Showing papers in "Financial Innovation in 2023"

The transaction behavior of cryptocurrency and electricity consumption

Abstract: Abstract Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production, increasing network hashrates and electricity consumption. Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity. These changes prompt cryptocurrency miners to become new investors, leading to cryptocurrency price increases. The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified. Hence, this research thus utilizes July 31 2015–July 12 2019 data from 13 cryptocurrencies to investigate the short- and long-run causal effects between cryptocurrency transaction and electricity consumption. Particularly, we consider structural breaks induced by external shocks through stationary analysis and comovement relationships. Over the examined time period, we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks, with prices trending together with hashrates. Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption. Therefore, owing to the computational power deployed to wherever high profit is found, transactions are vital determinants of electricity consumption.

Exploring the moderating role of financial development in environmental Kuznets curve for South Africa: fresh evidence from the novel dynamic ARDL simulations approach

Abstract: Abstract The extant literature has produced mixed evidence on the relationship between financial development and ecological sustainability. This work addresses this conundrum by investigating financial development’s direct and indirect consequences on ecological quality utilizing the environmental Kuznets curve (EKC) methodological approach. Our empirical analysis is based on the novel dynamic autoregressive distributed lag simulations approach for South Africa between 1960 and 2020. The results, which used five distinct financial development measures, demonstrate that financial development boosts ecological integrity and environmental sustainability over the long and short terms. In the instance of South Africa, we additionally confirm the validity of the EKC theory. More importantly, the outcomes of the indirect channels demonstrate that financial development increases energy usage’s role in causing pollution while attenuating the detrimental impacts of economic growth, trade openness, and foreign direct investment on ecological quality. Moreover, the presence of an inadequate financial system is a requirement for the basis of the pollution haven hypothesis (PHH), which we examine using trade openness and foreign direct investment variables. PHH for both of these variables disappears when financial development crosses specified thresholds. Finally, industrial value addition destroys ecological quality while technological innovation enhances it. This research provides some crucial policy recommendations and fresh perspectives for South Africa as it develops national initiatives to support ecological sustainability and reach its net zero emissions goal.

Asymmetric nexus between commercial policies and consumption-based carbon emissions: new evidence from Pakistan

Abstract: Abstract The current study extends the previous literature by exploring the effects of a newly discovered driver, i.e., import taxes (as a proxy for commercial policies), on the consumption-based carbon emissions (CCO2e) for 1990Q1-2017Q4. For empirical analysis, several tests and methods, including Augmented Dickey–Fuller unit root test, Zivot–Andrews unit root test, asymmetric cointegration bound testing approach, non-linear ARDL, Wald-test, Granger causality test and wavelet quantile correlation (WQC) method are utilized. Furthermore, NARDL technique estimates reveal that contractionary commercial policy enhances the environmental quality by disrupting the detrimental effects of CCO2e. However, expansionary commercial policy escalates the environmental pollution by boosting the carbon emissions. Also, the exports and the renewable energy improve the ecological quality; however, GDP deteriorates the atmospheric quality by increasing the CCO2e. Besides, WQC method and the trivariate Granger causality test are deployed to confirm the robustness of the results. Based on the findings, some crucial policies are also recommended for sustainable and green development in Pakistan.

Survey of feature selection and extraction techniques for stock market prediction

Abstract: In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications. We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011-2022. We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles. We also describe the combination of feature analysis techniques and ML methods and evaluate their performance. Moreover, we present other survey articles, stock market input and output data, and analyses based on various factors. We find that correlation criteria, random forest, principal component analysis, and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs

Abstract: Abstract The purpose of the paper is to identify the factors of financial development that have the greatest impact on open innovation in 7 emerging countries. The analysis was performed featuring the MF-X-DMA method, as well as its further verification for autocorrelation and heteroscedasticity. The time period covers years from 2002 to 2020. The article states that the main indicators to improve financial development should enhance the process of bank lending and equity market development. An important area is the development of competition by providing equal access to information to all market participants in a continuously refining technical infrastructure. Regression analysis with the MF-X-DMA method confirms the statistical significance of this influence. The article fills the knowledge gap into the link between open innovations and the relatively low capitalization of the modern emerging countries’ financial market, low liquidity in small cap stocks at the financial market and concentration of the banking sector, as well as risks arising in the process of globalization. Another analysis has also been conducted by generating a novel fuzzy decision-making model. In the first stage, the determinants of open innovation-based fintech potential are weighted for the emerging economies. For this purpose, M-SWARA methodology is taken into consideration based on bipolar q-ROFSs and golden cut. The second stage of the analysis includes evaluating the emerging economies with the determinants of open innovation-based fintech potential. In this context, emerging seven countries are examined with ELECTRE methodology. It found the most significant factor is the open innovation-based fintech potential.

