Risks

68 pages, 1040 KiB

Open AccessArticle

Managing Financial Risks of Global Companies Through Corporate Social Responsibility: The Specifics of Sustainable Employment in Developed and Developing Countries

by Bobir O. Tursunov, Chinara R. Kulueva, Olim K. Abdurakhmanov, Larisa V. Shabaltina and Tatyana I. Bezdenezhnykh

Risks 2024, 12(10), 168; https://doi.org/10.3390/risks12100168 - 21 Oct 2024

Viewed by 1281

Abstract

The motivation for this research was the desire to disclose the social nature of the financial risks of global companies: the authors attempted a scientific explanation of the influence of corporate social responsibility, which is manifested through the preservation and creation of additional [...] Read more.

The motivation for this research was the desire to disclose the social nature of the financial risks of global companies: the authors attempted a scientific explanation of the influence of corporate social responsibility, which is manifested through the preservation and creation of additional jobs, on the financial risks of global companies. The research aims to establish the interdependence between financial risks and sustainable employment in global companies. This goal is achieved using the SEM (structural equation modeling) method based on corporate statistics from the Fortune “Global 500” rankings for 2021–2023. As a result, the consequences of global companies’ CSR (corporate social responsibility) practices in personnel management and financial risk management are modeled and described through quantitative and qualitative patterns. The established regularities proved that for developed and developing countries, the larger the number of employees, the lower the financial risks of global companies—the risk of a decrease in profitability, the risk of loss of profit, and the risk of depreciation of assets. The main conclusion is that there is a close systemic relationship between the financial risks of global companies and their workforce size, suggesting that CSR is key to highly effective financial risk management. A clear distinction between the practices of financial risk management through CSR in developed and developing countries forms the basis of the theoretical significance of the research results. The authors provide recommendations to improve the current practice of financial risk management in global companies by integrating it more closely with personnel management practices, highlighting their managerial relevance. It is proposed that corporate strategies for global companies in developed countries should focus on reducing the risk of declining profitability, as CSR has the most pronounced and consistent impact on this particular financial risk. In developing countries, corporate strategies are recommended to be structured by diversifying the areas of CSR application, with the most promising in financial risk management being the reduction in asset depreciation risk and the reduction in profitability risk. The findings of this research have practical significance because they enhance the predictability of CSR activities of global companies and open up opportunities for highly accurate forecasting of the financial risk implications of ensuring sustainable employment by global companies, considering the specificities of developed and developing countries. Full article

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24 pages, 1278 KiB

Open AccessArticle

Enhancing Portfolio Decarbonization Through SensitivityVaR and Distorted Stochastic Dominance

by Aniq Rohmawati, Oki Neswan, Dila Puspita and Khreshna Syuhada

Risks 2024, 12(10), 167; https://doi.org/10.3390/risks12100167 - 19 Oct 2024

Viewed by 801

Abstract

Recent trends in portfolio management emphasize the importance of reducing carbon footprints and aligning investments with sustainable practices. This paper introduces Sensitivity Value-at-Risk (SensitivityVaR), an advanced distortion risk measure that combines Value-at-Risk (VaR) and Expected Shortfall (ES) with the Cornish–Fisher expansion. SensitivityVaR provides [...] Read more.

Recent trends in portfolio management emphasize the importance of reducing carbon footprints and aligning investments with sustainable practices. This paper introduces Sensitivity Value-at-Risk (SensitivityVaR), an advanced distortion risk measure that combines Value-at-Risk (VaR) and Expected Shortfall (ES) with the Cornish–Fisher expansion. SensitivityVaR provides a more robust framework for managing risk, particularly under extreme market conditions. By incorporating first- and second-order distorted stochastic dominance criteria, we enhance portfolio decarbonization strategies, aligning financial objectives with environmental targets such as the Paris Agreement’s goal of a 7% annual reduction in carbon intensity from 2019 to 2050. Our empirical analysis evaluates the impact of integrating carbon intensity data—including Scope 1, Scope 2, and Scope 3 emissions—on portfolio optimization, focusing on key sectors like technology, energy, and consumer goods. The results demonstrate the effectiveness of SensitivityVaR in managing both risk and environmental impact. The methodology led to significant reductions in carbon intensity across different portfolio configurations, while preserving competitive risk-adjusted returns. By optimizing tail risks and limiting exposure to carbon-intensive assets, this approach produced more balanced and efficient portfolios that aligned with both financial and sustainability goals. These findings offer valuable insights for institutional investors and asset managers aiming to integrate climate considerations into their investment strategies without compromising financial performance. Full article

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15 pages, 512 KiB

Open AccessArticle

Polynomial Moving Regression Band Stocks Trading System

by Gil Cohen

Risks 2024, 12(10), 166; https://doi.org/10.3390/risks12100166 - 18 Oct 2024

Viewed by 1056

Abstract

In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression [...] Read more.

