{
"last_scan": "2026-03-13 16:51",
"total_papers": 178,
"scan_days": 7,
"new_found": 18,
"new_relevant": 5,
"new_highly_relevant": 2,
"papers": [
{
"id": "gscholar_can_aaii_beat_the_market_a_long_horizon_backtest_of_sentimen",
"source": "google_scholar",
"title": "Can AAII Beat the Market? A Long-Horizon Backtest of Sentiment-Driven Strategies in E-mini S&P 500",
"abstract": "E-mini S&P 500 futures. Using 25 years of weekly data, we conduct a transaction-cost-adjusted backtest of simple contrarian trading rules that condition market high-threshold strategies",
"authors": [
"R G\u00f3mez-Mart\u00ednez",
"ML Medrano-Garc\u00eda"
],
"date": "NA-01-01",
"categories": [
"google_scholar",
"\u2026 Strategies in E-mini \u2026"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6143986",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=6143986",
"relevance_score": 50,
"high_keywords": [
"s&p 500",
"e-mini",
"e-mini"
],
"medium_keywords": [
"sentiment"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "gscholar_high_frequency_market_microstructure_market_quality_informed",
"source": "google_scholar",
"title": "High Frequency Market Microstructure: Market Quality, Informed Trading and the Limit Order Book",
"abstract": "market microstructure has evolved into a critical area of financial research, particularly in the modern era of automated and high-frequency trading it provides low latency infrastructures,",
"authors": [
"DM Donate"
],
"date": "NA-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://zaguan.unizar.es/record/161956/files/TESIS-2025-226.pdf?version=1",
"pdf": "https://zaguan.unizar.es/record/161956/files/TESIS-2025-226.pdf?version=1",
"relevance_score": 45,
"high_keywords": [
"high frequency",
"microstructure",
"high frequency"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_futures_sull_e_mini_s_p_500",
"source": "google_scholar",
"title": "FUTURES SULL'E-MINI S&P-500",
"abstract": "Il Riquadro 1.2 riporta \u00abspecifiche contrattuali\u00bb (contract specs) degli E-mini S&P-500 futures cos\u00ec come appaiono sul website del CME Group. Il simbolo (ticker) univoco che identifica",
"authors": [
"A Avella",
"E Barone",
"M Pascucci",
"C Cerasani"
],
"date": "NA-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://www.researchgate.net/profile/Francesco-Christian-Scionti/publication/390769229_09_Equities_E-mini_SP_500_Futures_ESF/links/67fd7f4fd1054b0207d3dc4b/09-Equities-E-mini-S-P-500-Futures-ESF.pdf",
"pdf": "https://www.researchgate.net/profile/Francesco-Christian-Scionti/publication/390769229_09_Equities_E-mini_SP_500_Futures_ESF/links/67fd7f4fd1054b0207d3dc4b/09-Equities-E-mini-S-P-500-Futures-ESF.pdf",
"relevance_score": 35,
"high_keywords": [
"e-mini",
"e-mini"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_variation_in_option_implied_skewness_and_stock_returns",
"source": "google_scholar",
"title": "Variation in Option-implied skewness and stock returns",
"abstract": "predictive capacity of option-implied skewness variability on the relationship between option-implied skewness and stock Lastly, negative implied skewness signals an undervalued",
"authors": [
"M Rusch"
],
"date": "NA-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://thesis.eur.nl/pub/72922/Final-Thesis-Mark-Rusch-2-.pdf",
"pdf": "https://thesis.eur.nl/pub/72922/Final-Thesis-Mark-Rusch-2-.pdf",
"relevance_score": 23,
"high_keywords": [
"skew"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_logistic_regression_based_systematic_trading_performance_on_",
"source": "google_scholar",
"title": "Logistic Regression-Based Systematic Trading: Performance on the S&P 500",
"abstract": "This paper examines the performance of a Logistic Regression-Based Systematic Trading (LRST) strategy, applied to stocks in the S&P 500 from November 1983 to July 2023. The",
"authors": [
"CO Voigt"
],
"date": "NA-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://conradvoigt.github.io/Logistic%20Regression%20Based%20Trading%20on%20the%20S&P%20500.pdf",
"pdf": "https://conradvoigt.github.io/Logistic%20Regression%20Based%20Trading%20on%20the%20S&P%20500.pdf",
"relevance_score": 23,
"high_keywords": [
"s&p 500"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Regression analysis"
],
"backtestable": false
},
{
"id": "2603.11408v1",
"source": "arxiv",
"title": "Beyond Polarity: Multi-Dimensional LLM Sentiment Signals for WTI Crude Oil Futures Return Prediction",
"abstract": "Forecasting crude oil prices remains challenging because market-relevant information is embedded in large volumes of unstructured news and is not fully captured by traditional polarity-based sentiment measures. This paper examines whether multi-dimensional sentiment signals extracted by large language models improve the prediction of weekly WTI crude oil futures returns. Using energy-sector news articles from 2020 to 2025, we construct five sentiment dimensions covering relevance, polarity, intensity, uncertainty, and forwardness based on GPT-4o, Llama 3.2-3b, and two benchmark models, FinBERT and AlphaVantage. We aggregate article-level signals to the weekly level and evaluate their predictive performance in a classification framework. The best results are achieved by combining GPT-4o and FinBERT, suggesting that LLM-based and conventional financial sentiment models provide complementary predictive information. SHAP analysis further shows that intensity- and uncertainty-related features are among the most important predictors, indicating that the predictive value of news sentiment extends beyond simple polarity. Overall, the results suggest that multi-dimensional LLM-based sentiment measures can improve commodity return forecasting and support energy-market risk monitoring.",
"authors": [
"Dehao Dai",
"Ding Ma",
"Dou Liu",
"Kerui Geng",
"Yiqing Wang"
],
"date": "2026-03-12",
"categories": [
"q-fin.ST",
"cs.CL"
],
"url": "https://arxiv.org/abs/2603.11408v1",
"pdf": "https://arxiv.org/pdf/2603.11408v1",
"relevance_score": 31,
"high_keywords": [],
"medium_keywords": [
"return prediction",
"alpha",
"sentiment"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.10857v1",
"source": "arxiv",
"title": "SPX-VIX Risk Computations Via Perturbed Optimal Transport",
"abstract": "We propose a model independent framework for generating SPX and VIX risk scenarios based on a joint optimal transport calibration of their market smiles. Starting from the entropic martingale optimal transport formulation of Guyon, we introduce a perturbation methodology that computes sensitivities of the calibrated coupling using a Fisher information linearization. This allows risk to be generated without performing a full recalibration after market shocks. We further introduce a dimension reduction method based on perturbed optimal transport that produces fast and stable risk estimates while preserving the structural properties of the calibrated model. The approach is combined with Skew Stickiness Ratio(SSR) dynamics to translate SPX shocks into perturbations of forward variance and VIX distributions. Numerical experiments show that the proposed method produces accurate risk estimates relative to full recalibration while being computationally much faster. A backtesting study also demonstrates improved hedging performance compared with stochastic local volatility models.",
"authors": [
"Charlie Che",
"Hanxuan Lin",
"Yudong Yang",
"Guofan Hu",
"Lei Fang"
],
"date": "2026-03-11",
"categories": [
"q-fin.CP",
"q-fin.MF"
],
"url": "https://arxiv.org/abs/2603.10857v1",
"pdf": "https://arxiv.org/pdf/2603.10857v1",
"relevance_score": 53,
"high_keywords": [
"spx",
"vix",
"skew"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.10559v1",
"source": "arxiv",
"title": "A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting",
"abstract": "This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limited. This informational asymmetry translates into economically meaningful performance differences and highlights how structured machine learning frameworks can uncover cross-market dependencies while maintaining interpretability.",
"authors": [
"Jing Liu",
"Maria Grith",
"Xiaowen Dong",
"Mihai Cucuringu"
],
"date": "2026-03-11",
"categories": [
"cs.LG",
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2603.10559v1",
"pdf": "https://arxiv.org/pdf/2603.10559v1",
"relevance_score": 41,
"high_keywords": [
"intraday"
],
"medium_keywords": [
"close-to-close",
"open-to-close"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.10327v1",
"source": "arxiv",
"title": "Weighted Generalized Risk Measure and Risk Quandrangle: Characterization, Optimization and Application",
"abstract": "Various financial market scenarios may cause heterogeneous risk assessments among analysts, which motivates the usage of the Generalized Risk Measure in Fadina et al. (2024, Finance and Stochastics). Effectively synthesizing these diverse assessments avoids over-relying on a single, potentially flawed or conservative forecast and promotes more robust decision-making. Motivated by this, we establish analytical characterizations of the Weighted Generalized Risk Measure (WGRM) under both discrete and continuous settings. Building upon the WGRM, we incorporate the Fundamental Risk Quadrangle (FRQ) in Rockafellar and Uryasev (2013, Surveys in Operations Research and Management Science) into the Weighted Risk Quadrangle (WRQ) and show that the intrinsic relationships among risk, deviation, regret, error, and statistics in FRQ are preserved under weighted aggregation across scenarios. Moreover, we demonstrate that certain complex risk optimization problems under the WGRM can be reformulated as tractable linear programs through the WRQ structure, thus ensuring computational feasibility. Finally, the WGRM and WRQ framework is applied to empirical analyses using constituents of the NASDAQ 100 and S&P 500 indices across recession and expansion regimes, which validates that WGRM-based portfolios exhibit superior risk-adjusted performance and enhanced downside resilience and effectively mitigate losses arising from erroneous single-scenario judgments.",
"authors": [
"Yang Liu",
"Yunran Wei",
"Xintao Ye"
],
"date": "2026-03-11",
"categories": [
"q-fin.RM"
],
"url": "https://arxiv.org/abs/2603.10327v1",
"pdf": "https://arxiv.org/pdf/2603.10327v1",
"relevance_score": 31,
"high_keywords": [
"s&p 500"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.10327v2",
"source": "arxiv",
"title": "Weighted Generalized Risk Measure and Risk Quadrangle: Characterization, Optimization and Application",
"abstract": "Various financial market scenarios may cause heterogeneous risk assessments among analysts, which motivates the usage of the Generalized Risk Measure in Fadina et al. (2024, Finance and Stochastics). Effectively synthesizing these diverse assessments avoids over-relying on a single, potentially flawed or conservative forecast and promotes more robust decision-making. Motivated by this, we establish analytical characterizations of the Weighted Generalized Risk Measure (WGRM) under both discrete and continuous settings. Building upon the WGRM, we incorporate the Fundamental Risk Quadrangle (FRQ) in Rockafellar and Uryasev (2013, Surveys in Operations Research and Management Science) into the Weighted Risk Quadrangle (WRQ) and show that the intrinsic relationships among risk, deviation, regret, error, and statistics in FRQ are preserved under weighted aggregation across scenarios. Moreover, we demonstrate that certain complex risk optimization problems under the WGRM can be reformulated as tractable linear programs through the WRQ structure, thus ensuring computational feasibility. Finally, the WGRM and WRQ framework is applied to empirical analyses using constituents of the NASDAQ 100 and S&P 500 indices across recession and expansion regimes, which validates that WGRM-based portfolios exhibit superior risk-adjusted performance and enhanced downside resilience and effectively mitigate losses arising from erroneous single-scenario judgments.",
"authors": [
"Yang Liu",
"Yunran Wei",
"Xintao Ye"
],
"date": "2026-03-11",
"categories": [
"q-fin.RM"
],
"url": "https://arxiv.org/abs/2603.10327v2",
"pdf": "https://arxiv.org/pdf/2603.10327v2",
"relevance_score": 31,
"high_keywords": [
"s&p 500"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.11046v1",
"source": "arxiv",
"title": "On Utility Maximization under Multivariate Fake Stationary Affine Volterra Models",
"abstract": "This paper is concerned with Merton's portfolio optimization problem in a Volterra stochastic environment described by a multivariate fake stationary Volterra--Heston model. Due to the non-Markovianity and non-semimartingality of the underlying processes, the classical stochastic control approach cannot be directly applied in this setting. Instead, the problem is tackled using a stochastic factor solution to a Riccati backward stochastic differential equation (BSDE). Our approach is inspired by the martingale optimality principle combined with a suitable verification argument. The resulting optimal strategies for Merton's problems are derived in semi-closed form depending on the solutions to time-dependent multivariate Riccati-Volterra equations. Numerical results on a two dimensional fake stationary rough Heston model illustrate the impact of stationary rough volatilities on the optimal Merton strategies.",
"authors": [
"Emmanuel Gnabeyeu"
],
"date": "2026-03-11",
"categories": [
"math.OC",
"math.PR",
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2603.11046v1",
"pdf": "https://arxiv.org/pdf/2603.11046v1",
"relevance_score": 20,
"high_keywords": [
"factor"
],
"medium_keywords": [
"portfolio optimization"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.10202v1",
"source": "arxiv",
"title": "Hybrid Hidden Markov Model for Modeling Equity Excess Growth Rate Dynamics: A Discrete-State Approach with Jump-Diffusion",
"abstract": "Generating synthetic financial time series that preserve statistical properties of real market data is essential for stress testing, risk model validation, and scenario design. Existing approaches, from parametric models to deep generative networks, struggle to simultaneously reproduce heavy-tailed distributions, negligible linear autocorrelation, and persistent volatility clustering. We propose a hybrid hidden Markov framework that discretizes continuous excess growth rates into Laplace quantile-defined market states and augments regime switching with a Poisson-driven jump-duration mechanism to enforce realistic tail-state dwell times. Parameters are estimated by direct transition counting, bypassing the Baum-Welch EM algorithm. Synthetic data quality is evaluated using Kolmogorov-Smirnov and Anderson-Darling pass rates for distributional fidelity, and ACF mean absolute error for temporal structure. Applied to ten years of SPY data across 1,000 simulated paths, the framework achieves KS and AD pass rates exceeding 97% and 91% in-sample and 94% out-of-sample (calendar year 2025), partially reproducing the ARCH effect that standard regime-switching models miss. No single model dominates all quality dimensions: GARCH(1,1) reproduces volatility clustering more accurately but fails distributional tests (5.5% KS pass rate), while the standard HMM without jumps achieves higher distributional fidelity but cannot generate persistent high-volatility regimes. The proposed framework offers the best joint quality profile across distributional, temporal, and tail-coverage metrics. A Single-Index Model extension propagates the SPY factor path to a 424-asset universe, enabling scalable correlated synthetic path generation while preserving cross-sectional correlation structure.",
"authors": [
"Abdulrahman Alswaidan",
"Jeffrey D. Varner"
],
"date": "2026-03-10",
"categories": [
"q-fin.ST",
"cs.LG",
"q-fin.RM"
],
"url": "https://arxiv.org/abs/2603.10202v1",
"pdf": "https://arxiv.org/pdf/2603.10202v1",
"relevance_score": 58,
"high_keywords": [
"regime switching",
"volatility regime",
"correlation",
"factor"
],
"medium_keywords": [],
"low_keywords": [
"defi"
],
"actionable": true,
"findings": [],
"methods": [
"GARCH model",
"Cross-sectional analysis",
"Time series analysis"
],
"backtestable": false
},
{
"id": "2603.09164v1",
"source": "arxiv",
"title": "Slippage-at-Risk (SaR): A Forward-Looking Liquidity Risk Framework for Perpetual Futures Exchanges",
"abstract": "We introduce $\\textbf{Slippage-at-Risk (SaR)}$, a quantitative framework for measuring liquidity risk in perpetual futures exchanges. Unlike backward-looking metrics such as Value-at-Risk computed on historical returns or realized deficit distributions, SaR provides a \\emph{forward-looking} assessment of liquidation execution risk derived from current order book microstructure. The framework comprises three complementary metrics: $SaR(\u03b1)$, the cross-sectional slippage quantile; $ESaR(\u03b1)$, the expected slippage in the distributional tail; and $TSaR(\u03b1)$, the aggregate dollar-denominated tail slippage. We extend the base framework with a \\emph{concentration adjustment} that penalizes fragile liquidity structures where a small number of market makers dominate quote provision. Drawing on recent work by Chitra et al. (2025) on autodeleveraging mechanisms and insurance fund optimization, we establish a direct mapping from SaR metrics to optimal capital requirements. Empirical analysis using Hyperliquid order book data, including the October 10, 2025 liquidation cascade, demonstrates SaR's predictive validity as a leading indicator of systemic stress. We conclude with practical implementation guidance and discuss philosophical implications for risk management in decentralized financial systems.",
"authors": [
"Otar Sepper"
],
"date": "2026-03-10",
"categories": [
"q-fin.RM"
],
"url": "https://arxiv.org/abs/2603.09164v1",
"pdf": "https://arxiv.org/pdf/2603.09164v1",
"relevance_score": 48,
"high_keywords": [
"market maker",
"microstructure",
"liquidity",
"market maker"
],
"medium_keywords": [],
"low_keywords": [
"defi",
"insurance"
],
"actionable": true,
"findings": [],
"methods": [
"Cross-sectional analysis"
],
"backtestable": true
},
{
"id": "2603.09219v1",
"source": "arxiv",
"title": "AlgoXpert Alpha Research Framework. A Rigorous IS WFA OOS Protocol for Mitigating Overfitting in Quantitative Strategies",
"abstract": "Transitioning a strategy from backtest to live trading is a common failure point for quantitative systems due to parameter overfitting, selection bias, and sensitivity to regime changes. This paper presents the AlgoXpert Alpha Research Framework, a standardized protocol that evaluates strategies across three stages: In Sample (IS), which focuses on stable parameter regions instead of single optima; Walk Forward Analysis (WFA) using rolling windows and purge gaps to reduce information leakage, supported by majority pass and catastrophic veto rules; and Out of Sample (OOS) testing under strict parameter lock with no further tuning. The framework applies a defense in depth structure that includes structural safeguards such as cliff veto, execution controls such as spread and leverage guards, and equity protection mechanisms such as circuit breakers and a kill switch. A case study on USDJPY M5 intraday data demonstrates how to detect overfitting through performance decay and drawdown behavior across chronological stages. A post validation comparison of four alpha variants (v1 to v4) shows rank reversal when the objective changes from maximizing Sharpe to minimizing maximum drawdown, highlighting the trade off between risk adjusted performance and tail risk control.",
"authors": [
"The Anh Pham",
"Bao Chan Nguyen",
"Nguyet Nguyen Thi"
],
"date": "2026-03-10",
"categories": [
"q-fin.PM"
],
"url": "https://arxiv.org/abs/2603.09219v1",
"pdf": "https://arxiv.org/pdf/2603.09219v1",
"relevance_score": 35,
"high_keywords": [
"intraday"
],
"medium_keywords": [
"alpha",
"tail risk",
"drawdown"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.09669v1",
"source": "arxiv",
"title": "Competition between DEXs through Dynamic Fees",
"abstract": "We find an approximate Nash equilibrium in a game between decentralized exchanges (DEXs) that compete for order flow by setting dynamic trading fees. We characterize the equilibrium via a coupled system of partial differential equations and derive tractable approximate closed-form expressions for the equilibrium fees. Our analysis shows that the two-regime structure found in monopoly models persists under competition: pools alternate between raising fees to deter arbitrage and lowering fees to attract noise trading and increase volatility. Under competition, however, the switching boundary shifts from the oracle price to a weighted average of the oracle and competitors' exchange rates. Our numerical experiments show that, holding total liquidity fixed, an increase in the number of competing DEXs reduces execution slippage for strategic liquidity takers and lowers fee revenue per DEX. Finally, the effect on noise traders' slippage depends on market activity: they are worse off in low-activity markets but better off in high-activity ones.",
"authors": [
"Leonardo Baggiani",
"Martin Herdegen",
"Leandro Sanchez-Betancourt"
],
"date": "2026-03-10",
"categories": [
"q-fin.MF",
"math.OC",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2603.09669v1",
"pdf": "https://arxiv.org/pdf/2603.09669v1",
"relevance_score": 30,
"high_keywords": [
"order flow",
"liquidity"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.10137v1",
"source": "arxiv",
"title": "Uncertainty-Aware Deep Hedging",
"abstract": "Deep hedging trains neural networks to manage derivative risk under market frictions, but produces hedge ratios with no measure of model confidence -- a significant barrier to deployment. We introduce uncertainty quantification to the deep hedging framework by training a deep ensemble of five independent LSTM networks under Heston stochastic volatility with proportional transaction costs. The ensemble's disagreement at each time step provides a per-time-step confidence measure that is strongly predictive of hedging performance: the learned strategy outperforms the Black-Scholes delta on approximately 80% of paths when model agreement is high, but on fewer than 20% when disagreement is elevated. We propose a CVaR-optimised blending strategy that combines the ensemble's hedge with the classical Black-Scholes delta, weighted by the level of model uncertainty. The blend improves on the Black-Scholes delta by 35-80 basis points in CVaR across several Heston calibrations, and on the theoretically optimal Whalley-Wilmott strategy by 100-250 basis points, with all improvements statistically significant under paired bootstrap tests. The analysis reveals that ensemble uncertainty is driven primarily by option moneyness rather than volatility, and that the uncertainty-performance relationship inverts under weak leverage -- findings with practical implications for the deployment of machine learning in hedging systems.",
"authors": [
"Manan Poddar"
],
"date": "2026-03-10",
"categories": [
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2603.10137v1",
"pdf": "https://arxiv.org/pdf/2603.10137v1",
"relevance_score": 23,
"high_keywords": [],
"medium_keywords": [
"stochastic volatility",
"neural network",
"lstm"
],
"low_keywords": [],
"actionable": false,
"findings": [
"Quantitative result: 80"
],
"methods": [
"Neural network",
"LSTM"
],
"backtestable": false
},
{
"id": "2603.10272v1",
"source": "arxiv",
"title": "An operator-level ARCH Model",
"abstract": "AutoRegressive Conditional Heteroscedasticity (ARCH) models are standard for modeling time series exhibiting volatility, with a rich literature in univariate and multivariate settings. In recent years, these models have been extended to function spaces. However, functional ARCH and generalized ARCH (GARCH) processes established in the literature have thus far been restricted to model ``pointwise'' variances. In this paper, we propose a new ARCH framework for data residing in general separable Hilbert spaces that accounts for the full evolution of the conditional covariance operator. We define a general operator-level ARCH model. For a simplified Constant Conditional Correlation version of the model, we establish conditions under which such models admit strictly and weakly stationary solutions, finite moments, and weak serial dependence. Additionally, we derive consistent Yule--Walker-type estimators of the infinite-dimensional model parameters. The practical relevance of the model is illustrated through simulations and a data application to high-frequency cumulative intraday returns.",
"authors": [
"Alexander Aue",
"Sebastian K\u00fchnert",
"Gregory Rice",
"Jeremy VanderDoes"
],
"date": "2026-03-10",
"categories": [
"stat.ME",
"econ.EM",
"math.ST",
"q-fin.ST"
],
"url": "https://arxiv.org/abs/2603.10272v1",
"pdf": "https://arxiv.org/pdf/2603.10272v1",
"relevance_score": 20,
"high_keywords": [
"intraday",
"correlation"
],
"medium_keywords": [],
"low_keywords": [
"defi"
],
"actionable": false,
"findings": [],
"methods": [
"GARCH model",
"Time series analysis"
],
"backtestable": false
},
{
"id": "2603.09006v1",
"source": "arxiv",
"title": "Spectral Portfolio Theory: From SGD Weight Matrices to Wealth Dynamics",
"abstract": "We develop spectral portfolio theory by establishing a direct identification: neural network weight matrices trained on stochastic processes are portfolio allocation matrices, and their spectral structure encodes factor decompositions and wealth concentration patterns. The three forces governing stochastic gradient descent (SGD) -- gradient signal, dimensional regularisation, and eigenvalue repulsion -- translate directly into portfolio dynamics: smart money, survival constraint, and endogenous diversification. The spectral properties of SGD weight matrices transition from Marchenko-Pastur statistics (additive regime, short horizon) to inverse-Wishart via the free log-normal (multiplicative regime, long horizon), mirroring the transition from daily returns to long-run wealth compounding. We unify the cross-sectional wealth dynamics of Bouchaud and Mezard (2000), the within-portfolio dynamics of Olsen et al. (2025), and the scalar Fokker-Planck framework via a common spectral foundation. A central result is the Spectral Invariance Theorem: any isotropic perturbation to the portfolio objective preserves the singular-value distribution up to scale and shift, while anisotropic perturbations produce spectral distortion proportional to their cross-asset variance. We develop applications to portfolio design, wealth inequality measurement, tax policy, and neural network diagnostics. In the tax context, the invariance result recovers and generalises the neutrality conditions of Fr\u00f8seth (2026).",
"authors": [
"Anders G Fr\u00f8seth"
],
"date": "2026-03-09",
"categories": [
"q-fin.PM",
"physics.soc-ph"
],
"url": "https://arxiv.org/abs/2603.09006v1",
"pdf": "https://arxiv.org/pdf/2603.09006v1",
"relevance_score": 55,
"high_keywords": [
"cross-asset",
"factor",
"decomposition"
],
"medium_keywords": [
"neural network",
"allocation"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Neural network",
"Cross-sectional analysis"
],
"backtestable": false
},
{
"id": "2603.07616v1",
"source": "arxiv",
"title": "SABR Type Libor (Forward) Market Model (SABR/LMM) with time-dependent skew and smile",
"abstract": "Volatility Skew and Smile of Interest Rate products (Swaption and Caplet) are represented by SABR (Stochastic Alpha Beta Rho model). So, the Interest Rate derivatives model for pricing the callable exotic swaps should be comparable to the SABR volatility surface. In the interest rate derivatives models, Libor Market Model (LMM) (in a post-Libor world, Forward Market Model (FMM)) is one of the most popular models used in the market. So, there are many attempts to develop LMMs that are comparable to the SABR surface. It is called SABR/LMM. There are many references for SABR/LMM, but most of them only treat SABR/LMM, which is not flexible enough to be used practically in global banks. The purpose of this paper is to provide a comprehensive definition of SABR/LMM and a complete description of how it is to be implemented.",
"authors": [
"Osamu Tsuchiya"
],
"date": "2026-03-08",
"categories": [
"q-fin.MF",
"q-fin.PR"
],
"url": "https://arxiv.org/abs/2603.07616v1",
"pdf": "https://arxiv.org/pdf/2603.07616v1",
"relevance_score": 40,
"high_keywords": [
"volatility surface",
"skew",
"volatility surface"
],
"medium_keywords": [
"alpha"
],
"low_keywords": [
"defi"
],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.07600v1",
"source": "arxiv",
"title": "Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps",
"abstract": "We present a differential machine learning method for zero-days-to-expiry (0DTE) options under a stochastic-volatility jump-diffusion model that computes prices and Greeks in a single network evaluation. To handle the ultra-short-maturity regime, we represent the price in Black--Scholes form with a maturity-gated variance correction, and combine supervision on prices and Greeks with a PIDE-residual penalty. To make the jump contribution identifiable, we introduce a separate jump-operator network and train it with a three-stage procedure. In Bates-model simulations, the method improves jump-term approximation relative to one-stage baselines, keeps price errors close to one-stage alternatives while improving Greeks accuracy, produces stable one-day delta hedges, and is substantially faster than a Fourier-based pricing benchmark.",
"authors": [
"Takayuki Sakuma"
],
"date": "2026-03-08",
"categories": [
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2603.07600v1",
"pdf": "https://arxiv.org/pdf/2603.07600v1",
"relevance_score": 35,
"high_keywords": [
"0dte",
"zero-day"
],
"medium_keywords": [
"stochastic volatility"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.05917v1",
"source": "arxiv",
"title": "Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis",
