Pub Date : 2024-03-08DOI: 10.1186/s40854-023-00602-2
Duc Hong Vo, Hung Le-Phuc Nguyen
Global economic downturns and multiple extreme events threaten Vietnam's economy, leading to a surge in stock market risk and significant spillovers. This study investigates market risk spillovers and explores the asymmetric effects of macroeconomic indicators on market risk across 24 sectors in Vietnam from 2012 to 2022. We use the value-at-risk (VaR) technique and a vector autoregression (VAR) model to estimate market risks and their spillovers across Vietnamese sectors. We then examine the asymmetric effects of macroeconomic indicators on market risk using a panel nonlinear autoregressive distribution lag (NARDL) model. Our results confirm that Vietnam’s market risk increases rapidly in response to extreme events. Additionally, market risks exhibit substantial inter-connectedness across the Vietnamese sectors. The Building Materials, Technology, and Securities sectors are primary risk transmitters, whereas the Minerals, Development Investment, and Education sectors are major risk absorbers. Our results also confirm that market risk responds asymmetrically to changes in interest rates, exchange rates (USD/VND), trade openness, financial development, and economic growth in the short and long run. Minerals, Oil & Gas, and Rubber are the sectors that are most affected by macroeconomic indicators in the long run. Based on these important findings, implications focused on limiting market risks and their spillovers, along with sustainable investing, have emerged.
{"title":"Market risk spillover and the asymmetric effects of macroeconomic fundamentals on market risk across Vietnamese sectors","authors":"Duc Hong Vo, Hung Le-Phuc Nguyen","doi":"10.1186/s40854-023-00602-2","DOIUrl":"https://doi.org/10.1186/s40854-023-00602-2","url":null,"abstract":"Global economic downturns and multiple extreme events threaten Vietnam's economy, leading to a surge in stock market risk and significant spillovers. This study investigates market risk spillovers and explores the asymmetric effects of macroeconomic indicators on market risk across 24 sectors in Vietnam from 2012 to 2022. We use the value-at-risk (VaR) technique and a vector autoregression (VAR) model to estimate market risks and their spillovers across Vietnamese sectors. We then examine the asymmetric effects of macroeconomic indicators on market risk using a panel nonlinear autoregressive distribution lag (NARDL) model. Our results confirm that Vietnam’s market risk increases rapidly in response to extreme events. Additionally, market risks exhibit substantial inter-connectedness across the Vietnamese sectors. The Building Materials, Technology, and Securities sectors are primary risk transmitters, whereas the Minerals, Development Investment, and Education sectors are major risk absorbers. Our results also confirm that market risk responds asymmetrically to changes in interest rates, exchange rates (USD/VND), trade openness, financial development, and economic growth in the short and long run. Minerals, Oil & Gas, and Rubber are the sectors that are most affected by macroeconomic indicators in the long run. Based on these important findings, implications focused on limiting market risks and their spillovers, along with sustainable investing, have emerged.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"49 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140073776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-07DOI: 10.1186/s40854-023-00570-7
Imran Yousaf, Manel Youssef, Mariya Gubareva
This study investigates the static and dynamic return and volatility spillovers between non-fungible tokens (NFTs) and conventional currencies using the time-varying parameter vector autoregressions approach. We reveal that the total connectedness between these markets is weak, implying that investors may increase the diversification benefits of their multicurrency portfolios by adding NFTs. We also find that NFTs are net transmitters of both return and volatility spillovers; however, in the case of return spillovers, the influence of NFTs on conventional currencies is more pronounced than that of volatility shock transmissions. The dynamic exercise reveals that the returns and volatility spillovers vary over time, largely increasing during the onset of the Covid-19 crisis, which deeply affected the relationship between NFTs and the conventional currencies markets. Our findings are useful for currency traders and NFT investors seeking to build effective cross-currency and cross-asset hedge strategies during systemic crises.
{"title":"Return and volatility spillovers between non-fungible tokens and conventional currencies: evidence from the TVP-VAR model","authors":"Imran Yousaf, Manel Youssef, Mariya Gubareva","doi":"10.1186/s40854-023-00570-7","DOIUrl":"https://doi.org/10.1186/s40854-023-00570-7","url":null,"abstract":"This study investigates the static and dynamic return and volatility spillovers between non-fungible tokens (NFTs) and conventional currencies using the time-varying parameter vector autoregressions approach. We reveal that the total connectedness between these markets is weak, implying that investors may increase the diversification benefits of their multicurrency portfolios by adding NFTs. We also find that NFTs are net transmitters of both return and volatility spillovers; however, in the case of return spillovers, the influence of NFTs on conventional currencies is more pronounced than that of volatility shock transmissions. The dynamic exercise reveals that the returns and volatility spillovers vary over time, largely increasing during the onset of the Covid-19 crisis, which deeply affected the relationship between NFTs and the conventional currencies markets. Our findings are useful for currency traders and NFT investors seeking to build effective cross-currency and cross-asset hedge strategies during systemic crises.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"64 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140054706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper specifically investigates the effects of US government emergency actions on the investor sentiment–financial institution stock returns relationship. Despite attempts by many studies, the literature still provides no answers concerning this nexus. Using a new firm-specific Twitter investor sentiment (TS) metric and performing a panel smooth transition regression for daily data on 66 S&P 500 financial institutions from January 1 to December 31, 2020, we find that TS acts asymmetrically, nonlinearly, and time varyingly according to the pandemic situation and US states’ responses to COVID-19. In other words, we uncover the nexus between TS and financial institution stock returns and determine that it changes with US states’ reactions to COVID-19. With a permissive government response (the first regime), TS does not impact financial institution stock returns; however, when moving to a strict government response (the overall government response index exceeds the 63.59 threshold), this positive effect becomes significant in the second regime. Moreover, the results show that the slope of the transition function is high, indicating an abrupt rather than a smooth transition between the first and second regimes. The results are robust and have important policy implications for policymakers, investment analysts, and portfolio managers.
{"title":"Do US states’ responses to COVID-19 restore investor sentiment? Evidence from S&P 500 financial institutions","authors":"Kaouther Chebbi, Aymen Ammari, Seyed Alireza Athari, Kashif Abbass","doi":"10.1186/s40854-023-00603-1","DOIUrl":"https://doi.org/10.1186/s40854-023-00603-1","url":null,"abstract":"This paper specifically investigates the effects of US government emergency actions on the investor sentiment–financial institution stock returns relationship. Despite attempts by many studies, the literature still provides no answers concerning this nexus. Using a new firm-specific Twitter investor sentiment (TS) metric and performing a panel smooth transition regression for daily data on 66 S&P 500 financial institutions from January 1 to December 31, 2020, we find that TS acts asymmetrically, nonlinearly, and time varyingly according to the pandemic situation and US states’ responses to COVID-19. In other words, we uncover the nexus between TS and financial institution stock returns and determine that it changes with US states’ reactions to COVID-19. With a permissive government response (the first regime), TS does not impact financial institution stock returns; however, when moving to a strict government response (the overall government response index exceeds the 63.59 threshold), this positive effect becomes significant in the second regime. Moreover, the results show that the slope of the transition function is high, indicating an abrupt rather than a smooth transition between the first and second regimes. The results are robust and have important policy implications for policymakers, investment analysts, and portfolio managers.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"63 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-05DOI: 10.1186/s40854-023-00591-2
Juan Laborda, Ricardo Laborda, Javier de la Cruz
This study evaluates whether exchange traded funds (ETFs) threaten financial market stability by testing two hypotheses relating the growing importance of ETFs to increased market volatility and rising equity valuations. We estimate quantile cointegration models using Standard & Poor's 500 Index (S&P 500) and Chicago Board Options Exchange volatility Index (VIX) data for 1994–2020. We found that an increase in ETFs is positively and significantly related to the long-term valuation of the S&P 500 for quantile values above the median. By contrast, ETFs have only a negative and significant effect on the VIX for quantiles around the median. Ultimately, two novel results were obtained. First, the distortion in the value of the S&P 500 relative to its fundamentals is driven by investor flow into ETFs during a bull market. Second, the impact of equity ETFs on the VIX is only affected when fundamental factors are in play, decreasing it. Therefore, ETFs contribute to forming equity bubbles and support valuation market dynamics. Both regulators and policymakers should consider these conclusions.
{"title":"Can ETFs affect U.S. financial stability? A quantile cointegration analysis","authors":"Juan Laborda, Ricardo Laborda, Javier de la Cruz","doi":"10.1186/s40854-023-00591-2","DOIUrl":"https://doi.org/10.1186/s40854-023-00591-2","url":null,"abstract":"This study evaluates whether exchange traded funds (ETFs) threaten financial market stability by testing two hypotheses relating the growing importance of ETFs to increased market volatility and rising equity valuations. We estimate quantile cointegration models using Standard & Poor's 500 Index (S&P 500) and Chicago Board Options Exchange volatility Index (VIX) data for 1994–2020. We found that an increase in ETFs is positively and significantly related to the long-term valuation of the S&P 500 for quantile values above the median. By contrast, ETFs have only a negative and significant effect on the VIX for quantiles around the median. Ultimately, two novel results were obtained. First, the distortion in the value of the S&P 500 relative to its fundamentals is driven by investor flow into ETFs during a bull market. Second, the impact of equity ETFs on the VIX is only affected when fundamental factors are in play, decreasing it. Therefore, ETFs contribute to forming equity bubbles and support valuation market dynamics. Both regulators and policymakers should consider these conclusions.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"1 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-04DOI: 10.1186/s40854-024-00612-8
Asil Azimli
This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the Russia–Ukraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices.
{"title":"Time-varying spillovers in high-order moments among cryptocurrencies","authors":"Asil Azimli","doi":"10.1186/s40854-024-00612-8","DOIUrl":"https://doi.org/10.1186/s40854-024-00612-8","url":null,"abstract":"This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the Russia–Ukraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"5 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-02DOI: 10.1186/s40854-024-00625-3
Blanco-Oliver Antonio, Lara-Rubio Juan, Irimia-Diéguez Ana, Liébana-Cabanillas Francisco
Disruptive innovations caused by FinTech (i.e., technology-assisted customized financial services) have brought digital peer-to-peer (P2P) payments to the fore. In this challenging environment and based on theories about customer behavior in response to technological innovations, this paper identifies the drivers of consumer adoption of mobile P2P payments and develops a machine learning model to predict the use of this thriving payment option. To do so, we use a unique data set with information from 701 participants (observations) who completed a questionnaire about the adoption of Bizum, a leading mobile P2P platform worldwide. The respondent profile was the average Spanish citizen within the framework of European culture and lifestyle. We document (in this order of priority) the usefulness of mobile P2P payments, influence of peers and other social groups such as friends, family, and colleagues on individual behavior (that is, subjective norms), perceived trust, and enjoyment of the user experience within the digital context and how those attributes better classify (potential) users of mobile P2P payments. We also find that nonparametric approaches based on machine learning algorithms outperform traditional parametric methods. Finally, our results show that feature selection based on random forest, such as the Boruta procedure, as a preprocessing technique substantially increases prediction performance while reducing noise, redundancy of the resulting model, and computational costs. The main limitation of this research is that it only has a place within the sociocultural and institutional framework of the Spanish population. It is therefore desirable to replicate this study by surveying people from other countries to analyze the effects of the institutional environment on the adoption of mobile P2P payments.
{"title":"Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption","authors":"Blanco-Oliver Antonio, Lara-Rubio Juan, Irimia-Diéguez Ana, Liébana-Cabanillas Francisco","doi":"10.1186/s40854-024-00625-3","DOIUrl":"https://doi.org/10.1186/s40854-024-00625-3","url":null,"abstract":"Disruptive innovations caused by FinTech (i.e., technology-assisted customized financial services) have brought digital peer-to-peer (P2P) payments to the fore. In this challenging environment and based on theories about customer behavior in response to technological innovations, this paper identifies the drivers of consumer adoption of mobile P2P payments and develops a machine learning model to predict the use of this thriving payment option. To do so, we use a unique data set with information from 701 participants (observations) who completed a questionnaire about the adoption of Bizum, a leading mobile P2P platform worldwide. The respondent profile was the average Spanish citizen within the framework of European culture and lifestyle. We document (in this order of priority) the usefulness of mobile P2P payments, influence of peers and other social groups such as friends, family, and colleagues on individual behavior (that is, subjective norms), perceived trust, and enjoyment of the user experience within the digital context and how those attributes better classify (potential) users of mobile P2P payments. We also find that nonparametric approaches based on machine learning algorithms outperform traditional parametric methods. Finally, our results show that feature selection based on random forest, such as the Boruta procedure, as a preprocessing technique substantially increases prediction performance while reducing noise, redundancy of the resulting model, and computational costs. The main limitation of this research is that it only has a place within the sociocultural and institutional framework of the Spanish population. It is therefore desirable to replicate this study by surveying people from other countries to analyze the effects of the institutional environment on the adoption of mobile P2P payments.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"36 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1186/s40854-023-00600-4
Fabian Mayer, Peter Bofinger
This study investigated the extent of currency competition within the cryptocurrency market through the Hayek’s concept of the denationalization of money. Hayek’s original analysis primarily centered on competition revolving around the medium of the exchange function. This study posited that cryptocurrencies compete across diverse monetary functions, particularly concerning their roles as speculative stores of value and exchange media. This assertion provided insight into the distinction between Hayek’s envisaged private currencies and the cryptocurrency paradigm. Utilizing an extensive dataset encompassing 101 cryptocurrencies spanning from 2016 to 2022, an empirical exploration was conducted to scrutinize the progression and intensity of competition within the broader cryptocurrency market and its submarkets. These findings reveal a robust competition among unpegged cryptocurrencies, predominantly contending for speculative investment purposes. Similarly, there is pronounced competition among stablecoins as stable stores of value. In contrast, competition is much less pronounced concerning the medium of the exchange function, potentially entailing network effects and the emergence of monopolistic tendencies within this specific submarket.
