Financial performance prediction using constraint-based evolutionary classification tree (CECT) approach

Abstract

Financial ratios are commonly employed to measure a corporate financial performance. In recent years a considerable amount of research has been directed towards the analysis of the predictive power of financial ratios as influential factors of corporate stock market behavior. In this paper we propose a constraint-based evolutionary classification tree (CECT) approach that combines both the constraint-based reasoning and evolutionary techniques to generate useful patterns from data in a more effective way. The proposed approach is experimented, tested and compared with a regular genetic algorithm (GA) to predict corporate financial performance using data from Taiwan Economy Journal (TEJ). Better prediction effectiveness of CECT approach is obtained than those of regular GA and C5.0. © Springer-Verlag Berlin Heidelberg 2005.