# Deep Learning for Predicting Asset Returns

arXiv: https://arxiv.org/abs/1804.09314 · HTML: https://mlsanity.com/paper/1804.09314/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Deep Sequence Modeling: Development and Applications in Asset Pricing](https://mlsanity.com/paper/2108.08999/) (2021)
- [Deep Partial Least Squares for Empirical Asset Pricing](https://mlsanity.com/paper/2206.10014/) (2022)
- [Non-linear dimension reduction in factor-augmented vector autoregressions](https://mlsanity.com/paper/2309.04821/) (2023)
- [Forex Trading Volatility Prediction using Neural Network Models](https://mlsanity.com/paper/2112.01166/) (2021)
- [Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training](https://mlsanity.com/paper/2604.14206/) (2026)

## Abstract (arXiv)

Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed in this way, asset pricing studies can be revisited using multi-layer deep learners, such as rectified linear units (ReLU) or long-short-term-memory (LSTM) for time-series effects. State-of-the-art algorithms including stochastic gradient descent (SGD), TensorFlow and dropout design provide imple- mentation and efficient factor exploration. To illustrate our methodology, we revisit the equity market risk premium dataset of Welch and Goyal (2008). We find the existence of nonlinear factors which explain predictability of returns, in particular at the extremes of the characteristic space. Finally, we conclude with directions for future research.