{"title":"Cryptocurrency competition: empirical testing of Hayek’s vision of private monies","authors":"Fabian Mayer, Peter Bofinger","doi":"10.1186/s40854-023-00600-4","DOIUrl":"https://doi.org/10.1186/s40854-023-00600-4","url":null,"abstract":"This study investigated the extent of currency competition within the cryptocurrency market through the Hayek’s concept of the denationalization of money. Hayek’s original analysis primarily centered on competition revolving around the medium of the exchange function. This study posited that cryptocurrencies compete across diverse monetary functions, particularly concerning their roles as speculative stores of value and exchange media. This assertion provided insight into the distinction between Hayek’s envisaged private currencies and the cryptocurrency paradigm. Utilizing an extensive dataset encompassing 101 cryptocurrencies spanning from 2016 to 2022, an empirical exploration was conducted to scrutinize the progression and intensity of competition within the broader cryptocurrency market and its submarkets. These findings reveal a robust competition among unpegged cryptocurrencies, predominantly contending for speculative investment purposes. Similarly, there is pronounced competition among stablecoins as stable stores of value. In contrast, competition is much less pronounced concerning the medium of the exchange function, potentially entailing network effects and the emergence of monopolistic tendencies within this specific submarket.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"6 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140020184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1186/s40854-023-00592-1
Aktham Maghyereh, Salem Adel Ziadat
The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment, economic conditions, and economic uncertainty can predict tail risk interconnectedness. Combining the Conditional Autoregressive Value-at-Risk (CAViaR) model with the time-varying parameter vector autoregressive (TVP-VAR) approach shows that the transmission of tail risks among cryptocurrencies changes dynamically over time. During crises and significant events, transmission bursts and tail risks change. Based on both in- and out-of-sample forecasts, we find that the information contained in investor sentiment, economic conditions, and uncertainty includes significant predictive content about the tail risk connectedness of cryptocurrencies.
{"title":"Pattern and determinants of tail-risk transmission between cryptocurrency markets: new evidence from recent crisis episodes","authors":"Aktham Maghyereh, Salem Adel Ziadat","doi":"10.1186/s40854-023-00592-1","DOIUrl":"https://doi.org/10.1186/s40854-023-00592-1","url":null,"abstract":"The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment, economic conditions, and economic uncertainty can predict tail risk interconnectedness. Combining the Conditional Autoregressive Value-at-Risk (CAViaR) model with the time-varying parameter vector autoregressive (TVP-VAR) approach shows that the transmission of tail risks among cryptocurrencies changes dynamically over time. During crises and significant events, transmission bursts and tail risks change. Based on both in- and out-of-sample forecasts, we find that the information contained in investor sentiment, economic conditions, and uncertainty includes significant predictive content about the tail risk connectedness of cryptocurrencies.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"358 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140020197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.1186/s40854-023-00509-y
Mutaz M. Al-Debei, Omar Hujran, Ahmad Samed Al-Adwan
Iris recognition technology (IRT)-based authentication is a biometric financial technology (FinTech) application used to automate user recognition and verification. In addition to being a controversial technology with various facilitators and inhibitors, the adoption of IRT-based FinTech is driven by contextual factors, such as customer perceptions, deployed biometric technology, and financial transaction settings. Due to its controversial and contextual properties, analyzing IRT-based FinTech acceptance is challenging. This study uses a net valence framework to investigate the salient positive and negative factors influencing the intention to use IRT-based FinTech in automated teller machines (ATMs) in Jordan. This study is pertinent because there is a dearth of research on IRT-based FinTech in the relevant literature; most previous research has taken purely engineering and technical approaches. Furthermore, despite considerable investments by banks and other financial institutions in this FinTech, target user adoption is minimal, and only 6% of Jordan’s ATM transactions are currently IRT-enabled. This study employs mixed methods. In the first qualitative study, 17 Jordanian customers were interviewed regarding the benefits and risks of IRT-based FinTech in ATMs. Content analyses determined the most important concepts or themes. The advantages include financial security, convenience, and FinTech-enabled hygiene, whereas the concerns include performance, financial, privacy, and physical risks. The research model is constructed based on the qualitative study and theoretical underpinnings, wherein 631 Jordanian bank customers with active ATM accounts were surveyed to validate the research model. The findings indicate that IRT-based FinTech usage in ATMs is proportional to its perceived value. In descending order of effect, financial security, FinTech-enabled hygiene, and convenience benefits positively impact perceived value. Privacy, financial, and physical risks have negative impacts on perceived value, whereas performance risk has no effect. This study contributes to the relatively untapped domain of biometric technology in information systems, with important theoretical and practical implications.
