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Given the difficulties in finding significant exchange rate exposure in the extant literature, this paper attempts to resolve the so-called “exposure puzzle” by investigating whether currency movements have any significant impact on international industry returns.

This paper utilizes the multivariate Generalized AutoRegressive Conditional Heteroskedasticity (MGARCH) methodology to estimate both symmetric and asymmetric exchange rate exposures for each industry common across 12 countries simultaneously.

The empirical results show that exchange rate exposure is not only statistically significant but also economically important based on the estimation of an asymmetric three-factor exposure model using MGARCH methodology. This is an extremely important finding as it suggests that the “exposure puzzle” may not be a puzzle at all once a better methodology is utilized in the estimation.

Because this study tries to resolve the exchange rate exposure puzzle by focusing on whether exchange rate movements affect ex-post returns as opposed to ex ante expected returns and given the significant exposures with respect to different risk factors found in the study, it is interesting to see if any of these risk factors commands a risk premium. In other words, a natural extension of this study is to test whether any of these risk factors is priced in international industry returns.

The findings of the study have interesting implications for international investors who would like to diversify their portfolios across different industries and are concerned about whether the unexpected movements in the bilateral exchange rates will affect their portfolio returns in addition to its interest rate and world market risk exposures.

The study utilizes the MGARCH methodology, which has not been fully exploited in the exchange rate exposure literature.

Given the dearth of thorough summaries in the literature, this systematic review and bibliometric analysis attempt to take a meticulous approach meant to present knowledge on the constantly developing subject of stock market volatility during crises. In outline, this study aims to map the extant literature available on stock market volatility during crisis periods.

The present study reviews 1,283 journal articles from the Scopus database published between 1994 and 2022, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow diagram. Bibliometric analysis through software like R studio and VOSviewer has been performed, that is, annual publication trend analysis, journal analysis, citation analysis, author influence analysis, analysis of affiliations, analysis of countries and regions, keyword analysis, thematic mapping, co-occurrence analysis, bibliographic coupling, co-citation analysis, Bradford’s law and Lotka’s law, to map the existing literature and identify the gaps.

The literature on the effects of crises on volatility in financial markets has grown in recent years. It was discovered that volatility intensified during crises. This increased volatility can be linked to COVID-19 and the global financial crisis of 2008, as both had massive effects on the world economy. Moreover, we identify specific patterns and factors contributing to increased volatility, providing valuable insights for further research and decision-making.

The present study is confined to the areas of economics, econometrics and finance, business, management and accounting and social sciences. Future studies could be conducted considering a broader perspective.

Most of the available literature has focused on the impact of some particular crises on the volatility of financial markets. The present study is not limited to some specific crises, and the suggested research directions will serve as a guide for future research.

Small and medium-sized enterprises (SMEs) are a major source of employment and revenue growth in developing nations like India, but they also face challenges from resource shortages, shifting consumer demand and heightened competition. This research aims to discover the aspects that enhance SMEs' competitiveness and performance.

By analyzing literature and consulting experts, 10 factors that boost a firm's competitiveness were identified. The total interpretive structural modeling (TISM) method was then used to determine their interaction and structural hierarchy. Neutrosophic-MICMAC analysis was employed to assess the driving-dependence power of each factor.

The study discovered that the factor, namely “entrepreneurial orientation,” was found to be a significant one. “Manufacturing strategy” was found to be extremely dependent on the remaining competitive advantage factors.

This SME-focused framework can be adopted by large businesses to enhance organizational performance by focusing on critical factors. The study depends on experts' judgment, which might be biased. Findings will assist SMEs in identifying significant factors influencing competitive advantage and relationships, increasing awareness of factors contributing to competitive edge.

The results of the research may encourage SME sector managers and practitioners to prioritize the factors that contribute to a firm's competitive advantage.

The majority of research on SME competitive advantage focuses on individual aspects. To add to the body of knowledge on the subject, this study applies the TISM technique to Indian SMEs to identify the contextual interactions among factors that increase long-term competitiveness.

This study aims to develop and test a multivariable psychosocial prediction model of subjective well-being in Ukrainian adults (n = 1,248) 1.5 years after the 2022 Russian invasion of Ukraine.

The research design followed the “Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis” checklist. The online survey combined a questionnaire on sociodemographic characteristics and specifics of living in wartime, as well as validated self-reported inventories: The Modified BBC Subjective Well-being Scale, Acceptance and Action Questionnaire – Version 2 and Connor–Davidson Resilience Scale-10.

The initially developed model was tested through regression analysis, which revealed nine variables as predictors of the subjective well-being scores within the sample, explaining 49.3% of its variance. Among them, the strongest were living with a friend and receiving mental health care systematically. They were almost twice as influential as forced displacement abroad and trauma exposure, which predicted lower well-being, and living with a spouse, which forecasted higher well-being scores. Two resilience subscales – adjustment and restoring and resistance – as predictors of better well-being and perceived unsuccess in life and age as predictors of lower well-being were relatively weaker but statistically significant.

The obtained results support the previous evidence on the essential role of accessible mental health services and social support in times of war, as well as the deteriorative effect of trauma exposure and forcible taking refuge on subjective well-being.

The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.