Predicting Financial Failure in Companies by Employing Machine Learning Methods

  • Safak Sönmez SOYDAS İrfan Can Kose Vocaional High School, Gümüşhane University, Gümüşhane, Turkey
  • Handan CAM Department of Management Informatıon Systems, Gumushane University, Gumushane, Turkey
Keywords: BIST; Financial Failure; Financial Ratios; Machine Learning


It is utterly crucial for the companies trading in a country to sustain their activities and the welfare they would deliver to the country’s economy. The worldwide economic globalization and the outbreaks of economic crises adversely influence the economies of the states as well as trading companies. It has become imperative for companies to attain good financial management and to take the vital precautions prior to failure either to prevent the existing companies from being influenced by these crises or to be less affected by them within the framework of all these circumstances. The study aims to generate a prediction and classification model in which the dependent variable, which is generated by taking into account the profit and loss criteria of the companies that maintain their activities, as well as independent variables by considering the generally accepted financial data of 178 manufacturing companies trading in Borsa Istanbul between the years 2015-2019, are used by employing machine learning methods. It also aims to assess the effectiveness of machine learning techniques in predicting failure. By courtesy of the comparative analysis, Machine learning methods of companies operating in Borsa Istanbul yield financially acceptable results in predicting and classifying successful-unsuccessful companies.

How to Cite
SOYDAS, S. S., & CAM, H. (2024). Predicting Financial Failure in Companies by Employing Machine Learning Methods. International Journal of Social Science Research and Review, 7(2), 111-125.