Machine Learning Algorithms for Financial Asset Price Forecasting

TL;DR

This paper evaluates various Machine Learning algorithms for financial asset price forecasting, demonstrating their superior performance over traditional models like CAPM using high-performance computing and extensive stock data.

Contribution

It provides a comprehensive comparison of modern ML algorithms against CAPM, highlighting their improved predictive accuracy in financial asset forecasting.

Findings

ML models outperform CAPM on out-of-sample data

Inclusion of macroeconomic variables enhances prediction accuracy

High-performance computing enables efficient large-scale analysis

Abstract

This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC) infrastructures versus the traditional and highly popular Capital Asset Pricing Model (CAPM) on U.S equities data. The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.