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Volume 16
Issue 12
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J. Risk Financial Manag., Volume 16, Issue 12 (December 2023) – 32 articles

Cover Story (view full-size image): The aim of this paper is to assess the impact generated by financial market shocks on the economic cycle in selected European countries. In addition to studies from the literature, which focus more on the developed economies, this paper also considered the situation at the level of a group of emerging economies. Both econometric models for the individual analyses of each state, such as the Bayesian vector autoregression model, and models for groups of states, such as panel regressions, were used. The results showed a strong connection between the dynamics of the financial system and those of the real economy. In addition, the impact of financial factors on the economic cycle tends to be much stronger and more significant in the case of developing countries compared to developed ones. View this paper
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21 pages, 652 KiB  
Article
Navigating the Intricate Relationship between Investments and Global Output: A Leontief Matrix Case Study of Romania
by Mihail Busu, Madalina Vanesa Vargas and Sorin Anagnoste
J. Risk Financial Manag. 2023, 16(12), 521; https://doi.org/10.3390/jrfm16120521 - 18 Dec 2023
Viewed by 2292
Abstract
This research delves into the intricate dynamics underlying the impact of investments on global output, employing the Leontief matrix as a robust analytical framework. Investments wield a profound influence on economies worldwide, with varying effects contingent upon investment types, development levels of countries, [...] Read more.
This research delves into the intricate dynamics underlying the impact of investments on global output, employing the Leontief matrix as a robust analytical framework. Investments wield a profound influence on economies worldwide, with varying effects contingent upon investment types, development levels of countries, and external factors such as trade conflicts and global shocks. The diverse range of investment forms, including physical capital, human capital, R&D, and technological investments, engenders distinct implications for productivity, innovation, and efficiency. Developing and developed economies navigate unique trajectories, with investments playing a pivotal role in bridging infrastructure gaps, improving technology, and spurring growth. However, external disruptions, such as trade wars and global shocks, introduce an element of complexity, reshaping investment patterns and altering global output trajectories. This study centers on harnessing the Leontief matrix’s prowess to evaluate the interplay of investments and global output, focusing on the Romanian economy. By analyzing input–output tables, encompassing 105 branches aggregated into 10 sectors, the research captures the intricate connections between economic segments. Notably, the Romanian context reveals the volatility of the matrix coefficients, an outcome of ongoing transitional processes, technological advancements, and fluctuating relative prices. In unraveling the intricate threads weaving investments and global output, this study contributes to a nuanced comprehension of these multifaceted interactions. The findings underscore the significance of tailoring investment strategies to specific economic contexts and advocate for robust frameworks, such as the input–output model, to inform policy decisions and drive sustainable growth in an increasingly complex global economy. Full article
(This article belongs to the Section Financial Markets)
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<p>The values of w, by sector. Source: own computations.</p> Full article ">
14 pages, 1387 KiB  
Article
Predicting the Profitability of Directional Changes Using Machine Learning: Evidence from European Countries
by Nicholas D. Belesis, Georgios A. Papanastasopoulos and Antonios M. Vasilatos
J. Risk Financial Manag. 2023, 16(12), 520; https://doi.org/10.3390/jrfm16120520 - 18 Dec 2023
Cited by 2 | Viewed by 2145
Abstract
In this paper, we follow the suggestions of past literature to further explore the prediction of the profitability direction by employing different machine learning algorithms, extending the research in the European setting and examining the effect of profits mean reversion for the prediction [...] Read more.
In this paper, we follow the suggestions of past literature to further explore the prediction of the profitability direction by employing different machine learning algorithms, extending the research in the European setting and examining the effect of profits mean reversion for the prediction of profitability. We provide evidence that simple algorithms like LDA can outperform classification trees if the data used are preprocessed correctly. Moreover, we use nested cross-validation and show that sample predictions can be obtained without using the classic train–test split. Overall, our prediction results are in line with previous studies, and we also found that cash flow-based measures like Free Cash Flow and Operating Cash Flow can be predicted more accurately compared to accrual-based measures like return on assets or return on equity. Full article
(This article belongs to the Special Issue Financial Valuation and Econometrics)
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<p>Decision surface plot. (<b>a</b>) Decision surface for ΔROA; (<b>b</b>) Decision surface for ΔROE.</p> Full article ">Figure 2
<p>Decision surface plot. (<b>a</b>) Decision surface for ΔROE using KNN; (<b>b</b>) Decision surface for ΔROE using DT.</p> Full article ">
19 pages, 2460 KiB  
Article
Risky Indebtedness Behavior: Impacts on Financial Preparation for Retirement and Perceived Financial Well-Being
by Kelmara Mendes Vieira, Taiane Keila Matheis and Ana Maria Heinrichs Maciel
J. Risk Financial Manag. 2023, 16(12), 519; https://doi.org/10.3390/jrfm16120519 - 17 Dec 2023
Cited by 1 | Viewed by 2200
Abstract
This study aimed to verify the impact of financial preparation for retirement and risky indebtedness behavior on perceived financial well-being. A survey was carried out with 2290 individuals from diverse sociodemographic and economic profiles who resided in Brazil. Confirmatory factor analysis and structural [...] Read more.
This study aimed to verify the impact of financial preparation for retirement and risky indebtedness behavior on perceived financial well-being. A survey was carried out with 2290 individuals from diverse sociodemographic and economic profiles who resided in Brazil. Confirmatory factor analysis and structural equation modeling were used as data analysis techniques. The results obtained indicate that risky indebtedness behavior negatively impacts financial preparation for retirement and perceived financial well-being and that there is a positive impact of financial preparation for retirement on perceived financial well-being. These findings highlight the importance of financial planning and savings behavior so that future expectations are achieved, and individuals may enjoy life with financial well-being. Thus, it is essential that public policies that promote new behaviors and healthy financial habits to the population, in addition to incentives for financial preparation for retirement, are built. Brazil needs to review the new credit concessions so that the individual does not acquire the behavior of using a financial resource that they do not have and that compromise financial well-being in the short and long term, negatively affecting retirement. Full article
(This article belongs to the Special Issue Subjective Well-Being and Financial Decision Making)
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<p>Proposed framework and its respective hypotheses (H). Source: Prepared by the authors (2023).</p> Full article ">Figure 2
<p>Frequency Distribution of the Scales.</p> Full article ">Figure 2 Cont.
<p>Frequency Distribution of the Scales.</p> Full article ">Figure 3
<p>Final model. Notes: * <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. A description of the items can be found in <a href="#app1-jrfm-16-00519" class="html-app">Appendix A</a>.</p> Full article ">
19 pages, 960 KiB  
Article
Influences of Internal Control on Enterprise Performance: Does an Information System Make a Difference?
by Hani Alshaiti
J. Risk Financial Manag. 2023, 16(12), 518; https://doi.org/10.3390/jrfm16120518 - 15 Dec 2023
Viewed by 5120
Abstract
It is generally perceived that the effective implementation of an adequate internal control system prevents and controls an entity’s risks and improves its procedures and performance. This study empirically investigates the relationship between the internal control system and firms’ performance, with particular emphasis [...] Read more.
It is generally perceived that the effective implementation of an adequate internal control system prevents and controls an entity’s risks and improves its procedures and performance. This study empirically investigates the relationship between the internal control system and firms’ performance, with particular emphasis on the moderation role of an integrated information system. For this purpose, a survey was developed and sent to 215 Saudi firms that had implemented an integrated information system. A hundred and two valid responses were received. Partial least squares structural equation modeling was utilized for the data analysis and hypothesis testing. The findings confirmed that organizational structure, prospectors’ strategy, information system quality, and management support significantly influence the internal control system for the study sample. The finding also supports the role of an information system as a moderator variable in the relationship between internal control and organizational performance. Additionally, the study elucidates the importance of information system maturity for information system quality. Full article
(This article belongs to the Section Business and Entrepreneurship)
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<p>The theoretical framework.</p> Full article ">Figure 2
<p>Path coefficients with a path diagram for the whole sample.</p> Full article ">Figure 3
<p>Moderator effect.</p> Full article ">
21 pages, 401 KiB  
Article
Environmental, Social, and Governance Performance and Value Creation in Product Market: Evidence from Emerging Economies
by Yasmeen Bashir, Yiwei Zhao, Huan Qiu, Zeeshan Ahmed and Josephine Tan-Hwang Yau
J. Risk Financial Manag. 2023, 16(12), 517; https://doi.org/10.3390/jrfm16120517 - 14 Dec 2023
Viewed by 3107
Abstract
Using a unique sample of 13,412 firm-year observations from 19 countries of the emerging economies for the period of 2011 to 2019, we investigate the association between the firms’ environmental, social, and governance (ESG) performance and their value creation in the product market. [...] Read more.
Using a unique sample of 13,412 firm-year observations from 19 countries of the emerging economies for the period of 2011 to 2019, we investigate the association between the firms’ environmental, social, and governance (ESG) performance and their value creation in the product market. Specifically, we first used the pooled OLS regression model for panel data as our baseline model and found that ESG performance (as well as its pillars) has a strong positive effect on the future value creation of the firms in the product market. We also conducted some additional analyses using various regression models, as well as adopting multiple tests for endogeneity, and the additional analyses revealed that the results are robust under different scenarios. Overall, the findings of this study highlight the importance of firm-level ESG performance for the value creation of firms in the product market in emerging economies and have theoretical and practical implications for academic researchers, market participants, and government entities in studying, evaluating, and governing firms’ ESG performance and reporting. Full article
(This article belongs to the Special Issue Contemporary Studies on Corporate Finance and Business Research)
23 pages, 1643 KiB  
Review
Private Placement of China-Listed Real Estate Firms: A Conceptual Idea
by Yuping Ning and Rohaya Binti Abdul Jalil
J. Risk Financial Manag. 2023, 16(12), 516; https://doi.org/10.3390/jrfm16120516 - 12 Dec 2023
Cited by 1 | Viewed by 2379
Abstract
This article conducts a review of the literature on private placement and analyzes the risks facing China’s real estate companies. It argues that, within the framework of China’s hybrid economic model, private placement can serve as a market-oriented financing mechanism and risk mitigation [...] Read more.
