Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry
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...
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| Format: | Article |
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MDPI AG
2021-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/20/2622 |
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| author | You-Shyang Chen Chien-Ku Lin Chih-Min Lo Su-Fen Chen Qi-Jun Liao |
| author_facet | You-Shyang Chen Chien-Ku Lin Chih-Min Lo Su-Fen Chen Qi-Jun Liao |
| author_sort | You-Shyang Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:24:51Z |
| format | Article |
| id | doaj.art-45548d4d8529483684a5fedc57bbdf34 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T06:24:51Z |
| publishDate | 2021-10-01 |
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| series | Mathematics |
| spelling | doaj.art-45548d4d8529483684a5fedc57bbdf342023-11-22T19:02:49ZengMDPI AGMathematics2227-73902021-10-01920262210.3390/math9202622Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics IndustryYou-Shyang Chen0Chien-Ku Lin1Chih-Min Lo2Su-Fen Chen3Qi-Jun Liao4Department of Information Management, Hwa Hsia University of Technology, New Taipei City 235, TaiwanDepartment of Business Management, Hsiuping University of Science and Technology, Taichung City 412, TaiwanDepartment of Digital Multimedia Design, National Taipei University of Business, Taipei City 100025, TaiwanNational Museum of Marine Science and Technology, Keelung City 202010, TaiwanDepartment of Information Management, Hwa Hsia University of Technology, New Taipei City 235, TaiwanIn 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.https://www.mdpi.com/2227-7390/9/20/2622financial bankruptcydata miningadvanced intelligent modellogistic regressionclassifier |
| spellingShingle | You-Shyang Chen Chien-Ku Lin Chih-Min Lo Su-Fen Chen Qi-Jun Liao Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry Mathematics financial bankruptcy data mining advanced intelligent model logistic regression classifier |
| title | Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry |
| title_full | Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry |
| title_fullStr | Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry |
| title_full_unstemmed | Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry |
| title_short | Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry |
| title_sort | comparable studies of financial bankruptcy prediction using advanced hybrid intelligent classification models to provide early warning in the electronics industry |
| topic | financial bankruptcy data mining advanced intelligent model logistic regression classifier |
| url | https://www.mdpi.com/2227-7390/9/20/2622 |
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