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Volume 17
Issue 9
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J. Risk Financial Manag., Volume 17, Issue 9 (September 2024) – 49 articles

Cover Story (view full-size image): Using data from 52 empirical studies, this study explores the relationship between climate-related regulations, such as environmental regulations (ERs) and climate-related disclosure (CRD) laws, and the financial markets using Meta-Analysis Structural Equation Modelling (MASEM). Across the examined studies, both the ERs and CRD laws exhibit significant influence on financial performance. The ERs produce mixed effects on the equity and support debt markets. The analysis indicates that, in more developed markets, the impact of the ERs and CRD laws on the equity markets tends to be less pronounced. The relationship between ERs/CRD laws and firm value is significantly influenced by market, industry, and firm-specific risks. The mandatory CRD laws have a downside effect on the equity markets, signalling that policymakers, firms, and investors should approach new CRD regulations carefully. View this paper
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19 pages, 1697 KiB  
Article
Risk Analysis of Conglomerates with Debt and Equity Links
by Arturo Cifuentes and Rodrigo Roman
J. Risk Financial Manag. 2024, 17(9), 426; https://doi.org/10.3390/jrfm17090426 - 23 Sep 2024
Viewed by 756
Abstract
Conglomerates play an important role in the functioning of capital markets. Therefore, assessing their response to external shocks is a significant risk management challenge not only for conglomerate executives but also for investors and regulators alike. In this context, a conglomerate refers to [...] Read more.
Conglomerates play an important role in the functioning of capital markets. Therefore, assessing their response to external shocks is a significant risk management challenge not only for conglomerate executives but also for investors and regulators alike. In this context, a conglomerate refers to a group of companies typically operating across different industries and interconnected through both equity and debt relationships. Essentially, a conglomerate functions as a financial network whose nodes are linked by two layers of reciprocal connections. This paper introduces an algorithm to evaluate a conglomerate’s response to external shocks. Additionally, it proposes a protocol based on five key metrics that collectively summarize the conglomerate’s overall resilience. These metrics offer two major advantages: they facilitate comparisons between the strengths of different conglomerates and help assess the effectiveness of various strategies, such as internal capital reallocations, aimed at enhancing a conglomerate’s resilience. The algorithm’s usefulness, including its ability to detect cascades or “second-wave” defaults, is demonstrated through two illustrative examples. Full article
(This article belongs to the Special Issue Risk Management in Capital Markets)
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<p>Seven-firm conglomerate: graphical representation of [α] and [β] matrices. Note: In the case of [α], the percentage indicates which fraction of the equity of the firm (node) at the end of the arrow is owned by the firm (node) at the origin of the arrow. The case of [β] is analogous, except that the arrows refer to debt holdings. Firm 1 (designated with a triangular symbol) is the controlling firm.</p> Full article ">Figure 2
<p>Equity of each firm after applying shocks of different sizes to firm 6.</p> Full article ">Figure 3
<p>Nine-firm conglomerate: graphical representation of [α] and [β] matrices. Note: In the case of [α], the percentage indicates which fraction of the equity of the firm (node) at the end of the arrow is owned by the firm (node) at the origin of the arrow. The case of [β] is analogous, except that the arrows refer to debt holdings. Firm 1 (designated with a triangular symbol) is the controlling firm.</p> Full article ">Figure 4
<p>Equity of each firm after applying shocks of different sizes to firm 7.</p> Full article ">
20 pages, 1347 KiB  
Article
Assessing the Impact of the ECB’s Unconventional Monetary Policy on the European Stock Markets
by Carlos J. Rincon and Anastasiia V. Petrova
J. Risk Financial Manag. 2024, 17(9), 425; https://doi.org/10.3390/jrfm17090425 - 23 Sep 2024
Viewed by 859
Abstract
This study assesses the effects of the European Central Bank’s (ECB) unconventional monetary policy (UMP) on the prices of selected European stock market indices during the European sovereign debt (2010–2012) and the COVID-19 pandemic (2020–2022) crises interventions. This research employs the instrumental variables [...] Read more.
This study assesses the effects of the European Central Bank’s (ECB) unconventional monetary policy (UMP) on the prices of selected European stock market indices during the European sovereign debt (2010–2012) and the COVID-19 pandemic (2020–2022) crises interventions. This research employs the instrumental variables (IV) two-stage least squares (2SLS) model approach to evaluate the effects of changes in the size of the ECB’s balance sheet on the pricing of key equity market indices in Europe. The results of this study suggest that the ECB’s asset value expansion had the opposite statistically significant effects on the European stock market indices’ prices between the interventions. That is, an increase in the ECB’s balance sheet size was associated with a decrease in the prices of the indices during the sovereign debt crisis and with a rise during the COVID-19 pandemic. This research pinpoints the price sensitivity of each of the European equity indices to the ECB’s UMP and determines the different outcomes of the ECB’s quantitative easing policy between the interventions. Full article
(This article belongs to the Special Issue Financial Valuation and Econometrics)
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<p>ECB’s balance sheet, German 10-year government bond yield, and European short-term rate.</p> Full article ">Figure 2
<p>Major European stock market indices’ prices.</p> Full article ">
21 pages, 4533 KiB  
Article
Forecasting Financial Investment Firms’ Insolvencies Empowered with Enhanced Predictive Modeling
by Ahmed Amer Abdul-Kareem, Zaki T. Fayed, Sherine Rady, Salsabil Amin El-Regaily and Bashar M. Nema
J. Risk Financial Manag. 2024, 17(9), 424; https://doi.org/10.3390/jrfm17090424 - 22 Sep 2024
Viewed by 844
Abstract
In the realm of financial decision-making, it is crucial to consider multiple factors, among which lies the pivotal concern of a firm’s potential insolvency. Numerous insolvency prediction models utilize machine learning techniques try to solve this critical aspect. This paper aims to assess [...] Read more.
In the realm of financial decision-making, it is crucial to consider multiple factors, among which lies the pivotal concern of a firm’s potential insolvency. Numerous insolvency prediction models utilize machine learning techniques try to solve this critical aspect. This paper aims to assess the financial performance of financial investment firms listed on the Iraq Stock Exchange (ISX) from 2012 to 2022. A Multi-Layer Perceptron predicting model with a parameter optimizer is proposed integrating an additional feature selection process. For this latter process, three methods are proposed and compared: Principal Component Analysis, correlation coefficient, and Particle Swarm Optimization. Through the fusion of financial ratios with machine learning, our model exhibits improved forecast accuracy and timeliness in predicting firms’ insolvency. The highest accuracy model is the integrated MLP + PCA model, at 98.7%. The other models, MLP + PSO and MLP + CC, also exhibit strong performance, with 0.3% and 1.1% less accuracy, respectively, compared to the first model, indicating that the first model serves as a powerful predictive approach. Full article
(This article belongs to the Special Issue Featured Papers in Corporate Finance and Governance)
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<p>Proposed Enhanced Forecast Insolvency Model.</p> Full article ">Figure 2
<p>Application of SMOTE to an Imbalanced Dataset.</p> Full article ">Figure 3
<p>Scatter Plot Without/With PCA.</p> Full article ">Figure 4
<p>Optimal Number of PCs.</p> Full article ">Figure 5
<p>Scree Plot.</p> Full article ">Figure 6
<p>Number of Components Needed to Explain Variance.</p> Full article ">Figure 7
<p>Correlation Heatmap of 10 Important Features With the Target.</p> Full article ">Figure 8
<p>Highest Absolute Correlation Value of 10 Important Features.</p> Full article ">Figure 9
<p>Confusion Matrix of Training and Testing Data Using PCA, CC, and PSO.</p> Full article ">Figure 9 Cont.
<p>Confusion Matrix of Training and Testing Data Using PCA, CC, and PSO.</p> Full article ">
10 pages, 665 KiB  
Article
Character Counts: Psychometric-Based Credit Scoring for Underbanked Consumers
by Saul Fine
J. Risk Financial Manag. 2024, 17(9), 423; https://doi.org/10.3390/jrfm17090423 - 22 Sep 2024
Viewed by 1054
Abstract
Psychometric-based credit scores measure important personality traits that are characteristic of good borrowers’ behaviors. While such data can potentially improve credit models for underbanked consumers, the utility of psychometric data in consumer lending is still largely understudied. The present study contributes to the [...] Read more.
