Mehriban Aliyeva

A high level of company bankruptcy in certain countries and a low level of profitability actualizes the need to find additional mechanisms for increasing the efficiency of their activities. One of such mechanisms is the growth of information transparency. The study deals with examining the effects of accounting information transparency on business performance on the example of construction companies in G7 countries. The transparency index was used as a parameter characterizing the level of accounting information transparency. The level of business performance was analyzed using the following indicators: value added of the construction industry, investment in the construction industry, number of construction firms, profitability of the construction industry, annual all-work construction output index, and total employees in construction firms. The dependence between the indicators was analyzed using the multiple regression analysis, Dickey-Fuller, Philips Perron, and Johansen tests. According to the results, the most vital link was between the level of accounting information transparency and the volume of investments (increased information transparency by 1 point leads to an increase in the volume of investments from 1.7% to 4.6%). At the same time, the level of accounting information transparency practically does not affect the number of employees (change by 0.1-0.2%) and added value (change by 0.1-0.3%). It was concluded that the policy of accounting information transparency should be an essential element of company strategy aimed to increase the level of its investment attractiveness and confidence of investors and consumers in its activities.

The study aims to discuss the impact of the analysis of events after the reporting date (subsequent events) on management decision-making. In the interval between the end of the reporting period and the publication of the annual financial report, company management may learn about events that either occurred during the reporting period but were previously unknown or occurred when the financial report was already prepared but not approved. The consequences of these events can be so serious that they require adjustments to the financial statements, changes in the company’s strategy and tactics, and radical management transformations. The paper structures such events depending on their impact on business performance and the procedure for reporting and identifies the determinants and mechanisms for their analysis and correct accounting. To assess the complex impact of events after the reporting period on the financial results of a company, an integral indicator is proposed, a set of management measures is defined in accordance with the values of this indicator, and the mechanism for its calculation and use is demonstrated on the example of a hypothetical scenario. The sensitivity analysis of this indicator to fluctuations in the weighting coefficients of its components was performed using the Monte Carlo method. In an environment where transparency, accountability, trust between key stakeholders, adaptability, and proactivity are crucial for effective management, this indicator can be used as an effective metric that is taken into account by auditors, regulators, clients, investors, company management, etc.

Type of the article: Research Article

Abstract
The banking and financial system of the countries of the world is constantly developing, but at different rates and ways, given their differences in the levels of economic, financial, and innovation development. The purpose of this article is to identify factors that link upper-middle- and high-income countries in terms of banking ecosystem digitalization, based on cluster analysis. The research sample includes 40 countries – 20 top-performing upper-middle-income and 20 high-income economies – based on the 2023 ICT Development Index. The analysis is based on 15 standardized indicators characterizing digitalization in the banking ecosystem, sourced from the International Monetary Fund, the World Bank, and the International Telecommunication Union. These indicators cover ICT development, AI readiness, cybersecurity, GovTech maturity, financial development, banking access, and digital transaction activity. Data standardization was performed in Stata (v19.5) using the built-in function to create new variables with a mean of 0 and a standard deviation of 1. Cluster analysis was conducted using the k-means method in Statgraphics (v19), with silhouette scores computed in Python to determine the optimal number of clusters. Cluster analysis revealed four distinct country groups, demonstrating that similarities in banking ecosystem digitalization transcend income levels. Key convergence factors include ICT development, GovTech maturity, mobile banking adoption, and AI readiness. Some upper-middle-income countries exhibit digitalization patterns comparable to high-income economies, highlighting the role of strategic investment and policy, rather than income, as primary drivers of digital financial advancement.