A spatiotemporal context aware hierarchical model for corporate bankruptcy prediction

Corporations getting bankrupt has been a severe issue for investors, businesses as well as ordinary individuals. Several research works have been conducted over the years to accurately predict bankruptcy, with the earliest works depending on the financial metrics of the company taken under consideration and the latest ones trying to predict bankruptcy from various kinds of data of organizations. However, the present works do not capture the dynamic nature of the business world and the possibility of a turnaround scenario. Hence, predictive models that are spatiotemporally aware when they predict a firm’s financial distress are needed. Considering this imminent problem, our work focuses on building a unique spatiotemporal context-aware bankruptcy prediction model that can predict bankruptcy with the help of daily news articles of a company or its related companies and key financial metrics to predict bankruptcy. Knowledge graphs were used to represent the vast amount of textual data. Their embeddings, along with the financial metrics, were used in the classification process. In the first stage, various machine learning algorithms were used for the financial metrics, while for the textual data or the embeddings, attention-based LSTM was used. Next, both were assembled together in the second stage to form the final predictive model, which has given an accuracy of 0.97 on the test set and an F1 score of 0.95. We hope our novel approach to this problem helps those who are uncertain about the future of any organization in predicting its bankruptcy beforehand and thereby timely decision making.