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Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model

Author

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  • Mahfuzur Rahman

    (Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Cheong Li Sa

    (Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Md. Abdul Kaium Masud

    (Department of Business Administration, Noakhali Science and Technology University, Noakhali 3814, Bangladesh)

Abstract

Financial performance of firms is very important to bankers, shareholders, potential investors, and creditors. The inability of firms to meet their liabilities will affect all its stakeholders and will result in negative consequences in the wider economy. The objective of the study is to explore the applicability of a distress prediction model which uses the F-Score and its components to identify firms which are at high risk of going into default. The study incorporates a prediction model and vast literature to address the research questions. The sample of the study is collected from publicly listed firms of the United States. In total, 81 financially distressed firms wereextracted from the UCLA-LoPucki Bankruptcy Research Database during 2009–2017. This study found that the relationship of the F-Score and probability of firms going into financial distress is significant. This study also demonstrated that firms which are at risk of distress tend to record a negative cash flow from operations (CFO) and showed a greater decline in return on assets (ROA) in the year prior to default. This study extends the existing literature by supporting a model which has not been widely used in the area of financial distress predictions.

Suggested Citation

  • Mahfuzur Rahman & Cheong Li Sa & Md. Abdul Kaium Masud, 2021. "Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model," JRFM, MDPI, vol. 14(5), pages 1-16, May.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:199-:d:547608
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    References listed on IDEAS

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    1. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.

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