Robert Gharios, Antoine B. Awad, Bashar Abu Khalaf, Lena A. Seissian
This study examines how board gender diversity affects listed non-financial European companies’ financial performance. Data from the Refinitiv Eikon Platform—LSEG and World Bank databases was used to complete the analysis. The total sample included 4257 companies for the period 2011–2023. This study examined board gender diversity and its interaction with liquidity while controlling for board characteristics such as board size, independence, and board meetings. Controlling for firm characteristics (firm size and leverage) and macroeconomic variables like inflation and GDP. This study estimated the connection using panel regression. Due to Hausman test significance, fixed effect estimation was used. The findings demonstrated a notable and favorable influence of board features, such as gender diversity, board independence, and board size, on European nonfinancial companies. Additionally, liquidity positively affects firm performance. Furthermore, the findings indicated that leverage had a significant negative impact on profitability. Finally, both the size and GDP have a significant beneficial impact on profitability. Our findings indicate that an increased representation of women on the board of directors is associated with greater independence among board members and a higher number of board members being hired. This, in turn, has a positive impact on profitability due to the extensive experience shared among board members. Additionally, this leads to improved governance, enabling better control over decisions and a greater focus on the long-term investment strategy of the company. Our results are robust, as are similar results reported by the GMM regression.
{"title":"The Impact of Board Gender Diversity on European Firms’ Performance: The Moderating Role of Liquidity","authors":"Robert Gharios, Antoine B. Awad, Bashar Abu Khalaf, Lena A. Seissian","doi":"10.3390/jrfm17080359","DOIUrl":"https://doi.org/10.3390/jrfm17080359","url":null,"abstract":"This study examines how board gender diversity affects listed non-financial European companies’ financial performance. Data from the Refinitiv Eikon Platform—LSEG and World Bank databases was used to complete the analysis. The total sample included 4257 companies for the period 2011–2023. This study examined board gender diversity and its interaction with liquidity while controlling for board characteristics such as board size, independence, and board meetings. Controlling for firm characteristics (firm size and leverage) and macroeconomic variables like inflation and GDP. This study estimated the connection using panel regression. Due to Hausman test significance, fixed effect estimation was used. The findings demonstrated a notable and favorable influence of board features, such as gender diversity, board independence, and board size, on European nonfinancial companies. Additionally, liquidity positively affects firm performance. Furthermore, the findings indicated that leverage had a significant negative impact on profitability. Finally, both the size and GDP have a significant beneficial impact on profitability. Our findings indicate that an increased representation of women on the board of directors is associated with greater independence among board members and a higher number of board members being hired. This, in turn, has a positive impact on profitability due to the extensive experience shared among board members. Additionally, this leads to improved governance, enabling better control over decisions and a greater focus on the long-term investment strategy of the company. Our results are robust, as are similar results reported by the GMM regression.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221250","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}
In construction, risk is inherent in each project, and success involves meeting defined objectives beyond budget and schedule. Factors vary for infrastructure projects, and their correlation with performance must be studied. In the case of public–private partnership (PPP) transportation, the level of complexity is higher due to more involved parties. Risks and success factors in PPP projects affect each other, which may lead to project failure. Recognizing the critical risk factors (CRFs) and critical success factors (CSFs) is indispensable to ensure the success of PPP infrastructure project implementation. However, the existing research on the PPP risk and success relationship has not gone into sufficient detail, and more support to address the existing gaps in the body of knowledge and literature is necessary. Therefore, in response to the missing area in the public–private partnership transportation industry, this paper analyzed the correlation between PPP risks and success factors. It identified, explored, and categorized various risk and success factors by combining a literature review, expert panel interviews, and a questionnaire survey among both the public and private sectors, a win–win principle. The data collected were analyzed using the structural equation modeling (SEM) approach and relative significance. Results show the relationship between risk and success factors, their influence on PPPs, and the most important factors, known as CRFs and CSFs, with high loading factors (LF > 0.5) and high relative importance (NMS > 0.5). The top five CRFs include “Contract quality (incomplete, conflicting)”, “Staff expertise and experience”, “Financial market risk”, “Conflicting objectives and expectations”, and “Inefficient feasibility study”. The top five CSFs were found as “Appropriate risk allocation and risk-sharing”, “Strong financial capacity and capability of the private sector”, “Government providing guarantees”, “Employment of professional advisors”, and “Realistic assessment of the cost and benefits”. This study advances the understanding of risk and success factors in PPPs and contributes to the theoretical foundations, which will benefit not only public management, policy consultants, and investors but also academics interested in studying PPP transportation projects.
