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Volume 17
Issue 12
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J. Risk Financial Manag., Volume 17, Issue 12 (December 2024) – 43 articles

Cover Story (view full-size image): This paper introduces a new metric that emphasizes the accurate forecasting of extreme prices, enabling better model selection for storage profit maximization. Maximizing storage profit relies on the purchase of energy at low prices and their subsequent sale at high prices. This requires accurate predictions of the price peak hours and the determination of the optimal times to buy or sell stored energy. While price prediction is crucial, conventional metrics like mean squared error (MSE) are inadequate for selecting prediction models, as the MSE equally weights all price prediction errors and fails to prioritize the high and low prices. The results show that the proposed metric outperforms the standard ones, leading to more precise profit estimation for storage and other trading activities. View this paper
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23 pages, 3054 KiB  
Article
The Spillover Effects of Market Sentiments on Global Stock Market Volatility: A Multi-Country GJR-GARCH-MIDAS Approach
by Sarula Bai, Jaewon Jung and Shun Li
J. Risk Financial Manag. 2024, 17(12), 569; https://doi.org/10.3390/jrfm17120569 - 18 Dec 2024
Abstract
In behavioral economics, it has widely been documented that there might be a close relationship between overall market sentiment and economic performance, such as GDP per capita. In this paper, we investigate the effects of market sentiment on stock market volatility, which has [...] Read more.
In behavioral economics, it has widely been documented that there might be a close relationship between overall market sentiment and economic performance, such as GDP per capita. In this paper, we investigate the effects of market sentiment on stock market volatility, which has widely been recognized as an important factor for economic sustainability. In particular, we aim to identify the existence of spillover effects of market sentiments on global stock market volatility. As a first attempt, we chose ten countries from major economic regions over the world (including America, Asia, Europe, and Oceania), and analyzed their interdependence and interconnectedness using a GJR-GARCH-MIDAS model. The results highlight that an individual country’s stock market volatility is significantly influenced not only by its own market sentiment (proxied by the consumer confidence index) but also by the overall market sentiments of other countries across the world. The results also highlight significant country-by-country heterogeneity in the time lags of the global spillover effects, which indicates substantial heterogeneity in the behavioral dynamics of individual countries. Full article
(This article belongs to the Special Issue Emerging Trends in Global Trade and Policy Dynamics)
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<p>Relationship between long-term and total volatility across countries.</p> Full article ">Figure 1 Cont.
<p>Relationship between long-term and total volatility across countries.</p> Full article ">Figure A1
<p>Time series of daily log returns (R), monthly realized volatility (RV), and monthly consumer confidence index (CCI).</p> Full article ">Figure A1 Cont.
<p>Time series of daily log returns (R), monthly realized volatility (RV), and monthly consumer confidence index (CCI).</p> Full article ">Figure A1 Cont.
<p>Time series of daily log returns (R), monthly realized volatility (RV), and monthly consumer confidence index (CCI).</p> Full article ">
14 pages, 494 KiB  
Article
Evaluating the Financial Performance of Colombian Companies: A Data Envelopment Analysis Without Explicit Inputs and Technique for Order Preference by Similarity to the Ideal Solution Approach
by Adel Mendoza-Mendoza, Daniel Mendoza Casseres and Enrique De La Hoz-Domínguez
J. Risk Financial Manag. 2024, 17(12), 568; https://doi.org/10.3390/jrfm17120568 - 18 Dec 2024
Viewed by 224
Abstract
The evaluation and ranking of companies in any sector are generally based on a single measure of financial success, so the results obtained vary according to the classification criteria used. This study applies a multi-criteria approach to develop a classification of the largest [...] Read more.
The evaluation and ranking of companies in any sector are generally based on a single measure of financial success, so the results obtained vary according to the classification criteria used. This study applies a multi-criteria approach to develop a classification of the largest companies in Colombia based on their financial results for the period 2022–2023. An analysis of 100 companies was conducted, utilizing four critical criteria: operating income, net profit, total assets, and equity. The evaluation followed a two-stage process. In the first stage, the weights or importance of each selected criterion were objectively established using data envelopment analysis without explicit inputs (DEA-WEIs). This approach reveals that operating income (35.23%) and total assets (28.57%) are the most influential criteria, while net profit is the least influential (13.51%). In the second stage, companies are ranked using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), with the results highlighting Refinería de Cartagena, Empresas Públicas de Medellín, and Terpel S.A. as the top-performing companies. The classification shows clear differentiation, forming two statistically distinct groups validated through discriminant analysis, achieving a 100% correct classification rate. These findings provide actionable insights for benchmarking and improving financial performance in the corporate sector. Full article
(This article belongs to the Section Mathematics and Finance)
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<p>Diagram of the proposed methodology.</p> Full article ">
15 pages, 627 KiB  
Article
Analysis of Financial Contagion and Prediction of Dynamic Correlations During the COVID-19 Pandemic: A Combined DCC-GARCH and Deep Learning Approach
by Victor Chung, Jenny Espinoza and Alan Mansilla
J. Risk Financial Manag. 2024, 17(12), 567; https://doi.org/10.3390/jrfm17120567 - 18 Dec 2024
Viewed by 277
Abstract
This study aims to combine the use of dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models and deep learning techniques in analyzing the dynamic correlation between stock markets. First, we examine the contagion effect of the high-risk financial crisis during COVID-19 [...] Read more.
This study aims to combine the use of dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models and deep learning techniques in analyzing the dynamic correlation between stock markets. First, we examine the contagion effect of the high-risk financial crisis during COVID-19 in the United States on the Latin American stock market using a dynamic conditional correlation approach. The study covers the period from 2014 to 2020, divided into the pre-COVID-19 period (January 2014–February 2020) and the COVID-19 period (March 2020–November 2020), to examine the sudden change in average conditional correlation from one period to the next and identify the contagion effect. The contagion test showed significant contagion between the S&P 500 and Latin American indices, except for Argentina’s MERVAL. Additionally, we applied deep learning models, specifically LSTM, to predict market dynamics and changes in volatility as an early warning system. The results indicate that incorporating LSTM improved the accuracy of predicting dynamic correlations and provided early risk signals during the crisis. This suggests that combining DCC-GARCH with deep learning techniques is a powerful tool for predicting and managing financial risk in highly uncertain markets. Full article
(This article belongs to the Section Financial Technology and Innovation)
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<p>Data Analysis Methodology.</p> Full article ">Figure 2
<p>Dynamic DCC-GARCH Correlations Between the S&amp;P 500 and Latin American Markets.</p> Full article ">Figure 3
<p>LSTM Predictions vs. DCC-GARCH Estimated Correlations with Early Warning Signal Detection.</p> Full article ">
17 pages, 2489 KiB  
Review
Higher Education Loan Schemes Across the Globe: A Systematic Review on the Utility Derived and Burden Associated with Educational Debt
by Daniel Frank, Rakshith Bhandary and Sudhir K. Prabhu
J. Risk Financial Manag. 2024, 17(12), 566; https://doi.org/10.3390/jrfm17120566 - 18 Dec 2024
Viewed by 278
Abstract
Education is considered an investment in human capital that is gained at the cost of knowledge acquisition. This cost is borne by the beneficiary along with subsidy provided by the government, if any, that is mainly collected through tax revenues. This article aims [...] Read more.
Education is considered an investment in human capital that is gained at the cost of knowledge acquisition. This cost is borne by the beneficiary along with subsidy provided by the government, if any, that is mainly collected through tax revenues. This article aims to systematically review the utility derived and the burden experienced with educational debt borrowers across the globe as per the three types of educational loan schemes present across the globe. This study follows the PRISMA guidelines for review selection, and 47 articles published between 1994 and 2024 were included for the final review. The study results reveal that education improves the quality of life; an educational debt servicing to income ratio above 8% is considered as a financial burden. Also, the results reveal that material benefits are high after education along with an increase in the psychological burden because of repayment concerns. This study highlights the need to move towards designing a flexible repayment system in the education loan scheme based on the income contingent schemes adopted in many countries. Income contingent schemes reduce the repayment burden of the borrowers but the return to the lender is limited to the income of the borrower, and mortgage-based schemes are associated with high repayment burden. Therefore, a dynamic scheme will fix the problems associated with the repayment burden by creating a dynamic link between the benefits received and the contributions made by the borrower. Full article
(This article belongs to the Special Issue Recent Development and Challenges in the Banking and Financial Sector: Toward a More Sustainable Financial System)
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<p>Review process for education loan schemes based on PRISMA protocol. Source: authors.</p> Full article ">Figure 2
<p>Annual scientific production. Source: authors.</p> Full article ">Figure 3
<p>Most relevant authors. Source: authors.</p> Full article ">Figure 4
<p>Co-occurrence network. Source: authors.</p> Full article ">Figure 5
<p>Thematic map. Source: authors.</p> Full article ">
18 pages, 897 KiB  
Article
A Non-Linear Approach to Current Account Sustainability—The Cases of Germany, China, and the USA
by Thanos Poulakis and Dimitrios Kyrkilis
J. Risk Financial Manag. 2024, 17(12), 565; https://doi.org/10.3390/jrfm17120565 - 17 Dec 2024
Viewed by 200
Abstract
This paper examines the sustainability of the current account balances for three leading economies that play significant roles in global financial and geopolitical developments: Using the concept of sustainability as the ability of an economy to meet its long-term intertemporal budget constraint, the [...] Read more.
