Labor markets worldwide are increasingly strained by skill shortages, mismatches, and migration pressures, disrupting workforce stability and economic growth. This study conducts a comparative sectoral analysis in North Macedonia, Türkiye, Ethiopia, and Ukraine, focusing on the manufacturing, information and communication technology, and hospitality sectors, to examine the root causes and economic consequences of these challenges. Using a qualitatively driven mixed-methods approach, the research integrates expert interviews, surveys, and labor market data to assess skill gaps, workforce imbalances, and the role of migration. The findings reveal education–industry misalignment, inadequate vocational training, and low wages as persistent drivers of shortages. Additionally, the war in Ukraine, internal conflicts in Ethiopia, and refugee inflows in Türkiye amplify workforce instability, while North Macedonia faces severe emigration, particularly in the sectors of manufacturing and information and communication technologies. These insights are essential for policymakers, industry leaders, and educators in designing labor market interventions that foster workforce resilience. The study recommends national qualification reforms, industry–education collaboration, and improved wage structures to mitigate talent loss and strengthen labor market sustainability. By offering empirical evidence from diverse socio-economic contexts, this research contributes to global discussions on workforce development, migration economics, and labor policy reforms.

In the realm of financial markets, the manifestation of volatility clustering serves as a pivotal element, indicative of the inherent fluctuations characterizing financial instruments. This attribute acquires pronounced relevance within the sphere of cryptocurrencies, a sector renowned for its elevated risk profile. The present analysis, conducted through the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model, seeks to elucidate the enduring nature of volatility clustering and the occurrence of leverage effects within this domain. Over the course of a four-year time frame, it was observed that Bitcoin diverges from the anticipated Autoregressive Conditional Heteroskedasticity (ARCH) effects, in contrast to Ethereum and Cardano, which exhibit marked volatility clustering. Binance Coin, Ripple, and Dogecoin, whilst demonstrating moderate clustering, uniformly reflect the existence of leverage effects. An exception to this pattern was identified in Ripple, where it was discerned that positive market news exerts a disproportionate influence on log returns. The findings of this study illuminate the critical influence of both leverage effects and volatility clustering on the pricing dynamics of cryptocurrencies. It underscores the imperative for a nuanced comprehension of risk management in the context of cryptocurrency investments, given their susceptibility to abrupt price fluctuations. The distinct degrees to which these phenomena are manifested across diverse cryptocurrencies accentuate the necessity for a tailored risk management approach, resonant with the unique attributes of the asset in question. Such strategies, accounting for the potential amplification of losses through leverage, may encompass prudent position sizing, portfolio diversification, and the implementation of stress tests, thereby fortifying the investment against the dual perils of volatility clustering and leverage effects. The implications of this analysis serve to inform investors, providing a foundation upon which to construct risk management tactics that are responsive to the idiosyncrasies of the cryptocurrency market.

Financial analysis plays a major role in investing the disposable income of various economic agents. Stock markets are predominantly made up of small investors with limited information and low capabilities for a suitable analysis. Researchers, as well as practitioners, are divided over the findings on the adequacy of technical analysis in investing. This paper examines the Markov chain process in the stock market to discover the essential links and probabilities for the stocks transition through three states of stagnation, growth, and decline (i.e., stagnant, bull, and bear markets). The subject of analysis is a randomly selected portfolio of 20 shares traded on the New York Stock Exchange. The data suggest that the portfolio relatively quickly, in four trading days, achieves equilibrium probabilities that allow a certain amount of predictability of future movements. At the same time, when analyzing the expected time intervals for the first transition, we found that the portfolio returns to a state of growth much faster than a decline. In addition, the results negate the basic habits of frequent trading, herding, and taking a short position in events of negative price fluctuations. Our research contributes towards observing regularities and stock market efficiency with a clear goal of improving expectations and technical analysis for small individual investors.

In the current millennium, the Indian general insurance market has witnessed major struc-tural changes because of the establishment of a market regulator and the initiation of entry deregulation. The present study evaluates the efficiency performance of fifteen Indian general insurance companies for the period 2011/12 -2016/17 using a robust nonparametric approach. The study also seeks to explain efficiency by considering the influence of environmental variables on the efficiency scores. The results indicate that efficiency is positively related to ownership, insurer age, market share, and return on equity but negatively related to size.

Besides being a buzzword, machine learning finds new areas of application in organizational decision-making processes by the day. We map the field's intellectual structure, thematic evolution, and application domains through a bibliometric analysis of 1,803 Web of Science and Scopus articles (1990-2024) to elucidate its strategic and operational roles. Six clusters, spanning risk modeling, predictive analytics, strategic intelligence, and human-centered AI, are revealed by co-authorship, keyword co-occurrence, and bibliographic coupling. The findings reveal a fragmented but methodologically diverse landscape, with algorithm adoption differing by decision type and industry. By connecting machine learning methods (like deep learning, natural language processing, and explainable AI) with decision functions (like forecasting, optimization, and classification), we can identify the situations in which machine learning has the biggest influence. We go beyond descriptive enumeration with our integration of conceptual and practical insights.