This article conducts a review of the literature on private placement and analyzes the risks facing China’s real estate companies. It argues that, within the framework of China’s hybrid economic model, private placement can serve as a market-oriented financing mechanism and risk mitigation strategy beyond the traditional banking system. The article focuses on the characteristics of private placement, prevalent hypotheses, and influencing factors. It also traces the evolution of financialization in the global real estate industry, outlines the development model of China’s real estate sector, and discusses the challenges and risks it encounters. Private placement offers various advantages, including reducing corporate leverage, strengthening working capital, and addressing information asymmetry issues. However, existing research in this field is still insufficient. Therefore, future research can provide a more robust theoretical foundation and guidance for policymakers, investors, and businesses. Full article
(This article belongs to the Section Business and Entrepreneurship)
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<p>Timeline of Private Placement in China (Source: <a href="#B82-jrfm-16-00516" class="html-bibr">Song</a> (<a href="#B82-jrfm-16-00516" class="html-bibr">2014</a>)).</p> Full article ">Figure 2
<p>Summarize of private placement hypotheses (source: author).</p> Full article ">Figure 3
<p>The statistics of China-listed real estate companies: (<b>a</b>) A comparison between the funds obtained by real estate enterprises through domestic loans and private equity placement. Source: CSMAR database; (<b>b</b>) The annual numbers of private equity placements declared by listed Chinese real estate companies.<a href="#fn006-jrfm-16-00516" class="html-fn">6</a> Source: Tonghuashun Wencai database; (<b>c</b>) The debt/asset ratio from 2006 to 2022 for China real estate companies. Source: CSMAR database.</p> Full article ">Figure 4
<p>Private placements as a micro-level solution for real estate company risk management (source: author).</p> Full article ">
16 pages, 1287 KiB  
Article
Flight-to-Liquidity and Excess Stock Return: Empirical Evidence from a Dynamic Panel Model
by Asif Ali, Habib Ur Rahman, Adam Arian and John Sands
J. Risk Financial Manag. 2023, 16(12), 515; https://doi.org/10.3390/jrfm16120515 - 12 Dec 2023
Cited by 1 | Viewed by 1817
Abstract
This study examines the impact of the flight-to-liquidity (FTL) phenomenon on the excess stock return by applying the previously developed generalised method of moments (GMM) framework. For this purpose, we use the data covering the period from 2004 to 2018 for 122 public [...] Read more.
This study examines the impact of the flight-to-liquidity (FTL) phenomenon on the excess stock return by applying the previously developed generalised method of moments (GMM) framework. For this purpose, we use the data covering the period from 2004 to 2018 for 122 public companies listed on the Pakistan Stock Exchange (PSX). This study uses six proxies to measure the expected and unexpected illiquidity. The empirical investigation reveals that expected and unexpected illiquidities greatly influence smaller firms more notably than larger ones, which induces FTL phenomena into the market. Moreover, a FTL phenomenon triggered the Pakistani equity market during the financial crisis, when a significant decline appeared and the less liquid stocks were strongly affected. The results reveal that FTL risk is priced in the Pakistan equity market, making large stocks relatively more attractive in times of dire liquidity. These findings further suggest that the market participants in the Pakistan equity market, including policymakers, regulators and investors, should not ignore FTL phenomena while designing their portfolios. Full article
(This article belongs to the Special Issue Corporate Finance: Financial Management of the Firm)
9 pages, 239 KiB  
Review
Financial Intermediation, Economic Growth, and Business Cycles
by Ioanna Konstantakopoulou
J. Risk Financial Manag. 2023, 16(12), 514; https://doi.org/10.3390/jrfm16120514 - 12 Dec 2023
Cited by 2 | Viewed by 8077
Abstract
This paper aims to examine the importance of financial intermediation in economic activity. We also explore the effects of monetary factors and financial frictions on the relationship between financial intermediation and economic growth, the drivers of business cycles, and how shocks spread through [...] Read more.
This paper aims to examine the importance of financial intermediation in economic activity. We also explore the effects of monetary factors and financial frictions on the relationship between financial intermediation and economic growth, the drivers of business cycles, and how shocks spread through the intermediation process. Financial intermediaries improve fund allocation, minimize monitoring costs, minimize liquidity risk, simplify risk management, and facilitate portfolio diversification and resource allocation to more productive activities. In addition, financial intermediaries collect and analyze information about investment projects, allocating resources and managing information more efficiently than individual investors. We conclude that financial intermediation is significant for economic growth. In addition, we show that financial market frictions can amplify exogenous shocks, affecting investment, economic growth rates, and macroeconomic stability. Reducing financial frictions through intermediation is crucial. Full article
(This article belongs to the Special Issue Business, Finance and Economic Development)
31 pages, 1971 KiB  
Article
Do Aid for Trade Flows Affect Technology Licensing in Recipient Countries?
by Sèna Kimm Gnangnon
J. Risk Financial Manag. 2023, 16(12), 513; https://doi.org/10.3390/jrfm16120513 - 11 Dec 2023
Viewed by 1788
Abstract
There is an abundant literature on the economic (including trade) effects of Aid for Trade (AfT) flows. However, little attention has been devoted to the effect of AfT flows on demand for technology licensing. The present article aims to fill this void in [...] Read more.
There is an abundant literature on the economic (including trade) effects of Aid for Trade (AfT) flows. However, little attention has been devoted to the effect of AfT flows on demand for technology licensing. The present article aims to fill this void in the literature by investigating the effect of AfT flows on technology licensing in developing countries. The analysis has used an unbalanced panel dataset of 77 countries over the period from 2002 to 2019 and mainly the two-step generalized method of moments estimator. It has been established that AfT flows foster technology licensing in countries that experience lower trade costs. In addition, the analysis has revealed that adverse environmental and external (economic and financial) shocks significantly hamper innovation, including the demand for technology licensing, and that AfT flows promote technology licensing in countries that experience lower magnitudes of such shocks. Finally, AfT flows foster technology licensing in countries that diversify export products. Full article
(This article belongs to the Special Issue Foreign Direct Investment & International Trade)
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<p>Total AfT and technology licensing_over the full sample. Source: Author. Note: The variable “RLFP” is the ‘non-transformed’ indicator of royalties and license fee payments and is expressed in millions of US<span>$</span>, constant 2015 prices. The variable “AfTTOT” (the gross disbursement of total Aid for Trade) is expressed in millions of US<span>$</span> at constant 2019 prices.</p> Full article ">Figure 2
<p>Correlation pattern between total AfT flows and technology licensing and between AfT flows and the overall trade costs_Over the full sample. Source: Author. Note: The variable “RLFP” is the ‘transformed’ indicator of the real values of royalties and license fee payments. The variable “AfTTOT” (the gross disbursement of total Aid for Trade) is expressed in constant 2019 prices.</p> Full article ">Figure 3
<p>Marginal Impact of “AfTTOT” on “RLFP” for varying overall trade costs. Source: Author.</p> Full article ">Figure 4
<p>Marginal Impact of “AfTTOT” on “RLFP” for varying tariff costs. Source: Author.</p> Full article ">Figure 5
<p>Marginal Impact of “AfTTOT” on “RLFP” for varying nontariff costs. Source: Author.</p> Full article ">Figure 6
<p>Correlation patterns between the magnitude of shocks, export product concentration, and technology licensing_Over the full sample. Source: Author. Note: The variable “RLFP” is the ‘transformed’ indicator of the real values of royalties and license fee payments. The variables “SHOCK” and “ECI” are, respectively, indicators of the magnitude of SHOCKs and export product concentration.</p> Full article ">Figure 7
<p>Marginal Impact of “AfTTOT” on “RLFP” for varying magnitudes of shocks. Source: Author.</p> Full article ">Figure 8
<p>Marginal Impact of “AfTTOT” on “RLFP” for varying levels of export product concentration. Source: Author.</p> Full article ">
21 pages, 624 KiB  
Article
Does the Cultural Dimension Influence the Relationship between Firm Value and Board Gender Diversity in Saudi Arabia, Mediated by ESG Scoring?
by Laila Mohamed Alshawadfy Aladwey and Raghad Abdulkarim Alsudays
J. Risk Financial Manag. 2023, 16(12), 512; https://doi.org/10.3390/jrfm16120512 - 11 Dec 2023
Cited by 2 | Viewed by 2816
Abstract
The scarcity of female directors on Saudi boards is linked to cultural and social barriers deeply rooted in traditional masculine norms. Our study investigates the mediating role of ESG scores in the relationship between board gender diversity and firm value within the Saudi [...] Read more.
The scarcity of female directors on Saudi boards is linked to cultural and social barriers deeply rooted in traditional masculine norms. Our study investigates the mediating role of ESG scores in the relationship between board gender diversity and firm value within the Saudi context. The Structural Equation Model (SEM) was utilized based on a sample of 54 Saudi-listed financial companies on (Tadawul) during 2021–2022. The study unveiled a negative correlation between female director presence and Saudi firm value. This association is attributed to the prevailing male-dominated Saudi societal norms, where boards with more female members may hesitate to prioritize performance-driven actions due to concerns about their perceived legitimacy within traditional gender roles. Conversely, a positive correlation was observed between female director presence and ESG scores, aligning with existing research highlighting the role of board gender diversity in improving sustainability performance. The sustainability framework prevails over the influence of gender diversity, fully integrating it within the broader context of sustainability to enhance the value of Saudi companies. Our results are consistent when considering alternative measures of firm value. Our findings offer valuable insights for investors assessing board gender diversity’s impact on company value and emphasize the role of gender diversity in enhancing sustainability. They suggest that greater female representation on boards is vital for ESG score improvement, promoting sustainable initiatives and overall firm value. This calls for policymakers to promote sustainability disclosures and establish guidelines for increased female board participation, considering the absence of mandatory quotas. Full article
(This article belongs to the Special Issue Corporate Finance and Environmental, Social, and Governance (ESG) Practices)
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<p>Board gender diversity and firm value: the mediating effect of ESG disclosure.</p> Full article ">
14 pages, 1048 KiB  
Article
The Impact of COVID-19 on the Internationalization Performance of Family Businesses: Evidence from Portugal
by Ana Roque and Maria-Ceu Alves
J. Risk Financial Manag. 2023, 16(12), 511; https://doi.org/10.3390/jrfm16120511 - 8 Dec 2023
Cited by 2 | Viewed by 1670
Abstract
Drawing on the internationalization and family business literature, this preliminary and exploratory study examines the impact of the COVID-19 pandemic on the internationalization performance of family firms. To the best of our knowledge, this is the first study to analyze the impact of [...] Read more.