{"title":"A Structural Equation Model on Critical Risk and Success in Public–Private Partnership: Exploratory Study","authors":"Medya Fathi","doi":"10.3390/jrfm17080354","DOIUrl":"https://doi.org/10.3390/jrfm17080354","url":null,"abstract":"In construction, risk is inherent in each project, and success involves meeting defined objectives beyond budget and schedule. Factors vary for infrastructure projects, and their correlation with performance must be studied. In the case of public–private partnership (PPP) transportation, the level of complexity is higher due to more involved parties. Risks and success factors in PPP projects affect each other, which may lead to project failure. Recognizing the critical risk factors (CRFs) and critical success factors (CSFs) is indispensable to ensure the success of PPP infrastructure project implementation. However, the existing research on the PPP risk and success relationship has not gone into sufficient detail, and more support to address the existing gaps in the body of knowledge and literature is necessary. Therefore, in response to the missing area in the public–private partnership transportation industry, this paper analyzed the correlation between PPP risks and success factors. It identified, explored, and categorized various risk and success factors by combining a literature review, expert panel interviews, and a questionnaire survey among both the public and private sectors, a win–win principle. The data collected were analyzed using the structural equation modeling (SEM) approach and relative significance. Results show the relationship between risk and success factors, their influence on PPPs, and the most important factors, known as CRFs and CSFs, with high loading factors (LF > 0.5) and high relative importance (NMS > 0.5). The top five CRFs include “Contract quality (incomplete, conflicting)”, “Staff expertise and experience”, “Financial market risk”, “Conflicting objectives and expectations”, and “Inefficient feasibility study”. The top five CSFs were found as “Appropriate risk allocation and risk-sharing”, “Strong financial capacity and capability of the private sector”, “Government providing guarantees”, “Employment of professional advisors”, and “Realistic assessment of the cost and benefits”. This study advances the understanding of risk and success factors in PPPs and contributes to the theoretical foundations, which will benefit not only public management, policy consultants, and investors but also academics interested in studying PPP transportation projects.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221245","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}
Moustafa Al Najjar, Mohamed Gaber Ghanem, Rasha Mahboub, Bilal Nakhal
This study investigates the impact of artificial intelligence (AI) on reducing accounting errors from two distinct angles: that of accounting software developers and of certified public accountants. We employ a questionnaire-based approach informed by prior research and validated through pilot testing. Our findings reveal significant benefits for software developers. AI effectively addresses various accounting errors, including tax rate discrepancies, cutoff period inaccuracies, principal violations, concealed transactions, mathematical mistakes, and manipulation errors. However, when considering users, AI’s effectiveness varies. While it successfully mitigates certain errors, such as those related to principles, it falls short in eliminating mathematical errors. This research contributes fresh insights into the role of AI in accounting within emerging markets, enhancing our understanding of its potential and limitations.
{"title":"The Role of Artificial Intelligence in Eliminating Accounting Errors","authors":"Moustafa Al Najjar, Mohamed Gaber Ghanem, Rasha Mahboub, Bilal Nakhal","doi":"10.3390/jrfm17080353","DOIUrl":"https://doi.org/10.3390/jrfm17080353","url":null,"abstract":"This study investigates the impact of artificial intelligence (AI) on reducing accounting errors from two distinct angles: that of accounting software developers and of certified public accountants. We employ a questionnaire-based approach informed by prior research and validated through pilot testing. Our findings reveal significant benefits for software developers. AI effectively addresses various accounting errors, including tax rate discrepancies, cutoff period inaccuracies, principal violations, concealed transactions, mathematical mistakes, and manipulation errors. However, when considering users, AI’s effectiveness varies. While it successfully mitigates certain errors, such as those related to principles, it falls short in eliminating mathematical errors. This research contributes fresh insights into the role of AI in accounting within emerging markets, enhancing our understanding of its potential and limitations.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221246","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 article provides exact analytical formulae for various kinds of rainbow step barrier options. These are highly flexible and sophisticated multi-asset barrier options based on the following principle: the option life is divided into several time intervals on which different barriers are monitored w.r.t. different underlying assets. From a mathematical point of view, new results are provided for the first passage time of a multidimensional geometric Brownian motion to a boundary defined as a step function. The article shows how to implement the obtained option valuation formulae in a simple and very efficient manner. Numerical results highlight a strong sensitivity of rainbow step barrier options to the correlations between the underlying assets.
