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

The extant literature has produced mixed evidence on the relationship between finan-cial development and ecological sustainability.This work addresses this conundrum by investigating financial development’s direct and indirect consequences on ecologi-cal 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 develop-ment 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 tech-nological innovation enhances it.This research provides some crucial policy recom-mendations and fresh perspectives for South Africa as it develops national initiatives to support ecological sustainability and reach its net zero emissions goal.

Survey of feature selection and extraction techniques for stock market prediction

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.

Revisiting the nexus between fiscal decentralization and CO_(2)emissions in South Africa:fresh policy insights

The argument over fiscal decentralization and carbon dioxide emission(CO_(2))reduction has received much attention.However,evidence to back this claim is limited.Economic theory predicts that fiscal decentralization affects environmental quality,but the specifics of this relationship are still up for debate.Some scholars noted that fiscal decentralization might lead to a race to the top,whereas others contended that it would result in a race to the bottom.In light of the current debates in environmental and development economics,this study aims to provide insight into how this relationship may function in South Africa from 1960 to 2020.In contrast to the existing research,the present study uses a novel dynamic autoregressive distributed lag simulation approach to assess the positive and negative changes in fiscal decentralization,scale effect,technique effect,technological innovation,foreign direct investment,energy consumption,industrial growth,and trade openness on CO_(2)emissions.The following are the main findings:(i)Fiscal decentralization had a CO_(2)emission reduction impact in the short and long run,highlighting the presence of the race to the top approach.(ii)Economic growth(as represented by the scale effect)eroded ecological integrity.However,its square(as expressed by technique effect)aided in strengthening ecological protection,validating the environmental Kuznets curve hypothesis.(iii)CO_(2)emissions were driven by energy utilization,trade openness,industrial value-added,and foreign direct investment,whereas technological innovation boosted ecological integrity.Findings suggest that further fiscal decentralization should be undertaken through further devolution of power to local entities,particularly regarding environmental policy issues,to maintain South Africa’s ecological sustainability.South Africa should also establish policies to improve environmental sustainability by strengthening a lower layer of government and clarifying responsibilities at the national and local levels to fulfill the energy-saving functions of fiscal expenditures.

Dynamic portfolio choice with uncertain rare‑events risk in stock and cryptocurrency markets

In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework.We reach the following conclusions in both markets:first,price diffusion and jump ambiguity mainly determine detection-error probability;second,optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity,and trivially affected by volatility diffusion ambiguity.In addition,investors tend to be more aggressive in a stable market than in a volatile one.Next,given a larger volatility jump size,investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps.Finally,the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market.These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market.The results have implications for both investors and regulators.

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

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.

Understanding the adoption context of China’s digital currency electronic payment

Central banks worldwide have started researching and developing central bank digital currencies(CBDCs).In the digital economy context,concerns regarding the integrity,competition,and privacy of CBDC systems have also gradually emerged.Against this backdrop,this study aims to evaluate users’willingness to use China’s digital currency electronic payment(DCEP)system,a digital payment and processing network,and its influencing factors by comprehensively considering and comparing the characteristics of cash and third-party payment services.Combining the push-pull-mooring frame-work(PPM)and task-technology fit(TTF)theory,we discuss the scenarios and mecha-nisms that may inspire users’DCEP adoption intention through an empirical study.The results reveal that privacy concerns regarding the original payment methods and technology-task fitting level of DCEP positively impact users’willingness to adopt DCEP.The technical characteristics of DCEP,users’payment requirements,and government support positively affect users’adoption intention by influencing the task-technology fitting degree of DCEP.Switching cost significantly and negatively impacts adop-tion intention,whereas relative advantage exhibits no significant effect.This research contributes to a better understanding of the factors that influence switching intentions and the actual use of DCEP,and provides policy guidance on promoting the efficiency and effectiveness of DCEP.

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

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.

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

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 blockchainbased 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.

COVID‑19 and tourism sector stock price in Spain:medium‑term relationship through dynamic regression models

The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.

Effect of blockchain technology initiatives on firms’market value

Despite blockchain’s potential to transform corporations by providing new ways of organizing business processes and handling information,extant research pays inadequate attention to how and under what conditions blockchain technology provides additional financial value for shareholders.Drawing on the efficient market hypothesis and signaling theory,we examined the relationship between firms’blockchain use,development announcements,and stock market reactions.We used the event study methodology to analyze a sample of blockchain projects initiated by US firms between 2016 and 2019.The sample contains 114 firm-event observations.The findings show that the average abnormal return over a 2 days event period(including the day of the announcement and the day after the announcement)was positive.This positive stock market reaction is even more substantial when firms announce blockchain projects that focus on saving cost or time.Our findings also indicate that blockchain announcements tend to elicit more positive market reactions from smaller firms.We analyzed 249 firm-event observations containing firms from around the world and conclude that blockchain technology has a non-significant long-term impact on operating performance.The contingency approach adopted in our research provides advice for selecting the right mix of blockchain investment initiatives that is most suitable for a given organizational context.

Prioritizing real estate enterprises based on credit risk assessment:an integrated multi‑criteria group decision support framework

Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects.Additionally,it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry.However,Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns,making subjective evalu-ation approaches inevitable,occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks.To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises,this study proposes an integrated multi-criteria group decision-making approach.First,a credit risk assessment index for Chinese real estate enterprises is established.Then,the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods.This approach is suitable for processing large amounts of data with high uncertainty,which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjec-tive evaluation information.Finally,the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility.This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise credit risk assessment.

