mlsanityData to 7 Oct 2026

Deep Learning in Asset Pricing

arXiv 1904.00745 ↗First posted 11 Mar 2019q-fin.ST stat.MEcited by 41

Not on the map. Why?

Abstract (arXiv)

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function, to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 5; 2024: 6; 2025: 12; 2026: 10.

41 papers.

Sort:

By area:

  1. 2022DualCF: Efficient Model Extraction Attack from Counterfactual Explanationscited by 31
  2. 2023Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Predictioncited by 16
  3. 2025Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimizationcited by 14
  4. 2025Retrieval-augmented Large Language Models for Financial Time Series Forecastingcited by 12
  5. 2021MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learningcited by 4
  6. 2023Label Delay in Online Continual Learningcited by 4
  7. 2024Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectorscited by 4
  8. 2023Maximally Machine-Learnable Portfolioscited by 3
  9. 2023Online Ensemble Learning for Sector Rotation: A Gradient-Free Frameworkcited by 2
  10. 2023Harnessing Collective Intelligence Under a Lack of Cultural Consensuscited by 2
  11. 2024Empirical Asset Pricing with Large Language Model Agentscited by 2
  12. 2024Time-Series Foundation AI Model for Value-at-Risk Forecastingcited by 2
29 more
  1. 2025Growing the Efficient Frontier on Panel Treescited by 2
  2. 2025Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Informationcited by 2
  3. 2021Interpretability in Safety-Critical FinancialTrading Systemscited by 1
  4. 2022Deep Partial Least Squares for Empirical Asset Pricingcited by 1
  5. 2022Model-Free Reinforcement Learning for Asset Allocationcited by 1
  6. 2022Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Modelscited by 1
  7. 2024Multi-Task Dynamic Pricing in Credit Market with Contextual Informationcited by 1
  8. 2025A Set-Sequence Model for Time Seriescited by 1
  9. 2025Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Predictioncited by 1
  10. 2025Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Constructioncited by 1
  11. 2026LambdaRankIC: Directly Optimizing Rank IC for Financial Predictioncited by 1
  12. 2021Estimating the Value-at-Risk by Temporal VAEcited by 0
  13. 2022Empirical Asset Pricing via Ensemble Gaussian Process Regressioncited by 0
  14. 2024RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Predictioncited by 0
  15. 2024Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Marketscited by 0
  16. 2025NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networkscited by 0
  17. 2025An Information Bottleneck Asset Pricing Modelcited by 0
  18. 2025FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Managementcited by 0
  19. 2025Attention Factors for Statistical Arbitragecited by 0
  20. 2025The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?cited by 0
  21. 2026Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Seriescited by 0
  22. 2026Learning Nonlinear Regime Transitions via Semi-Parametric State-Space Modelscited by 0
  23. 2026Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Trainingcited by 0
  24. 2026Generative Predictive Distributions for Time Seriescited by 0
  25. 2026Generating Input Distributions for Explaining Portfolio Optimization Pipelinescited by 0
  26. 2026Grounding Investor Views: Neural Predicates in the Black-Litterman Modelcited by 0
  27. 2026From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Modelscited by 0
  28. 2026Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investingcited by 0
  29. 2026AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Miningcited by 0