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 · Qi-Jun Liao

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Artificial Intelligence in Healthcare

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.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 2020Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection 2018Bankruptcy prediction using imaged financial ratios and convolutional neural networks 2020Remaining Financially Healthy and Competitive: The Role of Financial Predictors 2016Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks 2015An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes 2019Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?

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