$$DMDP^2$$: A Dynamic Multi-source Based Default Probability Prediction Framework

Abstract

In this paper, we propose a dynamic forecasting framework, named \(DMDP^2\) (Dynamic Multi-source based Default Probability Prediction), to predict the default probability of a company. The default probability is a very important factor to assess the credit risk of listed companies on a stock market. Aiming at aiding financial institutions in decision making, our \(DMDP^2\) framework not only analyses financial data to well capture the historical performance of a company, but also utilizes Long Short-Term Memory model (LSTM) to dynamically incorporate daily news from social media to take the perceptions of market participants and public opinions into consideration. The study of this paper makes two key contributions. First, we make use of unstructured news crawled from social media to alleviate the impact of financial fraud issue made on default probability prediction. Second, we propose a neural network method to integrate both structured financial factors and unstructured social media data with appropriate time alignment for default probability prediction. Extensive experimental results demonstrate the effectiveness of \(DMDP^2\) in predicting default probability for the listed companies in mainland China, compared with various baselines.