Empirical Asset Pricing via Ensemble Gaussian Process Regression
This work addresses asset pricing for investors by providing a computationally efficient and uncertainty-aware portfolio strategy, though it appears incremental as it builds on existing GPR techniques.
The paper tackles the problem of predicting stock returns using an ensemble Gaussian Process Regression method, achieving statistically and economically superior out-of-sample performance with higher R-squared and Sharpe ratios compared to existing models.
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.