Journal Description
FinTech
FinTech
is an international, peer-reviewed, open access journal on a variety of themes connected with financial technology, such as cryptocurrencies, risk management, robo-advising, crowdfunding, blockchain, new payment solutions, machine learning and AI for financial services, digital currencies, etc., published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within RePEc, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21 days after submission; acceptance to publication is undertaken in 4.7 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Latest Articles
Artificial Intelligence-Driven FinTech Valuation: A Scalable Multilayer Network Approach
FinTech 2024, 3(3), 479-495; https://doi.org/10.3390/fintech3030026 - 23 Sep 2024
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The integration of Artificial Intelligence (AI) in the FinTech industry has significantly reshaped operational workflows, product innovation, and risk management, all of which are pivotal to company valuation. This study investigates the impact of AI-enhanced multilayer networks on FinTech valuation, introducing a novel,
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The integration of Artificial Intelligence (AI) in the FinTech industry has significantly reshaped operational workflows, product innovation, and risk management, all of which are pivotal to company valuation. This study investigates the impact of AI-enhanced multilayer networks on FinTech valuation, introducing a novel, scalable multilayer network model with AI-driven Copula Nodes that serve as connectors across operational layers. By incorporating AI, the research unveils a dynamic and interconnected approach to FinTech valuation, revealing new pathways for value co-creation through real-time adjustments and predictive capabilities. The research reveals that while operational efficiency is a major driver of market value, a balanced integration of AI across risk management, product innovation, and market perception is essential for maximizing value. Additionally, the findings highlight the importance of managing AI-driven risks such as algorithmic bias and regulatory challenges. This comprehensive framework offers critical insights for FinTechs, investors, and regulators seeking to understand the complex role of AI in enhancing valuation within the evolving financial services landscape.
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Open AccessReview
A Comprehensive Review of Generative AI in Finance
by
David Kuo Chuen Lee, Chong Guan, Yinghui Yu and Qinxu Ding
FinTech 2024, 3(3), 460-478; https://doi.org/10.3390/fintech3030025 - 20 Sep 2024
Abstract
The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing
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The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing an advanced topic modeling method, BERTopic, we systematically categorize and analyze existing research to uncover predominant themes and emerging areas of interest. Our findings reveal the transformative impact of finance-specific large language models (LLMs), the innovative use of generative adversarial networks (GANs) in synthetic financial data generation, and the pressing necessity of a new regulatory framework to govern the use of GAI in the finance sector. This paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies.
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(This article belongs to the Special Issue Trends and New Developments in FinTech)
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Open AccessArticle
A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data
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Johannes K. Chiang and Renhe Chi
FinTech 2024, 3(3), 427-459; https://doi.org/10.3390/fintech3030024 - 18 Sep 2024
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Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial
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Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.
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Open AccessEditorial
Financial Technology and Innovation for Sustainable Development
by
Otilia Manta
FinTech 2024, 3(3), 424-426; https://doi.org/10.3390/fintech3030023 - 26 Aug 2024
Abstract
This Special Issue on “Financial Technology and Innovation for Sustainable Development” includes a diverse collection of research papers that explore the evolving landscape of financial technologies (FinTech) and their implications for sustainable development [...]