Smart cities from low cost to expensive solutions under an optimal analysis

Abstract: This scientific approach mainly aims to develop a smart city/smart community concept to objectively evaluate the progress of these organizational forms in relation to other classical/traditional forms of city organizations. The elaborated model allowed the construction of the dashboard of access actions in the smart city/smart community category on two levels of financial effort correlated with the effect on the sustainable development of smart cities. The validity of the proposed model and our approach was supported by the complex statistical analysis performed in this study. The research concluded that low-cost solutions are the most effective in supporting smart urban development. They should be followed by the other category of solutions, which implies more significant financial and managerial efforts as well as a higher rate of welfare growth for urban citizens. The main outcomes of this research include modelling solutions related to smart city development at a low-cost level and identifying the sensitivity elements that maximize the growth function. The implications of this research are to provide viable alternatives based on smart city development opportunities with medium and long-term effects on urban communities, economic sustainability, and translation into urban development rates. This study's results are useful for all administrations ready for change that want the rapid implementation of the measures with beneficial effects on the community or which, through a strategic vision, aim to connect to the European objectives of sustainable growth and social welfare for citizens. Practically, this study is a tool for defining and implementing smart public policies at the urban level.

Asymmetric relationship between global and national factors and domestic food prices: evidence from Turkey with novel nonlinear approaches

Abstract: Abstract This study investigates the asymmetric relationship between global and national factors and domestic food prices in Turkey, considering the recent rapid and continuous increase in domestic food prices. In this context, six global and three national explanatory variables were included, and monthly data for the period from January 2004 to June 2021 were used. In addition, novel nonlinear time-series econometric approaches, such as wavelet coherence, Granger causality in quantiles, and quantile-on-quantile regression, were applied for examination at different times, frequencies, and quantiles. Moreover, the Toda-Yamamoto (TY) causality test and quantile regression (QR) approach were used for robustness checks. The empirical results revealed that (i) there is a significant relationship between domestic food prices and explanatory variables at different times and frequencies; (ii) a causal relationship exists in most quantiles, excluding the lowest quantile, some middle quantiles, and the highest quantile for some variables; (iii) the power of the effect of the explanatory variables on domestic food prices varies according to the quantiles; and (iv) the results were validated by the TY causality test and QR, which show that the results were robust. Overall, the empirical results reveal that global and national factors have an asymmetric relationship with domestic food prices, highlighting the effects of fluctuations in global and national variables on domestic food prices. Thus, the results imply that Turkish policymakers should consider the asymmetric effects of global and national factors on domestic food prices at different times, frequencies, and quantiles.

Investigating the components of fintech ecosystem for distributed energy investments with an integrated quantum spherical decision support system

Abstract: Abstract This study aimed to evaluate the components of a fintech ecosystem for distributed energy investments. A new decision-making model was created using multiple stepwise weight assessment ratio analysis and elimination and choice translating reality techniques based on quantum spherical fuzzy sets. First, in this model, the criteria for distributed energy investment necessities were weighted. Second, we ranked the components of the fintech ecosystem for distributed energy investments. The main contribution of this study is that appropriate strategies can be presented to design effective fintech ecosystems to increase distributed energy investments, by considering an original fuzzy decision-making model. Capacity is the most critical issue with respect to distributed energy investment necessities because it has the greatest weight (0.261). Pricing is another significant factor for this condition, with a weight of 0.254. Results of the ranking of the components of the fintech ecosystem indicate that end users are of the greatest importance for the effectiveness of this system. It is necessary to develop new techniques for the energy storage process, especially with technological developments, to prevent disruptions in energy production capacity. In addition, customers’ expectations should be considered for the development of effective and user-friendly financial products that are preferred by a wider audience. This would have a positive effect on fintech ecosystem performance.

