Vol 14, No 4 (2020)

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.

Investors are interested in a quantitative measure of banks’ credit risk. This paper maps the credit ratings of Russian banks to default probabilities for different time horizons by constructing an empirical dynamic calibration scale. As such, we construct a dynamic scale of credit risk calibration to the probability of default (PD).
Our study is based on a random sample of 395 Russian banks (86 of which defaulted) for the period of 2007-2017. The scale proposed by this paper has three features which distinguish it from existing scales: dynamic nature (quarterly probability of default estimates), compatibility with all rating agencies (base scale credit ratings), and a focus on Russian banks.
Our results indicate that banks with high ratings are more stable just after the rating assignment, while a speculative bank’s probability of default decreases over time. Hence, we conclude that investors should account for not only the current rating grade of a bank, but also how long ago it was assigned. As a result, a rising capital strategy was formulated: the better a bank’s credit rating, the shorter the investment horizon should be and the closer the date of investment should be to the rating assignment date in order to minimise credit risk.
The scientific novelty of this paper arises from the process of calibration of a rating grade to dynamic PD in order to evaluate the optimal time horizon of investments into a bank in each rating class. In practical terms, investors may use this scale not only to obtain a desired credit rating, but also to identify quantitative measure of credit risk, which will help to plan investment strategies and to calculate expected losses.

A lot of obstacles stand in the way of small and medium enterprises formation. One of the first ones is an external financing gap in the majority of Central and Eastern Europe countries. Considering the totality of problems identified by entrepreneurs one may notice that the major part of them is made up of external impact factors.
The purpose of the present study is revealing the influence of country-specific determinants on performance of small and medium enterprises in 24 European countries. The research analyzed 54,512 SME within the period of 2013 to 2017.
The practical value of the research consists in the fact that its results may be useful for: governmental agencies in optimizing the existing small and medium enterprises support programs, emergent entrepreneurs when choosing the country to start up their business and for a better understanding of their environment, management when choosing the ways of entrepreneurship geographical expansion.
The applied importance of this paper consists in defining the most significant country-specific determinants of SME performance.
The regression analysis results show that the majority of small and medium enterprises from the point of view of macroeconomic and political conditions exist “in spite of, not thanks to something”. We also identified a negative relation between the government machinery efficiency as such and its efficiency in relation to SME which means that a lot of effort is necessary for improvement in this sphere. Analysis of the difference between developed and emerging countries revealed a slight positive influence of corruption on SME’s return on assets in emerging countries. Northern Europe is considered to be the friendliest region for small and medium enterprises while Eastern Europe is the least favourable one.

In recent years, the topic of ‘digital transformation’ has become a primary focus in the areas of business and research. Among digital technologies, the area attracting the most investment is artificial intelligence (AI). Research shows that AI can benefit corporate governance in a variety of ways.

In this article, we identify two academic streams on the topic and evaluate the existing literature. The first stream analyses AI-driven improvements in governance mechanisms such as boards of directors (BoD). The second stream explores the digital-driven organisational changes and broad governance adaptations necessary for AI improvements. We evaluate the evidence for AI implementation in improving and evolving traditional aspects of corporate governance.

The examined authors argue that digital technologies transform the nature of a firm, making it less based on traditional sources of authority. There is consensus that this environment calls for fundamental reconsideration of corporate governance and for the revision of regulatory models, moving towards decentralisation. Specific areas examined in these contexts include jobs automation, agency conflict, auditing processes, the selection of BoD members, compliance functions, data analytics, and capital allocation.
The examined research indicates that AI improves corporate governance and lowers agency cost by automating decision making using real-time big data analysis. However, while researchers propose multiple novel approaches to governance, practical implementation of those approaches or an empirical analysis of the results of such experiments is yet to occur.

Despite the consensus among researchers on the positive impact of AI for governance and implementations as making AI a part of BoD, open questions and skepticism persist. This is indicative of the immaturity of AI as a technology in terms of development and implementation, and as such there is ample scope for future research. We propose multiple areas within this article where opportunities exist for further insight within this burgeoning field.