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

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  • Hamid Waqas
  • Rohani Md-Rus

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

  • Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    • Handle: RePEc:pdc:jrnbeh:v:14:y:2018:i:2:p:389-401
    DOI: 10.15208/beh.2018.28
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    References listed on IDEAS

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    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. Munene Halldess Nguta & James Ndegwa, 2021. "The Mediating Effect of Firm Revenue on the Relationship Between 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. 10(1), pages 34-47, January.
    4. 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.
    5. 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.
    6. 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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    More about this item

    Keywords

    Financial distress; bankruptcy; logit regression; O-score model; financial distress; emerging market; Pakistan;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises

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