Liudmyla Huliaieva

This paper explores market efficiency in the Ukrainian stock market to determine whether there are differences between traditional and ESG indices. Different data properties related to market efficiency are explored: persistence (R/S analysis is used for these purposes), stationarity (ADF tests), normality (Kolmogorov-Smirnoff, Anderson-Darling test, etc.), resistance to market anomalies (Day of the week effect, abnormal returns and patterns they generate are tested using parametrical and non-parametrical statistical tests), etc. Database includes daily data from 2 conventional Ukrainian stock market indices (UX and PFTS) and ESG index (WIG Ukraine) over the period 2015–2022. The following hypothesis is tested in this paper: ESG indices are more efficient than traditional ones. The findings suggest that there are no significant differences between traditional and ESG indices: they have the same persistence, stationarity, do not fit normal distribution and are not influenced by explored market anomalies. So, despite the fact that companies listed in the ESG index are more transparent and thus characterized by lower information asymmetry, they are more liquid and popular among investors, ESG index is not more efficient than traditional ones. This might be the result of unfair practices called “washing” aimed at signaling the active ESG involvement with actual absence of it. This means that many ESG companies are actually traditional. To prevent such practices, the ESG reporting regulation needs to be revised.

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

This paper explores the perceived resilience of russia’s economy under severe sanctions, investigating the potential falsification of economic data to demonstrate the growth. The hypothesis is that the relationship between the official inflation rate and the FMCG deflator index during 2019–2021 significantly differs from that of 2022–2024. Statistical methods, such as correlation analysis, Granger causality tests, and differences tests (e.g., t-tests and Wilcoxon tests), are used along with vector autoregressive (VAR) models and robust linear regressions. The study covers the pre-invasion period (2019–2021) and the post-invasion period (2022–2024), focusing on indicators like the official inflation rate, inflation expectations, CPI, and the FMCG deflator index. Findings reveal a shift from a direct to an inverse correlation between official inflation and the FMCG deflator post-2022, suggesting data manipulation. Pre-2022 models predict inflation 2-3 times higher than both post-2022 models and official statistics, raising concerns about the reliability of russia’s economic data. Further research should explore indirect metrics, such as electricity production and cargo shipments, for additional evidence of data falsification.

Acknowledgments
Alex Plastun gratefully acknowledges financial support from the New Europe College (NEC), the Center for Advanced Study, and Sumy State University.
Anna Vorontsova gratefully acknowledges financial support from Sumy State University.