Psychometric-based credit scores measure important personality traits that are characteristic of good borrowers’ behaviors. While such data can potentially improve credit models for underbanked consumers, the utility of psychometric data in consumer lending is still largely understudied. The present study contributes to the literature in this respect, as it is one of the first studies to evaluate the efficacy of psychometric-based credit scores for predicting future loan defaults among underbanked consumers. The results from two culturally diverse samples of loan applicants (Sub-Saharan Africa, n = 1113; Western Europe, n = 1033) found that psychometric scores correlated significantly with future loan defaults (Gini = 0.28–0.31) and were incrementally valid above and beyond the banks’ own credit scorecards. These results highlight the theoretical basis for personality in financial behaviors, as well as the practical utility that psychometric scores can have for credit decisioning in general and the facilitation of financial inclusion for underbanked consumer groups in particular. Full article
(This article belongs to the Special Issue Recent Developments in Finance and Economic Growth)
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<p>ROC curves.</p> Full article ">Figure 2
<p>Default rates by psychometric score band. <span class="html-italic">Note</span>: Low and high score bands represent the bottom and top 15–20th score percentiles, respectively. Exact case numbers can be found in the tables below.</p> Full article ">
25 pages, 2148 KiB  
Article
Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability
by Constantinos Challoumis
J. Risk Financial Manag. 2024, 17(9), 422; https://doi.org/10.3390/jrfm17090422 - 22 Sep 2024
Cited by 3 | Viewed by 1186
Abstract
This paper integrates two theoretical frameworks to explore optimal resource allocation and the dynamics of the money cycle in a hypothetical economy. It examined the theoretical background of the problems of choice. The first framework considers an economy governed by an omniscient authority [...] Read more.
This paper integrates two theoretical frameworks to explore optimal resource allocation and the dynamics of the money cycle in a hypothetical economy. It examined the theoretical background of the problems of choice. The first framework considers an economy governed by an omniscient authority responsible for production and distribution decisions, focusing on the logic of choice and efficient resource allocation. The second framework introduces the concept of the new economic system of Economocracy, emphasizing the role of the Money Cycle theory in economic management and governance. By combining these frameworks, the paper provides a comprehensive understanding of productive and distributive efficiency and examines the impact of the money cycle on economic stability and growth. A mathematical modeling of the money cycle is presented to highlight the relationship between money distribution, economic capacity, and overall economic health. The integrated approach offers valuable insights for optimizing resource allocation and enhancing economic resilience. Full article
(This article belongs to the Special Issue Emerging Issues in Economics, Finance and Business—2nd Edition)
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<p>Comparison of Money Cycle (author’s results, see <a href="#app1-jrfm-17-00422" class="html-app">Appendix A</a>).</p> Full article ">Figure 2
<p>Metrics with and without of Money Cycle &amp; Economocracy (author’s results, see <a href="#app2-jrfm-17-00422" class="html-app">Appendix B</a>).</p> Full article ">Figure 3
<p>Box plot of F-statistics (author’s results, see <a href="#app3-jrfm-17-00422" class="html-app">Appendix C</a>).</p> Full article ">Figure 4
<p>Line plot of R<sup>2</sup> (author’s results, see <a href="#app4-jrfm-17-00422" class="html-app">Appendix D</a>).</p> Full article ">Figure 5
<p>Line 2D-plot of F-statistics (author’s results, see <a href="#app5-jrfm-17-00422" class="html-app">Appendix E</a>).</p> Full article ">Figure 6
<p>Line 3D-plot of F-statistics (author’s results, see <a href="#app5-jrfm-17-00422" class="html-app">Appendix E</a>).</p> Full article ">
21 pages, 843 KiB  
Article
Does ICT Investment Affect Market Share and Customer Acquisition Cost? A Comparative Analysis of Domestic and Foreign Banks Operating in India
by Gulam Goush Ansari and Rajorshi Sen Gupta
J. Risk Financial Manag. 2024, 17(9), 421; https://doi.org/10.3390/jrfm17090421 - 22 Sep 2024
Viewed by 1181
Abstract
Competitive banks aggressively invest in information and communication technologies (ICT) to enhance their market share and reduce Customer Acquisition Costs (CAC). This study examines the impact of cumulative stock of ICT investment on (a) deposit and loan market share and (b) CAC of [...] Read more.
Competitive banks aggressively invest in information and communication technologies (ICT) to enhance their market share and reduce Customer Acquisition Costs (CAC). This study examines the impact of cumulative stock of ICT investment on (a) deposit and loan market share and (b) CAC of banks operating in India. The analysis uses a longitudinal dataset of 84 domestic and 70 foreign banks from 2000 to 2020, employing a two-step system Generalized Method of Moment (GMM). It is found that ICT investment adversely affects the market share of domestic banks, indicating a need for these banks to strategically invest more in CAC. Conversely, foreign banks are able to increase their market share through ICT investment and reduced CAC, thereby demonstrating greater efficiency in utilizing ICT. The study underscores the strategic importance of cumulative stock of ICT investment for banks. Nonetheless, it is emphasized that ICT investment must be complemented with innovative marketing strategies to enhance customer experience, reduce CAC, and increase market share. Overall, while foreign banks are able to leverage ICT to boost efficiency, domestic banks must leverage ICT to implement targeted marketing strategies and strive to enhance their customer service. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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<p>Effect of age on DMS and LMS of domestic banks.</p> Full article ">Figure 2
<p>Effect of age on DMS and LMS of foreign banks.</p> Full article ">
18 pages, 1883 KiB  
Article
The Effect of Student Loan Debt on Emergency Savings and the Moderating Role of Financial Knowledge: Evidence from the U.S. Survey of Household Economics and Decisionmaking
by Thomas Korankye, Blain Pearson and Peter Agyemang-Mintah
J. Risk Financial Manag. 2024, 17(9), 420; https://doi.org/10.3390/jrfm17090420 - 21 Sep 2024
Viewed by 2127
Abstract
This study examines data from the U.S. 2018 and 2019 Survey of Household Economics and Decision making (SHED) to understand the association between student loan debt and emergency-saving decisions, including the moderating role of financial knowledge. Controlling self-selection bias through a propensity score [...] Read more.
This study examines data from the U.S. 2018 and 2019 Survey of Household Economics and Decision making (SHED) to understand the association between student loan debt and emergency-saving decisions, including the moderating role of financial knowledge. Controlling self-selection bias through a propensity score and coarsened exact matching approach, the findings reveal that individuals with student loan debt are less likely to save for financial emergencies. The findings also show that financial knowledge is positively associated with a higher likelihood of having emergency savings. Furthermore, the results from the moderating analysis indicate a statistically significant interaction effect. Based on the empirical results and the corresponding interaction plots, the findings suggest that targeted financial education may lead to improved financial outcomes for student loan borrowers, rather than assuming that such education occurred prior to a loan application. Full article
(This article belongs to the Special Issue Global Perspectives on Loan Debt Issues and Risks)
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<p>Distribution of propensity scores across treatment and control groups.</p> Full article ">Figure 2
<p>Interaction plot—propensity score matching, predictive margins with 95% confidence intervals.</p> Full article ">Figure 3
<p>Interaction plot—coarsened exact matching, predictive margins with 95% confidence intervals.</p> Full article ">
17 pages, 726 KiB  
Article
Investigating the Relationship between Energy Consumption and Environmental Degradation with the Moderating Influence of Technological Innovation
by Suzan Sameer Issa, Mosab I. Tabash, Adel Ahmed, Hosam Alden Riyadh, Mohammed Alnahhal and Manishkumar Varma
J. Risk Financial Manag. 2024, 17(9), 419; https://doi.org/10.3390/jrfm17090419 - 21 Sep 2024
Viewed by 1093
Abstract
Energy consumption (ECON) in BRICS countries is fueled by fossil fuels, mainly coal. Increased environmental degradation (ED) in BRICS countries is mostly driven by coal consumption. This study utilizes quantile regression for the analysis, enabling the development of targeted energy reorganization and emission [...] Read more.