Drawing on the internationalization and family business literature, this preliminary and exploratory study examines the impact of the COVID-19 pandemic on the internationalization performance of family firms. To the best of our knowledge, this is the first study to analyze the impact of COVID-19 on the internationalization strategy of Portuguese family firms. Using a questionnaire survey of private family firms, this paper adopts a quantitative approach. Our analysis of data from a single survey of 127 family firms shows that these firms mostly use the Uppsala model of internationalization. The results indicate that COVID-19 has a very negative and statistically significant impact on the different components of the internationalization performance of family businesses. This study contributes significantly to a better understanding of the impact of uncertainty caused by epidemiological scenarios on the strategy and performance of family firms. Full article
(This article belongs to the Special Issue Family Companies)
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<p>Questionnaire structure (adapted from: <a href="#B89-jrfm-16-00511" class="html-bibr">Roque et al. 2019a</a>; <a href="#B88-jrfm-16-00511" class="html-bibr">Ribau et al. 2017</a>; <a href="#B48-jrfm-16-00511" class="html-bibr">Jantunen et al. 2005</a>; <a href="#B55-jrfm-16-00511" class="html-bibr">Kuivalainen et al. 2007</a>; <a href="#B6-jrfm-16-00511" class="html-bibr">Aulakh et al. 2000</a>; <a href="#B104-jrfm-16-00511" class="html-bibr">Zou et al. 1998</a>; <a href="#B14-jrfm-16-00511" class="html-bibr">Cavusgil and Zou 1994</a>; <a href="#B64-jrfm-16-00511" class="html-bibr">Matthyssens and Pauwels 1996</a>; <a href="#B102-jrfm-16-00511" class="html-bibr">Ziegler et al. 2020</a>).</p> Full article ">
13 pages, 1775 KiB  
Article
Are Cryptocurrency Forks Wealth Creating?
by Bill Hu and Jonathan Miller
J. Risk Financial Manag. 2023, 16(12), 510; https://doi.org/10.3390/jrfm16120510 - 8 Dec 2023
Viewed by 1822
Abstract
We find that planned cryptocurrency forks, like voluntary corporate spin-offs, are wealth-creating. Involuntary forks that are forced due to hacks and other problems with the blockchain are not. We find diminishing returns for second-generation forks, alleviating the concern of forking solely for wealth [...] Read more.
We find that planned cryptocurrency forks, like voluntary corporate spin-offs, are wealth-creating. Involuntary forks that are forced due to hacks and other problems with the blockchain are not. We find diminishing returns for second-generation forks, alleviating the concern of forking solely for wealth creation. Full article
(This article belongs to the Special Issue Blockchain Technologies and Cryptocurrencies​)
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<p>Bitcoin–Bitcoin Gold Fork.</p> Full article ">Figure 2
<p>Bitcoin–Bitcoin Cash Fork.</p> Full article ">Figure 3
<p>Bitcoin–Bitcoin Diamond Fork.</p> Full article ">Figure 4
<p>Bitcoin–CashBitcoin Cash SV Fork.</p> Full article ">Figure 5
<p>Ethereum–Ethereum Classic Upgrade.</p> Full article ">
24 pages, 2106 KiB  
Article
Monte Carlo Sensitivities Using the Absolute Measure-Valued Derivative Method
by Mark Joshi, Oh Kang Kwon and Stephen Satchell
J. Risk Financial Manag. 2023, 16(12), 509; https://doi.org/10.3390/jrfm16120509 - 8 Dec 2023
Viewed by 1554
Abstract
Measure-valued differentiation (MVD) is a relatively new method for computing Monte Carlo sensitivities, relying on a decomposition of the derivative of transition densities of the underlying process into a linear combination of probability measures. In computing the sensitivities, additional paths are generated for [...] Read more.
Measure-valued differentiation (MVD) is a relatively new method for computing Monte Carlo sensitivities, relying on a decomposition of the derivative of transition densities of the underlying process into a linear combination of probability measures. In computing the sensitivities, additional paths are generated for each constituent distribution and the payoffs from these paths are combined to produce sample estimates. The method generally produces sensitivity estimates with lower variance than the finite difference and likelihood ratio methods, and can be applied to discontinuous payoffs in contrast to the pathwise differentiation method. However, these benefits come at the expense of an additional computational burden. In this paper, we propose an alternative approach, called the absolute measure-valued differentiation (AMVD) method, which expresses the derivative of the transition density at each simulation step as a single density rather than a linear combination. It is computationally more efficient than the MVD method and can result in sensitivity estimates with lower variance. Analytic and numerical examples are provided to compare the variance in the sensitivity estimates of the AMVD method against alternative methods. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Journal of Risk and Financial Management)
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Figure 1
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> and, on the right, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>. On the right, there are variance ratios at <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. The solid curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, the dashed curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, and the dotted curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> and, on the right, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>. On the right, there are variance ratios at <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. The solid curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, the dashed curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, and the dotted curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> and, on the right, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> and, on the right, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Comparison of double-barrier option vega standard deviations for the LR, MVD, and AMVD methods.</p> Full article ">
27 pages, 3402 KiB  
Article
Optimal and Non-Optimal MACD Parameter Values and Their Ranges for Stock-Index Futures: A Comparative Study of Nikkei, Dow Jones, and Nasdaq
by Byung-Kook Kang
J. Risk Financial Manag. 2023, 16(12), 508; https://doi.org/10.3390/jrfm16120508 - 7 Dec 2023
Cited by 1 | Viewed by 3351
Abstract
This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the [...] Read more.
This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the existence of specific ranges of optimal and non-optimal values for each of the three parameters of the MACD indicator in these indices. Sample models employing the optimal parameter values in the three index futures generated significantly higher returns, outperforming both a non-technical buy-and-hold strategy and a random strategy that did not incorporate any market information. This discovery suggests that the three market indices may not be weak-form efficient. Therefore, this study contributes to the research on market efficiency by verifying inefficiency using a new approach. The highlight of this study is identifying that the ranges of optimal parameter values for the three indices are different from each other, but the optimal parameter value combinations for each of the three indices share a unique characteristic form. This issue and its finding have not been explored in the existing literature. Several interesting findings and valuable insights for market participants and researchers arise from this study. The new methodology is unique in finding optimal and non-optimal parameter values through the analysis of parameter sets used in well-performing and poorly performing sample models. Its validity and reliability have been confirmed by this study, making a useful contribution to the field of technical analysis research, particularly in parameter optimization insight. Full article
(This article belongs to the Special Issue Low Frequency Algorithmic Trading)
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Figure 1
<p>Frequencies of the three parameter values (<span class="html-italic">n</span><sub>1</sub>, <span class="html-italic">n</span><sub>2</sub>, <span class="html-italic">n</span><sub>3</sub>) in the top/bottom 100, 500, and 1000 models.</p> Full article ">Figure 2
<p>Index values of the Nikkei 225, Dow Jones, and Nasdaq futures (2011–2021). Note: This figure shows the historical changes in the three indices: Nikkei 225 (blue), Dow Jones (red), and Nasdaq (black). The dataset for each index covers 2692, 2896, and 2877 trading days, respectively.</p> Full article ">Figure 3
<p>Annual market returns of Nikkei, Dow Jones, and Nasdaq. Note: The annual market returns for the three indices are obtained from the natural logarithm of the ratio between the year-end index value and the year-beginning index value.</p> Full article ">Figure 4
<p>Performance comparison: P-P models vs. Benchmark strategies.</p> Full article ">Figure 5
<p>Optimal and non-optimal parameter value ranges in Nikkei, Dow Jones, and Nasdaq. Note: The optimal parameter ranges are represented by dots in each rectangle, corresponding to the parameter values used in the ‘P-P’ models of the A<sub>n1</sub> − A<sub>n2</sub> − A<sub>n3</sub> group. In contrast, non-optimal parameter ranges are depicted by crosses in each rectangle, indicating the parameter values used in the sample models of the W<sub>n1</sub> − W<sub>n2</sub> − W<sub>n3</sub> group. Blue bands and gray bands indicate the whole parameter value ranges examined in this study.</p> Full article ">
22 pages, 563 KiB  
Article
ECB Monetary Policy and the Term Structure of Bank Default Risk
by Tom Beernaert, Nicolas Soenen and Rudi Vander Vennet
J. Risk Financial Manag. 2023, 16(12), 507; https://doi.org/10.3390/jrfm16120507 - 7 Dec 2023
Viewed by 2155
Abstract
Euro Area banks have been confronted with unprecedented monetary policy actions by the ECB. Monetary policy may affect bank risk profiles, but the consequences may differ for short-term risk versus long-term or structural bank risk. We empirically investigated the association between the ECB’s [...] Read more.