This paper examines the sustainability of the current account balances for three leading economies that play significant roles in global financial and geopolitical developments: Using the concept of sustainability as the ability of an economy to meet its long-term intertemporal budget constraint, the analysis evaluates whether the current accounts of these three economies can maintain this condition without requiring substantial policy interventions or significant changes in private sector behavior. The stationarity of the current account is considered a sufficient condition for testing the sustainability of the current account balance. However, it is argued that the common assumption of a linear process for the current account under the alternative hypothesis of stationarity may not accurately reflect reality, as the current account may exhibit non-linear behavior. Therefore, both linear and non-linear unit root tests are employed to investigate current account sustainability. The results of the unit root tests indicate that the current account balances of Germany, China, and the United States are unsustainable. These findings have significant implications for the economic stability and policy direction of these major economies. Full article
(This article belongs to the Special Issue Contemporary Trends in International Finance: Navigating Global Dynamics)
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<p>China. (<b>a</b>) Exports and imports as a percentage of GDP for the years 1960−2021; (<b>b</b>) GDP growth for the years 1961−2021; (<b>c</b>) Current account balance as a percentage of GDP for the years 1982−2022; (<b>d</b>) Real exchange rate for the years 1980−2022 (2010 = 100). Source: World Bank, authors’ elaborations.</p> Full article ">Figure 2
<p>USA. (<b>a</b>) Exports and imports as a percentage of GDP for the years 1971−2021; (<b>b</b>) GDP growth for the years 1970−2021; (<b>c</b>) Current account balance as a percentage of GDP for the years 19712−2022; (<b>d</b>) Real effective exchange rate for the years 1980−2022 (2010 = 100). Source: World Bank, authors’ elaborations.</p> Full article ">Figure 3
<p>Germany. (<b>a</b>) Exports and imports as a percentage of GDP for the years 1960−2021; (<b>b</b>) GDP growth for the years 1961−2021; (<b>c</b>) Current account balance as a percentage of GDP for the years 1971−2022 (2010 = 100); (<b>d</b>) Real exchange rate for the years 1978−2022. Source: World Bank, authors’ elaborations.</p> Full article ">
13 pages, 252 KiB  
Article
Dynamics of Dividend Payout in Korean Corporations: A Comprehensive Panel Analysis Across Economic Cycles
by SungSup Brian Choi and Kudzai Sauka
J. Risk Financial Manag. 2024, 17(12), 564; https://doi.org/10.3390/jrfm17120564 - 17 Dec 2024
Viewed by 255
Abstract
This research conducts a meticulous examination of the determinants influencing dividend payout dynamics among firms listed on the Korean Stock Exchange (KSE) from 1995 to 2021, a period characterized by profound economic fluctuations. By leveraging a dynamic panel data model and the Generalized [...] Read more.
This research conducts a meticulous examination of the determinants influencing dividend payout dynamics among firms listed on the Korean Stock Exchange (KSE) from 1995 to 2021, a period characterized by profound economic fluctuations. By leveraging a dynamic panel data model and the Generalized Method of Moments (GMM) for estimation, the study addresses endogeneity concerns while exploring the effects of firm-specific and macroeconomic variables on dividend yields. The investigation delineates three distinct economic phases: normal conditions, financial crises, and the aggregate study period, facilitating a granular understanding of firms’ dividend payout adaptability under varying economic landscapes. Empirical findings underscore the persistence of dividend payments, revealing a variable adjustment speed toward target dividend yields contingent upon the economic context, with an expedited adjustment observed during crises. Crucially, firm profitability emerges as a consistent determinant of dividend yields across all examined periods, whereas the influence of macroeconomic variables is notably more pronounced during periods of economic normalcy. This research elucidates the complex interplay between internal corporate strategies and external economic pressures in shaping dividend policies, thereby enriching the discourse on dividend payout behavior in the context of Korea’s economic evolution from an emerging to a developed market. Full article
(This article belongs to the Special Issue Advances in Macroeconomics and Financial Markets)
19 pages, 308 KiB  
Article
Does Profitability Moderate the Relationship Between the Leverage and Dividend Policy of Manufacturing Firms in Nigeria and South Africa?
by Ovbe Simon Akpadaka, Musa Adeiza Farouk, Dagwom Yohanna Dang and Musa Inuwa Fodio
J. Risk Financial Manag. 2024, 17(12), 563; https://doi.org/10.3390/jrfm17120563 - 16 Dec 2024
Viewed by 481
Abstract
This study examines the moderating role of profitability in the relationship between leverage and dividend policy in listed manufacturing firms in Nigeria and South Africa. Using a sample of 915 firm-year observations from 2013 to 2022, the analysis employs panel Tobit regression to [...] Read more.
This study examines the moderating role of profitability in the relationship between leverage and dividend policy in listed manufacturing firms in Nigeria and South Africa. Using a sample of 915 firm-year observations from 2013 to 2022, the analysis employs panel Tobit regression to manage the censored nature of dividend data, with logistic regression applied as a robustness check. The findings reveal a negative association between leverage and dividend payout ratio for Nigerian firms, while this association is less pronounced and statistically insignificant in South Africa, reflecting a more flexible financial environment. Profitability strengthens the leverage–dividend policy relationship in Nigeria, enabling firms to maintain dividends despite high leverage; however, this moderating effect is weaker in South Africa. These results underscore the importance of context-specific financial strategies, recommending that Nigerian policymakers improve access to affordable credit, while South African policymakers focus on sustaining market stability. This study advances the understanding of dividend policy in emerging markets by clarifying how leverage and profitability interact to shape dividend practices. Full article
(This article belongs to the Special Issue Featured Papers in Corporate Finance and Governance)
15 pages, 512 KiB  
Article
Assessment of Factors Affecting Tax Revenues: The Case of the Simplified Taxation System in the Russian Federation
by Kristina Alekseyevna Zakharova, Danil Anatolyevich Muravyev, Egine Araratovna Karagulian, Natalia Alekseyevna Baburina and Ekaterina Vladimirovna Degtyaryova
J. Risk Financial Manag. 2024, 17(12), 562; https://doi.org/10.3390/jrfm17120562 - 16 Dec 2024
Viewed by 234
Abstract
The simplified tax system is the most common special tax regime in the Russian Federation in terms of the number of taxpayers. Tax revenues from the simplified tax system account for 6% of the structure of tax revenues of the consolidated budgets of [...] Read more.
The simplified tax system is the most common special tax regime in the Russian Federation in terms of the number of taxpayers. Tax revenues from the simplified tax system account for 6% of the structure of tax revenues of the consolidated budgets of the constituent entities of the Russian Federation and more than 93% of the structure of tax revenues from special tax regimes. The purpose of this study is to identify and assess the factors influencing tax revenues from the tax levied in connection with applying the simplified system of taxation (taxable object—income reduced by the amount of expenses). The objective of this study is to determine a set of factors used by economists to model the level of tax revenues and to conduct a corresponding econometric analysis of the influence of the selected factors on the dependent variable to identify characteristics of the simplified taxation system functioning in the Russian Federation. The object of this study is the per capita tax revenue from the tax levied in connection with applying the simplified system of taxation (the object of taxation is income reduced by expenses) in the Russian Federation. The subject of the research is a set of economic relations, which arise because of tax-legal relations between tax authorities and taxpayers in relation to the calculation of the tax levied in connection with the application of the simplified taxation system. This study’s hypothesis is that the amount of tax revenues is influenced by factors characterizing the economic situation and development of small and medium businesses in the constituent territories of the Russian Federation. This study was conducted in 83 constituent territories of the Russian Federation in 2020–2022. The research methods are statistical analysis and econometric modeling on panel data. During this study, six econometric models were constructed. Based on the results of specification tests, the least squares dummy variables model was selected. The results of the modeling show that the tax rate, the number of taxpayers, and the real average per capita monetary income of the population have a statistically significant impact on the per capita tax revenue under the simplified tax system (the object of taxation is income reduced by the number of expenses). As a result, the focus of economic policy at both macro and meso levels should be on the support of small and medium-sized enterprises in the early stages of their life cycle, as well as on the increase of the purchasing power of the population. Based on the results obtained, it is possible to forecast the revenue side of the budgets of the constituent entities of the Russian Federation. Full article
(This article belongs to the Special Issue Financial Econometrics with Panel Data)
12 pages, 613 KiB  
Article
Sustainability, Risk Management, and Innovation: Enhancing Performance in Indonesian Social Enterprises
by Azhar Maksum, Munawarah, Yuni Lestari Br Sitepu and Fauziah Kumalasari
J. Risk Financial Manag. 2024, 17(12), 561; https://doi.org/10.3390/jrfm17120561 - 16 Dec 2024
Viewed by 246
Abstract
This study investigates the integration of sustainability practices and risk management in Indonesian social enterprises, emphasizing the role of innovation as a mediator and operational type as a moderator. Social enterprises face unique challenges in balancing economic sustainability with social impact, especially in [...] Read more.
This study investigates the integration of sustainability practices and risk management in Indonesian social enterprises, emphasizing the role of innovation as a mediator and operational type as a moderator. Social enterprises face unique challenges in balancing economic sustainability with social impact, especially in emerging markets like Indonesia. A structured survey was conducted with 118 social enterprises to assess their sustainable practices, risk management procedures, innovation scores, and operational models (permanent vs. project-based). Using Structural Equation Modeling (SEM) and Partial Least Squares (PLS) analysis, the results show that sustainability practices positively influence innovation, while both innovation and risk management significantly improve sustainable performance. Additionally, innovation mediates the relationship between sustainability practices, risk management, and performance. The operational type moderates the link between risk management and sustainable performance but does not influence the connection between sustainability practices and performance. These findings suggest that innovation is crucial for improving the sustainability of social enterprises and that risk management strategies should be tailored to the operational model. Social enterprises in Indonesia should prioritize innovative approaches and effective risk management to enhance their long-term sustainability and social impact. Full article
(This article belongs to the Special Issue Business, Finance and Economic Development—On Globalisation, Entrepreneurship and Emerging Markets)
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<p>Structure Model (Path coefficient and <span class="html-italic">p</span>-values).</p> Full article ">
25 pages, 458 KiB  
Article
Managerial Social Capital and Dividends: Evidence from the UK
by Omar Al-Bataineh, Abdullah Iqbal and Timothy King
J. Risk Financial Manag. 2024, 17(12), 560; https://doi.org/10.3390/jrfm17120560 - 15 Dec 2024
Viewed by 265
Abstract
We examine the relationship between managerial social capital (MSC) and firms’ dividend policies. For an unbalanced panel of publicly listed UK FTSE 350 firms from 2006 to 2017, we find that MSC has a negative impact on a firm’s dividend policy. Firms pay [...] Read more.
We examine the relationship between managerial social capital (MSC) and firms’ dividend policies. For an unbalanced panel of publicly listed UK FTSE 350 firms from 2006 to 2017, we find that MSC has a negative impact on a firm’s dividend policy. Firms pay lower dividends when a higher proportion of well-connected directors join corporate boards. Our main result is consistent with the notion that a high degree of social capital leads to better monitoring and control; therefore, social capital works under the substitution effect between governance quality and dividend payouts. In further analyses, we explore potential differences in this relationship between financial and non-financial firms and show that the association between MSC and dividend policy is weaker in financial firms than in non-financial firms. Taken together, our findings infer that investors should consider the social capital status of firms when they make investment decisions. Our results proved to be robust when subjected to a battery of tests, including alternative model specifications and definitions of MSC. Full article
(This article belongs to the Special Issue Corporate Finance: Financial Management of the Firm)
16 pages, 375 KiB  
Article
The Impact of Shipping Connectivity on Environmental Quality, Financial Development, and Economic Growth in Regional Comprehensive Economic Partnership Countries
by Xhelil Bekteshi, Sevdie Alshiqi, Bartosz Jóźwik, Fatma Gul Altin, Mesut Dogan and Tatyana Petrossyants
J. Risk Financial Manag. 2024, 17(12), 559; https://doi.org/10.3390/jrfm17120559 - 15 Dec 2024
Viewed by 587
Abstract
This study investigates the relationship between shipping connectivity, environmental quality, financial development, and economic growth among 14 countries in the Regional Comprehensive Economic Partnership (RCEP) from 2006 to 2019. Using panel-corrected standard error, Dynamic Seemingly Unrelated Regression, and Driscoll–Kraay estimation methods, the analysis [...] Read more.
This study investigates the relationship between shipping connectivity, environmental quality, financial development, and economic growth among 14 countries in the Regional Comprehensive Economic Partnership (RCEP) from 2006 to 2019. Using panel-corrected standard error, Dynamic Seemingly Unrelated Regression, and Driscoll–Kraay estimation methods, the analysis reveals that shipping connectivity significantly contributes to financial development and economic growth, while also exerting a negative impact on environmental quality. These findings suggest that the maritime sector can have significant impacts not only on economic growth and financial development but also on environmental sustainability. In countries where maritime shipping has increased, particularly with the growth of trade, positive outcomes are observed in terms of financial development and economic growth, while negative impacts on environmental quality are also evident. This study provides insights for policymakers to develop strategies that maximize economic benefits while reducing environmental harm in order to achieve sustainable development in the maritime sector. Full article
(This article belongs to the Special Issue Macroeconomic Policies and Economic Growth)
20 pages, 1490 KiB  
Article
The Predictive Grey Forecasting Approach for Measuring Tax Collection
by Pitresh Kaushik, Mohsen Brahmi, Shubham Kakran and Pooja Kansra
J. Risk Financial Manag. 2024, 17(12), 558; https://doi.org/10.3390/jrfm17120558 - 13 Dec 2024
Viewed by 390
Abstract
Taxation serves as a vital lifeline for government revenue, directly contributing to national development and the welfare of its citizens. Ensuring the efficiency and effectiveness of the tax collection process is essential for maintaining a sustainable economic framework. This study investigates (a) trends [...] Read more.
Taxation serves as a vital lifeline for government revenue, directly contributing to national development and the welfare of its citizens. Ensuring the efficiency and effectiveness of the tax collection process is essential for maintaining a sustainable economic framework. This study investigates (a) trends and patterns of direct tax collection, (b) the cost of tax collection, (c) the proportion of direct tax in total tax collection, and (d) the tax-to-GDP ratio in India. By utilizing a novel grey forecasting model (GM (1,1)), this study attempted to predict the future trends of India’s direct tax collections, through which it aims to provide a concurrent and accurate future outlook on tax revenue, ensuring resources are optimally allocated for the country’s growth. Results revealed that direct tax collection has consistently increased in the past two decades, and the proportion of direct tax in total tax has also improved significantly. On the contrary, the cost of tax collection has decreased regularly, indicating the efficiency of tax collection. Forecasting shows that the collection from direct tax is expected to reach INR 30.67 trillion in 2029–30, constituting around 54.41% of the total tax, leaving behind collections from indirect tax at a total of INR 25.70 trillion. Such findings offer insights that could enhance revenue management strategies with policy decisions relevant to economists, government, and other stakeholders to understand trends and the efficiency of direct tax collection in India. Full article
(This article belongs to the Special Issue Sustainable Tax and Accounting Reporting in Building a New Tax Culture)
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<p>Collection of direct tax and its constituents. Source: Author calculation using Central Board of Direct Tax (CBDT) data.</p> Full article ">Figure 2
<p>Contribution of direct tax to total tax revenue (Source: author calculation using data from CBDT database, CBIC).</p> Full article ">Figure 3
<p>Comparison of the tax growth rate, GDP growth rate, and Direct tax-to-GDP ratio (Source: MOSPI).</p> Full article ">Figure 4
<p>Comparison between actual and predicted data on direct tax.</p> Full article ">Figure 5
<p>Comparison between actual and predicted observations of indirect tax collection in India using the GM (1,1).</p> Full article ">
22 pages, 7903 KiB  
Article
Forecasting Forex Market Volatility Using Deep Learning Models and Complexity Measures
by Pavlos I. Zitis, Stelios M. Potirakis and Alex Alexandridis
J. Risk Financial Manag. 2024, 17(12), 557; https://doi.org/10.3390/jrfm17120557 - 13 Dec 2024
Viewed by 443
Abstract
In this article, we examine whether incorporating complexity measures as features in deep learning (DL) algorithms enhances their accuracy in predicting forex market volatility. Our approach involved the gradual integration of complexity measures alongside traditional features to determine whether their inclusion would provide [...] Read more.