Energy consumption (ECON) in BRICS countries is fueled by fossil fuels, mainly coal. Increased environmental degradation (ED) in BRICS countries is mostly driven by coal consumption. This study utilizes quantile regression for the analysis, enabling the development of targeted energy reorganization and emission reduction policies in BRICS countries. This study uses data spanning from 1990 to 2022 to explore the impact of ECON on ED. Additionally, technological innovation was used to create a moderating role in the nexus between ECON and ED. The model focuses on CO2 emissions and the ecological footprint across ten BRICS countries. Among the nations included in the panel, the results indicate a significant dependence on cross-sectional factors. The study shows that ECON has a detrimental impact on ED across all quantiles. However, technological innovation reduces ED. In terms of a moderating role, technological innovation mitigates the negative influence of ECON on ED. Therefore, it is necessary to implement distinct policies in order to accomplish carbon emission reduction goals in various countries. Full article
(This article belongs to the Special Issue Navigating Sustainable Development Goals (SDGs): Narrative Disclosure Approach)
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<p>CO<sub>2</sub> emissions, GDP, and Population trend in BRICS Economies. Source: Authors’ work (WDI data).</p> Full article ">Figure 2
<p>Flow of Analysis, Source: Authors’ work.</p> Full article ">
21 pages, 642 KiB  
Article
Human Trafficking and Gender Inequality: How Businesses Can Lower Risks and Costs
by Donald L. Ariail, Katherine Taken Smith and Lawrence Murphy Smith
J. Risk Financial Manag. 2024, 17(9), 418; https://doi.org/10.3390/jrfm17090418 - 21 Sep 2024
Cited by 1 | Viewed by 1388
Abstract
Human trafficking continues to be a profitable multi-billion dollar business. People are either callous toward human rights or they are unaware of the crime occurring. Many businesses may unknowingly facilitate human trafficking by providing services, such as transportation, hotels, or haircuts, or purchasing [...] Read more.
Human trafficking continues to be a profitable multi-billion dollar business. People are either callous toward human rights or they are unaware of the crime occurring. Many businesses may unknowingly facilitate human trafficking by providing services, such as transportation, hotels, or haircuts, or purchasing products from unfamiliar sources that secretly use forced labor. To be socially responsible, a business must establish effective enterprise governance policies that help prevent and detect trafficking. A business can incur legal fines, damage to its reputation, incur lost business, and be subject to litigation, all as a result of human trafficking. Worldwide, estimates are that 50 million people are being trafficked. Human trafficking is especially harmful to females, both adult women and girls, who comprise about 70 percent of all trafficking victims. Gender theory helps explain this disproportionate impact on women. This study provides an overview of human trafficking, an empirical analysis of the relationship of gender inequality to trafficking, and specific steps that a business can take to help prevent this crime, protect its reputation, and avoid fines and lost business. Full article
(This article belongs to the Collection Business Performance)
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<p>Major flows of human trafficking worldwide. Source: L.M. <a href="#B76-jrfm-17-00418" class="html-bibr">Smith</a> (<a href="#B76-jrfm-17-00418" class="html-bibr">2020</a>). Website: <a href="https://bit.ly/traffickingworldmap" target="_blank">https://bit.ly/traffickingworldmap</a>, accessed on 18 August 2024. Used by Permission.</p> Full article ">
16 pages, 312 KiB  
Article
Examining the Impact of Vulnerability and the Law of Justice on the IFRS Adoption Decision
by Khandokar Istiak, John Reid Cummings, Robert Forrester and Macy Adams
J. Risk Financial Manag. 2024, 17(9), 417; https://doi.org/10.3390/jrfm17090417 - 20 Sep 2024
Viewed by 671
Abstract
We investigate the impact of vulnerability and the law of justice indicators on the decision to adopt International Financial Reporting Standards (IFRS) by 133 countries. Applying robust Logit and Probit models to 2021 cross-sectional data, we find that the absence of corruption, state [...] Read more.
We investigate the impact of vulnerability and the law of justice indicators on the decision to adopt International Financial Reporting Standards (IFRS) by 133 countries. Applying robust Logit and Probit models to 2021 cross-sectional data, we find that the absence of corruption, state illegitimacy, a well-functioning civil justice system, and insufficient public services are helpful for IFRS adoption. On the other hand, results show that a country’s uneven economic development and human rights violations are detrimental to IFRS adoption. Our research confirms that requiring higher standards for financial and accounting reporting in the media, allocating sufficient budget amounts to support an equitable civil justice system, and coordinating efforts to reduce or eliminate economic inequality may help IFRS adoption. We argue that highlighting the positive benefits of IFRS adoption and the commensurate constructive policy outcomes may add the emphasis needed to convince governmental leaders to move toward IFRS adoption. Full article
(This article belongs to the Special Issue Financial Reporting and Auditing)
17 pages, 574 KiB  
Article
Intellectual Capital and Performance of Banking and Financial Institutions in Panama: An Application of the VAIC™ Model
by Oriana Jannett Pitre-Cedeño and Edila Eudemia Herrera-Rodríguez
J. Risk Financial Manag. 2024, 17(9), 416; https://doi.org/10.3390/jrfm17090416 - 20 Sep 2024
Viewed by 954
Abstract
In the knowledge era, intellectual capital has been considered a key factor in creating value within organisations. This study examines the relationships and interactions between the components of intellectual capital and the profitability of Panamanian banking and financial institutions listed on the Latin [...] Read more.
In the knowledge era, intellectual capital has been considered a key factor in creating value within organisations. This study examines the relationships and interactions between the components of intellectual capital and the profitability of Panamanian banking and financial institutions listed on the Latin American Stock Exchange (LATINEX) from 2014 to 2020. A theoretical framework based on agency theories, signalling theory, and stakeholder theory was employed to support the results. The Valued-Added Intellectual Coefficient (VAIC)™ model, which evaluates the intellectual capital of organisations based on information from financial statements, was also utilised. In this study, stepwise regression was applied to select the optimal number of predictors to be included in each multiple regression model to examine the relationship between the return on equity (ROE) and the components of the VAIC™ in addition to control variables such as size and indebtedness. The findings confirm this study’s hypothesis, demonstrating that the structural capital efficiency (SCE) and company size (SIZE) variables explain 57% of the variance in the ROE for the analysed institutions. The results suggest that the intellectual capital (IC) of financial sector institutions listed on LATINEX is significantly influenced by the SCE coefficient, which shows a negative relationship, suggesting that investment in structural capital does not enhance profitability. On the other hand, larger institutions exhibited higher profitability during the study period. This study was limited to the analysis of two sectors: banking and finance in companies listed on LATINEX. However, its rigorous theoretical and empirical foundation opens the way for future research in which other sectors can be considered, and cross-country comparisons can be made, strengthening the research in this field for Latin America. At the same time, this study offers market regulators a scientific methodology to oversee the activities of issuing companies. Full article
(This article belongs to the Section Banking and Finance)
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<p>Scatter plot (ZPRED = standardised predicted values; ZRESID = standardised residuals).</p> Full article ">
15 pages, 6826 KiB  
Article
Forecasting Crude Oil Price Using Multiple Factors
by Hind Aldabagh, Xianrong Zheng, Mohammad Najand and Ravi Mukkamala
J. Risk Financial Manag. 2024, 17(9), 415; https://doi.org/10.3390/jrfm17090415 - 19 Sep 2024
Viewed by 1917
Abstract
In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may [...] Read more.
In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical results show that the CNN–LSTM model outperforms these models. Full article
(This article belongs to the Section Financial Technology and Innovation)
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<p>Factors influencing crude oil prices.</p> Full article ">Figure 2
<p>Saudi production (blue) and WTI production percentage changes (red).</p> Full article ">Figure 3
<p>OPEC spare capacity (blue) and WTI crude oil prices (red).</p> Full article ">Figure 4
<p>Non-OPEC production changes (blue) and WTI crude oil prices (red).</p> Full article ">Figure 5
<p>World production change (blue) and WTI crude oil prices (red).</p> Full article ">Figure 6
<p>World production change (orange), non-OPEC production change (green), Saudi production change (blue) and WTI crude oil prices (red).</p> Full article ">Figure 7
<p>OECD consumption change (blue) and WTI crude oil prices (red).</p> Full article ">Figure 8
<p>Non-OECD consumption change (blue) and their GDP percentage growth (red).</p> Full article ">Figure 9
<p>World consumption change (blue) and WTI price (red).</p> Full article ">Figure 10
<p>World consumption change (orange), OECD consumption change (blue), non-OECD consumption change (green) and WTI crude oil prices (red).</p> Full article ">Figure 11
<p>US dollar index (2002–2023).</p> Full article ">Figure 12
<p>The tree-based representation of random forests.</p> Full article ">Figure 13
<p>The XGBoost architecture.</p> Full article ">Figure 14
<p>The actual versus the predicted oil price without preprocessing and the dollar index.</p> Full article ">Figure 15
<p>The actual versus the predicted oil price with the dollar index but no preprocessing.</p> Full article ">Figure 16
<p>The actual versus the predicted oil price using preprocessing and the dollar index.</p> Full article ">
18 pages, 489 KiB  
Article
Maximizing Profitability and Occupancy: An Optimal Pricing Strategy for Airbnb Hosts Using Regression Techniques and Natural Language Processing
by Luca Di Persio and Enis Lalmi
J. Risk Financial Manag. 2024, 17(9), 414; https://doi.org/10.3390/jrfm17090414 - 18 Sep 2024
Viewed by 1748
Abstract
In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve [...] Read more.
