Iryna Hrabchuk

In today’s fast-paced business environment, integrating sustainability into financial decision-making has been a key driver of change. As stakeholders increasingly demand greater corporate transparency and accountability, regulatory bodies have stepped in to ensure that sustainability reporting is standardized and robust. This paper aims to establish the relationship between the sustainability-related disclosure rules and the dynamic indicators of the financial market. The object of the study is 74 countries of the world, which are grouped into developed and developing countries. The time period is 2021, for the stock market capitalization indicators – 2020, as the most recent years with available data. The research methods are normality tests (Shapiro-Wilk and Shapiro-Francia test), comparison methods (Student’s t-test and Mann-Whitney U test, regression analysis with dummy variables), linear and non-linear correlation and regression analysis (logarithmic, polynomial). The results obtained confirmed that the sustainability-related disclosure rules are higher in developed countries than in developing ones. At the same time, in developed countries, the growth of such requirements affects the increase in stock price volatility, stock market capitalization, foreign direct and portfolio investments. For developing countries, there is also an increase in the stock market capitalization, portfolio investments and the volume of stock trading. Recognizing these trends can benefit both financial market regulators and participants to encourage the formation of a transparent and efficient financial market, thereby mitigating the problems associated with information asymmetry.

Type of the article: Research Article

This study proposes Agilance as a conceptual and technical framework for explainable strategic financial planning in data-intensive organizational environments. The framework is based on a custom transformer architecture that incorporates three sector-specific components: Financial Relevance Weighting, Context Shift Stabilization, and Output Compression. Because the evaluation was conducted on a confidential sector-specific dataset and through internal benchmarking procedures, the underlying source data and job-level operational records cannot be publicly released. Within these constraints, the internal evaluation yielded indicative results: 96.03% accuracy and 95.8% F1-score for priority classification on the held-out test set, 90.26% accuracy and 90.22% F1-score for implementation-duration classification, and an average 10-fold cross-validation accuracy of 91.7%. The expert explainability assessment produced mean scores of 4.67 for clarity, 4.53 for trustworthiness, and 4.48 for actionability, with inter-rater agreement ranging from 0.87 to 0.91. Internal operational benchmarks further suggested planning-cycle reductions and economic benefits, including 95.8% improvement in real-time data analysis and time-zone synchronization, 5.4% operational cost savings, and an illustrative first-year ROI of 46%. These results should be interpreted as preliminary internal evidence obtained under specific evaluation conditions, not as independently verified proof of broad organizational generalizability. The study contributes an auditable AI-supported framework and identifies the need for future validation using anonymized multi-organizational datasets, externally audited protocols, or independently reproducible benchmarks.

Acknowledgments
The publication fees of this manuscript have been financed by the MSc Tax and Financial Services Digital Transformation (DITAF), University of Patras.