{"title":"Rainbow Step Barrier Options","authors":"Tristan Guillaume","doi":"10.3390/jrfm17080356","DOIUrl":"https://doi.org/10.3390/jrfm17080356","url":null,"abstract":"This article provides exact analytical formulae for various kinds of rainbow step barrier options. These are highly flexible and sophisticated multi-asset barrier options based on the following principle: the option life is divided into several time intervals on which different barriers are monitored w.r.t. different underlying assets. From a mathematical point of view, new results are provided for the first passage time of a multidimensional geometric Brownian motion to a boundary defined as a step function. The article shows how to implement the obtained option valuation formulae in a simple and very efficient manner. Numerical results highlight a strong sensitivity of rainbow step barrier options to the correlations between the underlying assets.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221249","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}
Luis F. Cardona, Jaime A. Guzmán-Luna, Jaime A. Restrepo-Carmona
Crowdfunding platforms are important for startups, since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company information, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are important in analyzing large data sets, detecting anomalies and fraud, and enhancing decision-making and business strategies. A systematic review employed PRISMA guidelines, which studied how ML improves fraud detection on digital crowdfunding platforms. The analysis includes English-language studies from peer-reviewed journals published between 2018 and 2023 to analyze the pre- and post-COVID-19 pandemic. The findings indicate that ML techniques such as Random Forest, Support Vector Machine, and Artificial Neural Networks significantly enhance the predictive accuracy and utility of tax planning for startups considering equity crowdfunding. The United States, Germany, Canada, Italy, and Turkey do not present statistically significant differences at the 95% confidence level, standing out for their notable academic visibility. Florida Atlantic and Cornell Universities, Springer and John Wiley & Sons Ltd. publishing houses, and the Journal of Business Ethics and Management Science magazines present the highest citations without statistical differences at the 95% confidence level.
众筹平台对初创企业非常重要，因为它们通过投资者社区提供多样化的融资选择、市场验证和推广机会。这些平台提供详细的公司信息，有助于在受监管和安全的环境中做出明智的投资决策。机器学习（ML）技术在分析大型数据集、检测异常和欺诈行为以及加强决策和业务战略方面非常重要。一项系统性综述采用了 PRISMA 准则，研究了 ML 如何改进数字众筹平台的欺诈检测。分析包括2018年至2023年期间发表在同行评审期刊上的英文研究，分析了COVID-19大流行前后的情况。研究结果表明，随机森林（Random Forest）、支持向量机（Support Vector Machine）和人工神经网络（Artificial Neural Networks）等 ML 技术大大提高了考虑股权众筹的初创企业的预测准确性和税收筹划的实用性。美国、德国、加拿大、意大利和土耳其在 95% 的置信水平上没有统计学意义上的显著差异，因其显著的学术知名度而脱颖而出。佛罗里达大西洋大学和康奈尔大学、施普林格出版社和约翰-威利父子出版社以及《商业伦理杂志》和《管理科学》杂志的引用率最高，但在 95% 置信度下无统计学差异。
{"title":"Bibliometric Analysis of the Machine Learning Applications in Fraud Detection on Crowdfunding Platforms","authors":"Luis F. Cardona, Jaime A. Guzmán-Luna, Jaime A. Restrepo-Carmona","doi":"10.3390/jrfm17080352","DOIUrl":"https://doi.org/10.3390/jrfm17080352","url":null,"abstract":"Crowdfunding platforms are important for startups, since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company information, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are important in analyzing large data sets, detecting anomalies and fraud, and enhancing decision-making and business strategies. A systematic review employed PRISMA guidelines, which studied how ML improves fraud detection on digital crowdfunding platforms. The analysis includes English-language studies from peer-reviewed journals published between 2018 and 2023 to analyze the pre- and post-COVID-19 pandemic. The findings indicate that ML techniques such as Random Forest, Support Vector Machine, and Artificial Neural Networks significantly enhance the predictive accuracy and utility of tax planning for startups considering equity crowdfunding. The United States, Germany, Canada, Italy, and Turkey do not present statistically significant differences at the 95% confidence level, standing out for their notable academic visibility. Florida Atlantic and Cornell Universities, Springer and John Wiley & Sons Ltd. publishing houses, and the Journal of Business Ethics and Management Science magazines present the highest citations without statistical differences at the 95% confidence level.