Euro Area banks have been confronted with unprecedented monetary policy actions by the ECB. Monetary policy may affect bank risk profiles, but the consequences may differ for short-term risk versus long-term or structural bank risk. We empirically investigated the association between the ECB’s monetary policy stance and market-perceived short-term and long-term bank risk, using the term structure of default risk captured by bank CDS spreads. The results demonstrated that, during the period 2009–2020, ECB expansionary monetary policy diminished bank default risk in the short term. However, we did not observe a similar decline in long-term bank default risk, since we documented that monetary stimulus is associated with a steepening of the bank default risk curve. The reduction of bank default risk was most pronounced during the sovereign debt crisis and for periphery Euro Area banks. From 2018 onwards, monetary policy accommodation is associated with increased bank default risk, both short-term and structurally, which is consistent with the risk-taking hypothesis under which banks engage in excessive risk-taking behavior in their loan and securities portfolios to compensate profitability pressure caused by persistently low rates. The increase in perceived default risk is especially visible for banks with a high reliance on deposit funding. Full article
(This article belongs to the Section Banking and Finance)
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Figure 1
<p>Yield curve factors of the four-factor Svensson model (<a href="#B51-jrfm-16-00507" class="html-bibr">Svensson 1994</a>). The graph shows the evolution of each factor: level, slope, and two curvatures with a decay rate of <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.0609</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.0299</mn> </mrow> </semantics></math>, respectively.</p> Full article ">Figure 2
<p>Bank default risk curve loadings. The top row displays the time series of the level and slope parameters, whilst the bottom row shows the first and second curvature loadings. The level, slope, and curvatures are estimated weekly, by aggregating CDS spreads (daily) of the given week. The solid black line represents the median value of the variables in a given week, while the darker and lighter blue areas show the 25th−75th and the 10th−90th percentiles for a given week.</p> Full article ">Figure 3
<p>ECB monetary policy stance. Time series of the cumulative monetary policy shocks for the Euro Area, estimated using an identification-through-heteroskedasticity approach proposed by <a href="#B44-jrfm-16-00507" class="html-bibr">Rigobon and Sack</a> (<a href="#B44-jrfm-16-00507" class="html-bibr">2004</a>). An increase in the monetary policy stance reflects an accommodative monetary policy change; a decrease captures restrictive monetary policy changes. We highlight some of these announcement dates: (<b>a</b>) the ECB starts its first Covered Bond Purchase Program (CBPP1) and announces a one-year LTRO; (<b>b</b>) the ECB announces its Securities Markets Program; (<b>c</b>) the ECB increases its MRO interest rate; (<b>d</b>) ECB President Mario Draghi states that the ECB “is ready to do whatever it takes to preserve the euro”; (<b>e</b>) the ECB introduces the Outright Monetary Transactions (OMT) program; (<b>f</b>) the ECB announces it will start buying public-sector securities (EUR 60 billion per month until September 2016); (<b>g</b>) the ECB decreases the deposit facility rate to −0.3% and extends its APP program until the end of March 2017; (<b>h</b>) the ECB extends its APP for EUR 30 billion until at least September 2018; (<b>i</b>) the ECB offers the forward guidance that interest rates will remain low until the summer of 2019; (<b>j</b>) the ECB announces its pandemic emergency purchase program (EUR 750 billion until end 2020).</p> Full article ">Figure 4
<p>Evolution of the impact of monetary policy on short-term and long-term bank CDS spreads over time.</p> Full article ">Figure 5
<p>Evolution of the impact of monetary policy on short-term and long-term bank credit risk over time, for banks with a high and low reliance on deposit funding.</p> Full article ">
126 pages, 14996 KiB  
Article
Target2: The Silent Bailout System That Keeps the Euro Afloat
by David Blake
J. Risk Financial Manag. 2023, 16(12), 506; https://doi.org/10.3390/jrfm16120506 - 7 Dec 2023
Viewed by 2979
Abstract
Target2 is the Eurozone’s cross-border payment system, which is mandatory for the settlement of euro transactions involving Eurozone central banks. It is being used to save the Eurozone from imploding. A key underlying problem is that the Eurozone does not satisfy the economic [...] Read more.
Target2 is the Eurozone’s cross-border payment system, which is mandatory for the settlement of euro transactions involving Eurozone central banks. It is being used to save the Eurozone from imploding. A key underlying problem is that the Eurozone does not satisfy the economic conditions for being an Optimal Currency Area, i.e., a geographical area over which a single currency and monetary policy can operate on a sustainable, long-term basis. The different business cycles in the Eurozone, combined with poor labour and capital market flexibility, mean that systematic trade surpluses and deficits will build up because inter-regional exchange rates can no longer be changed. Surplus regions need to recycle the surpluses back into deficit regions via transfers to keep the Eurozone economies in balance. But the largest surplus country—Germany—refuses to formally accept that the European Union is a ‘transfer union’. However, deficit countries, including the largest of these—Italy—are using Target2 for this purpose. Target2 has become a giant credit card for Eurozone members that import more than they export to other members, but with two differences compared with normal credit card debt: neither the debt nor the interest that accrues on the debt ever needs to be repaid. Furthermore, the size of the deficits being built up is causing citizens in deficit countries to lose confidence in their banking systems, leading them to transfer their funds to banks in surplus countries. Target2 is also being used to facilitate this capital flight. However, these are not viable long-term solutions to systemic Eurozone trade imbalances and weakening national banking systems. There are only two realistic outcomes. The first is a full fiscal and political union, with Brussels determining the levels of tax and public expenditure in each member state—which has long been the objective of Europe’s political establishment. The second outcome is that the Eurozone breaks up. Full article
(This article belongs to the Special Issue Bank Lending and Monetary Policy)
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<p>Eurozone GDP real growth rate (% quarterly), 1999–2019.</p> Full article ">Figure 2
<p>UK GDP real growth rate (% quarterly), 1999–2019.</p> Full article ">Figure 3
<p>US GDP real growth rate (% quarterly), 1999–2019.</p> Full article ">Figure 4
<p>Eurozone unemployment rate (%), 1999–2019.</p> Full article ">Figure 5
<p>UK unemployment rate (%), 1999–2019.</p> Full article ">Figure 6
<p>US unemployment rate (%), 1999–2019.</p> Full article ">Figure 7
<p>Eurozone youth unemployment rate (%), 1999–2019.</p> Full article ">Figure 8
<p>UK youth unemployment rate (%), 1999–2019.</p> Full article ">Figure 9
<p>US youth unemployment rate (%), 1999–2019.</p> Full article ">Figure 10
<p>Eurozone government debt-to-GDP ratio (%), 1999–2019.</p> Full article ">Figure 11
<p>Individual Eurozone and UK government debt-to-GDP ratios (%) in 2019.</p> Full article ">Figure 12
<p>UK government debt-to-GDP ratio (%), 1999–2019.</p> Full article ">Figure 13
<p>US government debt-to-GDP ratio (%), 1999–2019.</p> Full article ">Figure 14
<p>Per capita GDP relative to the EU average in member states where the catch-up effect dominates. Sources: 1. European Commission. 2. <a href="#B162-jrfm-16-00506" class="html-bibr">Tilford</a> (<a href="#B162-jrfm-16-00506" class="html-bibr">2017</a>). Note: PPS—purchasing power standards.</p> Full article ">Figure 15
<p>Per capita GDP relative to the EU average in member states where the agglomeration effect dominates. Sources: 1. European Commission. 2. <a href="#B162-jrfm-16-00506" class="html-bibr">Tilford</a> (<a href="#B162-jrfm-16-00506" class="html-bibr">2017</a>). Note: PPS—purchasing power standards.</p> Full article ">Figure 16
<p>Germany’s intra-EU trade balance (EUR mn), 2010–2021. Source: Tradingeconomics.com; <a href="https://tradingeconomics.com/germany/intra-eu-trade-trade-balance-eurostat-data.html" target="_blank">https://tradingeconomics.com/germany/intra-eu-trade-trade-balance-eurostat-data.html</a> (accessed on 9 November 2022).</p> Full article ">Figure 17
<p>Italy’s intra-EU trade balance (EUR mn), 2010–2021. Source: Tradingeconomics.com; <a href="https://tradingeconomics.com/italy/intra-eu-trade-trade-balance-eurostat-data.html" target="_blank">https://tradingeconomics.com/italy/intra-eu-trade-trade-balance-eurostat-data.html</a> (accessed on 9 November 2022).</p> Full article ">Figure 18
<p>The Eurozone’s international trade in goods (EUR bn), 2012–2020. Source: Eurostat Newsrelease Euroindicators 93/2020–15 June 2020; <a href="https://ec.europa.eu/eurostat/documents/2995521/10294876/6-15062020-AP-EN.pdf/5a036ad2-8a36-faaf-4fd9-fb1a285d5ee4" target="_blank">https://ec.europa.eu/eurostat/documents/2995521/10294876/6-15062020-AP-EN.pdf/5a036ad2-8a36-faaf-4fd9-fb1a285d5ee4</a> (accessed on 9 November 2022).</p> Full article ">Figure 19
<p>Germany’s international trade balance (EUR bn and percentage of GDP), 1970–2020. Source: <a href="https://www.macrotrends.net/countries/DEU/germany/trade-balance-deficit" target="_blank">https://www.macrotrends.net/countries/DEU/germany/trade-balance-deficit</a> (accessed on 9 November 2022).</p> Full article ">Figure 20
