In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends.

This paper introduces a new family of distortion functions for measuring risks, developed using transmutation techniques. We identify the parameter spaces where the proposed distortions exhibit concavity. Considering that the choice of distortion parameters can be influenced by political factors or users’ risk aversion levels, we generate plots of the distortion functions to examine how these parameters impact the tasks and users’ attitudes toward risk. The coherent properties of the resulting risk measures are explored, outlining the conditions under which the transmuted Kumaraswamy and transmuted truncated normal distortions ensure coherence. Numerical analyses demonstrate the effects of parameter variations on the derived risk measures, highlighting the effectiveness of the proposed distortion functions in accurately assessing risk.

Purpose: This study investigates the relationship between product diversification strategy and cost stickiness, focusing on managerial overconfidence as a moderating factor. It aims to address a critical gap in the literature by providing empirical insights grounded in the Resource-Based View (RBV) theory, specifically examining firms listed on the Tehran Stock Exchange. Methodology: Utilizing a sample of 149 companies from the Tehran Stock Exchange in Iran spanning from 2015 to 2021, this study tests two hypotheses: (1) a positive relationship between product diversification and cost stickiness and (2) the amplification of this relationship by managerial overconfidence. Product diversification is quantified using the Herfindahl Index, while managerial overconfidence is measured through an investment-based index derived from capital expenditures. Cost stickiness is assessed by analysing the asymmetric behaviour of costs in response to changes in sales, focusing on how costs tend to remain high even when sales decrease. Findings: The empirical results substantiate both hypotheses, demonstrating a significant positive relationship between product diversification strategy and cost stickiness. Furthermore, managerial overconfidence amplifies this relationship, highlighting the role of internal resources and managerial perceptions in shaping cost behaviour. Originality: This study contributes substantially to the literature by being among the first to empirically examine the interplay between product diversification strategy, cost stickiness, and managerial overconfidence. Extending the RBV theory to cost behaviour and strategic management provides novel insights for scholars and practitioners in entrepreneurship, corporate strategy, and organizational behaviour. The findings underscore the importance of strategic choices and managerial traits in determining cost stickiness, offering valuable implications for financial analysts, auditors, and stakeholders.

Based on the typical positions of S&P 500 option market makers, we derive a funding illiquidity measure from quoted prices of S&P 500 derivatives. Our measure significantly affects the returns of leveraged managed portfolios; hedge funds with negative exposure to changes in funding illiquidity earn high returns in normal times and low returns in crisis periods when funding liquidity deteriorates. The results are not driven by existing measures of funding illiquidity, market illiquidity, and proxies for tail risk. Our funding illiquidity measure also affects leveraged closed-end mutual funds and, to an extent, asset classes where leveraged investors are marginal investors.

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can identify features of the data-generating process undetected by standard methods and rank the best-performing algorithms. Our tests use 95 years of CRSP data, from 1926 to 2021, encompassing the price history of the broad US stock market. Our findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors may be more attractive when more appropriate account is taken of the non-linear properties of the underlying assets.

This study examines the influence of fraud disclosure (FR_DISC) in annual reports on the financial stability of Takaful insurance (TKI) in Saudi Arabia over the period of 2014 to 2022. Moreover, the current study aims to explore the mediating impact of Shariah board size in shaping this relationship using agency theory and examines whether the different Islamic governance attributes could affect this stability differently. Using the dynamic generalized method of moments (GMM) approach to address the possibility of endogeneity, it was found that FR_DISC is significantly negatively related to the financial stability of a sample TKI. We also provide evidence that the larger the size of a Shariah board, the less FR_DISC affects TKI stability. Furthermore, significant negative influence of ownership concentration and the proportion of non-executives’ independent board members on the stability of insurance companies was also observed. Overall, our analysis reveals several significant challenges if accounting and whistleblowing are to contribute to financial stability.

This survey offers a succinct overview of the General Framework of Portfolio Theory (GFPT), consolidating Markowitz portfolio theory, the growth optimal portfolio theory, and the theory of risk measures. Central to this framework is the use of convex analysis and duality, reflecting the concavity of reward functions and the convexity of risk measures due to diversification effects. Furthermore, practical considerations, such as managing multiple risks in bank balance sheets, have expanded the theory to encompass vector risk analysis. The goal of this survey is to provide readers with a concise tour of the GFPT’s key concepts and practical applications without delving into excessive technicalities. Instead, it directs interested readers to the comprehensive monograph of Maier-Paape, Júdice, Platen, and Zhu (2023) for detailed proofs and further exploration.

This study contributes to the discussion about a fair and balanced pension system with a collectively funded pension scheme or social security and a defined contribution pillar. With an invigorated risk approach using financial option positions, it considers the variance of socioeconomic interests of different society-earning cohorts. By that, it enables the assumption of un-uniformity in interests about the fair and sustainable pension design. Specifically, we claim that the alternative cost of hedging the ideal position to the counterparty position studies the implied risks and returns that participants are willing to absorb and hence may lead to a fair compromise when there are different interests. The novelty of the introduced method is mainly based on the variety of participants’ risks and not on the utility function. Accordingly, we spare the discussion about the right shape of the utility function and the proper calibrations.

Our study investigates the relevance and application of key performance indicators (KPIs) and key risk indicators (KRIs) in business management from 1992 to 2023 through a comprehensive bibliometric analysis performed in RStudio using the Bibliometrix platform and in VOSviewer. Utilizing data from the Web of Science database, we identify trends, key themes, and influential research in this domain, observing an annual growth rate of 17.76%. Our analyses include the top 10 most globally cited documents, word clouds based on authors’ keywords and Keywords Plus, clustering by coupling, co-occurrence networks, and factorial analysis. Our findings reveal a significant increase in research interest post-2004, with sustainability and corporate social responsibility emerging as central themes. We confirm positive correlations between KPIs, improved organizational performance, and effective risk management via KRIs. This research underscores the importance of international collaboration and diverse thematic exploration in advancing the field.

The study described in this paper develops a new technique which permits the execution of an open straddle strategy based on the superior volatility forecast for analyzing historical data. We extend the current litearure by measuring the volatility of an underlying asset in the last predefined period and comparing the actual volatility in currency with historical volatility in currency to make predictions of implied volatility. We calculated stock price volatility through an optimal holding period (OHP) and set up bars of volatility in currency. To obtain this, we solved optimization equations to find maximum and minimum movements in the volatility in currency within the defined range. We placed volatility in currency into percentile rankings and designed a straddle trading strategy based on the last OHP’s volatility in currency. The technique allows for an investor (or trader) to open either short or long positions based on calculations for a selected OHP’s volatility in currency. We applied this strategy to 130 stocks which are traded on CBOE. We developed a trading algorithm which can be used by institutional as well as individual investors. The algorithm is set to determine historical volatility in currency and forecast upcoming volatilities in currency through the understanding of the market sentiment. The empirical findings show that the stocks analyzed with the algorithm generate positive returns along a spectrum of changing volatilities of the underlying assets.