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221211","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 COVID-19 pandemic, the Russia–Ukraine and the Israel–Hamas conflicts, and the resulting global economic shocks will affect the world economy for several years. This paper analyzes and discusses monetary finance (MF) using the Quantity Theory of Money (QTM) to understand economic dynamics. To achieve this goal, we utilize a Structural Vector Autoregressive (SVAR) identification scheme with sign restrictions on datasets from advanced economies. The findings indicate that monetary growth plays a role in short-term inflationary dynamics, while its effects are less significant in the medium to long term. The aim of the paper is to contribute to ongoing debate surrounding the potential strategies for addressing the economic downturn through the reintroduction of monetary finance (MF).
{"title":"A Re-Appraisal of the Role of Monetary Policy: The Quantity Theory of Money through a Structural Vector Autoregressive Approach","authors":"Antonio Focacci, Angelo Focacci","doi":"10.3390/jrfm17080355","DOIUrl":"https://doi.org/10.3390/jrfm17080355","url":null,"abstract":"The COVID-19 pandemic, the Russia–Ukraine and the Israel–Hamas conflicts, and the resulting global economic shocks will affect the world economy for several years. This paper analyzes and discusses monetary finance (MF) using the Quantity Theory of Money (QTM) to understand economic dynamics. To achieve this goal, we utilize a Structural Vector Autoregressive (SVAR) identification scheme with sign restrictions on datasets from advanced economies. The findings indicate that monetary growth plays a role in short-term inflationary dynamics, while its effects are less significant in the medium to long term. The aim of the paper is to contribute to ongoing debate surrounding the potential strategies for addressing the economic downturn through the reintroduction of monetary finance (MF).","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221248","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 evaluates the effect of earnings management on earnings quality and sustainability in the GCC region, particularly in distressed and non-distressed companies. Studies on earnings quality and sustainability have mostly concentrated on developed markets, with little attention paid to emerging markets like the GCC region. This research is the first to examine how manipulating earnings impacts the quality and sustainability of earnings in distressed and non-distressed companies. This study utilized a unique dataset that represents the GCC region, which has a specific socio-cultural context. We collected data from 839 publicly listed companies in the GCC region between 2011 and 2022 using DataStream®, WorldScope (WS), and Refinitiv Eikon. To test our hypotheses and ensure accuracy, we used three types of regressions (the fixed effects model, OLS, and 2SLS) and conducted robustness and endogeneity tests. The results of this study indicate that accruals-based earnings management has a negative impact on earnings quality for distressed and non-distressed firms but a positive effect on earnings sustainability for both types of companies. The results of this study also find variations in earnings management practices across industries. These findings provide valuable guidance for auditors, investors, and other stakeholders to evaluate the earnings quality and sustainability of distressed and non-distressed companies, benefiting the GCC economy and similar economies.
