Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction

Chou, Chih-Hsun; Hsieh, Su-Chen; Qiu, Chui-Jie

doi:10.1016/j.asoc.2017.03.014

Cited by 117 publications

(39 citation statements)

References 109 publications

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“…A distinction between the improved hybrid shape and different well-applied structures was also given. The experimental results of financial data based on less than a four-year period before bankruptcy occurrence were used to gauge the performance of the proposed prediction model (Chou et al 2017).…”

Section: Emergent Bankruptcy Prediction Systemsmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

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Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy.

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Section: Emergent Bankruptcy Prediction Systemsmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

View full text Add to dashboard Cite

show abstract

“…According to the analysis of scientific literature, research both of a theoretical and practical nature has been performed, focusing on early signs (Amoa-Gyarteng 2014; Karas and Reznakova 2013), factors and reasons for a bankruptcy (Uhrig-Homburg 2005;Carter and Auken 2006). New bankruptcy prediction models were being created (Etemadi et al 2009;Li et al 2011), their advantages and limitations of application were analyzed (Chou et al 2017;Sung et al 1999), and an integrated methodology for bankruptcy prediction was composed (Shin and Lee 2002; Kumar and Ravi 2007).…”

Section: Introductionmentioning

confidence: 99%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

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“…The model can use genetic algorithm and fuzzy clustering as it is designed to be applicable in all sectors. (Chou, Hsieh, & Qiu, 2017). Figure 1 illustrates how bankruptcy prediction uses the ANFIS model.…”

Section: Bankruptcy Predictionmentioning

confidence: 99%

Forecasting Corporate Bankruptcy Based on Managerial Overconfidence Using the Adaptive Neuro-Fuzzy Inference System

Seputra¹

2020

Proceedings of the 3rd International Conference on Vocational Higher Education (ICVHE 2018)

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This purpose of this study is to examine the relationship between managerial overconfidence and the prediction of bankruptcy and likelihood of the auditor's hesitation of the company's going concern over the financially distressed companies listed on the Indonesian Stock Exchange during 2014-2016. Managerial overconfidence is defined as the manager's excessive confidence that can be measured when the growth of the company's assets is higher than its sales growth. The financial condition is measured by the prediction of bankruptcy using the adaptive neuro-fuzzy inference system (ANFIS) model. The results showed that the likelihood of an auditor's hesitation of the company's going concerns is positively associated with managerial overconfidence in Indonesia because the auditor has a positive view on the ability of management. In addition, there is the likelihood that the auditor's hesitation of a company's going concerns is positively associated with the prediction of bankruptcy.

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Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction

Cited by 117 publications

References 109 publications

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Forecasting Corporate Bankruptcy Based on Managerial Overconfidence Using the Adaptive Neuro-Fuzzy Inference System

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