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(This article belongs to the Special Issue Financial Technology and Innovation Sustainable Development)
Open AccessReview
Transforming Financial Systems: The Role of Time Banking in Promoting Community Collaboration and Equitable Wealth Distribution
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Otilia Manta and Maria Palazzo
FinTech 2024, 3(3), 407-423; https://doi.org/10.3390/fintech3030022 - 22 Aug 2024
Abstract
The existing global multi-crises have generated significant transformations in the architecture of financial systems, impacting local communities. Furthermore, the digital era has created a conducive environment for the development of financial innovations that can generate financial instruments supporting financial inclusion. Our research aims
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The existing global multi-crises have generated significant transformations in the architecture of financial systems, impacting local communities. Furthermore, the digital era has created a conducive environment for the development of financial innovations that can generate financial instruments supporting financial inclusion. Our research aims to identify and develop innovative financial instruments that foster closer collaboration within communities and promote a more equitable distribution of wealth and resources, directly impacting financial inclusion and well-being. The methodology used in our study is based on existing empirical research in the specialized scientific literature, as well as on identifying variables within existing models. Additionally, the use of bibliometric analyses and research tools based on artificial intelligence allows us to structure the innovative financial instruments found in the scientific databases. Building on the existence of innovative financial instruments, our paper specifically explores the concept of time banking as an innovative financial instrument, offering a new approach to economic exchange and the construction of financial mechanisms at the local community level. By using technology, especially in digital and ecological eras, time banks can be efficiently managed through online platforms where individuals can register their contributed hours and access the services they need. This study’s conclusions emphasize that time banks have the potential to serve as innovative financial instruments. Furthermore, through the analysis conducted in this study and the identified models, this study contributes to redefining the concept of time banking as an innovative financial instrument. Time banks focus on the productivity and efficiency of local community activities, with direct implications for reducing dependence on traditional currency and promoting an equitable distribution of labor. This innovative approach is promising, especially in an increasingly digitized financial landscape. Our paper seeks to capture this transformative potential and highlight our personal contributions to redefining the time bank as an innovative financial instrument.
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(This article belongs to the Special Issue Financial Technology and Innovation Sustainable Development)
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Open AccessArticle
Monetary Transmission & Small Firm Credit Rationing: The Stablecoin Opportunity to Raise Business Credit Flows
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Richard Simmons
FinTech 2024, 3(3), 379-406; https://doi.org/10.3390/fintech3030021 - 13 Aug 2024
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Credit rationing, especially prevalent for smaller firms, impedes economic growth. A central bank-aligned not-for-profit managed business-to-business “stablecoin” (“synthetic central bank digital currency”) providing trade credit liquidity can provide additional monetary mass to mitigate small firm credit rationing. This raises growth by reducing monetary
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Credit rationing, especially prevalent for smaller firms, impedes economic growth. A central bank-aligned not-for-profit managed business-to-business “stablecoin” (“synthetic central bank digital currency”) providing trade credit liquidity can provide additional monetary mass to mitigate small firm credit rationing. This raises growth by reducing monetary transmission imperfections consequent upon asymmetric information, commercial bank underwriting restrictions, market power dynamics, and regulatory distortion. A simple framework is developed to contextualise small firm credit rationing and associated monetary transmission imperfections with broader credit flows into both the real and monetary sectors. Evidence is presented regarding monetary transmission efficacy to firms, paving the way to proposing a business-to-business central bank-mediated “trade credit stablecoin” to improve business credit supply. In addition to providing additional (estimated at more than 10%) industrial and commercial (including smaller) firm financing, the envisaged trade credit stablecoin provides an additional monetary transmission channel for central banks to manage credit supply to the real economy to support economic activity and raise growth. Available to all firms, the trade credit stablecoin offers additional low-cost liquidity to firms, thereby offering policymakers an additional contra-cyclical monetary transmission instrument to support growth and, where necessary, reduce real economic disruption consequent upon financial system crises and liquidity events.
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Open AccessFeature PaperArticle
Dynamics between Bitcoin Market Trends and Social Media Activity
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George Vlahavas and Athena Vakali
FinTech 2024, 3(3), 349-378; https://doi.org/10.3390/fintech3030020 - 24 Jul 2024
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This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus
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This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.
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Open AccessArticle
Assessing the Impact of Financial Technology Innovations on the Sustainable Profitability of Listed Commercial Banks in China
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Yueyao Wang, Xintong Yu, Qingyuan Yao, Yingnan Lu, Wenjia Che, Jingang Jiang and Sonia Chien-I Chen
FinTech 2024, 3(3), 337-348; https://doi.org/10.3390/fintech3030019 - 8 Jul 2024
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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.
