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Deep learning improves prediction capability, but limited interpretability constrains its value for explaining stock volatility and investment behavior. This study proposes a causality-based multivariate framework that jointly models news event attention sequences and sentiment index sequences, aiming to capture relationships among media signals and stock prices. Experiments use the GDELT dataset and analyze results across multiple news categories to support investment-related insights.",{"@graph":69,"@context":115},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/an-ensemble-model-for-stock-index-prediction-based-on-media-attention-and-emotional-causal-inference/152755/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/an-ensemble-model-for-stock-index-prediction-based-on-media-attention-and-emotional-causal-inference/152755.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-08-27",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",18,{"@type":107,"mainEntity":108},"FAQPage",[109],{"name":110,"@type":111,"acceptedAnswer":112},"Which dataset is used to demonstrate the method?","Question",{"text":113,"@type":114},"The study uses the Global Database of Events, Language, and Tone (GDELT) dataset to analyze attention and media sentiment across various news categories.","Answer","https://schema.org",{"og:url":83,"og:type":117,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":119,"canonical":83},"index,follow",{"doc_id":121,"site_id":62},152755,1787866930,{"code":4,"msg":5,"data":124},{"doc_id":121,"user_id":125,"nickname":92,"user_avatar":126,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":127,"file_id":128,"file_url":129,"file_type":130,"file_size":131,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":132,"language":133,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":134,"faqs":135,"seo_title":136,"seo_description":67,"update_tm":122,"read_time":137,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","Received: 22 July 2022 Revised: 26 September 2023 Accepted: 31 January 2024  \nDOI: 10.1002/for.3108  \nRESE ARCH ARTICL E  \nAn ensemble model for stock index prediction based on media attention and emotional causal inference  \nJuanjuan Wang 1 | Shujie Zhou 2 | Wentong Liu 1,3  | Lin Jiang 4  \n1School of Business Administration, Zhongnan University of Economics and Law, Wuhan, China  \n2School of Electronic Information, Wuhan University, Wuhan, China  \n3China Research Center for Social Entrepreneurship, Shanghai University of Finance and Economics, Shanghai, China 4Institute of Management Studies, Goldsmiths, University of London, London, UK  \nCorrespondence  \nWentong Liu, China Research Center for Social Entrepreneurship, Shanghai University of Finance and Economics, Shanghai, China.  \nEmail: [liuwentong@zuel.edu.cn](liuwentong@zuel.edu.cn)  \nAbstract  \nElectronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality-based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions.  \nKEYWOR DS  \ncausality analysis, direct transfer entropy, GDELT dataset, multivariate time series, stock price prediction  \n1 | INTRODUCTION  \nNumerous scholars have delved into the study of the stock market since its inception. The ability to accurately predict stock index prices and movements is pivotal for anticipating market trends and managing investment risks, which is fundamental for the establishment of robust and efficient financial markets. Numerous empirical studies have addressed the challenge of predicting stock price index direction, given its partially predictable nature. The development of effective trading strategies hinges on precise forecasts of stock price index  \nfluctuations (Leung et al., 2000) . Stock price indexes offer speculators and arbitrageurs opportunities to maximize capital gains while minimizing losses (Kumar & Thenmozhi, 2006) . Furthermore, the stock index holds a prominent position as a key evaluation metric for stock exchanges worldwide (Aldin et al., 2012) . Predicting market returns is exceptionally challenging due to the stock market's complexity, characterized by nonlinearity, nonstationarity, and a low signal-to-noise ratio. The digital age has ushered in a widespread proliferation of media and the broad dissemination of news reports. Consequently, the influence of political and economic news on  \n1998  \n© 2024 John Wiley & Sons Ltd.  \n[wileyonlinelibrary.com/journal/for](wileyonlinelibrary.com/journal/for)  \nJournal of Forecasting. 2024;43:1998–2020.  \ninvestors' decisions and market dynamics can be substantial. These news reports often encompass both subjective and objective opinions about companies, which are subsequently disseminated and magnified, potentially impacting the compan","cbCaieBdp87V0JL9","https://ap.wps.com/l/cbCaieBdp87V0JL9","pdf",2571033,23,"English","# Abstract\n# Introduction\n## Stock index prediction challenges\n## Role of media attention and sentiment\n## Endogeneity and causal interpretation","[{\"question\":\"Which dataset is used to demonstrate the method?\",\"answer\":\"The study uses the Global Database of Events, Language, and Tone (GDELT) dataset to analyze attention and media sentiment across various news categories.\"}]","An ensemble model for stock index prediction based on media attention and emotional causal inference | PDF",58]