In this article, we examine whether incorporating complexity measures as features in deep learning (DL) algorithms enhances their accuracy in predicting forex market volatility. Our approach involved the gradual integration of complexity measures alongside traditional features to determine whether their inclusion would provide additional information that improved the model’s predictive accuracy. For our analyses, we employed recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) as DL model architectures, while using the Hurst exponent and fuzzy entropy as complexity measures. All analyses were conducted on intraday data from four highly liquid currency pairs, with volatility estimated using the Range-Based estimator. Our findings indicated that the inclusion of complexity measures as features significantly enhanced the accuracy of DL models in predicting volatility. In achieving this, we contribute to a relatively unexplored area of research, as this is the first instance of such an approach being applied to the prediction of forex market volatility. Additionally, we conducted a comparative analysis of the three models’ performance, revealing that the LSTM and GRU models consistently demonstrated a superior accuracy. Finally, our findings also have practical implications, as they may assist risk managers and policymakers in forecasting volatility in the forex market. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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<p>Schematic representation of simple RNN cell (<a href="#B66-jrfm-17-00557" class="html-bibr">Yu et al. 2019</a>).</p> Full article ">Figure 2
<p>Schematic representation of LSTM cell (<a href="#B66-jrfm-17-00557" class="html-bibr">Yu et al. 2019</a>).</p> Full article ">Figure 3
<p>Schematic representation of gated recurrent unit (GRU) cell (<a href="#B66-jrfm-17-00557" class="html-bibr">Yu et al. 2019</a>).</p> Full article ">Figure 4
<p>Partitioning of the dataset and the grid search process: (<b>a</b>) the division of the dataset into training and test sets and (<b>b</b>) a schematic representation of grid search and cross-validation.</p> Full article ">Figure 5
<p>Evolution of four forex market currency exchange rate prices (blue curves, left vertical axis) and corresponding Range-Based volatility (grey curves, right vertical axis) over the period from 28 August 2014, to 29 December 2023: (<b>a</b>) EUR/USD, (<b>b</b>) GBP/USD, (<b>c</b>) USD/CAD, and (<b>d</b>) USD/CHF. The vertical red lines delineate the data area utilized for model training (on the left) and the data area employed for model testing (on the right).</p> Full article ">Figure 6
<p>Actual values of the Range-Based volatility for EUR/USD (grey curves) and the model predictions (colored curves) by feature case and DL model for the test dataset (i.e., over the period 8 April 2020, to 29 December 2023). More specifically, subfigures (<b>a</b>,<b>e</b>,<b>i</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case I (where the feature used was Range-Based Volatility). Subfigures (<b>b</b>,<b>f</b>,<b>j</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case II (where the features used were Range-Based Volatility, High, and Low). Subfigures (<b>c</b>,<b>g</b>,<b>k</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case III (where the features used were Range-Based Volatility, Hurst Exponent, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>u</mi> <mi>z</mi> <mi>z</mi> <mi>y</mi> <mi>E</mi> <mi>n</mi> </mrow> </semantics></math>). Subfigures (<b>d</b>,<b>h</b>,<b>l</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case IV (where the features used were Range-Based Volatility, High, Low, Hurst Exponent, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>u</mi> <mi>z</mi> <mi>z</mi> <mi>y</mi> <mi>E</mi> <mi>n</mi> </mrow> </semantics></math>).</p> Full article ">Figure 7
<p>Actual values of the Range-Based volatility for GBP/USD (grey curves) and the model predictions (colored curves) by feature case and DL model for the test dataset (i.e., over the period 8 April 2020, to 29 December 2023). More specifically, subfigures (<b>a</b>,<b>e</b>,<b>i</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case I (where the feature used was Range-Based Volatility). Subfigures (<b>b</b>,<b>f</b>,<b>j</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case II (where the features used were Range-Based Volatility, High, and Low). Subfigures (<b>c</b>,<b>g</b>,<b>k</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case III (where the features used were Range-Based Volatility, Hurst Exponent, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>u</mi> <mi>z</mi> <mi>z</mi> <mi>y</mi> <mi>E</mi> <mi>n</mi> </mrow> </semantics></math>). Subfigures (<b>d</b>,<b>h</b>,<b>l</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case IV (where the features used were Range-Based Volatility, High, Low, Hurst Exponent, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>u</mi> <mi>z</mi> <mi>z</mi> <mi>y</mi> <mi>E</mi> <mi>n</mi> </mrow> </semantics></math>).</p> Full article ">Figure 8
<p>Actual values of the Range-Based volatility for USD/CAD (grey curves) and the model predictions (colored curves) by feature case and DL model for the test dataset (i.e., over the period 8 April 2020, to 29 December 2023). More specifically, subfigures (<b>a</b>,<b>e</b>,<b>i</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case I (where the feature used was Range-Based Volatility). Subfigures (<b>b</b>,<b>f</b>,<b>j</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case II (where the features used were Range-Based Volatility, High, and Low). Subfigures (<b>c</b>,<b>g</b>,<b>k</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case III (where the features used were Range-Based Volatility, Hurst Exponent, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>u</mi> <mi>z</mi> <mi>z</mi> <mi>y</mi> <mi>E</mi> <mi>n</mi> </mrow> </semantics></math>). Subfigures (<b>d</b>,<b>h</b>,<b>l</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case IV (where the features used were Range-Based Volatility, High, Low, Hurst Exponent, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>u</mi> <mi>z</mi> <mi>z</mi> <mi>y</mi> <mi>E</mi> <mi>n</mi> </mrow> </semantics></math>).</p> Full article ">Figure 9
<p>Actual values of the Range-Based volatility for USD/CHF (grey curves) and the model predictions (colored curves) by feature case and DL model for the test dataset (i.e., over the period 8 April 2020, to 29 December 2023). More specifically, subfigures (<b>a</b>,<b>e</b>,<b>i</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case I (where the feature used was Range-Based Volatility). Subfigures (<b>b</b>,<b>f</b>,<b>j</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case II (where the features used were Range-Based Volatility, High, and Low). Subfigures (<b>c</b>,<b>g</b>,<b>k</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case III (where the features used were Range-Based Volatility, Hurst Exponent, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>u</mi> <mi>z</mi> <mi>z</mi> <mi>y</mi> <mi>E</mi> <mi>n</mi> </mrow> </semantics></math>). Subfigures (<b>d</b>,<b>h</b>,<b>l</b>) show the actual values of the Range-Based volatility and the predictions of the RNN, LSTM, and GRU models, respectively, for Case IV (where the features used were Range-Based Volatility, High, Low, Hurst Exponent, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>u</mi> <mi>z</mi> <mi>z</mi> <mi>y</mi> <mi>E</mi> <mi>n</mi> </mrow> </semantics></math>). A subsequent evaluation of the model performance on the test dataset was conducted using four statistical metrics (i.e., <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>A</mi> <mi>E</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>S</mi> </mrow> </semantics></math>), as detailed in <a href="#sec3dot4-jrfm-17-00557" class="html-sec">Section 3.4</a>. This evaluation was performed for each of the three models, across all currency rates, and for each of the four feature sets. The results are presented in <a href="#jrfm-17-00557-t002" class="html-table">Table 2</a>.</p> Full article ">
14 pages, 1123 KiB  
Article
Incorporating Artificial Intelligence into Finance: A Bibliometric Analysis
by Antonio Carlos Alcázar-Blanco, José Francisco Rangel-Preciado and Fiama Portillo-Santos
J. Risk Financial Manag. 2024, 17(12), 556; https://doi.org/10.3390/jrfm17120556 - 11 Dec 2024
Viewed by 514
Abstract
The aim of this study is to carry out an analysis of the intellectual structure of the introduction of AI into finance, in the period from 1995 to 2023, using SciMAT v.1.1.04 software. The results indicate how research on the incorporation of AI [...] Read more.
The aim of this study is to carry out an analysis of the intellectual structure of the introduction of AI into finance, in the period from 1995 to 2023, using SciMAT v.1.1.04 software. The results indicate how research on the incorporation of AI in finance has grown significantly, which shows the evolution and importance of this area of research. Eight main topics were obtained in this area: bank, prediction, impact, decision, valuesstock, genetic algorithm, big data analysis, and social data analysis. This study shows us how the incorporation of AI can strongly support the analysis of different financial situations such as decision making or the prediction of movements. Full article
(This article belongs to the Section Financial Technology and Innovation)
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<p>Evolution of IA–finance publications.</p> Full article ">Figure 2
<p>Strategic diagram of the research area.</p> Full article ">
19 pages, 977 KiB  
Article
Reinforcement Learning Pair Trading: A Dynamic Scaling Approach
by Hongshen Yang and Avinash Malik
J. Risk Financial Manag. 2024, 17(12), 555; https://doi.org/10.3390/jrfm17120555 - 11 Dec 2024
Viewed by 374
Abstract
Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can [...] Read more.
Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can enhance decision-making in cryptocurrency algorithmic trading compared to traditional methods. In order to address this question, we combined reinforcement learning with a statistical arbitrage trading technique, pair trading, which exploits the price difference between statistically correlated assets. We constructed RL environments and trained RL agents to determine when and how to trade pairs of cryptocurrencies. We developed new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1 min intervals (n = 263,520). The traditional non-RL pair trading technique achieved an annualized profit of 8.33%, while the proposed RL-based pair trading technique achieved annualized profits from 9.94% to 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as cryptocurrencies. Full article
(This article belongs to the Special Issue Financial Technologies (Fintech) in Finance and Economics)
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<p>Stretched pair trading view of price distance between <math display="inline"><semantics> <msub> <mi>p</mi> <mi>i</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>p</mi> <mi>j</mi> </msub> </semantics></math>. Figure (<b>b</b>), which shares the same time axis with (<b>a</b>), is a stretched view of (<b>a</b>). It presents the corresponding same actions with the crossing of Spread (S) and zones in two different views.</p> Full article ">Figure 2
<p>Architecture of trading strategies.</p> Full article ">Figure 3
<p>Window-size cut for correlation and co-integration testing.</p> Full article ">Figure 4
<p>The value of position observation based on investment.</p> Full article ">Figure 5
<p>Prices of BTCEUR and BTCGBP.</p> Full article ">Figure 6
<p>Comparison of portfolio value trends for RL<sub>1</sub> and RL<sub>2</sub> Pair Trading (A2C).</p> Full article ">Figure A1
<p>Comparison of Pair Trading strategies from (<b>a</b>) <a href="#B11-jrfm-17-00555" class="html-bibr">Gatev et al.</a> (<a href="#B11-jrfm-17-00555" class="html-bibr">2006</a>) and (<b>b</b>) <a href="#B17-jrfm-17-00555" class="html-bibr">Kim and Kim</a> (<a href="#B17-jrfm-17-00555" class="html-bibr">2019</a>).</p> Full article ">
19 pages, 2836 KiB  
Article
The Application of Machine Learning Techniques to Predict Stock Market Crises in Africa
by Muhammad Naeem, Hothefa Shaker Jassim and David Korsah
J. Risk Financial Manag. 2024, 17(12), 554; https://doi.org/10.3390/jrfm17120554 - 10 Dec 2024
Viewed by 518
Abstract
This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the [...] Read more.
