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Cited by 3Financial Markets and Investment StrategiesStock Market Forecasting MethodsComplex Systems and Time Series Analysis

On the impact of publicly available news and information transfer to financial markets

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Jazbec, Metod, Pàsztor, Barna, Faltings, Felix, Antulov-Fantulin, Nino, Kolm, Petter N.·2021·Royal Society Open Science
DOI 10.1098/rsos.202321PMC PMC8316821
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Abstract

We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a non-profit organization that crawls a large part of the web. We develop a processing pipeline to identify news articles associated with the constituent companies in the S&P 500 index, an equity market index that measures the stock performance of US companies. Using machine learning techniques, we extract sentiment scores from the Common Crawl News data and employ tools from information theory to quantify the information transfer from public news articles to the US stock market. Furthermore, we analyse and quantify the economic significance of the news-based information with a simple sentiment-based portfolio trading strategy. Our findings provide support for that information in publicly available news on the World Wide Web has a statistically and economically significant impact on events in financial markets.

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  • The Common Crawl web corpora from 2013 to 2026 had relatively stable data format and sampling procedures, allowing cross-year comparability.

    “We analyzed web corpora from 2013 to 2026, a period during which the data format and sampling procedures were relatively stable, allowing for cross-year comparability (Jazbec et al., 2021).”

    From The Attention Mismatch: Mapping the Structural Academic Governance Deficit in the Age of Generative AI (2026) · cites this paper as Jazbec et al., 2021

    The Methods section ('Common Crawl data'; page unavailable) says the crawler’s developers continued to improve the system and expand the number of websites visited, contrary to the claim of relatively stable sampling procedures. The Results section ('The Common Crawl and financial news coverage'; page unavailable) says coverage before August 2016 was insufficient for the study, which used news from 2016–2020. The paper does not establish stable formats or comparability for 2013–2026. (LLM, conf=0.85)

