FinTech最新文献

{"title":"Assessing the Impact of Financial Technology Innovations on the Sustainable Profitability of Listed Commercial Banks in China","authors":"Yueyao Wang, Xintong Yu, Qingyuan Yao, Yingnan Lu, Wenjia Che, Jingang Jiang, Sonia Chien-I Chen","doi":"10.3390/fintech3030019","DOIUrl":"https://doi.org/10.3390/fintech3030019","url":null,"abstract":"Commercial banks constitute a crucial segment of China’s financial system, and their efficient operation is directly linked to the development of other sectors within the national economy. The sustainable profitability of these banks is vital for maintaining the stability of China’s financial system. In the context of the current digital economy, it is of great theoretical and practical significance to conduct an in-depth analysis of the impact of financial technology (fintech) development on the sustainable profitability of commercial banks and its underlying mechanisms. Such research can promote the digital transformation of commercial banks, enhance risk supervision policies, and mitigate systemic financial risks. This study utilizes EViews software Version 13 to analyze annual data from 13 listed commercial banks in China over the period from 2011 to 2021. It examines the influence of fintech on the profitability of these banks, considering their unique characteristics and drawing insights from the existing literature on the mechanisms through which fintech affects bank profitability. Employing both a static panel fixed effects variable-intercept model and a dynamic panel generalized method of moments (GMM) model, the empirical findings indicate that fintech development significantly impacts the profitability of listed commercial banks.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"112 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systemic Risk and Bank Networks: A Use of Knowledge Graph with ChatGPT","authors":"Ren-Yuan Lyu, Ren-Raw Chen, San-Lin Chung, Yilu Zhou","doi":"10.3390/fintech3020016","DOIUrl":"https://doi.org/10.3390/fintech3020016","url":null,"abstract":"In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT (via OpenAI api). Our final graphs represent bank networks and further shed light on the systemic risk of the financial institutions. Financial news reflects live how financial institutions are connected, via graphs which provide information on conditional dependencies among the financial institutions. Our results show that in the year 2016, the chosen 22 top U.S. financial firms are not closely connected and, hence, present no systemic risk.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"29 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140971080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyses of Scientific Collaboration Networks among Authors, Institutions, and Countries in FinTech Studies: A Bibliometric Review","authors":"Carson Duan","doi":"10.3390/fintech3020015","DOIUrl":"https://doi.org/10.3390/fintech3020015","url":null,"abstract":"Purpose: FinTech research has grown rapidly, but few studies have measured the levels of scientific collaboration among authors, institutions, and nations. This study aimed to reveal the status and levels of scientific collaboration in this field. The results will help scholars to combine their knowledge and resources to generate new ideas that may not have been possible if they worked alone and enable them to work more efficiently, resulting in higher-quality results for all parties. Design/methodology/approach: Research papers in the FinTech field indexed in the Web of Science databases from 1999 to 2022 were included in the research dataset. Using R-bibliometrix and VOS viewer (Visualisation of Similarities viewer), co-authorship networks were drawn. Additionally, some measures of the co-authorship network were assessed, such as the links, total link strength, total number of articles, total citations, normalized total citations, average year of publication, average citations, and average normalized normal citations. Beyond bibliometric analyses, this research gathers other statistics for analysis to gain further insights. Result: A total of 1792 publications were identified, and a number of these revealed an increase in the forms of collaboration, including collaboration among authors and institutions. Three lists of the most collaborative authors, institutions, and countries were compiled. The top authors, affiliations, and countries were ranked according to their total links, citations, average citations, and annual normalized citations. There were six distinct clusters of collaboration among authors, thirteen among affiliations, and eleven among countries. In terms of author collaborations, the links and total link strength had three nodes and four nodes, respectively. John Goodell, Chi-Chuan Le, and Shaen Corbet were the top three collaborative authors. In terms of affiliations, the two strength attributes were 8 and 12 nodes, with Sydney University, Hong Kong University, and the Shanghai University of Finance and Economics topping the list. In terms of collaboration among countries, these two attributes had 14 and 34 nodes. Three of the most collaborative countries were England, the People’s Republic of China, and the United States. Originality/value: In contrast with previous systematic literature reviews, this study quantitatively examines the collaboration status in the FinTech field on three levels: authors, affiliations, and countries.