[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-202119-105":59,"doc-detail-202119-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","measuring-information-flux-between-social-media-and-stock-prices-with-transfer-entropy","Measuring information flux between social media and stock prices with Transfer Entropy","","Transfer Entropy was used to quantify correlations and the flow of information between 200,500 tweets and 23 large-cap companies across 6 years from 2013 to 2018. Tweets were collected via a text mining approach and organized by daily date and the mentioned company. A Sentiment Index was built using a natural language processing method to structure sentiment polarity for each dataset. Bootstrapped simulations applied Transfer Entropy between stock prices and Sentiment Indexes, indicating a clear information flux between public opinion and company stock prices.",{"@graph":69,"@context":123},[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/measuring-information-flux-between-social-media-and-stock-prices-with-transfer-entropy/202119/",{"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/measuring-information-flux-between-social-media-and-stock-prices-with-transfer-entropy/202119.png","ImageObject",300,407,{"name":92,"@type":93},"Arica Lee","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How did the study collect and organize the social media data?","Question",{"text":113,"@type":114},"Tweets were obtained using a text mining algorithm and classified by daily date and the company mentioned.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the Sentiment Index constructed?",{"text":118,"@type":114},"The Sentiment Index was created using a natural language processing algorithm, structuring sentiment polarity for each dataset.",{"name":120,"@type":111,"acceptedAnswer":121},"What does the Transfer Entropy simulation reveal about information flow?",{"text":122,"@type":114},"The results show a clear information flux between public opinion and companies’ stock prices, including substantial information flowing from general opinion to stock prices.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},202119,1788542451,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},8796096645457,"https://ap-avatar.wpscdn.com/avatar/800003749518d68ffe3?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345340919836971","PLOS ONE  \nOPEN ACCESS  \nCitation: Mendoza Urdiales RA, Garc´ıa-Medina A, Nuñez Mora JA (2021) Measuring information flux between social media and stock prices with Transfer Entropy. PLoS ONE 16(9): e0257686 .  \n[https://doi.org/10.1371/journal.pone.0257686](https://doi.org/10.1371/journal.pone.0257686)  \n[Editor:](Editor: J E)[ J E](Editor: J E). Trinidad Segovia, University of Almeria, SPAIN  \nReceived: April 3, 2021  \nAccepted: September 7, 2021  \nPublished: September 23, 2021  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0257686](https://doi.org/10.1371/journal.pone.0257686)  \n[Copyright:](Copyright:) © [2021](2021) Mendoza Urdiales et al. This isan open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: All relevant data are within the paper and its Supporting information files.  \nFunding: FOSEC SEP-INVESTIGACI´ON B´ASICA through Consejo Nacional de Ciencia y Tecnolog´ıa,  \nRESEARCH ARTICLE  \nMeasuring information flux between social media and stock prices with Transfer Entropy  \nRom´an Alejandro Mendoza Urdiales1☯*, Andr´es Garc´ıa-Medina2,3☯, Jos´e Antonio Nuñez Mora1  \n1 Department of Finance, Tecnol´ogico de Monterrey, EGADE Business School, Ciudad de M´exico, Alvaro Obregon, M´exico, 2 Consejo Nacional de Ciencia y Tecnolog´ıa, Ciudad de M´exico, M´exico, 3 Unidad Monterrey, Centro de Investigaci´on en Matem´aticas, PIIT, Apodaca, Nuevo Le´on, M´exico  \n☯ These authors contributed equally to this work.  \n* [A00939178@itesm.mx](A00939178@itesm.mx)  \nAbstract  \nTransfer Entropy was applied to analyze the correlations and flow of information between 200,500 tweets and 23 of the largest capitalized companies during 6 years along the period 2013-2018. The set of tweets were obtained applying a text mining algorithm and classified according to daily date and company mentioned. We proposed the construction of a Sentiment Index applying a Natural Processing Language algorithm and structuring the sentiment polarity for each data set. Bootstrapped Simulations of Transfer Entropy were performed between stock prices and Sentiment Indexes. The results of the Transfer Entropy simulations show a clear information flux between general public opinion and companies’stock prices. There is a considerable amount of information flowing from general opinion tostock prices, even between different Sentiment Indexes. Our results suggest a deep relationship between general public opinion and stock prices. This is important for trading strategies and the information release policies for each company.  \nIntroduction  \nThere is a large strand of the finance literature interested in demonstrating how the news related to a publicly-traded company affect its stock price performance, and how there is a direct correlation between the direction and magnitude of the stock price response and the nature of the published news [ 1] .  \nSeveral studies conclude that the news’ impact is related to the media coverage of the company [2] . Furthermore, still more studies propose that whether the information is public or private is not relevant and that what matters is that traders have access to it [3] . Eugene Fama stated that the information available about traded companies is fully reflected in the prices, naming this theory the Efficient Market Hypothesis [4] .  \nThe Efficient Market Hypothesis (EMH) has been challenged by evidence in diverse studies in different markets and periods of time. Different phenomena not explained by the EMH can be justified from Adaptive Market Hypothesis (AMH","cbCaikBy0krSt1cC","https://ap.wps.com/l/cbCaikBy0krSt1cC","pdf",3668445,"English","# Abstract\n# Introduction","[{\"question\":\"How did the study collect and organize the social media data?\",\"answer\":\"Tweets were obtained using a text mining algorithm and classified by daily date and the company mentioned.\"},{\"question\":\"How was the Sentiment Index constructed?\",\"answer\":\"The Sentiment Index was created using a natural language processing algorithm, structuring sentiment polarity for each dataset.\"},{\"question\":\"What does the Transfer Entropy simulation reveal about information flow?\",\"answer\":\"The results show a clear information flux between public opinion and companies’ stock prices, including substantial information flowing from general opinion to stock prices.\"}]","Measuring information flux between social media and stock prices with Transfer Entropy | PDF",48]