{"title":"Effect of Earnings Management on Earnings Quality and Sustainability: Evidence from Gulf Cooperation Council Distressed and Non-Distressed Companies","authors":"Khaled Aljifri, Tariq Elrazaz","doi":"10.3390/jrfm17080348","DOIUrl":"https://doi.org/10.3390/jrfm17080348","url":null,"abstract":"This study evaluates the effect of earnings management on earnings quality and sustainability in the GCC region, particularly in distressed and non-distressed companies. Studies on earnings quality and sustainability have mostly concentrated on developed markets, with little attention paid to emerging markets like the GCC region. This research is the first to examine how manipulating earnings impacts the quality and sustainability of earnings in distressed and non-distressed companies. This study utilized a unique dataset that represents the GCC region, which has a specific socio-cultural context. We collected data from 839 publicly listed companies in the GCC region between 2011 and 2022 using DataStream®, WorldScope (WS), and Refinitiv Eikon. To test our hypotheses and ensure accuracy, we used three types of regressions (the fixed effects model, OLS, and 2SLS) and conducted robustness and endogeneity tests. The results of this study indicate that accruals-based earnings management has a negative impact on earnings quality for distressed and non-distressed firms but a positive effect on earnings sustainability for both types of companies. The results of this study also find variations in earnings management practices across industries. These findings provide valuable guidance for auditors, investors, and other stakeholders to evaluate the earnings quality and sustainability of distressed and non-distressed companies, benefiting the GCC economy and similar economies.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931962","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}
Sonal Sahu, Alejandro Fonseca Ramírez, Jong-Min Kim
This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market.
{"title":"Exploring Calendar Anomalies and Volatility Dynamics in Cryptocurrencies: A Comparative Analysis of Day-of-the-Week Effects before and during the COVID-19 Pandemic","authors":"Sonal Sahu, Alejandro Fonseca Ramírez, Jong-Min Kim","doi":"10.3390/jrfm17080351","DOIUrl":"https://doi.org/10.3390/jrfm17080351","url":null,"abstract":"This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"131 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932073","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}
Mohammed R. M. Salem, Shahida Shahimi, Suhaili Alma’amun
This study identifies and synthesizes patterns and trends in the emerging body of literature of environmental, social, and corporate governance (ESG) endeavors on the financial performance (FP) of the banking firms. It specifically aims to highlight the relationship of ESG–FP. The scoping review analysis is based on 1856 journal articles from two online databases, namely Scopus and Web of Science (WoS) for the period of 2015 to 2023. The analysis reveals inconsistent results regarding the ESG–FP relationship, with some studies reporting positive impacts, others negative, and several showing no significant relationship. Notably, non-linear studies consistently identify an inverted U-shaped relationship, suggesting that there is a threshold level of ESG investment beyond which additional investments do not yield proportional benefits. This indicates that threshold-based policies may be more effective at maximizing ESG benefits. The study also found that numerous studies suggested exploring the indirect effect or mediating variables in the ESG–FP relationship to better explain the FP variance. Thus, the study identifies a need for future research to explore indirect relationships by testing potential moderators or mediators, particularly bank risk-taking, to better understand the ESG–FP dynamics. Policymakers and regulators should adopt non-linear analytical approaches and set threshold-based ESG investment policies, while bank management should strategically invest in ESG activities, integrating ESG considerations into risk management frameworks. Continuous monitoring and evaluation, along with stakeholder engagement, are crucial for optimizing ESG investments. By adopting these strategies, banks can enhance financial performance and contribute to sustainable and responsible banking practices.
本研究对有关环境、社会和公司治理（ESG）努力对银行公司财务绩效（FP）影响的新兴文献中的模式和趋势进行了识别和综合。本研究特别强调了环境、社会和公司治理与财务绩效之间的关系。范围审查分析基于两个在线数据库（即 Scopus 和 Web of Science (WoS)）中 2015 年至 2023 年期间的 1856 篇期刊论文。分析显示，ESG-FP 关系的结果并不一致，一些研究报告了积极影响，另一些报告了消极影响，还有几项研究显示没有显著关系。值得注意的是，非线性研究一致确定了一种倒 U 型关系，表明 ESG 投资存在一个门槛水平，超过这一水平，额外投资不会产生相应的收益。这表明，基于临界值的政策可能更有效地实现环境、社会和治理效益的最大化。研究还发现，许多研究建议探索环境、社会和公司治理与生产计划关系中的间接效应或中介变量，以更好地解释生产计划的差异。因此，该研究指出，未来的研究需要通过测试潜在的调节因素或中介变量（尤其是银行的风险承担）来探索间接关系，从而更好地理解 ESG-FP 的动态变化。政策制定者和监管者应采用非线性分析方法，制定基于阈值的环境、社会和治理投资政策，同时银行管理层应战略性地投资于环境、社会和治理活动，将环境、社会和治理因素纳入风险管理框架。持续监测和评估以及利益相关者的参与对于优化环境、社会和公司治理投资至关重要。通过采取这些战略，银行可以提高财务业绩，并为可持续和负责任的银行业务实践做出贡献。
