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Predicting financial distress: Applicability of O-score and logit model for Pakistani firms

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  • Waqas, Hamid
  • Md-Rus, Rohani

Abstract

Predicting financial distress have significant importance in corporate finance as it serves as an effective early warning system for the related stakeholders. The study applies the most admired financial distress prediction O-score model and compares its predictive accuracy with estimated logit model. The study estimates logit model by including the profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios. This study filled the gap by using the cash flow ratios to predict financial distress for Pakistani listed firms. The sample for the estimation model consists of 290 firms with 45 distressed and 245 healthy firms for the period 2006-2016 and covers all sectors of Pakistan Stock Exchange. The study provides important insights on the role of different financial ratio in predicting financial distress and shows that estimated logit model produces higher accuracy rate in predicting financial distress.

Suggested Citation

  • Waqas, Hamid & Md-Rus, Rohani, 2018. "Predicting financial distress: Applicability of O-score and logit model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 14(2), February.
    • Handle: RePEc:ags:pdcbeh:285182
    DOI: 10.22004/ag.econ.285182
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    Cited by:

    1. Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
    2. Halldess Nguta Munene & James Ndegwa & Thomas Senaji & Kenneth M. Mugambi, 2020. "Influence of Board Characteristics on Financial Distress of Deposit Taking SACCOs in Nairobi County, Kenya," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 9(4), pages 97-110, October.
    3. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.
    4. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    5. Rintala, Oskari & Laari, Sini & Solakivi, Tomi & Töyli, Juuso & Nikulainen, Reetta & Ojala, Lauri, 2022. "Revisiting the relationship between environmental and financial performance: The moderating role of ambidexterity in logistics," International Journal of Production Economics, Elsevier, vol. 248(C).

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    Keywords

    Financial Economics;

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