Journal of Finance and Data Science最新文献

{"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":"2025-12-01","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":"Leveraging financial interdependencies in emerging markets via graph neural networks","authors":"Nicolai Bloch Jessen","doi":"10.1016/j.jfds.2025.100168","DOIUrl":"10.1016/j.jfds.2025.100168","url":null,"abstract":"<div><div>This study examines the role of interdependencies in forecasting sovereign yield spreads in emerging markets using Graph Neural Networks (GNNs). Sovereign yield spreads reflect economic conditions and investor sentiment, making accurate predictions crucial for investors, policymakers, and financial institutions. Traditional forecasting models often treat sovereign risks in isolation, failing to account for financial spillovers and cross-country linkages. By structuring sovereign bonds within a graph-based framework, this study explicitly models these interdependencies to improve predictive accuracy. Using macroeconomic indicators such as GDP, inflation, and foreign exchange reserves, countries are represented as nodes in a financial network, with edges capturing key economic relationships. A Graph Convolutional Network (GCN) is trained to predict sovereign yield spreads, and its performance is benchmarked against a structurally identical feed-forward neural network, where the only difference is the use of graph convolution layers or dense layers. The results show that the GCN model consistently outperforms the feed-forward model, particularly in predicting extreme yield spread movements, demonstrating the importance of accounting for financial interdependencies. Our findings underline the potential of GNNs as a powerful tool in forecasting sovereign yield spreads in emerging markets. Considering the economic impact of these spreads, GNNs could present significant benefits for financial sector stakeholders.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100168"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623397","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":"Persistent cointegration and regime-sensitive market leadership: Evidence from international tobacco stocks","authors":"Juan Manuel Martín-Álvarez ,&nbsp;Aida Galiano ,&nbsp;Brenda Vázquez-La Hoz ,&nbsp;Miguel Flores","doi":"10.1016/j.jfds.2026.100178","DOIUrl":"10.1016/j.jfds.2026.100178","url":null,"abstract":"<div><div>This paper develops a data-driven framework combining fractional cointegration and structural break detection to examine long-run interdependence and market leadership among international tobacco equities. Using weekly data from May 2008 to October 2024 for Philip Morris International, Altria, British American Tobacco, Imperial Brands, and Japan Tobacco, the study applies the Fractionally Cointegrated Vector Autoregressive (FCVAR) model of Johansen and Nielsen (2012) integrated with the Bai–Perron multiple-break methodology. The empirical analysis supports the presence of a single fractionally cointegrated equilibrium relationship characterized by long memory and regime-dependent persistence. Three model-implied regime shifts, which align closely in timing with major regulatory and ESG-related events, such as the 2012 WHO-FCTC harmonization, the 2015 expansion of FDA regulation, and the 2021 post-COVID ESG rotation—mark distinct equilibrium regimes in the global tobacco market. Within these regimes, adjustment dynamics indicate recurrent long-run leadership by Japan Tobacco, more heterogeneous leadership patterns for Altria, and a clearly regime-dependent role for Philip Morris International. The integration of fractional modeling and break detection provides a robust data-science approach to disentangling persistence from structural change, offering new insights into leadership cycles, systemic risk, and sectoral resilience. These findings underscore how regulated and ESG-sensitive industries evolve through adaptive equilibrium processes, contributing to the broader literature on long-memory econometrics and data-driven financial analytics.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100178"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187554","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":"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":"2025-12-01","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":"Multifractal and low-dimensional representations of high-frequency return distribution sequences","authors":"Chun-Xiao Nie","doi":"10.1016/j.jfds.2025.100173","DOIUrl":"10.1016/j.jfds.2025.100173","url":null,"abstract":"<div><div>High-frequency returns are of great significance to financial risk management and investment. This study analyzes the dynamics of the high-frequency return distributions. The calculations show that the high-frequency return distribution sequence has a small intrinsic dimension and exhibits time-varying characteristics. We find that all subdistribution sequences included multifractal structures, and the global Rényi index (<em>GRI</em>) series showed that the multifractal features changed over time. We also constructed a benchmark distribution sequence, in which each distribution was sampled from a normal population and the mean and standard