{"title":"Net valence analysis of iris recognition technology-based FinTech","authors":"Mutaz M. Al-Debei, Omar Hujran, Ahmad Samed Al-Adwan","doi":"10.1186/s40854-023-00509-y","DOIUrl":"https://doi.org/10.1186/s40854-023-00509-y","url":null,"abstract":"Iris recognition technology (IRT)-based authentication is a biometric financial technology (FinTech) application used to automate user recognition and verification. In addition to being a controversial technology with various facilitators and inhibitors, the adoption of IRT-based FinTech is driven by contextual factors, such as customer perceptions, deployed biometric technology, and financial transaction settings. Due to its controversial and contextual properties, analyzing IRT-based FinTech acceptance is challenging. This study uses a net valence framework to investigate the salient positive and negative factors influencing the intention to use IRT-based FinTech in automated teller machines (ATMs) in Jordan. This study is pertinent because there is a dearth of research on IRT-based FinTech in the relevant literature; most previous research has taken purely engineering and technical approaches. Furthermore, despite considerable investments by banks and other financial institutions in this FinTech, target user adoption is minimal, and only 6% of Jordan’s ATM transactions are currently IRT-enabled. This study employs mixed methods. In the first qualitative study, 17 Jordanian customers were interviewed regarding the benefits and risks of IRT-based FinTech in ATMs. Content analyses determined the most important concepts or themes. The advantages include financial security, convenience, and FinTech-enabled hygiene, whereas the concerns include performance, financial, privacy, and physical risks. The research model is constructed based on the qualitative study and theoretical underpinnings, wherein 631 Jordanian bank customers with active ATM accounts were surveyed to validate the research model. The findings indicate that IRT-based FinTech usage in ATMs is proportional to its perceived value. In descending order of effect, financial security, FinTech-enabled hygiene, and convenience benefits positively impact perceived value. Privacy, financial, and physical risks have negative impacts on perceived value, whereas performance risk has no effect. This study contributes to the relatively untapped domain of biometric technology in information systems, with important theoretical and practical implications.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"7 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140008327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.1186/s40854-023-00501-6
Xian Zhuo, Felix Irresberger, Denefa Bostandzic
This paper provides a systematic literature review of text analysis methodologies used in blockchain-related research to comprehend and synthesize existing studies across disciplines and define future research directions. We summarize the research scope, text data, and methodologies of 124 papers and identify the two most common combinations of these dimensions: (1) papers that focus on specific cryptocurrencies tend to apply sentiment analysis to instant user-generated content or news articles to discover the correlations between public opinion and market behavior, and (2) studies that examine the broad concept of blockchain with text data from documents published by companies tend to apply topic modeling techniques to explore classifications and trends in blockchain development. We discover five major research topics in the academic literature: relationship discovery, cryptocurrency performance prediction, classification and trend, crime and regulation, and perception of blockchain. Based on these findings, we highlight three potential research directions for researchers to select topics and implement suitable methodologies for text analysis.
{"title":"How are texts analyzed in blockchain research? A systematic literature review","authors":"Xian Zhuo, Felix Irresberger, Denefa Bostandzic","doi":"10.1186/s40854-023-00501-6","DOIUrl":"https://doi.org/10.1186/s40854-023-00501-6","url":null,"abstract":"This paper provides a systematic literature review of text analysis methodologies used in blockchain-related research to comprehend and synthesize existing studies across disciplines and define future research directions. We summarize the research scope, text data, and methodologies of 124 papers and identify the two most common combinations of these dimensions: (1) papers that focus on specific cryptocurrencies tend to apply sentiment analysis to instant user-generated content or news articles to discover the correlations between public opinion and market behavior, and (2) studies that examine the broad concept of blockchain with text data from documents published by companies tend to apply topic modeling techniques to explore classifications and trends in blockchain development. We discover five major research topics in the academic literature: relationship discovery, cryptocurrency performance prediction, classification and trend, crime and regulation, and perception of blockchain. Based on these findings, we highlight three potential research directions for researchers to select topics and implement suitable methodologies for text analysis.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"7 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140008328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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