Contrasting Approaches for Forecasting the S&P 500 Index

This paper develops and compares several methods of forecasting the S&P 500 Index using only data based on the closing value and trained over a six-decade data set. The methodologies include a C5.0 decision tree, a neural network, and a group of forecasts based on training set patterns of directional change from one to seven days in length. Methods are compared by using the number of correct forecast directions, and by calculating the amount of gain/loss. We find that the neural network yielded the most gain, but the six-day string pattern did best predicting that the Index would move up