  Credit risk assessment of a company is commonly conducted by utilizing
financial ratios that are derived from its financial statements. However, this
approach may not fully encompass other significant aspects of a company. We
propose the utilization of a hybrid dataset that combines financial statements
with information about corporate restructuring behavior in order to construct
diverse machine learning models to predict bankruptcy. Utilizing a hybrid data
set provides a more comprehensive and holistic perspective on a company's
financial position and the dynamics of its business operations. The experiments
were carried out using publicly available records of all the files submitted by
small and medium-sized enterprises to Luxembourg Business Registers. We conduct
a comparative analysis of bankruptcy prediction using six machine learning
models. Furthermore, we validate the effectiveness of the hybrid dataset. In
addition to the conventional testing set, we deliberately chose the timeframe
encompassing the years of the Covid-19 pandemic as an additional testing set in
order to evaluate the robustness of the models. The experimental results
demonstrate that the hybrid data set can improve the performance of the model
by 4%-13% compared to a single source data set. We also identify suitable
models for predicting bankruptcy.
