Empirical Asset Pricing via Ensemble Gaussian Process Regression

• URL: http://arxiv.org/abs/2212.01048v1
• Date: Fri, 2 Dec 2022 09:37:29 GMT
• Title: Empirical Asset Pricing via Ensemble Gaussian Process Regression
• Authors: Damir Filipovi\'c and Puneet Pasricha
• Abstract summary: Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference. We find that our method dominates existing machine learning models statistically and economically. It appeals to an uncertainty averse investor and significantly dominates the equal- and value-weighted prediction-sorted portfolios, which outperform the S&P 500.
• Score: 4.111899441919165
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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