Details

Autor(en) / Beteiligte

• Gu, Shihao
• Kelly, Bryan
• Xiu, Dacheng
• Karolyi, Andrew

Titel

Empirical Asset Pricing via Machine Learning

Ist Teil von

• The Review of financial studies, 2020-05, Vol.33 (5), p.2223-2273

Ort / Verlag

Oxford University Press

Erscheinungsjahr

2020

Link zum Volltext

Quelle

EBSCOhost Business Source Ultimate

Beschreibungen/Notizen

• We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. 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 allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility.