Default Prediction for Small and Medium-sized Companies with Financial Indicators and Macroeconomic Indexes

Abstract

The goal of our research is to build default prediction models on the basis of machine learning models and to obtain useful information for corporate credit risk evaluation. The novelty of this work is twofold. The first point is on how to use time-series information of macroeconomic indexes for the default prediction model for small and medium-sized companies. Since macroeconomic indexes and financial data are different in frequency of being obtained, we considered how to combine these two kinds of data, as input of the default prediction model. In order to combine these data, we summarized time-series information of macroeconomic indexes in the form of mean, percentage change, and volatility. Regarding percentage change, some periods were adopted for the purpose of summarizing both of macrotrends and microtrends. The summarized forms and corporate financial indicators were used as input of the default prediction model in this research. As a result, the default prediction model with inputs of the financial indicators and the macroeconomic indexes outperformed the model with inputs of only financial indicators. Furthermore, the model, to the inputs of which the percentage changes in the fine periods summarizing microtrends were added, outperformed the model not considering the percentage changes in the fine periods. Therefore, considering macroeconomic indexes, especially our proposed method summarizing macrotrends and microtrends, has been found effective for default prediction. The second point is regarding which financial indicators are important in default prediction for small and medium-sized companies by industry sectors. We divided companies into eight industry sectors and investigated which financial indicators are important in each industry sector on the basis of variable importance evaluated with random forest.