Journal of Finance and Data Science最新文献_第2页

Finding a needle in a haystack: A machine learning framework for anomaly detection in payment systems 大海捞针：用于支付系统异常检测的机器学习框架

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2025-04-02 DOI: 10.1016/j.jfds.2025.100163

Ajit Desai , Anneke Kosse , Jacob Sharples

{"title":"Finding a needle in a haystack: A machine learning framework for anomaly detection in payment systems","authors":"Ajit Desai , Anneke Kosse , Jacob Sharples","doi":"10.1016/j.jfds.2025.100163","DOIUrl":"10.1016/j.jfds.2025.100163","url":null,"abstract":"<div><div>We propose a flexible machine learning (ML) framework for real-time transaction monitoring in high-value payment systems (HVPS), which are central to a country’s financial infrastructure and integral to financial stability. This framework can be used by system operators and overseers to detect anomalous transactions, which—if caused by a cyber attack or an operational outage and left undetected—could have serious implications for the HVPS, its participants and the financial system more broadly. Given the high volume of payments settled each day and the scarcity of actual anomalous transactions in HVPS, detecting anomalies resembles finding a needle in a haystack. Therefore, our framework employs a layered approach to manage the high volume of payments and isolate potential anomalies. In the first layer, a supervised ML algorithm is used to identify and separate ‘typical’ payments from ‘unusual’ payments. In the second layer, only the ‘unusual’ payments are run through an unsupervised ML algorithm for anomaly detection. We test this framework using artificially manipulated transactions and payments data from the Canadian HVPS. The ML algorithm employed in the first layer achieves a detection rate of 93 %, marking a significant improvement over commonly-used econometric models. The ML algorithm used in the second layer marks the artificially manipulated transactions as nearly twice as suspicious as the original transactions, proving its effectiveness.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891242","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}

引用次数: 0

Pairs trading with time-series deep learning models 配对交易与时间序列深度学习模型

IF 3.9

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2026-02-04 DOI: 10.1016/j.jfds.2026.100177

Selin Yilmaz , Emre Sefer

{"title":"Pairs trading with time-series deep learning models","authors":"Selin Yilmaz , Emre Sefer","doi":"10.1016/j.jfds.2026.100177","DOIUrl":"10.1016/j.jfds.2026.100177","url":null,"abstract":"<div><div>Pairs trading is a well-studied statistical arbitrage strategy including the identification of asset pairs exhibiting correlated changes in their historical prices. This statistical arbitrage strategy focuses on benefiting from non-permanent divergent behaviour of price, and it forecasts that the price relationship will revert to its usual and normal correlation. In this paper, we explore how more recent time-series-based deep learning techniques can be utilized in pairs trading, where cointegrated asset pairs are taken into account. We propose deep-learning and more traditional machine learning-based methods to predict the fluctuation of daily idiosyncratic residual terms between assets and their factor approximations. In our analysis, we focused on seven models: LSTM as a fundamental time-series method to capture interrelationships in dataset, Informer, Autoformer, iTransformer, Scaleformer, and Chronos as transformer-based deep time series methods, and AdaBoost, which is an ensemble learning-based machine learning method. We have assessed the performance of methods comprehensively over S&P 500 and cryptocurrency assets data starting from 2012 to 2020 respectively, and used a traditional statistical arbitrage-based relative value method as a baseline. All of our proposed learning-based methods turned out to be profitable strategies, obtaining higher Sharpe ratios and average returns by outperforming the baseline relative value method. Nevertheless, deep learning-based methods had a lower volume than the baseline, so when transaction costs are taken into account they showed better performance. Deep learning-based methods maximum drawdown was also lower than the traditional statistical arbitrage strategy. As a result, we show the benefits of time series-based deep learning methods in pairs trading across distinct asset classes.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100177"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147537866","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}

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

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2025-12-23 DOI: 10.1016/j.jfds.2025.100175

Fredrik Johannessen , Martin Jullum

{"title":"Finding money launderers using heterogeneous graph neural networks","authors":"Fredrik Johannessen , Martin Jullum","doi":"10.1016/j.jfds.2025.100175","DOIUrl":"10.1016/j.jfds.2025.100175","url":null,"abstract":"<div><div>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.</div><div>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.</div><div>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.</div><div>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.</div><div>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.</div><div>To the best of our knowledge, this is the first publication applying heterogeneous GNNs for AML purposes with a large real-world heterogeneous network.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100175"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883669","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}

引用次数: 0

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

IF 3.9

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2025-12-23 DOI: 10.1016/j.jfds.2025.100174

James J. Choi , Li Jin , Hongjun Yan

引用次数: 0

GARCH-PDE models for option pricing under stochastic volatility and their finite difference solvers 随机波动下期权定价的GARCH-PDE模型及其有限差分解

IF 3.9

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2026-02-17 DOI: 10.1016/j.jfds.2026.100176

Qi Wang , Lu Zhang , Qian Zhang

{"title":"GARCH-PDE models for option pricing under stochastic volatility and their finite difference solvers","authors":"Qi Wang , Lu Zhang , Qian Zhang","doi":"10.1016/j.jfds.2026.100176","DOIUrl":"10.1016/j.jfds.2026.100176","url":null,"abstract":"<div><div>This paper presents numerical solvers for generative and hybrid option pricing models that unify econometric and diffusion-based approaches. These models are formulated as systems of continuous partial differential equations (PDEs), with stochastic volatility updated at discrete reset dates according to generalized autoregressive conditional heteroskedasticity (GARCH) dynamics. In contrast to approaches that estimate volatility from option prices using the Black–Scholes model or Monte Carlo simulations, this method simplifies option pricing under stochastic volatility by exogenously supplying and updating latent intraday volatility using discrete return data. We then develop and analyze numerical techniques for solving the resulting system of parabolic partial differential equations, which feature time-varying diffusion coefficients governed by the stochastic volatility paths inferred from the return data. Convergence and stability analyses of the numerical schemes attest to the option pricing accuracy of the proposed framework using available discrete implied volatility samples without compromising its computational accuracy. Several sets of numerical tests using SPX data are presented to illustrate our approach and demonstrate its superiority over other empirically well-tested pricing methods.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100176"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394554","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}

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

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2025-10-28 DOI: 10.1016/j.jfds.2025.100167

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

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2025-11-01 DOI: 10.1016/j.jfds.2025.100169

Chengcheng Charlie Huang, Pauline Shum Nolan

引用次数: 0

Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients☆ 基于极值理论的策略梯度的灾难风险感知强化学习☆

IF 3.9

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2025-08-05 DOI: 10.1016/j.jfds.2025.100165

Parisa Davar , Frédéric Godin , Jose Garrido

引用次数: 0

Unsupervised generation of tradable topic indices through textual analysis 基于文本分析的交易话题指数的无监督生成

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2025-01-29 DOI: 10.1016/j.jfds.2025.100149

Marcel Lee , Alan Spark

引用次数: 0

Optimal rebalancing strategies reduce market variability 最优的再平衡策略减少了市场的可变性

Journal of Finance and Data Science Pub Date : 2025-12-01 Epub Date: 2025-01-10 DOI: 10.1016/j.jfds.2025.100151

Helge Holden , Lars Holden

引用次数: 0