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Family considerations are known to influence the decision to buy long-term care (LTC) insurance. This paper uses a Swiss survey to identify the characteristics of individuals willing to purchase LTC insurance, either to protect their children's bequest or because they cannot rely on family for care. First, it shows that the presence or absence of children plays an important role in the two motivations for buying LTC insurance. Second, it shows that individuals from the French-speaking part of Switzerland and those with lower self-perceived health are more likely to buy LTC insurance because of the unreliability of family care. On the other hand, individuals with higher self-perceived health and those with a right and center political orientation are more likely to buy LTC insurance for reasons of bequest protection. The results provide insights into designing more targeted strategies to promote LTC insurance. [ABSTRACT FROM AUTHOR]

This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting years that summarize multiple accident year loss metrics. By integrating spatial clustering techniques, the study not only improves the credibility of predictive models but also introduces a strategic dimension reduction method that concurrently enhances the interpretability of predictive models used. The evolving nature of spatial patterns over time poses a significant barrier to a better understanding of complex insurance systems as these patterns transform due to various factors. While spatial clustering effectively identifies regions with similar loss data characteristics, maintaining up-to-date clusters is an ongoing challenge. This research underscores the importance of studying spatial patterns of auto insurance claim data across major insurance coverage types, including Accident Benefits (AB), Collision (CL), and Third-Party Liability (TPL). The research offers regulators valuable insights into distinct risk profiles associated with different coverage categories and territories. By leveraging spatial loss data from pre-pandemic and pandemic periods, this study also aims to uncover the impact of the COVID-19 pandemic on auto insurance claims of major coverage types. From this perspective, we observe a statistically significant increase in insurance premiums for CL coverage after the pandemic. The proposed unified spatial clustering method incorporates a relabeling strategy to standardize comparisons across different accident years, contributing to a more robust understanding of the pandemic effects on auto insurance claims. This innovative approach has the potential to significantly influence data visualization and pattern recognition, thereby improving the reliability and interpretability of clustering methods. [ABSTRACT FROM AUTHOR]

The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to model the aggregate loss for all three coverages. To study the pairwise dependence of claims from all types of coverage, an independent model, a hierarchical model, and some copula-based models are proposed for the frequency component. Meanwhile, we applied composite distributions to capture the heavy-tailed severity component. It is shown that consideration of dependence for the multi-peril frequencies (i) significantly enhances model goodness-of-fit and (ii) provides more accurate risk measures of the aggregated losses for all types of coverage in total. [ABSTRACT FROM AUTHOR]

This study employs a robust quantitative ex post facto research design to investigate the complex relationship between audit fees and earnings management. The financial information of 164 firms admitted to the Tehran Stock Exchange (TSE) was used from 2010 to 2019 (pre-COVID period) to achieve the research goal. Analysing data from the Tehran Stock Exchange firms, the study uncovers an inverted U-shaped relationship between audit fees and earnings management. This suggests that moderate audit fees can lead to higher earnings management. Key contributions of this paper include highlighting the role of audit fees in influencing financial reporting quality and risk management, providing empirical evidence on the asymmetric effects of normal and abnormal audit fees on earnings management, and emphasising the need for balanced audit fee structures to ensure financial transparency and mitigate risk. The findings offer valuable insights for academics, practitioners, and policymakers in understanding the nuances of audit fees and their impact on corporate financial practices. This study advances the literature on financial risk management and corporate finance. It emphasises the importance of balanced audit fee structures for management teams, auditors, and policymakers to ensure transparent financial reporting practices.

This study investigates the impact of Research and Development (R&D) investment on the performance of Portuguese companies, specifically addressing the gap in understanding how R&D influences a company’s value and performance. We employ a dynamic panel data model estimated using the Generalized Method of Moments (GMM) to account for potential endogeneity issues. This approach allows us to analyze the influence of R&D investment on the Return on Operating Assets (ROA) for Portuguese companies with significant R&D investments between 2012 and 2019. The analysis reveals that while R&D investment itself may not have a statistically significant short-term impact on ROA, lagged financial performance, leverage, asset turnover ratio, and accounts payable turnover all demonstrate a statistically significant relationship with the dependent variable.