Size matters: analyzing bank profitability and efficiency under the Basel III framework

Abstract: Abstract The latest regulatory framework, which has been introduced globally in the form of Basel III, and its implementation in the legislation of the member states of the European Union has generated much interest in the impact of regulation on the efficiency and profitability of banks. This study aims to examine the impact of the introduction of two major regulatory changes (Basel II and Basel III) on bank performance, in terms of bank size and bank-specific and macroeconomic variables. A two-stage empirical analysis was conducted on a sample of 433 European commercial banks over the 2006–2015 period. In the first stage, relative efficiency was calculated using non-parametric data envelopment analysis. In the second stage, the generalized method of moments was used to examine the impact of bank-specific and macroeconomic variables as well as regulation on bank performance, that is, profitability and efficiency. Considering bank size, the results show a diverse impact of regulation on bank performance. Regarding large- and medium-sized banks, regulation positively affects both efficiency and profitability, whereas, for small banks, it negatively affects performance. The results suggest that larger banks have skillfully adapted to the new regulatory environment. In contrast, small banks have problems with profitability and efficiency because the new regulatory framework has imposed additional administrative and regulatory burdens. This could result in future failure or mergers with larger banks, resulting in a higher concentration in the banking sector and increased systemic risk. Our results strongly suggest that regulation should not be implemented equally for all banks; that is, on a one size fits all terms. A distinction between small and large banks when introducing new regulatory frameworks should be made if a reasonable level of competition is to be preserved.

Online payment fraud: from anomaly detection to risk management

Abstract: Abstract Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be combated systematically and economically if the risks and costs in payment channels are known. We define three models that overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud while considering countermeasures. The models were tested utilizing real data. Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15% compared to a benchmark consisting of static if-then rules. Optimizing the machine-learning model further reduces the expected losses by 52%. These results hold with a low false positive rate of 0.4%. Thus, the risk framework of the three models is viable from a business and risk perspective.

Empirical evidence on the ownership and liquidity of real estate tokens

Abstract: To better understand the potential and limitations of the tokenization of real asset markets, empirical studies need to examine this radically new organization of financial markets. In our study, we examine the financial and economic consequences of tokenizing 58 residential rental properties in the US, particularly those in Detroit. Tokenization aims at fragmented ownership. We found that the residential properties examined have 254 owners on average. Investors with a greater than USD 5,000 investment in real estate tokens, diversify their real estate ownership across properties within and across the cities. Property ownership changes about once yearly, with more changes for properties on decentralized exchanges. We report that real estate token prices move according to the house price index; hence, investing in real estate tokens provides economic exposure to residential house prices.

Optimal reduction and equilibrium carbon allowance price for the thermal power industry under China’s peak carbon emissions target

Abstract: Abstract As the largest source of carbon emissions in China, the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle. Its conversion to clean-energy generation technologies is also an important means of reducing CO 2 emissions and achieving the carbon peak and carbon neutral commitments. This study used fractional Brownian motion to describe the energy-switching cost and constructed a stochastic optimization model on carbon allowance (CA) trading volume and emission-reduction strategy during compliance period with the Hurst exponent and volatility coefficient in the model estimated. We defined the optimal compliance cost of thermal power enterprises as the form of the unique solution of the Hamilton–Jacobi–Bellman equation by combining the dynamic optimization principle and the fractional Itô’s formula. In this manner, we obtained the models for optimal emission reduction and equilibrium CA price. Our numerical analysis revealed that, within a compliance period of 2021–2030, the optimal reductions and desired equilibrium prices of CAs changed concurrently, with an increasing trend annually in different peak-year scenarios. Furthermore, sensitivity analysis revealed that the energy price indirectly affected the equilibrium CA price by influencing the Hurst exponent, the depreciation rate positively impacted the CA price, and increasing the initial CA reduced the optimal reduction and the CA price. Our findings can be used to develop optimal emission-reduction strategies for thermal power enterprises and carbon pricing in the carbon market.