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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.
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Open AccessArticle
Cryptocurrency, Gold, and Stock Exchange Market Performance Correlation: Empirical Evidence
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Kanellos Toudas, Démétrios Pafos, Paraskevi Boufounou and Athanasios Raptis
FinTech 2024, 3(2), 324-336; https://doi.org/10.3390/fintech3020018 - 18 Jun 2024
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This paper examines the correlation between three prospective investing options: the Bitcoin cryptocurrency price, gold, and the Dow Jones stock index. The main research question is whether there is a causal effect of gold and the DWJ on Bitcoin and how this effect
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This paper examines the correlation between three prospective investing options: the Bitcoin cryptocurrency price, gold, and the Dow Jones stock index. The main research question is whether there is a causal effect of gold and the DWJ on Bitcoin and how this effect varies on time. The study begins with a background analysis that explains the definitions and operation of cryptocurrencies, followed by a brief overview of gold and its derivatives. In addition, a historical review of stock markets is provided, with a focus on the Dow Jones index. Then, a literature review follows. Daily data from three separate periods are used, each spanning four years. The first period, running from October 2014 to September 2018, provides an overview of the introduction of official cryptocurrency price data. The second period, running from Oct 2018 to Sept 2022, captures more recent trends preceding COVID-19. The third period, from January 2020 to December 2023, is the whole COVID-19 period with the initiation, embedded, and terminal phases. Classical inductive statistical methods (descriptive, correlations, multiple linear regression) as well as time series analysis methods (autocorrelation, cross-correlation, Granger causality tests, and ARIMA modeling) are used to analyze the data. Rigorous testing for autocorrelation, multicollinearity, and homoskedasticity is performed on the estimated models. The results show a correlation of Bitcoin with gold and the DWJ. This correlation varies over time, as in the first period the correlation mainly concerns the DWJ and in the second it mainly concerns gold. By using ARIMA models, it was possible to make a forecast in a time horizon of a few days. In addition, the structure of the forecasting mechanism of gold and DWJ on Bitcoin seems to have changed during the COVID-19 crisis. The findings suggest that future research should encompass a broader dataset, facilitating comprehensive comparisons and enhancing the reliability of the conclusions drawn.
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Open AccessArticle
Is the Metaverse Dead? Insights from Financial Bubble Analysis
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Pascal Frank and Markus Rudolf
FinTech 2024, 3(2), 302-323; https://doi.org/10.3390/fintech3020017 - 31 May 2024
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This paper explores the economic trends and identifies speculative bubbles within the emerging metaverse, based on the specific example of Decentraland, which is represented by its underlying native token MANA.For comparability, we consider three further tokens: SAND, ETH, and BTC.The study shows price
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This paper explores the economic trends and identifies speculative bubbles within the emerging metaverse, based on the specific example of Decentraland, which is represented by its underlying native token MANA.For comparability, we consider three further tokens: SAND, ETH, and BTC.The study shows price prediction and provides further insight into bubble behavior to provide a deeper insight into the real trend and situation of the metaverse. When comparing all considered tokens, evidence of comovement and positive as well as negative bubbles is identified. This paper makes use of proven modeling techniques, such as SARIMA, for price prediction and LPPLS for financial bubble identification, visualization, and time stamping.
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Open AccessArticle
Systemic Risk and Bank Networks: A Use of Knowledge Graph with ChatGPT
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Ren-Yuan Lyu, Ren-Raw Chen, San-Lin Chung and Yilu Zhou
FinTech 2024, 3(2), 274-301; https://doi.org/10.3390/fintech3020016 - 16 May 2024
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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
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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.
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Open AccessReview
Analyses of Scientific Collaboration Networks among Authors, Institutions, and Countries in FinTech Studies: A Bibliometric Review
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Carson Duan
FinTech 2024, 3(2), 249-273; https://doi.org/10.3390/fintech3020015 - 17 Apr 2024
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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
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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.
