Predicting Corporate Bankruptcy with Financial Ratios and Macroeconomic Predictors : Evidence from Finnish data
Tanskanen, Aleksi (2020-09-20)
20.09.2020
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020092070151
https://urn.fi/URN:NBN:fi-fe2020092070151
Tiivistelmä
Bankruptcy is a severe and permanent state of a firm where all stakeholders are facing the consequences, not just the investors. The literature of bankruptcy prediction is an extensive area where new statistical methods have been applied recently.
The purpose of this thesis is to study benefits of using machine learning methods in bank-ruptcy prediction instead traditional methods such as logistic regression and Z-score by using Finnish data. Furthermore, this thesis tests the use of macroeconomic variables together with firm specific predictors. Lastly, machine learning algorithm called random forest is tested against logistic regression. The adaptation of random forest in bankruptcy prediction is not studied comprehensively.
This thesis employs dataset of 96 995 Finnish firms between the years 1999 and 2019. 2595 firms of this dataset are stated as bankrupt, representing 2.7% of all observations. The finan-cial ratios are derived from Altman’s Z-score’s variables which reflect the financial state of a firm. The effect of macroeconomic events on predictability of bankruptcy, is tested by em-ploying different macroeconomic predictors such as change in gross domestic product. The robustness checks include careful data cleaning and validating models by splitting data into training and test data.
The results from Finnish data encourage the use of machine learning methods in bankruptcy, especially random forest algorithm. Predictability by using random forest outperformed all other methods introduced in this thesis. Furthermore, the utilisation of macroeconomic predictor in bankruptcy prediction is justified together with firm specific predictors. Particularly, household debt as a proportion of available income shows a significant predictive power on bankruptcy. Lastly, the random forest performed better than logistic regression. This thesis provides encouraging results on bankruptcy prediction in practical purposes against traditional methods such as Z-score that are still used today.
The purpose of this thesis is to study benefits of using machine learning methods in bank-ruptcy prediction instead traditional methods such as logistic regression and Z-score by using Finnish data. Furthermore, this thesis tests the use of macroeconomic variables together with firm specific predictors. Lastly, machine learning algorithm called random forest is tested against logistic regression. The adaptation of random forest in bankruptcy prediction is not studied comprehensively.
This thesis employs dataset of 96 995 Finnish firms between the years 1999 and 2019. 2595 firms of this dataset are stated as bankrupt, representing 2.7% of all observations. The finan-cial ratios are derived from Altman’s Z-score’s variables which reflect the financial state of a firm. The effect of macroeconomic events on predictability of bankruptcy, is tested by em-ploying different macroeconomic predictors such as change in gross domestic product. The robustness checks include careful data cleaning and validating models by splitting data into training and test data.
The results from Finnish data encourage the use of machine learning methods in bankruptcy, especially random forest algorithm. Predictability by using random forest outperformed all other methods introduced in this thesis. Furthermore, the utilisation of macroeconomic predictor in bankruptcy prediction is justified together with firm specific predictors. Particularly, household debt as a proportion of available income shows a significant predictive power on bankruptcy. Lastly, the random forest performed better than logistic regression. This thesis provides encouraging results on bankruptcy prediction in practical purposes against traditional methods such as Z-score that are still used today.