Deep Learning in Asset Pricing

75 Pages Posted: 4 Apr 2019 Last revised: 23 Jul 2021

See all articles by Luyang Chen

Luyang Chen

Stanford University - Institute for Computational and Mathematical Engineering

Markus Pelger

Stanford University - Department of Management Science & Engineering

Jason Zhu

Stanford University - Management Science & Engineering

Date Written: April 4, 2019

Abstract

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.

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 (September 10, 2020). 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 - Department of 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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