Empirical Asset Pricing via Ensemble Gaussian Process Regression

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 takes into account the non-stationarity of the financial data. We conduct an empirical analysis on a large cross-section of US stocks from 1962 to 2016. Our method dominates existing machine learning models statistically and economically. Exploiting the Bayesian nature of GPR, we introduce the mean-variance optimal portfolio with respect to the predictive uncertainty distribution. It significantly dominates standard prediction-sorted portfolios and the S&P 500.

Authors: Damir Filipovic (EPFL) and Puneet Pasricha (EPFL)