Journal of Finance and Data Science最新文献

英文中文

Leveraging financial interdependencies in emerging markets via graph neural networks 通过图形神经网络利用新兴市场的金融相互依赖性

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-12-01 DOI: 10.1016/j.jfds.2025.100168

Nicolai Bloch Jessen

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.

本研究利用图神经网络（gnn）研究了相互依赖关系在预测新兴市场主权债券收益率息差中的作用。主权债券收益率息差反映了经济状况和投资者情绪，因此对投资者、政策制定者和金融机构做出准确预测至关重要。传统的预测模型往往孤立地对待主权风险，未能考虑到金融溢出效应和跨国联系。通过在基于图表的框架内构建主权债券，本研究明确地对这些相互依赖关系进行建模，以提高预测的准确性。使用GDP、通货膨胀和外汇储备等宏观经济指标，将各国表示为金融网络中的节点，其边缘捕获关键的经济关系。图卷积网络（GCN）被训练来预测主权债券收益率息差，其性能以结构相同的前馈神经网络为基准，其中唯一的区别是使用图卷积层或密集层。结果表明，GCN模型始终优于前馈模型，特别是在预测极端收益率差运动方面，这表明了考虑金融相互依赖性的重要性。我们的研究结果强调了gnn作为预测新兴市场主权债券收益率息差的有力工具的潜力。考虑到这些利差的经济影响，gnn可能为金融部门的利益相关者带来重大利益。

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引用次数: 0

Multifractal and low-dimensional representations of high-frequency return distribution sequences 高频回波分布序列的多重分形和低维表示

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-12-01 DOI: 10.1016/j.jfds.2025.100173

Chun-Xiao Nie

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 (GRI) 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 GRI 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 UMAP 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.

高频收益对金融风险管理和投资具有重要意义。本文分析了高频回波分布的动态特性。计算表明，高频回波分布序列具有较小的固有维数和时变特性。研究发现，各亚分布序列均具有多重分形结构，全球rsamunyi指数（GRI）序列显示多重分形特征随时间的变化而变化。我们还构建了一个基准分布序列，其中每个分布都从一个正态总体中抽样，并保持原始序列的均值和标准差。计算结果表明，基准序列也具有多重分形特征，且相关维数和GRI均小于原始序列。然而，计算表明，基准序列的维数序列与原始序列之间存在高度的相关性，表明均值和标准差是影响分形动力学的重要参数。特别是，我们使用UMAP算法来显示分布序列的低维表示，并发现小维子序列包括蚯蚓类簇，而大维子序列包括云类簇。该研究为分析高频收益的分布提供了一种新的方法，并探讨了分布序列的特征，有助于理解分布的动态。

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引用次数: 0

Integrating Choquet portfolios and machine learning interpretability for robust cryptocurrency investment strategies 为稳健的加密货币投资策略整合Choquet投资组合和机器学习可解释性

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-12-01 DOI: 10.1016/j.jfds.2025.100172

João Pedro M. Franco, Márcio P. Laurini

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.

本研究提出了一种通过应用Choquet积分投资组合来优化加密货币市场投资组合的替代方法，并将其定位在Robo-Advisor文献的更广泛背景下。该方法基于悲观决策，采用高阶矩并捕获资产相互依赖性，因此与传统的投资组合构建方法（包括均值方差优化，朴素多样化和仅比特币投资组合）相比，具有显着优势。此外，我们的研究应用机器学习可解释方法（Shapley和LIME）来识别随着时间的推移推动投资组合回报的加密货币。研究结果强调了将可解释的机器学习工具与先进的投资组合模型相结合的重要性，可以为投资组合绩效的决定因素提供更深刻的见解，从而促进更明智和透明的投资决策。此外，我们的研究结果有助于投资者理解这些自动化过程根据高度波动的加密货币市场中的投资组合模型分配权重的方法。

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引用次数: 0

Enhancing bookkeeper decision support through graph representation learning for bank reconciliation 通过图表示学习增强银行对账员决策支持

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-12-01 DOI: 10.1016/j.jfds.2025.100170

Justin Munoz , Mahdi Jalili , Laleh Tafakori

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.

