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Predicting financial distress: Importance of accounting and firm-specific market variables for Pakistan’s listed firms

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  • Hamid Waqas
  • Rohani Md-Rus

Abstract

This study is intended to identify the predictors of financial distress for the Pakistani firms. Variables used are the financial ratios representing profitability, liquidity, leverage, and cash flows, as well as two important market factors which are size and idiosyncratic standard deviation of each firm’s stock returns (SIG). The sample consists of 290 firms stretching from 2007 to 2016 and logit regression is applied to predict financial distress. The findings reveal that profitability, liquidity, leverage, cash flow ratios, and firm size are significant, while SIG is insignificant in predicting financial distress. Results of the estimated logit model I, model II, and holdout model reveal that the models perform consistently. This study contributes to the literature by testing the market variables in relation to financial distress as these variables were ignored by the previous studies in Pakistan. Findings of this study are precise as the study covers a longer time horizon and a larger sample size.

Suggested Citation

  • Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Importance of accounting and firm-specific market variables for Pakistan’s listed firms," Cogent Economics & Finance, Taylor & Francis Journals, vol. 6(1), pages 1545739-154, January.
    • Handle: RePEc:taf:oaefxx:v:6:y:2018:i:1:p:1545739
    DOI: 10.1080/23322039.2018.1545739
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    Cited by:

    1. Shilpa H. Shetty & Theresa Nithila Vincent, 2021. "The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model: Evidence from India," JRFM, MDPI, vol. 14(7), pages 1-13, July.
    2. Shafiqul Alam & Sumon Kumar Das & Umma Rumman Dipa & Syed Zabid Hossain, 2024. "Predicting financial distress through ownership pattern: dynamics of financial resilience of Bangladesh," Future Business Journal, Springer, vol. 10(1), pages 1-19, December.
    3. Jagjeevan Kanoujiya & Shailesh Rastogi, 2024. "Nexus between efficiency and financial distress of listed firms in India: a comparative study using frontier techniques," OPSEARCH, Springer;Operational Research Society of India, vol. 61(2), pages 835-866, June.
    4. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research," Management Review Quarterly, Springer, vol. 72(4), pages 927-966, December.
    5. Shilpa Shetty H. & Theresa Nithila Vincent, 2024. "Corporate Default Prediction Model: Evidence from the Indian Industrial Sector," Vision, , vol. 28(3), pages 344-360, June.
    6. Rafael Becerra-Vicario & David Alaminos & Eva Aranda & Manuel A. Fernández-Gámez, 2020. "Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry," Sustainability, MDPI, vol. 12(12), pages 1-15, June.
    7. Maryna Brychko & Tetyana Vasilyeva & Zuzana Rowland & Serhiy Lyeonov, 2021. "Does the real estate market behavior predict the trust crisis in the financial sector? The case of the ECB and the Euro," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 16(4), pages 711-740, December.
    8. Tamala Amelia Manda, 2024. "Bankruptcy Prediction in REITs," International Real Estate Review, Global Social Science Institute, vol. 27(1), pages 139-167.
    9. Jagjeevan Kanoujiya & Shailesh Rastogi & Rebecca Abraham & Venkata Mrudula Bhimavarapu, 2023. "Does Competition Affect Financial Distress of Non-Financial Firms in India: A Comparison Using the Lerner Index and Boone Indicator," JRFM, MDPI, vol. 16(7), pages 1-20, July.
    10. Soumya Ranjan Sethi & Dushyant Ashok Mahadik & Rajkiran V. Bilolikar, 2024. "Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric Study," International Journal of Economics and Financial Issues, Econjournals, vol. 14(1), pages 164-179, January.

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