"abstract": "Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market environments characterized by noise, non-stationarity, and behavioral dynamics. Traditional forecasting methods often fail to capture the intricate patterns and cross-sectional dependencies inherent in financial markets. This paper presents an integrated framework combining a node transformer architecture with BERT-based sentiment analysis for stock price forecasting. The proposed model represents the stock market as a graph structure where individual stocks form nodes and edges capture relationships including sectoral affiliations, correlated price movements, and supply chain connections. A fine-tuned BERT model extracts sentiment from social media posts and combines it with quantitative market features through attention-based fusion. The node transformer processes historical market data while capturing both temporal evolution and cross-sectional dependencies among stocks. Experiments on 20 S&P 500 stocks spanning January 1982 to March 2025 demonstrate that the integrated model achieves a mean absolute percentage error (MAPE) of 0.80% for one-day-ahead predictions, compared to 1.20% for ARIMA and 1.00% for LSTM. Sentiment analysis reduces prediction error by 10% overall and 25% during earnings announcements, while graph-based modeling contributes an additional 15% improvement by capturing inter-stock dependencies. Directional accuracy reaches 65% for one-day forecasts. Statistical validation through paired t-tests confirms these improvements (p < 0.05 for all comparisons). The model maintains MAPE below 1.5% during high-volatility periods where baseline models exceed 2%.",
"authors": [
"Mohammad Al Ridhawi",
"Mahtab Haj Ali",
"Hussein Al Osman"
],
"date": "2026-03-06",
"categories": [
"cs.LG",
"cs.AI",
"q-fin.ST"
],
"url": "https://arxiv.org/abs/2603.05917v1",
"pdf": "https://arxiv.org/pdf/2603.05917v1",
"relevance_score": 76,
"high_keywords": [
"s&p 500",
"earnings",
"announcement"
],
"medium_keywords": [
"sentiment",
"lstm",
"transformer"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"LSTM",
"Transformer model",
"Cross-sectional analysis"
],
"backtestable": false
},
{
"id": "2603.05862v1",
"source": "arxiv",
"title": "Impact of arbitrage between leveraged ETF and futures on market liquidity during market crash",
"abstract": "Leveraged ETFs (L-ETFs) are exchange-traded funds that achieve price movements several times greater than an index by holding index-linked futures such as Nikkei Stock Average Index futures. It is known that when the price of an L-ETF falls, the L-ETF uses the liquidity of futures to limit the decline through arbitrage trading. Conversely, when the price of a futures contract falls, the futures contract uses the liquidity of the L-ETF to limit its decline. However, the impact of arbitrage trading on the liquidity of these markets has been little studied. Therefore, the present study used artificial market simulations to investigate how the liquidity (Volume, SellDepth, BuyDepth, Tightness) of both markets changes when prices plummet in either (i.e., the L-ETF or futures market), depending on the presence or absence of arbitrage trading. As a result, it was found that when erroneous orders occur in the L-ETF market, the existence of arbitrage trading causes liquidity to be supplied from the futures market to the L-ETF market in terms of SellDepth and Tightness. When erroneous orders occur in the futures market, the existence of arbitrage trading causes liquidity to be supplied from the L-ETF market to the futures market in terms of SellDepth and Tightness, and liquidity to be supplied from the futures market to the L-ETF market in terms of Volume. We also analyzed the internal market mechanisms that led to these results.",
"authors": [
"Ryuki Hayase",
"Takanobu Mizuta",
"Isao Yagi"
],
"date": "2026-03-06",
"categories": [
"q-fin.CP",
"cs.MA"
],
"url": "https://arxiv.org/abs/2603.05862v1",
"pdf": "https://arxiv.org/pdf/2603.05862v1",
"relevance_score": 50,
"high_keywords": [
"index futures",
"liquidity",
"futures market"
],
"medium_keywords": [
"etf"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.06563v1",
"source": "arxiv",
"title": "Convergence of Neural Network Policies for Risk--Reward Optimization",
"abstract": "We develop a neural-network framework for multi-period risk--reward stochastic control problems with constrained two-step feedback policies that may be discontinuous in the state. We allow a broad class of objectives built on a finite-dimensional performance vector, including terminal and path-dependent statistics, with risk functionals admitting auxiliary-variable optimization representations (e.g.\\ Conditional Value-at-Risk and buffered probability of exceedance) and optional moment dependence. Our approach parametrizes the two-step policy using two coupled feedforward networks with constraint-enforcing output layers, reducing the constrained control problem to unconstrained training over network parameters. Under mild regularity conditions, we prove that the empirical optimum of the NN-parametrized objective converges in probability to the true optimal value as network capacity and training sample size increase. The proof is modular, separating policy approximation, propagation through the controlled recursion, and preservation under the scalarized risk--reward objective. Numerical experiments confirm the predicted convergence-in-probability behavior, show close agreement between learned and reference control heat maps, and demonstrate out-of-sample robustness on a large independent scenario set.",
"authors": [
"Chang Chen",
"Duy-Minh Dang"
],
"date": "2026-03-06",
"categories": [
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2603.06563v1",
"pdf": "https://arxiv.org/pdf/2603.06563v1",
"relevance_score": 21,
"high_keywords": [],
"medium_keywords": [
"neural network"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cboe_are-option-income-funds-suppressing-volatility",
"source": "cboe",
"title": "[Cboe] 0DTEs Decoded: Positioning, Trends, and Market Impact",
"abstract": "Cboe volatility_insights publication. See full article for details.",
"authors": [
"Cboe Derivatives Market Intelligence"
],
"date": "2026-03-05",
"categories": [
"cboe",
"volatility_insights"
],
"url": "https://www.cboe.com/insights/posts/are-option-income-funds-suppressing-volatility",
"pdf": "",
"relevance_score": 60,
"high_keywords": [
"0dte",
"positioning"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cboe_cboe-i-boxx-credit-futures-unpacked-examining-the-key-driver",
"source": "cboe",
"title": "[Cboe] A Fresh Look at Short-Dated Options and 0DTE SPX",
"abstract": "Cboe volatility_insights publication. See full article for details.",
"authors": [
"Cboe Derivatives Market Intelligence"
],
"date": "2026-03-05",
"categories": [
"cboe",
"volatility_insights"
],
"url": "https://www.cboe.com/insights/posts/cboe-i-boxx-credit-futures-unpacked-examining-the-key-drivers-behind-their-record-market-adoption",
"pdf": "",
"relevance_score": 60,
"high_keywords": [
"spx",
"0dte"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cboe_harnessing-bitcoin-volatility-with-mbtx-and-cbtx-options-",
"source": "cboe",
"title": "[Cboe] A Tale of Two Markets: SPX Options\u2019 Expanding Lead vs. Eminis",
"abstract": "Cboe volatility_insights publication. See full article for details.",
"authors": [
"Cboe Derivatives Market Intelligence"
],
"date": "2026-03-05",
"categories": [
"cboe",
"volatility_insights"
],
"url": "https://www.cboe.com/insights/posts/harnessing-bitcoin-volatility-with-mbtx-and-cbtx-options-",
"pdf": "",
"relevance_score": 45,
"high_keywords": [
"spx"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7133900967",
"source": "openalex",
"title": "PCA-APT Stress Index for Market Drawdowns",
"abstract": "This study develops a leakage-safe PCA\u2013APT framework that constructs an idiosyncratic market-stress index from cross-sectional residual dispersion and evaluates its usefulness for anticipating equity drawdowns. Using daily adjusted prices for SPY and 11 U.S. sector ETFs from 2020\u20132025, we compute sector excess returns (sector minus SPY), estimate a low-dimensional common component via principal component analysis (PCA), and define residual stress as the cross-sectional root-mean-square magnitude of PCA reconstruction residuals. To prevent look-ahead bias, the PCA mapping is estimated using information available only through t\u22121, stress is computed out-of-sample at t, and stress regimes are identified using a rolling train-only quantile threshold that is shifted forward by one trading day. Drawdown-warning performance is assessed using drawdown-onset events and early-warning classification metrics (ROC-AUC, PR-AUC, and horizon-H precision/recall). Empirically, residual stress spikes cluster around drawdown onsets and provides predictive information, although a volatility-based benchmark remains stronger on average across discrimination metrics. Importantly, residual stress exhibits state-dependent complementarity with volatility: conditional on low volatility, high residual stress is associated with a materially higher probability of a drawdown onset within the next H=21 trading days (approximately 17% vs. 8%), and the joint high-stress/high-volatility regime identifies the highest-risk states (approximately 36% onset probability). Event-level overlap diagnostics further indicate that residual stress can flag a subset of drawdown onsets not captured by a volatility-threshold rule, while some onsets are not preceded by either signal. Economic relevance is examined under transaction costs through (i) a residual-ranked sector long\u2013short portfolio and (ii) stress-managed SPY overlays that reduce exposure during detected regimes. In the baseline sample, a volatility-manag",
"authors": [
"Ting Liu"
],
"date": "2026-03-05",
"categories": [
"finance",
"Preprints.org"
],
"url": "https://doi.org/10.20944/preprints202603.0395.v1",
"pdf": "https://www.preprints.org/frontend/manuscript/4ee9cb4afc19576cbf2976bcfd4d285d/download_pub",
"relevance_score": 44,
"high_keywords": [
"volatility regime"
],
"medium_keywords": [
"excess return",
"etf",
"drawdown"
],
"low_keywords": [
"defi"
],
"actionable": true,
"findings": [],
"methods": [
"PCA",
"Cross-sectional analysis"
],
"backtestable": true
},
{
"id": "2603.05260v1",
"source": "arxiv",
"title": "Extreme Value Analysis for Finite, Multivariate and Correlated Systems with Finance as an Example",
"abstract": "Extreme values and the tail behavior of probability distributions are essential for quantifying and mitigating risk in complex systems of all kinds. In multivariate settings, accounting for correlations is crucial. Although extreme value analysis for infinite correlated systems remains an open challenge, we propose a practical framework for handling a large but finite number of correlated time series. We develop our approach for finance as a concrete example but emphasize its generality. We study the extremal behavior of high-frequency stock returns after rotating them into the eigenbasis of the correlation matrix. This separates and extracts various collective effects, including information on the correlated market as a whole and on correlated sectoral behavior from idiosyncratic features, while allowing us to use univariate tools of extreme value analysis. This holds even for high-frequency data where discretization effects normally complicate analysis. We employ a peaks-over-threshold approach and thereby fully avoid the analysis of block maxima. We estimate the tail shape of the rotated returns while explicitly accounting for nonstationarity, a key feature in finance and many other complex systems. Our framework facilitates tail risk estimation relative to larger trends and intraday seasonalities at both market and sectoral levels.",
"authors": [
"Benjamin K\u00f6hler",
"Anton J. Heckens",
"Thomas Guhr"
],
"date": "2026-03-05",
"categories": [
"q-fin.ST",
"physics.data-an",
"q-fin.RM"
],
"url": "https://arxiv.org/abs/2603.05260v1",
"pdf": "https://arxiv.org/pdf/2603.05260v1",
"relevance_score": 40,
"high_keywords": [
"intraday",
"correlation"
],
"medium_keywords": [
"tail risk"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Time series analysis"
],
"backtestable": true
},
{
"id": "2603.05264v1",
"source": "arxiv",
"title": "Asset Returns, Portfolio Choice, and Proportional Wealth Taxation",
"abstract": "We analyse the effect of a proportional wealth tax on asset returns, portfolio choice, and asset pricing. The tax is levied annually on the market value of all holdings at a uniform rate. We show that such a tax is economically equivalent to the government acquiring a proportional stake in the investor's portfolio each period, a form of risk sharing in which expected wealth and risk are reduced by the same factor, while the return per share is unaffected. This multiplicative separability drives four main results: (i) the coefficient of variation of wealth is invariant to the tax rate; (ii) optimal portfolio weights are independent of the tax rate; (iii) the wealth tax is orthogonal to portfolio choice, inducing a homothetic contraction of the opportunity set that preserves the Sharpe ratio of every portfolio; (iv) taxed and untaxed investors price assets identically. Results are derived under geometric Brownian motion and generalised to the location-scale family. A Modigliani-Miller analysis confirms pricing neutrality and identifies an inconsistency in the literature regarding the discount rate for after-tax cash flows. Under CAPM with CRRA preferences, after-tax betas equal pre-tax betas and the security market line contracts by the tax factor; general-equilibrium prices are unchanged. This resolves an error in Fama (2021). The neutrality results depend on three conditions commonly violated in practice: universal taxation at market value, frictionless markets, and dividend consumption. We formalise three channels through which relaxing these conditions breaks neutrality: book-value taxation, liquidity frictions, and dividend extraction, and show they have opposing effects on asset prices.",
"authors": [
"Anders G. Froeseth"
],
"date": "2026-03-05",
"categories": [
"physics.soc-ph",
"econ.GN",
"q-fin.PM"
],
"url": "https://arxiv.org/abs/2603.05264v1",
"pdf": "https://arxiv.org/pdf/2603.05264v1",
"relevance_score": 38,
"high_keywords": [
"factor",
"liquidity"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cboe_0-dt-es-decoded-positioning-trends-and-market-impact",
"source": "cboe",
"title": "[Cboe] Cboe\u00ae iBoxx\u00ae Credit Futures Unpacked - Examining the Key Drivers Behind Their Record Market Adoption",
"abstract": "Cboe volatility_insights publication. See full article for details.",
"authors": [
"Cboe Derivatives Market Intelligence"
],
"date": "2026-03-05",
"categories": [
"cboe",
"volatility_insights"
],
"url": "https://www.cboe.com/insights/posts/0-dt-es-decoded-positioning-trends-and-market-impact",
"pdf": "",
"relevance_score": 30,
"high_keywords": [],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cboe_beyond-60-40-using-options-based-strategies-in-portfolios",
"source": "cboe",
"title": "[Cboe] Beyond 60/40: Using Options-Based Strategies in Portfolios",
"abstract": "Cboe volatility_insights publication. See full article for details.",
"authors": [
"Cboe Derivatives Market Intelligence"
],
"date": "2026-03-05",
"categories": [
"cboe",
"volatility_insights"
],
"url": "https://www.cboe.com/insights/posts/beyond-60-40-using-options-based-strategies-in-portfolios",
"pdf": "",
"relevance_score": 30,
"high_keywords": [],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cboe_a-tale-of-two-markets-spx-options-expanding-lead-vs-eminis-",
"source": "cboe",
"title": "[Cboe] Are Option Income Funds Suppressing Volatility?",
"abstract": "Cboe volatility_insights publication. See full article for details.",
"authors": [
"Cboe Derivatives Market Intelligence"
],
"date": "2026-03-05",
"categories": [
"cboe",
"volatility_insights"
],
"url": "https://www.cboe.com/insights/posts/a-tale-of-two-markets-spx-options-expanding-lead-vs-eminis-",
"pdf": "",
"relevance_score": 30,
"high_keywords": [],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.05326v1",
"source": "arxiv",
"title": "Riemannian Geometry of Optimal Rebalancing in Dynamic Weight Automated Market Makers",
"abstract": "In Temporal Function Market Making (TFMM), a dynamic weight AMM pool rebalances from initial to final holdings by creating a series of arbitrage opportunities whose total cost depends on the weight trajectory taken. We show that the per-step arbitrage loss is the KL divergence between new and old weight vectors, meaning the Fisher--Rao metric is the natural Riemannian metric on the weight simplex. The loss-minimising interpolation under the leading-order expansion of this KL cost is SLERP (Spherical Linear Interpolation) in the Hellinger coordinates $\u03b7_i = \\sqrt{w_i}$, i.e.\\ a geodesic on the positive orthant of the unit sphere traversed at constant speed. The SLERP midpoint equals the (AM+GM)/normalise heuristic of prior work (Willetts & Harrington, 2024), so the heuristic lies on the geodesic. This identity holds for any number of tokens and any magnitude of weight change; using this link, all dyadic points on the geodesic can be reached by recursive AM-GM bisection without trigonometric functions. SLERP's relative sub-optimality on the full KL cost is proportional to the squared magnitude of the overall weight change and to $1/f^2$, where $f$ is the number of interpolation steps.",
"authors": [
"Matthew Willetts"
],
"date": "2026-03-05",
"categories": [
"q-fin.MF",
"cs.IT",
"math.DG",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2603.05326v1",
"pdf": "https://arxiv.org/pdf/2603.05326v1",
"relevance_score": 30,
"high_keywords": [
"market maker",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.05326v2",
"source": "arxiv",
"title": "Riemannian Geometry of Optimal Rebalancing in Dynamic Weight Automated Market Makers",
"abstract": "In Temporal Function Market Making (TFMM), a dynamic weight AMM pool rebalances from initial to final holdings by creating a series of arbitrage opportunities whose total cost depends on the weight trajectory taken. We show that the per-step arbitrage loss is the KL divergence between new and old weight vectors, meaning the Fisher--Rao metric is the natural Riemannian metric on the weight simplex. The loss-minimising interpolation under the leading-order expansion of this KL cost is SLERP (Spherical Linear Interpolation) in the Hellinger coordinates $\u03b7_i = \\sqrt{w_i}$, i.e. a geodesic on the positive orthant of the unit sphere traversed at constant speed. The SLERP midpoint equals the (AM+GM)/normalise heuristic of prior work (Willetts & Harrington, 2024), so the heuristic lies on the geodesic. This identity holds for any number of tokens and any magnitude of weight change; using this link, all dyadic points on the geodesic can be reached by recursive AM-GM bisection without trigonometric functions. SLERP's relative sub-optimality on the full KL cost is proportional to the squared magnitude of the overall weight change and to $1/f^2$, where $f$ is the number of interpolation steps.",
"authors": [
"Matthew Willetts"
],
"date": "2026-03-05",
"categories": [
"q-fin.MF",
"cs.IT",
"math.DG",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2603.05326v2",
"pdf": "https://arxiv.org/pdf/2603.05326v2",
"relevance_score": 30,
"high_keywords": [
"market maker",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cme_erik-norland.html",
"source": "cme",
"title": "[CME] Erik Norland.Html",
"abstract": "CME Group economic_research publication.",
"authors": [
"CME Group Research"
],
"date": "2026-03-05",
"categories": [
"cme",
"economic_research"
],
"url": "https://www.cmegroup.com/education/featured-reports/bios/erik-norland.html",
"pdf": "",
"relevance_score": 25,
"high_keywords": [],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cme_insights.html",
"source": "cme",
"title": "[CME] Insights.Html",
"abstract": "CME Group economic_research publication.",
"authors": [
"CME Group Research"
],
"date": "2026-03-05",
"categories": [
"cme",
"economic_research"
],
"url": "https://www.cmegroup.com/insights.html",
"pdf": "",
"relevance_score": 25,
"high_keywords": [],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cme_report-a-security-concern.html",
"source": "cme",
"title": "[CME] Report A Security Concern.Html",
"abstract": "CME Group economic_research publication.",
"authors": [
"CME Group Research"
],
"date": "2026-03-05",
"categories": [
"cme",
"economic_research"
],
"url": "https://www.cmegroup.com/report-a-security-concern.html",
"pdf": "",
"relevance_score": 25,
"high_keywords": [],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "cboe_a-fresh-look-at-short-dated-options-and-0-dte-spx",
"source": "cboe",
"title": "[Cboe] Harnessing Bitcoin Volatility with MBTX and CBTX Options",
"abstract": "Cboe volatility_insights publication. See full article for details.",
"authors": [
"Cboe Derivatives Market Intelligence"
],
"date": "2026-03-05",
"categories": [
"cboe",
"volatility_insights"
],
"url": "https://www.cboe.com/insights/posts/a-fresh-look-at-short-dated-options-and-0-dte-spx",
"pdf": "",
"relevance_score": 20,
"high_keywords": [],
"medium_keywords": [],
"low_keywords": [
"bitcoin"
],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7133559538",
"source": "openalex",
"title": "Determinant Factors Influencing the Commodity Futures Market Returns in India: A Markov Regime Switching Approach",
"abstract": "Abstract The study analyses the factors influencing returns in the Indian commodity futures market. It capitalises on various uncertainty factors, including global economic conditions, global real economic activity indices, the Indian volatility index (VIX), world industrial production, geopolitical risk (GPR), Indian economic policy uncertainty (IEPU), and oil price uncertainty (OPU). Further, the study uses monthly futures closing prices for eleven actively traded agricultural and non-agricultural commodity futures. Next, we consider the monthly uncertainty factors data from January 2012 to March 2024. The study employs the dynamic Markov Regime Switching model and Transition Probability. The results document mixed outcomes in the low- and high-volatility regimes.",
"authors": [
"M Thilaga",
"V Veeravel",
"K Prabhakar Rajkumar"
],
"date": "2026-03-04",
"categories": [
"finance"
],
"url": "https://doi.org/10.21203/rs.3.rs-8900737/v1",
"pdf": "https://www.researchsquare.com/article/rs-8900737/latest.pdf",
"relevance_score": 75,
"high_keywords": [
"vix",
"regime switching",
"volatility regime",
"factor",
"futures market"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7133774417",
"source": "openalex",
"title": "Integrating the implied regularity (Ht) into implied volatility models: A study on free arbitrage model",
"abstract": "In financial markets, implied volatility (IV) is a critical metric reflecting the market\u2019s expectations of future price fluctuations in an underlying asset. Research has focused on modeling IV in relation to moneyness. We study the relationship between implied volatility and the implied Hurst exponent H, particularly as they vary with moneyness. Our key finding is that H approaches 1/2 when moneyness is 1, suggesting a critical point in market efficiency expectations at this level. We developed an implied volatility model that integrates H to better capture these dynamics. This model considers the interaction between H and the underlying to strike price ratio S/K, which is crucial for reflecting variations in IV based on moneyness. Applied to many indexes and optimized using the Optuna method, our model, compared with SABR and fSABR, demonstrated promising accuracy, laying the groundwork for future applications in options pricing and volatility forecasting. This approach offers a more nuanced representation of market expectations and the dynamics between IV and H, enhancing both theoretical understanding and practical modeling in financial analysis.",
"authors": [
"Fabrizio Di Sciorio",
"Daniele Angelini"
],
"date": "2026-03-04",
"categories": [
"finance",
"Journal of Economic Analysis"
],
"url": "https://doi.org/10.58567/jea05020001",
"pdf": "https://www.anserpress.org/journal/jea/5/2/136/pdf",
"relevance_score": 33,
"high_keywords": [
"implied volatility"
],
"medium_keywords": [
"options pricing",
"market efficiency"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.04275v1",
"source": "arxiv",
"title": "Statistical Inference for Score Decompositions",
"abstract": "We introduce inference methods for score decompositions, which partition scoring functions for predictive assessment into three interpretable components: miscalibration, discrimination, and uncertainty. Our estimation and inference relies on a linear recalibration of the forecasts, which is applicable to general multi-step ahead point forecasts such as means and quantiles due to its validity for both smooth and non-smooth scoring functions. This approach ensures desirable finite-sample properties, enables asymptotic inference, and establishes a direct connection to the classical Mincer-Zarnowitz regression. The resulting inference framework facilitates tests for equal forecast calibration or discrimination, which yield three key advantages. They enhance the information content of predictive ability tests by decomposing scores, deliver higher statistical power in certain scenarios, and formally connect scoring-function-based evaluation to traditional calibration tests, such as financial backtests. Applications demonstrate the method's utility. We find that for survey inflation forecasts, discrimination abilities can differ significantly even when overall predictive ability does not. In an application to financial risk models, our tests provide deeper insights into the calibration and information content of volatility and Value-at-Risk forecasts. By disentangling forecast accuracy from backtest performance, the method exposes critical shortcomings in current banking regulation.",
"authors": [
"Timo Dimitriadis",
"Marius Puke"
],
"date": "2026-03-04",
"categories": [
"econ.EM",
"q-fin.RM",
"stat.ME",
"stat.ML"
],
"url": "https://arxiv.org/abs/2603.04275v1",
"pdf": "https://arxiv.org/pdf/2603.04275v1",
"relevance_score": 31,
"high_keywords": [
"decomposition"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Regression analysis"
],
"backtestable": true
},
{
"id": "2603.03671v1",
"source": "arxiv",
"title": "Is an investor stolen their profits by mimic investors? Investigated by an agent-based model",
"abstract": "Some investors say increasing investors with the same strategy decreasing their profits per an investor. On the other hand, some investors using technical analysis used to use same strategy and parameters with other investors, and say that it is better. Those argues are conflicted each other because one argues using with same strategy decreases profits but another argues it increase profits. However, those arguments have not been investigated yet. In this study, the agent-based artificial financial market model(ABAFMM) was built by adding \"additional agents\"(AAs) that includes additional fundamental agents (AFAs) and additional technical agents (ATAs) to the prior model. The AFAs(ATAs) trade obeying simple fundamental(technical) strategy having only the one parameter. We investigated earnings of AAs when AAs increased. We found that in the case with increasing AFAs, market prices are made stable that leads to decrease their profits. In the case with increasing ATAs, market prices are made unstable that leads to gain their profits more.",
"authors": [
"Takanobu Mizuta",
"Isao Yagi"
],
"date": "2026-03-04",
"categories": [
"q-fin.CP",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2603.03671v1",
"pdf": "https://arxiv.org/pdf/2603.03671v1",
"relevance_score": 20,
"high_keywords": [
"earnings"
],
"medium_keywords": [
"technical analysis"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.02820v1",
"source": "arxiv",
"title": "Optimal Consumption and Portfolio Choice with No-Borrowing Constraint in the Kim-Omberg Model",
"abstract": "In this paper, we study an intertemporal utility maximization problem in which an investor chooses consumption and portfolio strategies in the presence of a stochastic factor and a no-borrowing constraint. In the spirit of the Kim-Omberg model, the stochastic factor represents the excess return of the risky asset and follows an Ornstein-Uhlenbeck process, capturing the mean reversion of expected excess returns-a feature well supported by empirical evidence in financial markets. The investor seeks to maximize expected utility from consumption, subject to the constraint that wealth remains nonnegative at all times. To address the dynamic no-borrowing constraint, we use Lagrange duality to transform the primal problem into a singular control problem in the dual space. We then characterize the solution to the dual singular control problem via an auxiliary two-dimensional optimal stopping problem featuring stochastic volatility, and subsequently retrieve the primal value function as well as the optimal portfolio and consumption plans. Finally, a numerical study is conducted to derive economic and financial implications.",
"authors": [
"Giorgio Ferrari",
"Tim Niclas Sch\u00fctz"
],
"date": "2026-03-03",
"categories": [
"math.OC",
"math.PR",
"q-fin.MF"
],
"url": "https://arxiv.org/abs/2603.02820v1",
"pdf": "https://arxiv.org/pdf/2603.02820v1",
"relevance_score": 48,
"high_keywords": [
"mean reversion",
"factor"
],
"medium_keywords": [
"stochastic volatility",
"excess return"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.02620v1",
"source": "arxiv",
"title": "Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Series",
"abstract": "Neural networks applied to financial time series operate in a regime of underspecification, where model predictors achieve indistinguishable out-of-sample error. Using large-scale volatility forecasting for S$\\&$P 500 stocks, we show that different model-training-pipeline pairs with identical test loss learn qualitatively different functions. Across architectures, predictive accuracy remains unchanged, yet optimizer choice reshapes non-linear response profiles and temporal dependence differently. These divergences have material consequences for decisions: volatility-ranked portfolios trace a near-vertical Sharpe-turnover frontier, with nearly $3\\times$ turnover dispersion at comparable Sharpe ratios. We conclude that in underspecified settings, optimization acts as a consequential source of inductive bias, thus model evaluation should extend beyond scalar loss to encompass functional and decision-level implications.",
"authors": [
"Federico Vittorio Cortesi",
"Giuseppe Iannone",
"Giulia Crippa",
"Tomaso Poggio",
"Pierfrancesco Beneventano"
],
"date": "2026-03-03",
"categories": [
"cs.LG",
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2603.02620v1",
"pdf": "https://arxiv.org/pdf/2603.02620v1",
"relevance_score": 37,