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  1. [2]Mandelbrot B. 1963 The variation of certain speculative prices. J. Bus. 36, 394- 419. (10.1086/294632) · DOI
  2. [3]Jarrow R, Protter P. 2004 A short history of stochastic integration and mathematical finance: the early years, 1880–1970. In A festschrift for Herman Rubin (ed. A DasGupta), pp. 75–91. Beachwood, OH: Institute of Mathematical Statistics.
  3. [4]Fama EF. 1970 Efficient capital markets: a review of theory and empirical work. J. Finance 25, 383- 417. (10.2307/2325486) · DOI · Paper page
  4. [5]Clark PK. 1973 A subordinated stochastic process model with finite variance for speculative prices. Econometrica 41, 135- 155. (10.2307/1913889) · DOI
  5. [6]Jennings RH, Starks LT, Fellingham JC. 1981 An equilibrium model of asset trading with sequential information arrival. J. Finance 36, 143- 161. (10.1111/j.1540-6261.1981.tb03540.x) · DOI
  6. [7]Jorion P. 1988 On jump processes in the foreign exchange and stock markets. Rev. Financ. Stud. 1, 427- 445. (10.1093/rfs/1.4.427) · DOI
  7. [8]Stanley H, Mantegna R. 2000 An introduction to econophysics. Cambridge, UK: Cambridge University Press.
  8. [9]Chan WS. 2003 Stock price reaction to news and no-news: drift and reversal after headlines. J. Financ. Econ. 70, 223- 260. (10.1016/S0304-405X(03)00146-6) · DOI
  9. [10]Maheu JM, McCurdy TH. 2004 News arrival, jump dynamics, and volatility components for individual stock returns. J. Finance 59, 755- 793. (10.1111/j.1540-6261.2004.00648.x) · DOI
  10. [11]Vega C. 2006 Stock price reaction to public and private information. J. Financ. Econ. 82, 103- 133. (10.1016/j.jfineco.2005.07.011) · DOI
  11. [12]Tetlock PC. 2007 Giving content to investor sentiment: the role of media in the stock market. J. Finance 62, 1139- 1168. (10.1111/j.1540-6261.2007.01232.x) · DOI · Paper page
  12. [13]Groß-Klußmann A, Hautsch N. 2011 When machines read the news: using automated text analytics to quantify high frequency news-implied market reactions. J. Empir. Finance 18, 321- 340. (10.1016/j.jempfin.2010.11.009) · DOI
  13. [14]Birz G, Lott Jr JR. 2011 The effect of macroeconomic news on stock returns: new evidence from newspaper coverage. J. Bank. Finance 35, 2791- 2800. (10.1016/j.jbankfin.2011.03.006) · DOI
  14. [15]Vlastakis N, Markellos RN. 2012 Information demand and stock market volatility. J. Bank. Finance 36, 1808- 1821. (10.1016/j.jbankfin.2012.02.007) · DOI
  15. [16]Engelberg JE, Reed AV, Ringgenberg MC. 2012 How are shorts informed? Short sellers, news, and information processing. J. Financ. Econ. 105, 260- 278. (10.1016/j.jfineco.2012.03.001) · DOI
  16. [17]Lillo F, Miccichè S, Tumminello M, Piilo J, Mantegna RN. 2015 How news affects the trading behaviour of different categories of investors in a financial market. Quant. Finance 15, 213- 229. (10.1080/14697688.2014.931593) · DOI
  17. [18]Goodfellow I, Bengio Y, Courville A. 2016 Deep learning. New York, NY: MIT press.
  18. [19]Newman ME. 2011 Complex systems: a survey. (http://arxiv.org/abs/1112.1440) · arXiv
  19. [20]Schreiber T. 2000 Measuring information transfer. Phys. Rev. Lett. 85, 461- 464. (10.1103/PhysRevLett.85.461) 10991308 · DOI
  20. [21]Barnett L, Barrett AB, Seth AK. 2009 Granger causality and transfer entropy are equivalent for Gaussian variables. Phys. Rev. Lett. 103, 238701. (10.1103/PhysRevLett.103.238701) 20366183 · DOI
  21. [22]Jizba P, Kleinert H, Shefaat M. 2012 Rényi’s information transfer between financial time series. Physica A 391, 2971- 2989. (10.1016/j.physa.2011.12.064) · DOI
  22. [23]Fang B, Zhang P. 2016 Big data in finance. In Big data concepts, theories, and applications (eds S Yu, S Guo), pp. 391–412. Berlin, Germany: Springer.
  23. [24]Ranco G, Bordino I, Bormetti G, Caldarelli G, Lillo F, Treccani M. 2016 Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics. PLoS ONE 11, e0146576. (10.1371/journal.pone.0146576) 26808833 PMC4726698 · DOI
  24. [25]Heiberger RH. 2015 Collective attention and stock prices: evidence from Google trends data on Standard and Poor’s 100. PLoS ONE 10, e0135311. (10.1371/journal.pone.0135311) 26258498 PMC4530949 · DOI