<p>Italy’s international trade balance (EUR bn and percentage of GDP), 1970–2020. Source: <a href="https://www.macrotrends.net/countries/ITA/italy/trade-balance-deficit" target="_blank">https://www.macrotrends.net/countries/ITA/italy/trade-balance-deficit</a> (accessed on 9 November 2022).</p> Full article ">Figure 21
<p>Target2 balances of Germany and Italy (EUR bn), January 2001–December 2022. Source: Euro Crisis Monitor, Institute of Empirical Economic Research, Osnabrück University; <a href="http://www.eurocrisismonitor.com" target="_blank">http://www.eurocrisismonitor.com</a> (accessed on 10 March 2023).</p> Full article ">Figure 22
<p>Target2 balances of selected countries in the Eurozone (EUR bn), January 2001–December 2022. Source: Euro Crisis Monitor, Institute of Empirical Economic Research, Osnabrück University; <a href="http://www.eurocrisismonitor.com" target="_blank">http://www.eurocrisismonitor.com</a> (accessed on 10 March 2023).<a href="#fn143-jrfm-16-00506" class="html-fn">143</a></p> Full article ">Figure 23
<p>Interest rate on two-year German debt (%), 2012–2022. Source: <a href="https://tradingeconomics.com/germany/2-year-note-yield" target="_blank">https://tradingeconomics.com/germany/2-year-note-yield</a> (accessed on 12 January 2023).</p> Full article ">Figure 24
<p>Comparison of the credit risk pyramid in the UK and the Eurozone—no actual sovereign at the apex of the euro-pyramid. Source: <a href="#B147-jrfm-16-00506" class="html-bibr">Reynolds et al.</a> (<a href="#B147-jrfm-16-00506" class="html-bibr">2020, Figure 1</a>). Note: PSE—public sector entities.</p> Full article ">Figure 25
<p>Non-performing loans in EU member states in 2021 (% of bank equity). Sources: 1. ECB. 2. <a href="#B33-jrfm-16-00506" class="html-bibr">Teunis Brosens</a> (<a href="#B33-jrfm-16-00506" class="html-bibr">2020</a>), Bank non-performing loans in 30 October 2021; <a href="https://think.ing.com/articles/bank-non-performing-loans-the-silence-before-the-storm" target="_blank">https://think.ing.com/articles/bank-non-performing-loans-the-silence-before-the-storm</a> (accessed on 10 January 2022).</p> Full article ">Figure 26
<p>Share of vulnerable sectors in the total loan portfolio in EU member states in 2021 (%). Sources: 1. ECB. 2. <a href="#B33-jrfm-16-00506" class="html-bibr">Teunis Brosens</a> (<a href="#B33-jrfm-16-00506" class="html-bibr">2020</a>), Bank non-performing loans in 30 October 2021; <a href="https://think.ing.com/articles/bank-non-performing-loans-the-silence-before-the-storm" target="_blank">https://think.ing.com/articles/bank-non-performing-loans-the-silence-before-the-storm</a> (accessed on 10 January 2022).</p> Full article ">Figure 27
<p>Development of the doom loop in key Eurozone member states, 2007–2021. Sources: 1. ECB 2. (<a href="#B11-jrfm-16-00506" class="html-bibr">Arnold 2021</a>).</p> Full article ">Figure 28
<p>Development of the Eurozone doom loop, 2007–2021. Sources: 1. ECB 2. (<a href="#B11-jrfm-16-00506" class="html-bibr">Arnold 2021</a>).</p> Full article ">Figure 29
<p>Five-year credit default swaps on Eurozone banks (basis points). Source: (<a href="#B95-jrfm-16-00506" class="html-bibr">Foy 2023</a>).</p> Full article ">Figure 30
<p>Shadow banks are bigger than regular banks. Source: <a href="#B85-jrfm-16-00506" class="html-bibr">Evans-Pritchard</a> (<a href="#B85-jrfm-16-00506" class="html-bibr">2023a</a>).</p> Full article ">Figure 31
<p>The relative size of the shadow banking sector in selected countries. Source: <a href="#B85-jrfm-16-00506" class="html-bibr">Evans-Pritchard</a> (<a href="#B85-jrfm-16-00506" class="html-bibr">2023a</a>).</p> Full article ">Figure 32
<p>ECB Asset Purchase Programmes (EUR bn), January 2015–January 2023. Source: ECB Asset Purchase Programmes; <a href="https://www.ecb.europa.eu/mopo/implement/app/html/index.en.html" target="_blank">https://www.ecb.europa.eu/mopo/implement/app/html/index.en.html</a> (accessed on 20 February 2023). Note: PSPP—Public Sector Purchase Programme, CBPP3—Third Covered Bond Purchase Programme (CBPP3), CSPP—Corporate Sector Purchase Programme, and ABSPP—Asset-Backed Securities Purchase Programme (ABSPP). The data are cumulative.</p> Full article ">Figure 33
<p>ECB under-purchasing German bonds and over-purchasing Italian and Spanish bonds—Public Sector Purchase Programme, monthly deviation from adjusted capital key (EUR m), May 2015–July 2017. Source: European Central Bank, OMFIF analysis.</p> Full article ">Figure 34
<p>Italy–Germany 10-year government bond yield spread (basis points), April 2007–March 2023. Source: <a href="http://www.worldgovernmentbonds.com/spread/italy-10-years-vs-germany-10-years/" target="_blank">http://www.worldgovernmentbonds.com/spread/italy-10-years-vs-germany-10-years/</a> (accessed on 12 May 2023).</p> Full article ">Figure 35
<p>Share of Italian public debt held by foreign investors (%), 2008–2022. Sources: 1. <a href="https://www.bancaditalia.it/pubblicazioni/finanza-pubblica/2022-finanza-pubblica/en_statistiche_FPI_20220215.pdf" target="_blank">https://www.bancaditalia.it/pubblicazioni/finanza-pubblica/2022-finanza-pubblica/en_statistiche_FPI_20220215.pdf</a> (accessed on 10 January 2023). 2. <a href="https://scoperatings.com/ratings-and-research/research/EN/173351" target="_blank">https://scoperatings.com/ratings-and-research/research/EN/173351</a> (accessed on 26 August 2023).</p> Full article ">Figure 36
<p>Germany’s industrial output (index 2015 = 100). Source: (<a href="#B126-jrfm-16-00506" class="html-bibr">Lynn 2023b</a>).</p> Full article ">Figure 37
<p>Germany’s net public investment as a share of GDP. Source: (<a href="#B126-jrfm-16-00506" class="html-bibr">Lynn 2023b</a>).</p> Full article ">Figure 38
<p>The ECB’s balance sheet in October 2022 (EUR bn). Source: <span class="html-italic">Financial Times Europe Express</span>, 26 October 2022.</p> Full article ">Figure 39
<p>EU company bankruptcies 2019–2022. Source: <span class="html-italic">Financial Times</span>, 17 February 2023; <a href="https://www.ft.com/content/c90c3556-3218-47ff-aeda-3f23af217c11" target="_blank">https://www.ft.com/content/c90c3556-3218-47ff-aeda-3f23af217c11</a> (accessed on 19 February 2023).</p> Full article ">Figure 40
<p>Global debt as a share of global national income 2000–2027. Source: (<a href="#B46-jrfm-16-00506" class="html-bibr">Chan 2023b</a>).</p> Full article ">Figure 41
<p>Italian budget deficit, EUR bn, 2005–2023. Source: (<a href="#B87-jrfm-16-00506" class="html-bibr">Evans-Pritchard 2023c</a>).</p> Full article ">Figure 42
<p>Italian government bond yields at issuance, average, 2000–2023. Source: (<a href="#B87-jrfm-16-00506" class="html-bibr">Evans-Pritchard 2023c</a>).</p> Full article ">
23 pages, 1420 KiB  
Article
Understanding the Determinants of FinTech Adoption: Integrating UTAUT2 with Trust Theoretic Model
by Muhammed Basid Amnas, Murugesan Selvam, Mariappan Raja, Sakthivel Santhoshkumar and Satyanarayana Parayitam
J. Risk Financial Manag. 2023, 16(12), 505; https://doi.org/10.3390/jrfm16120505 - 6 Dec 2023
Cited by 6 | Viewed by 10514
Abstract
Financial technology (FinTech) is transforming the financial services industry by offering innovative, convenient solutions for businesses and individuals. This study examines the factors influencing FinTech adoption, with a special focus on trust. By integrating insights from both the unified theory of acceptance and [...] Read more.
Financial technology (FinTech) is transforming the financial services industry by offering innovative, convenient solutions for businesses and individuals. This study examines the factors influencing FinTech adoption, with a special focus on trust. By integrating insights from both the unified theory of acceptance and use of technology (UTAUT2), and the trust theoretic model (TTM), this research uncovers critical determinants of FinTech adoption. Utilizing survey responses obtained from 399 participants, this research employs the partial least squares structural equation modelling method. The findings reveal that performance expectancy, effort expectancy, social influence, habit, price value, and facilitating conditions significantly influence users’ intentions to use FinTech services. In addition, the study shows that trust plays a crucial role in FinTech use, as it influences both the intentions to use and the actual use of FinTech. Surprisingly, hedonic motivation was found not to affect users’ intentions, implying that people see FinTech as a practical, rather than enjoyable, endeavor. These insights provide valuable guidance for service providers and policymakers seeking to enhance FinTech adoption and utilization while ensuring the security and trustworthiness of these digital platforms. Full article
(This article belongs to the Special Issue Financial Technologies (Fintech) in Finance and Economics)
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<p>Research model.</p> Full article ">Figure 2
<p>Results of structural model.</p> Full article ">
9 pages, 268 KiB  
Article
The Effects of Corporate Financial Disclosure on Stock Prices: A Case Study of Korea’s Compulsory Preliminary Earnings Announcements
by Sun-Keun Yoo and Se-Hak Chun
J. Risk Financial Manag. 2023, 16(12), 504; https://doi.org/10.3390/jrfm16120504 - 6 Dec 2023
Viewed by 2189
Abstract
This paper examines the effects of Korea’s compulsory preliminary earnings announcements on stock prices using individual corporate financial disclosure data. Korea’s compulsory preliminary earnings announcements are similar to the US’s fair disclosures in that they are preliminary settlement disclosures. Disclosure regulation aims to [...] Read more.