"high_keywords": [],
"medium_keywords": [
"neural network"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Neural network",
"Time series analysis"
],
"backtestable": false
},
{
"id": "2603.02844v1",
"source": "arxiv",
"title": "Optimal Routing across Constant Function Market Makers with Gas Fees",
"abstract": "We study the optimal routing problem in decentralized exchanges built on Constant Function Market Makers when trades can be split across multiple heterogeneous pools and execution incurs fixed on-chain costs (gas fees). While prior routing formulations typically abstract from fixed activation costs, real on-chain execution presents non-negligible gas fees. They also become convex under concavity/convexity assumptions on the invariant functions. We propose a general optimization framework that allows differentiable invariant functions beyond global convexity and incorporates fixed gas fees through a mixed-integer model that induces activation thresholds. Subsequently, we introduce a relaxed formulation of this model, whereby we deduce necessary optimality conditions, obtaining an explicit Karush-Kuhn-Tucker system that links prices, fees, and activation. We further establish sufficient optimality conditions using tools from generalized convexity (pseudoconcavity/pseudoconvexity and quasilinearity), yielding a verifiable optimality characterization without requiring convex trade functions. Finally, we relate the relaxed solution to the original mixed-integer model by providing explicit approximation bounds that quantify the utility gap induced by relaxation. Our results extend the mathematical theory for routing by offering no-trade conditions in fragmented on-chain markets in the presence of gas fees.",
"authors": [
"Carlos Escudero",
"Felipe Lara",
"Miguel Sama"
],
"date": "2026-03-03",
"categories": [
"math.OC",
"q-fin.MF"
],
"url": "https://arxiv.org/abs/2603.02844v1",
"pdf": "https://arxiv.org/pdf/2603.02844v1",
"relevance_score": 30,
"high_keywords": [
"market maker",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "2603.01344v1",
"source": "arxiv",
"title": "Pricing and hedging for liquidity provision in Constant Function Market Making",
"abstract": "This paper develops a robust mathematical framework for Constant Function Market Makers (CFMMs) by transitioning from traditional token reserve analyses to a coordinate system defined by price and intrinsic liquidity. We establish a canonical parametrization of the bonding curve that ensures dimensional consistency across diverse trading functions, such as those employed by Uniswap and Balancer, and demonstrate that asset reserves and value functions exhibit a linear dependence on this intrinsic liquidity. This linear structure facilitates a streamlined approach to arbitrage-free pricing, delta hedging, and systematic risk management. By leveraging the Carr-Madan spanning formula, we characterize Impermanent Loss (IL) as a weighted strip of vanilla options, thereby defining a fine-grained implied volatility structure for liquidity profiles. Furthermore, we provide a path-dependent analysis of IL using the last-passage time. Empirical results from Uniswap v3 ETH/USDC pools and Deribit option markets confirm a volatility smile consistent with crypto-asset dynamics, validating the framework's utility in characterizing the risk-neutral fair value of liquidity provision.",
"authors": [
"Jimmy Risk",
"Shen-Ning Tung",
"Tai-Ho Wang"
],
"date": "2026-03-02",
"categories": [
"q-fin.MF"
],
"url": "https://arxiv.org/abs/2603.01344v1",
"pdf": "https://arxiv.org/pdf/2603.01344v1",
"relevance_score": 73,
"high_keywords": [
"market maker",
"implied volatility",
"delta hedging",
"liquidity",
"market maker"
],
"medium_keywords": [],
"low_keywords": [
"defi"
],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7133346046",
"source": "openalex",
"title": "Pricing and hedging for liquidity provision in Constant Function Market Making",
"abstract": "This paper develops a robust mathematical framework for Constant Function Market Makers (CFMMs) by transitioning from traditional token reserve analyses to a coordinate system defined by price and intrinsic liquidity. We establish a canonical parametrization of the bonding curve that ensures dimensional consistency across diverse trading functions, such as those employed by Uniswap and Balancer, and demonstrate that asset reserves and value functions exhibit a linear dependence on this intrinsic liquidity. This linear structure facilitates a streamlined approach to arbitrage-free pricing, delta hedging, and systematic risk management. By leveraging the Carr-Madan spanning formula, we characterize Impermanent Loss (IL) as a weighted strip of vanilla options, thereby defining a fine-grained implied volatility structure for liquidity profiles. Furthermore, we provide a path-dependent analysis of IL using the last-passage time. Empirical results from Uniswap v3 ETH/USDC pools and Deribit option markets confirm a volatility smile consistent with crypto-asset dynamics, validating the framework's utility in characterizing the risk-neutral fair value of liquidity provision.",
"authors": [
"Jimmy Risk",
"Shen-Ning Tung",
"Tai\u2010Ho Wang"
],
"date": "2026-03-02",
"categories": [
"finance",
"arXiv (Cornell University)"
],
"url": "https://doi.org/10.48550/arxiv.2603.01344",
"pdf": "https://doi.org/10.48550/arxiv.2603.01344",
"relevance_score": 73,
"high_keywords": [
"market maker",
"implied volatility",
"delta hedging",
"liquidity",
"market maker"
],
"medium_keywords": [],
"low_keywords": [
"defi"
],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7133239333",
"source": "openalex",
"title": "External Macroeconomic Variables and Stock Returns: Evidence from Conventional and Islamic Indices",
"abstract": "The study documents the impact of the external sector on movements of the Pakistan Stock Exchange (PSX), covering conventional and Islamic indices. Selected variables include international trade, foreign investment, remittances, oil, gold, and currency markets, as well as the KSE-100 and KMI-30 indices. The sample period covers the latest 130 months, from 2015/01 to 2025/10. Results are documented through descriptive statistics, pairwise correlations, and OLS regression. Stability of coefficients during the review period is checked by calculating BTC-Var and switching Var. Outstanding momentum is evident in market indices (in the final phase), accompanied by growth in remittances, while the national currency has experienced an alarming depreciation. The combined impact of the external sector is not in the higher range for either index (adjusted R-square values are low). A group of four variables (remittances, oil, gold, and currency markets) was significant for the conventional index, while a group of three variables (oil, gold, and currency markets) was significant for the Islamic index. All significant variables contribute positively to stock index movements, except the exchange rate. BTC-Var and switching var suggest instability of relationships and regime-dependent var dynamics. The findings are beneficial for managers and investors in predicting index movements and portfolio diversification, as well as for relevant authorities in making policy decisions that promote prudent exchange-rate management and facilitate remittances. To the best of the author\u2019s knowledge, this study is among the few that jointly examine the impact of external-sector variables on stock market movements.",
"authors": [
"Muhammad Hanif"
],
"date": "2026-03-02",
"categories": [
"finance",
"Forecasting"
],
"url": "https://doi.org/10.3390/forecast8020020",
"pdf": "https://www.mdpi.com/2571-9394/8/2/20/pdf",
"relevance_score": 53,
"high_keywords": [
"momentum",
"correlation",
"stock index"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Regression analysis"
],
"backtestable": false
},
{
"id": "oa_W7134119700",
"source": "openalex",
"title": "Social media, high\u2010frequency trading, and market making after\u2010hours \u2013 Evidence from presidential tweets",
"abstract": "Abstract I analyze differences between the core and extended trading sessions in the high\u2010frequency reaction of equity markets to potential news. Using presidential tweets as unanticipated, potentially market\u2010stirring events, I find that volatility increases and liquidity deteriorates within fractions of a second after a tweet. The speed of quote adjustments indicates that algorithmic traders monitor social media sources around the clock and automatically trade upon this information. Compared to the core trading session, the reduction in market quality is much stronger and faster during the extended trading hours, when liquidity is lower and designated market maker participation is optional.",
"authors": [
"Stefan Scharnowski"
],
"date": "2026-03-02",
"categories": [
"finance",
"The Journal of Financial Research"
],
"url": "https://doi.org/10.1111/jfir.70049",
"pdf": "https://doi.org/10.1111/jfir.70049",
"relevance_score": 45,
"high_keywords": [
"market maker",
"liquidity",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.01820v1",
"source": "arxiv",
"title": "Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance",
"abstract": "We present a large scale benchmark of modern deep learning architectures for a financial time series prediction and position sizing task, with a primary focus on Sharpe ratio optimization. Evaluating linear models, recurrent networks, transformer based architectures, state space models, and recent sequence representation approaches, we assess out of sample performance on a daily futures dataset spanning commodities, equity indices, bonds, and FX spanning 2010 to 2025. Our evaluation goes beyond average returns and includes statistical significance, downside and tail risk measures, breakeven transaction cost analysis, robustness to random seed selection, and computational efficiency. We find that models explicitly designed to learn rich temporal representations consistently outperform linear benchmarks and generic deep learning models, which often lead the ranking in standard time series benchmarks. Hybrid models such as VSN with LSTM, a combination of Variable Selection Networks (VSN) and LSTMs, achieves the highest overall Sharpe ratio, while VSN with xLSTM and LSTM with PatchTST exhibit superior downside adjusted characteristics. xLSTM demonstrates the largest breakeven transaction cost buffer, indicating improved robustness to trading frictions.",
"authors": [
"Adir Saly-Kaufmann",
"Kieran Wood",
"Jan Peter-Calliess",
"Stefan Zohren"
],
"date": "2026-03-02",
"categories": [
"q-fin.TR",
"cs.LG"
],
"url": "https://arxiv.org/abs/2603.01820v1",
"pdf": "https://arxiv.org/pdf/2603.01820v1",
"relevance_score": 44,
"high_keywords": [],
"medium_keywords": [
"tail risk",
"deep learning",
"lstm",
"transformer"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"LSTM",
"Transformer model",
"Time series analysis"
],
"backtestable": true
},
{
"id": "2603.02187v1",
"source": "arxiv",
"title": "Does the Market Anticipate? Can it? Should it?",
"abstract": "We explore a nuance to 'no arbitrage' in relation to 'informational efficiency': acting immediately on an arbitrage is sometimes suboptimal; in such cases optimised trading can suppress the anticipation of predictable risk-outcomes, thereby creating an apparent Status Quo Bias, with Momentum and Low-Risk effects. This is shown in continuous time under model- or event-risk, where, unlike existing approaches, pre-horizon risk-resolution and Risk-Neutral Equivalent pricing are allowed, with the technical challenges overcome through results from the 'weak viability' and 'side-inside information' literature. The 'tension' between 'no arbitrage', 'informational efficiency' and 'risk-anticipation' is thus exposed and treated in a practically relevant setting.",
"authors": [
"Kangda Ken Wren"
],
"date": "2026-03-02",
"categories": [
"q-fin.MF"
],
"url": "https://arxiv.org/abs/2603.02187v1",
"pdf": "https://arxiv.org/pdf/2603.02187v1",
"relevance_score": 23,
"high_keywords": [
"momentum"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7133208586",
"source": "openalex",
"title": "Quantitative Trading: Market Trend Analysis and Risk Management Strategies Research",
"abstract": "Quantitative trading, an integration of financial technology, statistics, and computer science, has become a dominant force in global financial markets, reshaping trading dynamics and liquidity structures. Driven by advancements in big data analytics, artificial intelligence, and high-frequency computing, its market share has surged to over 70% in major European and U.S. equity markets. However, the inherent complexity of quantitative trading, characterized by automated decision-making, high leverage, and interrelated strategy execution, amplifies market vulnerabilities. This study aims to systematically explore market trend characteristics and effective risk management frameworks for quantitative trading. Key findings reveal that market trends exhibit distinct persistence, cyclicality, and volatility clustering, which can be accurately identified through integrated technical, statistical, and machine learning methods. Additionally, the research demonstrates that a multi-layered risk management system, which incorporates VaR, expected shortfall, dynamic position sizing and diversification, significantly reduces extreme loss probabilities. The study concludes that adaptive strategy design and robust risk controls are critical for sustaining performance amid market structural changes. This research provides actionable insights for quantitative traders, risk managers, and regulators, contributing to the stability and efficiency of global financial markets.",
"authors": [
"Ziji Wang"
],
"date": "2026-03-02",
"categories": [
"finance",
"Advances in Economics Management and Political Sci"
],
"url": "https://doi.org/10.54254/2754-1169/2026.ld32014",
"pdf": "https://aemps.ewapub.com/article/view/32014.pdf",
"relevance_score": 23,
"high_keywords": [
"liquidity"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "2603.01109v1",
"source": "arxiv",
"title": "A stochastic correlation extension of the Vasicek credit risk model",
"abstract": "In the Vasicek credit portfolio model, tail risk is driven primarily by the asset-correlation parameter, yet empirically is subject to correlation risk. We propose a stochastic correlation extension of the Vasicek framework in which the correlation state evolves as a diffusion on the circle. This representation accommodates both non-mean-reverting and mean-reverting dependence regimes via circular Brownian motion and von Mises process, while retaining tractable transition densities. Conditionally on a fixed correlation state, we derive closed or semi-closed form expressions for the joint distribution of two assets, the joint first-passage (default) time distribution, and the joint survival probability. A simulation study quantifies how correlation volatility and persistence reshape joint default-at-horizon, survival, and joint barrier-crossing probabilities beyond marginal volatility effects. An empirical illustration using U.S. bank charge-off rates demonstrates economically interpretable time-variation in a dependence index and shows how inferred stochastic dependence translates into materially different joint tail-event probabilities. Overall, circular diffusion models provide a parsimonious and operationally tractable route to incorporating correlation risk into Vasicek structural credit calculations.",
"authors": [
"Dhruv Bansal",
"Mayank Goud",
"Sourav Majumdar"
],
"date": "2026-03-01",
"categories": [
"q-fin.RM"
],
"url": "https://arxiv.org/abs/2603.01109v1",
"pdf": "https://arxiv.org/pdf/2603.01109v1",
"relevance_score": 20,
"high_keywords": [
"correlation"
],
"medium_keywords": [
"tail risk"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2603.01109v2",
"source": "arxiv",
"title": "A stochastic correlation extension of the Vasicek credit risk model",
"abstract": "In the Vasicek credit portfolio model, tail risk is driven primarily by the asset-correlation parameter, yet empirically is subject to correlation risk. We propose a stochastic correlation extension of the Vasicek framework in which the correlation state evolves as a diffusion on the circle. This representation accommodates both non-mean-reverting and mean-reverting dependence regimes via circular Brownian motion and von Mises process, while retaining tractable transition densities. Conditionally on a fixed correlation state, we derive closed or semi-closed form expressions for the joint distribution of two assets, the joint first-passage (default) time distribution, and the joint survival probability. A simulation study quantifies how correlation volatility and persistence reshape joint default-at-horizon, survival, and joint barrier-crossing probabilities beyond marginal volatility effects. An empirical illustration using U.S. bank charge-off rates demonstrates economically interpretable time-variation in a dependence index and shows how inferred stochastic dependence translates into materially different joint tail-event probabilities. Overall, circular diffusion models provide a parsimonious and operationally tractable route to incorporating correlation risk into Vasicek structural credit calculations.",
"authors": [
"Dhruv Bansal",
"Mayank Goud",
"Sourav Majumdar"
],
"date": "2026-03-01",
"categories": [
"q-fin.RM"
],
"url": "https://arxiv.org/abs/2603.01109v2",
"pdf": "https://arxiv.org/pdf/2603.01109v2",
"relevance_score": 20,
"high_keywords": [
"correlation"
],
"medium_keywords": [
"tail risk"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7132822686",
"source": "openalex",
"title": "Cultural Values and Stock Market Liquidity Under Global Volatility: A Cross-Country Panel Analysis (2002\u20132021)",
"abstract": "This study examines how national culture influences financial market development and volatility across 18 countries from 2002 to 2021. Integrating Hofstede\u2019s cultural dimensions, particularly Uncertainty Avoidance (UAI) and Individualism (IDV), with market indicators such as capitalization, trading volume, and turnover, the analysis explores how socio-cultural factors interact with global risk conditions measured by the VIX index. Using fixed-effects and dynamic panel regressions, the results show that higher individualism is associated with deeper and more liquid markets, while greater uncertainty avoidance constrains trading intensity but supports more stable capitalization. Global volatility negatively affects liquidity, yet its impact is moderated by cultural characteristics, with stronger effects observed in emerging economies. These findings highlight that financial development is not purely institutional or macroeconomic but also culturally embedded. The study underscores the importance of tailoring financial policies to societal norms to enhance market efficiency and resilience amid global uncertainty.",
"authors": [
"Luz Maria Sipi Chevola",
"Xie Yamin"
],
"date": "2026-02-28",
"categories": [
"finance",
"European Scientific Journal ESJ"
],
"url": "https://doi.org/10.19044/esj.2026.v22n4p1",
"pdf": "https://eujournal.org/index.php/esj/article/download/20687/20116",
"relevance_score": 50,
"high_keywords": [
"vix",
"factor",
"liquidity"
],
"medium_keywords": [
"market efficiency"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Regression analysis"
],
"backtestable": true
},
{
"id": "2602.23784v1",
"source": "arxiv",
"title": "TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure",
"abstract": "Foundation models have transformed domains from language to genomics by learning general-purpose representations from large-scale, heterogeneous data. We introduce TradeFM, a 524M-parameter generative Transformer that brings this paradigm to market microstructure, learning directly from billions of trade events across >9K equities. To enable cross-asset generalization, we develop scale-invariant features and a universal tokenization scheme that map the heterogeneous, multi-modal event stream of order flow into a unified discrete sequence -- eliminating asset-specific calibration. Integrated with a deterministic market simulator, TradeFM-generated rollouts reproduce key stylized facts of financial returns, including heavy tails, volatility clustering, and absence of return autocorrelation. Quantitatively, TradeFM achieves 2-3x lower distributional error than Compound Hawkes baselines and generalizes zero-shot to geographically out-of-distribution APAC markets with moderate perplexity degradation. Together, these results suggest that scale-invariant trade representations capture transferable structure in market microstructure, opening a path toward synthetic data generation, stress testing, and learning-based trading agents.",
"authors": [
"Maxime Kawawa-Beaudan",
"Srijan Sood",
"Kassiani Papasotiriou",
"Daniel Borrajo",
"Manuela Veloso"
],
"date": "2026-02-27",
"categories": [
"cs.LG",
"cs.AI",
"q-fin.CP",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2602.23784v1",
"pdf": "https://arxiv.org/pdf/2602.23784v1",
"relevance_score": 65,
"high_keywords": [
"microstructure",
"order flow",
"correlation",
"cross-asset"
],
"medium_keywords": [
"transformer"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Transformer model"
],
"backtestable": true
},
{
"id": "oa_W7131778899",
"source": "openalex",
"title": "Geopolitical risks, market volatility, and tech firms involved in quantum computing",
"abstract": "This study examines how global uncertainty influences the financial dynamics of technology firms involved in quantum computing, a strategically significant but structurally fragile segment of emerging deep-tech markets. Using daily data from January 2015 to May 2025, the analysis integrates principal component decomposition, panel regression, Granger causality testing and volatility diagnostics to assess the transmission of market volatility and geopolitical risk. The findings show that market volatility, proxied by the VIX index, exerts a persistent and adverse influence on stock returns, confirming its role as a systemic risk factor. Geopolitical risk, measured through the ACT and THREAT sub-indices of the Geopolitical Risk Index (GPR), also affects return behaviour, but through asymmetric and time-varying transmission mechanisms that emerge under heightened uncertainty and global strategic tension. The results further reveal heterogeneous vulnerability profiles across firms, indicating conditional risk spillovers rather than uniform market reactions. The study contributes new empirical evidence on the interplay between financial and geopolitical risk in advanced technology sectors and offers a replicable framework for uncertainty modelling in frontier markets.",
"authors": [
"Oana Panazan",
"Catalin GHEORGHE"
],
"date": "2026-02-27",
"categories": [
"finance",
"Journal of Business Economics and Management"
],
"url": "https://doi.org/10.3846/jbem.2026.26193",
"pdf": "https://journals.vilniustech.lt/index.php/JBEM/article/download/26193/13327",
"relevance_score": 45,
"high_keywords": [
"vix",
"factor",
"decomposition"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Regression analysis",
"Granger causality"
],
"backtestable": false
},
{
"id": "oa_W7131862227",
"source": "openalex",
"title": "The Role of Global Volatility Indices and Domestic Economic Factors on Investor Risk Appetite in T\u00fcrkiye",
"abstract": "Risk appetite refers to investors propensity to take risks and is directly affected by changes in macroeconomic conditions. Risk appetite is expected to decrease as financial difficulties increase during periods of economic recession and contraction, whereas it is expected to increase during favorable economic conditions. This study analyzes the impact of the gold volatility index (GVZ), crude oil volatility index (OVX), and Chicago Futures Exchange Volatility Index (VIX) (also known as global risk appetite), as well as local economic factors such as inflation (CPI) and interest rates, on the REKS index (known as the Turkish investor risk appetite indicator), which reflects the risk tendencies of domestic (REKS Domestic) and foreign (REKS Foreign) investors in Turkey for the period April 2010-November 2024, using the VAR method. The study's findings show that the REKS Foreign Index is significantly sensitive to global risk and volatility indicators, particularly OVX and VIX. In contrast, the impact of CPI on this index is relatively limited compared to other global indicators. The REKS Domestic Index, on the other hand, is more strongly influenced by domestic macroeconomic variables such as CPI and interest rates, compared to OVX. These results indicate that domestic risk appetite is primarily dependent on domestic economic conditions, while the REKS Foreign Index is more sensitive to global risk perception and volatility dynamics.",
"authors": [
"Seda Turnac\u0131gil",
"Ecem Ar\u0131k"
],
"date": "2026-02-27",
"categories": [
"finance",
"Bulletin of Economic Theory and Analysis"
],
"url": "https://doi.org/10.25229/beta.1644438",
"pdf": "https://dergipark.org.tr/en/download/article-file/4630089",
"relevance_score": 30,
"high_keywords": [
"vix",
"factor"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "2602.23330v1",
"source": "arxiv",
"title": "Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks",
"abstract": "The advancement of large language models (LLMs) has accelerated the development of autonomous financial trading systems. While mainstream approaches deploy multi-agent systems mimicking analyst and manager roles, they often rely on abstract instructions that overlook the intricacies of real-world workflows, which can lead to degraded inference performance and less transparent decision-making. Therefore, we propose a multi-agent LLM trading framework that explicitly decomposes investment analysis into fine-grained tasks, rather than providing coarse-grained instructions. We evaluate the proposed framework using Japanese stock data, including prices, financial statements, news, and macro information, under a leakage-controlled backtesting setting. Experimental results show that fine-grained task decomposition significantly improves risk-adjusted returns compared to conventional coarse-grained designs. Crucially, further analysis of intermediate agent outputs suggests that alignment between analytical outputs and downstream decision preferences is a critical driver of system performance. Moreover, we conduct standard portfolio optimization, exploiting low correlation with the stock index and the variance of each system's output. This approach achieves superior performance. These findings contribute to the design of agent structure and task configuration when applying LLM agents to trading systems in practical settings.",
"authors": [
"Kunihiro Miyazaki",
"Takanobu Kawahara",
"Stephen Roberts",
"Stefan Zohren"
],
"date": "2026-02-26",
"categories": [
"cs.AI",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2602.23330v1",
"pdf": "https://arxiv.org/pdf/2602.23330v1",
"relevance_score": 51,
"high_keywords": [
"correlation",
"stock index"
],
"medium_keywords": [
"portfolio optimization"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.20856v1",
"source": "arxiv",
"title": "Stochastic Discount Factors with Cross-Asset Spillovers",
"abstract": "This paper develops a unified framework that links firm-level predictive signals, cross-asset spillovers, and the stochastic discount factor (SDF). Signals and spillovers are jointly estimated by maximizing the Sharpe ratio, yielding an interpretable SDF that both ranks characteristic relevance and uncovers the direction of predictive influence across assets. Out-of-sample, the SDF consistently outperforms self-predictive and expected-return benchmarks across investment universes and market states. The inferred information network highlights large, low-turnover firms as net transmitters. The framework offers a clear, economically grounded view of the informational architecture underlying cross-sectional return dynamics.",
"authors": [
"Doron Avramov",
"Xin He"
],
"date": "2026-02-24",
"categories": [
"q-fin.CP",
"econ.EM",
"q-fin.PM",
"stat.ML"
],
"url": "https://arxiv.org/abs/2602.20856v1",
"pdf": "https://arxiv.org/pdf/2602.20856v1",
"relevance_score": 54,
"high_keywords": [
"cross-asset",
"factor"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Cross-sectional analysis"
],
"backtestable": false
},
{
"id": "2602.21173v1",
"source": "arxiv",
"title": "Bayesian Parametric Portfolio Policies",
"abstract": "Parametric Portfolio Policies (PPP) estimate optimal portfolio weights directly as functions of observable signals by maximizing expected utility, bypassing the need to model asset returns and covariances. However, PPP ignores policy risk. We show that this is consequential, leading to an overstatement of expected utility and an understatement of portfolio risk. We develop Bayesian Parametric Portfolio Policies (BPPP), which place a prior on policy coefficients thereby correcting the decision rule. We derive a general result showing that the utility gap between PPP and BPPP is strictly positive and proportional to posterior parameter uncertainty and signal magnitude. Under a mean--variance approximation, this correction appears as an additional estimation-risk term in portfolio variance, implying that PPP overexposes when signals are strongest and when risk aversion is high. Empirically, in a high-dimensional setting with 242 signals and six factors over 1973--2023, BPPP delivers higher Sharpe ratios, substantially lower turnover, larger investor welfare, and lower tail risk, with advantages that increase monotonically in risk aversion and are strongest during crisis episodes.",
"authors": [
"Miguel C. Herculano"
],
"date": "2026-02-24",
"categories": [
"q-fin.PM"
],
"url": "https://arxiv.org/abs/2602.21173v1",
"pdf": "https://arxiv.org/pdf/2602.21173v1",
"relevance_score": 28,
"high_keywords": [
"factor"
],
"medium_keywords": [
"tail risk"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.21125v1",
"source": "arxiv",
"title": "An Infinite-Dimensional Insider Trading Game",
"abstract": "We generalize the seminal framework of Kyle (1985) to a many-asset setting, bridging the gap between informed-trading theory and modern trading practices. Specifically, we formulate an infinite-dimensional Bayesian trading game in which the informed trader's private information may concern arbitrary aspects of the cross-sectional payoff structure across a continuum of traded assets. In this general setting, we obtain a parsimonious equilibrium characterized by a single scalar fixed point, yielding closed-form characterizations of equilibrium trading strategy, price impact within and across markets, and the informational efficiency of equilibrium prices.",