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 explores the impact of digitalization on the performance of insurance intermediaries, who still play a key role in the revenue generation of insurance companies. By using an interdisciplinary approach, this study is the first to examine the extent and type of digital technologies used by intermediaries, their impact on performance with respect to revenue, productivity, and interaction with clients, and the role of digital stress in this context. The research is exploratory, which is why a research model with many variables and relationships between them was built. The quantitative multivariate method of Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied as it allows the simultaneous estimation of models with multiple dependent variables and their interconnections. In this context, data collected in 2022 from 671 insurance intermediaries from Germany, whose demographic distribution in the sample is representative of the German insurance market, were analyzed. The findings show that insurance intermediaries use many digital technologies compared to other industries, particularly those that create added value in their daily work. Empirical evidence also showed that using digital technologies positively affects performance but induces perceived digital stress. As this study reveals, the latter diminishes the positive effects on performance. Technology optimism, technological skills, and organizational support reduce the severity of stress. This means that insurers can start here to support intermediaries to mitigate the performance-limiting effects. This study adds to the insurance literature by providing a broader understanding of how insurance intermediaries deal with digitalization and what it means for their performance.

We present a statistical test for the long-term calibration in rating systems that can deal with overlapping time windows as required by the guidelines of the European Banking Authority (EBA), which apply to major financial institutions in the European System. In accordance with regulation, rating systems are to be calibrated and validated with respect to the long-run default rate. The consideration of one-year default rates on a quarterly basis leads to correlation effects which drastically influence the variance of the long-run default rate. In a first step, we show that the long-run default rate is approximately normally distributed. We then perform a detailed analysis of the correlation effects caused by the overlapping time windows and solve the problem of an unknown distribution of default probabilities.

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.

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.

Family considerations are known to influence the decision to buy long-term care (LTC) insurance. This paper uses a Swiss survey to identify the characteristics of individuals willing to purchase LTC insurance, either to protect their children’s bequest or because they cannot rely on family for care. First, it shows that the presence or absence of children plays an important role in the two motivations for buying LTC insurance. Second, it shows that individuals from the French-speaking part of Switzerland and those with lower self-perceived health are more likely to buy LTC insurance because of the unreliability of family care. On the other hand, individuals with higher self-perceived health and those with a right and center political orientation are more likely to buy LTC insurance for reasons of bequest protection. The results provide insights into designing more targeted strategies to promote LTC insurance.

When trading in the call and put contracts of option chains, the portfolios of strikes must be selected. The trader must also decide whether to take long or short positions at the selected strikes. Dynamic strategies for making these decisions are discussed in this paper. On any day, the strategies estimate the drift and volatility parameters of the future probability distribution of the price of the underlying asset. From this distribution, the trader can further estimate the future expected profit and expected loss that may be experienced for any portfolio of strikes of the call and put contracts. Expected profit and expected loss are the reward and risk metrics of such portfolios. An optimal portfolio can then be selected by making the reward as high as possible under the risk tolerance set by the trader. Extensive back-testing applications to historical data of SPY option chains illustrate the effectiveness of these strategies, particularly when dealing with short-term expiry options and when acting as a seller of put and call options.

The digital economy’s rise has fueled the growth of digital banking, but concerns linger about customer protection. While offering advantages like financial inclusion, this shift disrupts traditional banking experiences and introduces potential risks. Customer safety in this new landscape is paramount, as dissatisfied users may switch providers and institutions risk reputational damage. To remain competitive, financial institutions must prioritize a secure experience that aligns with customer expectations. This study investigates five key factors influencing customer protection in Pakistan’s digital financial services. Analysis reveals all factors positively impact customer protection, with information security holding the most weight. These findings highlight the need for robust information security measures as a critical driver for the Pakistani digital banking industry’s success.

The EU faces two economic challenges: managing non-performing exposures (NPEs) and climate change. This paper analyzes the relationship between the NPEs of domestic banking groups and climate risks, including macroeconomic variables such as the GDP growth rate, unemployment rate (UnEmp), and the voice and accountability percentile (VCA) and the interaction variable between the GHG and the Rule of Law Percentile (GhGRLP). The estimation uses ordinary least squares with time-fixed and individual effects. Physical and transition risks significantly affect NPEs, showing that both adverse climate events and the transition to a low-carbon economy worsen the financial situation of European banking institutions. The analysis also revealed that increased levels of VCA lead to a rise in NPEs, while an increase in GhGRLP reduces NPEs. In contrast, financial institutions tend to recognize and report NPEs more accurately in contexts with greater transparency and accountability. In comparison, UnEmp negatively affects NPEs, suggesting that economic support measures during high unemployment can reduce NPEs in the subsequent period. In conclusion, climate risk management represents a crucial challenge for the financial stability of banking institutions. Policymakers and financial institutions must continue to develop and implement climate change mitigation and adaptation strategies to preserve financial system stability amid growing climate uncertainties.

