NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH
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Empirical Asset Pricing via Machine Learning

Shihao Gu, Bryan Kelly, Dacheng Xiu

NBER Working Paper No. 25398
Issued in December 2018, Revised in June 2019

---- Acknowledgments ----

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.

---- Disclosure of Financial Relationships for Bryan Kelly ----

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.

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