The Impact of Corporate Social Responsibility on the Company’s Financial Performance

This article empirically evaluates the impact of CSR behaviour on the financial indicators of 286 companies from Brazil, Russia, India, and China over six years from 2013 to 2018. Company information and CSR ratings were retrieved from the Bloomberg and RobetaSAM databases, and hypotheses were proposed based on a literature review. We constructedvarious analytical models that differ in dependent variables to better evaluate of distinct CSR metrics through different regression methods. Analyzed factors include: (1) the presence of women on the board; (2) the presence of a company in CSR ratings, and (3) various cultural aspects of the society where companies operate. Our results support the conclusions of related research in this field of study. Among other consequences, our analysis indicates that CSR significantly influences financial performance, although this is also contingent on external factors. A company’s presence in the CSR rating scale has a more substantial impact on profitability and market capitalization indicatorsthan the actual score itself. CSR information disclosure has some effect on ROA and ROE, and the presence of women in the board of Directors showed a slight positive effect on market capitalization. Further, a high level of ‘power distance’ (i.e. the ostensible alienation of the general citizenry from political authority sources) in the society where company operatesharms the relationship between the rating score and financial performance.

ESG-risk Factors and Value Multiplier of Telecommunications Companies

The paper is devoted to the study of the impact of environmental, social responsibility and corporate governance (ESG) risk factors on the value of telecommunications industry enterprises expressed through the EV_EBITDA multiplier. The main goal was to assess the elimination of ESG risks from the standpoint of increasing the competitiveness of the company. The methodological basis of the study was the coordination of non-financial information of companies with their financial results. The paper implements the construction of a regression model within the framework of econometric modeling, the direction of which was proposed by A. Damodaran. The authors did not limit themselves to corporate information from one country, but identified five regions, such as the USA, the European Union, the UK, the rest of the developed countries (DEV), as well as the markets of developing countries (EM). The database was compiled on the basis of non-financial business activity parameters of 57 of the world's largest telecommunications companies as of 2021, where financial information is taken from the Bloomberg database, and the ESG risk coefficient of the rating of these companies is used from the Sustainalytics research center. The result of the study was that there is a stable relationship between the risk of the ESG rating and the EV/EBITDA parameter characterizing the cost of capital – that is, the lower the risk, the greater the cost of capital. For different country groups, the result was obtained with varying degrees of confidence: for "other developed countries" with a high 5% significance level, for European countries with a 10% level, for the USA, the insignificance of the coefficient is associated with a small sample size, and for developing country markets the coefficient is insignificant. The novelty of the results obtained lies in the use of a metric approach to confirm the stable dependence of ESG risk factors on the EV/EBITDA cost multiplier. The results obtained allow us to make a generalized conclusion that the elimination of ESG risks contributes to the growth of the company's competitiveness, where the results of the study are able to encourage companies to consider the disclosure of non-financial information as an important indicator of long-term sustainability. When ESG is considered as an integral factor in the future activity of the company, the end result is its higher evaluation by stakeholders.

Bankruptcy Prediction for Innovative Companies

The main purpose of this article is to identify the best neural network model algorithm and relevant set of variables for predicting financial distress/bankruptcy in innovative companies. While previous articles in this area considered neural network analysis for large companies from primary sectors of the economy, we take the novel approach of examining theless-explored area of innovative companies. First, we complete a comprehensive review of the relevant literature in order to define the best configuration of factors which can influence bankruptcy, network architecture and learning methodology. We apply our chosen method to a sample of companies from around the world, from industries which are considered innovative, and identify the dependence of bankruptcy probability on a set of factors which are reflected in the financial data of a company. Our evaluation is based on the financial data of 300 companies – 50 of them are bankrupts, and 250 are ‘healthy’. Our results represent the set of relevant factors for bankruptcy prediction and the appropriate neural network. We have applied a total of 19 factors characterising efficiency, liquidity, profitability, sustainability, and level of innovation. Our proposed analysis is appropriate for all sizes of companies. We provided two models in order to cater for the most confidence in terms of obtained results. The total predictive ability of the model developed in our research is almost 98%, which is extremely efficient, and corresponds to the results of the most modern methods. Both approaches demonstrated almost the same level of influence of factor groups on final bankruptcy probability.

Corporate Governance and Risk Disclosure in Emerging Countries

The study examines the influence of corporate governance attributes on the corporate risk disclosure in the emerging countries. Board size, non-executive directors, independent directors, board diversity and CEO-duality are the important board of director’s composition that is considered as corporate governance variables for this study. The study focuses on South Africa and Nigeria as these countries are among major players in the African emerging market. The sample comprises 42 financial and non-financial firms listed in Nigerian Stock Exchange and Johannesburg Stock Exchange. The data was drawn from 192 annual reports for the year 2014–2018. The analytical tools employed are manual content analysis and regression. The empirical results show that operational risk disclosure outweighs environmental and strategic risk disclosure. Meanwhile, past information, non-monetary and good news are considered less relevant, however dominatefuture, monetary and bad news which are more valuable to diverse stakeholders. Moreover, in considering the important factors that impact on the risk confession, that board size, independent director and diversity have greater influence in driving the risk disclosure upward. Nevertheless, non-executive director and CEO-Duality are statistically insignificant in determining the movement of risk information to divulge. The persistence of contemporary corporate risk practice jampacked with irrelevant information might promote greater agency cost. The implication for the current practice might increase investors’ uncertainty which in turn would raise the company cost of capital. This issue could be addressed by regulating risk disclosure in emerging countries instead of allowing corporate managers to report risk related information at their discretion. Corporate manager are also encourage to appreciate all the potential risk disclosure drivers in the African emerging countries.

