Journal of Finance and Data Science最新文献

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Financial inclusion, technologies, and worldwide economic development: A spatial Durbin model approach

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Journal of Finance and Data Science

Pub Date : 2025-02-13 DOI: 10.1016/j.jfds.2025.100155

Xiaoling Song , Xuan Qin , Wanmeng Wang , Rita Yi Man Li

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.

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引用次数: 0

Unsupervised generation of tradable topic indices through textual analysis

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Journal of Finance and Data Science

Pub Date : 2025-01-29 DOI: 10.1016/j.jfds.2025.100149

Marcel Lee , Alan Spark

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 Latent Dirichlet Allocation (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.

引用次数: 0

Optimal rebalancing strategies reduce market variability

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Journal of Finance and Data Science

Pub Date : 2025-01-10 DOI: 10.1016/j.jfds.2025.100151

Helge Holden , Lars Holden

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.

引用次数: 0

Symbolic Modeling for financial asset pricing

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Journal of Finance and Data Science

Pub Date : 2025-01-09 DOI: 10.1016/j.jfds.2025.100150

Xiangwu Zuo, Anxiao (Andrew) Jiang

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, etc.), Symbolic Regression does not limit the discovered function to specific forms (e.g., linear functions, polynomials, etc.). 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.

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引用次数: 0

Paper discussion at the 2024 ABFER-JFDS Conference on AI and FinTech

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Journal of Finance and Data Science

Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2025.100153

引用次数: 0

Interpretable machine learning model for predicting activist investment targets 预测激进投资目标的可解释机器学习模型

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Journal of Finance and Data Science

Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100146

Minwu Kim, Sidahmend Benahderrahmane, Talal Rahwan

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.

本研究提出了一个预测模型，以确定激进投资基金的潜在目标，即收购公司重大股权以影响战略和运营决策，最终提高股东价值的实体。对于希望降低干预风险的公司、寻求最佳投资的维权基金以及希望利用潜在股价上涨的投资者来说，预测这些目标至关重要。利用2016年至2022年罗素3000指数的数据，我们评估了123种模型配置，包括不同的imputation、过采样和机器学习技术。我们的最佳模型达到了0.782的AUC-ROC，表明它能够有效地预测激进基金目标。为了提高可解释性，我们采用Shapley值方法来识别影响公司成为目标可能性的关键因素，突出了维权基金目标选择的动态机制。这些见解为积极主动的公司治理和明智的投资策略提供了强有力的工具，促进了对积极投资决策驱动机制的理解。

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引用次数: 0

Technical patterns and news sentiment in stock markets 股票市场的技术形态和新闻情绪

Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100145

Markus Leippold , Qian Wang , Min Yang

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.

本文探讨了技术模式在预测资产价格和市场走势方面的有效性，强调了新闻情绪的作用。我们采用一种图像识别方法来检测价格图像中的技术模式，并评估这种方法是否比传统的基于规则的方法提供更多的信息。我们的研究结果表明，许多基于模型的模式在美国市场产生了显著的回报，而顶部模式在中国市场的效果较差。该模型具有较高的训练样本精度和较强的样本外性能。我们的实证分析得出结论，当与新闻情绪相结合时，技术模式在最近的股市中仍然有效，提供了一个有利可图的投资组合策略。此外，我们发现，在美国，模式更能预测具有高动量、机构所有权和先前模式的公司的回报，而在中国，模式对具有高动量和机构所有权的小企业更有效。这项研究强调了图像识别方法在市场数据分析中的潜力，并强调了情绪在技术分析中的重要性。

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引用次数: 0

Learning from AI-Finance: A selected synopsis

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Journal of Finance and Data Science

Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2025.100152

Yi Huang, Sung Kwan Lee, Bernard Yeung

引用次数: 0

Detecting Hawala network for money laundering by graph mining

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Journal of Finance and Data Science

Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100147

Marzhan Alenova, Assem Utaliyeva, Ki-Joune Li

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.

引用次数: 0

Post notes of 2024 ABFER-JFDS conference on AI and FinTech

Q1 Mathematics

Journal of Finance and Data Science

Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2025.100154

引用次数: 0

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