Ashraf Khallaf, Feras M. Salama, Musa Darayseh, Eid Alotaibi
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.
{"title":"Cross-Sectional Determinants of Analyst Coverage for R&D Firms","authors":"Ashraf Khallaf, Feras M. Salama, Musa Darayseh, Eid Alotaibi","doi":"10.3390/risks12060098","DOIUrl":"https://doi.org/10.3390/risks12060098","url":null,"abstract":"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.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"51 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Dependence Modelling for Heavy-Tailed Multi-Peril Insurance Losses","authors":"Tianxing Yan, Yi Lu, Himchan Jeong","doi":"10.3390/risks12060097","DOIUrl":"https://doi.org/10.3390/risks12060097","url":null,"abstract":"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.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"37 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Estimating Disease-Free Life Expectancy Based on Clinical Data from the French Hospital Discharge Database","authors":"Oleksandr Sorochynskyi, Quentin Guibert, Frédéric Planchet, Michaël Schwarzinger","doi":"10.3390/risks12060092","DOIUrl":"https://doi.org/10.3390/risks12060092","url":null,"abstract":"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.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"23 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fernando Tavares, Eulália Santos, Margarida Freitas Oliveira, Luís Almeida
Corporate indebtedness is a powerful tool in determining a company’s financial health with impacts on its image and reputation. The main objective of this research is to study the determining factors in corporate indebtedness in Portugal. It also has the secondary objectives of creating clusters of companies’ behaviour in relation to the use of credit and verifying their differences in relation to the characteristics of the companies. It uses a quantitative methodology based on a questionnaire survey of 1957 Portuguese companies. The results of the factor analysis show the formation of six determining factors in corporate indebtedness, namely the negotiating relationship with banks, financing, cycle and indebtedness, company operating performance, guarantees used to obtain bank financing and financing risk analysis as well as secondary forms of bank financing. The application of cluster analysis to the six factors formed led to the classification of companies into three clusters: the resilient financial cluster, the operational excellence cluster and the strategic financial cluster. There are several statistically significant differences in the corporate financing factors in relation to the clusters to which they belong. The evidence of the factors and clusters explaining company financing provides insights for improving credit access practices and for implementing public policies that facilitate access to credit and promote economic development.
{"title":"Determinants of Corporate Indebtedness in Portugal: An Analysis of Financial Behaviour Clusters","authors":"Fernando Tavares, Eulália Santos, Margarida Freitas Oliveira, Luís Almeida","doi":"10.3390/risks12060091","DOIUrl":"https://doi.org/10.3390/risks12060091","url":null,"abstract":"Corporate indebtedness is a powerful tool in determining a company’s financial health with impacts on its image and reputation. The main objective of this research is to study the determining factors in corporate indebtedness in Portugal. It also has the secondary objectives of creating clusters of companies’ behaviour in relation to the use of credit and verifying their differences in relation to the characteristics of the companies. It uses a quantitative methodology based on a questionnaire survey of 1957 Portuguese companies. The results of the factor analysis show the formation of six determining factors in corporate indebtedness, namely the negotiating relationship with banks, financing, cycle and indebtedness, company operating performance, guarantees used to obtain bank financing and financing risk analysis as well as secondary forms of bank financing. The application of cluster analysis to the six factors formed led to the classification of companies into three clusters: the resilient financial cluster, the operational excellence cluster and the strategic financial cluster. There are several statistically significant differences in the corporate financing factors in relation to the clusters to which they belong. The evidence of the factors and clusters explaining company financing provides insights for improving credit access practices and for implementing public policies that facilitate access to credit and promote economic development.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"3 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank equity researchers. The study used a total of 201 reports on South African banks (2018–2023), 56 reports on Nigerian banks (2018–2023), and 27 reports on Kenyan banks (2018–2023) to investigate the bank equity valuation methods utilised by analysts in the employ of Investec Ltd. and Standard Bank Group Ltd. The study’s findings show that Investec’s South African analysts predominantly used the warranted equity method, based on book value (BV), and return on equity (ROE), for valuing shares throughout the South African, Nigerian, and Kenyan banks surveyed. Furthermore, Standard Bank Group’s analysts employed this method, incorporating tangible net asset value (tNAV) and return on tangible equity (ROTE), for South African and Nigerian banks, but in Kenya their analysts used the residual income model to value the equities of the five Kenyan banks they covered. These findings suggest that the warranted equity method and the residual income model are the mostly used bank equity valuation methods in South Africa, Nigeria, and Kenya. The study concludes with relevant recommendations, offering significant insights for banks, regulators, and investors to make knowledgeable decisions concerning equity valuation.
