Journal of Finance and Data Science最新文献

Unsupervised generation of tradable topic indices through textual analysis

Journal of Finance and Data Science Pub 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

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

Helge Holden , Lars Holden

引用次数: 0

Symbolic Modeling for financial asset pricing

Journal of Finance and Data Science Pub Date : 2025-01-09 DOI: 10.1016/j.jfds.2025.100150

Xiangwu Zuo, Anxiao (Andrew) Jiang

{"title":"Symbolic Modeling for financial asset pricing","authors":"Xiangwu Zuo, 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}

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

Journal of Finance and Data Science Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100146

Minwu Kim, Sidahmend Benahderrahmane, Talal Rahwan

{"title":"Interpretable machine learning model for predicting activist investment targets","authors":"Minwu Kim, Sidahmend Benahderrahmane, 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}

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

Journal of Finance and Data Science Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100145

Markus Leippold , Qian Wang , Min Yang

{"title":"Technical patterns and news sentiment in stock markets","authors":"Markus Leippold , Qian Wang , 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}

引用次数: 0

Detecting Hawala network for money laundering by graph mining

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

引用次数: 0

NFT price and sales characteristics prediction by transfer learning of visual attributes

Journal of Finance and Data Science Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100148

Mustafa Pala, Emre Sefer

{"title":"NFT price and sales characteristics prediction by transfer learning of visual attributes","authors":"Mustafa Pala, Emre Sefer","doi":"10.1016/j.jfds.2024.100148","DOIUrl":"10.1016/j.jfds.2024.100148","url":null,"abstract":"<div><div>Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework.</div><div>Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point. Our code and NFT datasets are publicly available at <span><span>https://github.com/seferlab/deep_nft</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100148"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175785","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}

引用次数: 0

Time-mixing and feature-mixing modelling for realized volatility forecast: Evidence from TSMixer model

Journal of Finance and Data Science Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100143

Hugo Gobato Souto , Storm Koert Heuvel , Francisco Louzada Neto

{"title":"Time-mixing and feature-mixing modelling for realized volatility forecast: Evidence from TSMixer model","authors":"Hugo Gobato Souto , Storm Koert Heuvel , Francisco Louzada Neto","doi":"10.1016/j.jfds.2024.100143","DOIUrl":"10.1016/j.jfds.2024.100143","url":null,"abstract":"<div><div>This study evaluates the effectiveness of the TSMixer neural network model in forecasting stock realized volatility, comparing it with traditional and contemporary benchmark models. Using data from S&P 100 index stocks and three other datasets containing various financial securities, extensive analyses, including robustness tests, were conducted. Results show that TSMixer outperforms benchmark models in predicting individual stock volatility when applied to datasets with a large number of securities, leveraging its feature-mixing MLP techniques, which can properly model the financial tail dependence phenomenon. However, its superiority diminishes in datasets with fewer securities, such as stock indexes, foreign exchange rates, and commodities, where models like NBEATSx and NHITS often perform better. This indicates that TSMixer's performance is context-dependent, excelling when feature interdependencies can be fully exploited. The findings suggest that simplified neural network architectures like TSMixer can enhance forecasting accuracy in appropriate contexts but may have limitations in datasets with fewer securities.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175786","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}

引用次数: 0

Tail-driven portfolios: Unveiling financial contagion and enhancing risk management 尾部驱动的投资组合：揭示金融传染和加强风险管理

Journal of Finance and Data Science Pub Date : 2024-10-22 DOI: 10.1016/j.jfds.2024.100142

Tingyu Qu

引用次数: 0

Do commodity prices matter for global systemic risk? Evidence from ML variable selection 商品价格对全球系统性风险重要吗？多变量选择的证据

Journal of Finance and Data Science Pub Date : 2024-10-22 DOI: 10.1016/j.jfds.2024.100144

Mikhail Stolbov , Maria Shchepeleva

{"title":"Do commodity prices matter for global systemic risk? Evidence from ML variable selection","authors":"Mikhail Stolbov , Maria Shchepeleva","doi":"10.1016/j.jfds.2024.100144","DOIUrl":"10.1016/j.jfds.2024.100144","url":null,"abstract":"<div><div>We identify robust predictors of global systemic risk proxied by conditional capital shortfall (SRISK) among a comprehensive set of commodity prices for the period between January 2004 and December 2021. The search is based on a battery of ML variable selection algorithms which apply both to price levels and price shocks in the presence of control variables, including the first lag of SRISK, world industrial production, global economic policy uncertainty, geopolitical risk as well as the global stance of monetary and macroprudential policies. We find that these controls outweigh commodity prices as the predictors of global systemic risk. Of the commodities themselves, the prices for agricultural commodities, including food, e.g. chicken, bananas, beef, tea, cocoa, are more important predictors of global systemic risk than the prices for energy commodities, e.g. natural gas and oil prices. The financialization of agricultural commodities, bio-energy expansion as well as commodity-specific dependence of the major economies contributing to global systemic risk, e.g. China, account for our main finding. We also document the positive linkage between commodity prices and systemic risk for the majority of commodities. Thus, monitoring commodity prices to avoid their unbalanced growth is of vast importance to curb global systemic financial risk.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571479","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}

引用次数: 0