Novel feature selection methods to financial distress prediction

Fengyi Lin, Deron Liang, Ching Chiang Yeh, Jui Chieh Huang

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

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.

Original languageEnglish
Pages (from-to)2472-2483
Number of pages12
JournalExpert Systems with Applications
Volume41
Issue number5
DOIs
StatePublished - Apr 2014

Keywords

  • Feature selection
  • Financial distress prediction
  • Genetic algorithm
  • Integrated prediction model
  • Wrappers

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