Iryna Zhyhlei

This paper explores the influence of traditional and ESG stock market indices on a country’s net international investment position. To do this, different methods, including ANOVA analysis, multiply regression analysis, correlation analysis, VAR-analysis and R/S-analysis, as well as the Granger causality test, are applied to quarterly data on the net international investment position, traditional and ESG indices from Finland, Sweden, France, Spain and Ukraine over the period 2005–2021. The results of descriptive statistics show that ESG indices are more volatile than traditional, but these differences are statistically insignificant according to ANOVA analysis. Correlation analysis provides direct evidence that ESG indices are highly correlated with their traditional analogues (correlation level varies from 0.88 to 0.96). Regression analysis results show that traditional and ESG stock market indices have no significant impact on the net international investment position. ESG stock market indices and net international investment position data are persistent, and autoregressive models can be applied to these data sets. On average, Hurst exponent is above 0.75 for the case of ESG indices and above 0.85 for the net investment position. This paper provides recommendations to improve the responsible investment framework.

Acknowledgment
Alex Plastun gratefully acknowledges financial support from the Ministry of Education and Science of Ukraine (0121U100473).

This article highlights the transformation of views on the understanding of accounting as a science in the new conditions for the functioning of enterprises operating in globalized markets in a dynamic competitive environment. The necessity of considering external factors (corporate scandals, financial crisis, etc.) in the development of accounting as a science is emphasized. The reasons for the need to confirm the scientific status of accounting are considered, the hypotheses concerning the gradual crowding out and replacement of accounting by information systems with artificial intelligence are refuted. Accordingly, the study aims to confirm the scientific significance of accounting and justify the need for its further development as a social science aimed at solving social issues and having a deeper social context. Various accounting models and identification of factors affecting their construction, as a result of which the goals of accounting are transformed, make the theoretical basis of this study. It is concluded that accounting is a social science that studies the features of the functioning of the accounting system as a social and institutional practice. Such an understanding of accounting science is considered one of the ways out of the existing crisis. The reasons for the lack of understanding among Ukrainian researchers of accounting as a social science are highlighted, and the ways to overcome them are suggested. It is proved that accounting, on the one hand, is a product of the social environment, an instrument for reflecting the economic reality of an enterprise. On the other hand, it influences the formation of social reality, being an instrument for shaping social processes and relations arising from the functioning of accounting as a separate socio-economic institute.

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.