{"title":"Does Mediation Matter in Explaining the Relationship between ESG and Bank Financial Performance? A Scoping Review","authors":"Mohammed R. M. Salem, Shahida Shahimi, Suhaili Alma’amun","doi":"10.3390/jrfm17080350","DOIUrl":"https://doi.org/10.3390/jrfm17080350","url":null,"abstract":"This study identifies and synthesizes patterns and trends in the emerging body of literature of environmental, social, and corporate governance (ESG) endeavors on the financial performance (FP) of the banking firms. It specifically aims to highlight the relationship of ESG–FP. The scoping review analysis is based on 1856 journal articles from two online databases, namely Scopus and Web of Science (WoS) for the period of 2015 to 2023. The analysis reveals inconsistent results regarding the ESG–FP relationship, with some studies reporting positive impacts, others negative, and several showing no significant relationship. Notably, non-linear studies consistently identify an inverted U-shaped relationship, suggesting that there is a threshold level of ESG investment beyond which additional investments do not yield proportional benefits. This indicates that threshold-based policies may be more effective at maximizing ESG benefits. The study also found that numerous studies suggested exploring the indirect effect or mediating variables in the ESG–FP relationship to better explain the FP variance. Thus, the study identifies a need for future research to explore indirect relationships by testing potential moderators or mediators, particularly bank risk-taking, to better understand the ESG–FP dynamics. Policymakers and regulators should adopt non-linear analytical approaches and set threshold-based ESG investment policies, while bank management should strategically invest in ESG activities, integrating ESG considerations into risk management frameworks. Continuous monitoring and evaluation, along with stakeholder engagement, are crucial for optimizing ESG investments. By adopting these strategies, banks can enhance financial performance and contribute to sustainable and responsible banking practices.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931963","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}
Anastasios G. Malliaris, Mary Malliaris, Mark S. Rzepczynski
Competing macroeconomic hypotheses have been developed to explain the US housing market and possible bubble behavior. We employ both seasonally adjusted (SA) and non-seasonally adjusted (NSA) monthly data for about 30 independent variables to examine alternative macro hypotheses for home prices. Using a neural network model as an atheoretical non-linear approach to capture the relative importance of alternative macro variables, we show that these hypotheses generate different macro relevance. As an alternative to testing housing time series, we focus on bubble identification being hypothesis dependent. Model forecast errors (residuals) identify the potential presence of bubbles through standardized residual CUSUM tests for structural breaks. By testing for housing bubbles from these unstructured models, we generate conclusions on the presence of bubbles prior to the Great Financial Crisis and the post-pandemic periods. Competing macro hypotheses or narratives will generate different conclusions on the presence of bubbles and create bubble identification issues.
{"title":"One Man’s Bubble Is Another Man’s Rational Behavior: Comparing Alternative Macroeconomic Hypotheses for the US Housing Market","authors":"Anastasios G. Malliaris, Mary Malliaris, Mark S. Rzepczynski","doi":"10.3390/jrfm17080349","DOIUrl":"https://doi.org/10.3390/jrfm17080349","url":null,"abstract":"Competing macroeconomic hypotheses have been developed to explain the US housing market and possible bubble behavior. We employ both seasonally adjusted (SA) and non-seasonally adjusted (NSA) monthly data for about 30 independent variables to examine alternative macro hypotheses for home prices. Using a neural network model as an atheoretical non-linear approach to capture the relative importance of alternative macro variables, we show that these hypotheses generate different macro relevance. As an alternative to testing housing time series, we focus on bubble identification being hypothesis dependent. Model forecast errors (residuals) identify the potential presence of bubbles through standardized residual CUSUM tests for structural breaks. By testing for housing bubbles from these unstructured models, we generate conclusions on the presence of bubbles prior to the Great Financial Crisis and the post-pandemic periods. Competing macro hypotheses or narratives will generate different conclusions on the presence of bubbles and create bubble identification issues.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"303 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931961","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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