Journal of Finance and Data Science最新文献

{"title":"Financial inclusion, technologies, and worldwide economic development: A spatial Durbin model approach","authors":"Xiaoling Song ,&nbsp;Xuan Qin ,&nbsp;Wanmeng Wang ,&nbsp;Rita Yi Man Li","doi":"10.1016/j.jfds.2025.100155","DOIUrl":"10.1016/j.jfds.2025.100155","url":null,"abstract":"<div><div>Using panel data from 144 countries, this study constructed an inclusive financial evaluation index and depicted the inclusive finance development worldwide under digital empowerment through classification. It reviewed the spatial effect of financial inclusion in developed and developing countries by throwing light on demand, supply, and regulatory factors via the spatial Durbin model. The mediating and regulatory effects model examines the transmission mechanism of financial inclusion with a focus on financial literacy, scientific and technological levels, and regulatory quality. The results show that the level of financial inclusion in developed countries is significantly higher than in developing countries. The economic level of developed countries positively impacts financial inclusion in their countries and neighbouring ones. Enhancing financial literacy, science and technological level, and supervision quality improve the development of inclusive finance. While the economic level and urbanization rate in developing countries inhibit the development of financial inclusion, countries with lower economic development and urbanization rates have a greater incentive to develop digital financial inclusion. The improved economic development in developing countries favours financial inclusion in countries nearby. Moreover, financial literacy plays a positive moderating role in the effect of digital finance on financial inclusion. The technology level can exert a transmission effect on financial inclusion through an elevated level of digital finance. The impact of regulatory quality on financial inclusion can be conveyede by creating a stable economic and financial environment and improving economic development levels. This study expands the theoretical research on constructing an inclusive finance evaluation system and its impact mechanism. It provides essential decision-making references for governments, relevant decision-making departments, financial institutions and financial technology enterprises to develop inclusive finance.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised generation of tradable topic indices through textual analysis","authors":"Marcel Lee ,&nbsp;Alan Spark","doi":"10.1016/j.jfds.2025.100149","DOIUrl":"10.1016/j.jfds.2025.100149","url":null,"abstract":"<div><div>Stock returns are moved by many risk factors. Thematic stock indices try to represent these factors, but are limited by the fact that risk factors are not directly observable. This paper introduces a method to uncover hidden risk factors through text analysis. It applies the dynamic variant of the <em>Latent Dirichlet Allocation</em> (LDA) model to annual and quarterly reports to find a topic distribution for each stock. This is then interpreted as the risk factor partition and transformed into a standard normal basis which corresponds to pure risk factors. The weights indicate the proportions necessary to combine the equities into tradable topic indices. The need for human intervention is minimized by determining the optimal parameters automatically.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal rebalancing strategies reduce market variability","authors":"Helge Holden ,&nbsp;Lars Holden","doi":"10.1016/j.jfds.2025.100151","DOIUrl":"10.1016/j.jfds.2025.100151","url":null,"abstract":"<div><div>The increasing fraction of passive funds influences stock market variability since passive investors behave differently than active investors. We demonstrate via simulations how portfolios that rebalance between different classes of assets influence the market variability. We prove that the optimal strategy for such portfolios when we include transaction costs, is only to rebalance when the portfolio leaves a no-trade region in the state space. This is the case also when the expectation and volatility of the prices are inhomogeneous. We show that portfolios that apply an optimal rebalance strategy reduce the variability in the stock market measured in the sum of the distances between local minimum and maximum of the prices in the stock market, also when these portfolios constitute only a small part of the market. However, the more usual rebalance strategies that only consider to rebalance at the end of a month or a quarter, have a much weaker influence on the market variability.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Symbolic Modeling for financial asset pricing","authors":"Xiangwu Zuo,&nbsp;Anxiao (Andrew) Jiang","doi":"10.1016/j.jfds.2025.100150","DOIUrl":"10.1016/j.jfds.2025.100150","url":null,"abstract":"<div><div>Symbolic Regression is a machine learning technique that discovers an unknown function from its samples. Compared to conventional regression techniques (e.g., linear regression, polynomial regression, <em>etc.</em>), Symbolic Regression does not limit the discovered function to specific forms (e.g., linear functions, polynomials, <em>etc.