Journal Description
Journal of Risk and Financial Management
Journal of Risk and Financial Management
is an international, peer-reviewed, open access journal on risk and financial management, published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, EconBiz, EconLit, RePEc, and other databases.
- Journal Rank: CiteScore - Q1 (Business, Management and Accounting (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.1 days after submission; acceptance to publication is undertaken in 4.6 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
Does Board Gender Diversity Influence SDGs Disclosure? Insight from Top 15 JSE-Listed Mining Companies
J. Risk Financial Manag. 2024, 17(10), 429; https://doi.org/10.3390/jrfm17100429 (registering DOI) - 25 Sep 2024
Abstract
An assessment was made halfway into the sustainable development goals (SDGs) agenda period, and the findings indicated a slower than anticipated pace towards the implementation of the SDGs agenda. One of the possible causes of the slower pace is a lack of strong
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An assessment was made halfway into the sustainable development goals (SDGs) agenda period, and the findings indicated a slower than anticipated pace towards the implementation of the SDGs agenda. One of the possible causes of the slower pace is a lack of strong governance mechanisms such as gender diversity, sustainability committees, and board sustainability experience in institutions. The study sought to investigate the influence of board gender diversity on SDGs disclosure amongst the top 15 JSE-listed mining companies in light of their contribution towards the attainment of this global agenda. Mining in South Africa affects about nine percent of the country’s population. The study was anchored on the agency and the stakeholder theories. This is quantitative research which employed a keyword search to measure SDGs disclosure in the annual integrated reports for the sampled companies from 2019 to 2023. The study hypothesised that there is a significant positive relationship between a female-dominated board and SDGs disclosure in the sampled companies. Descriptive statistics, correlation analysis, as well as regression analysis were employed. The results established a lack of significant evidence of a positive or negative relationship between gender diversity and SDGs disclosure, a significant positive relationship between board size and SDGs disclosure, and no relationship between board independence and SDGs disclosure in the sampled mining companies. It was concluded that board gender diversity in corporate boards in the top 15 JSE-listed mining companies has no impact on the SDGs disclosure. The study recommends including more moderating factors and conducting more empirical studies towards the attainment of conclusive results in this space.
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(This article belongs to the Special Issue Risk Management in Accounting and Business)
Open AccessReview
Mapping the Knowledge Landscape of Money Laundering for Terrorism Financing: A Bibliometric Analysis
by
Himanshu Thakkar, Saptarshi Datta, Priyam Bhadra, Siddharth Baburao Dabhade, Haresh Barot and Shankar O. Junare
J. Risk Financial Manag. 2024, 17(10), 428; https://doi.org/10.3390/jrfm17100428 - 24 Sep 2024
Abstract
This study employs a bibliometric analysis of emerging trends in money laundering for terrorism financing (ML/TF) to provide direction for future research. The authors used VOSviewer and analyzed 2577 published documents retrieved from the SCOPUS database using the PRISMA methodology. The findings reveal
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This study employs a bibliometric analysis of emerging trends in money laundering for terrorism financing (ML/TF) to provide direction for future research. The authors used VOSviewer and analyzed 2577 published documents retrieved from the SCOPUS database using the PRISMA methodology. The findings reveal a growing research interest in understanding the complex interplay between money laundering and terrorism financing. This research emphasizes the significance of ML/TF for economic stability, as understanding terrorism financing mechanisms allows authorities to trace and block funds going to terrorist groups, which is crucial for national security. Critical insights for policymakers underscore the need for robust legislative frameworks, effective Financial Intelligence Units (FIUs), and international collaboration to combat these global threats. This analysis offers a foundation for future research, mapping the evolving knowledge landscape in ML/TF.
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(This article belongs to the Special Issue Fintech, Business, and Development)
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Analysing Network Dynamics: The Contagion Effects of SVB’s Collapse on the US Tech Industry
by
Fan Wu, Anqi Liu, Jing Chen and Yuhua Li
J. Risk Financial Manag. 2024, 17(10), 427; https://doi.org/10.3390/jrfm17100427 - 24 Sep 2024
Abstract
The collapse of Silicon Valley Bank in 2023 was historically significant, and based on past experiences with similar banking sector shocks, it is widely expected to trigger domino effects among tech giants and startups. However, based on the analysis of risk spillover networks
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The collapse of Silicon Valley Bank in 2023 was historically significant, and based on past experiences with similar banking sector shocks, it is widely expected to trigger domino effects among tech giants and startups. However, based on the analysis of risk spillover networks established by VARs estimation, we find little evidence of such a spread of risk contagion. We observe a clear downward trend in the total connectedness index of large-cap tech companies right after the the SVB collapse. Moreover, the market quickly responded in a way that isolated the financial services subcategory within the tech sector, forming a distinct community in the network. This explains how the risk contagion paths were cut off. We also provide visualised comparisons of contagion paths within the tech network before and after the SVB’s collapse.
