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

Daniel Ogachi, Richard Ndege, Peter Gaturu and Zeman Zoltan
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Daniel Ogachi: Department of Finance, Szent Istvan University, 2100 Gödöllő, Hungary
Richard Ndege: Twenty Four Secure Security Services, Nairobi 50353-00100, Kenya
Peter Gaturu: BSS Department, Jomo Kenyatta University of Agriculture and Technology, Karen 62000-00200, Nairobi, Kenya
Zeman Zoltan: Department of Finance, Szent Istvan University, 2100 Gödöllő, Hungary

JRFM, 2020, vol. 13, issue 3, 1-14

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.

Keywords: bankruptcy; insolvency; financial distress; default; failure; forecasting methods (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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