Unraveling the Mystery of Default Prediction: A Study on the Textile Industry in Pakistan

  • Jahanzaib Alvi IQRA University, Karachi, Pakistan
  • Imtiaz Arif IQRA University, Karachi, Pakistan
Keywords: Default Prediction, Default Modeling, Risk Assessment


Default events are inevitable in any economy and can have a considerable impact on economic stability. However, predicting defaults before occurrence has always been a challenging task for researchers around the world. In Pakistan, the textile industry experiences a high rate of default, which motivated us to conduct a study on predicting default in this sector. We analyzed data from 134 listed companies in the textile industry between 2000 and 2020, and segregated the industry into three sub-sectors (Composite, Spinning and Weaving with Textile Associated Products) for better analysis. After reviewing the literature, we identified five widely-used default prediction models which led us to perform a comparative study to validate their performance. Findings revealed that Grover’s G-Score Model was the best predictor of default, followed by Springate’s S-Score Model based on both model accuracy and model validation. However, it is important to note that our study is limited to the textile sector and future studies could include other sectors and more advanced methods to improve accuracy. This study can be useful for investors and financial analysts in assessing the risk of default in the textile industry and making informed investment decisions.


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How to Cite
Alvi, J., & Arif, I. (2023). Unraveling the Mystery of Default Prediction: A Study on the Textile Industry in Pakistan. Journal of Quantitative Methods, 7(2). https://doi.org/10.29145/jqm.0702.02