In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property’s market performance. Our primary goal was to introduce a solution that can augment property owners’ understanding of their property’s market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing’s financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings’ market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals. Full article
(This article belongs to the Section Mathematics and Finance)
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<p>Graph derived from the most important features in the used models.</p> Full article ">Figure 2
<p>Workflow chart of development of the research.</p> Full article ">Figure 3
<p>Neural network architecture.</p> Full article ">Figure 4
<p>Word cloud for most common words in the reviews. Here, we can see what guests talk about most in their reviews and what is worth mentioning.</p> Full article ">Figure 5
<p>The TF-IDF showed the most common phrases found in the review, from which we can understand how important are the location, the host behavior, and other features like <span class="html-italic">being close to the Colosseum</span> or having <span class="html-italic">an automated check-in</span>.</p> Full article ">
19 pages, 1799 KiB  
Article
Financial Contagion between German and BRICS Stock Markets under Multiscale Scrutiny
by Olivier Niyitegeka and Alexis Habiyaremye
J. Risk Financial Manag. 2024, 17(9), 413; https://doi.org/10.3390/jrfm17090413 - 17 Sep 2024
Viewed by 642
Abstract
We employ wavelet analysis using the maximum overlap discrete wavelet transform (MODWT) to examine the return and volatility interconnectedness between the German equity market (a prominent representative of the Eurozone market) and the BRICS countries over the period 2005–2017. Specifically, we investigate the [...] Read more.
We employ wavelet analysis using the maximum overlap discrete wavelet transform (MODWT) to examine the return and volatility interconnectedness between the German equity market (a prominent representative of the Eurozone market) and the BRICS countries over the period 2005–2017. Specifically, we investigate the presence of the pure form of financial contagion in the stock markets of Brazil, Russia, India, China, and South Africa subsequent to the Eurozone Sovereign Debt Crisis (EZDC). Our results indicate the presence of financial contagion between the Eurozone equity market and its counterparts in South Africa and Russia, characterised by co-movement and volatility spillover effects. This contagion is particularly evident at higher frequencies, suggesting that the transmission of shocks occurs rapidly across these markets in the short term. No financial contagion is observed in the Brazilian, Chinese, and Indian stock markets during the European Sovereign Debt Crisis. The absence of financial contagion observed in these three BRICS countries during the European Sovereign Debt Crisis suggests that policymakers in these countries should prioritise addressing idiosyncratic shock channels. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond, 3rd Edition)
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<p>Correlation of DAX with BOVESPA (panel <b>A</b>) and SSE (panel <b>B</b>) at different timescales. (<b>A</b>) Correlation of DAX and BOVESPA at different time scales. (<b>B</b>) Correlation of DAX and SSE at different time scales.</p> Full article ">Figure 2
<p>Cross-correlation between the return series of DAX and BOVESPA.</p> Full article ">Figure 3
<p>Cross-correlation between the return series of DAX and SSE.</p> Full article ">Figure 4
<p>Wavelet coherence between the Eurozone and Brazilian stock markets. Note: → (pointing to the right): the two time series are in phase and move together; ← (pointing to the left): the two time series are out of phase and move in opposite directions; ↑ (pointing upwards): the first time series leads the second; ↓ (pointing downwards): the second time series leads the first.</p> Full article ">Figure 5
<p>Wavelet coherence between the Eurozone and South African stock markets. Note: → (pointing to the right): the two time series are in phase and move together; ← (pointing to the left): the two time series are out of phase and move in opposite directions; ↑ (pointing upwards): the first time series (DAX) leads the second (JSE); ↓ (pointing downwards): the second time series leads the first.</p> Full article ">Figure 6
<p>Wavelet coherence between the Eurozone and Russian stock markets. Note: → (pointing to the right): the two time series are in phase and move together; ← (pointing to the left): the two time series are out of phase and move in opposite directions; ↑ (pointing upwards): the first time series (DAX) leads the second (RTS); ↓ (pointing downwards): the second time series leads the first.</p> Full article ">Figure 7
<p>Wavelet coherence between the Eurozone and Indian stock markets. Note: → (pointing to the right): the two time series are in phase and move together; ← (pointing to the left): the two time series are out of phase and move in opposite directions; ↑ (pointing upwards): the first time series (DAX) leads the second (SENSEX); ↓ (pointing downwards): the second time series leads the first.</p> Full article ">Figure 8
<p>Wavelet coherence between the Eurozone and Chinese stock markets. Note: → (pointing to the right): the two time series are in phase and move together; ← (pointing to the left): the two time series are out of phase and move in opposite directions; ↑ (pointing upwards): the first time series (DAX) leads the second (SSE); ↓ (pointing downwards): the second time series leads the first.</p> Full article ">
13 pages, 993 KiB  
Article
Changes in Revealed Comparative Advantage in Machinery and Equipment: Evidence for Emerging Markets
by Andrea Boltho
J. Risk Financial Manag. 2024, 17(9), 412; https://doi.org/10.3390/jrfm17090412 - 17 Sep 2024
Viewed by 1117
Abstract
The paper computes Balassa’s index of revealed comparative advantage for machinery and equipment (a rough proxy for high-tech goods) for a number of emerging areas (East Asia, South-East Asia, South Asia, Eastern Europe, Latin America, Africa, and the Middle East) and for selected [...] Read more.
The paper computes Balassa’s index of revealed comparative advantage for machinery and equipment (a rough proxy for high-tech goods) for a number of emerging areas (East Asia, South-East Asia, South Asia, Eastern Europe, Latin America, Africa, and the Middle East) and for selected individual countries over some 50 years, from the early 1970s to the early 2020s. The focus is on why some economies were successful in promoting high-tech sectors. As could be expected, experience differs hugely. In some countries, interventionist trade or industrial policies were crucial in fostering comparative advantage. In others, however, the role of policies appears to have been minor and successes were achieved thanks to the free play of market forces (including an important contribution, at least in some countries, coming from foreign direct investment). Full article
(This article belongs to the Special Issue Globalization and Economic Integration)
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<p>Emerging Markets-Revealed Comparative Advantage—Machinery and Transport Equipment (SITC 7). a. China, Hong Kong, Korea, Singapore, Taiwan; b. Czech Rep., Hungary, Poland, Romania, Slovakia; c. Indonesia, Malaysia, Philippines, Thailand, Vietnam; d. Argentina, Brazil, Chile, Colombia, Dominican Rep., Ecuador, Mexico, Peru; e. Kuwait, Oman, Qatar, Saudi Arabia, UAE; f. Algeria, Egypt, Morocco; g. Bangladesh, India, Pakistan. <span class="html-italic">Sources</span>: UN, <span class="html-italic">Commodity Trade Database</span>; WTO, <span class="html-italic">Statistics on Merchandise Trade</span>.</p> Full article ">Figure A1
<p>East Asia-Revealed Comparative Advantage—Machinery and Transport Equipment (SITC 7). <span class="html-italic">Source</span>: UN, <span class="html-italic">Commodity Trade Database</span>; WTO, <span class="html-italic">Statistics on Merchandise Trade</span>.</p> Full article ">Figure A2
<p>South-East Asia-Revealed Comparative Advantage—Machinery and Transport Equipment (SITC 7). <span class="html-italic">Source</span>: UN, <span class="html-italic">Commodity Trade Database</span>; WTO, <span class="html-italic">Statistics on Merchandise Trade</span>.</p> Full article ">Figure A3
<p>Eastern Europe-Revealed Comparative Advantage—Machinery and Transport Equipment (SITC 7). <span class="html-italic">Source</span>: UN, <span class="html-italic">Commodity Trade Database</span>; WTO, <span class="html-italic">Statistics on Merchandise Trade</span>.</p> Full article ">Figure A4
<p>Others-Revealed Comparative Advantage—Machinery and Transport Equipment (SITC 7). <span class="html-italic">Source</span>: UN, <span class="html-italic">Commodity Trade Database</span>; WTO, <span class="html-italic">Statistics on Merchandise Trade</span>.</p> Full article ">
21 pages, 1914 KiB  
Article
Long-Run Trade Relationship between the U.S. and Canada: The Case of the Canadian Dollar with the U.S. Dollar
by Ikhlaas Gurrib, Firuz Kamalov, Osama Atayah, Dalia Hemdan and Olga Starkova
J. Risk Financial Manag. 2024, 17(9), 411; https://doi.org/10.3390/jrfm17090411 - 15 Sep 2024
Viewed by 1206
Abstract
This study investigates the long-run relationship between the U.S. dollar and the Canadian dollar by analyzing the bilateral exchange rate induced by nominal and real shocks. The methodology centers on a structural vector autoregressive (SVAR) model, including the analysis of impulse response and [...] Read more.