  25. [26]Bordino I, Kourtellis N, Laptev N, Billawala Y. 2014 Stock trade volume prediction with yahoo finance user browsing behavior. In 2014 IEEE 30th Int. Conf. on Data Engineering, Chicago, IL, 31 March, pp. 1168–1173. New York, NY: IEEE.
  26. [27]Piškorec M, Antulov-Fantulin N, Novak PK, Mozetič I, Grčar M, Vodenska I, Šmuc T. 2014 Cohesiveness in financial news and its relation to market volatility. Sci. Rep. 4, 5038. (10.1038/srep05038) 24849598 PMC4030282 · DOI
  27. [28]Zhang Y, Feng L, Jin X, Shen D, Xiong X, Zhang W. 2014 Internet information arrival and volatility of SME price index. Physica A 399, 70- 74. (10.1016/j.physa.2013.12.034) · DOI
  28. [29]Curme C, Stanley HE, Vodenska I. 2015 Coupled network approach to predictability of financial market returns and news sentiments. Int. J. Theor. Appl. Finance 18, 1550043. (10.1142/S0219024915500430) · DOI
  29. [30]Alanyali M, Moat HS, Preis T. 2013 Quantifying the relationship between financial news and the stock market. Sci. Rep. 3, 3578. (10.1038/srep03578) 24356666 PMC3868958 · DOI
  30. [31]Dos Rieis JCS, de Souza FB, de Melo VPOS, Prates RO, Kwak H, An J. 2015 Breaking the news: first impressions matter on online news. In 9th Int. AAAI Conf. on Web and Social Media, Oxford, UK, 21 April.
  31. [32]Yang SY, Mo SYK, Liu A, Kirilenko AA. 2017 Genetic programming optimization for a sentiment feedback strength based trading strategy. Neurocomputing 264, 29- 41. (10.1016/j.neucom.2016.10.103) · DOI
  32. [33]Ruiz-Martínez JM et al. 2012 Semantic-based sentiment analysis in financial news. In Proc. of the 1st Int. Workshop on Finance and Economics on the Semantic Web, Heraklion, Greece, 27–28 May, pp. 38–51. Extended Semantic Web Conference.
  33. [34]Wang C-J, Tsai M-F, Liu T, Chang C-T. 2013 Financial sentiment analysis for risk prediction. In Proc. of the 6th Int. Joint Conf. on Natural Language Processing, Nagoya, Japan, October, pp. 802–808. Asian Federation of Natural Language Processing.
  34. [35]Schumaker RP, Chen H. 2009 Textual analysis of stock market prediction using breaking financial news: the AZFin text system. ACM Trans. Inf. Syst. (TOIS) 27, 1- 19. (10.1145/1462198.1462204) · DOI
  35. [37]Hisano R, Sornette D, Mizuno T, Ohnishi T, Watanabe T. 2013 High quality topic extraction from business news explains abnormal financial market volatility. PLoS ONE 8, e64846. (10.1371/journal.pone.0064846) 23762258 PMC3675119 · DOI
  36. [38]Ding X, Zhang Y, Liu T, Duan J. 2015 Deep learning for event-driven stock prediction. In 24th Int. Joint Conf. on Artificial Intelligence, Buenos Aires, Argentina, 25–31 July. AAAI Press.
  37. [39]Luss R, d’Aspremont A. 2015 Predicting abnormal returns from news using text classification. Quant. Fin. 15, 999- 1012. (10.1080/14697688.2012.672762) · DOI
  38. [41]Rao T, Srivastava S. 2012 Analyzing stock market movements using Twitter sentiment analysis. In Proc. of the 2012 Int. Conf. on Advances in Social Networks Analysis and Mining (ASONAM 2012), ASONAM ’12, (USA), Istanbul, Turkey, August, pp. 119–123. Washington, DC: IEEE Computer Society.
  39. [42]Mao H, Counts S, Bollen J. 2011 Predicting financial markets: comparing survey, news, Twitter and search engine data. (http://arxiv.org/abs/1112.1051). · arXiv
  40. [43]Zheludev I, Smith R, Aste T. 2014 When can social media lead financial markets? Sci. Rep. 4, 4213. (10.1038/srep04213) 24572909 PMC5379406 · DOI
  41. [44]Broadstock DC, Zhang D. 2019 Social-media and intraday stock returns: the pricing power of sentiment. Finance Res. Lett. 30, 116- 123. (10.1016/j.frl.2019.03.030) · DOI
  42. [45]Bordino I, Battiston S, Caldarelli G, Cristelli M, Ukkonen A, Weber I. 2012 Web search queries can predict stock market volumes. PLoS ONE 7, e40014. (10.1371/journal.pone.0040014) 22829871 PMC3400625 · DOI
  43. [46]Preis T, Moat HS, Stanley HE. 2013 Quantifying trading behavior in financial markets using google trends. Sci. Rep. 3, 1684. (10.1038/srep01684) 23619126 PMC3635219 · DOI