This paper is devoted to establishing the consequences of integrating CSR into the marketing mix for financial risks in modern companies. Based on the international statistics for 2023, we compiled a regression model of the dependence of financial risks on the integration of CSR into their marketing mix. Based on that, we developed a new system approach to managing companies’ sustainable development. Its features are marketing management of the sustainable development of companies, systemic management of social and financial risks to companies, and implementation of risk management through integrating CSR into the marketing mix. The main conclusion is that the sustainable development of companies in the Decade of Action requires a systemic integration of CSR into the marketing mix, for this will allow for the simultaneous reduction of social and financial risks. The theoretical significance of this conclusion consists in the disclosure of previously unknown cause-and-effect relationships between CSR and financial risks to companies, which are explained in this paper through the lens of the elements of the marketing mix in the 7P model. The originality of this research consists in developing novel marketing tools for the systemic management of social and financial risks for companies with the help of CSR. The practical significance relates to the fact that the offered recommendations on the more complete integration of CSR into their marketing mix will allow reducing financial risks and ensuring sustainable development of Russian companies in the Decade of Action (until 2030). The managerial significance is as follows: the developed system approach to managing companies’ sustainable development will improve the practice of risk management in companies due to previously unavailable joint management and the general reduction of social and financial risks.

Working capital management is critical because it affects a company’s profitability, liquidity, and investment decisions, all of which have an impact on financial performance. As a result, effective and efficient working capital management is an essential component for commercial and service businesses. Given the importance of the commercial and services industries to the Kenyan economy, the goal of this research was to investigate the impact of working capital management on the financial performance of these firms, particularly those listed on the Nairobi Securities Exchange (NSE), from 2003 to 2022. Working capital management was measured using the average age of inventory, average collection period, average payment period, and cash conversion cycle, whereas financial performance was measured using return on asset, return on equity, and net operating profit margin. Using panel regression analysis, the results showed that the average inventory age, average collection period, average payment period, and cash conversion cycle were all negatively related to financial performance for NSE-listed commercial and service firms. Based on the findings, it is recommended that Kenyan commercial and service firms adopt prudent optimal working capital management practices to improve firm financial performance and maximize shareholder wealth.

The goal of this paper was to study the role of corporate social responsibility (by the example of responsible HRM) in the sustainable development of entrepreneurship through operational risk management. The correlation analysis method was used to find a close connection between the number of employees and operational risks to international companies from “Global 500” in 2021–2023. The regression analysis method was used to compile the economic and mathematical model of the sustainable development of international entrepreneurship, which demonstrated wide opportunities for operational risk management through responsible HRM. The method of trend analysis allowed determining scenarios of the sustainable development of international entrepreneurship, which demonstrated that in the Decade of Action, the success of operational risk management is largely determined by the activity of the use of responsible HRM practices. The main conclusion is that responsible HRM facilitates the reduction of operational risks to modern companies, but practices of responsible HRM have different impacts on operational risks to companies: some practices (creation of knowledge-intensive jobs and stimulation of the innovative activity of employees through support for research talents) reduce operational risks, while some practices (stimulation of the growth of labor efficiency and attraction of female researchers to the staff) have a contradictory impact, and other practices (development of human capital through corporate training) increase operational risks. The theoretical significance is because the paper discloses the previously unknown consequences of responsible HRM as a special sphere of manifestation of corporate social responsibility for the operational risks of companies. The practical significance is because the compiled scenarios disclose the perspective of the sustainable development of companies through the improvement of the management of their operational risks based on responsible HRM. The managerial significance is that the proposed recommendations from the authors for the practical implementation of the optimistic scenario can be milestones for companies and can be used to improve the practice of operational risk management of companies.

This study aims to investigate the impact of financial stress and uncertainty on the returns of green and conventional bonds and stocks in the United States from 2010 to 2022. The research utilizes nonlinear and nonparametric analysis, which includes the quantile-on-quantile and nonparametric causality-in-quantiles approaches to examine the relationship between variables. The data analyzed using R programming language show that financial stress positively impacts the middle quantiles of both conventional and green equity, while financial uncertainty negatively impacts upper quantiles. The study also finds that financial stress has a more significant impact on all types of bonds compared to financial uncertainty, with conventional bonds being more affected. This study proposes a pyramid that classifies financial assets based on their susceptibility to financial stress, which could help investors evaluate risk levels and make better investment decisions. The study recommends that policymakers should encourage green investments by offering incentives, such as tax credits. They should also focus on enhancing the efficiency of volatile assets by implementing new investment rules and regulations.