基于云计算的记账平台的出现，使得繁琐的会计任务（如银行对账）的决策变得更加简化成为可能。银行调节涉及跟踪从发票和账单到企业银行feed中记录的实际付款的预期现金流。簿记员负责确保企业银行对账单上列出的每笔付款都与簿记系统中记录的财务活动准确匹配。这个过程对于保持企业财务记录的准确性和完整性至关重要。当前的决策支持系统利用自然语言处理来推荐接近匹配的银行输入。虽然这些方法对于一对一匹配是有效的，但它们在识别一对多匹配方面表现不佳，这对于大型企业来说是常见的，而且要复杂得多。在这项工作中，我们研究了嵌入关系数据以及自然语言在识别匹配以支持银行对账过程中的价值。我们提出的基于图的系统超越了一对一匹配的行业基准，并为一对多匹配的识别提供了更健壮的决策支持解决方案。此外，我们还引入了一种新的后处理技术——顶边界排序，增强了系统对分组匹配的检测能力。

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引用次数: 0

Finding money launderers using heterogeneous graph neural networks 利用异构图神经网络寻找洗钱者

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-12-01 DOI: 10.1016/j.jfds.2025.100175

Fredrik Johannessen , Martin Jullum

The finance industry depends on effective anti-money laundering (AML) systems to ensure compliance and maintain operational efficiency. However, existing AML systems, which are predominantly rule-based, frequently struggle to detect money laundering accurately.

In particular, their inability to learn from historical data and properly account for diverse customer behavior is problematic. Also accounting for the vast amounts of transactional data generated daily, this challenge calls for big data analytics and advanced machine learning techniques.

In line with this, the present paper explores a graph neural network (GNN) approach, a state-of-the-art machine learning technique, to identify money laundering activities within a large heterogeneous network constructed from real-world bank transactions and business role data from DNB, Norway’s largest bank.

To this end, we extend the (homogeneous) Message Passing Neural Network (MPNN) architecture to operate on a heterogeneous graph, and demonstrate its strong performance in detecting money laundering activities.

We showcase the suitability of utilizing GNN methodology to improve electronic surveillance systems for detecting money laundering, thereby contributing a pioneering approach to AML through the application of advanced data science techniques.

To the best of our knowledge, this is the first publication applying heterogeneous GNNs for AML purposes with a large real-world heterogeneous network.

金融行业依赖于有效的反洗钱（AML）系统来确保合规性和维持运营效率。然而，现有的“反洗钱”系统主要以规则为基础，往往难以准确地发现洗钱行为。特别是，他们不能从历史数据中学习，不能正确地解释不同的客户行为，这是有问题的。此外，由于每天产生的大量交易数据，这一挑战需要大数据分析和先进的机器学习技术。与此相一致，本文探讨了一种图神经网络（GNN）方法，这是一种最先进的机器学习技术，用于识别来自挪威最大银行DNB的真实银行交易和业务角色数据构建的大型异构网络中的洗钱活动。为此，我们扩展了（同构）消息传递神经网络（MPNN）架构，使其在异构图上运行，并证明了其在检测洗钱活动方面的强大性能。我们展示了利用GNN方法改进电子监控系统以检测洗钱的适用性，从而通过应用先进的数据科学技术为反洗钱提供了一种开创性的方法。据我们所知，这是首次将异构gnn应用于大型现实世界异构网络的AML目的。

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引用次数: 0

Informed trading and expected returns 知情交易和预期回报

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-12-01 DOI: 10.1016/j.jfds.2025.100174

James J. Choi , Li Jin , Hongjun Yan

Does information asymmetry affect the cross-section of expected stock returns? We explore this question using representative portfolio holdings data from the Shanghai Stock Exchange. We show that institutional investors have a strong information advantage, and that past aggressiveness of institutional trading in a stock positively predicts institutions’ future information advantage in this stock. Sorting stocks on this predictor and controlling for other correlates of expected returns, we find that the top quintile’s average annualized return in the next month is 10.8 % higher than the bottom quintile’s, indicating that information asymmetry increases expected returns.

信息不对称是否影响股票预期收益的横截面？我们使用上海证券交易所的代表性投资组合持股数据来探讨这个问题。我们发现机构投资者具有很强的信息优势，并且过去机构交易股票的侵略性正预测机构未来在该股票中的信息优势。根据这一预测指标对股票进行分类，并控制预期收益的其他相关因素，我们发现，前五分之一的股票在下个月的平均年化收益比后五分之一的股票高10.8%，这表明信息不对称增加了预期收益。

引用次数: 0

The economic impact of DeFi crime events on decentralized autonomous organizations (DAOs) DeFi犯罪事件对去中心化自治组织（dao）的经济影响

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-11-08 DOI: 10.1016/j.jfds.2025.100171

Stefan Kitzler , Masarah Paquet-Clouston , Bernhard Haslhofer

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.