"authors": [
"Christian Keller",
"Michael C. Tseng"
],
"date": "2026-02-24",
"categories": [
"q-fin.MF",
"econ.TH",
"q-fin.GN",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2602.21125v1",
"pdf": "https://arxiv.org/pdf/2602.21125v1",
"relevance_score": 23,
"high_keywords": [
"price impact"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Cross-sectional analysis"
],
"backtestable": false
},
{
"id": "2602.20771v1",
"source": "arxiv",
"title": "Market Inefficiency in Cryptoasset Markets",
"abstract": "We demonstrate market inefficiency in cryptoasset markets. Our approach examines investments that share a dominant risk factor but differ in their exposure to a secondary risk. We derive equilibrium restrictions that must hold regardless of how investors price either risk. Our empirical results strongly reject these necessary equilibrium restrictions. The rejection implies market inefficiency that cannot be attributed to mispriced risk, suggesting the presence of frictions that impede capital reallocation.",
"authors": [
"Joel Hasbrouck",
"Julian Ma",
"Fahad Saleh",
"Caspar Schwarz-Schilling"
],
"date": "2026-02-24",
"categories": [
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2602.20771v1",
"pdf": "https://arxiv.org/pdf/2602.20771v1",
"relevance_score": 20,
"high_keywords": [
"factor"
],
"medium_keywords": [
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.19590v1",
"source": "arxiv",
"title": "Metaorder modelling and identification from public data",
"abstract": "Market-order flow in financial markets exhibits long-range correlations. This is a widely known stylised fact of financial markets. A popular hypothesis for this stylised fact comes from the Lillo-Mike-Farmer (LMF) order-splitting theory. However, quantitative tests of this theory have historically relied on proprietary datasets with trader identifiers, limiting reproducibility and cross-market validation. We show that the LMF theory can be validated using publicly available Johannesburg Stock Exchange (JSE) data by leveraging recently developed methods for reconstructing synthetic metaorders. We demonstrate the validation using 3 years of Transaction and Quote Data (TAQ) for the largest 100 stocks on the JSE when assuming that there are either N=50 or N=150 effective traders managing metaorders in the market.",
"authors": [
"Ezra Goliath",
"Tim Gebbie"
],
"date": "2026-02-23",
"categories": [
"q-fin.TR",
"cs.CE",
"q-fin.ST",
"stat.CO"
],
"url": "https://arxiv.org/abs/2602.19590v1",
"pdf": "https://arxiv.org/pdf/2602.19590v1",
"relevance_score": 30,
"high_keywords": [
"order flow",
"correlation"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.19732v1",
"source": "arxiv",
"title": "VOLatility Archive for Realized Estimates (VOLARE)",
"abstract": "VOLARE (VOLatility Archive for Realized Estimates - https://volare.unime.it) is an open research infrastructure providing standardized realized volatility and covariance measures constructed from ultra-high-frequency financial data. The platform processes tick-level observations across equities, exchange rates, and futures using an asset-specific pipeline that addresses heterogeneous trading calendars, microstructure noise, and timestamp precision. For equities, price series are cleaned using a documented outlier detection procedure and sampled at regular intervals. VOLARE delivers a comprehensive set of realized estimators, including realized variance, range-based measures, bipower variation, semivariances, realized quarticity, realized kernels, and multivariate covariance measures, ensuring methodological consistency and cross-asset comparability. In addition to bulk dataset download, the platform supports interactive visualization and real-time estimation of established volatility models such as HAR and MEM specifications.",
"authors": [
"Fabrizio Cipollini",
"Giulia Cruciani",
"Giampiero M. Gallo",
"Alessandra Insana",
"Edoardo Otranto"
],
"date": "2026-02-23",
"categories": [
"q-fin.ST"
],
"url": "https://arxiv.org/abs/2602.19732v1",
"pdf": "https://arxiv.org/pdf/2602.19732v1",
"relevance_score": 30,
"high_keywords": [
"microstructure",
"cross-asset"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.19419v1",
"source": "arxiv",
"title": "RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMs",
"abstract": "Concentrated liquidity provision in decentralized exchanges presents a fundamental Impulse Control problem. Liquidity Providers (LPs) face a non-trivial trade-off between maximizing fee accrual through tight price-range concentration and minimizing the friction costs of rebalancing, including gas fees and swap slippage. Existing methods typically employ heuristic or threshold strategies that fail to account for market dynamics. This paper formulates liquidity management as an optimal control problem and derives the corresponding Hamilton-Jacobi-Bellman quasi-variational inequality (HJB-QVI). We present an approximate solution RAmmStein, a Deep Reinforcement Learning method that incorporates the mean-reversion speed (theta) of an Ornstein-Uhlenbeck process among other features as input to the model. We demonstrate that the agent learns to separate the state space into regions of action and inaction. We evaluate the framework using high-frequency 1Hz Coinbase trade data comprising over 6.8M trades. Experimental results show that RAmmStein achieves a superior net ROI of 0.72% compared to both passive and aggressive strategies. Notably, the agent reduces rebalancing frequency by 67% compared to a greedy rebalancing strategy while maintaining 88% active time. Our results demonstrate that regime-aware laziness can significantly improve capital efficiency by preserving the returns that would otherwise be eroded by the operational costs.",
"authors": [
"Pranay Anchuri"
],
"date": "2026-02-23",
"categories": [
"cs.LG",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2602.19419v1",
"pdf": "https://arxiv.org/pdf/2602.19419v1",
"relevance_score": 20,
"high_keywords": [
"liquidity"
],
"medium_keywords": [
"greed"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Reinforcement learning"
],
"backtestable": true
},
{
"id": "2602.18912v1",
"source": "arxiv",
"title": "Overreaction as an indicator for momentum in algorithmic trading: A Case of AAPL stocks",
"abstract": "This paper investigates whether short-term market overreactions can be systematically predicted and monetized as momentum signals using high-frequency emotional information and modern machine learning methods. Focusing on Apple Inc. (AAPL), we construct a comprehensive intraday dataset that combines volatility normalized returns with transformer-based emotion features extracted from Twitter messages. Overreactions are defined as extreme return realizations relative to contemporaneous volatility and transaction costs and are modeled as a three-class prediction problem. We evaluate the performance of several nonlinear classifiers, including XGBoost, Random Forests, Deep Neural Networks, and Bidirectional LSTMs, across multiple intraday frequencies (1, 5, 10, and 15 minute data). Model outputs are translated into trading strategies and assessed using risk-adjusted performance measures and formal statistical tests. The results show that machine learning models significantly outperform benchmark overreaction rules at ultra short horizons, while classical behavioral momentum effects dominate at intermediate frequencies, particularly around 10 minutes. Explainability analysis based on SHAP reveals that volatility and negative emotions, especially fear and sadness, play a central role in driving predicted overreactions. Overall, the findings demonstrate that emotion-driven overreactions contain a predictable structure that can be exploited by machine learning models, offering new insights into the behavioral origins of intraday momentum and the interaction between sentiment, volatility, and algorithmic trading.",
"authors": [
"Szymon Lis",
"Robert \u015alepaczuk",
"Pawe\u0142 Sakowski"
],
"date": "2026-02-21",
"categories": [
"q-fin.TR",
"q-fin.PM"
],
"url": "https://arxiv.org/abs/2602.18912v1",
"pdf": "https://arxiv.org/pdf/2602.18912v1",
"relevance_score": 79,
"high_keywords": [
"intraday",
"momentum"
],
"medium_keywords": [
"sentiment",
"fear",
"neural network",
"lstm",
"transformer"
],
"low_keywords": [
"defi"
],
"actionable": true,
"findings": [],
"methods": [
"Neural network",
"Random forest",
"LSTM",
"Transformer model"
],
"backtestable": false
},
{
"id": "oa_W7130712158",
"source": "openalex",
"title": "Payment for Order Flow and Option Internalization",
"abstract": "Abstract Option wholesalers specialize in purchasing and executing against retail option order flow. Orders are internalized via auctions (which provide price improvement) and the limit order book. Designated market makers (DMMs) have a key advantage in internalizing limit order book trades: they obtain the first five contracts of any order they bring to an exchange where they are a DMM. We exploit variation in DMM assignments and allocation rules to highlight how these rules create a barrier to entry in option wholesaling that does not exist for equity wholesaling, protecting wholesaler profits and high option PFOF.",
"authors": [
"Thomas Ernst",
"Chester S. Spatt"
],
"date": "2026-02-20",
"categories": [
"finance",
"Review of Financial Studies"
],
"url": "https://doi.org/10.1093/rfs/hhaf108",
"pdf": "https://doi.org/10.1093/rfs/hhaf108",
"relevance_score": 50,
"high_keywords": [
"market maker",
"order flow",
"market maker"
],
"medium_keywords": [
"allocation"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "s2_5239678b1e5b",
"source": "semantic_scholar",
"title": "Payment for Order Flow and Option Internalization",
"abstract": "Option wholesalers specialize in purchasing and executing against retail option order flow. Orders are internalized via auctions (which provide price improvement) and the limit order book. Designated market makers (DMMs) have a key advantage in internalizing limit order book trades: they obtain the first five contracts of any order they bring to an exchange where they are a DMM. We exploit variation in DMM assignments and allocation rules to highlight how these rules create a barrier to entry in option wholesaling that does not exist for equity wholesaling, protecting wholesaler profits and high option PFOF.",
"authors": [
"Thomas Ernst",
"Chester Spatt"
],
"date": "2026-02-20",
"categories": [
"finance",
"The Review of financial studies"
],
"url": "https://doi.org/10.1093/rfs/hhaf108",
"pdf": "",
"relevance_score": 35,
"high_keywords": [
"market maker",
"order flow"
],
"medium_keywords": [
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "2602.17851v1",
"source": "arxiv",
"title": "Beyond the Numbers: Causal Effects of Financial Report Sentiment on Bank Profitability",
"abstract": "This study establishes the causal effects of market sentiment on firm profitability, moving beyond traditional correlational analyses. It leverages a causal forest machine learning methodology to control for numerous confounding variables, enabling systematic analysis of heterogeneity and non-linearities often overlooked. A key innovation is the use of a pre-trained FinancialBERT to generate sentiment scores from quarterly reports, which are then treated as causal interventions impacting profitability dynamics like returns and volatilities. Utilizing a comprehensive dataset from NEPSE, NRB, and individual financial institutions, the research employs SHAP analysis to identify influential profit predictors. A two-pronged causal analysis further explores how sentiment's impact is conditioned by Loan Portfolio/Asset Composition and Balance Sheet Strength/Leverage. Average Treatment Effect analyses, combined with SHAP insights, reveal statistically significant causal associations between certain balance sheet and expense management variables and profitability. This advanced causal machine learning framework significantly extends existing literature, providing a more robust understanding of how financial sentiment truly impacts firm performance.",
"authors": [
"Krishna Neupane",
"Prem Sapkota",
"Ujjwal Prajapati"
],
"date": "2026-02-19",
"categories": [
"q-fin.CP",
"q-fin.ST"
],
"url": "https://arxiv.org/abs/2602.17851v1",
"pdf": "https://arxiv.org/pdf/2602.17851v1",
"relevance_score": 36,
"high_keywords": [
"correlation"
],
"medium_keywords": [
"sentiment"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.17890v1",
"source": "arxiv",
"title": "The Information Dynamics of Insider Intent: How Reporting Inversions (Form 144) Mask Informational Rents in Insider Sales (Form 4)",
"abstract": "This study identifies and quantifies a significant informational friction embedded in the SEC Form 144 disclosure regime, characterized as predictive decoupling. Drawing on a theoretical foundation of welfare economics, the article argues that the current reporting inversion -- where trade execution (Form 4) frequently precedes the public notice of intent (Form 144) -- violates the conditions for Pareto efficiency by inducing non-symmetric pricing. Utilizing an event-study framework of intent-to-sell windows, the analysis examines cases where insiders file a notice of proposed sale but fail to execute within the statutory 90-day period. The machine learning audit reveals a persistent 52.4 percent opacity rate, where aborted signals remain statistically indistinguishable from routine executions, creating a structural information ceiling that prevents the market from exhausting the signal's informational content. Contrary to the traditional small-firm effect, the study documents a large-cap significance paradox: while small-cap portfolios yield higher absolute abnormal returns (32.21 bps), statistically significant alpha is concentrated in large-cap firms (14.49 bps, $p = 0.021$). The results suggest that Institutional Salience enables more reliable processing of this negative non-event when reputational costs are maximized. Cross-sectional tests confirm that prior idiosyncratic volatility serves as a signal amplifier, with causal estimators identifying an illiquidity jump of up to 2.63 times. To mitigate this market failure, the study proposes a mandatory execution confirmation (Form 144-A) to transition the regime toward bilateral accountability, converting a predictive blind spot into a verifiable data stream and restoring the informational symmetry requisite for efficient capital allocation.",
"authors": [
"Krishna Neupane"
],
"date": "2026-02-19",
"categories": [
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2602.17890v1",
"pdf": "https://arxiv.org/pdf/2602.17890v1",
"relevance_score": 33,
"high_keywords": [
"liquidity"
],
"medium_keywords": [
"alpha",
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Cross-sectional analysis"
],
"backtestable": false
},
{
"id": "2602.17098v1",
"source": "arxiv",
"title": "Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization",
"abstract": "Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of achieving predetermined investment goals. Portfolio optimization is a key component that involves allocating the portfolio assets so as to maximize returns while minimizing risk taken. It is typically carried out by financial professionals who use a combination of quantitative techniques and investment expertise to make decisions about the portfolio allocation. Recent applications of Deep Reinforcement Learning (DRL) have shown promising results when used to optimize portfolio allocation by training model-free agents on historical market data. Many of these methods compare their results against basic benchmarks or other state-of-the-art DRL agents but often fail to compare their performance against traditional methods used by financial professionals in practical settings. One of the most commonly used methods for this task is Mean-Variance Portfolio Optimization (MVO), which uses historical time series information to estimate expected asset returns and covariances, which are then used to optimize for an investment objective. Our work is a thorough comparison between model-free DRL and MVO for optimal portfolio allocation. We detail the specifics of how to make DRL for portfolio optimization work in practice, also noting the adjustments needed for MVO. Backtest results demonstrate strong performance of the DRL agent across many metrics, including Sharpe ratio, maximum drawdowns, and absolute returns.",
"authors": [
"Srijan Sood",
"Kassiani Papasotiriou",
"Marius Vaiciulis",
"Tucker Balch"
],
"date": "2026-02-19",
"categories": [
"q-fin.PM",
"cs.AI",
"cs.LG"
],
"url": "https://arxiv.org/abs/2602.17098v1",
"pdf": "https://arxiv.org/pdf/2602.17098v1",
"relevance_score": 23,
"high_keywords": [],
"medium_keywords": [
"drawdown",
"portfolio optimization",
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Time series analysis",
"Reinforcement learning"
],
"backtestable": false
},
{
"id": "2602.16232v1",
"source": "arxiv",
"title": "A Wiener Chaos Approach to Martingale Modelling and Implied Volatility Calibration",
"abstract": "Calibration to a surface of option prices requires specifying a suitably flexible martingale model for the discounted asset price under a risk-neutral measure. Assuming Brownian noise and mean-square integrability, we construct an over-parameterized model based on the martingale representation theorem. In particular, we approximate the terminal value of the martingale via a truncated Wiener--chaos expansion and recover the intermediate dynamics by computing the corresponding conditional expectations. Using the Hermite-polynomial formulation of the Wiener chaos, we obtain easily implementable expressions that enable fast calibration to a target implied-volatility surface. We illustrate the flexibility and expressive power of the resulting model through numerical experiments on both simulated and real market data.",
"authors": [
"Pere Diaz-Lozano",
"Thomas K. Kloster"
],
"date": "2026-02-18",
"categories": [
"q-fin.MF",
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2602.16232v1",
"pdf": "https://arxiv.org/pdf/2602.16232v1",
"relevance_score": 30,
"high_keywords": [
"implied volatility",
"volatility surface"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.14670v1",
"source": "arxiv",
"title": "FactorMiner: A Self-Evolving Agent with Skills and Experience Memory for Financial Alpha Discovery",
"abstract": "Formulaic alpha factor mining is a critical yet challenging task in quantitative investment, characterized by a vast search space and the need for domain-informed, interpretable signals. However, finding novel signals becomes increasingly difficult as the library grows due to high redundancy. We propose FactorMiner, a lightweight and flexible self-evolving agent framework designed to navigate this complex landscape through continuous knowledge accumulation. FactorMiner combines a Modular Skill Architecture that encapsulates systematic financial evaluation into executable tools with a structured Experience Memory that distills historical mining trials into actionable insights (successful patterns and failure constraints). By instantiating the Ralph Loop paradigm -- retrieve, generate, evaluate, and distill -- FactorMiner iteratively uses memory priors to guide exploration, reducing redundant search while focusing on promising directions. Experiments on multiple datasets across different assets and Markets show that FactorMiner constructs a diverse library of high-quality factors with competitive performance, while maintaining low redundancy among factors as the library scales. Overall, FactorMiner provides a practical approach to scalable discovery of interpretable formulaic alpha factors under the \"Correlation Red Sea\" constraint.",
"authors": [
"Yanlong Wang",
"Jian Xu",
"Hongkang Zhang",
"Shao-Lun Huang",
"Danny Dongning Sun"
],
"date": "2026-02-16",
"categories": [
"q-fin.TR",
"cs.MA"
],
"url": "https://arxiv.org/abs/2602.14670v1",
"pdf": "https://arxiv.org/pdf/2602.14670v1",
"relevance_score": 43,
"high_keywords": [
"correlation",
"factor"
],
"medium_keywords": [
"alpha"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.14860v1",
"source": "arxiv",
"title": "Predicting the success of new crypto-tokens: the Pump.fun case",
"abstract": "We study the dynamics of token launched on Pump.fun, a Solana-based launchpad platform, to identify the determinants of the token success. Pump.fun employs a bonding curve mechanism to bootstrap initial liquidity possibly leading to graduation to the on-chain market, which can be seen as a token success. We build predictive models of the probability of graduation conditional on the current amount of Solana locked in the bonding curve and a set of explanatory variables that capture structural and behavioral aspects of the launch process. Conditioning the graduation probability on these variables significantly improves its predictive power, providing insights into early-stage market behavior, speculative and manipulative dynamics, and the informational efficiency of bonding-curve-based token launches.",
"authors": [
"Giulio Marino",
"Manuel Naviglio",
"Francesco Tarantelli",
"Fabrizio Lillo"
],
"date": "2026-02-16",
"categories": [
"q-fin.ST"
],
"url": "https://arxiv.org/abs/2602.14860v1",
"pdf": "https://arxiv.org/pdf/2602.14860v1",
"relevance_score": 23,
"high_keywords": [
"liquidity"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "oa_W7128947619",
"source": "openalex",
"title": "Information Transmission Across Markets: Tail Risk Spillovers and Cross-Market Volatility Forecasting",
"abstract": "This paper examines tail risk spillovers and cross-market volatility forecasting between the U.S. equity market and the crude oil market. Using realized and implied volatility within a heterogeneous autoregressive (HAR) framework, we document asymmetric and time-varying tail risk transmission across the two markets. Motivated by these findings, we propose several cross-market volatility forecasting strategies, including direct information augmentation, threshold-based designs, forecast averaging, and transfer learning. The results show that incorporating cross-market information improves volatility forecasts primarily at medium and longer horizons, consistent with the forward-looking nature of implied volatility. Moreover, the relative effectiveness of different transmission mechanisms varies across markets, with transfer learning performing particularly well in the crude oil market. Overall, the findings highlight the importance of linking tail risk spillovers to volatility forecasting and demonstrate that flexible cross-market information transmission can enhance predictive performance across markets and horizons.",
"authors": [
"Shig\u0113 P\u00e9ng",
"Yun Shi"
],
"date": "2026-02-15",
"categories": [
"finance",
"Mathematics"
],
"url": "https://doi.org/10.3390/math14040686",
"pdf": "https://www.mdpi.com/2227-7390/14/4/686/pdf?version=1771145412",
"relevance_score": 36,
"high_keywords": [
"implied volatility"
],
"medium_keywords": [
"tail risk"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "2602.14138v1",
"source": "arxiv",
"title": "Factor Engine: A Python Library for Systematic Financial Factor Computation and Analysis",
"abstract": "Factor Engine is a high-performance, open-source Python library designed for the systematic computation and analysis of financial factors. Built around a modular and extensible API that leverages Python decorators, Factor Engine enables users to define custom factors with ease and integrates seamlessly with the modern data science ecosystem. To assess its practical effectiveness, we compare the mispricing factors computed by Factor Engine to those generated using a reference Stata implementation, finding that both approaches yield highly similar results and comparable performance in backtesting analyses. Furthermore, we experimentally apply these factors within machine learning workflows for trading strategy development, illustrating their practical utility and potential for quantitative finance research.",
"authors": [
"Ata Keskin"
],
"date": "2026-02-15",
"categories": [
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2602.14138v1",
"pdf": "https://arxiv.org/pdf/2602.14138v1",
"relevance_score": 29,
"high_keywords": [
"factor"
],
"medium_keywords": [],
"low_keywords": [
"defi"
],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7129146840",
"source": "openalex",
"title": "Predictive Signals from VIX Spikes: A Comparative Study of Linear, Logistic, and GARCH-Based Return Forecasting Models",
"abstract": "This study investigates the relationship between market volatility, especially VIX levels and spikes above 45, and future equity returns. The analysis stems from the tactical decisions investors face during sharp market drawdowns. We explore whether volatility indicators, combined with investor sentiment measures, can inform shifts from defensive to opportunistic portfolio positions. Using U.S. data from 2008 to 2025, we test linear regression, logistic regression, and GARCH (1,1) models to evaluate return predictability. Results show that extreme VIX spikes offer contrarian signals, with significant positive returns over three-month horizons. Logistic regression confirms significance over both three-month and one-year periods. These findings remain robust after controlling for valuation, credit spreads, PMI, sentiment ratios, and interaction effects. While GARCH captures conditional variance, it lacks forward-looking predictive power in high-stress regimes. Overall, the evidence suggests that volatility timing merits consideration as part of a tactical allocation framework. Our findings also contribute to the market efficiency debate by integrating market expectations with economic indicators for risk-aware decision-making.",
"authors": [
"Philip L Fazio",
"David Spohn"
],
"date": "2026-02-14",
"categories": [
"finance",
"Preprints.org"
],
"url": "https://doi.org/10.20944/preprints202602.1048.v1",
"pdf": "https://www.preprints.org/frontend/manuscript/3a42b212752e8afd47d03d861e5ba402/download_pub",
"relevance_score": 51,
"high_keywords": [
"vix"
],
"medium_keywords": [
"market efficiency",
"sentiment",
"drawdown",
"allocation"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Regression analysis",
"GARCH model"
],
"backtestable": true
},
{
"id": "2602.12490v1",
"source": "arxiv",
"title": "Transformer-based CoVaR: Systemic Risk in Textual Information",
"abstract": "Conditional Value-at-Risk (CoVaR) quantifies systemic financial risk by measuring the loss quantile of one asset, conditional on another asset experiencing distress. We develop a Transformer-based methodology that integrates financial news articles directly with market data to improve CoVaR estimates. Unlike approaches that use predefined sentiment scores, our method incorporates raw text embeddings generated by a large language model (LLM). We prove explicit error bounds for our Transformer CoVaR estimator, showing that accurate CoVaR learning is possible even with small datasets. Using U.S. market returns and Reuters news items from 2006--2013, our out-of-sample results show that textual information impacts the CoVaR forecasts. With better predictive performance, we identify a pronounced negative dip during market stress periods across several equity assets when comparing the Transformer-based CoVaR to both the CoVaR without text and the CoVaR using traditional sentiment measures. Our results show that textual data can be used to effectively model systemic risk without requiring prohibitively large data sets.",
"authors": [
"Junyu Chen",
"Tom Boot",
"Lingwei Kong",
"Weining Wang"
],
"date": "2026-02-13",
"categories": [
"econ.EM",
"q-fin.RM",
"stat.ML"
],
"url": "https://arxiv.org/abs/2602.12490v1",
"pdf": "https://arxiv.org/pdf/2602.12490v1",
"relevance_score": 24,
"high_keywords": [],
"medium_keywords": [
"sentiment",
"transformer"
],
"low_keywords": [
"defi"
],
"actionable": false,
"findings": [],
"methods": [
"Transformer model"
],
"backtestable": true
},
{
"id": "2602.12104v1",
"source": "arxiv",
"title": "Liquidation Dynamics in DeFi and the Role of Transaction Fees",
"abstract": "Liquidation of collateral are the primary safeguard for solvency of lending protocols in decentralized finance. However, the mechanics of liquidations expose these protocols to predatory price manipulations and other forms of Maximal Extractable Value (MEV). In this paper, we characterize the optimal liquidation strategy, via a dynamic program, from the perspective of a profit-maximizing liquidator when the spot oracle is given by a Constant Product Market Maker (CPMM). We explicitly model Oracle Extractable Value (OEV) where liquidators manipulate the CPMM with sandwich attacks to trigger profitable liquidation events. We derive closed-form liquidation bounds and prove that CPMM transaction fees act as a critical security parameter. Crucially, we demonstrate that fees do not merely reduce attacker profits, but can make such manipulations unprofitable for an attacker. Our findings suggest that CPMM transaction fees serve a dual purpose: compensating liquidity providers and endogenously hardening CPMM oracles against manipulation without the latency of time-weighted averages or medianization.",
"authors": [
"Agathe Sadeghi",
"Zachary Feinstein"
],
"date": "2026-02-12",
"categories": [
"q-fin.MF",
"math.DS",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2602.12104v1",
"pdf": "https://arxiv.org/pdf/2602.12104v1",
"relevance_score": 43,
"high_keywords": [
"market maker",
"liquidity",
"market maker"
],
"medium_keywords": [],
"low_keywords": [
"defi"
],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "2602.10785v1",
"source": "arxiv",
"title": "A novel approach to trading strategy parameter optimization using double out-of-sample data and walk-forward techniques",