deviation of the original series were maintained. The calculations show that the benchmark sequence also includes multifractal features, and the correlation dimension and <em>GRI</em> are smaller than those of the original sequence. However, the calculations show that there is a high correlation between the dimensional series of the benchmark sequence and the original sequence, suggesting that the mean and standard deviation are important parameters affecting fractal dynamics. In particular, we use the <em>UMAP</em> algorithm to show the low-dimensional representation of the distribution sequence, and find that the small-dimensional subsequences include earthworm-like clusters, while the subsequences with large-dimensions include cloud-like clusters. This study provides a new way to analyze the distribution of high-frequency returns, and explores the characteristics of distribution sequences, which is helpful for understanding the dynamics of distributions.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100173"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883668","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":"Integrating Choquet portfolios and machine learning interpretability for robust cryptocurrency investment strategies","authors":"João Pedro M. Franco,&nbsp;Márcio P. Laurini","doi":"10.1016/j.jfds.2025.100172","DOIUrl":"10.1016/j.jfds.2025.100172","url":null,"abstract":"<div><div>This study proposes an alternative approach to portfolio optimization in the cryptocurrency market by applying the Choquet integral portfolio, positioning it within the broader context of Robo-Advisor literature. This approach is based on pessimistic decision-making and employs higher order moments and captures asset interdependencies, thereby offering a notable advantage over traditional portfolio construction methods, including mean-variance optimization, naive diversification, and a Bitcoin-only portfolio. Furthermore, our study applies Machine Learning Interpretable Methods (Shapley and LIME) to identify the cryptocurrencies that drive portfolio returns over time. The findings highlight the significance of integrating interpretable machine learning tools with advanced portfolio models to furnish more profound insights into the determinants of portfolio performance, which consequently facilitates more informed and transparent investment decisions. Moreover, our findings aid investors in comprehending the methodology by which these automated processes allocate weights according to a portfolio model within the highly volatile cryptocurrency market.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100172"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883670","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":"Enhancing bookkeeper decision support through graph representation learning for bank reconciliation","authors":"Justin Munoz ,&nbsp;Mahdi Jalili ,&nbsp;Laleh Tafakori","doi":"10.1016/j.jfds.2025.100170","DOIUrl":"10.1016/j.jfds.2025.100170","url":null,"abstract":"<div><div>The emergence of cloud-based bookkeeping platforms has made it possible to streamline decision-making in tedious accounting tasks, such as bank reconciliation. Bank reconciliation involves tracing the expected cash flow from invoices and bills to the actual payments recorded in a business's bank feeds. A bookkeeper is responsible for ensuring that every payment listed in a business's bank statement is accurately matched to financial activity recorded in the bookkeeping system. This process is crucial for maintaining the accuracy and integrity of the business's financial records. Current decision-support systems leverage natural language processing to recommend close matches for incoming bank feeds. While these approaches are effective for one-to-one matching, they underperform in identifying one-to-many matches, which are common and significantly more complex for larger businesses. In this work, we investigate the value of embedding relational data along with natural language in identifying matches to support the bank reconciliation process. Our proposed graph-based system surpasses industry benchmarks on one-to-one matching and offers a more robust decision support solution for the identification of one-to-many matches. Additionally, we introduce a novel post-processing technique, Top Boundary Ranking, which enhances the system's detection of group-based matches.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100170"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690480","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 economic impact of DeFi crime events on decentralized autonomous organizations (DAOs)","authors":"Stefan Kitzler ,&nbsp;Masarah Paquet-Clouston ,&nbsp;Bernhard Haslhofer","doi":"10.1016/j.jfds.2025.100171","DOIUrl":"10.1016/j.jfds.2025.100171","url":null,"abstract":"<div><div>The Decentralized Finance (DeFi) ecosystem has experienced over $10 billion in direct losses due to crime events. Beyond these immediate losses, such events often trigger broader market reactions, including price declines, trading activity changes, and reductions in market capitalization. Decentralized Autonomous Organizations (DAOs) govern DeFi applications through tradable