{"title":"A Case Study of Bank Equity Valuation Methods Employed by South African, Nigerian and Kenyan Equity Researchers","authors":"Vusani Moyo, Ayodeji Michael Obadire","doi":"10.3390/risks12060089","DOIUrl":"https://doi.org/10.3390/risks12060089","url":null,"abstract":"The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank equity researchers. The study used a total of 201 reports on South African banks (2018–2023), 56 reports on Nigerian banks (2018–2023), and 27 reports on Kenyan banks (2018–2023) to investigate the bank equity valuation methods utilised by analysts in the employ of Investec Ltd. and Standard Bank Group Ltd. The study’s findings show that Investec’s South African analysts predominantly used the warranted equity method, based on book value (BV), and return on equity (ROE), for valuing shares throughout the South African, Nigerian, and Kenyan banks surveyed. Furthermore, Standard Bank Group’s analysts employed this method, incorporating tangible net asset value (tNAV) and return on tangible equity (ROTE), for South African and Nigerian banks, but in Kenya their analysts used the residual income model to value the equities of the five Kenyan banks they covered. These findings suggest that the warranted equity method and the residual income model are the mostly used bank equity valuation methods in South Africa, Nigeria, and Kenya. The study concludes with relevant recommendations, offering significant insights for banks, regulators, and investors to make knowledgeable decisions concerning equity valuation.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"19 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abootaleb Shirvani, Stefan Mittnik, William Brent Lindquist, Svetlozar Rachev
We propose a doubly subordinated Lévy process, the normal double inverse Gaussian (NDIG), to model the time series properties of the cryptocurrency bitcoin. By using two subordinated processes, NDIG captures both the skew and fat-tailed properties of, as well as the intrinsic time driving, bitcoin returns and gives rise to an arbitrage-free option pricing model. In this framework, we derive two bitcoin volatility measures. The first combines NDIG option pricing with the Chicago Board Options Exchange VIX model to compute an implied volatility; the second uses the volatility of the unit time increment of the NDIG model. Both volatility measures are compared to the volatility based on the historical standard deviation. With appropriate linear scaling, the NDIG process perfectly captures the observed in-sample volatility.
{"title":"Bitcoin Volatility and Intrinsic Time Using Double-Subordinated Lévy Processes","authors":"Abootaleb Shirvani, Stefan Mittnik, William Brent Lindquist, Svetlozar Rachev","doi":"10.3390/risks12050082","DOIUrl":"https://doi.org/10.3390/risks12050082","url":null,"abstract":"We propose a doubly subordinated Lévy process, the normal double inverse Gaussian (NDIG), to model the time series properties of the cryptocurrency bitcoin. By using two subordinated processes, NDIG captures both the skew and fat-tailed properties of, as well as the intrinsic time driving, bitcoin returns and gives rise to an arbitrage-free option pricing model. In this framework, we derive two bitcoin volatility measures. The first combines NDIG option pricing with the Chicago Board Options Exchange VIX model to compute an implied volatility; the second uses the volatility of the unit time increment of the NDIG model. Both volatility measures are compared to the volatility based on the historical standard deviation. With appropriate linear scaling, the NDIG process perfectly captures the observed in-sample volatility.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"11 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141152255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to SAD, noting a significant impact on retail trading volumes but not on institutional trading or bond returns. This discovery extends the understanding of behavioral finance within the context of bond markets, diverging from established findings in equity and Treasury markets. Additionally, our analysis delineates the influence of macroeconomic announcements on trading activities, offering new insights into the market’s reaction to economic news. This study’s findings contribute to the broader literature on market microstructure and behavioral finance, providing empirical evidence on the interplay between psychological factors and macroeconomic information flow within corporate bond markets. By addressing these specific aspects with rigorous econometric techniques, our research enhances the comprehension of trading dynamics in less transparent markets, offering valuable perspectives for academics, investors, risk managers, and policymakers.
