Using News Articles and Financial Data to predict the likelihood of bankruptcy
It addresses the need for better bankruptcy prediction to mitigate economic impacts on stakeholders, but appears incremental as it builds on existing studies.
This paper tackles the problem of predicting company bankruptcy by combining financial data with sentiment analysis of news articles, aiming to provide a framework that uses external perspectives to improve prediction accuracy.
Over the past decade, millions of companies have filed for bankruptcy. This has been caused by a plethora of reasons, namely, high interest rates, heavy debts and government regulations. The effect of a company going bankrupt can be devastating, hurting not only workers and shareholders, but also clients, suppliers and any related external companies. One of the aims of this paper is to provide a framework for company bankruptcy to be predicted by making use of financial figures, provided by our external dataset, in conjunction with the sentiment of news articles about certain sectors. News articles are used to attempt to quantify the sentiment on a company and its sector from an external perspective, rather than simply using internal figures. This work builds on previous studies carried out by multiple researchers, to bring us closer to lessening the impact of such events.