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

Abstract

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.

An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

“…By comparing our logistic regression results obtained by the stepwise selection technique, we can say that they are well above the average obtained by other studies on the topic of prediction of financial distress (Bateni and Asghari 2020;Cohen et al 2017;Vu et al 2019;Guan et al 2020;Ogachi et al 2020;Tong and Serrasqueiro 2021;Rahman et al 2021;Park et al 2021). On a sample of 64 listed companies in the Nairobi Securities Exchange, Ogachi et al (2020) correctly classified 83% of the companies through logistic regression with the following significant ratios: working capital ratio, current ratio, debt ratio, total asset, debtors turnover, debt-equity ratio, asset turnover, and inventory turnover. Tong and Serrasqueiro (2021) used logistic regression to predict the financial distress of Portuguese small and mid-sized enterprises operating in Portuguese technology manufacturing sectors.…”

Financial statement manipulation in failing Small and Medium-Sized Enterprises in Bosnia and Herzegovina

“…The findings of that study demonstrate that such an approach helps a company's stakeholders to guide their decisionmaking process and auditors to focus their further investigation. Furthermore, Ogachi, Ndege, Gaturu, and Zoltan (2020) argue that the combination of different ratios that are used as classification tools and for bankruptcy prediction can help in the selection of financial ratios that would enhance predication accuracy. Also, Ogachi et al (2020) state that financially distressed companies are usually smaller, highly leveraged, and with very low liquidity, solvency, and profitability ratios.…”

Financial Distress Phenomenon of Non-financial Companies in Indonesia

“…Investor dapat memperoleh pemahaman mengenai kinerja perusahaan sebagai tolok ukur perolehan keuntungan dalam perdagangan sekuritas serta dapat mempertimbangkan probabilitas terjadinya kebangkrutan (Hosaka, 2018). Bagi pemberi pinjaman baik lembaga keuangan seperti bank maupun kreditor, kajian financial distress memberikan informasi yang bermanfaat bagi pengambilan keputusan mengenai pemberian kredit agar potensi kerugian dapat diminimalisir (Ogachi et al, 2020).…”