Artikel
Predicting firms' financial distress: An empirical analysis using the F-Score model
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.
- Sprache
-
Englisch
- Erschienen in
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 5 ; Pages: 1-16 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
accounting-based bankruptcy
bankruptcy prediction
F-Score
financial distress prediction
logistic regression
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Mahfuzur Rahman
Cheong Li Sa
Masud, Md. Abdul Kaium
- Ereignis
-
Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2021
- DOI
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doi:10.3390/jrfm14050199
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Mahfuzur Rahman
- Cheong Li Sa
- Masud, Md. Abdul Kaium
- MDPI
Entstanden
- 2021
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