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Open AccessArticle
Argumentation Schemes for Blockchain Deanonymisation
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Dominic Deuber, Jan Gruber, Merlin Humml, Viktoria Ronge and Nicole Scheler
FinTech 2024, 3(2), 236-248; https://doi.org/10.3390/fintech3020014 - 27 Mar 2024
Cited by 1
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Cryptocurrency forensics have become standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand,
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Cryptocurrency forensics have become standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand, this implicitness complicates investigations. On the other hand, it can have far-reaching consequences for the rights of those affected. Argumentation schemes could remedy this untenable situation by rendering the underlying premises more transparent. Additionally, they can aid in critically evaluating the probative value of any results obtained by cryptocurrency deanonymisation techniques. In the argumentation theory and AI community, argumentation schemes are influential as they state the implicit premises for different types of arguments. Through their critical questions, they aid the argumentation participants in critically evaluating arguments. We specialise the notion of argumentation schemes to legal reasoning about cryptocurrency deanonymisation. Furthermore, we demonstrate the applicability of the resulting schemes through an exemplary real-world case. Ultimately, we envision that using our schemes in legal practice can solidify the evidential value of blockchain investigations, as well as uncover and help to address uncertainty in the underlying premises—thus contributing to protecting the rights of those affected by cryptocurrency forensics.
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Open AccessArticle
Comparative Analysis of Linear Models and Artificial Neural Networks for Sugar Price Prediction
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Tathiana M. Barchi, João Lucas Ferreira dos Santos, Priscilla Bassetto, Henrique Nazário Rocha, Sergio L. Stevan, Jr., Fernanda Cristina Correa, Yslene Rocha Kachba and Hugo Valadares Siqueira
FinTech 2024, 3(1), 216-235; https://doi.org/10.3390/fintech3010013 - 12 Mar 2024
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Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work
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Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners.
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Open AccessArticle
Reimagining Peer-to-Peer Lending Sustainability: Unveiling Predictive Insights with Innovative Machine Learning Approaches for Loan Default Anticipation
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Ly Nguyen, Mominul Ahsan and Julfikar Haider
FinTech 2024, 3(1), 184-215; https://doi.org/10.3390/fintech3010012 - 5 Mar 2024
Cited by 1
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Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly
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Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly predict loan defaults to lessen the damage brought on by defaulters. The goal of this study is to fill the gap in the literature by exploring the feasibility of developing prediction models for P2P loan defaults without relying heavily on personal data while also focusing on identifying key variables influencing borrowers’ repayment capacity through systematic feature selection and exploratory data analysis. Given this, this study aims to create a computational model that aids lenders in determining the approval or rejection of a loan application, relying on the financial data provided by applicants. The selected dataset, sourced from an open database, contains 8578 transaction records and includes 14 attributes related to financial information, with no personal data included. A loan dataset is first subjected to an in-depth exploratory data analysis to find behaviors connected to loan defaults. Subsequently, diverse and noteworthy machine learning classification algorithms, including Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naïve Bayes, and XGBoost, were employed to build models capable of discerning borrowers who repay their loans from those who do not. Our findings indicate that borrowers who fail to comply with their lenders’ credit policies, pay elevated interest rates, and possess low FICO ratings are at a higher likelihood of defaulting. Furthermore, elevated risk is observed among clients who obtain loans for small businesses. All classification models, including XGBoost and Random Forest, successfully developed and performed satisfactorily and achieved an accuracy of over 80%. When the decision threshold is set to 0.4, the best performance for predicting loan defaulters is achieved using logistic regression, which accurately identifies 83% of the defaulted loans, with a recall of 83%, precision of 21% and f1 score of 33%.