In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression models to identify the stocks’ trends and two standard deviations from the regression model to generate the trading signals. This way, the MRB was transformed into a momentum indicator designed to identify strong uptrends that can be used by a fully automated trading system. Our results indicate that the behavior of Nasdaq100 stocks can be tracked using all three examined polynomial models and can be traded profitably using fully automated systems based on those models. The best performing model was the model that used a four-degree polynomial MRB achieving the highest average net profit (USD 162.73). Regarding the risks involved, the third model has the lowest loss in dollar value (USD −95.52), and the highest minimum percent of profitable trades (41.51%) and profit factor (0.55) that indicates that this strategy is relatively less risky than the other two strategies. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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19 pages, 620 KiB

Open AccessArticle

The Role of Entrepreneur’s Face Disclosure on Crowdfunding Success

by Lenny Phulong Mamaro and Athenia Bongani Sibindi

Risks 2024, 12(10), 165; https://doi.org/10.3390/risks12100165 - 15 Oct 2024

Viewed by 649

Abstract

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the project’s creativity, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data [...] Read more.

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the project’s creativity, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data were collected from multiple crowdfunding platforms for projects in Africa. That is, cross-country data from 54 African countries, to overcome data limitations from a single country. An econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success by 3%. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges to the success of online crowdfunding. Full article

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18 pages, 925 KiB

Open AccessArticle

Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

by M. K. Nallakaruppan, Himakshi Chaturvedi, Veena Grover, Balamurugan Balusamy, Praveen Jaraut, Jitendra Bahadur, V. P. Meena and Ibrahim A. Hameed

Risks 2024, 12(10), 164; https://doi.org/10.3390/risks12100164 - 15 Oct 2024

Viewed by 2146

Abstract

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are [...] Read more.

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates. Full article

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25 pages, 2301 KiB

Open AccessArticle

Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

by Hossein Ghanbari, Emran Mohammadi, Amir Mohammad Larni Fooeik, Ronald Ravinesh Kumar, Peter Josef Stauvermann and Mostafa Shabani

Risks 2024, 12(10), 163; https://doi.org/10.3390/risks12100163 - 11 Oct 2024

Viewed by 788

Abstract

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these [...] Read more.

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape. Full article

(This article belongs to the Special Issue Cryptocurrency Pricing and Trading)

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16 pages, 408 KiB

Open AccessArticle

Behavioral Biases in Panic Selling: Exploring the Role of Framing during the COVID-19 Market Crisis

by Yu Kuramoto, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2024, 12(10), 162; https://doi.org/10.3390/risks12100162 - 10 Oct 2024

Viewed by 1357

Abstract

Panic selling causes long-term losses and hinders investors’ return to the market. It has been explained using prospect theory aspects such as loss and regret aversion. Additionally, overconfidence and overreaction contribute to the disposition effect, leading investors to sell stocks prematurely. However, the [...] Read more.

Panic selling causes long-term losses and hinders investors’ return to the market. It has been explained using prospect theory aspects such as loss and regret aversion. Additionally, overconfidence and overreaction contribute to the disposition effect, leading investors to sell stocks prematurely. However, the framing effect, another disposition effect attribute, has been underexplored in the context of panic selling. This study investigates how the framing effect influences panic selling, particularly during market crises, when investors perceive information differently, depending on its positive or negative framing. Utilizing data from a collaborative survey, we examine Japanese investors’ behavior during the COVID-19 market crisis. Negative framing is negatively associated with complete or partial sale of securities, whereas positive framing has the opposite effect. During market crises, investors presented with negative framing are less likely to panic sell, whereas those presented with positive framing are more prone to it. Other significant factors include gender; men tend to engage more in panic selling. Conversely, higher education, financial literacy, and greater household income and assets are associated with a reduced likelihood of panic selling. These findings underscore the critical role of framing in investor behavior during market crises, providing new insights into the mechanisms underlying panic selling. Full article

