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

Authors:
Chi-I Hsu;Yuan Lin Hsu;Pei Lun Hsu
Affiliations:
Department of Information Management, Kai Nan University, Taiwan, R.O.C.;Department of Finance, National Cheng Chi University, Taiwan, R.O.C.;Department of Electricity Engineering, Chin Yun University, Taiwan, R.O.C.
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

Citing 8
Cited 0

Tree search and ARC consistency in constraint satisfaction algorithms

Search in Artificial Intelligence
C4.5: programs for machine learning

C4.5: programs for machine learning
Using Genetic Algorithms for Concept Learning

Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning

Machine Learning - Special issue on genetic algorithms
Competition-Based Induction of Decision Models from Examples

Machine Learning - Special issue on genetic algorithms
Search rearrangement backtracking and polynomial average time

Artificial Intelligence
A constraint-based genetic algorithm approach for mining classification rules

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
CAN: chain of nodes approach to direct rule induction

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

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.