This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the exchange rate of the respective countries’ currencies against the US dollar, each spanning from 1 May 2007 to 1 April 2023. It was revealed that extreme gradient boosting (XGBoost) emerged as the most effective way of predicting crises. Historical stock prices and exchange rates were found to be the most important features, exerting strong influences on stock market crises. Regarding the sentiment front, investors’ perceptions of possible volatility on the S&P 500 (Chicago Board Options Exchange (CBOE) VIX) and the Daily News Sentiment Index were identified as influential predictors. The study advances an understanding of market sentiment and emphasizes the importance of employing advanced computational techniques for risk management and market stability. Full article
(This article belongs to the Special Issue Investment Management in the Age of AI)
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<p>Model performance across countries. Source: the authors’ construct (2024).</p> Full article ">Figure 2
<p>Receiver operating characteristic (ROC). Source: the authors’ construct (2024).</p> Full article ">Figure 2 Cont.
<p>Receiver operating characteristic (ROC). Source: the authors’ construct (2024).</p> Full article ">Figure 3
<p>Feature importance. Source: the authors’ construct (2024).</p> Full article ">
23 pages, 867 KiB  
Article
Bachelier’s Market Model for ESG Asset Pricing
by Svetlozar Rachev, Nancy Asare Nyarko, Blessing Omotade and Peter Yegon
J. Risk Financial Manag. 2024, 17(12), 553; https://doi.org/10.3390/jrfm17120553 - 10 Dec 2024
Viewed by 358
Abstract
Environmental, Social, and Governance (ESG) finance is a cornerstone of modern finance and investment, as it changes the classical return-risk view of investment by incorporating an additional dimension to investment performance: the ESG score of the investment. We define the ESG price process [...] Read more.
Environmental, Social, and Governance (ESG) finance is a cornerstone of modern finance and investment, as it changes the classical return-risk view of investment by incorporating an additional dimension to investment performance: the ESG score of the investment. We define the ESG price process and include it in an extension of Bachelier’s market model in both discrete and continuous time, enabling option pricing valuation. Full article
(This article belongs to the Section Economics and Finance)
29 pages, 1079 KiB  
Article
Large Drawdowns and Long-Term Asset Management
by Eric Jondeau and Alexandre Pauli
J. Risk Financial Manag. 2024, 17(12), 552; https://doi.org/10.3390/jrfm17120552 - 10 Dec 2024
Viewed by 381
Abstract
Long-term investors are often hesitant to invest in assets or strategies prone to significant drawdowns, primarily due to the challenge of predicting these drawdowns. This study presents a multivariate Markov-switching model for small- and large-cap returns in the U.S. equity markets, demonstrating that [...] Read more.
Long-term investors are often hesitant to invest in assets or strategies prone to significant drawdowns, primarily due to the challenge of predicting these drawdowns. This study presents a multivariate Markov-switching model for small- and large-cap returns in the U.S. equity markets, demonstrating that three distinct regimes are necessary to capture the negative trends in expected returns during financial crises. Our findings indicate that this framework enhances the prediction of conditional drawdowns compared to standard alternative models of financial returns. Furthermore, out-of-sample analysis shows that investment strategies based on these predictions outperform those relying on models with one or two regimes. Full article
(This article belongs to the Special Issue Featured Papers in Mathematics and Finance)
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<p>Evolution of ADD, CDD, and MDD over non-overlapping subsamples. The figure displays the evolution in percentage of ADD, <math display="inline"><semantics> <mrow> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math> CDD, and MDD over various non-overlapping subsamples (from one to four quarters) between 1926 and 2020. The straight line on right plots corresponds to <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> CED. The black lines correspond to the small caps, the red dashed lines to the large caps. CED is computed with 376, 188, and 94 observations for the one-quarter, two-quarter, and four-quarter horizons, respectively.</p> Full article ">Figure 2
<p>Filtered Probability of Being in the Bear State. The figure displays the filtered probability <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mrow> <mi>d</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </semantics></math> of being in the low expected return regime (bear state), for the two-regime and three-regime models. The horizontal blue line corresponds to the stationary probability of being in the bear state <math display="inline"><semantics> <mrow> <msub> <mi>π</mi> <mrow> <mi>b</mi> <mo>,</mo> <mo>∞</mo> </mrow> </msub> <mo>=</mo> <mo form="prefix">Pr</mo> <mrow> <mo>[</mo> <msub> <mi>S</mi> <mrow> <mi>d</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>k</mi> <mi>b</mi> </msub> <mo>]</mo> </mrow> </mrow> </semantics></math>, where <math display="inline"><semantics> <msub> <mi>k</mi> <mi>b</mi> </msub> </semantics></math> denotes the bear state.</p> Full article ">Figure 3
<p>Out-of-Sample Optimal Weights—Two-quarter Horizon (1990–2020). The figure displays the temporal evolution of the optimal weight of small caps for the <math display="inline"><semantics> <mrow> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math> CDD, MDD, <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> CED, and MV portfolios over the two-quarter horizon, when predictions are based on the one-regime, two-regime, and three-regime models.</p> Full article ">Figure A1
<p>Model Parameters: One-regime Models.</p> Full article ">Figure A2
<p>Model Parameters: Two-regime Models.</p> Full article ">Figure A3
<p>Model Parameters: Three-regime Models.</p> Full article ">
33 pages, 3351 KiB  
Article
Risk-Averse, Integrated Contract, and Open Market Procurement with Quantity Adjustment Costs
by Santosh Mahapatra, Santosh Kar and Shlomo Levental
J. Risk Financial Manag. 2024, 17(12), 551; https://doi.org/10.3390/jrfm17120551 - 9 Dec 2024
Viewed by 384
Abstract
This paper examines the issue cost-effective procurement of a commodity product when its spot (open) market prices are stochastic, contract prices are previously determined, and there are costs associated with adjusting (i.e., switching) the procurement quantities from an alternative. Spot (open) market and [...] Read more.
This paper examines the issue cost-effective procurement of a commodity product when its spot (open) market prices are stochastic, contract prices are previously determined, and there are costs associated with adjusting (i.e., switching) the procurement quantities from an alternative. Spot (open) market and contract as sole modes of procurement could present risks of high magnitude and uncertainty of expenses for the buyer. To address these risks, a risk-averse buyer may consider simultaneous use of both alternatives with adjustment of the purchase quantities from both the alternatives over time. Scenarios when the switching costs depend on the relative prices of the two alternatives are considered. The problem being analytically intractable, a mixed method decision model combining analytical and computational techniques to analyze the problem is proposed. The model helps identify expected optimal contract and spot market procurement quantities with respect to unknown spot prices and known contract prices over the planned procurement horizon when procurement quantity adjustment costs are influenced by the spends. The analysis reveals that it is cost-effective to continue purchasing with an existing pattern of procurement from the two alternatives until the contract to spot market price ratio reaches a threshold level and then to change the proportion of quantity purchased from the two alternatives. Using numerical analysis, we illustrate the theoretical and managerial significance of this stickiness to continue with an existing pattern until an adjustment. Full article
(This article belongs to the Collection Business Performance)
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<p>Binomial tree: The binomial lattice represents the evolution of the relative price process <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>=</mo> <msub> <mi>P</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>P</mi> <mn>2</mn> </msub> </mrow> </semantics></math> over four time intervals. The integers represent the nodes corresponding to a specific price level and probability of occurrence. In the interest of brevity, only the probabilities of branching into the up state and down state at an instant and at the terminal nodes are presented.</p> Full article ">Figure 2
<p>The binomial representation of the evolution of relative price process over one year for <math display="inline"><semantics> <mrow> <mi>P</mi> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <msub> <mi>P</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>P</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math> at <span class="html-italic">t</span> = 0 in market price state 1 and the probabilities at terminal nodes.</p> Full article ">Figure 3
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for market price state 1 when the switching cost ratio (SCR) = 1.</p> Full article ">Figure 4
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for market price state 1 when SCR = 50.</p> Full article ">Figure 5
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for market price state 1 when SCR = 10.</p> Full article ">Figure 6
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for market price state 2 when SCR = 25.</p> Full article ">Figure 7
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for market price state 2 when SCR = 40.</p> Full article ">Figure 8
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for market price state 3 when SCR = 1 and 5.</p> Full article ">Figure 9
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for <math display="inline"><semantics> <mrow> <mi>P</mi> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <msub> <mi>P</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>P</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math> and SCR, <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> </mrow> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> = 20 with <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> for market price state 3.</p> Full article ">Figure 10
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for <math display="inline"><semantics> <mrow> <mi>P</mi> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <msub> <mi>P</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>P</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math> and SCR, <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> </mrow> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> = 1 with <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> in the alternative market price states 1, 2, and 3. The numbers in the parentheses represent the optimal fractions for states 1, 2, and 3, respectively.</p> Full article ">Figure 11
<p>Optimal fraction of units “<span class="html-italic">u</span>” to source from the market over one year for <math display="inline"><semantics> <mrow> <mi>P</mi> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <msub> <mi>P</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>P</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math> and SCR, <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> </mrow> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> = 20 with <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> in the alternative market price states 1, 2, and 3. The numbers in the parentheses represent the optimal fractions in states 1, 2, and 3, respectively.</p> Full article ">
31 pages, 1616 KiB  
Article
Conceptualizing an Institutional Framework to Mitigate Crypto-Assets’ Operational Risk
by Deepankar Roy, Ashutosh Dubey and Daitri Tiwary
J. Risk Financial Manag. 2024, 17(12), 550; https://doi.org/10.3390/jrfm17120550 - 9 Dec 2024
Viewed by 811
Abstract
Extent ecosystems of crypto financial assets (crypto-assets) lack parity and coherence across the globe. This asymmetry is further heightened with a knowledge gap in operational risk management, wherein the global landscape of crypto-assets is characterized by unprecedented external risks and internal vulnerabilities. In [...] Read more.
