Comparison of instance-based techniques for learning to predict changes in stock prices

Abstract

This paper is a practical guide to the application of instance-based machine learning techniques to the solution of a financial problem. A broad class of instance-based families is considered for classification using the WEKA software package. The problem selected for analysis is a common one in financial and econometric work: the use of publicly available economic data to forecast future changes in a stock market index. This paper examines various stages in the analysis of this problem including: identification of the problem, considerations in obtaining and preprocessing data, model and parameter selection, and interpretation of results. Finally, the paper offers suggestions of areas of future study for applying instance-based machine learning in the setting of solving financial problems. © 2007 Springer-Verlag Berlin Heidelberg.