"abstract": "This study introduces a novel approach to walk-forward optimization by parameterizing the lengths of training and testing windows. We demonstrate that the performance of a trading strategy using the Exponential Moving Average (EMA) evaluated within a walk-forward procedure based on the Robust Sharpe Ratio is highly dependent on the chosen window size. We investigated the strategy on intraday Bitcoin data at six frequencies (1 minute to 60 minutes) using 81 combinations of walk-forward window lengths (1 day to 28 days) over a 19-month training period. The two best-performing parameter sets from the training data were applied to a 21-month out-of-sample testing period to ensure data independence. The strategy was only executed once during the testing period. To further validate the framework, strategy parameters estimated on Bitcoin were applied to Binance Coin and Ethereum. Our results suggest the robustness of our custom approach. In the training period for Bitcoin, all combinations of walk-forward windows outperformed a Buy-and-Hold strategy. During the testing period, the strategy performed similarly to Buy-and-Hold but with lower drawdown and a higher Information Ratio. Similar results were observed for Binance Coin and Ethereum. The real strength was demonstrated when a portfolio combining Buy-and-Hold with our strategies outperformed all individual strategies and Buy-and-Hold alone, achieving the highest overall performance and a 50 percent reduction in drawdown. A conservative fee of 0.1 percent per transaction was included in all calculations. A cost sensitivity analysis was performed as a sanity check, revealing that the strategy's break-even point was around 0.4 percent per transaction. This research highlights the importance of optimizing walk-forward window lengths and emphasizing the value of single-time out-of-sample testing for reliable strategy evaluation.",
"authors": [
"Tomasz Mroziewicz",
"Robert \u015alepaczuk"
],
"date": "2026-02-11",
"categories": [
"q-fin.TR",
"q-fin.MF",
"q-fin.PM"
],
"url": "https://arxiv.org/abs/2602.10785v1",
"pdf": "https://arxiv.org/pdf/2602.10785v1",
"relevance_score": 24,
"high_keywords": [
"intraday"
],
"medium_keywords": [
"drawdown"
],
"low_keywords": [
"bitcoin",
"ethereum"
],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.09887v1",
"source": "arxiv",
"title": "Partially Active Automated Market Makers",
"abstract": "We introduce a new class of automated market maker (AMM), the \\emph{partially active automated market maker} (PA-AMM). PA-AMM divides its reserves into two parts, the active and the passive parts, and uses only the active part for trading. At the top of every block, such a division is done again to keep the active reserves always being \\(\u03bb\\)-portion of total reserves, where \\(\u03bb\\in (0, 1]\\) is an activeness parameter. We show that this simple mechanism reduces adverse selection costs, measured by loss-versus-rebalancing (LVR), and thereby improves the wealth of liquidity providers (LPs) relative to plain constant-function market makers (CFMMs). As a trade-off, the asset weights within a PA-AMM pool may deviate from their target weights implied by its invariant curve. Motivated by the optimal index-tracking problem literature, we also propose and solve an optimization problem that balances such deviation and the reduction of LVR.",
"authors": [
"Sunghun Ko"
],
"date": "2026-02-10",
"categories": [
"q-fin.MF"
],
"url": "https://arxiv.org/abs/2602.09887v1",
"pdf": "https://arxiv.org/pdf/2602.09887v1",
"relevance_score": 45,
"high_keywords": [
"market maker",
"liquidity",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.10071v1",
"source": "arxiv",
"title": "Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets",
"abstract": "Electricity price forecasting (EPF) plays a critical role in power system operation and market decision making. While existing review studies have provided valuable insights into forecasting horizons, market mechanisms, and evaluation practices, the rapid adoption of deep learning has introduced increasingly diverse model architectures, output structures, and training objectives that remain insufficiently analyzed in depth. This paper presents a structured review of deep learning methods for EPF in day-ahead, intraday, and balancing markets. Specifically, We introduce a unified taxonomy that decomposes deep learning models into backbone, head, and loss components, providing a consistent evaluation perspective across studies. Using this framework, we analyze recent trends in deep learning components across markets. Our study highlights the shift toward probabilistic, microstructure-centric, and market-aware designs. We further identify key gaps in the literature, including limited attention to intraday and balancing markets and the need for market-specific modeling strategies, thereby helping to consolidate and advance existing review studies.",
"authors": [
"Runyao Yu",
"Derek W. Bunn",
"Julia Lin",
"Jochen Stiasny",
"Fabian Leimgruber"
],
"date": "2026-02-10",
"categories": [
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2602.10071v1",
"pdf": "https://arxiv.org/pdf/2602.10071v1",
"relevance_score": 43,
"high_keywords": [
"intraday",
"microstructure"
],
"medium_keywords": [
"deep learning"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.18481v1",
"source": "arxiv",
"title": "AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models",
"abstract": "The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge tests to interactive trading simulations. However, current evaluations of real-time trading performance overlook a critical failure mode: severe behavioral instability in sequential decision-making under uncertainty. We empirically show that LLM-based trading agents exhibit extreme run-to-run variance, inconsistent action sequences even under deterministic decoding, and irrational action flipping across adjacent time steps. These issues stem from stateless autoregressive architectures lacking persistent action memory, as well as sensitivity to continuous-to-discrete action mappings in portfolio allocation. As a result, many existing financial trading benchmarks produce unreliable, non-reproducible, and uninformative evaluations. To address these limitations, we propose AlphaForgeBench, a principled framework that reframes LLMs as quantitative researchers rather than execution agents. Instead of emitting trading actions, LLMs generate executable alpha factors and factor-based strategies grounded in financial reasoning. This design decouples reasoning from execution, enabling fully deterministic and reproducible evaluation while aligning with real-world quantitative research workflows. Experiments across multiple state-of-the-art LLMs show that AlphaForgeBench eliminates execution-induced instability and provides a rigorous benchmark for assessing financial reasoning, strategy formulation, and alpha discovery.",
"authors": [
"Wentao Zhang",
"Mingxuan Zhao",
"Jincheng Gao",
"Jieshun You",
"Huaiyu Jia"
],
"date": "2026-02-10",
"categories": [
"q-fin.TR",
"cs.AI"
],
"url": "https://arxiv.org/abs/2602.18481v1",
"pdf": "https://arxiv.org/pdf/2602.18481v1",
"relevance_score": 33,
"high_keywords": [
"factor"
],
"medium_keywords": [
"alpha",
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.08527v1",
"source": "arxiv",
"title": "Consumption-Investment with anticipative noise",
"abstract": "We revisit the classical Merton consumption--investment problem when risky-asset returns are modeled by stochastic differential equations interpreted through a general $\u03b1$-integral, interpolating between It\u00f4, Stratonovich, and related conventions. Holding preferences and the investment opportunity set fixed, changing the noise interpretation modifies the effective drift of asset returns in a systematic way. For logarithmic utility and constant volatilities, we derive closed-form optimal policies in a market with $n$ risky assets: optimal consumption remains a fixed fraction of wealth, while optimal portfolio weights are shifted according to $\u03b8_\u03b1^\\ast = V^{-1}(\u03bc-r\\mathbf{1})+\u03b1\\,V^{-1}\\operatorname{diag}(V)\\mathbf{1}$, where $V$ is the return covariance matrix and $\\operatorname{diag}(V)$ denotes the diagonal matrix with the same diagonal as $V$. In the single-asset case this reduces to $\u03b8_\u03b1^\\ast=(\u03bc-r)/\u03c3^{2}+\u03b1$. We then show that genuinely state-dependent effects arise when asset volatility is driven by a stochastic factor correlated with returns. In this setting, the $\u03b1$-interpretation generates an additional drift correction proportional to the instantaneous covariation between factor and return noise. As a canonical example, we analyze a Heston stochastic volatility model, where the resulting optimal risky exposure depends inversely on the current variance level.",
"authors": [
"Mario Ayala",
"Benjamin Vallejo Jim\u00e9nez"
],
"date": "2026-02-09",
"categories": [
"q-fin.MF"
],
"url": "https://arxiv.org/abs/2602.08527v1",
"pdf": "https://arxiv.org/pdf/2602.08527v1",
"relevance_score": 28,
"high_keywords": [
"factor"
],
"medium_keywords": [
"stochastic volatility"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7128378329",
"source": "openalex",
"title": "A Hybrid Framework for Multi-Stock Trading: Deep Q-Networks with Portfolio Optimization",
"abstract": "This paper presents a hybrid framework for multi-stock trading that combines the decision-making ability of Deep Q-Networks (DQN) with the allocation precision of portfolio optimization models. Realistic markets are noisy and non-stationary, and complex action spaces can hinder reinforcement learning (RL) performance. The DQN generates buy/sell signals based on market conditions. The framework passes buy-listed assets to an optimizer, which computes portfolio weights. Five allocation strategies are examined: na\u00efve 1/N, Markowitz Mean\u2013Variance, Global Minimum Variance, Risk Parity, and Sharpe Ratio Maximization. Empirical evaluations on emerging-market exchange-traded funds (ETFs), as well as additional tests on U.S. equities, show that even the baseline DQN outperforms traditional technical indicators. Furthermore, integrating any of the optimization approaches with DQN yields measurable improvements in return-risk performance metrics. Among the hybrid frameworks, DQN combined with Sharpe Ratio Maximization delivers the most consistent gains. The findings highlight the value of decomposing stock selection from capital allocation and demonstrate the effectiveness of the proposed DQN-optimization framework on our testbed.",
"authors": [
"Soroush Shahsafi",
"Farnoosh Naderkhani"
],
"date": "2026-02-09",
"categories": [
"finance",
"Journal of risk and financial management"
],
"url": "https://doi.org/10.3390/jrfm19020132",
"pdf": "https://www.mdpi.com/1911-8074/19/2/132/pdf?version=1770707021",
"relevance_score": 28,
"high_keywords": [],
"medium_keywords": [
"etf",
"portfolio optimization",
"risk parity",
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Reinforcement learning"
],
"backtestable": true
},
{
"id": "2602.07659v1",
"source": "arxiv",
"title": "Continuous Program Search",
"abstract": "Genetic Programming yields interpretable programs, but small syntactic mutations can induce large, unpredictable behavioral shifts, degrading locality and sample efficiency. We frame this as an operator-design problem: learn a continuous program space where latent distance has behavioral meaning, then design mutation operators that exploit this structure without changing the evolutionary optimizer. We make locality measurable by tracking action-level divergence under controlled latent perturbations, identifying an empirical trust region for behavior-local continuous variation. Using a compact trading-strategy DSL with four semantic components (long/short entry and exit), we learn a matching block-factorized embedding and compare isotropic Gaussian mutation over the full latent space to geometry-compiled mutation that restricts updates to semantically paired entry--exit subspaces and proposes directions using a learned flow-based model trained on logged mutation outcomes. Under identical $(\u03bc+\u03bb)$ evolution strategies and fixed evaluation budgets across five assets, the learned mutation operator discovers strong strategies using an order of magnitude fewer evaluations and achieves the highest median out-of-sample Sharpe ratio. Although isotropic mutation occasionally attains higher peak performance, geometry-compiled mutation yields faster, more reliable progress, demonstrating that semantically aligned mutation can substantially improve search efficiency without modifying the underlying evolutionary algorithm.",
"authors": [
"Matthew Siper",
"Muhammad Umair Nasir",
"Ahmed Khalifa",
"Lisa Soros",
"Jay Azhang"
],
"date": "2026-02-07",
"categories": [
"cs.LG",
"cs.AI",
"q-fin.ST"
],
"url": "https://arxiv.org/abs/2602.07659v1",
"pdf": "https://arxiv.org/pdf/2602.07659v1",
"relevance_score": 39,
"high_keywords": [
"factor"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "oa_W7133122421",
"source": "openalex",
"title": "Predictive Finance: Leveraging Machine Learning to Anticipate Market Volatility in Emerging Economies",
"abstract": "Emerging macroeconomic economies have financial markets that are highly volatile, structurally ineffective, and are prone to global macroeconomic shocks. The nonlinear dependence and high frequency fluctuations of such markets are hard to model in the tradition econometric models. This paper discusses how machine learning can be used to forecast the volatility of the market in the emerging economies to improve the forecasting performance and promote the proactive decision-making in investment. The study combines historical prices, macroeconomic factors, trading volumes and sentiment-based variables to build predictive models using supervised learning algorithms, such as random forests, support vector machines, and Long Short-Term Memory (LSTM) networks. It is compared to the traditional volatility models like GARCH to analyze the performance improvements. The results reveal that machine learning models have a better predictive power, especially when it comes to abrupt volatility spikes that are caused by changes in policies, geopolitics and reversed capital flows. The significance of features analysis also suggests that the fact that there are indicators of macroeconomic instability and cross-market spillover effects is a crucial variable to the establishment of volatility patterns. The study also refers to the issues of data quality, model interpretability and overfitting in the new market environment. This paper builds upon the empirical evidence of the utility of advanced predictive analytics and, therefore, can be considered contributing to the growing list of literature at the intersection of finance and artificial intelligence. Their findings offer workable conclusions to institutional investors, portfolio managers and policymakers who require early warning mechanisms of financial instability. Lastly, machine learning-based volatility predictions can make risk management systems stronger and enable stronger financial systems in emerging economies.",
"authors": [
"Ms. Sandhya V",
"Ms. Shailaja S",
"Ms. Sai Ramya K"
],
"date": "2026-02-06",
"categories": [
"finance",
"International Journal of Integrated Research and P"
],
"url": "https://doi.org/10.65579/31075037.0119",
"pdf": "https://ijirp.org/index.php/files/article/download/141/132",
"relevance_score": 71,
"high_keywords": [
"high frequency",
"factor",
"high frequency"
],
"medium_keywords": [
"sentiment",
"lstm"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Random forest",
"LSTM",
"GARCH model"
],
"backtestable": true
},
{
"id": "2602.07085v1",
"source": "arxiv",
"title": "QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining",
"abstract": "Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose QuantaAlpha, an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors through trajectory-level mutation and crossover operations. QuantaAlpha localizes suboptimal steps in each trajectory for targeted revision and recombines complementary high-reward segments to reuse effective patterns, enabling structured exploration and refinement across mining iterations. During factor generation, QuantaAlpha enforces semantic consistency across the hypothesis, factor expression, and executable code, while constraining the complexity and redundancy of the generated factor to mitigate crowding. Extensive experiments on the China Securities Index 300 (CSI 300) demonstrate consistent gains over strong baseline models and prior agentic systems. When utilizing GPT-5.2, QuantaAlpha achieves an Information Coefficient (IC) of 0.1501, with an Annualized Rate of Return (ARR) of 27.75% and a Maximum Drawdown (MDD) of 7.98%. Moreover, factors mined on CSI 300 transfer effectively to the China Securities Index 500 (CSI 500) and the Standard & Poor's 500 Index (S&P 500), delivering 160% and 137% cumulative excess return over four years, respectively, which indicates strong robustness of QuantaAlpha under market distribution shifts.",
"authors": [
"Jun Han",
"Shuo Zhang",
"Wei Li",
"Zhi Yang",
"Yifan Dong"
],
"date": "2026-02-06",
"categories": [
"q-fin.ST",
"cs.AI",
"q-fin.CP"
],
"url": "https://arxiv.org/abs/2602.07085v1",
"pdf": "https://arxiv.org/pdf/2602.07085v1",
"relevance_score": 61,
"high_keywords": [
"s&p 500",
"factor"
],
"medium_keywords": [
"alpha",
"excess return",
"drawdown"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "2602.06424v1",
"source": "arxiv",
"title": "Single- and Multi-Level Fourier-RQMC Methods for Multivariate Shortfall Risk",
"abstract": "Multivariate shortfall risk measures provide a principled framework for quantifying systemic risk and determining capital allocations prior to aggregation in interconnected financial systems. Despite their well established theoretical properties, the numerical estimation of multivariate shortfall risk and the corresponding optimal allocations remains computationally challenging, as existing Monte Carlo based approaches can be numerically expensive due to slow convergence. In this work, we develop a new class of single and multilevel numerical algorithms for estimating multivariate shortfall risk and the associated optimal allocations, based on a combination of Fourier inversion techniques and randomized quasi Monte Carlo (RQMC) sampling. Rather than operating in physical space, our approach evaluates the relevant expectations appearing in the risk constraint and its optimization in the frequency domain, where the integrands exhibit enhanced smoothness properties that are well suited for RQMC integration. We establish a rigorous mathematical framework for the resulting Fourier RQMC estimators, including convergence analysis and computational complexity bounds. Beyond the single level method, we introduce a multilevel RQMC scheme that exploits the geometric convergence of the underlying deterministic optimization algorithm to reduce computational cost while preserving accuracy. Numerical experiments demonstrate that the proposed Fourier RQMC methods outperform sample average approximation and stochastic optimization benchmarks in terms of accuracy and computational cost across a range of models for the risk factors and loss structures. Consistent with the theoretical analysis, these results demonstrate improved asymptotic convergence and complexity rates relative to the benchmark methods, with additional savings achieved through the proposed multilevel RQMC construction.",
"authors": [
"Chiheb Ben Hammouda",
"Truong Ngoc Nguyen"
],
"date": "2026-02-06",
"categories": [
"q-fin.CP",
"math.NA",
"q-fin.MF",
"q-fin.RM"
],
"url": "https://arxiv.org/abs/2602.06424v1",
"pdf": "https://arxiv.org/pdf/2602.06424v1",
"relevance_score": 20,
"high_keywords": [
"factor"
],
"medium_keywords": [
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Monte Carlo simulation"
],
"backtestable": true
},
{
"id": "2602.06198v1",
"source": "arxiv",
"title": "Insider Purchase Signals in Microcap Equities: Gradient Boosting Detection of Abnormal Returns",
"abstract": "This paper examines whether SEC Form 4 insider purchase filings predict abnormal returns in U.S. microcap stocks. The analysis covers 17,237 open-market purchases across 1,343 issuers from 2018 through 2024, restricted to market capitalizations between \\$30M and \\$500M. A gradient boosting classifier trained on insider identity, transaction history, and market conditions at disclosure achieves AUC of 0.70 on out-of-sample 2024 data. At an optimized threshold of 0.20, precision is 0.38 and recall is 0.69. The distance from the 52-week high dominates feature importance, accounting for 36% of predictive signal. A momentum pattern emerges in the data: transactions disclosed after price appreciation exceeding 10% yield the highest mean cumulative abnormal return (6.3%) and the highest probability of outperformance (36.7%). This contrasts with the simple mean-reversion intuition often applied to post-run-up entries. The result is robust to winsorization and holds across subsamples. These patterns are consistent with slower information incorporation in illiquid markets, where trend confirmation may filter for higher-conviction insider signals.",
"authors": [
"Hangyi Zhao"
],
"date": "2026-02-05",
"categories": [
"q-fin.ST",
"q-fin.TR"
],
"url": "https://arxiv.org/abs/2602.06198v1",
"pdf": "https://arxiv.org/pdf/2602.06198v1",
"relevance_score": 31,
"high_keywords": [
"momentum"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Gradient boosting"
],
"backtestable": true
},
{
"id": "2602.05241v1",
"source": "arxiv",
"title": "On the Skew Stickiness Ratio",
"abstract": "The skew stickiness ratio is a statistic that captures the joint dynamics of an asset price and its volatility. We derive a representation formula for this quantity using the It\u00f4-Wentzell and Clark-Ocone formulae, and we apply it to analyze its asymptotics under Bergomi-type stochastic volatility models.",
"authors": [
"Masaaki Fukasawa"
],
"date": "2026-02-05",
"categories": [
"q-fin.MF",
"math.PR"
],
"url": "https://arxiv.org/abs/2602.05241v1",
"pdf": "https://arxiv.org/pdf/2602.05241v1",
"relevance_score": 20,
"high_keywords": [
"skew"
],
"medium_keywords": [
"stochastic volatility"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "2602.07048v2",
"source": "arxiv",
"title": "LLM as a Risk Manager: LLM Semantic Filtering for Lead-Lag Trading in Prediction Markets",
"abstract": "Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this challenge: (i) a statistical stage that uses Granger causality to identify candidate leader-follower pairs from market-implied probability time series, and (ii) an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism based on event descriptions. Because causal ground truth is unobserved, we evaluate the ranked pairs using a fixed, signal-triggered trading protocol that maps relationship quality into realized profit and loss (PnL). On Kalshi Economics markets, our hybrid approach consistently outperforms the statistical baseline. Across rolling evaluations, the win rate increases from 51.4% to 54.5%. Crucially, the average magnitude of losing trades decreases substantially from 649 USD to 347 USD. This reduction is driven by the LLM's ability to filter out statistically fragile links that are prone to large losses, rather than relying on rare gains. These improvements remain stable across different trading configurations, indicating that the gains are not driven by specific parameter choices. Overall, the results suggest that LLMs function as semantic risk managers on top of statistical discovery, prioritizing lead-lag relationships that generalize under changing market conditions.",
"authors": [
"Sumin Kim",
"Minjae Kim",
"Jihoon Kwon",
"Yoon Kim",
"Nicole Kagan"
],
"date": "2026-02-04",
"categories": [
"q-fin.RM",
"q-fin.ST"
],
"url": "https://arxiv.org/abs/2602.07048v2",
"pdf": "https://arxiv.org/pdf/2602.07048v2",
"relevance_score": 23,
"high_keywords": [
"correlation"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Granger causality",
"Time series analysis"
],
"backtestable": true
},
{
"id": "oa_W7127055379",
"source": "openalex",
"title": "Latent Profiles of Turkish Pre-Service Teachers\u2019 Attitudes Toward Artificial Intelligence",
"abstract": "The rapid proliferation of artificial intelligence technologies in educational contexts demands a nuanced understanding of how future educators perceive and position themselves in relation to these tools. This study adopts a person-centred approach to identify latent attitudinal profiles of Turkish pre-service teachers concerning AI integration in education. Utilizing a cross-sectional survey design, data were collected from 529 participants and analysed through Latent Profile Analysis, complemented by confirmatory factor analysis, ANOVA, and chi-square tests. The results uncovered three distinct profiles: Artificial Intelligence (AI)-Confident Advocates, Balanced Readiness, and Low-Readiness Sceptics. Each profile demonstrated unique patterns of AI-related self-efficacy, pedagogical technology engagement, and attitudinal orientation, indicating substantial intra-group variation. While the majority exhibited moderate openness to AI, only a small subgroup reported the confidence and practical readiness necessary for meaningful classroom integration. Notably, demographic predictors such as gender, and programme type did not significantly influence profile membership, whereas academic seniority emerged as a differentiating factor. These findings underscore the limitations of one-size-fits-all approaches in teacher education and highlight the necessity of differentiated, profile-sensitive training models, as pre-service teachers differ in AI confidence and readiness, making uniform training unable to address their diverse learning needs. Furthermore, the study suggests that fostering AI readiness requires not only technical proficiency but also the cultivation of pedagogical confidence and reflective engagement with emerging technologies. By mapping the attitudinal landscape of pre-service teachers, this research offers critical insights for curriculum developers, educational policymakers, and teacher educators seeking to align technological innovation with human-centre",
"authors": [
"Bahaddin Demirdi\u015f"
],
"date": "2026-02-01",
"categories": [
"finance",
"Bart\u0131n University Journal of Faculty of Education"
],
"url": "https://doi.org/10.14686/buefad.1710784",
"pdf": "https://dergipark.org.tr/en/download/article-file/4921803",
"relevance_score": 23,
"high_keywords": [
"factor"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Cross-sectional analysis"
],
"backtestable": false
},
{
"id": "oa_W7127360201",
"source": "openalex",
"title": "Explainable Patterns in Cryptocurrency Microstructure",
"abstract": "We document stable cross-asset patterns in cryptocurrency limit-order-book microstructure: the same engineered order book and trade features exhibit remarkably similar predictive importance and SHAP dependence shapes across assets spanning an order of magnitude in market capitalization (BTC, LTC, ETC, ENJ, ROSE). The data covers Binance Futures perpetual contract order books and trades on 1-second frequency starting from January 1st, 2022 up to October 12th, 2025. Using a unified CatBoost modeling pipeline with a direction-aware GMADL objective and time-series cross validation, we show that feature rankings and partial effects are stable across assets despite heterogeneous liquidity and volatility. We connect these SHAP structures to microstructure theory (order flow imbalance, spread, and adverse selection) and validate tradability via a conservative top-of-book taker backtest as well as fixed depth maker backtest. Our primary novelty is a robustness analysis of a major flash crash, where the divergent performance of our taker and maker strategies empirically validates classic microstructure theories of adverse selection and highlights the systemic risks of algorithmic trading. Our results suggest a portable microstructure representation of short-horizon returns and motivate universal feature libraries for crypto markets.",
"authors": [
"Bartosz Bieganowski",
"Robert \u015alepaczuk"
],
"date": "2026-01-31",
"categories": [
"finance",
"arXiv (Cornell University)"
],
"url": "https://doi.org/10.48550/arxiv.2602.00776",
"pdf": "https://doi.org/10.48550/arxiv.2602.00776",
"relevance_score": 58,
"high_keywords": [
"microstructure",
"order flow",
"cross-asset",
"liquidity"
],
"medium_keywords": [],
"low_keywords": [
"cryptocurrency"
],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7125787303",
"source": "openalex",
"title": "Adaptive Multi-Asset Trading Strategy Optimization via Genetic Algorithms with Walk-Forward Robustness Analysis",
"abstract": "The stochastic, non-linear, and dynamic nature of financial markets significantly diminishes the effectiveness of traditional trading stratsgies relying on fixed parameters over extended periods. While the Efficient Market Hypothesis (EMH) suggests that asset prices reflect all available information, rendering systematic profit generation impossible, the field of algorithmic trading operates on the premise that temporary market inefficiencies and behavioral anomalies can be exploited. This study presents a comprehensive Genetic Algorithm (GA) framework designed to develop and optimize an adaptive trading strategy for multi-asset portfolios consisting of high-liquidity technology stocks (Apple, Microsoft, Google). Unlike traditional optimization methods that focus solely on parameter tuning for a single indicator, the proposed system introduces a novel \"genetic switch\" mechanism. This mechanism allows the algorithm to simultaneously optimize the structural components of the strategy determining which combination of indicators (EMA, MACD, RSI, Momentum) yields the best performance and their respective parameters. The model\u2019s fitness function prioritizes risk-adjusted returns by utilizing a Calmar-like ratio, explicitly penalizing excessive drawdowns. To ensure robustness and mitigate the prevalent risk of overfitting (data snooping bias), a rigorous Walk-Forward Optimization (WFO) technique was applied to daily data spanning the 2020-2024 period. The findings demonstrate that the proposed GA framework generates a robust trading system that statistically outperforms the passive \"buy-and-hold\" strategy, achieving a higher Sortino Ratio (1.98 vs 1.21) and significantly lower maximum drawdown (-18.5% vs -35.1%). The outperformance over the buy-and-hold benchmark is statistically validated across all walk-forward windows, indicating robustness rather than data snooping effects.",
"authors": [
"Hakan K\u00f6r",
"Sad\u0131k Hazar Zengin"
],
"date": "2026-01-27",
"categories": [
"finance",
"Information technology in economics and business."
