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The primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.

We used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.

Results showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.

Understanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.

This research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.

The measurement of women's economic welfare and exploring its underlying factors have been undervalued in the context of Khyber Pakhtunkhwa, Pakistan. This study addressed this gap by focusing on assessing women's subjective economic welfare and its socioeconomic and cultural determinants in the education and health sectors within Mardan, Northern Pakistan.

The study used stratified random sampling techniques for the selection of sample respondents and collected data through a well-structured questionnaire. To measure women’s economic welfare, the study utilizes Lorenz curves, the Gini index, the Sen Social Welfare function and an individual's gross monthly income. Furthermore, the ordinary least squares method was utilized to analyze the determinants of economic welfare.

The findings show greater income inequality and a lower welfare level for women in the education sector compared to the health sector. Likewise, the study identifies several key determinants, such as age, educational qualification, job experience, respect for working women, outside and work-place problems and the suffering of family members of working women for their economic well-being.

This study makes valuable contributions to the literature by focusing on the cultural perspective of Pakhtun women in Mardan and providing a context-specific understanding of subjective economic welfare. Additionally, the authors collected first-hand data, which gave an original outlook on working women's current economic welfare level. Furthermore, this study undertakes a comparative analysis of working women's welfare in the health and education sectors. This comparison offers a more accurate portrayal of the challenges and opportunities specific to these occupations.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0246