Empirical Asset Pricing via Machine Learning / Shihao Gu, Bryan Kelly, Dacheng Xiu.

Format:

Book

Author/Creator:

Gu, Shihao.

Contributor:

National Bureau of Economic Research.

Kelly, Bryan.

Xiu, Dacheng.

Series:

Working Paper Series (National Bureau of Economic Research) no. w25398.

NBER working paper series no. w25398

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2018.

Summary:

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.

Notes:

Print version record

December 2018.