| Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability by Avramov et al. | 228 | Neural networks | 2023 |
| BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability by Hiew et al. | 40 | Language models +1 | 2022 |
| Option Return Predictability with Machine Learning and Big Data by Bali et al. | 20 | | 2021 |
| Intraday Market Predictability: A Machine Learning Approach by Huddleston et al. | 15 | Linear factor models | 2021 |
| Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis by Glasserman and Lin | 13 | Language models | 2023 |
| Empirical Asset Pricing with Missing Data by Beckmeyer and Wiedemann | 13 | | 2022 |
| Asset Pricing: Time-Series Predictability by Rapach and Zhou | 11 | | 2021 |
| The Virtue of Complexity in Return Prediction by Kelly et al. | 11 | | 2021 |
| A Comparison of Different Hybrid ARIMA - Neural Network Models for Stock Index Return Forecasting and Trading Strategy by Kumar and Thenmozhi | 8 | Classical time series +1 | 2007 |
| Intraday Market Return Predictability Culled from the Factor Zoo by Aleti et al. | 6 | | 2023 |
| The Anatomy of Machine Learning-Based Portfolio Performance by Coulombe et al. | 6 | | 2023 |
| Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting by Boubaker et al. | 6 | Neural networks | 2020 |
| Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction by Ito et al. | 6 | | 2020 |
| Time-Series and Cross-Sectional Stock Return Forecasting: New Machine Learning Methods by Rapach and Zhou | 6 | | 2019 |
| Accounting vs Market Information: What Matters More for Stock Return Predictability? by Cakici and Zaremba | 5 | | 2023 |
| Divide and Conquer: Financial Ratios and Industry Returns Predictability by Bianchi and McAlinn | 5 | Linear factor models | 2018 |
| What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts by Chen et al. | 4 | Language models | 2024 |
| Machine-Learning in the Chinese Stock Market by Leippold et al. | 4 | | 2021 |
| A Tweet-based Dataset for Company-Level Stock Return Prediction by Sowinska and Madhyastha | 4 | | 2020 |
| Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning by Zhao et al. | 4 | Neural networks | 2019 |
| Exploiting Supply Chain Interdependencies for Stock Return Prediction: A Full-State Graph Convolutional LSTM by Liu | 3 | Neural networks | 2025 |
| Intraday Stock Predictability Everywhere by Liu and Stentoft | 2 | Linear factor models | 2023 |
| Is machine learning a necessity? A regression-based approach for stock return prediction by Cheng et al. | 2 | | 2023 |
| Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing by Asgarov | 2 | Neural networks | 2023 |
| Can a Machine Learn from Behavioral Biases? Evidence from Stock Return Predictability of Deep Learning Models by Byun et al. | 2 | Neural networks | 2022 |
| Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach by Sun et al. | 2 | Neural networks | 2022 |
| RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction by Wang and Guo | 1 | Bayesian +2 | 2024 |
| Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions by Xu et al. | 1 | Neural networks | 2023 |
| Application of Machine Learning to Short-Term Equity Return Prediction by Yan et al. | 1 | | 2006 |
| (Mis)Understanding Benign Overfitting in Equity Return Prediction by Guo et al. | 0 | | 2026 |
| A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting by Liu et al. | 0 | | 2026 |
| A Multi-Horizon Machine Learning Framework for Stock Prediction in Emerging Market: Evidence from Thailand by Sriharee | 0 | Tree ensembles | 2026 |
| Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction by Sarkar | 0 | Neural networks | 2026 |
| Autonomous Market Intelligence: Agentic AI Nowcasting Predicts Stock Returns by Chen and Pu | 0 | Language models +1 | 2026 |
| Causal Momentum: Information Channel Alignment in Network-Based Cross-Sectional Stock Prediction by Shah | 0 | Tree ensembles +1 | 2026 |
| Cross-Sectional Return Prediction Using Machine Learning: A Practitioner Pipeline by Lama | 0 | Tree ensembles +1 | 2026 |
| Cross-Stock Predictability via LLM-Augmented Semantic Networks by Huang et al. | 0 | Language models | 2026 |
| Deep learning-based analysis of investor sentiment and stock return prediction: an experimental finance perspective by Zheng et al. | 0 | Language models +1 | 2026 |
| Dependence-Informed Sparse Neural Architecture for Stock Return Prediction by Lin et al. | 0 | Neural networks | 2026 |
