Deep Sequence Modeling: Development and Applications in Asset Pricing

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See all articles by Lin William Cong

Lin William Cong

Cornell University - Samuel Curtis Johnson Graduate School of Management

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University

Jingyuan Wang

Beihang University (BUAA)

Yang Zhang

Beihang University (BUAA)

Date Written: June 1, 2020

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.

Keywords: Artificial Intelligence, Asset Pricing, Machine Learning, Risk Premia, Time Series

Suggested Citation

Cong, Lin and Tang, Ke and Wang, Jingyuan and Zhang, Yang, Deep Sequence Modeling: Development and Applications in Asset Pricing (June 1, 2020). Available at SSRN: https://ssrn.com/abstract=

Lin Cong (Contact Author)

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.linwilliamcong.com/

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University ( email )

No.1 Tsinghua Garden
Beijing, 100084
China

Jingyuan Wang

Beihang University (BUAA) ( email )

37 Xue Yuan Road
Beijing 100083
China

Yang Zhang

Beihang University (BUAA) ( email )

37 Xue Yuan Road
Beijing 100083
China

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