Deep Sequence Modeling: Development and Applications in Asset Pricing

TL;DR

This paper explores the use of deep sequence modeling, especially LSTM models, for predicting asset returns and measuring risk premia, highlighting their advantages over traditional methods in capturing complex sequential dependencies.

Contribution

It provides an overview of deep sequence models, applies them to asset pricing, and demonstrates their superior out-of-sample performance in financial data.

Findings

LSTM models outperform other sequence models in asset return prediction.

Deep sequence models effectively capture complex historical dependencies.

Sequence modeling enhances asset pricing accuracy.

Abstract

We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time series models, sequence modeling offers a promising path with its data-driven approach and superior performance. In this paper, we first overview the development of deep sequence models, introduce their applications in asset pricing, and discuss their advantages and limitations. We then perform a comparative analysis of these methods using data on U.S. equities. We demonstrate how sequence modeling benefits investors in general through incorporating complex historical path dependence, and that Long- and Short-term Memory (LSTM) based models tend to have the best out-of-sample performance.