Extent ecosystems of crypto financial assets (crypto-assets) lack parity and coherence across the globe. This asymmetry is further heightened with a knowledge gap in operational risk management, wherein the global landscape of crypto-assets is characterized by unprecedented external risks and internal vulnerabilities. In this study, we present a critical examination and comprehensive analysis of current crypto-asset operational guidelines across geographies. We benchmark these guidelines to the Basel Committee for Banking Supervision (BCBS) risk classification framework for crypto-assets, identifying gaps in the operations across organizations. We, hence, conceptualize a novel institutional framework which may help in understanding and mitigating the gaps in operational risks’ regulation of crypto-assets. Our proposed Crypto-asset Operational Risk Management (CORM) framework determines how operational risk associated with crypto-assets of financial institutions can be mitigated to respond to the increasing demand for crypto-assets, cross border payments, electronic money, and cryptocurrencies, across countries. Applicable to firms irrespective of their size and scale of operations, CORM aligns with global regulatory initiatives, facilitating compliance and fostering trust among stakeholders. Strengthening our argument of CORM’s applicability, we present its efficacy in the form of alternate hypothetical outcomes in two distinct real-life cases wherein crypto-asset exchanges succumbed to either external risks, such as hacking, or internal vulnerabilities. It paves the way for future regulatory response with a structured approach to addressing the unique operational risks associated with crypto-assets. The framework advocates for collaborative efforts among industry stakeholders, ensuring its adaptability to the rapidly evolving crypto landscape. It further contributes to the establishment of a more resilient and regulated financial ecosystem, inclusive of crypto-assets. By implementing CORM, institutions can navigate the complexities of crypto-assets while safeguarding their interests and promoting sustainable growth in the digital asset market. Full article
(This article belongs to the Special Issue The Future of Money: Central Bank Digital Currencies, Cryptocurrencies and Stablecoins)
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<p>Market capitalization of cryptocurrencies, including stablecoins and tokens. <span class="html-italic">Source</span>: Authors’ Creation.</p> Full article ">Figure 2
<p>Crypto-asset ecosystem. Source: Authors’ Creation.</p> Full article ">Figure 3
<p>Global regulations for crypto-assets. <span class="html-italic">Source:</span> Thomson Reuters. Cryptos Report Compendium 2022.</p> Full article ">Figure 4
<p>CORM framework. <span class="html-italic">Source:</span> Created by the Authors.</p> Full article ">
20 pages, 699 KiB  
Article
Evaluating Financial Inclusion in Peru: A Cluster Analysis Using Self-Organizing Maps
by Alvaro Talavera, Rocío Maehara, Luis Benites, Benjamin Arriaga and Alejandro Aybar-Flores
J. Risk Financial Manag. 2024, 17(12), 549; https://doi.org/10.3390/jrfm17120549 - 4 Dec 2024
Viewed by 574
Abstract
This study evaluates financial inclusion in Peru through self-organizing maps. Financial inclusion is a multidimensional issue of great importance on the global agenda and continues to concern various actors internationally. In this context, the objective is to assess the financial inclusion situation in [...] Read more.
This study evaluates financial inclusion in Peru through self-organizing maps. Financial inclusion is a multidimensional issue of great importance on the global agenda and continues to concern various actors internationally. In this context, the objective is to assess the financial inclusion situation in the country and determine how self-organizing maps can complement standard models for this purpose. The empirical aim is to demonstrate how this technique can help identify priority areas and vulnerable groups, thus facilitating decision-making and policy design to improve the access to and use of financial services among Peruvian consumers by finding clearly defined profiles that allow the identification of potential problems within each category. This makes it possible to create customized strategies for each group, such as addressing the financial inclusion barriers faced by rural residents, compounded by low income and educational levels. Full article
(This article belongs to the Special Issue Applied Statistics and Big Data Analysis in Finance: Exploring Emerging Trends and Opportunities)
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<p>Distribution of records within clusters.</p> Full article ">Figure 2
<p>Records pertaining to Accounts Access.</p> Full article ">Figure 3
<p>Records pertaining to Accounts Use.</p> Full article ">Figure 4
<p>Records pertaining to Credit Access.</p> Full article ">Figure 5
<p>Records pertaining to Gender.</p> Full article ">Figure 6
<p>Distribution of records by cluster, and Credit Access and Income.</p> Full article ">
22 pages, 1429 KiB  
Article
Determinants of Sustainable Entrepreneurship in Morocco: The Role of Entrepreneurial Orientation, Financial Literacy, and Inclusion
by Ikram Zouitini, Hamza El Hafdaoui, Hajar Chetioui, Pierre-Martin Tardif and Mohamed Makhtari
J. Risk Financial Manag. 2024, 17(12), 548; https://doi.org/10.3390/jrfm17120548 - 30 Nov 2024
Viewed by 561
Abstract
This paper investigates the relationship between sustainable entrepreneurship and financial inclusion, financial literacy, and entrepreneurial orientation. As sustainable entrepreneurship gains academic and practical interest, understanding factors that enable entrepreneurs to operate sustainably is fundamental. The manuscript uses an electronic questionnaire distributed to key [...] Read more.
This paper investigates the relationship between sustainable entrepreneurship and financial inclusion, financial literacy, and entrepreneurial orientation. As sustainable entrepreneurship gains academic and practical interest, understanding factors that enable entrepreneurs to operate sustainably is fundamental. The manuscript uses an electronic questionnaire distributed to key economic stakeholders and performs partial least squares structural equation modeling on data from 169 respondents. The results show that entrepreneurial orientation has a positive and significant impact on sustainable entrepreneurship, with a beta coefficient of 0.878 and a probability value of less than 0.01. Financial literacy significantly influences sustainable entrepreneurship, with a beta coefficient of 0.389 and a probability value of less than 0.001, and it partially mediates its relationship with financial inclusion, showing a beta coefficient of 0.3 and a probability value of 0.013. Financial literacy and financial inclusion are positively correlated, with a beta coefficient of 0.771 and a probability value of less than 0.05. However, the impact of financial inclusion on sustainable entrepreneurship is negative and insignificant, with a beta coefficient of −0.392, and there is no evidence that entrepreneurial orientation moderates the link between financial literacy and sustainable entrepreneurship. The findings provide valuable insights for Moroccan policymakers to promote entrepreneurship, suggesting that financial literacy plays a crucial role in enhancing sustainable business practices. The study emphasizes the need for Morocco to adapt to current programs and create a supportive financial environment for entrepreneurs. Due to a lack of comprehensive datasets, the study’s conclusions are limited and might not accurately reflect the entire landscape. Full article
(This article belongs to the Special Issue The New Horizons of Global Financial Literacy)
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<p>Determinants of sustainable entrepreneurship in literature.</p> Full article ">Figure 2
<p>Descriptive statistics of respondents.</p> Full article ">Figure 3
<p>Unstandardized structural model assessment.</p> Full article ">Figure 4
<p>Moderator role.</p> Full article ">
27 pages, 458 KiB  
Article
Inflation Targeting with an Optimal Nonlinear Monetary Rule—The Case Study of Colombia
by Martha Misas, Edgar Villa and Andres Giraldo
J. Risk Financial Manag. 2024, 17(12), 547; https://doi.org/10.3390/jrfm17120547 - 30 Nov 2024
Viewed by 437
Abstract
This article examines whether Banco de la República (Banrep), Colombia’s central bank, has operated under a dual-regime policy framework—one for recessionary periods and another for periods of economic overheating—since adopting inflation targeting (IT) from Q4 2000 to Q4 2019. We modify the canonical [...] Read more.
This article examines whether Banco de la República (Banrep), Colombia’s central bank, has operated under a dual-regime policy framework—one for recessionary periods and another for periods of economic overheating—since adopting inflation targeting (IT) from Q4 2000 to Q4 2019. We modify the canonical New Keynesian inflation model to accommodate an optimal nonlinear monetary rule aligned with a two-regime policy framework. Using a LSTAR model estimated over the study period, with the output gap lagged by three periods as the transition variable, we identify two distinct monetary regimes. Our findings reveal that the smooth transitions between regimes were driven by shifts in Banrep’s preferences related to its loss function, alongside adjustments in the parameters of the aggregate demand and supply curves within the Colombian economy. Notably, we observe that a modified Taylor principle is not met in either identified monetary regime. This suggests that, in this context, IT has been a successful policy framework even without requiring the policy interest rate to respond aggressively to inflation gaps, as the Taylor principle would otherwise dictate. Full article
(This article belongs to the Special Issue Open Economy Macroeconomics)
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<p>Time series.</p> Full article ">Figure 2
<p>Quarterly time series of estimated <math display="inline"><semantics> <msub> <mi>G</mi> <mi>t</mi> </msub> </semantics></math>.</p> Full article ">Figure 3
<p>LSTAR transition function <math display="inline"><semantics> <msub> <mi>G</mi> <mi>t</mi> </msub> </semantics></math> with c = 0.0023.</p> Full article ">
19 pages, 1931 KiB  
Article
Researching the Impact of Corporate Social Responsibility on Economic Growth and Inequality: Methodological Aspects
by Mihail Chipriyanov, Galina Chipriyanova, Radosveta Krasteva-Hristova, Atanas Atanasov and Kiril Luchkov
J. Risk Financial Manag. 2024, 17(12), 546; https://doi.org/10.3390/jrfm17120546 - 30 Nov 2024
Viewed by 525
Abstract
The study focuses on analyzing the impact of corporate social responsibility (CSR) on economic growth and reducing inequality, highlighting the importance of CSR in achieving sustainable development and social justice. The main aim is to analyze how different CSR initiatives contribute to economic [...] Read more.