This study investigates the long-run relationship between the U.S. dollar and the Canadian dollar by analyzing the bilateral exchange rate induced by nominal and real shocks. The methodology centers on a structural vector autoregressive (SVAR) model, including the analysis of impulse response and variance decomposition to account for the impact of nominal and real shocks on exchange rate movements. This study also decomposes real shocks into demand and supply factors from both Canada and the U.S. and compares their impacts on the nominal and real exchange rates. The results are compared to shocks driven by country-specific nominal factors. This study uses quarterly data from December 1972 to December 2023. The findings suggest that real shocks have a permanent impact on both the nominal and real exchange rates, compared to nominal shocks, which have a temporary impact. Country-specific real supply-side factors have a more significant impact than country-specific real demand-side factors. Country-specific nominal factors barely impacted the nominal and real exchange rates between the U.S. and Canada. Full article
(This article belongs to the Section Financial Markets)
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<p>Presence of Canadian dollar and USD in foreign currency trades. Note: <a href="#jrfm-17-00411-f001" class="html-fig">Figure 1</a> reports the average daily turnover of Over-The-Counter (OTC) foreign exchange instruments for the Canadian dollar (CAD) and U.S. dollar (USD) for 2001–2022. Source: <a href="#B8-jrfm-17-00411" class="html-bibr">Bank for International Settlements</a> (<a href="#B8-jrfm-17-00411" class="html-bibr">2022</a>).</p> Full article ">Figure 2
<p>NER and RER for Canada (December 1972–December 2023). Note: <a href="#jrfm-17-00411-f002" class="html-fig">Figure 2</a> displays the nominal exchange rates (NER) and real exchange rates (RER) for Canada, using quarterly wholesale price ratios for the period December 1972–December 2023.</p> Full article ">Figure 3
<p>Response of RER and NER to nominal and real shocks. Note: <a href="#jrfm-17-00411-f003" class="html-fig">Figure 3</a> displays the accumulated response of real USD/CAD (RER) and nominal USD/CAD (NER) to real shocks (shock 1) and nominal shocks (shock 2).</p> Full article ">Figure 4
<p>Impulse responses of real and nominal shocks from the U.S. and Canada on NER and RER (December 1972–December 2023). Note: <a href="#jrfm-17-00411-f004" class="html-fig">Figure 4</a> displays the response of the real and nominal USD/CAD to real and nominal shocks from Canada and the U.S. Differenced logs of real government expenditure and differenced logs of real GNP were used as real demand and supply shocks, and money supply (M2) was used as a measure of nominal shock. Accumulated responses for 30 first quarters following the shocks are reported, using Cholesky (degree of adjusted) decomposition. All variables were stationary after first-order differencing using the ADF stationary test. Based on maximizing Schwarz, Hannan–Quinn, and Akaike information criteria, 1 lag and 4 lags were used in VAR models with U.S. and Canadian shock variables, respectively. The period of study is December 1972–December 2023.</p> Full article ">Figure 4 Cont.
<p>Impulse responses of real and nominal shocks from the U.S. and Canada on NER and RER (December 1972–December 2023). Note: <a href="#jrfm-17-00411-f004" class="html-fig">Figure 4</a> displays the response of the real and nominal USD/CAD to real and nominal shocks from Canada and the U.S. Differenced logs of real government expenditure and differenced logs of real GNP were used as real demand and supply shocks, and money supply (M2) was used as a measure of nominal shock. Accumulated responses for 30 first quarters following the shocks are reported, using Cholesky (degree of adjusted) decomposition. All variables were stationary after first-order differencing using the ADF stationary test. Based on maximizing Schwarz, Hannan–Quinn, and Akaike information criteria, 1 lag and 4 lags were used in VAR models with U.S. and Canadian shock variables, respectively. The period of study is December 1972–December 2023.</p> Full article ">
29 pages, 432 KiB  
Article
Social Media for Investment Advice and Financial Satisfaction: Does Generation Matter?
by Olamide Olajide, Sabina Pandey and Ichchha Pandey
J. Risk Financial Manag. 2024, 17(9), 410; https://doi.org/10.3390/jrfm17090410 - 13 Sep 2024
Viewed by 4971
Abstract
This study explores the relationship between social media usage for investment advice and financial satisfaction across different generations. Ten ordered logit models were estimated using Stata to explore this relationship. Ordered logit analyses using data from the 2021 National Financial Capability Study State-by-State [...] Read more.
This study explores the relationship between social media usage for investment advice and financial satisfaction across different generations. Ten ordered logit models were estimated using Stata to explore this relationship. Ordered logit analyses using data from the 2021 National Financial Capability Study State-by-State and Investor survey reveal that Generation X and millennials are less financially satisfied than baby boomers. While general social media use shows no statistically significant association, platform-specific analysis finds that Instagram and TikTok users report higher financial satisfaction, whereas YouTube users report lower satisfaction. Notably, millennials who use social media for investment advice are more financially satisfied than their peers. Detailed analyses reveal that Instagram, TikTok, and Twitter positively influence financial satisfaction across Gen Z, millennials, and Gen X, with more platform-specific associations observed for Facebook, LinkedIn, and Reddit among millennials and Gen X, respectively. These findings provide valuable insights for policymakers, financial professionals, and researchers, highlighting the need for targeted strategies to enhance financial well-being through social media. Full article
(This article belongs to the Special Issue Financial Technologies (Fintech) in Finance and Economics)
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<p>Proposed research framework.</p> Full article ">
20 pages, 1035 KiB  
Article
Identifying Information Types in the Estimation of Informed Trading: An Improved Algorithm
by Oguz Ersan and Montasser Ghachem
J. Risk Financial Manag. 2024, 17(9), 409; https://doi.org/10.3390/jrfm17090409 - 12 Sep 2024
Viewed by 775
Abstract
The growing frequency of news arrivals, partly fueled by the proliferation of data sources, has made the assumptions of the classical probability of informed trading (PIN) model outdated. In particular, the model’s assumption of a single type of information event no longer reflects [...] Read more.
The growing frequency of news arrivals, partly fueled by the proliferation of data sources, has made the assumptions of the classical probability of informed trading (PIN) model outdated. In particular, the model’s assumption of a single type of information event no longer reflects the complexity of modern financial markets, making the accurate detection of information types (layers) crucial for estimating the probability of informed trading. We propose a layer detection algorithm to accurately find the number of distinct information types within a dataset. It identifies the number of information layers by clustering order imbalances and examining their homogeneity using properly constructed confidence intervals for the Skellam distribution. We show that our algorithm manages to find the number of information layers with very high accuracy both when uninformed buyer and seller intensities are equal and when they differ from each other (i.e., between 86% and 95% accuracy rates). We work with more than 500,000 simulations of quarterly datasets with various characteristics and make a large set of robustness checks. Full article
(This article belongs to the Special Issue Financial Technologies (Fintech) in Finance and Economics)
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<p>Distribution of order imbalances when <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>b</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Distribution of order imbalances when <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>b</mi> </mrow> </msub> <mo>≠</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">
33 pages, 3455 KiB  
Article
The Stock Market Reaction to Green Bond Issuance: A Study Based on a Multidimensional Scaling Approach
by Wided Khiari, Ines Ben Flah, Azhaar Lajmi and Fida Bouhleli
J. Risk Financial Manag. 2024, 17(9), 408; https://doi.org/10.3390/jrfm17090408 - 10 Sep 2024
Viewed by 1720
Abstract
The aim of this study is to examine the impact of green bond issuance on the stock market, based on the share prices of 29 companies located in different countries around the world. Using our financial map and applying clustering techniques, we study [...] Read more.
