Towards Bankruptcy Prediction: Deep Sentiment Mining to Detect Financial Distress from Business Management Reports

Abstract

Due to their disclosure required by law, business management reports have become publicly available for a large number of companies, and these reports offer the opportunity to assess the financial health or distress of a company, both quantitatively from the balance sheets and qualitatively from the text. In this paper, we analyze the potential of deep sentiment mining from the textual parts of business management reports and aim to detect signals for financial distress. We (1) created the largest corpus of business reports analyzed qualitatively to date, (2) defined a non-trivial target variable based on the so-called Altman Z-score, (3) developed a filtering of sentences based on class-correlated pattern mining to reduce the complexity of these long and complex texts, and (4) employed one of the best-performing machine learning methods for this type of task, Dependency Sensitive Convolutional Neural Networks (DSCNNs). Experimental results show that strong prediction performance can be achieved by a suitable bundle of methods, with an F1-score of more than 0.86 and a Kappa score of more than 65%. To better understand the parts of management reports that indicate financial distress, the prediction engine is complemented by a visualization tool that highlights critical text passages.