Vol 16, No 1 (2022)
- Year: 2022
- Articles: 8
- URL: https://journal-vniispk.ru/2073-0438/issue/view/19991
- DOI: https://doi.org/10.17323/j.jcfr.2073-0438.16.1.2022
Full Issue
New Research
Empirical Analysis of Motives for Intra-Group Lending in Russian Business Groups
Abstract
This study examines the factors behind the companies’ financial roles on the internal capital markets of Russian business groups. The main goal is to determine the driving motives for intra-group lending in Russia. To find relevant answers, we use logit- and ordered logit-models based on 2018–2020 panel data for 239 Russian joint stock companies representing 21 business groups. Considering the findings of prior studies on debt financing in business groups, we analyze the influence of company size and age, asset tangibility and profitability, leverage, liquidity, sales growth, and the cash-flow rights of controlling shareholders on the probability of a company being a provider (receiver) of intra-group loans.
The novelty of our findings is ensured by the use of data from the State Information Resource of Financial Accounts that were made publicly available in 2020, enabling us to significantly expand the set of companies under examination. The results of this empirical analysis reveal that internal capital markets of Russian business groups serve as a tool for fund reallocation from older and larger, but less capital-intensive and leveraged companies to smaller, more capital-intensive and leveraged members of the group. The findings demonstrate that the financing advantage motive for intra-group lending is currently predominant in the leading Russian business groups. Thus, Russian business groups use their internal capital markets as
an alternative source of funds that alleviates the financing constraints of group members. In the context of continuing anti-Russian sanctions, the limited depth of the Russian financial market and a lack of “long” money in the economy, the research results can be useful for financial managers and policymakers seeking ways to enhance the financial security of group-affiliated companies.
5-13
Impact of Intellectual Property Rights on Activity of Cross-Border Mergers and Acquisitions
Abstract
We study the impact of the IPR (intellectual property rights) protection in the target and acquirer countries on the intensity of inbound cross-border M&As (mergers and acquisitions) in the target countries on a sample of 509,216 cross-border and domestic M&As in 64 developed and developing countries over 1985-2017. Our results show that better IPR protection in the target countries has a positive impact on M&A activity for the targets from the emerging and developed markets. We also find an inversed U-shaped relationship between the IPR protection in target countries and cross-border M&A activity at the post-TRIPS period after the global increase in IPR protection. Our results also show that developed acquires make less cross-border M&A deals when IPR protection improves in their own countries. The opposite happens when IPR protection improves in the countries of the emerging acquirers, which acquire the targets from the developed countries. IPR protection in the emerging targets motivates developed acquirers to make more international M&A deals, while the opposite happens when the developed acquirers would like to purchase targets from the developed markets.
14-37
Do ESG Factors Influence Investment Attractiveness of the Public Companies?
Abstract
Even though there are numerous papers on the impact of ESG disclosure or performance on company performance, the topic remains disputable and controversial. The growing importance of ESG scores in investment decision-making has raised a question of whether the ESG score and its pillars influence the investment attractiveness of public companies. Using a sample of S&P 500 American and S&P 350 European companies in the period between 2010 and 2020, we examine the relationship between ESG performance and investment attractiveness, expressed by Tobin’s Q, ROE, cost of capital and probability of paying dividends. We use the difference in means, panel regression and propensity score matching analysis
and conclude that higher ESG performance positively influences Tobin’s Q for both markets, while also providing evidence that ESG score transition to the above-median level may lead to a fairer valuation, higher probability of paying dividends and lower cost of capital, while return on equity is not subject to change. While previous research mainly focuses on one indicator, such as company value or cost of debt, this paper develops a set of investment attractiveness indicators and covers not only composite ESG performance, but also its environmental, social and governance pillars separately; it also emphasizes the influence on the industrial sector. Overall, our results suggest that managers pay close attention to ESG performance if it falls below median, although good ESG performance does not guarantee investment attractiveness.
38-64
Statistical Analysis of Assigning a Corporate Credit Rating with Regard to the Sovereign Rating in the Russian Federation
Abstract
The paper presents the results of a statistical study of the formation of a corporate credit rating, with regard to the sovereign rating. The research is based on the data from 19 non–financial companies in Russia’s leading industries for 2014–2018. It is shown that the sovereign credit rating, despite the relaxation of the sovereign “ceiling” rule by Fitch, Moody’s and S&P rating agencies in 1997, remains closely correlated to the risk level of Russian companies. The obtained results related to macroeconomic and idiosyncratic risk indicators denote the peculiarities of credit rating formation for Russian companies. In particular, in contrast to the results of similar studies, it reveals the negative effect of certain profitability and liquidity variables, as well as the country’s foreign trade turnover on the corporate rating. It also demonstrates that a
credit rating has a “short memory” – its current value is historically determined only by the level in the previous period.
This paper is of practical relevance for private and institutional investors and lenders that use credit ratings to form their own perception of the default risk level in the corporate sector.
65-82
Accounting for ESG Risks in the Discount Rate for Business Valuation
Abstract
The research is aimed at developing tools for determining and justifying specific ESG risks for the purpose of accounting for projected cash flows in the discount rate in business valuation. A study of modern methods, standards and publications in this area has been conducted, and the need for their refinement and development for practical use has been determined.
