Vol 17, No 1 (2023)

The purpose of this work is to examine the relationship of various financial and non-financial (qualitative) factors of performance of non-financial companies and their credit ratings.

We developed the scoring model which was based on the methodologies of international and Russian rating agencies. The modelled ratings of non-financial companies for 2018–2020 were compared with actual ratings assigned by the rating agencies and discrepancies were explained. The sample includes companies from retail, protein and agriculture, steel, oil and gas sectors from Russia, USA, Luxembourg, England, Canada, India, Ukraine and Brazil.

The paper proved that addition of business and environmental, social and governance factors improved the quality ofscoring models in comparison to those including only financial metrics. There are strong patterns in the resulting ratings of companies for some industries. Retail industry companies are associated with high sales indicators, while steel industry companies have high interest expenses coverage ratios. Oil and gas industry companies mostly show high results in reserves coefficients.

The study developed a credit rating forecasting tool that emulates the work of analysts of rating agencies and therefore has a high predictive power. The developed model can be used by financial market practitioners to predict the credit ratings of Russian companies in the face of the refusal of international rating agencies to rate Russian issuers.

The paper evaluates influence of CEO`s education and her experience on amounts invested in R&D in pharmaceutical industry. The sample consists of 270 pharmaceutical high-tech companies from S&P BMI index in 1999-2018 from 23 countries, both developed and emerging. The pharmaceutical industry is of particular interest since the projects there require specialized education to understand and manage the process. Therefore, the payback period of investments is long. As a result, in pharmaceuticals there is a high rate of intangible assets comparing to other sectors.

At the first glance, according to the results of regression analysis, business education, financial, managerial and economic education has no significant impact on investments. However, education in specific sphere, like physics, chemistry, biology, mathematics, has a significant positive impact on R&D expenses. We can see the similar effect for the CEO`s with two or more degrees in different fields. These results are in line with the findings of previous studies regarding CEO`s education.

At the next stage, educational level of CEO and its field are analyzed simultaneously. A degree obtained in the industry-specific field or a degree supplemented by a financial or managerial education has a positive impact on R&D expenses, while standalone financial degree, by contrast, has a negative effect.

So, we contribute to the academic literature with the idea that to get the unbiased results in similar studies we should account for not only degree but also the field. In practical sphere, the results can be useful for those who choose the educational track on their career path to CEO position as well as for HRs, Boards of directors, and other stakeholders while making decision on CEO turnover. Moreover, the results provide insights that could be useful for market analyst`s and investors` predictive models.

The article examines the impact of innovations on the performance of commercial banks. The size of intangible assets is used as a proxy for innovation, since most innovations in the banking sector, unlike those in industry, are intangible and include licenses, software, employees’ knowledge and experience, corporate culture, etc. Most researchers agree that an increase in efficiency and performance of financial companies is mainly underpinned by intangible assets, especially their unobservable part. The purpose of the study is to identify the relationship between innovation and financial performance of banks. Thirteen largest systemically important Russian banks of various forms of ownership in 2011-2020 were considered in the course of the research. This choice stems from the fact that these banks account for over 2/3 of the entire banking system in terms of assets, and have their own specifics compared to other banks both within Russia and in the world.

This study is limited by the fact that only large Russian banks were considered, while the specifics of medium and small banks, which have significantly fewer opportunities and are ready to take on higher risks, were not assessed. The scientific novelty lies in the fact that intangible assets are reflected in the work by a quantitative change used to assess the innovative activity of banks, for which an suitable approach is proposed. The results of empirical analysis demonstrate that the growth of intangible assets allows banks to increase the volume of both interest and commission income and slow down the rise of expenses, thereby ensuring profit growth. In turn, this stimulates increased liabilities, however, due to regulatory requirements, the share of equity remains almost unchanged at about 10% of the asset value. This trend indicates that even smaller banks can compete with larger ones by implementing innovation and building intangible assets.

The author tested the hypothesis that default prediction based on financial data may be inapplicable to Russian service sector organizations by analyzing the differences in the accuracy of models based solely on financial data for service providers from Russia and developed European countries.

Logistic regression, Random Forest and K-nearest neighbors machine learning methods were used as modeling tools on a sample of 404 Russian firms and 304 firms from developed European countries.
The results suggest that the prediction error is significantly higher in the case of Russian firms than in the case of firms from the control group (European service firms). Thus, the use of financial ratios for default prediction for service firms in Russia seems insufficient.

These findings can be used by organizations that provide credit scoring, and by any other market participants interested in the financial stability assessment of their counterparties.

This paper investigates the impact of financial and non-financial factors on the value of companies in the telecommunications industry. Such variables as the debt ratio, tangibility, return on assets, etc. were selected as financial factors. ESG indicators were used as non-financial factors. The research topic is relevant for decision-making and the development of recommendations for companies, as it assesses the level of companies’ involvement in solving environmental, social, and governmental problems. The work uses data from two databases (Bloomberg and Capital IQ) for 94 companies in the telecommunications industry between 2011 and 2021. The results suggest that the companies’ disclosure of information about
the overall ESG ratio and the “G”indicator has a positive effect on the company’s value, while the disclosure of “E” and “S” indicators does not affect the value of a business.