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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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