This paper examines the effects of Korea’s compulsory preliminary earnings announcements on stock prices using individual corporate financial disclosure data. Korea’s compulsory preliminary earnings announcements are similar to the US’s fair disclosures in that they are preliminary settlement disclosures. Disclosure regulation aims to prevent insider trading and resolve information asymmetry among investors by promptly disclosing unconfirmed internal settlement information prior to an external audit. The disclosure of such changes in profit or loss is generally expected to affect stock prices. Many studies have analyzed the relationship between accounting profit disclosure and stock prices, but most have focused on the relationship between net profit disclosure and stock price without considering other disclosure information such as sales and operating profit. In addition, previous studies analyzed the information effect of accounting profits based on annual reports, which are based on analysts’ predicted values and limited datasets. This study investigates the impact of Korea’s compulsory disclosure on stock prices through a multiple regression analysis, considering three types of accounting information, including sales, operating profit, and net profit, based on actual announcement data and daily trading volumes. The effect of corporate financial disclosure might vary with stock market type and industry sector. For this reason, we analyze the relationship between financial disclosure and stock prices for different stock market types and industry sectors. Results show that sales information affected KOSPI-listed companies’ stock prices, and operating profit information affected KOSDAQ-listed companies’ stock prices. In terms of financial market efficiency, the results show weak-form efficiency for both the KOSPI and KOSDAQ markets in general. However, this implies that there is still information asymmetry in sales information for the KOSPI, which consists of large and valued stocks and is not completely efficient, whereas information asymmetry might occur in operating profit information for the KOSDAQ, which consists of relatively small-to-medium innovative growing companies. In addition, results show that operating profits affect manufacturing industries’ stock prices, and that trading volumes significantly impact stock prices for all markets and industries. Full article
(This article belongs to the Special Issue Corporate Finance and Environmental, Social, and Governance (ESG) Practices)
22 pages, 11557 KiB  
Article
A Hybrid Deep Learning Approach for Crude Oil Price Prediction
by Hind Aldabagh, Xianrong Zheng and Ravi Mukkamala
J. Risk Financial Manag. 2023, 16(12), 503; https://doi.org/10.3390/jrfm16120503 - 6 Dec 2023
Cited by 3 | Viewed by 3886
Abstract
Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil [...] Read more.
Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). We compared our one-step CNN–LSTM model with other LSTM models, the CNN model, support vector machine (SVM), and the autoregressive integrated moving average (ARIMA) model. Also, we compared our multi-step CNN–LSTM model with LSTM, CNN, and the time series encoder–decoder model. Extensive experiments were conducted using short-, medium-, and long-term price data of one, five, and ten years, respectively. In terms of accuracy, the proposed model outperformed existing models in both one-step and multi-step predictions. Full article
(This article belongs to the Special Issue Financial Technologies (Fintech) in Finance and Economics)
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<p>Calculation of the output <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mn>1,1</mn> </mrow> </msub> </mrow> </semantics></math> by applying a convolution filter <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>×</mo> <mi>F</mi> </mrow> </semantics></math> to an input layer represented by the <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>×</mo> <mi>N</mi> </mrow> </semantics></math> matrix.</p> Full article ">Figure 2
<p>An unrolled recurrent neural network.</p> Full article ">Figure 3
<p>One memory cell of a long short-term memory network.</p> Full article ">Figure 4
<p>The proposed hybrid model.</p> Full article ">Figure 5
<p>The vector output LSTM model.</p> Full article ">Figure 6
<p>The encoder–decoder LSTM model.</p> Full article ">Figure 7
<p>Daily crude oil prices for the long-term period.</p> Full article ">Figure 8
<p>Daily crude oil prices for the medium-term period.</p> Full article ">Figure 9
<p>Daily crude oil prices for the short-term period.</p> Full article ">Figure 10
<p>The training and testing data for long-, medium-, and short-term datasets.</p> Full article ">Figure 11
<p>The actual versus the predicted oil price using the hybrid model on the long-term dataset.</p> Full article ">Figure 12
<p>The actual versus the predicted oil price using the hybrid model on the medium-term dataset.</p> Full article ">Figure 13
<p>The actual versus the predicted oil price using the hybrid model on the short-term dataset.</p> Full article ">Figure 14
<p>(<b>a</b>) Simple moving average of the actual prices versus the predicted prices on the short-term dataset. (<b>b</b>) Simple moving average of the actual prices versus the predicted prices on medium-term dataset.</p> Full article ">Figure 15
<p>Simple moving average of the actual prices versus the predicted prices on the long-term dataset with an enlarged view of six time-intervals.</p> Full article ">Figure 16
<p>The actual versus the predicted oil price, using the vector output CNN–LSTM model on the long-term dataset for the t+1 day price prediction.</p> Full article ">Figure 17
<p>The actual versus the predicted oil price, using the encoder–decoder model on the long-term dataset for the t+1 day price prediction.</p> Full article ">Figure 18
<p>The actual versus the predicted oil price, using the vector output CNN–LSTM model on the long-term dataset for the t+7 day price prediction.</p> Full article ">Figure 19
<p>The actual versus the predicted oil price, using the encoder–decoder LSTM model on the long-term dataset for the t+7 day price prediction.</p> Full article ">Figure 20
<p>The actual versus the predicted oil price, using the vector output CNN–LSTM model on the medium-term dataset for the t+1 day price prediction.</p> Full article ">Figure 21
<p>The actual versus the predicted oil price, using the encoder–decoder model on the medium-term dataset for the t+1 day price prediction.</p> Full article ">Figure 22
<p>The actual versus the predicted oil price, using the vector output CNN–LSTM model on the medium-term dataset for the t+7 day price prediction.</p> Full article ">Figure 23
<p>The actual versus the predicted oil price, using the encoder–decoder LSTM model on the medium-term dataset for the t+7 day price prediction.</p> Full article ">Figure 24
<p>The actual versus the predicted oil price, using the vector output CNN–LSTM model on the short-term dataset for the t+1 day price prediction.</p> Full article ">Figure 25
<p>The actual versus the predicted oil price, using the encoder–decoder model on the short-term dataset for the t+1 day price prediction.</p> Full article ">Figure 26
<p>The actual versus the predicted oil price, using the vector output CNN–LSTM model on the short-term dataset for the t+7 day price prediction.</p> Full article ">Figure 27
<p>The actual versus the predicted oil price, using the encoder–decoder LSTM model on the short-term dataset for the t+7 day price prediction.</p> Full article ">
2 pages, 157 KiB  
Editorial
Financial Technology (Fintech) and Sustainable Financing: A New Paradigm for Risk Management
by Sisira Colombage
J. Risk Financial Manag. 2023, 16(12), 502; https://doi.org/10.3390/jrfm16120502 - 5 Dec 2023
Cited by 1 | Viewed by 3518
Abstract
Financial technology (fintech) is transforming the financial services industry, and its impact on sustainable financing is becoming increasingly profound [...] Full article
(This article belongs to the Special Issue Financial Technology (Fintech) and Sustainable Financing, 2nd Edition)
7 pages, 245 KiB  
Communication
Information Theory and the Pricing of Contingent Claims: An Alternative Derivation of the Black–Scholes–Merton Formula
by Thomas P. Davis
J. Risk Financial Manag. 2023, 16(12), 501; https://doi.org/10.3390/jrfm16120501 - 5 Dec 2023
Viewed by 1713
Abstract
This paper seeks to determine the best subjective probability to use to carry out expectation values of uncertain future cash flows with the smallest number of assumptions. This results in the unique distribution that guarantees no more information is present other than the [...] Read more.
This paper seeks to determine the best subjective probability to use to carry out expectation values of uncertain future cash flows with the smallest number of assumptions. This results in the unique distribution that guarantees no more information is present other than the stated assumptions. The result is a novel derivation of the well-known Black–Scholes equation without the need to introduce high-level mathematical machinery. This formalism fits nicely into introductory courses of finance, where the value of any financial instrument is given by the present value of uncertain future cash flows. Full article
(This article belongs to the Special Issue Featured Papers in Mathematics and Finance)
21 pages, 648 KiB  
Article
An Attempt to Understand Stock Market Investors’ Behaviour: The Case of Environmental, Social, and Governance (ESG) Forces in the Pakistani Stock Market
by Samina Rooh, Hatem El-Gohary, Imran Khan, Sayyam Alam and Syed Mohsin Ali Shah
J. Risk Financial Manag. 2023, 16(12), 500; https://doi.org/10.3390/jrfm16120500 - 5 Dec 2023
Cited by 1 | Viewed by 3185
Abstract
The present study investigates the decision-making process of investors on the Pakistan Stock Exchange with regard to portfolio construction, explicitly focusing on the incorporation of ESG concerns. A quantitative research approach has been implemented for this paper. The hypotheses have been developed and [...] Read more.
The present study investigates the decision-making process of investors on the Pakistan Stock Exchange with regard to portfolio construction, explicitly focusing on the incorporation of ESG concerns. A quantitative research approach has been implemented for this paper. The hypotheses have been developed and tested through the adapted questionnaires. The data were collected from individual Pakistani investors. The present study employed SmartPLS-SEM to quantitatively assess data received from a sample of 421 out of 500 respondents. Based on the available data, investors participating in the Pakistan Stock Exchange are notably impacted by ESG aspects. The findings of this study hold significance for emerging economy firms, regulators, and investors, in terms of both theoretical and practical ramifications. The study’s findings demonstrate a clear indication of investors’ significant emphasis on ESG matters. This research made a significant contribution to the field of behavioural finance with a focus on ESG-related issues. This work contributes to the literature on ESG elements by using the Theory of Planned Behaviour (TPB) to adapt the ESG components from the United Nations Global Compact (UNGC) and Thomson Reuters Corporate Responsibility Index (TRCRI). Furthermore, it provides valuable insights for stakeholders who are involved in the ever-evolving realm of sustainable finance within developing countries. Full article
(This article belongs to the Special Issue Contemporary Studies on Corporate Finance and Business Research)
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<p>Conceptual framework.</p> Full article ">
17 pages, 496 KiB  
Article
The Nexus between Corporate Performance and State Ownership in Vietnam: Evidence of State Ownership’s Inverted U-Shape and Provincial Business Environment Influences
by Tran Thai Ha Nguyen, Susilo Nur Aji Cokro Darsono, Gia Quyen Phan, Thi Hong Van Pham, Huyen Bach Thi and Sobar M. Johari
J. Risk Financial Manag. 2023, 16(12), 499; https://doi.org/10.3390/jrfm16120499 - 2 Dec 2023
Cited by 1 | Viewed by 2447
Abstract
The level of state ownership in corporations is still a controversial topic because of its duality: on the one hand, it brings resource advantages, and on the other hand, it causes agency problems. Thus, our study aims to investigate the relationship between state [...] Read more.
