Predicting Financial Failure Using Decision Tree Algorithms: An Empirical Test on the Manufacturing Industry at Borsa Istanbul

  •  Nurcan Ocal    
  •  Metin Kamil Ercan    
  •  Eyup Kadioglu    


This study aims to develop a model using C5.0 and CHAID decision tree algorithms to estimate the financial failure and/or success of a given manufacturing company. Within the scope of this study, 35 financial ratios are used as independent variables calculated on the grounds of both company’s annual financial statements and notes from 2007 to 2013. The dependent variable is the successful or unsuccessful status in terms of financial capability of 206 manufacturing firms listed on the Borsa Istanbul. Qualitative criteria are used to categorize the companies as successful or unsuccessful. The rates of accurate classification for both models are found to be at acceptable levels. Although the CHAID algorithm’s general rate of accuracy and its rate for successful companies are greater than the rates obtained from the C5.0 algorithm for the same observations, the CHAID algorithm yielded much lower results than the C5.0 algorithm in predicting unsuccessful companies.

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