PREDICTING AND BEATING THE STOCK MARKET WITH MACHINE LEARNING AND TECHNICAL ANALYSIS
The paper studies whether machine learning or technical analysis best predicts the stock market and in turn generates the best return. The research back tests machine learning and technical analysis methods ten years in the past to predict ten years in the future. After prediction stage, the research incorporates the main findings into trading strategies to beat the S&P 500 index. To further this analysis, the paper examines all market periods and then examines the results specifically in up market and down-market periods. The sampling period is January 1995 through December 2005, and the trading period is January 2006 through December 2016. The null hypothesis is that machine learning and technical analysis would generate returns with no statistically significant difference. The study uses State Street’s SPDR® SPY ETF as the benchmark. Data is retrieved from Bloomberg and Yahoo Finance. Outputs are calculated in R, MATLAB, SPSS, EVIEWS, Python, and SAS languages.