MACHINE LEARNING FOR CROSS-SECTIONAL RETURN PREDICTABILITY: EVIDENCE FROM GLOBAL STOCK MARKETS

Authors : Ahmet Salih Kurucan, Ali Hepşen

Pages : 315-338

Doi:10.31671/doujournal.1534375

View : 24 | Download : 50

Publication Date : 2025-01-24

Article Type : Research Paper

Abstract :This work examines cross-sectional stock returns with machine learning models using global stock market data. By calculating 63 firm level characteristics, we find that our model outperforms linear models in terms of both economic and statistical performance. Shallow models, such as gradient boosted decision trees, provides more consistent and reliable performance compared to deeper ones in the context of asset pricing, likely due to a low signal-to-noise ratio and sensitivity to parameters. The results revealed that machine learning models can be developed into effective portfolios, complexity is welcomed when it enhances performance such as Sharpe ratios. Taken together, these results demonstrate the relative importance of machine learning for a modern financial system, and specifically, the ability to synthesize information from various characteristics that impact stock returns. This study challenges traditional notions of a preference for parsimony and, based on certain degrees of complexity, demonstrates strategic economic gains.
Keywords : Makine Öğrenimi, Varlık Fiyatlaması, Hisse Senedi Risk Primi, Tahminsel Modelleme, Getirilerin Tahmin Edilebilirliği, Finansal Analiz