Machine learning and sentiment analysis: Projecting bank insolvency risk

Abstract

The main motivation of this paper is to use machine learning techniques to build a new insolvency risk rating metric for banks traded on Brazilian stock exchange. Then, a set of prediction models will be used to project the risk rating of these institutions. Conventionally, the literature analyzes bank insolvency risk from accounting data and macroeconomic variables. In addition to these variables, this paper will construct a series of bank institution manager sentiment, via quarterly reports (ITR), and this will be used to improve the accuracy of bank risk predictions. The results indicate that the bank risk classification, via the k-means algorithm, was able to classify 17% of the sample into the highest risk group (1), while 83% of the sample was in the lowest bankruptcy risk group (0). Using the Z-score metric, we found that 65% of the sample is in the low-risk group, and 35% of the sample is in the high-risk group. Thus, the k-means algorithm is more rigorous in classifying a bank in the highest risk category. Next we used the data already described to project the risk of bank insolvency. The results of this step showed that the decision tree model performed the best for the test sample. In addition, it was found that the inclusion of the bank sentiment variable was able to improve the performance of the prediction models, especially, when bank sentiment is constructed from a time-varying dictionary.