Application of an instance based learning algorithm for predicting the stock market index

Abstract

Instance based learning is a class of data mining learning paradigms that applies specific cases or experiences to new situations by matching known cases and experiences with new cases. This paper presents an application of the instance-based learning algorithm for predicting daily stock index price changes of the S&P 500 stock index between October 1995 and September 2000, given the daily changes in the exchange rate of the Canadian Dollar, the Pound Sterling, the French Franc, the Deutsche Mark and the Yen, the monthly changes in the consumer price index, GDP, and the changes in the monthly rates of certificates of deposit. The algorithm is used to predict an increase, decrease or no change in the S&P 500 stock index between a business day and the previous business day. The predictions are carried out using the IB3 variant of the IBL algorithms. The objective is to determine the feasibility of stock price prediction using the IB3 variant of the IBL algorithms. Various testing proportions and normalization methods are experimented with to obtain good predictions. © 2007 Springer-Verlag Berlin Heidelberg.