Predicting Financial Distress

Abstrakt

Financial Distress Prediction (FDP) models largely revolve around the utilization of financial information to predict the probability of financial distress of companies. Accurate financial distress predictions are relevant for stakeholders as financial distress can have lasting impacts on both internal stakeholders and external stakeholders. Despite the widely studied area of FDP that has seen recent developments from machine learning, the academic literature has primarily focused on financial information, leaving the potential impact of quantitative non-financial ownership information sparsely studied. The potentially underdeveloped aspect of including non-financial ownership information as a predictor in FDP, the latest development of high-performance models using machine learning, and a considerable amount of data on limited Danish companies, leads to the research question: “How does the inclusion of non-financial ownership information affect the performance of financial distress prediction models on Danish companies?” Using public data from the Danish Business Authority, linear discriminant analysis (LDA), logistic regression (LR), and gradient boosted trees (GBT) models are trained on reduced (dense) data using cross-validation and randomized grid search – first trained without the proxy for non-financial ownership information, i.e., company ownership default risk (CODR), and then trained similarly with CODR. Additional GBT-models were trained on the complete (sparse) data for better generalization with and without CODR. The results show that the sparse-GBTCODR is the best-performing model (퐴푈퐶 = 0.8409) over other models. Following a discussion on limitations, implications, operationalization approaches, and statistical tests, the thesis concludes that there presumably are potential positive impacts of using non-financial ownership information for FDP on Danish companies but calls for further research.