The aim of this study is to examine the impact of green bond issuance on the stock market, based on the share prices of 29 companies located in different countries around the world. Using our financial map and applying clustering techniques, we study price fluctuations and identify the influences shaping them. Our contribution lies in methodological innovation through a Multidimensional Scaling approach. Based on this innovative approach, the results of this investigation revealed a complex dynamic in which various factors such as company size, issue volume, total number of issues, geographical location, country GDP, and even governance indices such as the corruption index interact significantly. Full article
(This article belongs to the Special Issue Emerging Issues in Economics, Finance and Business—2nd Edition)
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<p>Financial map.</p> Full article ">
26 pages, 607 KiB  
Article
The Impact of Changing External Auditors, Auditor Tenure, and Audit Firm Type on the Quality of Financial Reports on the Saudi Stock Exchange
by Abdulkarim Hamdan J. Alhazmi, Sardar Islam and Maria Prokofieva
J. Risk Financial Manag. 2024, 17(9), 407; https://doi.org/10.3390/jrfm17090407 - 10 Sep 2024
Viewed by 1803
Abstract
The purpose of this study is to examine the influences of external auditor firm type, auditor tenure, and external auditor changes on the quality of Saudi Arabian financial reports. In particular, this study examines the quality of financial reports of companies listed on [...] Read more.
The purpose of this study is to examine the influences of external auditor firm type, auditor tenure, and external auditor changes on the quality of Saudi Arabian financial reports. In particular, this study examines the quality of financial reports of companies listed on the Saudi Stock Exchange using a widely accepted evaluation model modified by JonesThis study aims to determine whether Big Four and non-Big Four audit firms, auditor tenures of three or more years, and external auditor changes have any impact on the quality of financial reports of Saudi-listed companies. This study uses 175 firm-year observations of 35 companies listed on the Tadawul Saudi Stock Exchange between 2017 and 2021. Using discretionary accruals (DACC) as modified by Jones to measure the quality of financial reports, the findings illustrate that there is a significant negative relationship between Big Four audit firms and DACC. However, the study also shows a significant positive correlation between auditor tenure and DACC. The research revealed that there is no significant relationship between auditor change and DACC. These results have practical implications for policy development. According to the outcomes of this research, there are numerous ramifications for both companies and the government in Saudi Arabia in terms of enhancing the relationship between companies and audit firms and determining the most suitable auditor tenure to improve the quality of financial reports. Full article
(This article belongs to the Section Business and Entrepreneurship)
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<p>Conceptual framework.</p> Full article ">
20 pages, 332 KiB  
Article
Joint Impact of Market Volatility and Cryptocurrency Holdings on Corporate Liquidity: A Comparative Analysis of Cryptocurrency Exchanges and Other Firms
by Namryoung Lee
J. Risk Financial Manag. 2024, 17(9), 406; https://doi.org/10.3390/jrfm17090406 - 9 Sep 2024
Viewed by 1648
Abstract
This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, [...] Read more.
This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges. Full article
(This article belongs to the Section Financial Technology and Innovation)
12 pages, 283 KiB  
Article
The Effect of Twitter Messages and Tone on Stock Return: The Case of Saudi Stock Market “Tadawul”
by Mohammed S. Albarrak
J. Risk Financial Manag. 2024, 17(9), 405; https://doi.org/10.3390/jrfm17090405 - 9 Sep 2024
Viewed by 768
Abstract
This research aims to examine whether corporate Twitter messages and tone have an effect on corporate stock return (RET) for the Saudi Stock Exchange “Tadawul”. The study also investigates whether the association differs across large- and small-sized firms. We used a sample of [...] Read more.
This research aims to examine whether corporate Twitter messages and tone have an effect on corporate stock return (RET) for the Saudi Stock Exchange “Tadawul”. The study also investigates whether the association differs across large- and small-sized firms. We used a sample of 11,099 firm-daily observations for non-financial firms that were traded on the Saudi Stock Exchange “Tadawul” across the period 1 April 2020 to 31 December 2020. Using panel ordinary least square (OLS) and two-stage least square (2SLS), we found that corporate Twitter (currently renamed ‘X’) messages is positively and significantly associated with stock return (RET). The findings also suggest that the message tone increases the stock returns. Furthermore, our results show different effects of Twitter messages and tone on stock return across small- and large-sized firms. In addition, our findings show that Twitter tone is positively associated with RET when the firm is large in size. However, when the firm is small, Twitter messages has a stronger effect on RET. Our findings provide policy implications for regulators and investors. Regulators might monitor the information in accurate ways. Also, investors might start to show interest in Twitter channels to follow the firm’s news. Full article
(This article belongs to the Section Financial Markets)
52 pages, 6746 KiB  
Article
COVID-19 and Uncertainty Effects on Tunisian Stock Market Volatility: Insights from GJR-GARCH, Wavelet Coherence, and ARDL
by Emna Trabelsi
J. Risk Financial Manag. 2024, 17(9), 403; https://doi.org/10.3390/jrfm17090403 - 9 Sep 2024
Viewed by 861
Abstract
This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are [...] Read more.
This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended. Full article
(This article belongs to the Special Issue Stability of Financial Markets and Sustainability Post-COVID-19)
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<p>Evolution of TUNINDEX stock return (2 January 2020–30 December 2022). Source: The Author.</p> Full article ">Figure 2
<p>Wavelet transform coherence: Tunisian stock return volatility versus COVID-19 cases rate. Notes: The black contour shows where the spectrum is significantly different from red noise at the 5% level. The lighter shade represents the cone of influence, marking high-power areas and indicating the autocorrelation of wavelet power at each scale. The horizontal axis represents time from 3 March 2020 to 30 December 2022, and the vertical axis denotes scale bands with daily frequency. Arrows to the right (left) indicate in-phase (out-of-phase) relationships, meaning a positive (negative) connection. If arrows move right and up (down), the first variable “m” (cases rate) drives (follows), while if arrows move left and up (down), variable “n” (TUNINDEX volatility) leads (lags). This visualization helps understand the dynamic relationships between variables. Source: The Author.</p> Full article ">Figure 3
<p>Wavelet transform coherence: Tunisian stock return volatility versus COVID-19 death rate. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 23 March 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 4
<p>Wavelet transform coherence: Tunisian stock return volatility versus stringency index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 5
<p>Wavelet transform coherence: Tunisian stock return volatility versus containment health index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 6
<p>Wavelet transform coherence: Tunisian stock return volatility versus economic support index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 7
<p>Wavelet transform coherence: Tunisian stock return volatility versus government response index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 8
<p>Wavelet transform coherence: Tunisian stock return volatility versus school closing index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 9
<p>Wavelet transform coherence: Tunisian stock return volatility versus workplace closing. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 10
<p>Wavelet transform coherence: Tunisian stock return volatility versus public events canceling. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 11
<p>Wavelet transform coherence: Tunisian stock return volatility versus public gathering restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 12
<p>Wavelet transform coherence: Tunisian stock return volatility versus public transport closure. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 13
<p>Wavelet transform coherence: Tunisian stock return volatility versus stay-at-home requirements. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 14
<p>Wavelet transform coherence: Tunisian stock return volatility versus internal movement restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 15
<p>Wavelet transform coherence: Tunisian stock return volatility versus international travel control. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 16
<p>Wavelet transform coherence: Tunisian stock return volatility versus public information gathering. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 17
<p>Wavelet transform coherence: Tunisian realized volatility versus the COVID-19 cases rate. Notes: The black contour shows where the spectrum is significantly different from red noise at the 5% level. The lighter shade represents the cone of influence, marking high-power areas, indicating autocorrelation of wavelet power at each scale. The horizontal axis represents time from 3 March 2020 to 30 December 2022, and the vertical axis denotes scale bands with daily frequency. Arrows to the right (left) indicate in-phase (out-of-phase) relationships, meaning a positive (negative) connection. If arrows move right and up (down), the first variable “m” (cases rate) drives (follows), while if arrows move left and up (down), variable “n” (TUNINDEX volatility) leads (lags). This visualization helps understand the dynamic relationships between variables. Source: The Author.</p> Full article ">Figure 18
<p>Wavelet transform coherence: Tunisian realized volatility versus COVID-19 death rate. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 23 March 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 19
<p>Wavelet transform coherence: Tunisian realized volatility versus stringency index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 20
<p>Wavelet transform coherence: Tunisian realized volatility versus containment health index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 21
<p>Wavelet transform coherence: Tunisian realized volatility versus economic support index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 22
<p>Tunisian realized volatility versus government response index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 23
<p>Wavelet transform coherence: Tunisian realized volatility versus school closing index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 24
<p>Wavelet transform coherence: Tunisian realized volatility versus workplace closing. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 25
<p>Wavelet transform coherence: Tunisian stock return volatility versus public events canceling. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 26
<p>Wavelet transform coherence: Tunisian realized volatility versus public gathering restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 27
<p>Wavelet transform coherence: Tunisian realized volatility versus public transport closure. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 28
<p>Wavelet transform coherence: Tunisian realized volatility versus stay-at-home requirements. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 29
<p>Wavelet transform coherence: Tunisian realized volatility versus internal movement restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 30
<p>Wavelet transform coherence: Tunisian realized volatility versus international travel control. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">Figure 31
<p>Wavelet transform coherence: Tunisian realized volatility versus public information gathering. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p> Full article ">
18 pages, 764 KiB  
Article
Digital Financial Capability Scale
by Kelmara Mendes Vieira, Taiane Keila Matheis and Eliete dos Reis Lehnhart
J. Risk Financial Manag. 2024, 17(9), 404; https://doi.org/10.3390/jrfm17090404 - 8 Sep 2024
Viewed by 1261
Abstract
Financial digitization is an irreversible phenomenon. The objective of this study is to construct the Digital Financial Capability Scale (DFCS). Starting with the development of a definition, we created a multidimensional scale composed of digital financial knowledge, digital financial behavior, and digital financial [...] Read more.