7 pages, 317 KiB

Open AccessArticle

Operating Cost Flexibility and Implications for Stock Returns

by Roi D. Taussig

Risks 2024, 12(10), 161; https://doi.org/10.3390/risks12100161 - 10 Oct 2024

Viewed by 574

Abstract

This study suggests a new measure for a firm’s operating cost flexibility. Flexible firms are less risky and, therefore, require lower stock returns. This analysis of 126,202 firm-year observations from the U.S. cross-section of stock returns finds that the new measure explains a [...] Read more.

This study suggests a new measure for a firm’s operating cost flexibility. Flexible firms are less risky and, therefore, require lower stock returns. This analysis of 126,202 firm-year observations from the U.S. cross-section of stock returns finds that the new measure explains a negative significant rate of return. The new measure’s impact extends beyond that of operating leverage. In addition, the new measure’s impact is both statistically and economically significant, and it is sustainable for a variety of in-sample and out-of-sample robustness tests. The new findings are beneficial to researchers and practitioners alike. Full article

23 pages, 789 KiB

Open AccessArticle

Risk Retention and Management Implications of Medical Malpractice in the Italian Health Service

by Ilaria Colivicchi, Tommaso Fabbri and Antonio Iannizzotto

Risks 2024, 12(10), 160; https://doi.org/10.3390/risks12100160 - 8 Oct 2024

Viewed by 1033

Abstract

This work provides an economic exploration of the multifaceted world of medical malpractice risk. Third party liability insurance plays a central role in protecting healthcare providers and public care institutions from the financial consequences of medical malpractice claims, although in recent years, the [...] Read more.

This work provides an economic exploration of the multifaceted world of medical malpractice risk. Third party liability insurance plays a central role in protecting healthcare providers and public care institutions from the financial consequences of medical malpractice claims, although in recent years, the industry landscape has been characterised by periods of distress for this type of protection, with rising litigations and reimbursement costs, resulting in a peculiarly complex market. For the Italian context, the study focuses on the financial repercussions for healthcare institutions of the growing trend towards risk retention practises, legally empowered by the introduction of Law No. 24/2017. The analysis employs Generalised Linear Models for the regressive approach to incorporate the structural and organisational characteristics of hospitals and uses quantitative simulations to explore different scenarios at a regional aggregate level. Due to the limited existing literature and data on the topic, this research aims to provide new methods for effectively understanding and managing this type of risk, thereby supporting decision-making processes in the healthcare sector. Full article

(This article belongs to the Special Issue Integrating New Risks into Traditional Risk Management)

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19 pages, 493 KiB

Open AccessArticle

Why Do Companies Share Buybacks? Evidence from the UK

by Yasmin Jamadar, Hossain Mohammad Reyad, Md. Kausar Alam, Oli Ahad Thakur and Syed A. Mamun

Risks 2024, 12(10), 159; https://doi.org/10.3390/risks12100159 - 8 Oct 2024

Viewed by 1573

Abstract

We examine the key drivers behind management decisions on share repurchase from various theoretical perspectives, including the free cash flow theory and the signaling theory/hypothesis. Specifically, we investigate the relationship between share repurchase and three key drivers: surplus cash, undervaluation, and leverage, along [...] Read more.

We examine the key drivers behind management decisions on share repurchase from various theoretical perspectives, including the free cash flow theory and the signaling theory/hypothesis. Specifically, we investigate the relationship between share repurchase and three key drivers: surplus cash, undervaluation, and leverage, along with several control variables. Using a sample of UK-listed non-financial companies from 2012 to 2022, we apply logistic regression, standard OLS regression, and Tobit regression to identify the factors influencing share repurchase. Our findings reveal that firms repurchase shares to distribute cash to shareholders with surplus cash and Surplus investing cash flow. This study also finds that undervalued smaller firms with lower market-to-book ratios and lower leverage are more likely to repurchase shares. Our study highlights the key factors motivating companies’ share repurchases, such as undervaluation, surplus cash, and leverage, examined from various theoretical perspectives, including the free cash flow theory and signaling theory. Focusing on the UK context, as well as adding a new angle in regard to applying logistic regression, standard OLS regression, and Tobit regression in combination, this research contributes to the existing body of knowledge in corporate finance. The outcome of the study has plausible implications for financial managers and investors in selecting stocks. Its practical implications will help investors gain a better understanding of the factors and forces influencing share repurchase decisions. Full article

