Abstract
This study is aimed at investigating the application of news sentiment analysis to bankruptcy prediction models in the context of the Russian retail sector.
 We analyse 190 companies: 95 Russian retail companies that went bankrupt in 2015-2019, and 95 non-defaulting analogue companies. This figure was attained from a larger pool of 312 companies retrieved from the Spark database on the basis of analysis of relevant financial data and further validated by the presence of pertinent news media coverage within 3 years of default date. The methodological base of this analysis is the logistic regression approach, used as a benchmark model, and several machine learning models: random forest, support vector machine, and multilayer perceptron.
 The predictor set applied consists of 34 financial variables and sentiment variables, aggregated using the ‘bag-of-words’ from a total sample of 4877 news articles, from more than 800 distinct online resource locations. We establish a set of hypotheses based on a review of existing literature in the area, and evaluate their accuracy on the basis of our technical analysis.
 Our results show that sentiment variables are statistically significant, and that adding sentiment variables improves the performance of bankruptcy prediction models. Also, the results indicate some reference characteristics of companies in terms of word-choice and descriptions in the news, indicating word choices correlated with financially stability and those correlated with financially instability.
Highlights
Accurate analyses of a firm’s financial stability are essential for multiple aspects of a company’s planning and strategic processes, and are relied upon by other market participants, banks
The problem of forecasting bankruptcy on the basis of financial reporting is connected with the fact that analysing the actual results of company reporting is possible only in the year subsequent to publication, which means that bankruptcy forecast in the short term is more challenging
Using news media resources for forecasting purposes assists with short term predictions by providing more current and recent data for analysis
Summary
Accurate analyses of a firm’s financial stability are essential for multiple aspects of a company’s planning and strategic processes, and are relied upon by other market participants, banks. The problem of forecasting bankruptcy on the basis of financial reporting is connected with the fact that analysing the actual results of company reporting is possible only in the year subsequent to publication, which means that bankruptcy forecast in the short term is more challenging. Using news media resources for forecasting purposes assists with short term predictions by providing more current and recent data for analysis. The overall effects of COVID-19 are not yet apparent, but it is already becoming clear the number of bankruptcies will rise enormously. The most novel and accurate methods of bankruptcy prediction are especially relevant today
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