本研究调查了美国公司债券市场交易活动的决定因素，重点关注季节性情感障碍（SAD）和宏观经济公告的影响。我们采用一般到特定（Gets）的 Autometrics 方法，发现了散户和机构投资者对 SAD 的不同行为反应，注意到对散户交易量有显著影响，但对机构交易或债券回报没有影响。这一发现拓展了人们对债券市场行为金融学的理解，与股票和国债市场的既有发现有所不同。此外，我们的分析界定了宏观经济公告对交易活动的影响，为市场对经济新闻的反应提供了新的见解。本研究的发现为更广泛的市场微观结构和行为金融学文献做出了贡献，为公司债券市场中心理因素和宏观经济信息流之间的相互作用提供了经验证据。通过利用严格的计量经济学技术解决这些具体问题，我们的研究增强了对透明度较低市场中交易动态的理解，为学术界、投资者、风险管理者和政策制定者提供了宝贵的视角。
{"title":"Trading Activity in the Corporate Bond Market: A SAD Tale of Macro-Announcements and Behavioral Seasonality?","authors":"James J. Forest, Ben S. Branch, Brian T. Berry","doi":"10.3390/risks12050080","DOIUrl":"https://doi.org/10.3390/risks12050080","url":null,"abstract":"This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to SAD, noting a significant impact on retail trading volumes but not on institutional trading or bond returns. This discovery extends the understanding of behavioral finance within the context of bond markets, diverging from established findings in equity and Treasury markets. Additionally, our analysis delineates the influence of macroeconomic announcements on trading activities, offering new insights into the market’s reaction to economic news. This study’s findings contribute to the broader literature on market microstructure and behavioral finance, providing empirical evidence on the interplay between psychological factors and macroeconomic information flow within corporate bond markets. By addressing these specific aspects with rigorous econometric techniques, our research enhances the comprehension of trading dynamics in less transparent markets, offering valuable perspectives for academics, investors, risk managers, and policymakers.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"42 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antoine B. Awad, Robert Gharios, Bashar Abu Khalaf, Lena A. Seissian
This study examined the relationship between the board characteristics and stock performance of commercial banks. Our analysis is based on a sample of 65 banks across 10 MENA countries and their quantitative data extracted between 2013 and 2022. This research employed pooled OLS, and fixed and random effect regression to confirm the association between board size, board independence, number of board meetings, and CEO duality with stock performance measured by the bank’s share price and market-to-book ratio. Further, several control variables were utilized such as the bank’s capital adequacy, profitability, and size. The empirical findings reveal that board independence positively affects the bank stock performance while the board size shows a negative relationship. This suggests that banks with fewer board members and high independence levels have their shares outperforming others. However, we found that having frequent board meetings per year and separate roles for the CEO and chairman have no impact on bank stock performance. Moreover, the findings indicate that the bank’s capital adequacy, size, and profitability have a positive effect on the stock performance. To test the robustness of our analysis, we implemented a one-limit Tobit model, which enables lower-bound censoring, and obtained similar findings thus confirming our hypotheses. From a practical perspective, our findings highlight the importance of the board size and the directors’ independence to MENA regulators and policymakers in an effort to implement an effective corporate governance system. Specifically, MENA banks are advised to decrease the number of board members, and this should reduce the number of annual board meetings which, in turn, should maximize performance.
{"title":"Board Characteristics and Bank Stock Performance: Empirical Evidence from the MENA Region","authors":"Antoine B. Awad, Robert Gharios, Bashar Abu Khalaf, Lena A. Seissian","doi":"10.3390/risks12050081","DOIUrl":"https://doi.org/10.3390/risks12050081","url":null,"abstract":"This study examined the relationship between the board characteristics and stock performance of commercial banks. Our analysis is based on a sample of 65 banks across 10 MENA countries and their quantitative data extracted between 2013 and 2022. This research employed pooled OLS, and fixed and random effect regression to confirm the association between board size, board independence, number of board meetings, and CEO duality with stock performance measured by the bank’s share price and market-to-book ratio. Further, several control variables were utilized such as the bank’s capital adequacy, profitability, and size. The empirical findings reveal that board independence positively affects the bank stock performance while the board size shows a negative relationship. This suggests that banks with fewer board members and high independence levels have their shares outperforming others. However, we found that having frequent board meetings per year and separate roles for the CEO and chairman have no impact on bank stock performance. Moreover, the findings indicate that the bank’s capital adequacy, size, and profitability have a positive effect on the stock performance. To test the robustness of our analysis, we implemented a one-limit Tobit model, which enables lower-bound censoring, and obtained similar findings thus confirming our hypotheses. From a practical perspective, our findings highlight the importance of the board size and the directors’ independence to MENA regulators and policymakers in an effort to implement an effective corporate governance system. Specifically, MENA banks are advised to decrease the number of board members, and this should reduce the number of annual board meetings which, in turn, should maximize performance.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"43 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robin Van Oirbeek, Félix Vandervorst, Thomas Bury, Gireg Willame, Christopher Grumiau, Tim Verdonck
Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA’s effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector.
{"title":"Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm","authors":"Robin Van Oirbeek, Félix Vandervorst, Thomas Bury, Gireg Willame, Christopher Grumiau, Tim Verdonck","doi":"10.3390/risks12050079","DOIUrl":"https://doi.org/10.3390/risks12050079","url":null,"abstract":"Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA’s effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"52 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolò Giunta, Giuseppe Orlando, Alessandra Carleo, Jacopo Maria Ricci
This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks.
{"title":"Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact","authors":"Nicolò Giunta, Giuseppe Orlando, Alessandra Carleo, Jacopo Maria Ricci","doi":"10.3390/risks12050078","DOIUrl":"https://doi.org/10.3390/risks12050078","url":null,"abstract":"This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"26 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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