Empirical Asset Pricing via Machine Learning
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. All methods agree on the same set of dominant predictive signals which includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation.
We benefitted from discussions with Joseph Babcock, Si Chen (Discussant), Rob Engle, Andrea Frazzini, Amit Goyal (Discussant), Lasse Pedersen, Lin Peng (Discussant), Alberto Rossi (Discussant), Guofu Zhou (Discussant), and seminar and conference participants at Erasmus School of Economics, NYU, Northwestern, Imperial College, National University of Singapore, UIBE, Nanjing University, Tsinghua PBC School of Finance, Fannie Mae, U.S. Securities and Exchange Commission, City University of Hong Kong, Shenzhen Finance Institute at CUHK, NBER Summer Institute, New Methods for the Cross Section of Returns Conference, Chicago Quantitative Alliance Conference, Norwegian Financial Research Conference, EFA, China International Conference in Finance, 10th World Congress of the Bachelier Finance Society, Financial Engineering and Risk Management International Symposium, Toulouse Financial Econometrics Conference, Chicago Conference on New Aspects of Statistics, Financial Econometrics, and Data Science, Tsinghua Workshop on Big Data and Internet Economics, Q group, IQ-KAP Research Prize Symposium, Wolfe Re- search, INQUIRE UK, Australasian Finance and Banking Conference, Goldman Sachs Global Alternative Risk Premia Conference, AFA, and Swiss Finance Institute. We gratefully acknowledge the computing support from the Research Computing Center at the University of Chicago. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
I have received consulting income from AQR Capital Management exceeding $5,000 over the past year. AQR Capital Management is a global investment management firm, which may or may not apply similar investment techniques or methods of analysis as described herein. The views expressed here are those of the authors and not necessarily those of AQR.
Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, vol 33(5), pages 2223-2273. citation courtesy of