governance assets that function like corporate shares for voting and decision-making. Leveraging DeFi's granular trading data, we conduct an event study on 22 crime events between 2020 and 2022 to assess their economic impact on governance asset prices, trading volumes, and market capitalization. Using a dynamic difference-in-differences (DiD) framework with counterfactual governance assets, we aim for causal inference of intraday temporal effects. Our results show that 55 % of crime events lead to significant negative price impacts, with an average decline of about 14 %. Additionally, 68 % of crime events lead to increased governance asset trading volume. Based on these impacts, we estimate indirect economic losses of over $1.3 billion in DAO market capitalization, far exceeding direct victim costs and accounting for 74 % of total losses. Our study provides valuable insights into how crime events shape market dynamics and affect DAOs. Moreover, our methodological approach is reproducible and applicable beyond DAOs, offering a framework to assess the indirect economic impact on other cryptoassets.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100171"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145576047","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":"Using Bell violations as an indicator for financial market crisis","authors":"Arefeh Zarifian ,&nbsp;Christoph Gallus ,&nbsp;Ludger Overbeck ,&nbsp;Emmanuel M. Pothos ,&nbsp;Pawel Blasiak","doi":"10.1016/j.jfds.2025.100164","DOIUrl":"10.1016/j.jfds.2025.100164","url":null,"abstract":"<div><div>The failure to identify and measure financial risk carries significant social and economic consequences. This paper introduces a novel framework for analyzing financial stress and crises, based on the Bell inequalities, a foundational framework in causal analysis, originally developed in quantum mechanics. Traditional approaches to crisis analysis do not, in general, adequately represent event-based dependencies and the distribution of tail risks inherent in complex financial systems. The proposed approach is underwritten by a generic causal framework, which we think is suitable for financial analysis: we offer an index for financial stress and we explore its value in detecting extreme market co-movements, which may serve as an early crisis warning signal.</div><div>Our analyses employ a rolling-window approach to analyze financial time series data. We utilize S&amp;P 500 and STOXX Europe 600 stocks and consider three historical crises, namely the 2008 financial crisis, the EU debt crisis and the COVID-19 pandemic, which mark some of the largest downturns of financial markets in the last two decades. The findings demonstrate the framework's ability to align the number of observed Bell inequalities violations with observed peaks in market stress. In particular, the framework shows good performance against CDS spreads as a crisis indicator and is less erratic than the traditional Pearson correlation of price returns. It aligns well with implied equity option volatility as measured by VIX. Overall, we think the present causal framework has promising properties and merits further examination.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100164"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770754","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":"Integrating credit and debit data for enhanced insights into borrowing behavior and predictive modeling of credit card delinquency","authors":"Håvard Huse ,&nbsp;Sven A. Haugland ,&nbsp;Auke Hunneman","doi":"10.1016/j.jfds.2025.100166","DOIUrl":"10.1016/j.jfds.2025.100166","url":null,"abstract":"<div><div>This research delves into the predictive modeling of credit card delinquency by harnessing both credit and debit data, offering a nuanced perspective on consumer financial behavior. The study introduces a novel hierarchical Bayesian regression model that significantly surpasses traditional machine learning algorithms in predictive accuracy. By integrating behavioral aspects of financial decision-making, the model provides a profound understanding of the factors influencing delinquency, such as payment timing and repayment ability.</div><div>We found that the combination of credit and debit data allows for a more comprehensive assessment of a cardholder's financial behavior and risk potential. The model effectively captures individual variations in financial behavior, making it possible to predict delinquency with higher precision. This approach not only enhances the predictive power but also aids in understanding the underlying patterns of financial behavior that lead to credit risk.</div><div>The practical implications of this research are substantial for financial institutions, which can leverage these insights to refine risk assessment processes and develop targeted strategies for managing credit risk. The findings advocate for a more informed approach to credit scoring that considers broader behavioral factors, offering a strategic advantage in the competitive financial services market.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100166"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525689","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}