| DistillSem: LLM-Distilled Financial Headline Semantics for Cross-Sectional Return Prediction by Guo | 0 | Language models | 2026 |
| Does CNN-Based Feature Extraction Improve High-Frequency Return Prediction? Evidence from the CSI 300 Index by Fan and Wang | 0 | Neural networks | 2026 |
| Empirical Asset Pricing via Machine Learning: Evidence from Indian Equity Markets by Sharma | 0 | Tree ensembles +2 | 2026 |
| Enhanced Prediction of Stock Returns: Optimizing Variable Selection in Machine Learning by Huo et al. | 0 | | 2026 |
| Feature Scope and Cross-Sectional Return Prediction: Evidence from US Large-Cap Equities by Rocchi | 0 | Linear factor models | 2026 |
| Feature-enriched deep learning approach for stock market return forecasting using time-variant Hurst exponent and feature selection by Kumbure et al. | 0 | Neural networks | 2026 |
| From Index to Equity: Pre-Training Transformers for Stock Return Prediction by Coolsaet et al. | 0 | Tree ensembles +1 | 2026 |
| From Pixels to Signals: Hierarchical Vision Transformers for Return Prediction by Bøjstrup et al. | 0 | Neural networks | 2026 |
| Getting the Target Right in Return Prediction by Cakici and Zaremba | 0 | | 2026 |
| Google Trends as Alternative Data: A Systematic Review with Implications for the Indian Equity Markets by Yeware and Kanade | 0 | Neural networks | 2026 |
| Hybrid Quantum FT-Transformer for Stock Return Prediction: Evidence from Chinese Rare Earth Firms by Zhu et al. | 0 | Neural networks | 2026 |
| Incorporating Price Limits into Deep Learning for Stock Return Prediction in the Chinese A-Share Market by Fan and Wang | 0 | Neural networks | 2026 |
| Innovation Theater: Frontier Narrative Adoption, Investment Decoupling, and Cross-sectional Return Predictability by Tang et al. | 0 | | 2026 |
| Interpretable Factor Decomposition for Decision Intelligence in Large-Scale Financial Markets: Evidence from China's A-Share Market by Han et al. | 0 | Tree ensembles +1 | 2026 |
| Machine Learning for Cross-Sectional Return Prediction: A Comparative Study of US and Indian Equities by Khatu | 0 | Tree ensembles +1 | 2026 |
| Machine Learning, Return Predictability, and Implementable Alpha on the Ghana Stock Exchange by Rahman et al. | 0 | Classical time series | 2026 |
| Machine Learning-Based Stock Return Prediction for NEPSE: Do Global Feature Sets Outperform Traditional Technical Indicators in an Emerging, Illiquid Frontier Market? An Independent Study Submitted in Partial Fulfilment of the Requirements for the Bachelor of Management Studies (BMS) by Basnet | 0 | Tree ensembles | 2026 |
| Market Prices Encode Future Profitability Beyond Standard Accounting Signals by Zhang | 0 | Tree ensembles +2 | 2026 |
| Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study by Chen et al. | 0 | Neural networks | 2026 |
| Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction by Ahmad et al. | 0 | Classical time series +1 | 2026 |
| Regime Conditioning Alters Risk, Not Predictability in Short-Horizon U.S. Equity ETF Models by Somani | 0 | Tree ensembles | 2026 |
| Scaling State-Space Models: A Deep Learning Approach to Firm-level Return Forecasting by Liu and Zivot | 0 | State-space models +2 | 2026 |
| Spatiotemporal Signals in Residuals: A Two-Stage Boosting Graph Neural Network for Stock Return Prediction by Liu et al. | 0 | Tree ensembles +2 | 2026 |
| Stock Return Prediction Guided by a Revised Random Walk Hypothesis: Adaptive Information Smoothing and Signed Correlation-Aware TimeMixing by Seo and Yang | 0 | Neural networks | 2026 |
| Symbolic Regime Laws: Automated Discovery of Time-Varying Alpha Factor Equations via Symbolic Regression by Merlini | 0 | Linear factor models | 2026 |
| The Geometry of Alpha Manifold Learning, Topological Data Analysis, and Non-Linear Factor Structures in Global Equity Markets by Kholia | 0 | State-space models +2 | 2026 |
| Understanding the Convolutional Neural Network's Stock Return Predictability by Guan et al. | 0 | Neural networks | 2026 |
| Visualizing Income Statements by Garfinkel et al. | 0 | Neural networks | 2026 |
| Volatility Forecasting and Return Prediction under Market Regimes: Evidence from High-Frequency Chinese Equity Data by Fang and Ślepaczuk | 0 | Classical time series +1 | 2026 |
| Why Does Stock Return Predictability Disappear? Evidence from Information Structure by Wang et al. | 0 | | 2026 |
| Bay Street Meets Machine Learning: Predicting Stock Risk Premium by Attig and Attig | 0 | Tree ensembles +1 | 2025 |
