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Novel feature selection methods to financial distress prediction
Affiliation:1. Department of Business Management, National Taipei University of Technology, Taipei, Taiwan;2. Department of Computer Science and Information Engineering, and Software Research Center, National Central University, Taoyuan, Taiwan;3. Department of Business Administration, National Taipei College of Business, Taipei, Taiwan;1. Institute for Infocomm Research, Singapore;2. University of Hong Kong, Hong Kong;3. Centre for Computer and Information Security Research, School of Computer Science and Software Engineering, University of Wollongong, Australia;1. Sobey School of Business, Saint Mary’s University, Halifax, NS B3H 2W3, Canada;2. Charlton College of Business, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA;1. Research Institute of Computer Science, Technical University of Loja, San Cayetano alto, Loja, Ecuador;2. Department of Computing, Polytechnic University of Madrid, Boadilla del Monte, Madrid, Spain;1. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia;2. Department of Electrical and Computer Engineering, Concordia University, Montreal H3G 1T7, QC, Canada;3. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal H3G 1T7, QC, Canada;1. CMR Institute of Technology, AECS Layout, Bangalore, Karnataka 560037, India;2. Christ University, Hosur Road, Bangalore, Karnataka 560029, India;1. Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, Brazil;2. Universidade Federal de Minas Gerais, Computer Science Department, 31.270-010 Belo Horizonte, MG, Brazil
Abstract:Financially distressed prediction (FDP) has been a widely and continually studied topic in the field of corporate finance. One of the core problems to FDP is to design effective feature selection algorithms. In contrast to existing approaches, we propose an integrated approach to feature selection for the FDP problem that embeds expert knowledge with the wrapper method. The financial features are categorized into seven classes according to their financial semantics based on experts’ domain knowledge surveyed from literature. We then apply the wrapper method to search for “good” feature subsets consisting of top candidates from each feature class. For concept verification, we compare several scholars’ models as well as leading feature selection methods with the proposed method. Our empirical experiment indicates that the prediction model based on the feature set selected by the proposed method outperforms those models based on traditional feature selection methods in terms of prediction accuracy.
Keywords:Financial distress prediction  Genetic algorithm  Wrappers  Integrated prediction model  Feature selection
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