The research used the results of the works by foreign and domestic authors, as well as their own professional experience. The authors used general scientific methods of cognition, such as classification, logical and system analysis, typology and generalization.
The proposed tools are aimed at substantiating, supplementing and clarifying the discount rate model (CAPM) by introducing additional coefficients that take into account the influence of ESG factors. The article proposes a scoring model for assessing risks on a point scale and tools for their subsequent translation into correction coefficients using the method of expert assessments, which already allow them to be applied in practice today. The model of accounting for specific risks is based on data from literary sources, and demonstrated using a practical example.
The author’s tool is designed to provide analysts, appraisers and experts with a qualitative justification and calculation of specific risks associated with ESG factors when evaluating a business. It is also assumed that the proposed tools will serve as one of the criteria for managing business value, allowing for measures to reduce specific risks and increase company capitalization.
83-98
Comparative Analysis of the Predictive Power of Machine Learning Models for Forecasting the Credit Ratings of Machine-Building Companies
Abstract
The purpose of this study is to compare the predictive power of different machine learning models to reproduce the credit ratings of Moody's assigned to machine-building companies. The study closes several gaps found in the literature related to the choice of explanatory variables and the formation of a sample of data for modeling. The task to be solved is highly relevant. There is a growing need for high-precision and low-cost models for reproducing the credit ratings of machine-building companies (internal credit ratings). This is due to the ongoing growth of credit risks of companies in the industry, as well as the limited number of assigned public ratings to these companies from international rating agencies due to the high cost of rating process. The study compares the predictive power of three machine learning models: ordered logistic regression, random forest, and gradient boosting. The sample of companies includes 109 enterprises of the machine-building industry from 18 countries for the period from 2005 to 2016. The financial indicators of companies that correspond to the industry methodology of Moody's and the macroeconomic indicators of the home countries of the companies are used as explanatory variables. The results show that among models studied the artificial intelligence models have the greatest predictive ability. The random forest model showed a prediction accuracy of 50%, the gradient boosting model showed accuracy of 47%. Their predictive power is almost twice as high as the accuracy of ordered logistic regression (25%). In addition, the article tested two different ways of forming a sample: randomly and taking into account the time factor. The result showed that the use of random sampling increases the predictive power of the models. The inclusion of macroeconomic variables into the models does not improve their predictive power. The explanation is that rating agencies follow a "through the cycle" rating approach to ensure the stability of ratings. The results of the study may be useful for researchers who are engaged in assessing the accuracy of empirical methods for modeling credit ratings, as well as practitioners in banking industry who directly use such models to assess the creditworthiness of machine-building companies.
99-112
Cash Balance Management in Innovative Companies
Abstract
Since the 1980s, innovative companies all over the world have been holding a substantial cash balance on their books due
to transactional, preventive, agency, tax-related and macroeconomic motives and by limitations in capital availability due
to information asymmetry. Our research examines the determinants that influence the analysis of cash holdings of hightech
and non-high-tech companies.
Financial information for 38,386 unique companies was obtained from the 2009–2017 Compustat database. The final sample
version comprised 12,083 companies, of which 2,909 were innovative. We used the panel regression method, selecting
the appropriate calculation model and a number of proxy variables.
Our research confirmed the existence of innovative companies’ significant cash holdings. Adding a macroeconomic factor
variable (GDP growth) to the research model was justified for innovative companies only. In spite of the insignificant impact
of GDP, increased GDP growth resulted in a decreased cash ratio for innovative companies. The authors also reveal
the insignificance of R&D expenditures for innovative companies and prove that ranking companies by the amount of
R&D expenditures and using this variable as innovation proxy was inexpedient. In addition, the authors confirm a positive
relationship between growth opportunities, company size and cash ratio and a negative relationship between dividend
payout and the amount of cash holdings.
An understanding of the reasons for cash accumulation facilitates prudential management of cash holdings in companies.
This paper contributes new evidence to the study of corporate cash holding, focusing specifically on innovative companies,
which have not been examined separately in the past.
113-135
Discussions
Ceo Power And Risk-Taking: Intermediate Role of Personality Traits
Abstract
In our paper we discuss how different personal characteristics of a CEO, being affected by CEO power, may in turn affect personal risk-taking. Agency theory states that managers have non-changing risk preferences and are either risk-averse or risk-neutral. In contrast to that, there may be cases, when managers are risk-seekers and power of executives is positively related to excessive risk-taking. Additionally, agency theory assumes that CEOs are homogenous in power use and ignores difference of CEOs in term of personality traits as well as its impact on corporate decisions. Therefore, our aim is to focus specifically on factors that connect CEO power with CEO risk-taking and to analyze possible effects of that relationship on firm. Based on both psychological and managerial studies, we conclude that, on the one hand, CEO’s power can affect CEO’s personal traits by producing [in the case of overconfidence or hubris] or by enhancing them [in case of narcissism]. On the other hand, CEO’s personal traits affect CEO’s risk-taking. It can be either by changing the perception of risk or because of behavior patterns inherent to those traits. Finally, we hypothesize that CEO power can affect CEO personal risk-taking through personality traits. By examining relationship between CEO power and CEO risk-taking based on individual-level determinants, our paper adds to the behavioral corporate finance and corporate governance literature.
136-145