Predicting Fintech Innovation Adoption: the Mediator Role of Social Norms and Attitudes

Abstract: Digital innovation is challenging the traditional way of offering financial services to companies; the so-called Fintech phenomenon refers to startups that use the latest technologies to offer innovative financial services. Within the framework of the Theory of Planned Behavior (TPB) and the Theory of Reasoned Action (TRA), the primary purpose of this paper is to develop a causal-predictive analysis of the relationship between Subjective Norms, Attitudes, and Perceived Behavioral Control with the Intention to Use and Behavioral Use of the Fintech services by companies. Partial Least Squares Structural Equation Modeling methodology was used with data collected from a survey of 300 companies. Our findings support the TRA and TPB models and confirm their robustness in predicting companies' intention and use of Fintech services. Financial technology innovators must understand the processes involved in users' adoption to design sound strategies that increase the viability of their services. Studying the antecedents of behavioral intention to adopt Fintech services can greatly help understand the pace of adoption, allowing these players to attract and retain customers better. This study contributes to the literature by formulating and validating TPB to predict Fintech adoption, and its findings provide useful information for banks and Fintech companies and lead to an improvement in organizational performance management in formulating marketing strategies.

A hybrid decision support system with golden cut and bipolar q-ROFSs for evaluating the risk-based strategic priorities of fintech lending for clean energy projects

Abstract: In the last decade, the risk evaluation and the investment decision are among the most prominent issues of efficient project management. Especially, the innovative financial sources could have some specific risk appetite due to the increasing return of investment. Hence, it is important to uncover the risk factors of fintech investments and investigate the possible impacts with an integrated approach to the strategic priorities of fintech lending. Accordingly, this study aims to analyze a unique risk set and the strategic priorities of fintech lending for clean energy projects. The most important contributions to the literature can be listed as to construct an impact-direction map of risk-based strategic priorities for fintech lending in clean energy projects and to measure the possible influences by using a hybrid decision making system with golden cut and bipolar q-rung orthopair fuzzy sets. The extension of multi stepwise weight assessment ratio analysis (M-SWARA) is applied for weighting the risk factors of fintech lending. The extension of elimination and choice translating reality (ELECTRE) is employed for constructing and ranking the risk-based strategic priorities for clean energy projects. In this process, data is obtained with the evaluation of three different decision makers. The main superiority of the proposed model by comparing with the previous models in the literature is that significant improvements are made to the classical SWARA method so that a new technique is created with the name of M-SWARA. Hence, the causality analysis between the criteria can also be performed in this proposed model. The findings demonstrate that security is the most critical risk factor for fintech lending system. Moreover, volume is found as the most critical risk-based strategy for fintech lending. In this context, fintech companies need to take some precautions to effectively manage the security risk. For this purpose, the main risks to information technologies need to be clearly identified. Next, control steps should be put for these risks to be managed properly. Furthermore, it has been determined that the most appropriate strategy to increase the success of the fintech lending system is to increase the number of financiers integrated into the system. Within this framework, the platform should be secure and profitable to persuade financiers.

Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm

Abstract: Abstract The study aims to investigate the financial technology (FinTech) factors influencing Chinese banking performance. Financial expectations and global realities may be changed by FinTech’s multidimensional scope, which is lacking in the traditional financial sector. The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization. The future of FinTech will be shaped by technologies like the Internet of Things, blockchain, and artificial intelligence. The involvement of these platforms in financial services is a major concern for global business growth. FinTech is becoming more popular with customers because of such benefits. FinTech has driven a fundamental change within the financial services industry, placing the client at the center of everything. Protection has become a primary focus since data are a component of FinTech transactions. The task of consolidating research reports for consensus is very manual, as there is no standardized format. Although existing research has proposed certain methods, they have certain drawbacks in FinTech payment systems (including cryptocurrencies), credit markets (including peer-to-peer lending), and insurance systems. This paper implements blockchain-based financial technology for the banking sector to overcome these transition issues. In this study, we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’ algorithm. The chaotic improved foraging optimization algorithm is used to optimize the proposed method. The rolling window autoregressive lag modeling approach analyzes FinTech growth. The proposed algorithm is compared with existing approaches to demonstrate its efficiency. The findings showed that it achieved 91% accuracy, 90% privacy, 96% robustness, and 25% cyber-risk performance. Compared with traditional approaches, the recommended strategy will be more convenient, safe, and effective in the transition period.