The level of state ownership in corporations is still a controversial topic because of its duality: on the one hand, it brings resource advantages, and on the other hand, it causes agency problems. Thus, our study aims to investigate the relationship between state ownership and corporate performance within the Vietnamese context, unraveling the impacts of state ownership’s non-linear and provincial business environment. Analyzing financial data spanning over a decade from 359 listed corporations on the Vietnamese stock markets (2010–2021), our empirical findings derived through the General Method of Moments (GMM) reveal that state ownership emerges as a potent “strategic asset” with a positive influence on corporate performance. However, a critical point is identified when state ownership surpasses the threshold of 32 percent and a decline in corporate performance ensues—a confirmation of an inverted U-shaped impact. These results substantiate the necessity of the equitization process and underscore the imperative of judiciously managing state ownership in Vietnam. Notably, our study unveils a more critical dimension: the enhanced provincial business environment bolsters corporate performance and amplifies the positive impact of state ownership. Thus, a strategic dual approach is suggested to improve corporate performance: improving the business environment and recalibrating the percentage of state shareholders. Our study serves as empirical evidence, referencing Vietnam and other transitional economies, toward mannerly policy decision-making related to state ownership and the business environment to boost corporate performance. Full article
(This article belongs to the Special Issue Emerging Markets II)
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<p>Analytical framework among state ownership, corporate performance, and the local business environment.</p> Full article ">
9 pages, 365 KiB  
Article
Farmers’ Willingness to Purchase Weather Index Crop Insurance: Evidence from Battambang, Cambodia
by Bungchay Lay, Isriya Bunyasiri and Ravissa Suchato
J. Risk Financial Manag. 2023, 16(12), 498; https://doi.org/10.3390/jrfm16120498 - 29 Nov 2023
Cited by 1 | Viewed by 2604
Abstract
The weather index crop insurance (WICI) scheme was introduced under a pilot project for rice in Cambodia in 2021. The adoption rate was low and the loss ratio was higher than 200%. The increase in farmers’ participation would help reduce the loss ratio, [...] Read more.
The weather index crop insurance (WICI) scheme was introduced under a pilot project for rice in Cambodia in 2021. The adoption rate was low and the loss ratio was higher than 200%. The increase in farmers’ participation would help reduce the loss ratio, which can sustain the WICI scheme. This study, therefore, examines Cambodian rice farmers’ willingness to purchase WICI in Cambodia. The hypothesis is that the low adoption rate is due to a lack of awareness, lack of understanding of WICI, lack of trust in weather stations, and the problem of basis risk. This study would like to test the influence of those factors on the willingness to purchase in Cambodia. Battambang Province was chosen as the study area as it is the largest area for rice production and has the largest take-up rate of farmers buying WICI. Detailed interviews of 232 farmers were conducted in the districts of Bavel and Moung Ruessei. The probit regression model was used to identify factors that significantly impact farmers’ willingness to purchase WICI. The results indicate that land size, level of trust in weather stations, level of farmers’ understanding of WICI, and joining the WICI awareness program have positive effects on the probability of farmers’ willingness to buy WICI, whereas the number of household laborers and expectation of floods have negative influences. The probability of willingness to purchase by farmers who attended the awareness program on WICI was 38% higher than those who did not. The size of farmland, level of trust in weather stations, and level of understanding of WICI increase in one unit affecting the probability of willingness to purchase WICI by 4%, 16%, and 25%, respectively. On the other hand, the increase in the number of household laborers in the rice field by one person and the increase in the probability of expected flood increase by 0.1 drag back the probability of farmers’ willingness to purchase by 16% and 5%, respectively. The results suggest the government to raise the insurance awareness and understanding of WICI. Development of weather station infrastructure, as well as maintenance of weather stations, is needed to guarantee the accuracy of data generated from the weather station. Full article
(This article belongs to the Section Economics and Finance)
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<p>Map showing the study area. (<b>a</b>) The map of Cambodia. The part highlighted in gray is Battambang Province. (<b>b</b>) The map of Battambang Province. The parts highlighted in red are the study areas (districts of Bavel and Moung Ruessei).</p> Full article ">
22 pages, 3399 KiB  
Article
Examining the Impact of Agency Issues on Corporate Performance: A Bibliometric Analysis
by Vinay Khandelwal, Prasoon Tripathi, Varun Chotia, Mohit Srivastava, Prashant Sharma and Sushil Kalyani
J. Risk Financial Manag. 2023, 16(12), 497; https://doi.org/10.3390/jrfm16120497 - 28 Nov 2023
Cited by 2 | Viewed by 8057
Abstract
An agency problem is defined as a conflict of interest arising due to a misalignment of interests among the managers and other stakeholders of the company. This article aims to review the articles addressing the agency problem and their impact on business performance. [...] Read more.
An agency problem is defined as a conflict of interest arising due to a misalignment of interests among the managers and other stakeholders of the company. This article aims to review the articles addressing the agency problem and their impact on business performance. This article reviews the contributions of prominent theorists on agency problems and agency costs. Using bibliometric attributes of 740 articles from the Scopus database, this study highlights the publishing trend and outlets, along with leading contributors and collaborators in terms of authors, institutions, and countries. This study identifies the clusters through the bibliographic coupling technique and a trend topics analysis. Most researchers have focused on corporate governance and expressed the agency problem as one of the impact areas. This study is unique as no study to date specifically focuses solely on agency theory or the agency problem through the lens of bibliometric analysis. Future research directions on agency problems and their solutions conclude this study. Full article
(This article belongs to the Special Issue Corporate Finance and Environmental, Social, and Governance (ESG) Practices)
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<p>Publishing Trend of Research on Agency Theory.</p> Full article ">Figure 2
<p>Leading Publishing Outlets of Research on Agency Theory (Minimum of Six Articles).</p> Full article ">Figure 3
<p>Leading Authors Contributing to Research on Agency Theory (Minimum of Three Research Articles).</p> Full article ">Figure 4
<p>Prominent Collaborators (Authors) on the Research Topic of Agency Theory.</p> Full article ">Figure 5
<p>Leading Institutions Contributing to Research on AP (Minimum of Six Research Articles).</p> Full article ">Figure 6
<p>Prominent Collaborators (Institutions) on the Research Topic of Agency Theory.</p> Full article ">Figure 7
<p>Geographic Heat Map of Countries Contributing to Research on Agency Theory.</p> Full article ">Figure 8
<p>Prominent Collaborators (Countries) on the Research Topic of Agency Theory.</p> Full article ">Figure 9
<p>Knowledge Clusters Identified as a Result of “Bibliographic by Coupling” Analysis.</p> Full article ">Figure 10
<p>In-trend keywords on the research in the field of AT over the past seven years.</p> Full article ">
21 pages, 3167 KiB  
Article
Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach
by Nicolas Suhadolnik, Jo Ueyama and Sergio Da Silva
J. Risk Financial Manag. 2023, 16(12), 496; https://doi.org/10.3390/jrfm16120496 - 27 Nov 2023
Cited by 7 | Viewed by 10686
Abstract
Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a financial institution. We also summarize key statistical and machine learning [...] Read more.
Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a financial institution. We also summarize key statistical and machine learning models in credit scoring and review current research findings. Our results indicate that ensemble models, particularly XGBoost, outperform traditional algorithms such as logistic regression in credit classification. Researchers and experts in the subject of credit risk can use this work as a practical reference as it covers crucial phases of data processing, exploratory data analysis, modeling, and evaluation metrics. Full article
(This article belongs to the Special Issue The Effects of Interest Rates and Macroprudential Policies on Banks' Credit Portfolio)
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<p>Hypothetical example of a separation hyperplane defined by a SVM algorithm.</p> Full article ">Figure 2
<p>Artificial neural network with four attributes, two intermediate layers, and binary output.</p> Full article ">Figure 3
<p>The higher the AUC value, or the closer the ROC curve is to the upper left corner, the better the classifier’s performance. Source: <a href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py" target="_blank">https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py</a> (accessed on 15 November 2023).</p> Full article ">Figure 4
<p>Loan status.</p> Full article ">Figure 5
<p>Loans made each year.</p> Full article ">Figure 6
<p>Missing matrix. Each column in the matrix represents a variable found in the original dataset, and any empty spaces denote missing values.</p> Full article ">Figure 7
<p>Only the top two quantitative features appear to be related to loan status.</p> Full article ">Figure 8
<p>Selected features and loan status.</p> Full article ">Figure 9
<p>Selected features and loan status. Source: Adapted from <a href="https://scikit-learn.org/stable/modules/cross_validation.html" target="_blank">https://scikit-learn.org/stable/modules/cross_validation.html</a>. Access on 15 November 2023.</p> Full article ">Figure 10
<p>Model performance when resampled. The error bars represent the standard deviation.</p> Full article ">Figure 11
<p>ROC curve and AUC. The AUC is the area under the ROC curve, calculated by plotting the false positive rate against the true positive rate.</p> Full article ">Figure 12
<p>Relative importance of features.</p> Full article ">
27 pages, 495 KiB  
Article
Global Financial Market Integration: A Literature Survey
by Sama Haddad
J. Risk Financial Manag. 2023, 16(12), 495; https://doi.org/10.3390/jrfm16120495 - 27 Nov 2023
Cited by 3 | Viewed by 9541
Abstract
This article undertakes a literature review on the topic of market integration, covering over 380 articles from the 1980s to 2024. The review consists of a qualitative analysis for context and a quantitative analysis for content, identifying key research streams and proposing directions [...] Read more.