20 pages, 3514 KiB

Open AccessReview

Community-Based Disaster Insurance for Sustainable Economic Loss Risk Mitigation: A Systematic Literature Review

by Titi Purwandari, Hilda Azkiyah Surya, Riaman, Yuyun Hidayat, Sukono and Moch Panji Agung Saputra

Risks 2024, 12(10), 158; https://doi.org/10.3390/risks12100158 - 7 Oct 2024

Viewed by 862

Abstract

This systematic literature review (SLR) explores the role of community-based catastrophe insurance (CBCI) as a tool for sustainable economic loss risk mitigation. Utilizing bibliometric analysis and a literature review, this study aims to reveal the methods employed in CBCI schemes from a novel [...] Read more.

This systematic literature review (SLR) explores the role of community-based catastrophe insurance (CBCI) as a tool for sustainable economic loss risk mitigation. Utilizing bibliometric analysis and a literature review, this study aims to reveal the methods employed in CBCI schemes from a novel perspective, highlighting their effectiveness in mitigating catastrophe risks. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was employed to systematically collect and analyze articles sourced from the Scopus, ScienceDirect, and Dimensions databases. The findings provide a comprehensive summary of the CBCI implementation, including various considerations such as risk-sharing mechanisms, premium determination, and policy frameworks. This research offers a fresh perspective on CBCI as a sustainable approach to catastrophe risk mitigation, contributing valuable insights to policymakers, practitioners, and researchers interested in community resilience and disaster risk management. Full article

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25 pages, 694 KiB

Open AccessFeature PaperArticle

Advantages of Accounting for Stochasticity in the Premium Process

by Yang Miao and Kristina P. Sendova

Risks 2024, 12(10), 157; https://doi.org/10.3390/risks12100157 - 3 Oct 2024

Viewed by 537

Abstract

In this paper, we study a risk model with stochastic premium income and its impact on solvency risk management. It is assumed that both the premium arrival process and the claim arrival process are modelled by homogeneous Poisson processes, and that the premium [...] Read more.

In this paper, we study a risk model with stochastic premium income and its impact on solvency risk management. It is assumed that both the premium arrival process and the claim arrival process are modelled by homogeneous Poisson processes, and that the premium amounts are modelled by independent and identically distributed random variables. While this model has been studied in the existing literature and certain explicit results are known under more restrictive assumptions, these results are relatively difficult to apply in practice. In this paper, we investigate the factors that differentiate this model and the classical risk model. After reviewing various known results of this model, we derive a simulation approach for obtaining the probability of ultimate ruin based on importance sampling, which does not require specific distributions for the premium and the claim. We demonstrate this approach first with examples where the distribution of the sampling random variable can be identified. We then provide additional examples where we use the fast Fourier transform to obtain an approximation of the sampling random variable. The simulated results are compared with the known results for the probability of ruin. Using the simulation approach, we apply this model to a real-life auto-insurance data set. Differences with the classical model are then discussed. Finally, we comment on the suitability and impact of using this model in the context of solvency risk management. Full article

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15 pages, 468 KiB

Open AccessArticle

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

by Johanna M. Orozco-Castañeda, Sebastián Alzate-Vargas and Danilo Bedoya-Valencia

Risks 2024, 12(10), 156; https://doi.org/10.3390/risks12100156 - 30 Sep 2024

Viewed by 825

Abstract

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the [...] Read more.