],
"url": "https://doi.org/10.69882/adba.iteb.2026013",
"pdf": "https://journals.adbascientific.com/iteb/article/download/126/78",
"relevance_score": 59,
"high_keywords": [
"momentum",
"liquidity"
],
"medium_keywords": [
"drawdown"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "oa_W7125580831",
"source": "openalex",
"title": "Intraday Timing and Market Volatility in Indian Financial Markets: An Econometric Approach",
"abstract": "Abstract This paper examines the effect of intraday timing on market volatility in Indian equity markets, emphasizing the interaction between liquidity, information flow, and investor behavior. Using one and five-minute data from the National Stock Exchange (NSE) between 2018 and 2025, volatility is modeled through GARCH-type econometric models frameworks such as Wavelet Realized Volatility and LSTM-GARCH. The results reveal a distinct U-shaped intraday volatility curve with peaks at market opening and closing hours and heightened fluctuations during macroeconomic announcements. The hybrid LSTM-GARCH model demonstrates superior predictive accuracy, outperforming conventional GARCH by roughly 25 percent. Findings highlight that combining econometric structure with deep-learning flexibility improves real-time volatility forecasting in emerging markets like India.",
"authors": [
"G. Dr. Ram Raj",
"B. Dr. Sathish Kumar"
],
"date": "2026-01-24",
"categories": [
"finance",
"Zenodo (CERN European Organization for Nuclear Res"
],
"url": "https://doi.org/10.5281/zenodo.18360938",
"pdf": "https://doi.org/10.5281/zenodo.18360938",
"relevance_score": 66,
"high_keywords": [
"intraday",
"announcement",
"liquidity"
],
"medium_keywords": [
"investor behavior",
"lstm"
],
"low_keywords": [
"emerging market"
],
"actionable": true,
"findings": [],
"methods": [
"LSTM",
"GARCH model"
],
"backtestable": false
},
{
"id": "oa_W7125581952",
"source": "openalex",
"title": "Intraday Timing and Market Volatility in Indian Financial Markets: An Econometric Approach",
"abstract": "Abstract This paper examines the effect of intraday timing on market volatility in Indian equity markets, emphasizing the interaction between liquidity, information flow, and investor behavior. Using one and five-minute data from the National Stock Exchange (NSE) between 2018 and 2025, volatility is modeled through GARCH-type econometric models frameworks such as Wavelet Realized Volatility and LSTM-GARCH. The results reveal a distinct U-shaped intraday volatility curve with peaks at market opening and closing hours and heightened fluctuations during macroeconomic announcements. The hybrid LSTM-GARCH model demonstrates superior predictive accuracy, outperforming conventional GARCH by roughly 25 percent. Findings highlight that combining econometric structure with deep-learning flexibility improves real-time volatility forecasting in emerging markets like India.",
"authors": [
"G. Dr. Ram Raj",
"B. Dr. Sathish Kumar"
],
"date": "2026-01-24",
"categories": [
"finance",
"Zenodo (CERN European Organization for Nuclear Res"
],
"url": "https://doi.org/10.5281/zenodo.18360939",
"pdf": "https://doi.org/10.5281/zenodo.18360939",
"relevance_score": 66,
"high_keywords": [
"intraday",
"announcement",
"liquidity"
],
"medium_keywords": [
"investor behavior",
"lstm"
],
"low_keywords": [
"emerging market"
],
"actionable": true,
"findings": [],
"methods": [
"LSTM",
"GARCH model"
],
"backtestable": false
},
{
"id": "oa_W7124970133",
"source": "openalex",
"title": "Sentiment, social media and meme stock return predictability",
"abstract": "Purpose The purpose of this paper is to investigate how investor sentiment surrounding \u201cmeme stocks\u201d predicts subsequent returns over varying horizons. We construct two value-weighted meme-stock indices via textual analysis of Reddit's r/WallStreetBets and develop three sentiment measures\u2013Google search volume, Bloomberg Twitter sentiment and Bloomberg news sentiment\u2013each rescaled to a 0\u2013100 range. Employing univariate and multivariate regressions with lags from one to fourteen days, we demonstrate that Google search sentiment forecasts returns over 3\u20137 days, Bloomberg news sentiment over 7\u201314 days, and Bloomberg Twitter sentiment primarily over one trading day, thereby illuminating platform-specific information dissemination dynamics. Design/methodology/approach We identify meme stocks by extracting ticker mentions from Reddit's r/WallStreetBets and construct monthly and semi-annual value-weighted indices. We build three sentiment measures\u2013Google Trends, Bloomberg Twitter and Bloomberg news\u2013each scaled to 0\u2013100 and transformed via the Abnormal Search Volume Index. Using daily data from January 2021 to December 2022, we estimate univariate and multivariate regressions with sentiment lags of 1\u201314 days. To assess robustness, we include control variables (term spread, 14-day volatility and 14-day volume) and compare predictive power across horizons and platforms. Findings Google search sentiment significantly predicts meme stock returns at 3\u20137 days horizons. Bloomberg news sentiment forecasts returns over 7\u201314 days horizons, whereas Bloomberg Twitter sentiment only predicts one-day returns. These relationships remain robust after controlling for term spread, 14-day volatility and 14-day volume. Multivariate regressions show that combining sentiment measures improves short- and medium-term return forecasts. The differing forecast horizons reflect platform-specific information speeds and user profiles, highlighting the need to match sentiment sources to the desired predic",
"authors": [
"J. Li",
"Zijian Li"
],
"date": "2026-01-20",
"categories": [
"finance",
"Review of Behavioral Finance"
],
"url": "https://doi.org/10.1108/rbf-05-2025-0212",
"pdf": "",
"relevance_score": 21,
"high_keywords": [],
"medium_keywords": [
"sentiment"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Regression analysis"
],
"backtestable": true
},
{
"id": "oa_W7124259253",
"source": "openalex",
"title": "Sector Rotation Strategies in the TSX 60: A Comprehensive Analysis of Risk-Adjusted Returns, Machine Learning Applications, and Out-of-Sample Validation (2000\u20132025)",
"abstract": "We investigate the profitability of systematic sector rotation strategies in the Canadian equity market using TSX 60 constituents (2000\u20132025). Testing 72 distinct strategies across three theoretical frameworks\u2014momentum, mean-reversion, and balanced approaches\u2014with varying rebalancing frequencies, we identify that median-performer selection combined with quarterly rebalancing generates statistically significant risk-adjusted returns (Sharpe ratio 0.922 versus 0.624 for equal-weighted buy-and-hold). Our primary contributions include rigorous out-of-sample validation, demonstrating performance persistence from 2020 to 2025, machine learning regime classification with 72.7% accuracy, and a comprehensive transaction cost analysis. Results support intermediate-horizon mean reversion in sector returns and challenge strict efficient market hypothesis interpretations in concentrated markets. Findings inform tactical asset allocation practices and contribute to the momentum-reversal literature by documenting conditions under which rotation strategies generate economically meaningful alpha.",
"authors": [
"Gourav Salotra",
"Eugene Pinsky"
],
"date": "2026-01-15",
"categories": [
"finance",
"Journal of risk and financial management"
],
"url": "https://doi.org/10.3390/jrfm19010070",
"pdf": "https://www.mdpi.com/1911-8074/19/1/70/pdf",
"relevance_score": 72,
"high_keywords": [
"mean reversion",
"momentum"
],
"medium_keywords": [
"alpha",
"allocation"
],
"low_keywords": [],
"actionable": true,
"findings": [
"Quantitative result: ('72.7', 'accuracy')",
"Quantitative result: 0.922"
],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7124549828",
"source": "openalex",
"title": "Cheap Options Are Expensive",
"abstract": "Abstract We show that demand pressure from retail investors makes options on low-price stocks relatively expensive\u2014delta-hedged options on low-price stocks underperform those on high-price stocks by 0.63% per week for calls and 0.36% for puts. Natural experiments corroborate this finding: options become more expensive following stock splits, options on mini indices are more expensive than those on main indices, and mini contract options are more expensive than standard options. We attribute our findings to retail investors\u2019 preference for skewness and divergence of opinion. Limits to arbitrage and strategic quote setting by market makers contribute to, but do not fully explain, this effect. (JEL G13, G14)",
"authors": [
"Alexei Boulatov",
"Assaf Eisdorfer",
"Amit Goyal",
"Alexei Zhdanov"
],
"date": "2026-01-14",
"categories": [
"finance",
"The Review of Asset Pricing Studies"
],
"url": "https://doi.org/10.1093/rapstu/raag001",
"pdf": "",
"relevance_score": 45,
"high_keywords": [
"market maker",
"skew",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W1610434158",
"source": "openalex",
"title": "The Illiquidity of Water Markets",
"abstract": "Abstract We investigate the efficiency of a market relative to a non-market institution\u2014an auction relative to a quota\u2014as allocation mechanisms in the presence of frictions. We use data from water markets in southeastern Spain and explore a specific change in the institutions to allocate water. On the one hand, frictions arose because poor farmers were liquidity constrained. On the other hand, farmers who were part of the wealthy elite were not liquidity constrained. We estimate a structural dynamic demand model by taking advantage of the fact that water demand for both types of farmers is determined by the technological constraint imposed by the crop\u2019s production function. This approach allows us to differentiate liquidity constraints from unobserved heterogeneity. We show that the institutional change from an auction to a quota increased total efficiency for the farmers considered. Welfare increased by 23.4 real pesetas per farmer per tree, a 6 % increase in total production relative to the market.",
"authors": [
"Javier D. Donna",
"Jos\u00e9\u2010Antonio Esp\u00edn\u2010S\u00e1nchez"
],
"date": "2026-01-14",
"categories": [
"finance",
"The Review of Economic Studies"
],
"url": "https://doi.org/10.1093/restud/rdag004",
"pdf": "https://mpra.ub.uni-muenchen.de/109544/5/MPRA_paper_109542.pdf",
"relevance_score": 20,
"high_keywords": [
"liquidity"
],
"medium_keywords": [
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7122959019",
"source": "openalex",
"title": "Explainable Spectrum Prediction Based on VMD-LSTM",
"abstract": "To improve the accuracy and interpretability of neural network enabled spectrum prediction, an explainable spectrum prediction framework based on Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks, integrated with the Shapley Additive Explanations (SHAP) method (VMD-LSTM), is proposed in this work. Firstly, the raw spectrum data is decomposed into multiple Intrinsic Mode Functions (IMFs) via VMD to reduce sequence complexity. These IMFs are then fed into the LSTM network in parallel to improve prediction accuracy. Secondly, the SHAP method is incorporated to evaluate the impact weights of individual IMF components on the prediction outcomes, revealing the model's decision-making logic. Finally, we weight the input data by multiplying each IMF by its SHAP value to optimize prediction performance. Simulation results based on real spectrum data demonstrate that the proposed VMD-LSTM significantly outperforms baseline models on the metrics of Weighted Quality Evaluation Index (WQE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and mean absolute error (MAE). By incorporating SHAP weights to refine the model input features, the framework not only provides transparent explanations for the black-box model but also reduces the average WQE, RMSE, and MAPE by 3.99%, 3.23%, and 3.67%, respectively.",
"authors": [
"W. Xu",
"J. S. Zhang",
"Z. Su"
],
"date": "2026-01-12",
"categories": [
"finance",
"Radioengineering"
],
"url": "https://doi.org/10.13164/re.2026.0015",
"pdf": "https://doi.org/10.13164/re.2026.0015",
"relevance_score": 33,
"high_keywords": [
"decomposition"
],
"medium_keywords": [
"neural network",
"lstm"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Neural network",
"LSTM"
],
"backtestable": true
},
{
"id": "oa_W7125517541",
"source": "openalex",
"title": "AI-Driven Asset Management with Behavioral Profiling: A Dual-Strategy Prototype",
"abstract": "Conventional portfolio strategies often over-rely on static risk measures or neglect the behavioral tendencies of both clients and markets. While AI-driven systems already exist, their role in strategy allocation remains limited, particularly when psychological dynamics and risk capacity are not explicitly integrated. This paper addresses that gap by introducing a dual-strategy asset management prototype designed to adapt allocations dynamically to client profiles and evolving market conditions. The framework combines two modules: a safe-risk strategy that emphasizes structural reliability, and a high-risk strategy that embraces volatility through liquidity-trap identification. At its core, allocation decisions are guided by an AI-driven profiling system that balances quantitative metrics with behavioral scoring, ensuring that discipline and psychology jointly inform risk exposure. A central innovation is the formalization of the Bi-Directional Liquidity Trap (BDLT), a concept developed in this work to identify and exploit dual-trap market structures. This is integrated into the allocation process alongside a behavioral weighting scheme, where 60% of the decision score derives from client behavior and 40% from numerical portfolio metrics. The prototype was tested in live market conditions, with equity curve results illustrating controlled drawdowns, adaptability in stagnant phases, and scalability across client profiles. By merging client psychology with algorithmic structure, this work outlines a model for asset management that is not only risk-aware but also adaptive. This framework sets the stage for future research into AI-assisted portfolio design that has potential that bridges market mechanics with human behavior.",
"authors": [
"Abhijeet More"
],
"date": "2026-01-11",
"categories": [
"finance",
"International Journal For Multidisciplinary Resear"
],
"url": "https://doi.org/10.36948/ijfmr.2026.v08i01.66176",
"pdf": "https://www.ijfmr.com/papers/2026/1/66176.pdf",
"relevance_score": 25,
"high_keywords": [
"liquidity"
],
"medium_keywords": [
"drawdown",
"allocation"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7123258629",
"source": "openalex",
"title": "When the Rules Change: Adaptive Signal Extraction via Kalman Filtering and Markov-Switching Regimes",
"abstract": "Most empirical microstructure research assumes that order flow--return parameters are constant, yet these relationships shift substantially across market regimes. Combining adaptive Kalman filtering, Markov-switching regime identification, and asymmetric response estimation, we characterize regime-dependent investor behavior in the Korean stock market during 2020--2024 using daily transaction data disaggregated by investor type. Three principal findings emerge: foreign investor predictive power increases several-fold during crisis periods relative to bull markets; individual investors chase momentum asymmetrically, reacting far more strongly to positive than to negative shocks; and independent information-theoretic validation corroborates both patterns. Rigorous out-of-sample testing reveals that these in-sample regularities do not generalize reliably, underscoring the need for proper validation methodology in microstructure research.",
"authors": [
"Sungwoo Kang"
],
"date": "2026-01-09",
"categories": [
"finance",
"arXiv (Cornell University)"
],
"url": "https://doi.org/10.48550/arxiv.2601.05716",
"pdf": "https://doi.org/10.48550/arxiv.2601.05716",
"relevance_score": 66,
"high_keywords": [
"microstructure",
"order flow",
"momentum"
],
"medium_keywords": [
"investor behavior"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "oa_W7120092211",
"source": "openalex",
"title": "Jump Volatility Forecasting for Crude Oil Futures Based on Complex Network and Hybrid CNN\u2013Transformer Model",
"abstract": "The crude oil futures market is highly susceptible to policy changes and international relations, which often trigger abrupt jumps in prices. The existing literature rarely considers jump volatility and the underlying impact mechanisms. This study proposes a hybrid forecasting model integrating a convolutional neural network (CNN) and self-attention (Transformer) for high-frequency financial data, based on the complex network characteristics between trading information and multi-market financialization indicators. Empirical results demonstrate that incorporating complex network indicators enhances model performance, with the CNN\u2013Transformer model with a complex network achieving the highest predictive accuracy. Furthermore, we verify the model\u2019s effectiveness and robustness in the WTI crude oil market via Diebold\u2013Mariano tests and external event shock. Notably, this study also extends the analytical framework to jump intensity, thereby providing a more accurate and robust jump forecasting model for risk management and trading strategies in the crude oil futures market.",
"authors": [
"Yuqi He",
"Po Ning",
"Yuping Song"
],
"date": "2026-01-09",
"categories": [
"finance",
"Mathematics"
],
"url": "https://doi.org/10.3390/math14020258",
"pdf": "https://www.mdpi.com/2227-7390/14/2/258/pdf?version=1767966655",
"relevance_score": 41,
"high_keywords": [
"futures market"
],
"medium_keywords": [
"neural network",
"transformer"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Neural network",
"Transformer model"
],
"backtestable": true
},
{
"id": "gscholar_option_implied_volatility_and_trading_strategies_based_on_ne",
"source": "google_scholar",
"title": "Option Implied Volatility and Trading Strategies Based on Neural Network Correction",
"abstract": "yields the implied volatility estimate \u03c3 i , t AHBS for each option. This semi-parametric method captures essential characteristics of the implied volatility surface, such as skewness and",
"authors": [
"X Duan",
"Q Liu",
"Z Xu",
"Z Ying"
],
"date": "2026-01-01",
"categories": [
"google_scholar",
"Journal of Futures \u2026"
],
"url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/fut.70046",
"pdf": "",
"relevance_score": 65,
"high_keywords": [
"implied volatility",
"volatility surface",
"skew",
"volatility surface"
],
"medium_keywords": [
"neural network"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7117963739",
"source": "openalex",
"title": "Information-Neutral Hedging of Derivatives Under Market Impact and Manipulation Risk",
"abstract": "The literature on derivative pricing in illiquid markets has mostly focused on computing optimal hedging controls, but empirical microstructure studies show that large order flow generates persistent and predictable price effects. Therefore, these controls can themselves induce endogenous market manipulation because traders can internalize the impact of their own trades. We identify the key shortcoming as the absence of a formal separation between a large trader\u2019s informational advantage and the mechanical price impact and temporary cost-of-hedging. To address this gap, we introduce a counterfactual informed observer\u2014an agent who knows the large trader\u2019s strategy but does not face trading frictions\u2014and use this device to isolate informational order-flow effects from mechanical price impact, a distinction explicitly observed in microstructure data. We prove the existence of information-neutral probability measures under which the discounted asset is a martingale for this observer and derive a hedging framework that jointly accounts for transaction costs and permanent market impact. Numerical experiments show that because price pressure and order-flow effects create non-linear execution costs, the optimal hedge for an out-of-the-money call can deviate substantially from the Black\u2013Scholes hedge, with implications for risk management and regulatory monitoring.",
"authors": [
"Behzad Alimoradian",
"Karim Barigou",
"Anne Eyraud"
],
"date": "2026-01-01",
"categories": [
"finance",
"International Journal of Financial Studies"
],
"url": "https://doi.org/10.3390/ijfs14010002",
"pdf": "https://doi.org/10.3390/ijfs14010002",
"relevance_score": 53,
"high_keywords": [
"microstructure",
"order flow",
"price impact"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_dynamic_incentives_in_reit_option_markets",
"source": "google_scholar",
"title": "Dynamic incentives in REIT option markets",
"abstract": "-seeking trading activity within the REIT options market, we Specifically, we partition a REIT\u2019s options trading order flow in previous REIT market microstructure investigations. Footnote",
"authors": [
"GD Cashman",
"DM Harrison",
"H Sheng"
],
"date": "2026-01-01",
"categories": [
"google_scholar",
"The Journal of Real Estate \u2026"
],
"url": "https://link.springer.com/article/10.1007/s11146-025-10022-x",
"pdf": "https://vtechworks.lib.vt.edu/bitstreams/bb647de4-9bf8-43d3-93c7-b0cb58a89a8e/download",
"relevance_score": 45,
"high_keywords": [
"options market",
"microstructure",
"order flow"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_forecasting_the_worst_is_implied_volatility_forward_looking_",
"source": "google_scholar",
"title": "Forecasting the worst: is implied volatility forward-looking enough?",
"abstract": "various alternative methodologies for estimating the risk factor\u2019s volatility. The aim is to determine whether using option-implied volatility (IV) produces superior results compared to other",
"authors": [
"C Confalonieri",
"P De Vincentiis"
],
"date": "2026-01-01",
"categories": [
"google_scholar",
"Journal of Banking Regulation"
],
"url": "https://link.springer.com/article/10.1057/s41261-025-00306-w",
"pdf": "https://link.springer.com/article/10.1057/s41261-025-00306-w",
"relevance_score": 38,
"high_keywords": [
"implied volatility",
"factor"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7133341099",
"source": "openalex",
"title": "Comparative analysis of static and dynamic approaches to constructing factor investment strategies",
"abstract": "The article compares static and dynamic approaches to constructing factor investment strategies, focusing on performance, risks, and practical implementation. The aim of the study is to formalize criteria for selecting a factor portfolio architecture that accounts for the cyclicality of premia, liquidity constraints, and transaction costs, and to clarify under which conditions a more complex dynamic specification is justified relative to fixed rules. The relevance of the work is driven by the widespread adoption of factor investing and, simultaneously, by mounting doubts regarding the persistence of premia and the out-of-sample transportability of factor-timing results. The scientific contribution lies in a comprehensive typology of static and dynamic schemes (exposure timing, volatility scaling, regime filters), an explicit treatment of premium degradation channels via costs, capacity, and model risk, and the rationale for a hybrid design with a robust static core and a minimally parametric dynamic risk-management layer. The main findings indicate that static strategies are superior in terms of interpretability and robustness to overfitting, whereas dynamic constructions can increase return density per unit of risk at the cost of greater sensitivity to estimation errors and to the structure of trading frictions. The article is intended for portfolio managers, risk managers, and researchers engaged in developing and testing factor strategies.",
"authors": [
"Abdelmadjid Laouedj"
],
"date": "2026-01-01",
"categories": [
"finance",
"International Journal of Advanced engineering Mana"
],
"url": "https://doi.org/10.22161/ijaems.122.3",
"pdf": "https://ijaems.com/upload_images/issue_files/3IJAEMS-102202615-Comparative.pdf",
"relevance_score": 38,
"high_keywords": [
"factor",
"liquidity"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_intraday_trading_volume_patterns_of_market_index_etfs",
"source": "google_scholar",
"title": "Intraday trading volume patterns of market index etfs",
"abstract": "The spikes are more pronounced during negative return periods, and our models suggest of the percentage of daily trading volumes for each minute of the trading day. The two figures",
"authors": [
"E Giudici",
"A Grossmann"
],
"date": "2026-01-01",
"categories": [
"google_scholar",
"Journal of Corporate Accounting & \u2026"
],
"url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/jcaf.70005",
"pdf": "",
"relevance_score": 35,
"high_keywords": [
"intraday",
"day trading"
],
"medium_keywords": [
"etf"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7125825251",
"source": "openalex",
"title": "Toward responsible investment: A deep learning frame-work for ESG-differentiated portfolio optimization",
"abstract": "Aim/purpose \u2013 This paper proposes a framework that integrates deep learning-based return forecasting with environmental, social, and governance (ESG)-differentiated op- timization to align portfolio performance with financial and sustainability goals, ena- bling data-driven responsible investment decisions. The study hypothesizes that ESG risk dimensions influence portfolio performance differently: mitigating environmental risk imposes higher financial costs due to regulatory and operational pressures, whereas social and governance risks yield more balanced return-sustainability trade-offs. Design/methodology/approach \u2013 This study employs the N-BEATS deep learning model to forecast one-day-ahead returns for S&P 100 stocks. The predicted returns serve as inputs to an enhanced Mean-Variance with Forecasting (MVF) model that integrates ESG risk as a penalty term. ESG factors are analyzed both collectively and across individual di- mensions using a tunable risk-aversion parameter that reflects investor preferences. The dataset includes 99 S&P 100 stocks from January 2017 to December 2024, with distinct training, validation, and test sets for model development and evaluation. Findings \u2013 The study reveals that incorporating ESG risk into portfolio optimization with forecasted returns produces distinct trade-offs across ESG dimensions. Mitigating environmental risk entails the greatest return cost, whereas social and governance risks allow more favorable balances between return and sustainability. The N-BEATS model achieves sufficient forecasting accuracy to inform investment decisions. Moreover, the elbow point method offers a practical means for selecting optimal ESG sensitivity levels, enabling investors to effectively balance performance and sustainability objectives. Research implications/limitations \u2013 This research demonstrates that combining deep learning-based forecasting with ESG-differentiated optimization enables more nuanced and responsible investment s",
"authors": [
"Minh To\u1ea3n Nguy\u1ec5n"
],
"date": "2026-01-01",
"categories": [
"finance",
"Journal of Economics and Management"
],
"url": "https://doi.org/10.22367/jem.2026.48.01",
"pdf": "https://doi.org/10.22367/jem.2026.48.01",
"relevance_score": 31,
"high_keywords": [
"factor"
],
"medium_keywords": [
"deep learning",
"portfolio optimization"
],
"low_keywords": [
"esg"
],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7118922968",
"source": "openalex",
"title": "Market Power and Capital Constraints",
"abstract": "We explore how traders' equity capitalization influences asset prices in a framework that accounts for market power.In our model, traders with capital constraints engage in transactions in an imperfectly competitive market.We demonstrate that looser capital constraints elevate both asset prices and price impact, the latter diminishing market liquidity.Using Canadian Treasury auction data, we illustrate how to apply our model to quantify these effects.We estimate the shadow costs of capital constraints by leveraging a temporary policy exemption during 2020-2021.We show that while these constraints are only infrequently binding, their relative impact when activated can be sizable.",
"authors": [
"Milena Wittwer",
"Jason Allen"
],
"date": "2026-01-01",
"categories": [
"finance"
],
"url": "https://doi.org/10.3386/w34645",
"pdf": "https://doi.org/10.3386/w34645",
"relevance_score": 30,
"high_keywords": [
"price impact",
"liquidity"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7118951997",
"source": "openalex",
"title": "Effect of Macro-economic Factors on the Trading Styles of Institutional Investors",
"abstract": "Institutional investors, known for their professional expertise and high-volume trading, vary in their investment horizons and strategies, particularly in how they respond to macro-economic factors.This study investigates whether foreign institutional investors(FIIs) and domestic institutional investors (DIIs) in India adopt different trading styles in relation to macro-economic factors, with a particular focus on momentum and contrarian trading. The study uses the monthly aggregated investment flows of institutional investors in the equity market to measure the trading style from 1st April 2012 to 31st March 2024. The trading style of institutional investors is measured using the Buy Ratio. Further to determine which macro-economic factors increase the likelihood of institutional investors going momentum or contrarian is examined using the logistic regression model. The findings show institutional investors do not mimic each other trading style with respect to macro-economic announcements. The results reveal that there exist significant positive and negative relations between a few macro-economic factors and institutional investors. The study finds evidence that FIIs chase the market return and pursue a momentum trading style while DIIs adopt a contrarian trading style.",
"authors": [
"Amit Naik",
"Sankaranarayanan K G",
"Kavir Kashinath Shirodkar"
],
"date": "2025-12-31",
"categories": [
"finance",
"South India Journal of Social Sciences"
],
"url": "https://doi.org/10.62656/sijss.v23i7.2291",
"pdf": "https://journal.sijss.com/index.php/home/article/download/2291/423",
"relevance_score": 45,
"high_keywords": [
"momentum",
"factor",
"announcement"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Regression analysis"
],
"backtestable": false
},
{
"id": "oa_W7117368405",
"source": "openalex",
"title": "Market Risk of Gold Futures and Gold Spot under Geopolitical Risk",
"abstract": "With the heightening financial globalization, the market risk has increased strongly especially at times of geopolitical uncertainty. Gold that has traditionally been considered a safe-haven asset is highly volatile when there is an escalation of geopolitical tensions. Nonetheless, the variability that occurs to the spot and futures markets has not been fully investigated. The paper determines the factor of Geopolitical Risk (GPR) on conditional volatility of gold futures and spot returns by the GARCH-X(1,1) model that incorporates the Student-t innovations. A standardized GPR index is introduced as an exogenous variable in the equation of variance using monthly data between the year 1980 and 2023. We find that, lagged GPR has a positive effect on futures volatility, but that effect is somewhat significant, indicating that futures markets that are more vulnerable to geopolitical shocks are speculative and leveraged derivatives markets. By comparison, the spot market volatility does not exhibit any important effect of GPR, meaning that it is self-enhanced by past shocks. Ljung-Box diagnostics indicate some residual autocorrelation in the futures model, indicating that there is some possibility to refine the model by using alternative GARCH specification, or adding more terms to the mean equation. GARKH-X(t) model is highly suitable in terms of volatility dynamics in the futures, whereas spot volatility is more stable. The study has significant implications on risk management, portfolio hedging policies, and regulatory controls on derivatives markets in times of geopolitical crises.",
