  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.
