An evolution-based approach with modularized evaluations to forecast financial distress
- 31 March 2006
- journal article
- research article
- Published by Elsevier BV in Knowledge-Based Systems
- Vol. 19 (1), 84-91
- https://doi.org/10.1016/j.knosys.2005.11.006
Abstract
Due to the radical changing of the global economy, a more precise forecasting of corporate financial distress helps provide important judgment principles to decision-makers. Although financial statements reflect a firm's business activities, it is very challenging to discover critical information from these statements. Applying machine learning algorithms can be demonstrated to improve forecasting accuracy in predicting corporate bankruptcy. In this paper, we introduce an evolutionary approach with modularized evaluation functions to forecast financial distress, which allows using any evolutionary algorithm to extract the set of critical financial ratios and integrates more evaluation function modules to achieve a better forecasting accuracy by assigning distinct weights. To achieve a more precise predicting accuracy, the undesirable forecasting results from some modules are weeded out, if their predicting accuracies are out of the allowable tolerance range as learned from our mechanism.Keywords
This publication has 24 references indexed in Scilit:
- A data mining approach to the prediction of corporate failureKnowledge-Based Systems, 2001
- The integrated methodology of rough set theory and artificial neural network for business failure predictionExpert Systems with Applications, 2000
- Mining multi-dimensional data for decision supportFuture Generation Computer Systems, 1999
- Neural networks and genetic algorithms for bankruptcy predictionsExpert Systems with Applications, 1996
- Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy predictionExpert Systems with Applications, 1996
- Hybrid neural network models for bankruptcy predictionsDecision Support Systems, 1996
- Neural network prediction analysis: The bankruptcy caseNeurocomputing, 1996
- Genetically optimized neural network classifiers for bankruptcy prediction-an empirical studyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Effectiveness of neural network types for prediction of business failureExpert Systems with Applications, 1995
- Bankruptcy prediction using neural networksDecision Support Systems, 1994