Machine Learning Algorithms for Financial Asset Price Forecasting
This work addresses the challenging problem of asset price prediction for quantitative finance practitioners, but it is incremental as it applies existing ML methods to financial data.
This paper tackled the problem of financial asset price forecasting by comparing machine learning algorithms against the traditional Capital Asset Pricing Model (CAPM) on U.S. equities data, finding that the ML models significantly outperformed CAPM on out-of-sample test data.
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.