Innovations Creation Process: CEO and Board of Directors Roles

There are innovation creation process, innovations’ classifications, and measures considered in the paper. Recently focus on the literature moving from innovations relationship with financials to the role of people. This review considers board of directors group characteristics and CEO individual characteristics (the part of which impacts only firms from innovative industries) significant for innovation creation. The paper predicts investment in innovation, innovation outcome, and optimal for shareholders’ wealth board of directors’ type in dependence on CEO individual characteristics.

Approaches to Building Default Probability Models for Financial Instruments of Project Financing at Long Time Horizons

Project financing is one of the priority tools for stimulating the country's economic growth around the world, which allows the implementation of large-scale and capital-intensive projects, providing favorable credit conditions with insufficient creditworthiness of the project beneficiaries [1]. As a rule, project financing instruments are long-term (10-30 years, depending on the type of transaction), so this asset class is interesting for the implementation of the task of building long-term models for assessing credit risk associated with the introduction in 2018 of the new international financial reporting standard IFRS 9 "Financial Instruments". The new standard requires financial institutions to calculate their expected credit loss (ECL) at the time of granting loans and other banking products exposed to credit risk [2], taking into account different time horizons, which significantly changes the traditional approaches to assessing credit risk by commercial banks [3], [4]. As part of this work, a model was built to assess the long-term probability of default for the portfolio of assets of a Russian commercial bank belonging to the project finance segment in accordance with the requirements of the International Financial Reporting standard IFRS 9 "Financial Instruments". At present, the topic of this work is extremely relevant and may be of interest both for commercial banks that are faced with the problem of improving credit risk assessment models

How Patents Influence Market Value of Industrial Enterprises' Assets?

Scientists and engineers are continuously patenting innovative ideas such as inventions, industrial designs, and utility models. It is therefore relevant to pose the question of the influence of intellectual property in the field of innovative technologies on the market value of the assets of industrial enterprises. We analyse the dependence of the results of intellectual activity in the field of advanced technologies on the capitalization of innovative industrial production after the adoption of the developed technologies. We consider patent landscapes, analyse research publications, and study the dependence of financial indicators on the results of intellectual activity at enterprises producing computers, optics and electronic equipment. Our research methodology is based on the statistical analysis of the dependence of the financial results of industrial enterprises on the actual application of the results of intellectual activity to the technological process. We define the object of analysis by citing research articles and surveys from the WoS database. The patent landscape is assessed using data from commercial information systems such as Orbit Intelligence (Questel) and Amadeus (Bureau van Dijk, Moody’s Analytics) that make it possible to visually show the links between patent activity and technological trends in the computer and electronic technology industries. The research results shall be useful for assessing the effectiveness of employing patents in manufacturing and the prospects of improving production technologies for the formation of corporate innovative technological policy. We conclude that the use of information on patent trends is an effective tool for increasing the competitiveness of enterprises producing electronic equipment. The priority financing of innovative technologies ensures the sustainable development of the manufacturing industry and have a positive impact on the profitability of enterprise assets.

Cash Management in Russian Metallurgical and Oil and Gas Companies

The problem of money management has remained relevant over the past years. The aim of the study is to assess the impact of debt and cash flow on the amount of cash on companies with and without financial constraints. The main hypothesis of the study is that the impact of debt and cash flow on the level of cash depends on financial constraints which were taken as two proxy variables – dividend payment and bond rating. To substantiate the hypothesis put forward, a regression model of the influence of debt and cash flow on the level of cash is built in the work. For the analysis, large Russian companies in the metallurgical and oil and gas industries were sorted in accordance with financial constraints. Based on the results of the constructed regression model, the following conclusions can be drawn. Borrowed funds of companies negatively affect the amount of cash on the balance sheet, regardless of the presence andtype of financial constraints. Cash flow is not statistically significant for companies without financial constraints. This study has some limitations. The research results can be useful for corporate CFOs in order to optimize cash balances.

The Determinants of Debt Load for Companies in Emerging Markets

Corporate capital structure is one of the key elements of long-term development, which determines the company value.Consequently, defining the factors that influence the debt load level of a company and, hence, its capital structure is also of great importance. In this paper we have collected a sample of data of 753 Russian companies and 292 Brazilian companies for 2020 to evaluate the influence of various factors on their debt-load level. The data was downloaded from Bloomberg database and the basis of the analysis focuses on evaluation of conventional academic theories on capital structure, and a regression analysis based on variables extracted from a set of original hypotheses. Among our results, our analysis illustrates that individual sets of determinants differ significantly in explanatory power, and operate unequally when contrasting Russian companies and Brazilian ones. Additionally, it was established that when companies define their debt load, they do not limit themselves to a single theory of capital structure. We conclude, inter alia, that it is impossible to identify with confidence which of the examined theories companies are most likely to follow in their actions, because observed interrelations between relevant variables and debt load have indications of various academic theories.

Testing Market Reaction On Stock Market Delisting In Russia

This paper expands the available information on the effects of delisting in Russia, and represents a rare empirical analysis of the impact of external events on securities prices in this major global market. We seek to evaluate how stock prices of competing companies fluctuate around the dates of stock market delisting announcements and completion. We analyse stock prices as correlated with company delisting events from 2004 to 2019 on 552 companies on the Russian MOEX Exchange. The event study methodology is used to evaluate the abnormal returns of rival companies close to relevant delisting dates. These data were checked for statistical significance using the standardised Patell residual test. The results indicate a significant competitive effect on stock prices both on the dates of delisting announcement and on completion, with more significant returns close to announcement dates. These effects were found to influence the prospects not just of individual groups of companies, but of all market participants. We may conclude from our results that delisting is not an event limited in effect to only one company, but impacts the industry as a whole, temporarily changing its value. As such, it will interest both shareholders and managers of public companies, and any participants of industries in which delisting occurs.