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns. Full article

(This article belongs to the Special Issue Volatility Modeling in Financial Market)

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44 pages, 9032 KiB

Open AccessArticle

Modifying Sequential Monte Carlo Optimisation for Index Tracking to Allow for Transaction Costs

by Leila Hamilton-Russell, Thomas Malan O’Callaghan, Dmitrii Savin and Erik Schlögl

Risks 2024, 12(10), 155; https://doi.org/10.3390/risks12100155 - 30 Sep 2024

Viewed by 677

Abstract

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also [...] Read more.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also known as particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper modifies the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance. Full article

(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)

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35 pages, 7452 KiB

Open AccessArticle

Mapping Financial Connections: Market Integration in Emerging Economies through Graph Theory

by Marc Cortés Rufé and Jordi Martí Pidelaserra

Risks 2024, 12(10), 154; https://doi.org/10.3390/risks12100154 - 29 Sep 2024

Viewed by 813

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)

17 pages, 3297 KiB

Open AccessArticle

Transmuted Distortion Functions for Measuring Risks

by Muna Alkasasbeh, Carl Lee and Felix Famoye

Risks 2024, 12(10), 153; https://doi.org/10.3390/risks12100153 - 26 Sep 2024

Viewed by 624

Abstract

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 [...] Read more.

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. Full article

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17 pages, 12461 KiB

Open AccessArticle

A Contrast-Tree-Based Approach to Two-Population Models

by Matteo Lizzi

Risks 2024, 12(10), 152; https://doi.org/10.3390/risks12100152 - 25 Sep 2024

Viewed by 466

Abstract

Building small-population mortality tables has great practical importance in actuarial applications. In recent years, several works in the literature have explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations, and many models dealing with [...] Read more.

Building small-population mortality tables has great practical importance in actuarial applications. In recent years, several works in the literature have explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations, and many models dealing with this problem usually use a two-population approach, modeling a mortality spread between a larger reference population and the population of interest, via likelihood-based techniques. To broaden the tools at actuaries’ disposal to build small-population mortality tables, a general structure for a two-step two-population model is proposed, its main element of novelty residing in a machine-learning-based approach to mortality spread estimation. In order to obtain this, Contrast Trees and the related Estimation Contrast Boosting techniques have been applied. A quite general machine-learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from the Human Mortality Database. Finally, results from the ML-based model have been compared to those obtained from the traditional model. Full article

(This article belongs to the Special Issue Life Insurance and Pensions: Latest Advances and Prospects)

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17 pages, 440 KiB

Open AccessArticle

The Impact of Value-Added Intellectual Capital on Corporate Performance: Cross-Sector Evidence

by Darya Dancaková and Jozef Glova

Risks 2024, 12(10), 151; https://doi.org/10.3390/risks12100151 - 25 Sep 2024

Viewed by 1902

Abstract

This study explores the relationship between intellectual capital (IC) and the financial performance of 250 publicly traded companies in France, Germany, and Switzerland from 2009 to 2018, addressing the gaps in prior research regarding the differential impacts of IC components across countries and [...] Read more.

This study explores the relationship between intellectual capital (IC) and the financial performance of 250 publicly traded companies in France, Germany, and Switzerland from 2009 to 2018, addressing the gaps in prior research regarding the differential impacts of IC components across countries and industries in Western and Central Europe. Using the Value-Added Intellectual Coefficient (VAIC™) approach, this study evaluates human capital efficiency (HCE), structural capital efficiency (SCE), and capital employed efficiency (CEE). Panel regression analyses at the country and industry levels were conducted to assess their effects on financial metrics, such as return on equity (ROE), return on assets (ROA), and asset turnover ratio (ATO). The findings reveal a significant positive association between SCE, CEE, and firm performance, with CEE showing the most substantial effect, while HCE had a relatively weaker impact. Additionally, the study uncovers a trade-off between the accumulation of patents and trademarks and short-term financial performance, raising new considerations for intellectual property management. This research contributes to the literature by providing a nuanced understanding of how IC components influence financial outcomes across different contexts and offers practical insights for firms aiming to optimize structural capital and capital-employed strategies for improved financial performance while acknowledging the limitations regarding the sample of publicly traded firms. Full article

(This article belongs to the Special Issue Corporate Finance and Intellectual Capital Management)

21 pages, 763 KiB

Open AccessArticle

Risk Management in Product Diversification: The Role of Managerial Overconfidence in Cost Stickiness—Evidence from Iran

by Mona Parsaei, Davood Askarany, Mahtab Maleki and Ali Rahmani

Risks 2024, 12(10), 150; https://doi.org/10.3390/risks12100150 - 24 Sep 2024

Viewed by 873

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)

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Risks, Volume 12, Issue 10 (October 2024) – 19 articles

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