The shadow economy’s size and impact remain subjects of extensive research and debate, holding significant implications for economic policy and social welfare. In Lebanon, the ongoing crisis since 2019 has exacerbated severe economic challenges, with the national currency’s collapse, bank crisis, and foreign reserve deficits. The World Bank reports Lebanon’s financial deficit surpassed $72 billion, three times the GDP in 2021. Despite a drastic decline in GDP, imports have surged to near-pre-crisis levels, exacerbating economic woes and indicating a constant outflow of foreign currencies. Considering such contracting facts, this paper aims to investigate global factors influencing the shadow economy and discern their manifestations in Lebanon during financial crises. Our methodology involves a comprehensive literature review, alongside a case study approach specific to Lebanon. This dual-method strategy ensures a detailed understanding of the shadow economy’s impact and the development of actionable insights for policy and economic reform. Through this approach, we seek to contribute to a nuanced understanding of Lebanon’s economic landscape and provide valuable guidance for policy decisions aimed at reducing corruption, promoting transparency, and fostering a robust formal economy. The increase in the shadow economy raises the formal economy risk, as resources and activities diverted to informal channels hinder the growth and stability of the official economic sector. Although focusing on Lebanon, this analysis deepens the comprehension of the economic landscape and provides valuable guidance for policymakers, researchers, and stakeholders, aiming to address the root causes of informal economic activities and promote sustainable growth in developing countries in general.

This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting years that summarize multiple accident year loss metrics. By integrating spatial clustering techniques, the study not only improves the credibility of predictive models but also introduces a strategic dimension reduction method that concurrently enhances the interpretability of predictive models used. The evolving nature of spatial patterns over time poses a significant barrier to a better understanding of complex insurance systems as these patterns transform due to various factors. While spatial clustering effectively identifies regions with similar loss data characteristics, maintaining up-to-date clusters is an ongoing challenge. This research underscores the importance of studying spatial patterns of auto insurance claim data across major insurance coverage types, including Accident Benefits (AB), Collision (CL), and Third-Party Liability (TPL). The research offers regulators valuable insights into distinct risk profiles associated with different coverage categories and territories. By leveraging spatial loss data from pre-pandemic and pandemic periods, this study also aims to uncover the impact of the COVID-19 pandemic on auto insurance claims of major coverage types. From this perspective, we observe a statistically significant increase in insurance premiums for CL coverage after the pandemic. The proposed unified spatial clustering method incorporates a relabeling strategy to standardize comparisons across different accident years, contributing to a more robust understanding of the pandemic effects on auto insurance claims. This innovative approach has the potential to significantly influence data visualization and pattern recognition, thereby improving the reliability and interpretability of clustering methods.

The frequency and economic damage of natural disasters have increased globally over the last two decades due to climate change. This increase has an impact on the disaster insurance field, particularly in the calculation of premiums. Many regions have a shortcoming in employing insurance because the premium is too high compared with their budget allocation. As one of the solutions, the premium calculation can be developed by applying the cross-subsidies mechanism based on economic growth. Therefore, this research aims to develop premium models of natural disaster insurance that uniquely involve two new variables of an insured region: cross-subsidies and the economic growth rate. Another novelty is the development of the Black–Scholes model, considering the two new variables, and it is used to formulate the premium model. Following the modeling process, this study uses the model to estimate the premiums for natural disaster insurance in each province of Indonesia. The estimation results show that all new variables involved in the model novelties significantly affect the premiums. This research can be used by insurance companies to determine the premium of natural disaster insurance, which involves cross-subsidies and economic growth.

Financial risk aversion and financial risk tolerance are sometimes considered to be ‘opposite sides of the same coin’, with the implication being that risk aversion (a term describing the unwillingness of an investor to take risks based on a probability assessment) and risk tolerance (an investor’s willingness to engage in a behavior based on their subjective evaluation of the uncertainty of the outcomes) are inversely-related substitutes. The purpose of this paper is to present an alternative way of viewing these constructs. We show that risk aversion and risk tolerance act as complementary factors in models designed to describe the degree of risk observed in household investment portfolios. A series of multivariate tests were used to determine that financial risk aversion is inversely related to portfolio risk, whereas financial risk tolerance is positively associated with portfolio risk. When used in the same model, the amount of explained variance in portfolio risk was increased compared to models where one, but not the other, measure was used. Overall, financial risk tolerance exhibited the largest model effect, although financial risk aversion was also important across the models analyzed in this study.

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.

Risk management in the project portfolio can contribute to more effective implementation of the goals of the projects, the portfolio, and the entire organization. However, in the literature on the subject, relatively little attention is paid to the determinants of this process. Moreover, the process course is rarely analyzed in a strategic context relating to the entire organization. For these reasons, this article’s primary goal is to identify the determinants of the effectiveness of risk management in the project portfolio. Research in this area was carried out in the FinTech industry, and the results were analyzed using structural equation modeling. The results indicated that the most important dimensions of the examined effectiveness are the strategic orientation of the organization and the risk management process in the project portfolio. At the level of strategic orientation, this highlights the need for coherence between the organization’s strategy and the project portfolio. At the level of risk management in the project portfolio, the primacy of ownership and control of individual risks is clearly visible.