Tax avoidance and earnings management: a neural network approach for the largest European economies

Abstract: Abstract In this study, we investigate the relationship between tax avoidance and earnings management in the largest five European Union economies by using artificial neural network regressions. This methodology allows us to deal with nonlinearities detected in the data, which is the principal contribution to the previous literature. We analyzed Compustat data for Germany, the United Kingdom, France, Italy, and Spain for the 2006–2015 period, focusing on discretionary accruals. We considered three tax avoidance measures, two based on the effective tax rate (ETR) and one on book-tax differences (BTD). Our results indicate the presence of nonlinear patterns and a positive, statistically significant relationship between discretionary accruals and both ETR indicators implying that when companies resort to earnings management, a larger taxable income—and thus higher ETR and lesser tax avoidance– would ensue. Hence, as also highlighted by the fact that discretionary accruals do not appear to affect BTD, our evidence does not suggest that companies are exploiting tax manipulation to reduce their tax payments; thus, the gap between accounting and taxation seems largely unaffected by earnings management.

Stock profiling using time–frequency-varying systematic risk measure

Abstract: Abstract This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model (CAPM) framework. The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness. Furthermore, we emphasize the effect of an investor’s investment horizon on the robustness of portfolio characteristics. We use a daily panel of French stocks from 2012 to 2022. Results show that varying systematic risk varies in time and frequency, and that its short and long-run evolutions differ. We observe differences in short and long dynamics, indicating that a stock’s betas differently fluctuate to early announcements or signs of events. However, short-run and long-run betas exhibit similar dynamics during persistent shocks. Betas are more volatile during times of crisis, resulting in greater or lesser robustness of risk profiles. Significant differences exist in short-run and long-run risk profiles, implying a different asset allocation. We conclude that the standard CAPM assumes short-run investment. Then, investors should consider time–frequency CAPM to perform systematic risk analysis and portfolio allocation.

The effect of overseas investors on local market efficiency: evidence from the Shanghai/Shenzhen–Hong Kong Stock Connect

Abstract: Abstract Using a recent stock market liberalization reform policy in China—the Stock Connect—as a quasi-natural experiment, this study examines the effect of stock market liberalization on market efficiency. Employing a dataset of 17,086 Chinese listed firms covering 2009 to 2018, we find that stock market liberalization improves the market efficiency of the Chinese mainland stock market. We further explore the potential channels through which the Stock Connect can enhance the efficiency of the A-share (A-shares refer to shares issued by Chinese companies incorporated in mainland China, traded in the Shanghai Stock Exchange and the Shenzhen Stock Exchange. They are denominated in Chinese RMB (the local currency). A-shares were restricted to local Chinese investors before 2003, are open to foreign investors via the Qualified Foreign Institutional Investor, RMB Qualified Foreign Institutional Investor, or the Stock Connect programs.) market. The findings show that liberalizing capital markets could benefit local market efficiency by increasing stock price informational efficiency and improving corporate governance quality. The additional analysis shows that stock market liberalization has a significant and positive impact on local market efficiency, enhancing firm value and reducing stock crash risk. We conduct various robustness checks to corroborate our findings. This study provides important policy implications for emerging countries liberalizing capital markets for foreign investors.

Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19

Abstract: This paper aims to probe the influence of innovation spillovers in the artificial intelligence (AI) and financial technology (Fin-tech) industries on the value of the internet of things (IoT) companies. Python was utilized to download public information from Yahoo Finance, and then the GARCH model was used to extract the fluctuations of cross-industry innovation spillovers. Next, the Fama-French three-factor model was used to explore the interactive changes between variables. The panel data regression analysis indicates that the more firms accept innovation spillovers from other industries, the better the excess return; however, this effect differs because of industrial attributes and the environmental changes induced by COVID-19. Additionally, this study finds that investing in large-cap growth stocks of IoT firms is more likely to yield excess returns. Finally, the study yields lessons for policy leverage to accelerate the upgrading and transformation of innovation-interactive industries by referring to the practices of Singapore and South Korea.

