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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Daniel Ogachi & Richard Ndege & Peter Gaturu & Zeman Zoltan, 2020. "Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya," JRFM, MDPI, vol. 13(3), pages 1-14, March.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:3:p:47-:d:328331
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    References listed on IDEAS

    as
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    Cited by:

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    2. Yuan Gao & Biao Jiang & Jietong Zhou, 2023. "Financial Distress Prediction For Small And Medium Enterprises Using Machine Learning Techniques," Papers 2302.12118, arXiv.org.
    3. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
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    6. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    7. Muhammad Ramadhani Kesuma & Felisitas Defung & Anisa Kusumawardani, 2021. "Bankruptcy Prediction And Its Effect On Stock Prices As Impact Of The COVID-19 Pandemic," Technium Social Sciences Journal, Technium Science, vol. 25(1), pages 567-582, November.
    8. Yong Sun & Hui Liu & Jiwei Liu & Mingyu Sun & Qun Li, 2023. "Analysis of Factors Influencing the Corporate Performance of Listed Companies in China’s Agriculture and Forestry Sector Based on a Panel Threshold Model," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
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    10. Antonio Pelaez-Verdet & Pilar Loscertales-Sanchez, 2021. "Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation," Sustainability, MDPI, vol. 13(3), pages 1-17, January.

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