We introduce the function principal component regression (FPCR) forecasting method to model and forecast age-specific survival functions observed over time. The age distribution of survival functions is an example of constrained data whose values lie within a unit interval. Because of the constraint, such data do not reside in a linear vector space. A natural way to deal with such a constraint is through an invertible logit transformation that maps constrained onto unconstrained data in a linear space. With a time series of unconstrained data, we apply a functional time-series forecasting method to produce point and interval forecasts. The forecasts are then converted back to the original scale via the inverse logit transformation. Using the age- and sex-specific survival functions for Australia, we investigate the point and interval forecast accuracies for various horizons. We conclude that the functional principal component regression (FPCR) provides better forecast accuracy than the Lee–Carter (LC) method. Therefore, we apply FPCR to calculate annuity pricing and compare it with the market annuity price.

This paper aimed to investigate the effects of government borrowing banks’ capital structure using a sample of banks registered in South Africa from 2012 to 2021. Despite the extensive literature on this association, few prominent researchers have studied this phenomenon in the banking sector. Applying the generalised method of moments (GMM) model, the study established a positive but significant effect on the South African banks’ capital structure from total government borrowing, local government borrowing and foreign government borrowing, and capital structure. Contrary to the crowding-out effects detected, the results revealed a positive and significant relationship between government borrowing and banks’ capital structure. The crowding-in effect better explains these results, where government borrowing stimulates the local market for goods and services, motivating banks to borrow more in order to meet the demand for loans. Future research should test the cointegrating and causality relationship between government borrowing and bank capital structure. Also, given that the banking sector is constrained by Basel III’s capital adequacy requirement, controlling for this factor is critical in future research.

The objective of this study is to examine the relationship between macroeconomic variables and the financial liquidity of companies. In this context, two main research questions were formulated. Firstly, which macroeconomic variables impact the financial liquidity of companies? Secondly, what is the direction and strength of the influence of these macroeconomic variables on the financial liquidity of companies? This study employed panel data analysis conducted on an unbalanced panel of 5327 Polish enterprises over the period 2003–2021. The primary research method employed was linear regression (pooled OLS) with robust standard errors clustered at the firm level. The main results of this study indicate that (1) the majority of macroeconomic variables, which illustrate the overall efficiency of the economic system (GDP per capita, ratio of foreign trade goods balance to GDP, CPI, and money supply), demonstrate a positive relationship with corporate liquidity; only the consumption-to-GDP ratio exhibits a negative relationship; (2) a positive relationship was observed between the number of building permits for housing and financial liquidity; (3) variables from the informal institutional environment indicate a positive relationship for the employment rate and a negative relationship for the share of the pre-working age population in the overall population; (4) the relationship between the ratio of internal expenditures on research and development to GDP and corporate liquidity is positive. This study addresses limitations of previous research by examining the impact of macroeconomic factors, particularly those from the institutional and technical environment, on corporate financial liquidity.

A company’s cyber defenses are based on a secure infrastructure and risk-aware behavior by employees. With rising cyber threats and normative training efforts showing limited impact, raising cyber risk awareness is emerging as a challenging effort. The review of the extant literature on awareness diagnosis shows interdisciplinary but mainly theoretical approaches to understanding attitudes and influencing risk behavior. We propose and test a novel methodology to combine and operationalize two tools, deep metaphor interviews and the IDEA risk communication model, to apply them for the first time in the context of behavioral cyber vulnerabilities. The results show a link between diagnosed attitudes and effective risk behavior in a real-life organizational setting, indicating the potential for an expanded diagnostic effort. We propose to develop a broader diagnostic and intervention set to improve cyber awareness and a toolkit to support the business practice of cyber risk management.

This study investigates the impact of FinTech adoption on traditional financial inclusion in 22 countries in sub-Saharan Africa (SSA). The study utilizes the World Bank’s World Development Indicators data and the International Monetary Fund’s Financial Access Survey data. This study employed Principal Component Analysis (PCA) to construct the dimensions of traditional financial inclusion and the overall financial inclusion index. Applying the Generalized Method of Moments estimation technique to annual data spanning from 2004 to 2022, the findings show that FinTech has a negative and statistically significant effect on the geographic and usage dimensions. However, it has a positive and statistically significant impact on the demographic dimension and the overall traditional financial inclusion index. These findings indicate that FinTech does not have a detrimental impact on traditional financial inclusion, which is contrary to the findings of other studies. Therefore, in order to enhance the degree of financial inclusion in SSA, it is important for traditional financial inclusion to effectively utilize FinTech.