Financial digitization is an irreversible phenomenon. The objective of this study is to construct the Digital Financial Capability Scale (DFCS). Starting with the development of a definition, we created a multidimensional scale composed of digital financial knowledge, digital financial behavior, and digital financial confidence. The validation process involved a qualitative stage, consisting of focus groups, expert validation, and pre-testing, and a quantitative stage, with exploratory and confirmatory factor analyses and structural equation modeling. The DFCS assesses an individual’s perception of their ability to apply financial knowledge, adopt appropriate financial behaviors, and feel confident in making financial decisions in a digital environment. The final version of the DFCS consists of a set of 33 items divided into the three dimensions. The scale can be very useful for researchers who wish to study financial capability in the digital environment, for financial agents to evaluate clients, and for assessing the outcomes of public policies aimed at enhancing the financial capability of the population. Full article
(This article belongs to the Special Issue The New Horizons of Global Financial Literacy)
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<p>Proposed theoretical model for the digital financial capability scale. Source: Prepared by the authors (2024).</p> Full article ">Figure 2
<p>Final model of the Digital Financial Capability Scale. Note: * <span class="html-italic">p</span> &lt; 0.01; <sup>1</sup> z-value not calculated, where the parameter was set to 1, due to model requirements. For simplicity, the correlations between the errors were not represented in the Figure.</p> Full article ">
11 pages, 285 KiB  
Article
Impairing Globalization: The Russo-Ukrainian War, Western Economic Sanctions and Asset Seizures
by Steven Rosefielde
J. Risk Financial Manag. 2024, 17(9), 402; https://doi.org/10.3390/jrfm17090402 - 8 Sep 2024
Viewed by 1059
Abstract
The potency of economic sanctions imposed on nations depends on demand and supply adjustment possibilities. Adverse GDP impacts will be maximal when import, export, production, distribution and finance are inflexible (universal non-substitution). This paper elaborates on these conditions and quantifies the maximum GDP [...] Read more.
The potency of economic sanctions imposed on nations depends on demand and supply adjustment possibilities. Adverse GDP impacts will be maximal when import, export, production, distribution and finance are inflexible (universal non-substitution). This paper elaborates on these conditions and quantifies the maximum GDP loss that Western sanctions could have inflicted on Russia in 2022–2023. It reports the World Bank’s predictions, contrasts them with the results and draws inferences about the efficiency of Russia’s workably competitive markets. This paper shows that Russia’s economic system exhibits moderate universal substitutability and is less vulnerable to punitive discipline than Western policymakers suppose. The likelihood that economic sanctions will compel the Kremlin to restore Ukraine’s territorial integrity ceteris paribus is correspondingly low, even though war reduces Russia’s quality of existence. Western economic sanctions serve narrow geostrategic ends that are reconcilable with Pareto-efficient free trade and globalization, if precision-targeted, but as the Russo-Ukrainian war intensifies, an expanded array of novel and dubiously legal sanctions is degrading free trade, and spurring de-globalization and anti-Western coalitions. If this armed combat is prolonged, the goals of free trade and globalization could be set back for decades. Full article
(This article belongs to the Special Issue Globalization and Economic Integration)
17 pages, 935 KiB  
Article
Analyzing the Selective Stock Price Index Using Fractionally Integrated and Heteroskedastic Models
by Javier E. Contreras-Reyes, Joaquín E. Zavala and Byron J. Idrovo-Aguirre
J. Risk Financial Manag. 2024, 17(9), 401; https://doi.org/10.3390/jrfm17090401 - 7 Sep 2024
Cited by 1 | Viewed by 918
Abstract
Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty [...] Read more.
Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China’s economic slowdown in 2012 and the subprime crisis in 2008. Full article
(This article belongs to the Special Issue Political Risk Management in Financial Markets)
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<p>Selective Stock Price index (<b>top</b>) and log returns (<b>bottom</b>), 2 January 2010 to 30 September 2023.</p> Full article ">Figure 2
<p>Sample autocorrelation function of SSP log-returns (<b>top</b>). Absolute value of SSP log returns (<b>middle</b>) and squares of SSP log returns (<b>bottom</b>).</p> Full article ">Figure 3
<p>Histogram of SSP log returns (2 January 2010 to 30 September 2023).</p> Full article ">Figure 4
<p>Left to right: Histogram of standardized residuals and sample ACF of residuals and square residuals of the GARCH<math display="inline"><semantics> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math> model.</p> Full article ">Figure 5
<p>Left to right: Histogram of standardized residuals, sample ACF of residuals, square residuals of ARFIMA<math display="inline"><semantics> <mrow> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mi>d</mi> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math>-GARCH<math display="inline"><semantics> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math> model.</p> Full article ">Figure 6
<p>Left to right: Histogram of standardized residuals, sample ACF of residuals, square residuals of FIGARCH<math display="inline"><semantics> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mi>d</mi> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math> model.</p> Full article ">Figure 7
<p>Estimated volatility under GARCH, ARFIMA-GARCH, and FIGARCH models for SSP log returns (January 2010–September 2023).</p> Full article ">
18 pages, 756 KiB  
Article
An Empirical Analysis of Tax Evasion among Companies Engaged in Stablecoin Transactions
by Rubens Moura de Carvalho, Helena Coelho Inácio and Rui Pedro Marques
J. Risk Financial Manag. 2024, 17(9), 400; https://doi.org/10.3390/jrfm17090400 - 6 Sep 2024
Viewed by 987
Abstract
This research investigates the relationship between stablecoin usage and tax evasion. We present a model that includes variables related to transactions such as intensity, frequency, environment on-chain (P2P) vs. off-chain (IntraVasp), and company characteristics such as age, sector, and size. Our model was [...] Read more.
