Repository Article

·2019 OPEN ACCESS

Bankruptcy Risk Forecasting Based on Company Balance Sheet Data Using Feature Selection Methods And Machine Learning

Necip Bulut , Saber Shakeri , Seçil YÜZÜK , Mehmet S. Aktaş YTU

DergiPark (Istanbul University)

Abstract

The success of a company has a significant issue for both the interlocutors of companies and other related persons.Financial failure sometimes ends up bankrupt and can have a critical effect on the company's interlocutors.Prediction of bankruptcy is significant for investors, backers, director sand sometimes policy makers.Although there are a lot of models to predict bankruptcy in the financial literature, Ohlson O-score and Altman Z-score are models that are used quite often.The fact that the set wo models are both linear models and companies are only interested in their latest balance sheets can sometimes lead to incorrect predictions.We argue that bankruptcy should be viewed as a process.Hence, modeling bankruptcy based on only the latest financial reports of the companies has some

Keywords

Bankruptcy Feature selection Balance sheet Feature (linguistics) Bankruptcy prediction Artificial intelligence Computer science Business Selection (genetic algorithm) Actuarial science Machine learning Finance

Subject Areas

Financial Distress and Bankruptcy Prediction ·Accounting ·Social Sciences