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Default Prediction Using Piotroski’s F-score

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  • Khushbu Agrawal

Abstract

This article is an attempt to explore the usefulness of a model that uses the Piotroski’s F -score and its individual components for predicting the risk of default for a sample of Indian firms. The study uses logistic regression as the prediction technique. The Piotroski’s F -score is found to be statistically significant in predicting defaults. Higher values of the score are associated with lower probability of default. Among the individual components of the score, change in leverage is found to be statistically significant in predicting defaults. Increases in leverage are associated with higher probability of default. The model using the individual components of the score is found to have better prediction accuracy as compared to the model using the aggregate score. The findings of the study hold important implications for investment and lending decisions. The study contributes to the existing literature on default prediction by making available yet another measure which has so far not been explicitly used for distress prediction.

Suggested Citation

  • Khushbu Agrawal, 2015. "Default Prediction Using Piotroski’s F-score," Global Business Review, International Management Institute, vol. 16(5_suppl), pages 175-186, October.
    • Handle: RePEc:sae:globus:v:16:y:2015:i:5_suppl:p:175s-186s
    DOI: 10.1177/0972150915601261
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    References listed on IDEAS

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    1. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    2. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    3. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    4. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    5. Arindam Bandyopadhyay, 2006. "Predicting probability of default of Indian corporate bonds: logistic and Z-score model approaches," Journal of Risk Finance, Emerald Group Publishing, vol. 7(3), pages 255-272, May.
    6. Casey, C & Bartczak, N, 1985. "Using Operating Cash Flow Data To Predict Financial Distress - Some Extensions," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 384-401.
    7. N. Ramu, 2009. "Dimensions of Non-performing Assets in Urban Cooperative Banks in Tamil Nadu," Global Business Review, International Management Institute, vol. 10(2), pages 279-297, July.
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    9. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    10. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    11. Altman, Edward I, 1984. "A Further Empirical Investigation of the Bankruptcy Cost Question," Journal of Finance, American Finance Association, vol. 39(4), pages 1067-1089, September.
    12. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 179-192.
    13. Abdul RASHID & Qaiser ABBAS, 2011. "Predicting Bankruptcy in Pakistan," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(9(562)), pages 103-128, September.
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