Correlation in cyber risk represents an additional source of concern for utility and industrial infrastructures, where risks may be introduced by connected systems. A major means of reducing risk is to transfer it through insurance. In this paper, we consider a company which has peripheral branches in addition to its headquarters, where risk correlation is present between all of its sites and insurance is adopted to hedge against economic losses. We employ the expected utility principle (which leads to the well-known mean variance premium formula) to derive the insurance premium under risk correlation under several risk scenarios. Under a first-order approximation, a quasi-linear relationship between the premium and the two major risk factors (the number of branches and the risk correlation coefficient) is determined. [ABSTRACT FROM AUTHOR]

Greening the environment cannot be achieved satisfactorily, considering that the private sector lags behind the public sector in participation levels. The purpose of this study was to determine the reasons behind the gap in green finance between the two sectors using numerically derived outcomes. Six-year data in the form of total shareholder returns, comprising capital gains and dividends paid from the largest banks in China, the USA, and Europe involved in financing fossil fuels, were extracted from Yahoo.com finance and Macrotrends public forums. Equity premiums were calculated from the total shareholder returns and risk-free rates. A 95% confidence interval was established to determine the lower and upper limits of the equity premiums. The resulting upper limits were used to project premiums that could attract the private sector by 2030. Equity premiums averaged 2.73%, 9.73%, and 4.31% for China, the USA, and Europe, respectively, indicating the substantial task in the USA of attracting the private sector compared to Europe and China. The projections of total shareholder returns showed the same patterns in equity premiums among China, the United States (USA), and Europe. To bridge the gap, the significant need for economic benefits for the private sector should ideally be addressed through green bonds, tailored to green financing projects that are earmarked for revenue generation.

The relationship between intellectual capital and firm performance represents a critical facet of corporate governance, warranting comprehensive investigation. By analyzing data from 1151 non-financial firms listed on the Indonesia Stock Exchange over the period from 2018 to 2022, the authors utilize fixed effect regression analysis to test their hypothesis. This study’s findings reveal a positive and significant relationship between intellectual capital and firm performance. Additionally, the interaction model incorporating political connections yields statistically significant results, indicating that political connections can moderate the relationship between intellectual capital and firm performance. This study makes a substantial contribution to the literature, particularly by advancing the understanding of corporate governance through the lens of intellectual capital’s influence on firm performance. It offers both theoretical and practical insights into the Indonesian context, highlighting the moderating role of political connections. Notably, this study is the first to incorporate interaction models to assess the impact of political connections on this relationship.

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility.

In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed.

This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, ensure accuracy and reliability for auditing, and adhere to tax compliance. The design involves the selection of data from Web of Science Core Collection (WoS) and Scopus, published between 2007 and 2023. The technique helps identify influential publications, collaboration networks, thematic clusters, and trends in research on CA using tools VOSviewer, Biblioshiny, and MS Excel. The originality of the study lies in its dual role as a support for accounting professionals and academics to develop innovative solutions for the challenges posed by crypto technology across core accounting areas: financial and managerial accounting, taxation, and auditing. The findings offer insights into the themes mentioned, and even if the collaboration between the authors is not very developed, the innovation and public recognition of the subject could raise researchers’ interest. The limitation of the dataset is that it does not cover all relevant publications in a different period from the one in which the data were retrieved, 9–11 May 2024. This review might need periodic updates because the CA landscape is constantly changing.

The development of health indicators to measure healthy life expectancy (HLE) is an active field of research aimed at summarizing the health of a population. Although many health indicators have emerged in the literature as critical metrics in public health assessments, the methods and data to conduct this evaluation vary considerably in nature and quality. Traditionally, health data collection relies on population surveys. However, these studies, typically of limited size, encompass only a small yet representative segment of the population. This limitation can necessitate the separate estimation of incidence and mortality rates, significantly restricting the available analysis methods. In this article, we leverage an extract from the French National Hospital Discharge database to define health indicators. Our analysis focuses on the resulting Disease-Free Life Expectancy (Dis-FLE) indicator, which provides insights based on the hospital trajectory of each patient admitted to hospital in France during 2008–2013. Through this research, we illustrate the advantages and disadvantages of employing large clinical datasets as the foundation for more robust health indicators. We shed light on the opportunities that such data offer for a more comprehensive understanding of the health status of a population. In particular, we estimate age-dependent hazard rates associated with sex, alcohol abuse, tobacco consumption, and obesity, as well as geographic location. Simultaneously, we delve into the challenges and limitations that arise when adopting such a data-driven approach.

Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider the prediction of the movement direction of the mid-price on an option order book, using machine learning tools. The applicability of such tools on the options market is currently missing. On an intraday tick-level dataset of options on an exchange traded fund from the Chinese market, we apply a variety of machine learning methods, including decision tree, random forest, logistic regression, and long short-term memory neural network. As machine learning models become more complex, they can extract deeper hidden relationship from input features, which classic market microstructure models struggle to deal with. We discover that the price movement is predictable, deep neural networks with time-lagged features perform better than all other simpler models, and this ability is universal and shared across assets. Using an interpretable model-agnostic tool, we find that the first two levels of features are the most important for prediction. The findings of this article encourage researchers as well as practitioners to explore more sophisticated models and use more relevant features.

The purpose of this paper was to reveal the influence of the support of the sustainable development goals (SDGs) on the financial risks of responsible universities in Russia. This paper fills the gap in the literature that exists regarding the unknown consequences of SDGs’ support by responsible Russian universities concerning their financial risks. Based on the experience of the top 30 most responsible Russian universities in 2023, we used regression analysis to compile a model for their financial risk management. This model mathematically describes the cause-and-effect relationships of financial risk management in responsible Russian universities. This paper offers a new approach to financial risk management in responsible Russian universities. In it, financial risks to Russian universities are reduced due to universities accepting responsibility for state and private investors. A feature of the new approach is that the effective use of university funds is ensured not by cost savings but by the support of the SDGs. The potential for a reduction in financial risk in responsible universities in Russia through alternative approaches to financial risk management was disclosed. The proposed new approach can potentially raise (to a large extent) the aggregate incomes of responsible universities in Russia compared to the existing approach. The main conclusion is that the existing approach to financial risk management in Russian universities is based on low-efficiency managerial measures which risk burdening universities. This burden could be prevented with the newly developed approach to financial risk management in responsible universities in Russia through support of the SDGs. The theoretical significance lies in clarifying the specific list of the SDGs whose support makes the largest contribution to reducing financial risks for the universities—namely, SDG 4, SDG 8, and SDG 9. The practical significance is that the new approach will allow for full disclosure of the potential reduction in financial risks in responsible universities in Russia in the Decade of Action (2020–2030). The managerial significance is as follows: the proposed recommendations will allow improved financial risk management in Russian universities through optimization of the support of the SDGs.

The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to model the aggregate loss for all three coverages. To study the pairwise dependence of claims from all types of coverage, an independent model, a hierarchical model, and some copula-based models are proposed for the frequency component. Meanwhile, we applied composite distributions to capture the heavy-tailed severity component. It is shown that consideration of dependence for the multi-peril frequencies (i) significantly enhances model goodness-of-fit and (ii) provides more accurate risk measures of the aggregated losses for all types of coverage in total.

Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment returns modeled by a stochastically ordered family of random variables under the convolution order, including Poisson, Gamma, and exponential distributions. Utilizing fractional calculus, we derive explicit, closed-form expressions for the derivatives of expected utility for various utility functions, significantly broadening the potential for analytical and computational applications. We apply these theoretical advancements to a case study involving the optimal production strategies of competitive firms, demonstrating the practical implications of our findings in economic decision making.

Previous research predicts an increasing financial deficit in Algeria’s PAYG retirement system, mainly due to rapid population aging, and parametric adjustments will be insufficient to alleviate this imbalance. Mitigating the effects of population aging will necessitate further intervention. In this work, we analyze how capping contributed salaries can help to mitigate the effects of population aging on the retirement system. Under generous Pay-As-You-Go schemes, promised pension payouts far exceed contributions. Thus, restricting contributions is expected to reduce the burden of future benefits by accepting lower contributions today, while directing public subsidies to low-income individuals. We simulate the future evolution of the financial balance of Algeria’s retirement system under various contributable salary caps versus various scenarios of environmental evolution and potential parametric reform actions. The results demonstrated that a 40% cap, along with major parametric reforms and an ideal environment, would help achieve a cumulatively balanced system in the long run.

In recent decades, the importance of transport and logistics companies has increased considerably, especially for Lithuania, where this sector is on the rise and creating benefits for various users. Therefore, this study aims to analyse the economic–financial situation of transport and logistics companies operating in Lithuania, focusing mainly on their financial risk, probability of bankruptcy, and level of solvency. To achieve these results, 416 companies were analysed based on their data from 2022. The employed methodology included descriptive analysis, quartile ratio analysis, the use of Altman’s Z-score model to predict bankruptcy, and, finally, logistic regression analysis to answer the hypotheses. The results show that the companies analysed in this study were highly profitable, with a high level of solvency and liquidity that did not compromise their continuity in the market. These results were confirmed by the Z-score analysis. In addition, it was observed that the age and size of the companies did not affect their survival on the market. This study presents results that are of great interest for the academic literature, as well as for the management of logistics companies. The originality of the study lies in its relevance and timeliness, presenting robust results for different stakeholders, such as policymakers or new entrepreneurs, among others.