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Open AccessArticle
Account Information and Payment Initiation Services and the Related AML Obligations in the Law of the European Union
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Michał Grabowski
FinTech 2024, 3(1), 173-183; https://doi.org/10.3390/fintech3010011 - 4 Mar 2024
Abstract
The Second Payment Services Directive introduced new services into the European Union legal system—Payment Initiation and Account Information Services. These services are based on payment accounts already opened and maintained for customers by the Account Servicing Payment Service Provider (bank, payment institution, electronic
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The Second Payment Services Directive introduced new services into the European Union legal system—Payment Initiation and Account Information Services. These services are based on payment accounts already opened and maintained for customers by the Account Servicing Payment Service Provider (bank, payment institution, electronic money institution). The Account Services Payment Service provider performs AML/CFT verification of the account holder and applies customer due diligence measures to the account holder, such as identifying beneficial owners, obtaining information on the purpose and intended nature of the business relationship, and ongoing monitoring of the business relationship. Payment Initiation and Account Information services are therefore provided to a previously verified client and based on the payment account currently maintained for him. European Union law does not clearly specify whether a Third-Party Service Provider offering Payment Initiation or Account Information Services is obliged to re-apply financial security measures to customers. The aim of this article was to perform a legal analysis of the regulations and soft law acts in force in the European Union and to answer the question. The purposive (teleological) and linguistic–logical (grammatical) methods of interpretation of regulations were used for the analysis. The structure of the legal system of the European Union as a civil law (code law) system was taken into account. This article shows that in the current legal situation, there is no doubt that Third-Party Service Providers are obliged entities in terms of AML/CFT law and are obliged to apply the AML/CFT to customers using Payment Initiation and Account Information services. However, the degree to which customer due diligence measures have to be applied varies depending on the adopted model of providing Payment Initiation and Account Information services. Third-Party Service Providers will be obliged to apply financial security measures in cases where the relationship between the customer and the service providers will have a continuing character. In the case of occasional provision of services, when the transaction value does not exceed a certain threshold, the supplier may only perform simplified customer verification. In particular, this applies to Payment Initiation service models, where the Payment Initiation Service Provider works for merchants, enabling them to accept payments for goods and services sold. In such a model, the Service Provider has a continuous relationship with the merchant but only performs an occasional transaction for the user. The analysis also allowed for the conclusion that European Union law, including that in the draft phase, does not regulate in a sufficiently precise manner when a given model of Account Services and Payment Initiation Services may be treated as based on an occasional transaction. This made it possible to formulate a de lege ferenda request to include this issue in the proposal for an EU Regulation on the prevention of the use of the financial system for the purposes of money laundering or terrorist financing.
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(This article belongs to the Special Issue The Impact of Digitalisation on Financial Services and Financial Literacy)
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Open AccessArticle
Navigating Uncertainty: Enhancing Markowitz Asset Allocation Strategies through Out-of-Sample Analysis
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Vijaya Krishna Kanaparthi
FinTech 2024, 3(1), 151-172; https://doi.org/10.3390/fintech3010010 - 17 Feb 2024
Cited by 1
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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
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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.
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Open AccessArticle
The Role of Financial Sanctions and Financial Development Factors on Central Bank Digital Currency Implementation
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Medina Ayta Mohammed, Carmen De-Pablos-Heredero and José Luis Montes Botella
FinTech 2024, 3(1), 135-150; https://doi.org/10.3390/fintech3010009 - 15 Feb 2024
Cited by 2
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
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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.
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(This article belongs to the Special Issue Financial Technology and Innovation Sustainable Development)
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Open AccessArticle
A Crypto Yield Model for Staking Return
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Julien Riposo and Maneesh Gupta
FinTech 2024, 3(1), 116-134; https://doi.org/10.3390/fintech3010008 - 15 Feb 2024
Cited by 1
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
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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.
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(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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Open AccessArticle
Robo Advising and Investor Profiling
by
Raquel M. Gaspar and Madalena Oliveira
FinTech 2024, 3(1), 102-115; https://doi.org/10.3390/fintech3010007 - 3 Feb 2024
Abstract
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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
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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.
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