{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:14:05Z","timestamp":1783016045131,"version":"3.54.6"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,16]],"date-time":"2021-05-16T00:00:00Z","timestamp":1621123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"H2020 FIN-TECH","award":["825215"],"award-info":[{"award-number":["825215"]}]},{"name":"ESRC","award":["ES\/K002309\/1"],"award-info":[{"award-number":["ES\/K002309\/1"]}]},{"name":"EPSRC","award":["EP\/P031730\/1"],"award-info":[{"award-number":["EP\/P031730\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The interaction between the flow of sentiment expressed on blogs and media and the dynamics of the stock market prices are analyzed through an information-theoretic measure, the transfer entropy, to quantify causality relations. We analyzed daily stock price and daily social media sentiment for the top 50 companies in the Standard &amp; Poor (S&amp;P) index during the period from November 2018 to November 2020. We also analyzed news mentioning these companies during the same period. We found that there is a causal flux of information that links those companies. The largest fraction of significant causal links is between prices and between sentiments, but there is also significant causal information which goes both ways from sentiment to prices and from prices to sentiment. We observe that the strongest causal signal between sentiment and prices is associated with the Tech sector.<\/jats:p>","DOI":"10.3390\/e23050621","type":"journal-article","created":{"date-parts":[[2021,5,16]],"date-time":"2021-05-16T23:17:16Z","timestamp":1621207036000},"page":"621","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Information Theoretic Causality Detection between Financial and Sentiment Data"],"prefix":"10.3390","volume":"23","author":[{"given":"Roberta","family":"Scaramozzino","sequence":"first","affiliation":[{"name":"Department of Economics and Management, University of Pavia, Via San Felice 7, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paola","family":"Cerchiello","sequence":"additional","affiliation":[{"name":"Department of Economics and Management, University of Pavia, Via San Felice 7, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4219-0215","authenticated-orcid":false,"given":"Tomaso","family":"Aste","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University College London, Gower Street, London WC1E 6EA, UK"},{"name":"UCL Centre for Blockchain Technologies, University College London, London WC1E 6BT, UK"},{"name":"Systemic Risk Centre, London School of Economics and Political Sciences, London WC2A 2AE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,16]]},"reference":[{"key":"ref_1","first-page":"424","article-title":"Investigating causal relations by econometric models and cross-spectral methods","volume":"37","author":"Granger","year":"1969","journal-title":"Econom. J. Econom. Soc."},{"key":"ref_2","unstructured":"Cover, T.M. (1999). Elements of Information Theory, John Wiley & Sons."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Engelberg, J. (2009, January 3\u20135). Costly information processing: Evidence from earnings announcements. Proceedings of the AFA 2009 San Francisco Meetings Paper, San Francisco, CA, USA.","DOI":"10.2139\/ssrn.1107998"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tirea, M., and Negru, V. (2013, January 26\u201328). Investment portfolio optimization based on risk and trust management. Proceedings of the 2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia.","DOI":"10.1109\/SISY.2013.6662604"},{"key":"ref_5","unstructured":"Jothimani, D., Shankar, R., and Yadav, S.S. (2018). A big data analytical framework for portfolio optimization. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4213","DOI":"10.1038\/srep04213","article-title":"When can social media lead financial markets?","volume":"4","author":"Zheludev","year":"2014","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cerchiello, P., and Nicola, G. (2018). Assessing news contagion in finance. Econometrics, 6.","DOI":"10.3390\/econometrics6010005"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1111\/j.1540-6261.2007.01232.x","article-title":"Giving content to investor sentiment: The role of media in the stock market","volume":"62","author":"Tetlock","year":"2007","journal-title":"J. Financ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.2308\/accr-50833","article-title":"Evidence on the information content of text in analyst reports","volume":"89","author":"Huang","year":"2014","journal-title":"Account. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","article-title":"Twitter mood predicts the stock market","volume":"2","author":"Bollen","year":"2011","journal-title":"J. Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.sbspro.2011.10.562","article-title":"Predicting stock market indicators through twitter \u201cI hope it is not as bad as I fear\u201d","volume":"26","author":"Zhang","year":"2011","journal-title":"Procedia Soc. Behav. Sci."},{"key":"ref_12","unstructured":"Rao, T., and Srivastava, S. (2012, January 26\u201329). Analyzing stock market movements using twitter sentiment analysis. Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), Istanbul, Turkey."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ranco, G., Aleksovski, D., Caldarelli, G., Gr\u010dar, M., and Mozeti\u010d, I. (2015). The effects of Twitter sentiment on stock price returns. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0138441"},{"key":"ref_14","first-page":"13","article-title":"Event studies in economics and finance","volume":"35","author":"MacKinlay","year":"1997","journal-title":"J. Econ. Lit."},{"key":"ref_15","unstructured":"Souza, T.T.P., Kolchyna, O., Treleaven, P.C., and Aste, T. (2015). Twitter sentiment analysis applied to finance: A case study in the retail industry. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"You, Q., and Luo, J. (2013, January 11). Towards social imagematics: Sentiment analysis in social multimedia. Proceedings of the Thirteenth International Workshop on Multimedia Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2501217.2501220"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Carvalho, J., Prado, A., and Plastino, A. (2014, January 11\u201314). A statistical and evolutionary approach to sentiment analysis. Proceedings of the 2014 IEEE\/WIC\/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland.","DOI":"10.1109\/WI-IAT.2014.87"},{"key":"ref_18","unstructured":"Kolchyna, O., Souza, T.T., Treleaven, P., and Aste, T. (2015). Twitter sentiment analysis: Lexicon method, machine learning method and their combination. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40537-016-0053-4","article-title":"Big data analysis for financial risk management","volume":"3","author":"Cerchiello","year":"2016","journal-title":"J. Big Data"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_21","first-page":"85","article-title":"Using transfer entropy to measure information flows between financial markets","volume":"17","author":"Dimpfl","year":"2013","journal-title":"Stud. Nonlinear Dyn. Econom."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.1016\/j.physa.2008.01.007","article-title":"Information flow between composite stock index and individual stocks","volume":"387","author":"Kwon","year":"2008","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1103\/PhysRevLett.85.461","article-title":"Measuring information transfer","volume":"85","author":"Schreiber","year":"2000","journal-title":"Phys. Rev. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1140\/epjb\/e2002-00379-2","article-title":"Analysing the information flow between financial time series","volume":"30","author":"Marschinski","year":"2002","journal-title":"Eur. Phys. J. Condens. Matter Complex Syst."},{"key":"ref_25","unstructured":"Baek, S.K., Jung, W.S., Kwon, O., and Moon, H.T. (2005). Transfer entropy analysis of the stock market. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nicola, G., Cerchiello, P., and Aste, T. (2020). Information network modeling for US banking systemic risk. Entropy, 22.","DOI":"10.3390\/e22111331"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Keskin, Z., and Aste, T. (2019). Information-theoretic measures for nonlinear causality detection: Application to social media sentiment and cryptocurrency prices. arXiv.","DOI":"10.1098\/rsos.200863"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ahelegbey, D.F., Cerchiello, P., and Scaramozzino, R. (2021). Network Based Evidence of the Financial Impact of Covid-19 Pandemic. SSRN.","DOI":"10.2139\/ssrn.3780954"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","article-title":"Community detection in graphs","volume":"486","author":"Fortunato","year":"2010","journal-title":"Phys. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"085009","DOI":"10.1088\/1367-2630\/12\/8\/085009","article-title":"Correlation structure and dynamics in volatile markets","volume":"12","author":"Aste","year":"2010","journal-title":"New J. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.eswa.2015.08.047","article-title":"Conditional graphical models for systemic risk estimation","volume":"43","author":"Cerchiello","year":"2016","journal-title":"Expert Syst. Appl."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/5\/621\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:02:19Z","timestamp":1760162539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/5\/621"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,16]]},"references-count":32,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["e23050621"],"URL":"https:\/\/doi.org\/10.3390\/e23050621","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,16]]}}}