The study focuses on analyzing the impact of corporate social responsibility (CSR) on economic growth and reducing inequality, highlighting the importance of CSR in achieving sustainable development and social justice. The main aim is to analyze how different CSR initiatives contribute to economic development, social prosperity, and the reduction in inequality by reviewing the methods used to assess their impact. The research methodology includes a detailed literature review, bibliometric analysis and scientific mapping, surveys of various business organizations, and a gap analysis regarding the identification of gaps between the current state of CSR activities and the expected outcomes. The research shows that companies perceive CSR as a key tool for improving corporate image, responding to stakeholder expectations, and investing in social justice. Despite positive intentions, challenges include the lack of clearly defined methodologies for measuring the impact on economic inequality, as well as difficulties in assessing the long-term effects of CSR initiatives. Key conclusions highlight the need for more structured approaches to assessing the social and economic effects of CSR, recommending that companies improve their transparency and accountability and implement clear indicators of success to achieve sustainable economic and social outcomes. Full article
(This article belongs to the Special Issue Research on Economic Growth and Inequality)
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<p>Three Fields Plot with data on keywords, country/region, and source of publications in the sample. (Source: prepared by the authors using Bibliometrix).</p> Full article ">Figure 2
<p>Measuring the impact of CSR initiatives in percentage. (Source: authors’ own research).</p> Full article ">Figure 3
<p>Indicators used for identifying the contribution of CSR initiatives to reducing economic inequality in percentage. (Source: authors’ own research).</p> Full article ">Figure 4
<p>Methodological approaches for assessing the long-term impact of CSR initiatives on economic inequality in percentage. (Source: authors’ own research).</p> Full article ">Figure 5
<p>Thematic map of conceptual structure in statics. (Source: prepared by the authors using Bibliometrix).</p> Full article ">Figure 6
<p>Synergy between CSR, economic growth, and inequality. (Source: authors’ own research).</p> Full article ">
21 pages, 3180 KiB  
Article
Trends in the Literature About the Adoption of Digital Banking in Emerging Economies: A Bibliometric Analysis
by Julio César Acosta-Prado, Joan Sebastián Rojas Rincón, Andrés Mauricio Mejía Martínez and Andrés Ricardo Riveros Tarazona
J. Risk Financial Manag. 2024, 17(12), 545; https://doi.org/10.3390/jrfm17120545 - 29 Nov 2024
Viewed by 655
Abstract
This study examines the trends in the literature about adopting digital banking in emerging economies. It is based on the concepts of digital transformation and technological adoption, which significantly impact the development of the banking industry. A quantitative approach was used through a [...] Read more.
This study examines the trends in the literature about adopting digital banking in emerging economies. It is based on the concepts of digital transformation and technological adoption, which significantly impact the development of the banking industry. A quantitative approach was used through a bibliometric analysis using data from Scopus to achieve the objective. The search equation allowed 118 publications to be extracted and analyzed. The results show that digital banking in emerging countries is a growing field of research that has driven the introduction of new information technologies. The perceived usefulness of digital banking is a key factor in promoting its adoption in the market. Attributes such as security and trust were identified as affecting the level of user satisfaction. Most studies are based on technological adoption, where perceived risk, usefulness, and ease of use are key to understanding the intention to use these technologies. Some countries’ concerns about financial inclusion, cyber security, and trust in financial technology are evident. While digital banking has the potential to increase the coverage of financial services, there are concerns about cybersecurity risks and user data protection. Full article
(This article belongs to the Special Issue Financial Technology (Fintech) and Sustainable Financing, 3rd Edition)
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<p>Bibliometric analysis protocol. <span class="html-italic"><b>Note</b></span>: Based on (<a href="#B16-jrfm-17-00545" class="html-bibr">Donthu et al. 2021</a>).</p> Full article ">Figure 2
<p>Analysis of thematic trends. <b><span class="html-italic">Note</span></b>: Based on data from Scopus, prepared with Bibliometrix software (version 4.3.0) and edited in Microsoft Excel.</p> Full article ">Figure 3
<p>Thematic areas. <b><span class="html-italic">Note</span></b>: Based on data from Scopus.</p> Full article ">Figure 4
<p>Productivity by region. <b><span class="html-italic">Note</span></b>: A choropleth map was prepared in Microsoft Excel based on data from Scopus to differentiate the countries with the highest level of productivity. Higher intensity of the blue color indicates higher productivity.</p> Full article ">Figure 5
<p>Thematic map. <b><span class="html-italic">Note</span></b>: Based on Scopus data and prepared with Bibliometrix.</p> Full article ">Figure 6
<p>Bibliographic linkage map. <b><span class="html-italic">Note</span></b>: Based on Scopus data and elaborated with VOSviewer.</p> Full article ">Figure 7
<p>Map of documents citation. <b><span class="html-italic">Note</span></b>: Based on Scopus data and elaborated with VOSviewer (<a href="#B47-jrfm-17-00545" class="html-bibr">Polatoglu and Ekin 2001</a>; <a href="#B58-jrfm-17-00545" class="html-bibr">Simpson 2002</a>; <a href="#B17-jrfm-17-00545" class="html-bibr">Eriksson et al. 2005</a>, <a href="#B18-jrfm-17-00545" class="html-bibr">2008</a>; <a href="#B41-jrfm-17-00545" class="html-bibr">Nilsson 2007</a>; <a href="#B4-jrfm-17-00545" class="html-bibr">Agarwal et al. 2009</a>; <a href="#B12-jrfm-17-00545" class="html-bibr">Cruz et al. 2010</a>; <a href="#B26-jrfm-17-00545" class="html-bibr">Khare et al. 2010</a>; <a href="#B2-jrfm-17-00545" class="html-bibr">Adil 2013</a>; <a href="#B44-jrfm-17-00545" class="html-bibr">Onay and Ozsoz 2013</a>; <a href="#B66-jrfm-17-00545" class="html-bibr">Yadav et al. 2015</a>; <a href="#B56-jrfm-17-00545" class="html-bibr">Sikdar and Makkad 2015</a>; <a href="#B57-jrfm-17-00545" class="html-bibr">Siddik et al. 2016</a>; <a href="#B67-jrfm-17-00545" class="html-bibr">Youssef et al. 2017</a>; <a href="#B51-jrfm-17-00545" class="html-bibr">Roy et al. 2017</a>; <a href="#B37-jrfm-17-00545" class="html-bibr">Nagved and Rajesh 2018</a>; <a href="#B25-jrfm-17-00545" class="html-bibr">Kaur et al. 2021</a>).</p> Full article ">
20 pages, 288 KiB  
Article
The Relationship Between Sociodemographic Attributes and Financial Well-Being of Low-Income Urban Families Amid the COVID-19 Pandemic: A Case Study of Malaysia
by Abdullah Sallehhuddin Abdullah Salim, Norzarina Md Yatim and Al Mansor Abu Said
J. Risk Financial Manag. 2024, 17(12), 544; https://doi.org/10.3390/jrfm17120544 - 29 Nov 2024
Viewed by 525
Abstract
The COVID-19 pandemic and the Movement Control Order (MCO) have had a negative impact on the financial well-being of low-income families in urban areas. This study involved respondents living in the public housing project (PPR) residential areas in Kuala Lumpur—the capital of Malaysia. [...] Read more.
The COVID-19 pandemic and the Movement Control Order (MCO) have had a negative impact on the financial well-being of low-income families in urban areas. This study involved respondents living in the public housing project (PPR) residential areas in Kuala Lumpur—the capital of Malaysia. The key finding is that the financial well-being of low-income urban families was negatively impacted due to the COVID-19 pandemic and the MCO implementation. Furthermore, the impact on the financial well-being of low-income urban families is significantly different in terms of types of families, type and sector of employment, type of home ownership, household monthly income, and education level. Reforms to the financial assistance system and the community empowerment of low-income urban families are necessary to increase the community’s preparedness and resilience in the face of new shocks in the future. Full article
(This article belongs to the Special Issue Featured Papers in Finance and Society Wellbeing—in Honor of Professors Joe Gani and Chris Heyde)
28 pages, 803 KiB  
Article
Impact of International Oil Price Shocks and Inflation on Bank Efficiency and Financial Stability: Evidence from Saudi Arabian Banking Sector
by Fathi Mohamed Bouzidi, Aida Arbi Nefzi and Mohammed Al Yousif
J. Risk Financial Manag. 2024, 17(12), 543; https://doi.org/10.3390/jrfm17120543 - 29 Nov 2024
Viewed by 601
Abstract
This study examines the short-run and long-run equilibrium relationship between the banking sector’s efficiency and stability and its endogenous and exogenous determinants, such as inflation and international oil price shocks in Saudi Arabia from 2004 to 2022. This study differentiates between the direct [...] Read more.
