# Deep Learning in Asset Pricing

arXiv: https://arxiv.org/abs/1904.00745 · HTML: https://mlsanity.com/paper/1904.00745/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 30 of 41)

- [DualCF: Efficient Model Extraction Attack from Counterfactual Explanations](https://mlsanity.com/paper/2205.06504/) (2022)
- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
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- [Retrieval-augmented Large Language Models for Financial Time Series Forecasting](https://mlsanity.com/paper/2502.05878/) (2025)
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- [MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning](https://mlsanity.com/paper/2102.11448/) (2021)
- [Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors](https://mlsanity.com/paper/2404.12120/) (2024)
- [Maximally Machine-Learnable Portfolios](https://mlsanity.com/paper/2306.05568/) (2023)
- [Online Ensemble Learning for Sector Rotation: A Gradient-Free Framework](https://mlsanity.com/paper/2304.09947/) (2023)
- [Harnessing Collective Intelligence Under a Lack of Cultural Consensus](https://mlsanity.com/paper/2309.09787/) (2023)
- [Empirical Asset Pricing with Large Language Model Agents](https://mlsanity.com/paper/2409.17266/) (2024)
- [Time-Series Foundation AI Model for Value-at-Risk Forecasting](https://mlsanity.com/paper/2410.11773/) (2024)
- [Multi-Task Dynamic Pricing in Credit Market with Contextual Information](https://mlsanity.com/paper/2410.14839/) (2024)
- [Growing the Efficient Frontier on Panel Trees](https://mlsanity.com/paper/2501.16730/) (2025)
- [Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information](https://mlsanity.com/paper/2501.17992/) (2025)
- [Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction](https://mlsanity.com/paper/2510.15691/) (2025)
- [Interpretability in Safety-Critical FinancialTrading Systems](https://mlsanity.com/paper/2109.15112/) (2021)
- [Deep Partial Least Squares for Empirical Asset Pricing](https://mlsanity.com/paper/2206.10014/) (2022)
- [Model-Free Reinforcement Learning for Asset Allocation](https://mlsanity.com/paper/2209.10458/) (2022)
- [Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models](https://mlsanity.com/paper/2212.00852/) (2022)
- [A Set-Sequence Model for Time Series](https://mlsanity.com/paper/2505.11243/) (2025)
- [Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction](https://mlsanity.com/paper/2511.07014/) (2025)
- [LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction](https://mlsanity.com/paper/2605.00501/) (2026)
- [Estimating the Value-at-Risk by Temporal VAE](https://mlsanity.com/paper/2112.01896/) (2021)
- [Empirical Asset Pricing via Ensemble Gaussian Process Regression](https://mlsanity.com/paper/2212.01048/) (2022)
- [RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction](https://mlsanity.com/paper/2403.02500/) (2024)
- [Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets](https://mlsanity.com/paper/2409.05192/) (2024)
- [NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks](https://mlsanity.com/paper/2505.06864/) (2025)
- [An Information Bottleneck Asset Pricing Model](https://mlsanity.com/paper/2507.23218/) (2025)
- [FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management](https://mlsanity.com/paper/2510.02986/) (2025)

## 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.