"authors": [
"Ruoyan Mo"
],
"date": "2025-12-27",
"categories": [
"finance",
"Highlights in Business Economics and Management"
],
"url": "https://doi.org/10.54097/fqw8mz14",
"pdf": "https://hbemdata.org/index.php/ojs/article/download/140/120",
"relevance_score": 45,
"high_keywords": [
"correlation",
"factor",
"futures market"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"GARCH model"
],
"backtestable": true
},
{
"id": "oa_W7117372898",
"source": "openalex",
"title": "Predictive modeling and statistical inference for Commodity Trading Advisors",
"abstract": "This article focuses on predicting trends in Commodity Trading Advisors (CTAs), also known as trend-following hedge funds. The article applies a Hidden Markov Model (HMM) for classifying trends. By incorporating additional features, a regularized logistic regression model is used to enhance prediction capability. The model demonstrates success in identifying positive trends in CTA funds, with particular emphasis on precision and risk-adjusted return metrics. In the context of regularized regression models, techniques for statistical inference such as bootstrap resampling and Markov Chain Monte Carlo are applied to estimate the distribution of parameters. The findings suggest the model\u2019s effectiveness in predicting favorable CTA performance and mitigating equity market drawdowns.",
"authors": [
"Christian Oliver Ewald",
"Oskar Fransson"
],
"date": "2025-12-26",
"categories": [
"finance",
"Cogent Economics & Finance"
],
"url": "https://doi.org/10.1080/23322039.2025.2602329",
"pdf": "https://doi.org/10.1080/23322039.2025.2602329",
"relevance_score": 36,
"high_keywords": [
"hedge fund"
],
"medium_keywords": [
"drawdown"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Regression analysis",
"Monte Carlo simulation"
],
"backtestable": false
},
{
"id": "oa_W7118452639",
"source": "openalex",
"title": "TRADING DYNMICS OF FUTURE AND OPTIONS: A CONCEPTUAL FRAME WORK IN THE INDIAN DERIVATIVES MARKET",
"abstract": "One of the most active parts of India's whole financial sector is now the Indian derivatives market. A vital component of that ecosystem, futures and options (F&O) are essential for risk hedging, permitting speculation, and offering price discovery to both traders and investors. By examining Market Structure, Regulatory Framework, and Trends in Market Participation, it will investigate the fundamental elements propelling the growth and functioning of the Indian F&O Market. While the asymmetrical reward structure of an option provides flexibility to extend different trading methods, futures allow market players to trade standardized contracts for risk transfer. The way the F&O Market functions will be greatly influenced by additional factors like volatility profiles, the type of algorithmic trading, high-frequency trade execution, open interest characteristics, and margining mechanisms. Additionally, this study will describe how SEBI (Securities and Exchange Board of India) regulations\u2014such as modifications to lot sizes, physical settlement requirements, and increased transparency requirements\u2014have affected market participation and stability. It talks about the growth of institutional traders, proprietary firms, and retail investors and how they can give the market depth and liquidity. Lastly, it will focus on the Trend Shifts in the Derivative Market, such as the Increasing Integration of Worldwide macroeconomic Signals, the Growing Number of Weekly Index Options, and the Growing Use of Retail Options Selling. The connections between futures and options and their effects on India's financial system as a whole are covered in detail in this article. While they increase efficiency, they also raise concerns about volatility control, speculative behaviour, and systemic risk.",
"authors": [
"KASHAP TILAK SINGH",
"K. HARI CHANDANA"
],
"date": "2025-12-25",
"categories": [
"finance",
"International Journal of Web of Multidisciplinary "
],
"url": "https://doi.org/10.71366/ijwos02120636953",
"pdf": "https://doi.org/10.71366/ijwos02120636953",
"relevance_score": 45,
"high_keywords": [
"open interest",
"factor",
"liquidity"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7118169837",
"source": "openalex",
"title": "Assessing the Nexus Between Indonesia\u2019s Government Bond Yields and Global Volatility Index (VIX) Sentiment",
"abstract": "This study rigorously assesses the intricate long-run and short-run nexus between Indonesia's 10-year government bond yields and the Global Volatility Index (VIX) sentiment, using high-frequency daily data spanning the turbulent 2019\u20132023 period. Employing the flexible Autoregressive Distributed Lag (ARDL) model, we simultaneously analyze the impact of global volatility alongside crucial domestic macro-financial factors, namely the Bank Indonesia benchmark interest rate and the USD/IDR exchange rate. The results firmly establish a significant long-run cointegrating relationship, demonstrating that persistently elevated VIX levels positively and structurally correlate with increased bond yields, quantitatively confirming the demand for a higher sovereign risk premium by international investors during times of global uncertainty. The analysis also confirms the dominant influence of domestic factors, particularly the strong monetary policy transmission through interest rates. Crucially, the Error Correction Mechanism (ECM) reveals a rapid adjustment speed (????day), signifying the high responsiveness and efficiency of the market in incorporating both global and domestic shocks. These robust findings emphasize the critical necessity for policymakers and investors in emerging markets to systematically integrate VIX as a key macroprudential indicator into resilient sovereign debt management and strategic investment allocation frameworks.",
"authors": [
"Fahmi Sahlan",
"Diamond Limbonb",
"Fitriyani",
"Rizki Ramadhani",
"Fithri Suciati"
],
"date": "2025-12-24",
"categories": [
"finance",
"Perspectives on Advanced New Generations of Global"
],
"url": "https://doi.org/10.69855/panggaleh.v1i3.301",
"pdf": "https://gpijournal.com/index.php/panggaleh/article/download/301/309",
"relevance_score": 43,
"high_keywords": [
"vix",
"monetary policy",
"factor"
],
"medium_keywords": [
"sentiment",
"allocation"
],
"low_keywords": [
"sovereign debt",
"emerging market"
],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "oa_W7117100296",
"source": "openalex",
"title": "The Predictive Power of Bitcoin Return for American Major Stock Indexes Return",
"abstract": "Virtual currency has become one of the most sought-after alternative assets in the past decade with bitcoin being a leading example. value leapt from its starting price of $0.0025 to increase by more than 40 million times that amount, creating one of the greatest rises in value in the entire history of finance. In the past few years, many academic studies show that even though Bitcoin runs independently from traditional finance, but still there is a high correlation between Bitcoin and stock market. In particular, following the introduction of Bitcoin options back in 2017, Bitcoin now appears more predictive of stock return movements than before. Research by Afees A. Salisu and his coworkers display that a solitary Bitcoin price prediction model using an optimized predictive regression framework notably surpasses older ones. but dont say how long this goes on Therefore this research will go to try and determine the time frame when Bitcoin is better at predicting the future of the stock market as opposed to stock options. Also, well use machine learning techniques to train machine learning models to predict the movements of the stock market and see if they work.",
"authors": [
"Yu Gao",
"Bowen Liu",
"Yishan Yang",
"Junxiong Huang"
],
"date": "2025-12-24",
"categories": [
"finance",
"Advances in Economics Management and Political Sci"
],
"url": "https://doi.org/10.54254/2754-1169/2025.bl30746",
"pdf": "https://aemps.ewapub.com/article/view/30746.pdf",
"relevance_score": 28,
"high_keywords": [
"correlation",
"stock index"
],
"medium_keywords": [],
"low_keywords": [
"bitcoin"
],
"actionable": false,
"findings": [],
"methods": [
"Regression analysis"
],
"backtestable": false
},
{
"id": "oa_W7117235180",
"source": "openalex",
"title": "The Impact of Increasing News Intensity and Number of Investors on the Relationship between News Sentiment and Price Movement in the Developing Country: Indonesian Evidence",
"abstract": "This study examines how news intensity and investor numbers affect the link between news sentiment and equity price movements in Indonesia, using the LQ45 Index. Applying methods such as correlation analysis, CAPM, VAR, Granger causality tests, and rolling correlations, we find that higher news intensity and investor participation strengthen the connection between news sentiment and stock returns while also increasing volatility. The findings suggest that incorporating news sentiment analysis can improve market stability and investment decisions in developing economies.",
"authors": [
"Za\u00e4fri Ananto Husodo",
"Muhamad Nagib Alatas"
],
"date": "2025-12-24",
"categories": [
"finance",
"Bulletin of Monetary Economics and Banking"
],
"url": "https://doi.org/10.59091/2460-9196.2353",
"pdf": "https://bulletin.bmeb-bi.org/cgi/viewcontent.cgi?article=2353&context=bmeb",
"relevance_score": 20,
"high_keywords": [
"correlation"
],
"medium_keywords": [
"sentiment"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Granger causality"
],
"backtestable": true
},
{
"id": "oa_W7117275088",
"source": "openalex",
"title": "Quantitative Financial Modeling for Sri Lankan Markets: Approach Combining NLP, Clustering and Time-Series Forecasting",
"abstract": "This research introduces a novel quantitative methodology tailored for quantitative finance applications, enabling banks, stockbrokers, and investors to predict economic regimes and market signals in emerging markets, specifically Sri Lankan stock indices (S&P SL20 and ASPI) by integrating Environmental, Social, and Governance (ESG) sentiment analysis with macroeconomic indicators and advanced time-series forecasting. Designed to leverage quantitative techniques for enhanced risk assessment, portfolio optimization, and trading strategies in volatile environments, the architecture employs FinBERT, a transformer-based NLP model, to extract sentiment from ESG texts, followed by unsupervised clustering (UMAP/HDBSCAN) to identify 5 latent ESG regimes, validated via PCA. These regimes are mapped to economic conditions using a dense neural network and gradient boosting classifier, achieving 84.04% training and 82.0% validation accuracy. Concurrently, time-series models (SRNN, MLP, LSTM, GRU) forecast daily closing prices, with GRU attaining an R-squared of 0.801 and LSTM delivering 52.78% directional accuracy on intraday data. A strong correlation between S&P SL20 and S&P 500, observed through moving average and volatility trend plots, further bolsters forecasting precision. A rule-based fusion logic merges ESG and time-series outputs for final market signals. By addressing literature gaps that overlook emerging markets and holistic integration, this quant-driven framework combines global correlations and local sentiment analysis to offer scalable, accurate tools for quantitative finance professionals navigating complex markets like Sri Lanka.",
"authors": [
"Linuk Perera"
],
"date": "2025-12-23",
"categories": [
"finance",
"arXiv (Cornell University)"
],
"url": "https://doi.org/10.48550/arxiv.2512.20216",
"pdf": "https://doi.org/10.48550/arxiv.2512.20216",
"relevance_score": 56,
"high_keywords": [
"intraday",
"correlation"
],
"medium_keywords": [
"sentiment",
"neural network",
"lstm",
"transformer",
"portfolio optimization"
],
"low_keywords": [
"emerging market",
"esg"
],
"actionable": true,
"findings": [],
"methods": [
"Neural network",
"Gradient boosting",
"LSTM",
"Transformer model",
"PCA"
],
"backtestable": true
},
{
"id": "oa_W7117150709",
"source": "openalex",
"title": "Optimal Signal Extraction from Order Flow: A Matched Filter Perspective on Normalization and Market Microstructure",
"abstract": "We establish a general matched filter principle for order flow normalization: optimal normalization must match the scaling behaviour of the signal-generating process. For capacity-constrained institutional investors, market capitalization normalization ($S^{MC}$) is the matched filter; for volume-targeting traders (e.g., VWAP/TWAP algorithms), trading value normalization ($S^{TV}$) is optimal. Monte Carlo simulations confirm this principle works bidirectionally, with matched filters achieving up to $1.99\\times$ higher signal correlation. Empirical validation using 2.7 million stock-day observations from the Korean market (2020--2024) reveals symmetric normalization dominance across investor types: domestic institutional flows predict next-day returns significantly under $S^{MC}$ ($t = 9.65$), while foreign flows exhibit stronger predictability under $S^{TV}$ ($t = 16.35$) -- with no sign reversal at longer horizons, indicating durable private information rather than temporary price impact. These findings motivate the ``Informed Executor'' hypothesis: sophisticated foreign investors possess genuine private information but employ volume-targeting algorithms for stealth execution -- volume-scaling reflects execution methodology, not absence of information. Information-theoretic validation using KL divergence independently corroborates these results. The matched filter principle generalises to any market where signal scaling varies across trader types, with implications for trading algorithms, factor construction, and market microstructure methodology.",
"authors": [
"Sungwoo Kang"
],
"date": "2025-12-21",
"categories": [
"finance",
"arXiv (Cornell University)"
],
"url": "https://doi.org/10.48550/arxiv.2512.18648",
"pdf": "https://doi.org/10.48550/arxiv.2512.18648",
"relevance_score": 83,
"high_keywords": [
"microstructure",
"order flow",
"correlation",
"factor",
"price impact"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Monte Carlo simulation"
],
"backtestable": false
},
{
"id": "oa_W7117243895",
"source": "openalex",
"title": "Retail Investor Social Media Sentiment as a Determinant of Technology Sector Stock Price Movements",
"abstract": "The Technology sector is increasingly influenced by decentralized, real-time sentiment from retail investors disseminated via social media, fundamentally challenging market efficiency assumptions and raising systemic risk concerns (Guzman et al., 2025). This study performs a quantitative assessment to quantify the determinant influence of retail investor social media sentiment on technology sector stock price movements relative to conventional market indicators (trading volume and momentum). The investigation utilized a six-month dataset from five highly-traded technology stocks (AAPL, MSFT, NVDA, TSLA, AMD). Sentiment was accurately classified using a fine-tuned BERT model (Chen & Liu, 2024). The core analysis applied a novel PCA-Hidden Markov Model (PCA-HMM) framework used to mitigate multicollinearity and identify distinct market regimes (stable vs. volatile) followed by regime-switching multivariate regression (Zhou et al., 2025). The analysis reveals that social media sentiment is a significant predictor of daily stock returns (Novak & Smith, 2024). Crucially, the influence of sentiment was markedly magnified and more potent during the volatile market regime (Kim & Singh, 2024). This conditional effect confirms that sentiment acts as a powerful multiplier of price instability when the market is under stress. These findings necessitate the institutionalization of social media monitoring by investment practitioners for alpha generation (Taylor & Wirth, 2024) and by regulators for behavior-based surveillance to mitigate flash volatility and systemic risk (Rodriguez, 2025). The research advocates for the adoption of dynamic, regime-switching models in asset pricing and risk management.",
"authors": [
"Burhanuddin",
"Nasution",
"Azzahra Nikmatul Ilmi",
"Wa Ode Irma Sari"
],
"date": "2025-12-20",
"categories": [
"finance",
"Perspectives on Advanced New Generations of Global"
],
"url": "https://doi.org/10.69855/panggaleh.v1i3.298",
"pdf": "https://gpijournal.com/index.php/panggaleh/article/download/298/272",
"relevance_score": 46,
"high_keywords": [
"momentum"
],
"medium_keywords": [
"market efficiency",
"alpha",
"sentiment"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Regression analysis",
"PCA"
],
"backtestable": true
},
{
"id": "oa_W7115900551",
"source": "openalex",
"title": "The Architecture of Market Regimes: Modeling DXY through Structural and Reflexive Layers",
"abstract": "UPDATE Model Update: The Mathematics of Forgetting (and Artificial Remembering) In refining the Deconstructing Dollar Dynamics model, we addressed a critical structural critique: Crisis Memory ($M_{shock}$) cannot be a constant. A trader entering the market in 2028 will not possess the visceral \"muscle memory\" of the 2008 GFC. Fear decays as the old guard retires. To model this accurately, $M_{shock}$ must be treated as a time-dependent function of Generational Decay. 1. Psycho-Technical Amplification via $H_{soc}$ (Homogeneity) New Concept: Introduced the Psycho-Technical Layer (4.4) with $H_{soc}$ (Homogeneity of Collective Consciousness) as a meta-modulator. $H_{soc} \\to 1$ represents near-perfect convergence of collective perception due to factors like algorithms ($A_{alg}$), content concentration ($C_{src}$), AI ($P_{AI}$), and exposure time ($T_{exp}$). Main Impact: $H_{soc}$ amplifies $\\kappa_{system}$ (Reflexivity) by destroying the diversity of agents, leading to synchronized, \"wall-like\" market reactions instead of dispersed \"waves\". It also makes $R_{US}$ (Internal Risk) more volatile by facilitating rapid narrative contagion. Formula Change: The core change is the amplification of reflexivity: $$\\kappa_{system} = f(\\kappa_{base}, V_{USD}, M_{shock}, A_{meta}) \\times \\mathbf{H_{soc}}$$ 2. Psycho-Technical Acceleration via $I_{impulse}$ (Immediacy) New Concept: Introduced $I_{impulse}$ (Impulse Efficiency Index) within Layer 4.4, measuring the near-instant conversion of thought/signal to action. $I_{impulse} \\to 1$ is driven by factors like speed of access ($S_{access}$), cognitive loop shortening ($C_{loop}$), habit strength ($P_{habit}$), system coupling ($E_{system}$), overcoming friction ($R_{friction}$). Its function is approximated as: $I_{impulse} \\approx \\frac{S_{access} \\cdot C_{loop} \\cdot P_{habit} \\cdot E_{system}}{1 + R_{friction}}$ Main Impact: $I_{impulse}$ acts as a second amplifier on $\\kappa_{system}$ (synchronizing when actions occur), c",
"authors": [
"Son, Vi"
],
"date": "2025-12-17",
"categories": [
"finance",
"Zenodo (CERN European Organization for Nuclear Res"
],
"url": "https://doi.org/10.5281/zenodo.17958350",
"pdf": "https://doi.org/10.5281/zenodo.17958350",
"relevance_score": 20,
"high_keywords": [
"factor"
],
"medium_keywords": [
"fear"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W4417195137",
"source": "openalex",
"title": "Theory vs. Practice: Revisiting the Applicability of CAPM and Black-Scholes in Real-World Markets",
"abstract": "This study is an empirical assessment of the Capital Asset Pricing Model (CAPM), the BlackScholes option pricing model (Black-Scholes), and the PutCall Parity principle (PCP)in China's A-share equity market and the U.S. derivatives market. Using data from 2020 to 2023, the research will investigate whether these classical models can reliably describe real-world market behaviors. Precisely, the low R obtained from CAPM analysis of Kweichow Moutai and the CSI 300 Index suggests that the model is not sufficient to explain market dynamics. In the meantime, beta coefficients in the standard CAPM are negative and statistically insignificant, indicating the failure of single-factor risk measures to capture the market dynamics properly. For derivatives, testing of European options on AMZN and SPY shows that the BlackScholes model does have directionally consistent pricing. However, systematic deviations and volatility smiles indicate that, in a real market, there are persistent and unavoidable violations of constant volatility assumptions. Also, the putcall parity principle, although generally held, has small but persistent deviations caused by transaction costs, liquidity constraints, and other market frictions. All in all, findings show that the empirical accuracy of CAPM, BlackScholes and Put-Call Parity is limited by market structure, behavioral factors, and unrealistic assumptions. This study further demonstrates the need for multifactor modeling approaches that could enhance the validity of asset pricing models.",
"authors": [
"Guoyi Pei"
],
"date": "2025-12-10",
"categories": [
"finance",
"Advances in Economics Management and Political Sci"
],
"url": "https://doi.org/10.54254/2754-1169/2025.bj30424",
"pdf": "https://aemps.ewapub.com/article/view/30424.pdf",
"relevance_score": 53,
"high_keywords": [
"put-call",
"factor",
"liquidity"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Factor model"
],
"backtestable": false
},
{
"id": "oa_W4417195042",
"source": "openalex",
"title": "Adaptive LongShort Equity Strategies with Salience Theory and Hidden Markov Regimes",
"abstract": "This study bridges a gap in behavioral finance by integrating Salience Theory with Hidden Markov Models to develop adaptive long-short strategies. Existing salience-based approaches, while effective predictors of mispricing, remain static and vulnerable to shifting market regimes. The hybrid framework dynamically adjusts signals between momentum (in Bull states) and reversal (in Bear states), while applying a refined salience metric. The results demonstrate that this synthesis significantly improves performance stability and reduces drawdowns during volatile periods, but it does not consistently outperform the CRSP benchmark after accounting for transaction costs. The hybrid strategy provides a distinct risk-return profile, making it suitable for certain market conditions but not universally superior. The study confirms the cross-market applicability of Salience Theory and suggests a framework for future adaptive models combining behavioral insights with dynamic market timing.",
"authors": [
"Xin Lin",
"Qichen Huang",
"Zhenhong Ni",
"Zhanrong Li",
"Y.-H. Zhang"
],
"date": "2025-12-10",
"categories": [
"finance",
"Advances in Economics Management and Political Sci"
],
"url": "https://doi.org/10.54254/2754-1169/2025.gl30493",
"pdf": "https://aemps.ewapub.com/article/view/30493.pdf",
"relevance_score": 28,
"high_keywords": [
"momentum"
],
"medium_keywords": [
"drawdown"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W4417184429",
"source": "openalex",
"title": "Managing Drought Related Financial Risks with Water Futures",
"abstract": "Abstract Water is an essential resource for agricultural and municipal interests, and increasing water scarcity poses a fundamental risk for water users and suppliers worldwide. Although well-designed water markets can ease these pressures by reallocating limited supplies to high value uses, markets in water-scarce regions are often thin and volatile. Interannual hydrologic variability can drive sharp price fluctuations, exposing both buyers and sellers to significant financial risk. Using California as a case study, which operates one of the world\u2019s most active and institutionally complex water markets, we combine water price indices with a century of streamflow data and a detailed simulation of California's supply system to predict future prices at 1-, 3-, and 6-month horizons that match typical decision timeframes. Our results reveal strong short-term predictability from lagged prices and increasing dominance of reservoir storage at longer horizons, with r\u00b2 values of 0.90, 0.79, and 0.61 for 1-, 3-, and 6-month forecasts. Hedging strategies that incorporate forecast-uncertainty filters reduce tail risk and expected water costs by 14.1% and 16.7%, respectively, when using 6-month futures with a 50% hedging target. This scalable framework offers a structured way to quantify forecast capacity and assess financial risk management strategies in water markets.",
"authors": [
"Dan Li",
"Rohini S. Gupta",
"Harrison B. Zeff",
"Gregory W. Characklis"
],
"date": "2025-12-10",
"categories": [
"finance"
],
"url": "https://doi.org/10.21203/rs.3.rs-8275145/v1",
"pdf": "https://www.researchsquare.com/article/rs-8275145/latest.pdf",
"relevance_score": 21,
"high_keywords": [],
"medium_keywords": [
"tail risk"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "oa_W7117127131",
"source": "openalex",
"title": "Inferring Latent Market Forces: Evaluating LLM Detection of Gamma Exposure Patterns via Obfuscation Testing",
"abstract": "We introduce obfuscation testing, a novel methodology for validating whether large language models detect structural market patterns through causal reasoning rather than temporal association. Testing three dealer hedging constraint patterns (gamma positioning, stock pinning, 0DTE hedging) on 242 trading days (95.6% coverage) of S&P 500 options data, we find LLMs achieve 71.5% detection rate using unbiased prompts that provide only raw gamma exposure values without regime labels or temporal context. The WHO-WHOM-WHAT causal framework forces models to identify the economic actors (dealers), affected parties (directional traders), and structural mechanisms (forced hedging) underlying observed market dynamics. Critically, detection accuracy (91.2%) remains stable even as economic profitability varies quarterly, demonstrating that models identify structural constraints rather than profitable patterns. When prompted with regime labels, detection increases to 100%, but the 71.5% unbiased rate validates genuine pattern recognition. Our findings suggest LLMs possess emergent capabilities for detecting complex financial mechanisms through pure structural reasoning, with implications for systematic strategy development, risk management, and our understanding of how transformer architectures process financial market dynamics.",
"authors": [
"Christopher Regan",
"Ying Xie"
],
"date": "2025-12-08",
"categories": [
"finance",
"arXiv (Cornell University)"
],
"url": "https://doi.org/10.48550/arxiv.2512.17923",
"pdf": "https://doi.org/10.48550/arxiv.2512.17923",
"relevance_score": 100,
"high_keywords": [
"gamma exposure",
"dealer hedging",
"0dte",
"gamma",
"positioning"
],
"medium_keywords": [
"transformer"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Transformer model"
],
"backtestable": false
},
{
"id": "gscholar_structural_decomposition_of_vix_spikes_and_their_implication",
"source": "google_scholar",
"title": "Structural Decomposition Of Vix Spikes And Their Implications For Equity Market Dynamics",
"abstract": "shifts of the volatility surface, changes in skew gradients, and volatility spike events using the CBOE VIX Decomposition implied volatility surface contribute to observed VIX spike peaks",
"authors": [
"F La Manna"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Available at SSRN 5978355"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5978355",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5978355",
"relevance_score": 100,
"high_keywords": [
"implied volatility",
"vix",
"volatility surface",
"skew",
"decomposition",
"skew gradient",
"volatility surface"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_rough_multi_factor_volatility_for_spx_and_vix_options",
"source": "google_scholar",
"title": "Rough multi-factor volatility for SPX and VIX options",
"abstract": ", implied volatility surfaces from options on the VIX and the the level, skew, and curvature of the VIX implied volatility in a case a well-known decomposition formula and then prove a",
"authors": [
"A Jacquier",
"A Muguruza",
"A Pannier"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Advances in Applied Probability"
],
"url": "https://www.cambridge.org/core/journals/advances-in-applied-probability/article/rough-multifactor-volatility-for-spx-and-vix-options/4484E86B90CC07981E275943FD27C336",
"pdf": "https://www.cambridge.org/core/services/aop-cambridge-core/content/view/4484E86B90CC07981E275943FD27C336/S0001867824000454a.pdf/rough_multifactor_volatility_for_spx_and_vix_options.pdf",
"relevance_score": 100,
"high_keywords": [
"spx",
"implied volatility",
"vix",
"volatility surface",
"skew",
"factor",
"decomposition",
"volatility surface"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_convexity_in_motion_leveraging_gamma_exposure_to_predict_equ",
"source": "google_scholar",
"title": "Convexity in Motion: Leveraging Gamma Exposure to Predict Equity Market Returns and Improve Predictive Modeling",
"abstract": "gamma exposure (GEX) in the S&P 500 index options market the US options market has expanded significantly, with 2024 delta hedging resulting from the aggregate gamma exposure",
"authors": [
"T Nyberg",
"G Jonsson"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://www.diva-portal.org/smash/record.jsf?pid=diva2:1972044",
"pdf": "https://www.diva-portal.org/smash/get/diva2:1972044/FULLTEXT01.pdf",
"relevance_score": 98,
"high_keywords": [
"s&p 500",
"gamma exposure",
"gex",
"options market",
"gamma",
"delta hedging"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_capturing_smile_dynamics_with_the_quintic_volatility_model_s",
"source": "google_scholar",
"title": "Capturing Smile Dynamics with the Quintic Volatility Model: SPX, Skew-Stickiness Ratio and VIX",
"abstract": "captures the volatility surfaces of SPX and VIX while aligning with the skew-stickiness ratio ( in terms of cumulated asymmetry Z and the speed of decay of \u03c1. One can expect even better",
"authors": [
"EA Jaber"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"arXiv preprint arXiv:2503.14158"
],
"url": "https://arxiv.org/abs/2503.14158",
"pdf": "https://arxiv.org/pdf/2503.14158",
"relevance_score": 75,
"high_keywords": [
"spx",
"vix",
"volatility surface",
"skew",
"volatility surface"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_liquidity_management_with_stock_index_futures",
"source": "google_scholar",
"title": "Liquidity Management with Stock-Index Futures",
"abstract": "derivative strategies such as rolling futures contracts and mixed approaches using options and most active index futures in the United States. The E-mini S&P 500 futures contract has a",
"authors": [
"K Shen",
"S Zhang"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Derivatives Applications in Asset Management: From"
],
"url": "https://link.springer.com/chapter/10.1007/978-3-031-86354-7_9",
"pdf": "",
"relevance_score": 75,
"high_keywords": [
"s&p 500",
"e-mini",
"index futures",
"liquidity",
"e-mini"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar__in_modern_markets_the_hidden_fragility_of_hedging_and_trend",
"source": "google_scholar",
"title": "\u2026 in Modern Markets: The Hidden Fragility of Hedging and Trend-following Strategies A Structural Analysis of Gamma Exposure, Delta Hedging, and CTA Feedback \u2026",
"abstract": "Using S&P 500 options data from 1990 to 2009, the study More recently, in August 2024, a sudden unwinding of yen arbitrage, dealer gamma exposure, dynamic delta hedging, and",
"authors": [
"Y Du"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Delta Hedging, and CTA Feedback Mechanisms (May \u2026"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5238185",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5238185",
"relevance_score": 65,
"high_keywords": [
"s&p 500",
"gamma exposure",
"gamma",
"delta hedging"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_do_s_p500_options_increase_market_volatility_evidence_from_0",
"source": "google_scholar",
"title": "Do S&P500 Options Increase Market Volatility? Evidence from 0DTEs",
"abstract": "Net Gamma We then aggregate the hedging needs in to December 2024 Intraday volume for each option contract is the number of shares in the S&P 500 index. Order Imbalance is",
"authors": [
"G Adams",
"C Dim",
"B Eraker",
