Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

You-Shyang Chen, Chien-Ku Lin, Chih-Min Lo, Su-Fen Chen and Qi-Jun Liao
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You-Shyang Chen: Department of Information Management, Hwa Hsia University of Technology, New Taipei City 235, Taiwan
Chien-Ku Lin: Department of Business Management, Hsiuping University of Science and Technology, Taichung City 412, Taiwan
Chih-Min Lo: Department of Digital Multimedia Design, National Taipei University of Business, Taipei City 100025, Taiwan
Su-Fen Chen: National Museum of Marine Science and Technology, Keelung City 202010, Taiwan
Qi-Jun Liao: Department of Information Management, Hwa Hsia University of Technology, New Taipei City 235, Taiwan

Mathematics, 2021, vol. 9, issue 20, 1-26

Abstract: In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

Keywords: financial bankruptcy; data mining; advanced intelligent model; logistic regression; classifier (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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