The influence of CEO’s financial literacy on SMEs technological innovation:the mediating effects of MCS and risk‑taking

Previous literature showed mixed results about the impact of CEOs’financial literacy(CFL)on small and medium-sized enterprises’(SMEs)innovation.This relationship can be motivated by relevant variables,which are missing in the previous literature and make a difference as mediators.In this sense,based on the theoretical framework related to upper echelon theory and resource-based view,this study focuses on the mediating effect of risk-taking attitude and management control systems(MCS)varia-bles.Empirical data from 310 SMEs gathered using a qualitative research questionnaire are analyzed using structural equation modeling methodology.Specifically,estimations are carried out considering the partial least square method.Findings show that MCS and managers’risk attitudes fully mediate the relationship between financial literacy(FL)and innovation.Between these two mediating variables,the implementation of MCS stands out because it also enables the mediating effect of CEOs’risk-taking in the CFL–technological innovation relationship.As the results do not support the significant direct relationship between FL and risk attitude,they confirm an indirect effect through MCS.Furthermore,based on the study findings,SMEs’directors and owners,business associations,and public authorities can improve SMEs’technological innovation by implementing training programs and policies to foster CFL.They can also acknowledge the interdependency between organizational factors and individual characteristics to enhance SMEs’technological innovation.

Management disclosure of risk factors and COVID‑19

Public companies in the United States are required to file annual reports(i.e.,Form 10-K)and disclose,among other things,the risk factors that may harm their stock price.The risk of a pandemic was well-known before the recent crisis,and we now know that the initial impact on many shareholders was significant and negative.To what extent did managers forewarn their shareholders about this valuation risk?We examine all 10-K filings from 2018,before any knowledge of the current pandemic,and find that less than 21%of them contain any reference to pandemic-related terms.Given the management’s presumed in-depth knowledge of their business and the general awareness that pandemics have been identified as a significant global risk for at least the past decade,this number should have been higher.We find an unexpectedly posi-tive correlation(0.137)between the use of pandemic-related words in annual reports and realized stock returns during the actual pandemic at the industry level.Some industries most severely impacted by COVID-19 barely mentioned pandemic risk in their financial disclosures to shareholders,indicating that managers were ineffective in highlighting their exposure to pandemic risks to investors.

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

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.

Empirical evidence on the ownership and liquidity of real estate tokens

To better understand the potential and limitations of the tokenization of real asset mar-kets,empirical studies need to examine this radically new organization of financial mar-kets.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 proper-ties on decentralized exchanges.We report that real estate token prices move accord-ing to the house price index;hence,investing in real estate tokens provides economic exposure to residential house prices.

Correction:Nonlinear dynamics in Divisia monetary aggregates:an application of recurrence quantification analysis

Following publication of the original article(Andreadis et al.2023),the authors reported that they mistakenly omitted the affiliations 1,2 and 3 for Ioannis Andreadis,Athanasios D.Fragkou and Theodoros E.Karakasidis.The correct affiliations for each author should read.

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

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.

Financial literacy,behavioral traits,and ePayment adoption and usage in Japan

This study investigates how financial literacy and behavioral traits affect the adoption of electronic payment(ePayment)services in Japan.We construct a financial literacy index using a representative sample of 25,000 individuals from the Bank of Japan’s 2019 Financial Literacy Survey.We then analyze the relationship between this index and the extensive and intensive usage of two types of payment services:electronic money(e-money)and mobile payment apps.Using an instrumental variable approach,we find that higher financial literacy is positively associated with a higher likelihood of adopting ePayment services.The empirical results suggest that individuals with higher financial literacy use payment services more frequently.We also find that risk-averse people are less likely to adopt and use ePayment services,whereas people with herd behavior tend to adopt and use ePayment services more.Our empirical results also suggest that the effects of financial literacy on the adoption and use of ePayment differ among people with different behavioral traits.

Data envelopment analysis for scale elasticity measurement in the stochastic case:with an application to Indian banking

In the nonparametric data envelopment analysis literature,scale elasticity is evaluated in two alternative ways:using either the technical efficiency model or the cost efficiency model.This evaluation becomes problematic in several situations,for example(a)when input proportions change in the long run,(b)when inputs are heterogeneous,and(c)when firms face ex-ante price uncertainty in making their production decisions.To address these situations,a scale elasticity evaluation was performed using a value-based cost efficiency model.However,this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data.Therefore,in this study,we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty.An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years(1998–2005)was made to compare inferences about their efficiency and scale properties.The key findings are as follows:First,both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints.However,both models yield the same results at a tolerance level of 0.5,implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks.Second,the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart.Third,public banks exhibit higher efficiency than private and foreign banks.Finally,public and old private banks mostly exhibit either decreasing or constant returns to scale,whereas foreign and new private banks experience either increasing or decreasing returns to scale.Although the application of our proposed stochastic model is illustrative,it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs,which have ample potential for reaping scale and scope benefits.

Impact of trading hours extensions on foreign exchange volatility:intraday evidence from the Moscow exchange

Using transaction-level tick-by-tick data of same-and next-day settlement of the Russian Ruble versus the US Dollar exchange rate(RUB/USD)traded on the Moscow Exchange Market during the period 2005–2013,we analyze the impact of trading hours extensions on volatility.During the sample period,the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate.To analyze the effect of the implementations,various measures of historical and realized volatility are calculated for 5-and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions.Besides historical volatility measures,we also examine volume and spread.We apply an autoregressive moving average-autoregressive conditional heteroscedasticity(ARMA-GARCH)model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading.The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes.Volatility changes mostly occur after the opening of the market.The length of the extension has a significant positive effect on realized volatility.The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement,whereas this is not observed for next-day settlement.Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets,they may also lead to higher volatility in the market.Furthermore,this distortion is more significant at opening and midday.A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.