由于犯罪事件，去中心化金融（DeFi）生态系统已经经历了超过100亿美元的直接损失。除了这些直接损失之外，此类事件通常会引发更广泛的市场反应，包括价格下跌、交易活动变化和市值减少。去中心化自治组织（dao）通过可交易的治理资产来管理DeFi应用程序，这些资产的功能类似于用于投票和决策的公司股票。利用DeFi的粒度交易数据，我们对2020年至2022年期间的22起犯罪事件进行了事件研究，以评估其对治理资产价格、交易量和市值的经济影响。使用具有反事实治理资产的动态差异中差异（DiD）框架，我们的目标是对日内时间效应进行因果推断。我们的研究结果表明，55%的犯罪事件导致显著的负面价格影响，平均下降约14%。此外，68%的犯罪事件导致治理资产交易量增加。基于这些影响，我们估计DAO市值的间接经济损失超过13亿美元，远远超过直接受害者成本，占总损失的74%。我们的研究为犯罪事件如何塑造市场动态和影响dao提供了有价值的见解。此外，我们的方法方法是可重复的，适用于dao之外，为评估对其他加密资产的间接经济影响提供了一个框架。

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引用次数: 0

Dumb money? Social network attention herding, sentiment, and markets “傻钱”?社交网络注意力聚集、情绪和市场

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-11-01 DOI: 10.1016/j.jfds.2025.100169

Chengcheng Charlie Huang, Pauline Shum Nolan

Wallstreetbets (WSB) is the perfect echo chamber to study retail investor behaviour and markets. We introduce a direct measure of individual stock attention and the concept of forum-wide attention herding. We fine-tune a large language model to classify investor sentiment. We find that WSB sentiment is inversely related to the VIX. In general, more individual stock attention leads to more stock purchases, and sentiment is a contrarian predictor of future returns. However, when attention herds on a stock with high user engagement, trades peak but there is no reversal in returns. Finally, our monthly attention herding portfolio generates sizable alphas.

华尔街投注（Wallstreetbets）是研究散户投资者行为和市场的完美回音室。我们引入了个体股票注意力的直接度量和论坛范围内注意力羊群的概念。我们对一个大型语言模型进行微调，对投资者情绪进行分类。我们发现WSB的情绪与VIX呈负相关。一般来说，更多的个股关注会导致更多的股票购买，而情绪是未来回报的反向预测指标。然而，当注意力集中在用户参与度高的股票上时，交易达到峰值，但回报不会逆转。最后，我们每月的注意力组合产生了相当大的阿尔法。

引用次数: 0

Paper discussion at the third annual conference on capital market research in the era of AI 第三届人工智能时代资本市场研究年会论文讨论

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-10-28 DOI: 10.1016/j.jfds.2025.100167

引用次数: 0

Integrating credit and debit data for enhanced insights into borrowing behavior and predictive modeling of credit card delinquency 整合信用卡和借记卡数据，增强对借贷行为和信用卡拖欠预测建模的洞察力

IF 3.9 Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2025-10-24 DOI: 10.1016/j.jfds.2025.100166

Håvard Huse , Sven A. Haugland , Auke Hunneman

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.

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.

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.

这项研究通过利用信用卡和借记卡数据，深入研究了信用卡拖欠的预测模型，为消费者的金融行为提供了细致入微的视角。该研究引入了一种新的分层贝叶斯回归模型，该模型在预测精度方面显著优于传统的机器学习算法。通过整合财务决策的行为方面，该模型提供了对影响拖欠的因素的深刻理解，如付款时间和还款能力。我们发现，结合信用卡和借记卡数据可以更全面地评估持卡人的财务行为和潜在风险。该模型有效地捕获了金融行为的个体变化，从而可以更精确地预测违约行为。这种方法不仅提高了预测能力，而且有助于理解导致信用风险的金融行为的潜在模式。本研究对金融机构的实际意义是重大的，它们可以利用这些见解来完善风险评估流程并制定有针对性的信贷风险管理策略。研究结果提倡采用更明智的方法进行信用评分，考虑更广泛的行为因素，在竞争激烈的金融服务市场中提供战略优势。

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引用次数: 0

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