Predicting financial distress in private companies :|the case of Turkish firms
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Date
2021.
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Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2021.
Abstract
This thesis provides a discriminant score to predict the financial distress of privately held companies. Providing a discriminant score named PF-Score is intended to fill the gap in the literature of the private firms’ financial distress prediction. In this paper, we used discriminant analysis which used in literature predominantly. Our sample consists of Turkish privately held companies involving 2.391 financially failed companies and 345.426 healthy firms’ observations. Having determined coefficients of the PF-Score model, we observed that profitability ratios are more effective in distress prediction. Moreover, the coefficients of efficiency, liquidity, and leverage ratios were also found convenient estimators in the ranking of importance. After determining the threshold, we obtained that our model can distinguish distressed firms with 60% accuracy and can isolate healthy firms with a 75% accuracy rate. We also tested the accuracy of the Altman Z-Score models. Comparative ROC and AUC analyses of the prediction models are also provided in the paper. Eventually, we found that PF-Score outperformed other discriminant analysis prediction models of private firms.