This research investigates the relationship between stablecoin usage and tax evasion. We present a model that includes variables related to transactions such as intensity, frequency, environment on-chain (P2P) vs. off-chain (IntraVasp), and company characteristics such as age, sector, and size. Our model was empirically tested using a logistic regression based on data from the Brazilian Federal Revenue Service (Receita Federal do Brasil (RFB)) in 2021. This novel approach aims to understand the tax behaviours associated with stablecoin use in corporate financial practices. Our results indicate that the intensity, frequency, environment of transactions (specifically IntraVasp and P2P transactions), age, sector, and size are factors significantly associated with tax evasion behaviour. However, we found no evidence to suggest that firms engaging in only P2P transactions have a higher propensity for tax evasion than those engaging only in IntraVasp transactions. Our findings reveal that younger and medium-sized companies with intensive use of stablecoin, with high stablecoin transaction frequency, engaging in IntraVasp and P2P transactions, and belonging to the service sector are more likely to evade tax. Therefore, our research provides a detailed understanding of how digital financial practices with crypto assets (blockchain-based technology) intersect with corporate tax strategies, which can offer valuable insights for regulators, industry practitioners, and policymakers. 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>Monthly volume of crypto assets transactions. Source: RFB<a href="#fn004-jrfm-17-00400" class="html-fn">4</a>.</p> Full article ">
15 pages, 261 KiB  
Article
Bankruptcy Prediction for Restaurant Firms: A Comparative Analysis of Multiple Discriminant Analysis and Logistic Regression
by Yang Huo, Leo H. Chan and Doug Miller
J. Risk Financial Manag. 2024, 17(9), 399; https://doi.org/10.3390/jrfm17090399 - 6 Sep 2024
Viewed by 1212
Abstract
In this paper, we used data from publicly traded restaurant firms between 2000 and 2019 to test the effectiveness of multiple discriminant analysis (MDA) and logistic regression (logit) in predicting the probability of bankruptcy in the restaurant industry. We constructed various financial ratios [...] Read more.
In this paper, we used data from publicly traded restaurant firms between 2000 and 2019 to test the effectiveness of multiple discriminant analysis (MDA) and logistic regression (logit) in predicting the probability of bankruptcy in the restaurant industry. We constructed various financial ratios extracted from the financial information and analyzed them to determine the optimal models. Our results show that liquid ratios (particularly the quick ratio), operating cash flow, and working capital emerge as the most crucial indicators of potential bankruptcy filings for restaurant firms. The results also show that the logit model performs better within the sample. However, both models exhibit similar predictive capacities with out-of-sample data. Full article
(This article belongs to the Special Issue Advances in Financial and Hospitality Management Accounting)
19 pages, 1451 KiB  
Review
Climate-Related Regulations and Financial Markets: A Meta-Analytic Literature Review
by Linh Tu Ho, Christopher Gan and Zhenzhen Zhao
J. Risk Financial Manag. 2024, 17(9), 398; https://doi.org/10.3390/jrfm17090398 - 5 Sep 2024
Viewed by 946
Abstract
Countries are confronting climate change using climate-related regulations that require firms and investors to disclose their green strategies and activities. Using the Meta-Analysis Structural Equation Modeling (MASEM) technique, this study evaluates the relationship between climate-related regulations and financial markets. The meta-regression analysis is [...] Read more.
Countries are confronting climate change using climate-related regulations that require firms and investors to disclose their green strategies and activities. Using the Meta-Analysis Structural Equation Modeling (MASEM) technique, this study evaluates the relationship between climate-related regulations and financial markets. The meta-regression analysis is conducted based on the outcomes of 52 empirical studies screened from 143 relevant articles. The results show the predictive power of the climate-related disclosure (CRD) laws and environmental regulations (ERs) on financial performance across all studies. ERs create mixed impacts on the equity market and support the debt market. Firm value is affected by ERs either negatively or positively. Methodologies and risk-related factors (market, industry, and firm risks) are important in explaining the relationships between ER/CRD and financial performance. The more developed the market, the less the impact of ERs and CRD on the equity market. Considering industry risk is recommended because different industries are exposed to changes in policies differently. The ER/CRD–firm value relationship is affected by all market, industry, and firm risks. The downside effect of mandatory CRD on the equity market suggests that policy makers, firms, and investors should be cautious in passing a new CRD regulation for transformation towards a sustainable economy. Full article
(This article belongs to the Special Issue Featured Papers in Climate Finance)
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<p>The theoretical model. Source: Authors’ illustration.</p> Full article ">Figure 2
<p>Data collection framework. Source: Authors’ illustration.</p> Full article ">Figure 3
<p>Empirical study search and quality screening process. Source: Authors’ illustration.</p> Full article ">Figure 4
<p>Number of studies by year, 2000–2023. Source: Authors’ illustration.</p> Full article ">Figure 5
<p>Number of studies by market coverage, 2000–2023. Source: Authors’ illustration.</p> Full article ">Figure 6
<p>Methodology and risk-related effects on the ER/CRD–financial market relationship. Source: Authors’ illustration.</p> Full article ">
18 pages, 1843 KiB  
Article
Capturing Tail Risks in Cryptomarkets: A New Systemic Risk Approach
by Itai Barkai, Elroi Hadad, Tomer Shushi and Rami Yosef
J. Risk Financial Manag. 2024, 17(9), 397; https://doi.org/10.3390/jrfm17090397 - 5 Sep 2024
Viewed by 1168
Abstract
Using daily returns of Bitcoin, Litecoin, Ripple and Stellar, we introduce a novel risk measure for quantitative-risk management in the cryptomarket that accounts for the significant co-movements between cryptocurrencies. We find that our model has a lower error margin when forecasting the extent [...] Read more.
Using daily returns of Bitcoin, Litecoin, Ripple and Stellar, we introduce a novel risk measure for quantitative-risk management in the cryptomarket that accounts for the significant co-movements between cryptocurrencies. We find that our model has a lower error margin when forecasting the extent of future losses than traditional risk measures, such as Value-at-Risk and Expected Shortfall. Most notably, we observe this in Litecoin’s results, where Expected Shortfall, on average, overestimates the potential fall in the price of Litecoin by 8.61% and underestimates it by 3.92% more than our model. This research shows that traditional risk measures, while not necessarily inappropriate, are imperfect and incomplete representations of risk when it comes to the cryptomarket. Our model provides a suitable alternative for risk managers, who prioritize lower error margins over failure rates, and highlights the value in exploring how risk measures that incorporate the unique characteristics of cryptocurrencies can be used to supplement and complement traditional risk measures. Full article
(This article belongs to the Special Issue Financial Technologies (Fintech) in Finance and Economics)
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<p>Scaled cryptocurrency prices over time. <b>Notes</b>: The figure shows the co-movements between different cryptocurrency prices. All prices have been scaled as follows: Bitcoin is divided by 350, Litecoin is divided by 10, Ripple is multiplied by 10, and Stellar is multiplied by 10.</p> Full article ">Figure 2
<p>Cryptocurrency log returns separated into drawup and drawdown periods. <b>Notes</b>: Drawup periods describe low-risk market periods characterized by predominantly positive returns; drawdown periods denote predominantly negative returns and higher risk.</p> Full article ">Figure 3
<p>Bull and bear regimes for all cryptocurrencies. <b>Notes</b>: The figure illustrates bull and bear regimes over the period from 8 August 2015 to 21 July 2019.</p> Full article ">Figure 4
<p>Litecoin loss forecasts by systemic and traditional risk measures. <b>Notes</b>: The figure illustrates Litecoin daily loss forecasts from 8 August 2015 to 21 July 2019. Due to high volatility, the figure depicts the loss forecast in different timeframes.</p> Full article ">Figure A1
<p>Bitcoin loss forecasts by systemic and traditional risk measures. <b>Notes</b>: The figure illustrates Bitcoin daily loss forecasts from 8 August 2015 to 21 July 2019. Due to high volatility, the figure depicts the loss forecast in different timeframes.</p> Full article ">Figure A2
<p>Ripple loss forecasts by systemic and traditional risk measures. <b>Notes</b>: The figure illustrates Ripple daily loss forecasts from 8 August 2015 to 21 July 2019. Due to high volatility, the figure depicts the loss forecast in different timeframes.</p> Full article ">Figure A3
<p>Stellar loss forecasts by systemic and traditional risk measures. <b>Notes</b>: The figure illustrates Stellar daily loss forecasts from 8 August 2015 to 21 July 2019. Due to high volatility, the figure depicts the loss forecast in different timeframes.</p> Full article ">
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