"JS Fontaine"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"\u2026 (October 17, 2025)"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5641974",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5641974",
"relevance_score": 60,
"high_keywords": [
"s&p 500",
"intraday",
"0dte",
"gamma"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_short_term_market_reversals_and_the_s_p_500_index_option_ret",
"source": "google_scholar",
"title": "Short-term market reversals and the S&P 500 index option returns",
"abstract": "We study S&P 500 equity index options, the most liquid options in the world. We identify a This measure is calculated using 1 hour intraday futures data. First, for each hour within the",
"authors": [
"A Kajander",
"M Suominen"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Available at SSRN"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5284206",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5284206",
"relevance_score": 45,
"high_keywords": [
"s&p 500",
"intraday",
"equity index"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_effect_of_macro_economic_factors_on_the_trading_styles_of_in",
"source": "google_scholar",
"title": "Effect of Macro-economic Factors on the Trading Styles of Institutional Investors",
"abstract": "aggregated investment flows of institutional investors in the the institutional trading style, which indicates the trading indicators such as the RUS, S&P 500 index performance, FD, and",
"authors": [
"A Naik",
"KG Sankaranarayanan"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"South India Journal of \u2026"
],
"url": "https://journal.sijss.com/index.php/home/article/view/2291",
"pdf": "https://journal.sijss.com/index.php/home/article/download/2291/423",
"relevance_score": 45,
"high_keywords": [
"s&p 500",
"factor",
"institutional trading"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "gscholar_intelligent_decision_making_and_risk_management_in_stock_ind",
"source": "google_scholar",
"title": "Intelligent decision making and risk management in stock index futures markets under the influence of global geopolitical volatility",
"abstract": "trading strategies and risk management methods in the stock index -driven market state division strategy. By analyzing market trading system that combines the Geopolitical Risk Index",
"authors": [
"J Gao",
"C Fan",
"L Xu",
"H Chen",
"H Chen"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Omega"
],
"url": "https://www.sciencedirect.com/science/article/pii/S0305048324002366",
"pdf": "",
"relevance_score": 45,
"high_keywords": [
"index futures",
"stock index",
"futures market"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_why_does_volatility_demand_fall_during_market_turmoil_a_mark",
"source": "google_scholar",
"title": "Why Does Volatility Demand Fall During Market Turmoil? A Market Maker Perspective",
"abstract": "This paper studies how market participants use VIX options for hedging and how they adjust We demonstrate a gradual buildup of market maker inventory risk leading up to the",
"authors": [
"K Jacobs",
"AT Mai",
"P Pederzoli"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://paolapederzoli.com/wp-content/uploads/2024/12/jmp_vix_options_nov18_2024.pdf",
"pdf": "https://paolapederzoli.com/wp-content/uploads/2024/12/jmp_vix_options_nov18_2024.pdf",
"relevance_score": 45,
"high_keywords": [
"market maker",
"vix",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_ai_powered_trading_algorithmic_collusion_and_price_efficienc",
"source": "google_scholar",
"title": "Ai-powered trading, algorithmic collusion, and price efficiency",
"abstract": "The market maker observes the total order flow from action taken in a given state can affect future states and rewards. In Market makers are sophisticated individuals and institutions that",
"authors": [
"WW Dou",
"I Goldstein",
"Y Ji"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://www.nber.org/papers/w34054",
"pdf": "https://www.nber.org/system/files/working_papers/w34054/w34054.pdf",
"relevance_score": 45,
"high_keywords": [
"market maker",
"order flow",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_technical_analysis_based_unsupervised_intraday_trading_djia_",
"source": "google_scholar",
"title": "Technical analysis-based unsupervised intraday trading djia index stocks: is it profitable in long term? MA Rahim et al.",
"abstract": "two trading algorithms based on directional changes in the price and applied it for testing in multiple stock market equity indices on the link between trading volume and stock returns. To",
"authors": [
"MA Rahim",
"M Mushafiq",
"SD Khan",
"R Ullah",
"S Khan"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Applied \u2026"
],
"url": "https://link.springer.com/article/10.1007/s10489-024-05903-2",
"pdf": "",
"relevance_score": 43,
"high_keywords": [
"intraday",
"day trading"
],
"medium_keywords": [
"technical analysis"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_bitcoin_trading_performance_evaluation_a_comparative_study_o",
"source": "google_scholar",
"title": "Bitcoin trading performance evaluation: A comparative study of momentum and mean reversion strategies from 2020 to 2025",
"abstract": "momentum and mean reversion. The focus is on two widely used technical analysis tools \u2014 the moving average crossover strategy (momentum in Bitcoin\u2019s intraday price dynamics and",
"authors": [
"M Ekstr\u00f6m"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://www.theseus.fi/handle/10024/902903",
"pdf": "https://www.theseus.fi/bitstream/handle/10024/902903/Ekstrom%20Marja.pdf?sequence=2",
"relevance_score": 40,
"high_keywords": [
"intraday",
"mean reversion",
"momentum"
],
"medium_keywords": [
"technical analysis"
],
"low_keywords": [
"bitcoin"
],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_online_learning_of_order_flow_and_market_impact_with_bayesia",
"source": "google_scholar",
"title": "Online learning of order flow and market impact with Bayesian change-point detection methods",
"abstract": "of order flow regimes can be effectively utilized to improve predictions of order flow and price However we decided to choose these interval lengths in order to avoid any microstructure",
"authors": [
"IY Tsaknaki",
"F Lillo",
"P Mazzarisi"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Quantitative Finance"
],
"url": "https://www.tandfonline.com/doi/abs/10.1080/14697688.2024.2337300",
"pdf": "https://www.tandfonline.com/doi/pdf/10.1080/14697688.2024.2337300",
"relevance_score": 38,
"high_keywords": [
"microstructure",
"order flow"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_does_it_take_two_to_contango_",
"source": "google_scholar",
"title": "Does It Take Two to ConTango?",
"abstract": "market is in backwardation (contango) or the term structure of the VIX exhibits a negative (positive) curvature in period t. Additionally, I propose a fourth dynamic trading strategy that",
"authors": [
"T Lehnert"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Journal of Derivatives"
],
"url": "https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=10741240&AN=187979602&h=Lkfpmgy%2BZsxHtPlz%2FKtSDZUvWCy4WAmPmAqiFd%2Bh8s3ASA2P1t%2BmStuWroleUZYhNpy%2Bx2o3x4v%2BB%2B8fyPlfng%3D%3D&crl=c",
"pdf": "",
"relevance_score": 38,
"high_keywords": [
"vix",
"term structure"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_intraday_stock_return_prediction_a_comparative_study_of_econ",
"source": "google_scholar",
"title": "Intraday Stock Return Prediction: A Comparative Study of Econometrics and Machine Learning Models",
"abstract": "stock returns and order-flow imbalances between 1:00 pm and the close. Using ten years of intraday data from major North American exchanges, three targets are considered: return,",
"authors": [
"Z Hashemi"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://biblos.hec.ca/biblio/memoires/hashemi_zahrasadat_m2025.pdf",
"pdf": "https://biblos.hec.ca/biblio/memoires/hashemi_zahrasadat_m2025.pdf",
"relevance_score": 33,
"high_keywords": [
"intraday"
],
"medium_keywords": [
"return prediction"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_intermediary_option_pricing",
"source": "google_scholar",
"title": "Intermediary option pricing",
"abstract": "for S&P 500 options offers high-frequency variation in hedging dealer inventory gamma for the scenario where the S&P 500 in the context of S&P 500 index options, the concept applies",
"authors": [
"J Terstegge"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Available at SSRN 5877762"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5877762",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5877762",
"relevance_score": 30,
"high_keywords": [
"s&p 500",
"gamma"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_scale_invariant_dynamics_in_market_price_momentum",
"source": "google_scholar",
"title": "Scale Invariant Dynamics in Market Price Momentum",
"abstract": "-level data, we find that momentum dynamics at intraday-to-daily exhibit meanreversion at short horizons and momentum at appears across all asset classes: equity indices (ES, NQ),",
"authors": [
"B Dean"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Available at SSRN 5990674"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5990674",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5990674",
"relevance_score": 30,
"high_keywords": [
"intraday",
"momentum"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "gscholar_regulating_market_microstructure",
"source": "google_scholar",
"title": "Regulating Market Microstructure",
"abstract": "of the regulation of market microstructure, particularly in equity and option markets. We on broker pricing, the impact of payment for order flow (PFOF), the contrasting functions of",
"authors": [
"TH Ernst",
"CS Spatt"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Annual Review of Financial Economics"
],
"url": "https://www.annualreviews.org/content/journals/10.1146/annurev-financial-112923-112656",
"pdf": "https://www.annualreviews.org/content/journals/10.1146/annurev-financial-112923-112656?crawler=true&mimetype=application/pdf",
"relevance_score": 30,
"high_keywords": [
"microstructure",
"order flow"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_mechanisms_of_high_frequency_financial_data_on_market_micros",
"source": "google_scholar",
"title": "Mechanisms of High-Frequency Financial Data on Market Microstructure",
"abstract": "have emerged as flexible, nonparametric alternatives capable of detecting hidden patterns in market microstructure research by enabling fine-grained theoretical modeling of order flow,",
"authors": [
"S Yuan"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Modern Economics & Management Forum"
],
"url": "https://en.front-sci.com/index.php/memf/article/view/4249/4549",
"pdf": "https://en.front-sci.com/index.php/memf/article/view/4249/4549",
"relevance_score": 30,
"high_keywords": [
"microstructure",
"order flow"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_decay_in_vix_futures",
"source": "google_scholar",
"title": "Decay in VIX futures",
"abstract": "both contango and backwardation runs. Further, we find that the median term structure of VIX The findings carry implications for the practical trading of VIX futures. Notably, the instability",
"authors": [
"J Wallenius"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://aaltodoc.aalto.fi/items/017db850-54ca-47b5-a439-4ab4c973ea9d",
"pdf": "https://aaltodoc.aalto.fi/bitstreams/de7e2c47-87dc-4c21-95a4-a3bb827b5726/download",
"relevance_score": 30,
"high_keywords": [
"vix",
"term structure"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": true
},
{
"id": "gscholar_essays_on_institutional_trading_and_sustainable_finance",
"source": "google_scholar",
"title": "Essays on Institutional Trading and Sustainable Finance",
"abstract": "flowinduced trading (HFIT) into mechanical flow-induced trading (MFIT) and discretionary flow-induced trading ( market (as measured by the S&P 500 index) peak to trough return drop (",
"authors": [
"A Qin"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://search.proquest.com/openview/47cfca43da4db583e1605dbc30c1a23a/1?pq-origsite=gscholar&cbl=18750&diss=y",
"pdf": "https://rucore.libraries.rutgers.edu/rutgers-lib/74314/PDF/1/play/",
"relevance_score": 30,
"high_keywords": [
"s&p 500",
"institutional trading"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_option_market_making_with_hedging_induced_market_impact",
"source": "google_scholar",
"title": "Option market making with hedging-induced market impact",
"abstract": "A salient possibility in our setting is end-of-maturity manipulation: near T, a market maker holding a nonzero terminal option inventory i may attempt to move the underlying to tilt the",
"authors": [
"P Aubert",
"E Chevalier",
"VL Vath"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"arXiv preprint arXiv:2511.02518"
],
"url": "https://arxiv.org/abs/2511.02518",
"pdf": "https://arxiv.org/pdf/2511.02518",
"relevance_score": 30,
"high_keywords": [
"market maker",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_tweeting_for_money_social_media_and_mutual_fund_flows",
"source": "google_scholar",
"title": "Tweeting for money: Social media and mutual fund flows",
"abstract": "impact on flows to funds that cater mainly to retail investors. Our results confirm this prediction: the effect of social media on mutual fund flows is to prevailing market circumstances. In",
"authors": [
"J Gil-Bazo",
"JF Imbet"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Management Science"
],
"url": "https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2024.07584",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3719169",
"relevance_score": 23,
"high_keywords": [
"mutual fund"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_latency_arbitrage_in_cryptocurrency_markets_analyzing_execut",
"source": "google_scholar",
"title": "Latency Arbitrage in Cryptocurrency Markets: Analyzing Execution Speeds & Liquidity Dynamics",
"abstract": "of latency arbitrage in cryptocurrency markets, focusing on the interplay between execution speeds, high-frequency trading ( To model the effects of latency on market microstructure, we",
"authors": [
"A Alexander"
],
"date": "2025-01-01",
"categories": [
"google_scholar",
"Available at SSRN 5143158"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5143158",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5143158",
"relevance_score": 20,
"high_keywords": [
"microstructure",
"liquidity"
],
"medium_keywords": [],
"low_keywords": [
"cryptocurrency"
],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_a_model_for_the_hedging_impact_of_option_market_makers",
"source": "google_scholar",
"title": "A model for the hedging impact of option market makers",
"abstract": "contributions that focus on the gamma effect, we include an Inspired by Bangsgaard and Kokholm (2025), we include a trading volume in S&P 500 index options and S&P 500 E-Mini",
"authors": [
"S Egebjerg",
"T Kokholm"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Available at SSRN 4936978"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4936978",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4936978",
"relevance_score": 90,
"high_keywords": [
"s&p 500",
"e-mini",
"market maker",
"gamma",
"e-mini",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_the_market_for_0dte_the_role_of_liquidity_providers_in_volat",
"source": "google_scholar",
"title": "The Market for 0DTE: The Role of Liquidity Providers in Volatility Attenuation",
"abstract": "between how 0DTE and non-0DTE hedging needs impact the negative intraday relationship between market makers\u2019 net , 2024, How does zero-day-to-expiry options trading affect the",
"authors": [
"G Adams",
"JS Fontaine"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Available at SSRN \u2026"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4881008",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4881008",
"relevance_score": 90,
"high_keywords": [
"market maker",
"intraday",
"0dte",
"zero-day",
"liquidity",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_design_and_development_of_mean_reversion_strategies_on_quant",
"source": "google_scholar",
"title": "Design and Development of Mean Reversion Strategies on QuantConnect Platform",
"abstract": "of Mean Reversion strategies within the realm of Intraday trading, focusing specifically on the New York Stock The Relative Strength Index (RSI) serves as a momentum oscillator",
"authors": [
"DL Vu",
"R Bhattacharyya"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Available at SSRN 4878676"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4878676",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4878676",
"relevance_score": 60,
"high_keywords": [
"intraday",
"mean reversion",
"momentum",
"day trading"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_ai_driven_intraday_trading_applying_machine_learning_and_mar",
"source": "google_scholar",
"title": "AI-driven intraday trading: applying machine learning and market activity for enhanced decision support in financial markets",
"abstract": "day of Taiwan Capitalization Weighted Stock Index (TAIEX) futures. The results show that the accuracy was 57.45% and the returns of day trading in Taiwan\u2019s weighted index futures",
"authors": [
"MC Hung",
"AP Chen",
"WT Yu"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"IEEE Access"
],
"url": "https://ieeexplore.ieee.org/abstract/document/10403877/",
"pdf": "https://ieeexplore.ieee.org/iel7/6287639/6514899/10403877.pdf",
"relevance_score": 60,
"high_keywords": [
"index futures",
"intraday",
"stock index",
"day trading"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_vix_decomposition_tail_risk_premia_and_the_cross_section_of_",
"source": "google_scholar",
"title": "VIX Decomposition, Tail Risk Premia, and the Cross-Section of Stock Returns",
"abstract": "Unlike Harvey and Siddique (2000), which focuses on conditional skewness, this study investigates the role of tail risk and its informational value in cross-sectional asset pricing. We",
"authors": [
"V Chow",
"B Li",
"J Li"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Available at SSRN 5287877"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5287877",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5287877",
"relevance_score": 50,
"high_keywords": [
"vix",
"skew",
"decomposition"
],
"medium_keywords": [
"tail risk"
],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [
"Cross-sectional analysis"
],
"backtestable": false
},
{
"id": "gscholar_the_impact_of_index_futures_crash_risk_on_bitcoin_futures_re",
"source": "google_scholar",
"title": "The impact of index futures crash risk on bitcoin futures returns and volatility",
"abstract": "E-mini S&P 500 futures' crash risk and Bitcoin futures' returns construct informed trading strategies, leverage Bitcoin futures as on traditional market indicators like E-mini S&P 500 crash",
"authors": [
"CH Tang",
"YH Lee",
"YL Huang",
"YX Liu"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Heliyon"
],
"url": "https://www.cell.com/heliyon/fulltext/S2405-8440(24)00157-9",
"pdf": "https://www.cell.com/heliyon/fulltext/S2405-8440(24)00157-9",
"relevance_score": 50,
"high_keywords": [
"s&p 500",
"e-mini",
"index futures",
"e-mini"
],
"medium_keywords": [],
"low_keywords": [
"bitcoin"
],
"actionable": true,
"findings": [],
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"backtestable": true
},
{
"id": "gscholar_zero_day_to_expiry_options_trading_and_variance_risk_premium",
"source": "google_scholar",
"title": "Zero-Day-to-Expiry Options Trading and Variance Risk Premium",
"abstract": "intraday macroeconomic events or speculate on intraday , I examine the impact of 0DTE option trading on the underlying control variables that impact the 0DTE trading as well as the",
"authors": [
"O Khalil"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "https://research-api.cbs.dk/ws/portalfiles/portal/105671291/1775874_O._Khalil_Zero_Day_to_Expiry_Options_Trading_and_Variance_Risk_Premium.pdf",
"pdf": "https://research-api.cbs.dk/ws/portalfiles/portal/105671291/1775874_O._Khalil_Zero_Day_to_Expiry_Options_Trading_and_Variance_Risk_Premium.pdf",
"relevance_score": 45,
"high_keywords": [
"intraday",
"0dte",
"zero-day"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_are_e_mini_s_p_500_futures_prices_random_",
"source": "google_scholar",
"title": "Are E-mini S&P 500 Futures Prices Random?",
"abstract": "number of trading ticks N in an E-mini S&P 500 futures daily trading session reaches hundreds of thousands. On Thursday September 3, 2020, the December 2020 contract ESZ20 had",
"authors": [
"V Salov"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Annual Reviews in Modern Quantitative Finance \u2026"
],
"url": "https://www.worldscientific.com/doi/pdf/10.1142/13553#page=258",
"pdf": "",
"relevance_score": 45,
"high_keywords": [
"s&p 500",
"e-mini",
"e-mini"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_forecasting_the_cboe_vix_and_skew_indices_using_heterogeneou",
"source": "google_scholar",
"title": "Forecasting the CBOE VIX and SKEW indices using heterogeneous autoregressive models",
"abstract": "of the CBOE Volatility Index and the Skew Index. The choice extended to risk-neutral skewness, and thus to the S K E W of decomposed components of the S K E W , the Positive Skew",
"authors": [
"M Guidolin",
"GF Panzeri"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Forecasting"
],
"url": "https://www.mdpi.com/2571-9394/6/3/40",
"pdf": "https://www.mdpi.com/2571-9394/6/3/40",
"relevance_score": 43,
"high_keywords": [
"vix",
"skew"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": true,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_trading_volume_concentration_across_s_p_500_index_constituen",
"source": "google_scholar",
"title": "Trading Volume Concentration across S&P 500 Index Constituents\u2014A Gini-Based Analysis and Concentration-Driven (Daily Rebalanced) Portfolio Performance \u2026",
"abstract": "institutional trading volume increases (Shu 2013). These studies suggest that the rise of index funds and ETFs alters institutional The process involved retrieving data for each company",
"authors": [
"D Metelski",
"J Sobieraj"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Journal of Risk and Financial Management"
],
"url": "https://www.mdpi.com/1911-8074/17/8/325",
"pdf": "https://www.mdpi.com/1911-8074/17/8/325",
"relevance_score": 35,
"high_keywords": [
"s&p 500",
"institutional trading"
],
"medium_keywords": [
"etf"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_financial_and_energy_exchange_traded_funds_futures_an_eviden",
"source": "google_scholar",
"title": "Financial and energy exchange traded funds futures: an evidence of spillover and portfolio hedging",
"abstract": "We thus chose ETFs against indices, mutual funds, or derivatives hedge funds and develop portfolio hedging strategies. Also, ETFs are recently available and not very old traded funds,",
"authors": [
"MP Yadav",
"S Bhatia",
"N Singh",
"MT Islam"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Annals of Operations Research"
],
"url": "https://link.springer.com/article/10.1007/s10479-022-04538-1",
"pdf": "https://gala.gre.ac.uk/id/eprint/42031/7/42031_ISLAM_Financial_and_energy_exchange_traded_funds_futures_AAM.pdf",
"relevance_score": 35,
"high_keywords": [
"hedge fund",
"mutual fund"
],
"medium_keywords": [
"etf"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_0dte_asset_pricing",
"source": "google_scholar",
"title": "0DTE asset pricing",
"abstract": "We now investigate the implications of 0DTE options for intra-day market risk premia. Panel (a) of Figure 7 plots the decomposition of the equity premium across return states for different",
"authors": [
"C Almeida",
"G Freire",
"R Hizmeri"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Proceedings of the EUROFIDAI \u2026"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4701401",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4701401",
"relevance_score": 30,
"high_keywords": [
"0dte",
"decomposition"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_cross_sectional_variation_of_option_implied_volatility_skew",
"source": "google_scholar",
"title": "Cross-sectional variation of option-implied volatility skew",
"abstract": "to the cross-sectional variation of the skew: the company\u2019s business cyclicality and implied volatility skew and is particularly informative during and after recessions. The remaining skew",
"authors": [
"L Wu",
"M Tian"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Management Science"
],
"url": "https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.4872",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3707006",
"relevance_score": 30,
"high_keywords": [
"implied volatility",
"skew"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [
"Cross-sectional analysis"
],
"backtestable": false
},
{
"id": "gscholar_causality_of_price_movements_in_vix_exchange_traded_products",
"source": "google_scholar",
"title": "Causality of price movements in VIX exchange-traded products and VIX futures contracts",
"abstract": "of term structure on lead\u2013lag relations; traders operating in the futures curve moves from contango to backwardation [2] . shifts in VIX futures from contango to backwardation (and",
"authors": [
"M O'Neill",
"G Rajaguru"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Journal of Accounting Literature"
],
"url": "https://www.emerald.com/jal/article/46/2/153/1230106",
"pdf": "https://www.emerald.com/jal/article/46/2/153/1230106",
"relevance_score": 30,
"high_keywords": [
"vix",
"term structure"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_extracting_price_signals_from_the_term_structure_of_commodit",
"source": "google_scholar",
"title": "Extracting Price Signals from the Term Structure of Commodity and Credit Markets using Austrian Business Cycle Theory",
"abstract": "goods leads to contango in the term structure and thus extends causing contango in oil markets example cause backwardation Prices in the VIX term structure directly impact the stock",
"authors": [
"P Deussen",
"MR Angeley"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Procesos de Mercado: Revista \u2026"
],
"url": "https://discovery.ucl.ac.uk/id/eprint/10216721/",
"pdf": "https://discovery.ucl.ac.uk/id/eprint/10216721/1/09_Notas_Deussen%20and%20Rangeley%20-%20Colour%20Version.pdf",
"relevance_score": 30,
"high_keywords": [
"vix",
"term structure"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_monthly",
"source": "google_scholar",
"title": "Monthly",
"abstract": "VIX-based exchange-traded products in markets characterized by contango and backwardation of the VIX futures term structure. for VIX futures in general terms, as the VIX futures are in",
"authors": [
"VIXO Are"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"NA"
],
"url": "http://www.snifferquant.com/gyantal/Incode/papers/Options/ExpiringMonthly/expmonthly_vol3no1_mar20_2.pdf",
"pdf": "http://www.snifferquant.com/gyantal/Incode/papers/Options/ExpiringMonthly/expmonthly_vol3no1_mar20_2.pdf",
"relevance_score": 30,
"high_keywords": [
"vix",
"term structure"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_decision_trees_for_intuitive_intraday_trading_strategies",
"source": "google_scholar",
"title": "Decision trees for intuitive intraday trading strategies",
"abstract": "of decision trees as a valuable tool for enhancing intraday trading performance on a stock-bystock basis and could be of interest to traders seeking to improve their trading strategies.",
"authors": [
"P Naga",
"D Balivada",
"SC Nirmala"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"arXiv preprint arXiv \u2026"
],
"url": "https://arxiv.org/abs/2405.13959",
"pdf": "https://arxiv.org/pdf/2405.13959",
"relevance_score": 30,
"high_keywords": [
"intraday",
"day trading"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_on_the_other_side_of_hedge_fund_equity_trades",
"source": "google_scholar",
"title": "On the other side of hedge fund equity trades",
"abstract": "advisors or banks), as well as when we directly use index mutual funds from CRSP. Short Equities\u201d as its main investment strategy and \u201cNorth America\u201d as its geographical mandate. If a",
"authors": [
"X Cui",
"O Kolokolova",
"J Wang"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Management Science"
],
"url": "https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.4877",
"pdf": "https://research-information.bris.ac.uk/files/388599801/SSRN-id3304606.pdf",
"relevance_score": 30,
"high_keywords": [
"hedge fund",
"mutual fund"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_option_pricing_under_market_maker_s_inventory_risk_a_case_st",
"source": "google_scholar",
"title": "Option pricing under market maker's inventory risk: A case study of China",
"abstract": ") by incorporating market maker's inventory risk into option pricing for the Chinese market. Building on empirical findings, the dynamic of the ratio of market makers\u2019 inventory risks to",
"authors": [
"Z Deng",
"Y Yao"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Finance Research Letters"
],
"url": "https://www.sciencedirect.com/science/article/pii/S1544612324006469",
"pdf": "",
"relevance_score": 30,
"high_keywords": [
"market maker",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_market_making_and_hedging_with_market_impact_using_deep_rein",
"source": "google_scholar",
"title": "Market-making and hedging with market impact using deep reinforcement learning",
"abstract": "effect of inventory risk considerations on a monopolistic market maker\u2019 quoting strategy for a market maker with exponential utility. Optimal static-dynamic hedges for exotic options under",
"authors": [
"J Shi",
"SH Tang",
"C Zhou"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Proceedings of the 5th ACM International \u2026"
],
"url": "https://dl.acm.org/doi/abs/10.1145/3677052.3698646",
"pdf": "https://dl.acm.org/doi/fullHtml/10.1145/3677052.3698646",
"relevance_score": 30,
"high_keywords": [
"market maker",
"market maker"
],
"medium_keywords": [],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_exploiting_overestimated_volatility_risk_premium_a_contraria",
"source": "google_scholar",
"title": "Exploiting Overestimated Volatility Risk Premium: A Contrarian ETF Trading Strategy",
"abstract": "of a trading strategy that leverages the Volatility Risk Premium (VRP) to inform trading The VRP serves as a predictor for market volatility. The core hypothesis posits that the VRP",
"authors": [
"D Requejo"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Available at SSRN 4841308"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4841308",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4841308",
"relevance_score": 21,
"high_keywords": [],
"medium_keywords": [
"etf"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_flow",
"source": "google_scholar",
"title": "Flow",
"abstract": "Only active mutual fund and ETF flows produce stockand aggregate-level price pressure. impacts from active mutual funds and ETFs and no effect for index mutual funds. We estimate",
"authors": [
"CD Dannhauser",
"J Pontiff"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Available at SSRN 3428702"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3428702",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3428702",
"relevance_score": 20,
"high_keywords": [
"mutual fund"
],
"medium_keywords": [
"etf"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
},
{
"id": "gscholar_high_frequency_trading_asset_pricing_and_market_microstructu",
"source": "google_scholar",
"title": "High-Frequency Trading, Asset Pricing, and Market Microstructure",
"abstract": "market microstructure, capturing transaction costs and seasonality. It delves into the impact of non-stationarity with active trading on efficient markets and its lag and divided by its market",
"authors": [
"WM Tse"
],
"date": "2024-01-01",
"categories": [
"google_scholar",
"Available at SSRN 4858807"
],
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4858807",
"pdf": "https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4858807",
"relevance_score": 20,
"high_keywords": [
"microstructure"
],
"medium_keywords": [
"seasonality"
],
"low_keywords": [],
"actionable": false,
"findings": [],
"methods": [],
"backtestable": false
}
]
}