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140692064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating Uncertainty: Enhancing Markowitz Asset Allocation Strategies through Out-of-Sample Analysis","authors":"V. Kanaparthi","doi":"10.3390/fintech3010010","DOIUrl":"https://doi.org/10.3390/fintech3010010","url":null,"abstract":"This research paper explores the complicated connection between uncertainty and the Markowitz asset allocation framework, specifically investigating how mistakes in estimating parameters significantly impact the performance of strategies during out-of-sample evaluations. Drawing on relevant literature, we highlight the importance of our findings. In contrast to common assumptions, our study systematically compares these approaches with alternative allocation strategies, providing insights into their performance in both anticipated and real-world out-of-sample events. The research demonstrates that incorporating methods to address uncertainty enhances the Markowitz framework, challenging the idea that longer sample periods always lead to better outcomes. Notably, imposing a short-sale constraint proves to be a valuable strategy for improving the effectiveness of the initial portfolio. While revealing the complexities of uncertainty, our study also highlights the surprising resilience of basic asset allocation approaches, such as equally weighted allocation, which exhibit commendable performance. Methodologically, we employ a rigorous out-of-sample evaluation, emphasizing the practical implications of parameter uncertainty on asset allocation outcomes. Investors, portfolio managers, and financial practitioners can use these insights to refine their strategies, considering the dynamic nature of markets and the limitations internal to the traditional models. In conclusion, this paper goes beyond the theoretical scope to provide substantial value in enhancing real-world investment decisions.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"170 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Crypto Yield Model for Staking Return","authors":"Julien Riposo, Maneesh Gupta","doi":"10.3390/fintech3010008","DOIUrl":"https://doi.org/10.3390/fintech3010008","url":null,"abstract":"We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a forward approach, we prove that the interest is given by the ratio of the average staking gain to the total staked coins. Some additional PoS features are considered in the model, such as slash rate and Maximal Extractable Value (MEV), which marks the originality of this approach. In particular, we prove that slashing diminishes the rewards, reflecting the fact that the blockchain can consider stakers to potentially validate incorrectly. Regarding MEV, the approach we have sheds light on the relation between transaction fees and the average staking gain. We illustrate the developed model with Ethereum 2.0 and apply a similar process in a Proof-of-Work consensus context.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"322 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Financial Sanctions and Financial Development Factors on Central Bank Digital Currency Implementation","authors":"Medina Ayta Mohammed, Carmen De-Pablos-Heredero, José Luis Montes Botella","doi":"10.3390/fintech3010009","DOIUrl":"https://doi.org/10.3390/fintech3010009","url":null,"abstract":"This study investigates the influence of a country’s financial access and stability and the adoption of retail central bank digital currencies (CBDCs) across 71 countries. Using an ordinal logit model, we examine how individual financial access, the ownership of credit cards, financing accessibility by firms, offshore loans, financial sanctions, and the ownership structure of financial institutions influence the probability of CBDC adoption in nations. These findings reveal that nations facing financial sanctions and those with substantial offshore bank loans are more inclined to adopt CBDCs. Furthermore, a significant relationship is observed in countries where many people have restricted financial access, indicating heightened interest in CBDC adoption. Interestingly, no statistically significant relationship was found between the adoption of CBDCs and the percentage of foreign-owned banks in each country. The results show that countries with low financial stability and financial access adopt CBDCs faster. This study expands our knowledge of how a nation’s financial situation influences its adoption of CBDCs. The results provide important and relevant insights into the current discussion of the direction of global finance.