The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the insurer. If this intuition should be confirmed, it will serve to address the decade-long concern with risk selection both in the economic literature and on the part of policy makers. As an illustrative example, a two-peril model is developed in which consumers deploy effort in search of a policy offering them maximum coverage at the current market price while insurers deploy effort designed to stave off unfavorable risks. Two types of Nash equilibria are compared: one in which the insurer is confronted with high-risk and low-risk types, and another one where both types are a “better risk” with regard to a second peril. The difference in the insurer’s selection effort directed at high-risk and low-risk types is indeed shown to be lower in the latter case, resulting in a mitigation of adverse selection.

Prior research document a positive association between analyst coverage and R&D. However, they do not investigate what particular attribute of R&D leads to this positive association. In this study we aim to fill the gap in the extant literature and explore the cross-sectional determinants of the association between R&D and analyst coverage. We investigate four cross-sectional determinants: reporting biases arising from expensing of R&D compared to capitalization of R&D, uncertainty associated with R&D, investors’ attention, and scale effects of R&D. We find that while reporting biases and uncertainty decrease analyst coverage for R&D firms, investors’ attention and scale effects of R&D increase analyst coverage. Furthermore, we find that the positive association between R&D and analyst coverage documented by Barth et al. is fully explained by scale effects of R&D.

This study analyzes the impact of knowledge capital (KC), a key element of firms’ innovation and competitiveness, on stock returns during economic crises when sustainable competitiveness becomes particularly important. We analyze the impact of the Global Financial Crisis and COVID-19 as economic crises, focusing on manufacturing industries with a high proportion of investment shifts from physical capital to KC. Our findings indicate that KC is positively associated with stock returns during the Global Financial Crisis and COVID-19. This positive relationship is strengthened by the firm’s ability to leverage KC, as measured by greater product market share, higher Tobin’s Q, and larger cash holdings. This study emphasizes the protective role of KC during the economic crisis when the market pays more attention to corporate sustainability and provides implications to corporate managers and investors.

Commodity price co-movements significantly impact investment decisions. High correlations constrain portfolio diversification and limit risk mitigation potential. While international markets often exhibit strong price linkages, understanding national-level dynamics is crucial for effective portfolio optimization. In this paper, we examine the commodity price co-movements within three key sectors—energy, metals, and agriculture—in the specific context of Pakistan. Utilizing data from 13 January 2013 to 20 August 2020 and employing an autoregressive distributed lag (ARDL) model, we reveal a surprising finding: co-movement among these sectors is weak and primarily short-term. This challenges the conventional assumption of tight coupling in national markets and offers exciting implications for investors. Our analysis suggests that Pakistani commodities hold significant diversification potential, opening promising avenues for risk-reduction strategies within the national market.

Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments by adaptively improving a predictive regression model. This is achieved by augmenting data for model training with strategically selected informative samples. Successful application of active learning requires an effective metric in order to gauge the informativeness of data. Current sampling methods, which focus on prediction error-based informativeness, typically rely solely on prediction variance and assume an unbiased predictive model. In this paper, we address the fact that prediction bias can be nonnegligible in large VA portfolio valuation and investigate the impact of prediction bias in both the modeling and sampling stages of active learning. Our experimental results suggest that bias-based sampling can rival the efficacy of traditional ambiguity-based sampling, with its success contingent upon the extent of bias present in the predictive model.

The Sharpe ratio is a widely used tool for assessing investment strategy performance. An essential part of investing involves creating an appropriate portfolio by determining the optimal weights for desired assets. Before constructing a portfolio, selecting a set of investment opportunities is crucial. In the absence of a risk-free asset, investment opportunities can be identified based on the Sharpe ratios of risky assets and their correlation. The maximum squared Sharpe ratio serves as a useful metric that summarizes the performance of an investment opportunity in a single value, considering the Sharpe ratios of assets and their correlation coefficients. However, the assumption of a normal distribution in asset returns, as implied by the Sharpe ratio and related metrics, may not always hold in practice. Non-normal returns with a non-linear dependence structure can result in an overestimation or underestimation of these metrics. Copula functions are commonly utilized to address non-normal dependence structures. This study examines the impact of asset dependence on the squared maximum Sharpe ratio using copulas and proposes a copula-based approach to tackle the estimation issue. The performance of the proposed estimator is illustrated through simulation and real-data analysis.

We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory networks (LSTMs) to predict the intraday volatility of a collection of equity indices known to have a long memory. Generally, the number of epochs needed to train the extended LSTMs is divided by about two, while the variation in validation and test losses among models with the same hyperparameters is much smaller. We also show that the single model with the smallest validation loss systemically outperforms rough volatility predictions for the average intraday volatility of equity indices by about 20% when trained and tested on a dataset with multiple time series.