Predicting Bankruptcy with Machine Learning Models

This thesis explores the predictive power of different machine learning algorithms in Swedish firm defaults. Both firm-specific variables and macroeconomic variables are used to calculate the estimated probabilities of firm default. Four different algorithms are used to predict default; Random Forest, Adaboost, Feed Forward Neural Network and Long Short Term Memory Neural Network (LSTM). These models are compared to a classical Logistic Classification model that acts as a benchmark model. The data used is a panel data set of quarterly observations. The study is done on data for the period 2000 to 2018. To evaluate the models Precision and Recall are calculated and compared between the models. The LSTM model performs the best of all five fitted models and correctly classifies 60 % of all defaults in the test data. The data is supplied by the Riksbank, the Swedish central bank. It consists of two data sets, one from Upplysningscentralen AB with firm specific variables, and one from the Riksbank with the macroeconomic variables. Keywords: LTSM, Neural Network, Adaboost, Random Forest, Machine Learning, Default, Panel Data, Longitudinal Data, Risk, Prediction, Precision, Recall