Employing Machine Learning Algorithms to build Trading Strategies with higher than Risk-Free Returns
International Econometric Review
This research aims at exploring whether simple trading strategies developed using state-ofthe-art Machine Learning (ML) algorithms can guarantee more than the risk-free rate of return or not. For this purpose, the direction of S&P 500 Index returns on every 6th day (SPYRETDIR6) and magnitude of S&P 500 Index daily returns (SPYMAG) were predicted on a broad selection of independent variables using various ML techniques. Using five consecutive data spans of equal length, GBM was found to provide
... s found to provide highest prediction accuracy on SPYRETDIR6, consistently. In terms of magnitude prediction of daily returns (SPYMAG), Random Forest results indicated that there is a very high correlation between actual/predicted values of SPY. Based on these results, Trading Strategy #1 (using SPYRETDIR6 predictions) and Trading Strategy #2 (using SPYMAG predictions) were developed and tested against a simple Buy & Hold benchmark of the same index. It was found that Trading Strategy #1 provides negative returns on all data spans, while Trading Strategy #2 has positive returns on average when data is separated into consecutive data spans. None of the trading strategies have a positive Sharpe ratio on average, but Trading Strategy #2 is almost as profitable as investing in T-bills using the risk-free rate.