Empirical Asset Pricing via Ensemble Gaussian Process Regression

TL;DR

This paper presents an ensemble Gaussian Process Regression method for predicting stock returns, which improves computational efficiency and outperforms existing models in empirical tests, offering a new approach for asset pricing.

Contribution

It introduces a scalable ensemble GPR approach for asset pricing, enhancing prediction accuracy and portfolio performance compared to prior machine learning models.

Findings

Ensemble GPR outperforms existing models in out-of-sample R-squared.

The method yields higher Sharpe ratios for prediction-sorted portfolios.

Bayesian portfolio optimization based on GPR uncertainty surpasses traditional benchmarks.

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 prediction 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.