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"227 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Crypto Yield Model for Staking Return","authors":"Julien Riposo, Maneesh Gupta","doi":"10.3390/fintech3010008","DOIUrl":"https://doi.org/10.3390/fintech3010008","url":null,"abstract":"We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a forward approach, we prove that the interest is given by the ratio of the average staking gain to the total staked coins. Some additional PoS features are considered in the model, such as slash rate and Maximal Extractable Value (MEV), which marks the originality of this approach. In particular, we prove that slashing diminishes the rewards, reflecting the fact that the blockchain can consider stakers to potentially validate incorrectly. Regarding MEV, the approach we have sheds light on the relation between transaction fees and the average staking gain. We illustrate the developed model with Ethereum 2.0 and apply a similar process in a Proof-of-Work consensus context.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"69 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Financial Sanctions and Financial Development Factors on Central Bank Digital Currency Implementation","authors":"Medina Ayta Mohammed, Carmen De-Pablos-Heredero, José Luis Montes Botella","doi":"10.3390/fintech3010009","DOIUrl":"https://doi.org/10.3390/fintech3010009","url":null,"abstract":"This study investigates the influence of a country’s financial access and stability and the adoption of retail central bank digital currencies (CBDCs) across 71 countries. Using an ordinal logit model, we examine how individual financial access, the ownership of credit cards, financing accessibility by firms, offshore loans, financial sanctions, and the ownership structure of financial institutions influence the probability of CBDC adoption in nations. These findings reveal that nations facing financial sanctions and those with substantial offshore bank loans are more inclined to adopt CBDCs. Furthermore, a significant relationship is observed in countries where many people have restricted financial access, indicating heightened interest in CBDC adoption. Interestingly, no statistically significant relationship was found between the adoption of CBDCs and the percentage of foreign-owned banks in each country. The results show that countries with low financial stability and financial access adopt CBDCs faster. This study expands our knowledge of how a nation’s financial situation influences its adoption of CBDCs. The results provide important and relevant insights into the current discussion of the direction of global finance.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"38 04","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robo Advising and Investor Profiling","authors":"Raquel M. Gaspar, Madalena Oliveira","doi":"10.3390/fintech3010007","DOIUrl":"https://doi.org/10.3390/fintech3010007","url":null,"abstract":"The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo advisors gather information about clients’ preferences, financial situations, and future goals through questionnaires. Subsequently, they recommend ETF-based portfolios tailored to match the investor’s risk profile. However, these questionnaires often appear vague, and robo advisors seldom disclose the methodologies employed for investor profiling or asset allocation. This study aims to contribute by introducing an investor profiling method relying solely on investors’ relative risk aversion (RRA), which, in addition, allows for the determination of optimal allocations. We also show that, for the period under analysis and using the same ETF universe, our RRA portfolios consistently outperform those recommended by the Riskalyze platform, which may suffer from ultraconservadorism in terms of the proposed volatility.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robo Advising and Investor Profiling","authors":"Raquel M. Gaspar, Madalena Oliveira","doi":"10.3390/fintech3010007","DOIUrl":"https://doi.org/10.3390/fintech3010007","url":null,"abstract":"The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo advisors gather information about clients’ preferences, financial situations, and future goals through questionnaires. Subsequently, they recommend ETF-based portfolios tailored to match the investor’s risk profile. However, these questionnaires often appear vague, and robo advisors seldom disclose the methodologies employed for investor profiling or asset allocation. This study aims to contribute by introducing an investor profiling method relying solely on investors’ relative risk aversion (RRA), which, in addition, allows for the determination of optimal allocations. We also show that, for the period under analysis and using the same ETF universe, our RRA portfolios consistently outperform those recommended by the Riskalyze platform, which may suffer from ultraconservadorism in terms of the proposed volatility.","PeriodicalId":472258,"journal":{"name":"FinTech","volume":"51 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}