A socioeconomic approach to the profile of microcredit holders from the Hispanic minority in the USA

Abstract: The subject of this study is the microcredit market in the USA, more specifically in Florida. The justification for choosing this specific state is the massive presence of the Hispanic population. This will facilitate a generalization of the obtained results to the microcredit market in Latin American countries. Thus, the objective of this study is to analyze the profile of microcredit holders and their companies from socioeconomic and financial points of view. As our data also consider the degree of repayment of the microloans included in the sample, the clients' profile is related to the punctuality or default of their corresponding loan repayments using the methodology of multinomial logit regression. The variables used in this study refer to personal information concerning borrowers (gender, age, education level, and marital status), the economic situation of their respective companies (closeness to the lender, number of workers, and revenues), and the characteristics of granted loans (principal, term, and purpose). However, the results of the regression show that only two variables are significant at the 5% significance level: the borrower's age, which has a positive effect on repayment punctuality, and the loan term, which exhibits a negative effect. The findings of this study have clear implications, as they can help lenders design suitable microloans adjusted to customer profiles. Finally, future research should include other demographics and characteristics of affected companies.

Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach

Abstract: Abstract In 2021, the abnormal short-term price fluctuations of GameStop, which were triggered by internet stock discussions, drew the attention of academics, financial analysts, and stock trading commissions alike, prompting calls to address such events and maintain market stability. However, the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals. Furthermore, data mining methods are less often used to predict stock trading despite their higher accuracy. This study adopts an innovative approach using social media data to obtain stock rumors, and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior. Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior. The study serves as an impetus for further research using data mining as a method of inquiry.

Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach

Abstract: Abstract Because the U.S. is a major player in the international oil market, it is interesting to study whether aggregate and state-level economic conditions can predict the subsequent realized volatility of oil price returns. To address this research question, we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility (HAR-RV) model. To estimate the models, we use quantile-regression and quantile machine learning (Lasso) estimators. Our estimation results highlights the differential effects of economic conditions on the quantiles of the conditional distribution of realized volatility. Using weekly data for the period April 1987 to December 2021, we document evidence of predictability at a biweekly and monthly horizon.

The linkage between Bitcoin and foreign exchanges in developed and emerging markets

Abstract: This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries: the developed G7 and the emerging BRICS. The methodology adopts the regular (R)-vine copula and compares it with two benchmark models: the multivariate t copula and the dynamic conditional correlation (DCC) GARCH model. Moreover, this study examines whether the Bitcoin meltdown of 2013, selloff of 2018, COVID-19 pandemic, 2021 crash, and the Russia-Ukraine conflict impact the linkage with conventional currencies. The results indicate that for both currency baskets, R-vine beats the benchmark models. Hence, the dependence is better modeled by providing sufficient information on the shock transmission path. Furthermore, the cross-market linkage slightly increases during the Bitcoin crashes, and reaches significant levels during the 2021 and 2022 crises, which may indicate the end of market isolation of the virtual currency.

Toward a sustainable growth path in Arab economies: an extension of classical growth model

Abstract: Abstract Background/Objectives Many economies are on the trajectory of alternative growth drivers other than conventional capital and labor. Access to credit facilities is a pertinent indicator of economic growth. In line with the United Nations Sustainable Development Goals (UNSDGs-8) agenda, the national goal for sustainable development for most economies and Arab economies is no exception. Therefore, the current study adopts a traditional growth model by exploring the relationship between gross domestic product (GDP) per capita, credit for private sectors, ratio of exports, real GDP, and per labor force participants for selected Arab economies annually from 2001 to 2020. Research design This study leverages the Fourier Kwiatkowski–Phillips–Schmidt–Shin (KPSS) unit root test and second-generation panel econometrics as estimation techniques, such as Westerlund and Edgerton panel cointegration test, and the use of two estimators, namely the augmented mean group (AMG) and common correlated error mean group (CCEMG), to obtain robust results. Findings Empirical findings from Westerlund and Edgerton panel cointegration tests validate the long-run equilibrium relationship among the outlined variables. Further empirical results indicate that the share of exports is negatively significant with economic growth in countries such as Kuwait, Lebanon, Tunisia, and Jordan. Additionally, savings and labor force participation have a positive relationship with economic growth in individual countries such as Algeria and Bahrain. As per the panel, there is no significant relationship between labor force participation and economic growth. This indicates that the skilled labor force enhanced economic growth. Conclusions These findings come with inherent far-reaching policy suggestions for economies and panels. Further details on country-specific policy actions are presented in the concluding section. Graphical Abstract