This study examines the short-run and long-run equilibrium relationship between the banking sector’s efficiency and stability and its endogenous and exogenous determinants, such as inflation and international oil price shocks in Saudi Arabia from 2004 to 2022. This study differentiates between the direct and indirect effects of international oil price changes on bank efficiency and stability and investigates how these changes can affect the banking sector through inflation. The first stage uses a panel Autoregressive Distributive Lag (ARDL). The empirical result confirms a long/short-run relationship between oil price shocks and the stability and efficiency of banks. In the long run, the relationship is statistically significant and positive, and it is negative in the short run. On the other hand, this study finds that oil price shocks directly affect the stability and efficiency of banks. In the second stage, this study uses a nonlinear ARD (NARD) to examine the short- and long-run asymmetric impacts of oil price shocks on the stability and efficiency of banks by decomposing the oil price index into positive and negative changes. The findings confirm an asymmetric relationship between oil prices and the stability and efficiency of banks in Saudi Arabia. In addition, a positive change in oil price can affect the stability and efficiency of banks more than a negative one. Overall, the findings highlight the need for policymakers in Saudi Arabia to be vigilant in addressing potential risks arising from oil price fluctuations and to adopt appropriate policy measures to maintain stability and efficiency in the banking sector. Full article
(This article belongs to the Section Economics and Finance)
24 pages, 4901 KiB  
Article
Compliance Behavior in Environmental Tax Policy
by Suci Lestari Hakam, Agus Rahayu, Lili Adi Wibowo, Lazuardi Imani Hakam, Muhamad Adhi Nugroho and Siti Sarah Fuadi
J. Risk Financial Manag. 2024, 17(12), 542; https://doi.org/10.3390/jrfm17120542 - 29 Nov 2024
Viewed by 640
Abstract
This study examines compliance behavior in the context of environmental tax policies, highlighting the essential role that these policies play in achieving the objectives of the Sustainable Development Goals (SDGs). Environmental taxes are crucial instruments for reducing environmental damage and increasing energy efficiency. [...] Read more.
This study examines compliance behavior in the context of environmental tax policies, highlighting the essential role that these policies play in achieving the objectives of the Sustainable Development Goals (SDGs). Environmental taxes are crucial instruments for reducing environmental damage and increasing energy efficiency. Nevertheless, taxpayer compliance, which is impacted by several variables, including social acceptability, regulatory quality, and perceptions of fairness, is a key component of these policies’ efficacy. In contrast to earlier research, which frequently concentrated on certain kinds of tax or discrete policy mechanisms, this study takes a broad approach, looking at a range of environmental taxation instruments. Emerging trends, significant factors influencing compliance behavior, and noteworthy contributions from eminent authors and organizations are all identified via bibliometric and scientometric analyses. To create fair and effective environmental tax policies, interdisciplinary approaches and international collaboration are required. Along with presenting policies to improve environmental regulation compliance, this study offers insightful advice for businesses that can help them innovate toward sustainability and adjust to shifting policy. It also provides a solid theoretical base for future researchers by highlighting important areas that require more investigation, especially when it comes to the wider effects of environmental taxes on various industries. Full article
(This article belongs to the Special Issue Sustainable Tax and Accounting Reporting in Building a New Tax Culture)
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<p>Methods for article selection and analysis procedures (<a href="#B34-jrfm-17-00542" class="html-bibr">L. I. Hakam et al. 2023</a>).</p> Full article ">Figure 2
<p>Trend analysis.</p> Full article ">Figure 3
<p>Most relevant source.</p> Full article ">Figure 4
<p>The most cited countries.</p> Full article ">Figure 5
<p>Most relevant affiliations.</p> Full article ">Figure 6
<p>Authors with the highest number of article citations.</p> Full article ">Figure 7
<p>Trend topics.</p> Full article ">Figure 8
<p>Thematic evolution.</p> Full article ">Figure 9
<p>Thematic map. (<b>a</b>) Periode: 2000–2010. (<b>b</b>) Periode: 2010–2020. (<b>c</b>) Periode: 2020–2024.</p> Full article ">Figure 9 Cont.
<p>Thematic map. (<b>a</b>) Periode: 2000–2010. (<b>b</b>) Periode: 2010–2020. (<b>c</b>) Periode: 2020–2024.</p> Full article ">Figure 10
<p>Coupling author keywords.</p> Full article ">Figure 11
<p>Coupling countries.</p> Full article ">
21 pages, 2096 KiB  
Article
The Determinants and Growth Effects of Foreign Direct Investment: A Comparative Study
by Sheng-Ping Yang
J. Risk Financial Manag. 2024, 17(12), 541; https://doi.org/10.3390/jrfm17120541 - 29 Nov 2024
Viewed by 648
Abstract
This study examines the factors determining inward foreign direct investment (FDI) and its effects on productivity, ultimately contributing to economic growth. Using a two-step generalized method of moments (GMM) approach, we analyzed a panel of 84 countries, comprising 34 OECD and 50 non-OECD [...] Read more.
This study examines the factors determining inward foreign direct investment (FDI) and its effects on productivity, ultimately contributing to economic growth. Using a two-step generalized method of moments (GMM) approach, we analyzed a panel of 84 countries, comprising 34 OECD and 50 non-OECD countries, from 2010 to 2019. The findings suggest that FDI positively impacts productivity and benefits both OECD and non-OECD countries. Economic freedom plays a significant role in attracting FDI, particularly in OECD countries, and contributes to economic growth in non-OECD countries. However, economic freedom alone does not guarantee strong economic growth in OECD countries but significantly enhances growth in non-OECD countries. The results also highlight that only economies with robust economic infrastructure and development levels benefit more from FDI. It appears that FDI by itself has no direct effect on output growth. Instead, the impact of FDI is contingent on the level of economic freedom in the host countries. This paper presents a key finding on how policy decisions influence the effects of foreign capital investment on productivity and income. It indicates that countries promoting economic freedom can more effectively leverage productivity gains from FDI. Full article
(This article belongs to the Special Issue Globalization and Economic Integration)
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<p>Scatter plots of economic growth vs. FDI and economic freedom. Note: economic growth (in percentage) was computed using real GDP per capita downloaded from the World Bank World Development Indicators, Foreign direct investments are the net inflows of investment to acquire a lasting management interest, and the economic freedom index was taken from the Fraser Institute.</p> Full article ">Figure 1 Cont.
<p>Scatter plots of economic growth vs. FDI and economic freedom. Note: economic growth (in percentage) was computed using real GDP per capita downloaded from the World Bank World Development Indicators, Foreign direct investments are the net inflows of investment to acquire a lasting management interest, and the economic freedom index was taken from the Fraser Institute.</p> Full article ">Figure 2
<p>Scatter plots of economic growth vs. FDI for non-OECD and OECD countries.</p> Full article ">Figure 2 Cont.
<p>Scatter plots of economic growth vs. FDI for non-OECD and OECD countries.</p> Full article ">Figure 3
<p>Scatter plots of economic growth vs. economic freedom.</p> Full article ">Figure 3 Cont.
<p>Scatter plots of economic growth vs. economic freedom.</p> Full article ">
15 pages, 270 KiB  
Article
The Impact of CEO Characteristics on Investment Efficiency in Jordan: The Moderating Role of Political Connections
by Loona Shaheen, Zakarya Alatyat, Qasem Aldabbas, Ruba Nimer Abu Shihab and Murad Abuaddous
J. Risk Financial Manag. 2024, 17(12), 540; https://doi.org/10.3390/jrfm17120540 - 29 Nov 2024
Viewed by 449
Abstract
This study investigates the impact of CEO characteristics—specifically CEO age, founder status, and family membership—on investment efficiency in Jordanian non-financial companies, with a focus on the moderating role of political connections. Drawing on the existing literature, we identify conflicting views regarding how these [...] Read more.
This study investigates the impact of CEO characteristics—specifically CEO age, founder status, and family membership—on investment efficiency in Jordanian non-financial companies, with a focus on the moderating role of political connections. Drawing on the existing literature, we identify conflicting views regarding how these characteristics influence investment decisions. Some studies suggest that younger CEOs may adopt more aggressive investment strategies, while older CEOs tend to be conservative, leading to balanced resource allocation. Similarly, CEOs with founder status and family membership are thought to have an emotional attachment to the company, theoretically resulting in cautious investment behavior. However, empirical evidence remains mixed. By using data from 62 non-financial firms listed on the Amman Stock Exchange (ASE) from 2019 to 2023, this study employs regression analysis to explore these relationships. The findings reveal that CEO age contributes to investment efficiency by mitigating both over- and under-investment. Contrary to expectations, CEO founder status shows no significant effect on investment efficiency. Additionally, family-member CEOs exhibit a tendency toward under-investment, driven by a desire to preserve family wealth. Political connections further complicate these dynamics, encouraging riskier investment strategies while diluting the positive effects of CEO characteristics. These results provide new insights into the intricate interplay between CEO traits and political networks, contributing to the discourse on corporate governance in emerging markets. The study concludes with practical implications for policymakers and company boards, emphasizing the need for balanced leadership selection strategies to optimize investment efficiency. Full article
(This article belongs to the Special Issue Featured Papers in Corporate Finance and Governance)
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