Empirical Asset Pricing via Ensemble Gaussian Process Regression

Damir Filipović and Puneet Pasricha
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Damir Filipović: Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute
Puneet Pasricha: École Polytechnique Fédérale de Lausanne (EPFL)

No 22-95, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and lends itself to general online learning tasks. We conduct an empirical analysis on a large cross-section of US stocks from 1962 to 2016. We find that our method dominates existing machine learning models statistically and economically in terms of out-of-sample R-squared and Sharpe ratio of prediction-sorted portfolios. Exploiting the Bayesian nature of GPR, we introduce the mean-variance optimal portfolio with respect to the predictive uncertainty distribution of the expected stock returns. It appeals to an uncertainty averse investor and significantly dominates the equal- and value-weighted prediction-sorted portfolios, which outperform the S&P 500.

Keywords: empirical asset pricing; Gaussian process regression; portfolio selection; ensemble learning; machine learning; firm characteristics (search for similar items in EconPapers)
JEL-codes: C11 C14 C52 C55 G11 G12 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2022-12
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2295

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