| Deep Learning Enhanced Multi-Day Turnover Quantitative Trading Algorithm for Chinese A-Share Market by Du | 0 | Neural networks | 2025 |
| Integration of Wavelet Transform Convolution and Channel Attention with LSTM for Stock Price Prediction based Portfolio Allocation by Guo | 0 | Neural networks | 2025 |
| Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting by Yang et al. | 0 | Linear factor models +1 | 2025 |
| Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies by Dave et al. | 0 | Neural networks | 2025 |
| Structured Event Representation and Stock Return Predictability by Li et al. | 0 | Language models +1 | 2025 |
| The Cross-Section of Stock Returns: Rank-Based, Linear, and Non-Linear Models (slides) by Molinaro | 0 | Tree ensembles +2 | 2025 |
| Unleashing Expert Opinion from Social Media for Stock Prediction by Zhou et al. | 0 | Neural networks | 2025 |
| When Reasoning Fails: Evaluating 'Thinking' LLMs for Stock Prediction by Sodha | 0 | State-space models +2 | 2025 |
| Analysts' Conditional Biases and the Cross Section of Corporate Bond Returns by Yang and Liu | 0 | | 2024 |
| Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading by Lyu et al. | 0 | Neural networks | 2024 |
| StockGPT: A GenAI Model for Stock Prediction and Trading by Mai | 0 | Classical time series +2 | 2024 |
| Trading Volume Alpha by Goyenko et al. | 0 | | 2024 |
| Transforming Investment Strategies and Strategic Decision-Making: Unveiling a Novel Methodology for Enhanced Performance and Risk Management in Financial Markets by Tian et al. | 0 | Neural networks | 2024 |
| Choices Matter When Training Machine Learning Models For Return Prediction by Howard | 0 | | 2023 |
| Decoding the Unique Price Behavior in the Japanese Stock Market with Convolutional Neural Networks by Okada et al. | 0 | Neural networks | 2023 |
| Intraday Returns Forecasting Using Machine Learning: Evidence from the Brazilian Stock Market by Arismendi-Zambrano et al. | 0 | | 2023 |
| Multi-Measures Stock Return Prediction Using Neural Network Models by Wang | 0 | Linear factor models +1 | 2023 |
| Can Deep Neural Networks Outperform Fama-MacBeth Regression and Other Supervised Learning Approaches in Stock Returns Prediction With Asset-Pricing Factors? by Teng and Li | 0 | Linear factor models +1 | 2022 |
| Environmental Sustainability and Stock Returns * by Brown et al. | 0 | Neural networks | 2022 |
| Machine Forecast Disagreement and Equity Returns by Bali et al. | 0 | Linear factor models | 2022 |
| Network of Economic Sectors and Return Prediction by CNN-LSTM by Phuensane and Boonpong | 0 | Neural networks | 2022 |
| Robust Monitoring Machine: A Machine Learning Solution for Out-of-Sample R2-Hacking in Return Predictability Monitoring by Yae and Luo | 0 | | 2022 |
| The Connectedness of Business Sectors and Return Predictions by a Hybrid Machine Learning Method by Boonpong and Phuensane | 0 | Classical time series +1 | 2022 |
| Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating by Singh and Khushi | 0 | | 2021 |
| The Macroeconomy and the Cross-Section of International Equity Index Returns: A Machine Learning Approach by Popescu | 0 | Neural networks | 2019 |
| Anomalous Returns in a Neural Network Equity-Ranking Predictor by Satinover and Sornette | 0 | Neural networks | 2008 |
| A Hybrid LSTM-XGBoost Framework for Multi-Horizon Stock Return Prediction Across Diversified Equity Portfolios by Mostafa et al. | | Tree ensembles +1 | 2026 |
| ResNLS: An Improved Model for Stock Price Forecasting by Jia et al. | | Neural networks | 2026 |
| The cross-sectional stock return predictions via quantum neural network and tensor network by Kobayashi et al. | | Neural networks | 2024 |
| AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment by Cui et al. | | | 2021 |
| Multi-Horizon Equity Returns Predictability via Machine Learning | | Tree ensembles +1 | 2021 |
| Time-varying neural network for stock return prediction by Wong et al. | | Neural networks | 2021 |
| Predicting prices of S&P500 index using classical methods and recurrent neural networks | | Neural networks | 2020 |
| A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy by Nakagawa et al. | | Neural networks | 2019 |
| Directional Predictability of Daily Stock Returns | | Neural networks | 2018 |
| Empirical Asset Pricing via Machine Learning by Gu et al. | | Tree ensembles +2 | 2018 |