This article undertakes a literature review on the topic of market integration, covering over 380 articles from the 1980s to 2024. The review consists of a qualitative analysis for context and a quantitative analysis for content, identifying key research streams and proposing directions for future research. I have identified six research groups: (1) market segmentation, (2) portfolio diversification, (3) market integration evidence from developed and emerging markets, (4) spillovers and linkages, (5) economic market integration, and (6) financial market integration and volatility. The literature focuses on market integration; it aims to answer the following questions: (1) What is the scope of market integration research? (2) What are the direct influences of market integration looking at top journals and authors and characteristics of most studied and cited topics? (3) What are the past and recent topics studied within the area of market integration? (4) What are the potential future research questions to explore in market integration? The topic of market integration has been controversial in many studies, as seen in policy decision-making, investments, and other related areas; this literature will provide great benefit for such an audience. Full article
(This article belongs to the Special Issue Emerging Markets II)
16 pages, 1143 KiB  
Article
Fossil Fuel-Based versus Electric Vehicles: A Volatility Spillover Perspective Regarding the Environment
by Shailesh Rastogi, Jagjeevan Kanoujiya, Satyendra Pratap Singh, Adesh Doifode, Neha Parashar and Pracheta Tejasmayee
J. Risk Financial Manag. 2023, 16(12), 494; https://doi.org/10.3390/jrfm16120494 - 22 Nov 2023
Viewed by 2518
Abstract
Due to environmental concerns, electric vehicles (EVs) are gaining traction over fossil fuel-based vehicles. For electronic devices, including vehicles, copper is the key material used for building. This situation draws attention to the impact of copper prices, crude oil prices, and exchange rates [...] Read more.
Due to environmental concerns, electric vehicles (EVs) are gaining traction over fossil fuel-based vehicles. For electronic devices, including vehicles, copper is the key material used for building. This situation draws attention to the impact of copper prices, crude oil prices, and exchange rates on the economic viability of using EVs over fossil fuels. We use the volatility spillover effect (VSE) to determine the financial viability of these two types of vehicles in the context of environmental issues. Daily data on copper prices, crude oil, exchange rate, and the BSE100 ESG (“Bombay Stock Exchange 100 Environmental, Social and Governance”) index are taken from 1 November 2017 to 20 September 2022. Two popular multivariate GARCH (“Multivariate Generalized Autoregressive Conditional Heteroscedasticity”) family models, i.e., the BEKK (“Baba–Engle–Kraft–Kroner”)-GARCH (BG) and DCC (“Dynamic Conditional Correlation”)-GARCH (DG) models, are utilized to find volatility connections between these variables. These are appropriate GARCH models to observe the volatility dependence of one market on another market. It is found that there exist volatility effects of copper and exchange rate on the S&P BSE100 ESG Equity Index Price, which we will refer to here as ESG. However, crude oil is found to be insignificant for ESG. The novelty of this study is in the use of volatility spillover to determine economic viability. The volatility effects of copper prices are positive for ESG in the short run and negative for long-term volatility. The exchange rate has a positive volatility effect on ESG in the long run. Surprisingly, we find that EVs are technologically better than fossil fuel-based vehicles as a possible sustainable energy source. We observe studies that have raised similar concerns about EVs’ lack of business sense compared to fossil fuels. However, using VSE to explore financial viability offers a fresh perspective. Based on the findings of the current study, it is recommended that policymakers and researchers revisit their support for EVs as an alternate and sustainable source of energy. Full article
(This article belongs to the Special Issue Green Finance Risk Management under Peak Carbon Dioxide Emissions and Carbon Neutrality Goals)
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<p>Flowchart of methodology.</p> Full article ">Figure 2
<p>Conditional correlation between copper and ESG. Note: cor-x represents the conditional correlation between copper and ESG derived from the DG model.</p> Full article ">Figure 3
<p>Conditional correlation between crude oil and ESG. Note: cor-x represents the conditional correlation between crude oil and ESG derived from the DG model.</p> Full article ">Figure 4
<p>Conditional correlation between exchange rate and ESG. Note: cor-x represents the conditional correlation between the exchange rate and ESG derived from the DG model.</p> Full article ">
13 pages, 607 KiB  
Review
Tourism Forecasting of “Unpredictable” Future Shocks: A Literature Review by the PRISMA Model
by Sergej Gricar
J. Risk Financial Manag. 2023, 16(12), 493; https://doi.org/10.3390/jrfm16120493 - 21 Nov 2023
Cited by 1 | Viewed by 2727
Abstract
This study delves into the intricate process of predicting tourism demand, explicitly focusing on econometric and quantitative time series analysis. A meticulous review of the existing literature is carried out to comprehensively understand the various methods for forecasting “unpredictable” shocks of tourism demand [...] Read more.
This study delves into the intricate process of predicting tourism demand, explicitly focusing on econometric and quantitative time series analysis. A meticulous review of the existing literature is carried out to comprehensively understand the various methods for forecasting “unpredictable” shocks of tourism demand on an ex-ante basis. The PRISMA method has been implemented. Drawing on scholarly research, this study pinpoints the critical challenges in accurately predicting tourism demand, making it a valuable resource for tourism professionals and researchers seeking to stay on top of the latest forecasting techniques. Moreover, the study includes an overview of published manuscripts from the current decade, with mixed results from the 32 manuscripts reviewed. The study concludes that virtual tourism, augmented reality, virtual reality, big data, and artificial intelligence all have the potential to enhance demand forecasting in time series econometrics. Full article
(This article belongs to the Special Issue Financial Econometrics and Quantitative Economic Analysis)
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<p>PRISMA diagram. Source: Authors’ compilation using software designed by <a href="#B26-jrfm-16-00493" class="html-bibr">Haddaway et al.</a> (<a href="#B26-jrfm-16-00493" class="html-bibr">2022</a>).</p> Full article ">
17 pages, 1919 KiB  
Article
Impact of Financial Factors on the Economic Cycle Dynamics in Selected European Countries
by Bogdan Andrei Dumitrescu and Robert-Adrian Grecu
J. Risk Financial Manag. 2023, 16(12), 492; https://doi.org/10.3390/jrfm16120492 - 21 Nov 2023
Cited by 1 | Viewed by 2034
Abstract
The aim of this paper was to assess the impact generated by the financial market shocks on the economic cycle in European countries. In addition to the studies from the literature, which focus more on the developed economies, this paper also considered the [...] Read more.
The aim of this paper was to assess the impact generated by the financial market shocks on the economic cycle in European countries. In addition to the studies from the literature, which focus more on the developed economies, this paper also considered the situation at the level of a group of emerging economies to highlight the potential differences. In this sense, it was analyzed how the shocks at the level of the banking sector, those at the level of the capital market, and those at the level of the real estate market influence the dynamics of the economic cycle. Both econometric models for the individual analyses of each state, such as the Bayesian vector autoregression model, and models at the level of groups of states, such as panel regressions, were used for the period 2007–2022. The results showed a strong connection between the dynamics of the financial system and that of the real economy. In addition, the impact of financial factors on the economic cycle tends to be much stronger and more significant in the case of developing countries, compared to developed ones. In this regard, it was recommended that fiscal and monetary policies should be coordinated to generate the expected effect on the economy. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond, 3rd Edition)
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<p>Dynamics of the economic cycle due to the shock on the level of loans granted to non-financial companies in the CEE states. Note: the line represents the response of the economic cycle due to the shock, and the highlighted area represents the 95% confidence interval.</p> Full article ">Figure 2
<p>Dynamics of the economic cycle due to the CLIFS shock in the CEE states. Note: the line represents the response of the economic cycle due to the shock, and the highlighted area represents the 95% confidence interval.</p> Full article ">Figure 3
<p>Economic cycle dynamics due to the real estate price shock in CEE states. Note: the line represents the response of the economic cycle due to the shock, and the highlighted area represents the 95% confidence interval.</p> Full article ">Figure A1
<p>Dynamics of the economic cycle as a result of the shock from the level of loans granted to non-financial companies in the states of the central-western region of Europe. Note: the line represents the response of the economic cycle due to the shock, and the highlighted area represents the 95% confidence interval. Source of the data: own processing based on data provided by Eurostat and ECB.</p> Full article ">Figure A2
<p>Dynamics of the economic cycle as a result of the shock at the level of credits granted to the population in the CEE states. Note: the line represents the response of the economic cycle due to the shock, and the highlighted area represents the 95% confidence interval. Source of the data: own processing based on data provided by Eurostat and ECB.</p> Full article ">Figure A3
<p>Dynamics of the economic cycle as a result of the shock on the level of credits granted to the population in the states of the central-western region of Europe. Note: the line represents the response of the economic cycle due to the shock, and the highlighted area represents the 95% confidence interval. Source of the data: own processing based on data provided by Eurostat and ECB.</p> Full article ">Figure A4
<p>Dynamics of the economic cycle as a result of the shock at the CLIFS level in the states of the central-western region of Europe. Note: the line represents the response of the economic cycle due to the shock, and the highlighted area represents the 95% confidence interval. Source of the data: own processing based on data provided by Eurostat and ECB.</p> Full article ">Figure A5
<p>Dynamics of the economic cycle as a result of the real estate price shock in the states of the cCentral-wWestern region of Europe. Note: the line represents the response of the economic cycle due to the shock, and the highlighted area represents the 95% confidence interval. Source of the data: own processing based on data provided by Eurostat and ECB.</p> Full article ">
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