An impact assessment of the COVID-19 pandemic on Japanese and US hotel stocks

Abstract: This study proposes two new regime-switching volatility models to empirically analyze the impact of the COVID-19 pandemic on hotel stock prices in Japan compared with the US, taking into account the role of stock markets. The first model is a direct impact model of COVID-19 on hotel stock prices; the analysis finds that infection speed negatively affects Japanese hotel stock prices and shows that the regime continues to switch to high volatility in prices due to COVID-19 until September 2021, unlike US stock prices. The second model is a hybrid model with COVID-19 and stock market impacts on the hotel stock prices, which can remove the market impacts on regime-switching volatility; this analysis demonstrates that COVID-19 negatively affects hotel stock prices regardless of whether they are in Japan or the US. We also observe a transition to a high-volatility regime in hotel stock prices due to COVID-19 until around summer 2021 in both Japan and the US. These results suggest that COVID-19 is likely to affect hotel stock prices in general, except for the influence of the stock market. Considering the market influence, COVID-19 directly and/or indirectly affects Japanese hotel stocks through the Japanese stock market, and US hotel stocks have limited impacts from COVID-19 owing to the offset between the influence on hotel stocks and no effect on the stock market. Based on the results, investors and portfolio managers should be aware that the impact of COVID-19 on hotel stock returns depends on the balance between the direct and indirect effects, and varies from country to country and region to region.

Gazing through the bubble: an experimental investigation into financial risk-taking using eye-tracking

Abstract: Abstract Eye tracking can facilitate understanding irrational decision-making in contexts such as financial risk-taking. For this purpose, we develop an experimental framework in which participants trade a risky asset in a simulated bubble market to maximize individual returns while their eye movements are recorded. Returns are sensitive to eye movement dynamics, depending on the presented visual stimuli. Using eye-tracking data, we investigated the effects of arousal, attention, and disengagement on individual payoffs using linear and nonlinear approaches. By estimating a nonlinear model using attention as a threshold variable, our results suggest that arousal positively influences trading returns, but its effect becomes smaller when attention exceeds a certain threshold, whereas disengagement has a higher negative impact on reduced attention levels and becomes almost irrelevant when attention increases. Hence, we provide a neurobehavioral metric as a function of attention that predicts financial gains in boom-and-bust scenarios. This study serves as a proof-of-concept for developing future psychometric measures to enhance decision-making.

A multidimensional review of the cash management problem

Abstract: In this paper, we summarize and analyze the relevant research on the cash management problem appearing in the literature. First, we identify the main dimensions of the cash management problem. Next, we review the most relevant contributions in this field and present a multidimensional analysis of these contributions, according to the dimensions of the problem. From this analysis, several open research questions are highlighted.

Markets in crypto-assets regulation: Does it provide legal certainty and increase adoption of crypto-assets?

Abstract: This study discusses the European Union's proposal for a Regulation on Markets in Crypto-Assets, now subject to formal approval by the European Parliament. The objective is to explore whether it will positively impact the adoption of crypto-assets in the financial sector. The use of crypto-assets is growing. However, some stakeholders in the financial service sector remain skeptical and hesitant to adopt assets that are yet to be defined and have an unclear legal status. This regulatory uncertainty has been identified as the primary reason for the reluctant adoption. The proposed regulation (part of the EU's Digital Finance Strategy) aims to provide this legal certainty for currently unregulated crypto-assets. This study investigates whether or not the proposed regulation can be expected to have the intended effect by reviewing the proposed regulation itself, the opinions and reactions of the various stakeholders, and secondary literature. Findings reveal that such regulation will most likely not accelerate the adoption of crypto-assets in the EU financial services sector, at least not sufficiently or as intended. Some suggestions are made to improve the proposal.