</em>). Its recent developments are enabling its application to various fields, including both scientific study and engineering research. However, in spite of its flexibility, Symbolic Regression still faces one limitation: given datasets from different systems in the same domain, Symbolic Regression needs to find a distinct function for each dataset, instead of finding a more general yet succinct function that can fit all the datasets through the adjustments of its coefficients. The latter approach, which is termed “Symbolic Modeling” in this work, can be seen as a generalization of Symbolic Regression and has important applications to both academia and industry. This work elucidates Symbolic Modeling and unveils a cutting-edge algorithm, deriving its principles from deep learning and genetic programming. This algorithm is implemented into an application, showcasing its practical utility in the field of financial asset pricing, an integral facet of finance that concentrates on asset valuation. It is shown that Symbolic Modeling compares favorably to existing asset pricing models in multiple aspects.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Paper discussion at the 2024 ABFER-JFDS Conference on AI and FinTech","authors":"","doi":"10.1016/j.jfds.2025.100153","DOIUrl":"10.1016/j.jfds.2025.100153","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable machine learning model for predicting activist investment targets","authors":"Minwu Kim,&nbsp;Sidahmend Benahderrahmane,&nbsp;Talal Rahwan","doi":"10.1016/j.jfds.2024.100146","DOIUrl":"10.1016/j.jfds.2024.100146","url":null,"abstract":"<div><div>This research presents a predictive model to identify potential targets of activist investment funds—entities that acquire significant corporate stakes to influence strategic and operational decisions, ultimately enhancing shareholder value. Predicting such targets is crucial for companies aiming to mitigate intervention risks, activist funds seeking optimal investments, and investors looking to leverage potential stock price gains. Using data from the Russell 3000 index from 2016 to 2022, we evaluated 123 model configurations incorporating diverse imputation, oversampling, and machine learning techniques. Our best model achieved an AUC-ROC of 0.782, demonstrating its capability to effectively predict activist fund targets. To enhance interpretability, we employed the Shapley value method to identify key factors influencing a company’s likelihood of being targeted, highlighting the dynamic mechanisms underlying activist fund target selection. These insights offer a powerful tool for proactive corporate governance and informed investment strategies, advancing understanding of the mechanisms driving activist investment decisions.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technical patterns and news sentiment in stock markets","authors":"Markus Leippold ,&nbsp;Qian Wang ,&nbsp;Min Yang","doi":"10.1016/j.jfds.2024.100145","DOIUrl":"10.1016/j.jfds.2024.100145","url":null,"abstract":"<div><div>This paper explores the effectiveness of technical patterns in predicting asset prices and market movements, emphasizing the role of news sentiment. We employ an image recognition method to detect technical patterns in price images and assess whether this approach provides more information than traditional rule-based methods. Our findings indicate that many model-based patterns yield significant returns in the US market, whereas top-type patterns are less effective in the Chinese market. The model demonstrates high accuracy in training samples and strong out-of-sample performance. Our empirical analysis concludes that technical patterns remain effective in recent stock markets when combined with news sentiment, offering a profitable portfolio strategy. Moreover, we find patterns better predict returns for firms with high momentum, institutional ownership, and prior patterns in US, while in China, they are more effective for small firms with high momentum and institutional ownership. This study highlights the potential of image recognition methods in market data analysis and underscores the importance of sentiment in technical analysis.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning from AI-Finance: A selected synopsis","authors":"Yi Huang,&nbsp;Sung Kwan Lee,&nbsp;Bernard Yeung","doi":"10.1016/j.jfds.2025.100152","DOIUrl":"10.1016/j.jfds.2025.100152","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting Hawala network for money laundering by graph mining","authors":"Marzhan Alenova,&nbsp;Assem Utaliyeva,&nbsp;Ki-Joune Li","doi":"10.1016/j.jfds.2024.100147","DOIUrl":"10.1016/j.jfds.2024.100147","url":null,"abstract":"<div><div>Hawala, a traditional but informal money transfer system, has been prevalent in many parts of the world, such as money laundering. Despite the regulatory actions taken by financial institutions, Hawala is still a key node in terror financing schemes and its extent of misuse is unknown. Due to the hidden transactions and limited knowledge about the Hawala, it is difficult for legal enforcement authorities such as financial intelligence units (FIU) of each country to detect and investigate the Hawala network. In this paper, we present a novel approach to detect the potential Hawala instances in the stream of financial transaction data by using graph mining techniques. In order to reflect the properties of Hawala, we apply graph mining methods such as graph centrality, Blackhole metric, and Hidden link metric as well as anomaly detection methods using graph convolutional network. Experiments demonstrate that the proposed method gives a meaningful result in detecting Hawala network and can be used as a complementary tool to the existing transactional monitoring tracks.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post notes of 2024 ABFER-JFDS conference on AI and FinTech","authors":"","doi":"10.1016/j.jfds.2025.100154","DOIUrl":"10.1016/j.jfds.2025.100154","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}