Deep Learning in Asset Pricing

89 Pages Posted: 4 Apr 2019 Last revised: 28 Jun 2019

See all articles by Luyang Chen

Luyang Chen

Stanford University - Institute for Computational and Mathematical Engineering

Markus Pelger

Stanford University - Management Science & Engineering

Jason Zhu

Stanford University - Management Science & Engineering

Date Written: March 14, 2019

Abstract

We propose a novel approach to estimate asset pricing models 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. Our general non-linear asset pricing model is estimated with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage. Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors. We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative adversarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network finds a small set of hidden economic state processes. A feedforward network captures the non-linear effects of the conditioning variables. Our model allows us to identify the key factors that drive asset prices and generate profitable investment strategies.

Keywords: No-arbitrage, stock returns, conditional asset pricing model, non-linear factor model, machine learning, deep learning, neural networks, big data, hidden states, GMM

JEL Classification: C14, C38, C55, G12

Suggested Citation

Chen, Luyang and Pelger, Markus and Zhu, Jason, Deep Learning in Asset Pricing (March 14, 2019). Available at SSRN: https://ssrn.com/abstract=3350138 or http://dx.doi.org/10.2139/ssrn.3350138

Luyang Chen

Stanford University - Institute for Computational and Mathematical Engineering ( email )

Huang Building, 475 Via Ortega
Suite 060 (Bottom level)
Stanford, CA 94305-4042
United States

Markus Pelger (Contact Author)

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Jason Zhu

Stanford University - Management Science & Engineering ( email )

314L Huang Engineering Center
475 Via Ortega
Stanford, CA 94305
United States

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