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(This article belongs to the Special Issue Post SVB Banking Sector Outlook)
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Risk Analysis of Conglomerates with Debt and Equity Links
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Arturo Cifuentes and Rodrigo Roman
J. Risk Financial Manag. 2024, 17(9), 426; https://doi.org/10.3390/jrfm17090426 - 23 Sep 2024
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
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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.
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(This article belongs to the Special Issue Risk Management in Capital Markets)
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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
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
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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.
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(This article belongs to the Special Issue Financial Valuation and Econometrics)
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Forecasting Financial Investment Firms’ Insolvencies Empowered with Enhanced Predictive Modeling
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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
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
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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.
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(This article belongs to the Special Issue Featured Papers in Corporate Finance and Governance)
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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
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
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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.
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(This article belongs to the Special Issue Recent Developments in Finance and Economic Growth)
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Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability
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Constantinos Challoumis
J. Risk Financial Manag. 2024, 17(9), 422; https://doi.org/10.3390/jrfm17090422 - 22 Sep 2024
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
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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.
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(This article belongs to the Special Issue Emerging Issues in Economics, Finance and Business—2nd Edition)
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Does ICT Investment Affect Market Share and Customer Acquisition Cost? A Comparative Analysis of Domestic and Foreign Banks Operating in India
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Gulam Goush Ansari and Rajorshi Sen Gupta
J. Risk Financial Manag. 2024, 17(9), 421; https://doi.org/10.3390/jrfm17090421 - 22 Sep 2024
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
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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.
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(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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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
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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
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
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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.
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(This article belongs to the Special Issue Global Perspectives on Student Loan Debt Issues and Risks)
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Investigating the Relationship between Energy Consumption and Environmental Degradation with the Moderating Influence of Technological Innovation
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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
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
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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.
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(This article belongs to the Special Issue Navigating Sustainable Development Goals (SDGs): Narrative Disclosure Approach)
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Human Trafficking and Gender Inequality: How Businesses Can Lower Risks and Costs
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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
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
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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.
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(This article belongs to the Collection Business Performance)
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Examining the Impact of Vulnerability and the Law of Justice on the IFRS Adoption Decision
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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
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
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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.
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(This article belongs to the Special Issue Financial Reporting and Auditing)
Open AccessArticle
Intellectual Capital and Performance of Banking and Financial Institutions in Panama: An Application of the VAIC™ Model
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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
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
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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.
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(This article belongs to the Section Banking and Finance)
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Forecasting Crude Oil Price Using Multiple Factors
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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
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
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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.
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(This article belongs to the Section Financial Technology and Innovation)
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Maximizing Profitability and Occupancy: An Optimal Pricing Strategy for Airbnb Hosts Using Regression Techniques and Natural Language Processing
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Luca Di Persio and Enis Lalmi
J. Risk Financial Manag. 2024, 17(9), 414; https://doi.org/10.3390/jrfm17090414 - 18 Sep 2024
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
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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.
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(This article belongs to the Section Mathematics and Finance)
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Financial Contagion between German and BRICS Stock Markets under Multiscale Scrutiny
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Olivier Niyitegeka and Alexis Habiyaremye
J. Risk Financial Manag. 2024, 17(9), 413; https://doi.org/10.3390/jrfm17090413 - 17 Sep 2024
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
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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.
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(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond, 3rd Edition)
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Changes in Revealed Comparative Advantage in Machinery and Equipment: Evidence for Emerging Markets
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Andrea Boltho
J. Risk Financial Manag. 2024, 17(9), 412; https://doi.org/10.3390/jrfm17090412 - 17 Sep 2024
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
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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).
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(This article belongs to the Special Issue Globalization and Economic Integration)
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Long-Run Trade Relationship between the U.S. and Canada: The Case of the Canadian Dollar with the U.S. Dollar
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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
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
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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.
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(This article belongs to the Section Financial Markets)
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Social Media for Investment Advice and Financial Satisfaction: Does Generation Matter?
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Olamide Olajide, Sabina Pandey and Ichchha Pandey
J. Risk Financial Manag. 2024, 17(9), 410; https://doi.org/10.3390/jrfm17090410 - 13 Sep 2024
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
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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.
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(This article belongs to the Special Issue Financial Technologies (Fintech) in Finance and Economics)
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