Vol 18, No 2 (2024)

The aim of this study is to investigate the determinants of corporate cash holdings in emerging countries. The sample comprises non-financial firms from six emerging countries, five of them commonly referred to as BRICS, plus Turkey. The dataset includes the data of 4,769 firms and covers a ten-year period from 2012 to 2021, resulting in a total of 47,690 firm-year observations. We run panel regressions, specifically fixed and random effects models, and conduct the J.A. Hausman [1] test to choose between the latter. We use several firm-specific variables as independent variables and the GDP growth rate and the inflation rate as country-specific control variables. The results reveal that firm size, leverage, capital expenditures, net working capital, operating cash flow, dividend payments, firm age, and research and development (R&D) expenditures are significant determinants of corporate cash holdings, with some differences among countries and/or industries in terms of the sign and the significance levels. The macroeconomic variables showed significant results in some countries and industries, yet they were not consistent enough to make general conclusions. This study provides new empirical evidence on the determinants of corporate cash holdings by using a large dataset from major emerging countries. Our findings have important implications for corporate managers and policymakers in designing cash holding and liquidity policies. A comprehensive understanding of the main determinants of corporate cash holdings enables managers to adopt appropriate financing and investing strategies in the long term, as well as better short-term financial policies.

The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitative
literature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.

The paper aims to identify the key trends and risks associated with the introduction and spread of digital assets, including the digital ruble, in the Russian financial market. This study is timely due to the rapid growth of market capitalization for digital financial assets based on blockchain technology in both international and Russian financial markets, as more and more countries prepare to launch their own national digital currencies. However, there is a dearth of information in scientific and practical literature regarding the prospects, challenges, and risks associated with introducing digital financial assets, including the digital ruble, into Russia. The data for the analysis included official statistics and analytics from the Bank of
Russia and central banks of countries considering introducing digital national currencies. It also included the content of key Russian legislative acts related to digital currencies, as well as examples of the implementation of digital financial assets from Russian and international practices. The main research methods employed were a systematic approach, analysis of fundamental theoretical propositions in the literature, and case analysis. The paper discusses the existing types of digital financial assets, both internationally and in Russia, and assesses the demand for these assets in the Russian capital market, as well as their potential for lending to small and medium-sized businesses. It also considers the most common problems associated with the development of this segment and possible solutions, including regulatory measures. The second part of
the paper explores the prospects and challenges of introducing a digital ruble into the Russian financial system. It assesses the potential impact of this new currency on the stability of domestic banks and monetary conditions in Russia, including inflation. Future research could focus on quantifying a wide range of risks associated with the introduction of digital financial assets, as well as modelling supply and demand for these assets.

The study examines the impact of ownership structure on corporate risk disclosure in African emerging countries. The sample includes 42 firms that are listed on the Johannesburg Stock Exchange and the Nigerian Stock Exchange. The data for the independent variables were taken from the Bloomberg data stream, whereas the data for the dependent variable were taken from annual reports retrieved from the website of the sample companies. The study’s time period runs from 2014 to 2018. Regression and content analysis were employed as the analytical tools. We perform text analysis on company annual reports to ascertain the risks that companies disclose, and regression analysis was used to establish the extent to which ownership structure influenced corporate risk disclosure. The result shows that strategic and environmental risk disclosures are dominated by operational risk disclosure. It has become a convention for the firms to divulge considerable positive, past, non-monetary information rather than negative, future and monetary risk information. Moreover, it is discovered that the decision to improve risk disclosure is largely influenced by company size and profitability. In contrast, firms are reluctant to unveil risk information provided the shares of the company are not concentrated in the hands of few individuals. Nonetheless, company risk disclosure practice is unaffected by institutional investors, government, foreigners, insider ownership and leverage. It can be concluded that the enterprises operating in emerging African markets have made improvements to
their risk disclosure practices. However, there is still room for further improvement. Monetary, future, and negative risk information are the most important risk disclosures that various stakeholder groups, such as investors, demand to see. Hence, there is a need for regulation that can compel corporations to publish the most pertinent risk information. Even though risk disclosure is voluntary in these two African emerging countries, ownership structure is one of significant predictors of corporate risk disclosure.