Deep Learning in Asset Pricing
75 Pages Posted: 4 Apr 2019 Last revised: 5 Aug 2021
Date Written: April 4, 2019
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
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