In this study, a decision support system for stock market prediction is proposed. This model uses the historical data of 180K data points obtained from the 215 highest volume ETFs that are open for trade in NYSE. The data is analyzed with several different criteria such as next 1,2,3,4,5 days percent increase∕decrease, percent moves with respect to 50∕200 day Moving Averages, changes in RSI, MACD values, direction of movement within Bollinger Bands, etc. The next day prediction is made by statistical analysis on the data using a Bayesian Maximum Likelihood decision model and the best course of action (which ETF...