Machine Learning Execution Time in Asset Pricing

Demirbaga, Umit; Xu, Yue

Abstract

In the fast-paced world of finance, where timely decisions can yield substantial gains or losses, machine learning models with time-consuming training and prediction may miss crucial market timing opportunities. This study examines the machine learning model execution time including both training and prediction phases, in empirical asset pricing. We conduct a comprehensive analysis of machine learning execution time, examining ten models and introducing two strategies to save time: feature reduction and the reduction of time observations. Our findings reveal that XGBoost stands out as a top performer, demonstrating relatively low execution times compared to other machine learning models, with exceptional accuracy, boasting an out-of-sample R-Squared of 0.78 and a Sharpe ratio of 1.76. Furthermore, feature reduction and shorter time observations reduce execution time by as much as 18 times while also slightly enhancing investment performance. This research underscores the vital interplay between model accuracy and execution time to make accurate and prompt investment decisions in practice.

Citation

Demirbaga, U., & Xu, Y. (2023). Machine Learning Execution Time in Asset Pricing

• Working Paper Type: Working Paper
• Publication Date: Nov 2, 2023
• Deposit Date: May 29, 2024
• Publicly Available Date: May 29, 2024
• Public URL: https://durham-repository.worktribe.com/output/1961164