  44. [47]Zhang W, Shen D, Zhang Y, Xiong X. 2013 Open source information, investor attention, and asset pricing. Econ. Model. 33, 613- 619. (10.1016/j.econmod.2013.03.018) · DOI
  45. [48]De Choudhury M, Sundaram H, John A, Seligmann DD. 2008 Can blog communication dynamics be correlated with stock market activity? In Proc. of the 19th ACM Conf. on Hypertext and Hypermedia, Pittsburgh, PA, 19–21 June, pp. 55–60. New York, NY: Association for Computing Machinery.
  46. [49]Ruiz EJ, Hristidis V, Castillo C, Gionis A, Jaimes A. 2012 Correlating financial time series with micro-blogging activity. In Proc. of the 5th ACM Int. Conf. on Web Search and Data Mining, pp. 513–522.
  47. [51]Brown TBet al. 2020 Language models are few-shot learners. (http://arxiv.org/abs/2005.14165) · arXiv
  48. [52]Wang S, Zhang Z. 2019 Construction of financial news sentiment indices using deep neural networks. Available at SSRN 3477690.
  49. [54]Ke Z, Kelly BT, Xiu D. 2019 Predicting returns with text data. Available at SSRN: https://ssrn.com/abstract=3489226.
  50. [55]Shannon CE. 1948 A mathematical theory of communication. Bell Syst. Tech. J. 27, 379- 423. (10.1002/j.1538-7305.1948.tb01338.x) · DOI · Paper page
  51. [56]Dimpfl T, Peter FJ. 2013 Using transfer entropy to measure information flows between financial markets. Stud. Nonlinear Dyn. Econom. 17, 85- 102. (10.1515/snde-2012-0044) · DOI
  52. [57]Hlavackova-Schindler K, Palus M, Vejmelka M, Bhattacharya J. 2007 Causality detection based on information-theoretic approaches in time series analysis. Phys. Rep. 441, 1- 46. (10.1016/j.physrep.2006.12.004) · DOI
  53. [59]Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. 2013 Distributed representations of words and phrases and their compositionality. In Proc. of the 26th Int. Conf. on Neural Information Processing Systems – vol. 2, NIPS’13, Red Hook, NY, USA, pp. 3111–3119. Curran Associates Inc.
  54. [60]Pennington J, Socher R, Manning C. 2014 GloVe: global vectors for word representation. In Proc. of the 2014 Conf. on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543. Association for Computational Linguistics.
  55. [61]MacKinnon JG. 1994 Approximate asymptotic distribution functions for unit-root and cointegration tests. J. Bus. Econ. Stat. 12, 167- 176. (10.1080/07350015.1994.10510005) · DOI
  56. [63]Simon B, Thomas D, Franziska JP, David JZ. 2019 Rtransferentropy–quantifying information flow between different time series using effective transfer entropy. SoftwareX 10, 1- 9. (10.1016/j.softx.2019.100265) · DOI
  57. [64]Marschinski R, Kantz H. 2002 Analysing the information flow between financial time series. Eur. Phys. J. B-Condens. Matter Complex Syst. 30, 275- 281. (10.1140/epjb/e2002-00379-2) · DOI
  58. [65]Müller UA, Dacorogna MM, Davé RD, Olsen RB, Pictet OV, Von Weizsäcker JE. 1997 Volatilities of different time resolutions—analyzing the dynamics of market components. J. Empir. Finance 4, 213- 239. (10.1016/S0927-5398(97)00007-8) · DOI
  59. [66]Corsi F. 2009 A simple approximate long-memory model of realized volatility. J. Financ. Econom. 7, 174- 196. (10.1093/jjfinec/nbp001) · DOI
  60. [67]Araci D. 2019 FinBERT: financial sentiment analysis with pre-trained language models. (http://arxiv.org/abs/1908.10063). · arXiv
  61. [69]Verdes PF. 2005 Assessing causality from multivariate time series. Phys. Rev. E 72, 026222. (10.1103/PhysRevE.72.026222) 16196699 · DOI
  62. [70]Vakorin VA, Krakovska OA, McIntosh AR. 2009 Confounding effects of indirect connections on causality estimation. J. Neurosci. Methods 184, 152- 160. (10.1016/j.jneumeth.2009.07.014) 19628006 · DOI
  63. [71]Sugihara G, May R, Ye H, Hsieh C-h, Deyle E, Fogarty M, Munch S. 2012 Detecting causality in complex ecosystems. Science 338, 496- 500. (10.1126/science.1227079) 22997134 · DOI
  64. [72]Yang AC, Peng C-K, Huang NE. 2018 Causal decomposition in the mutual causation system. Nat. Commun. 9, 1- 10. (10.1038/s41467-017-02088-w) 30140008 PMC6107666 · DOI · Paper page
  65. [73]Kugiumtzis D. 2013 Partial transfer entropy on rank vectors. Eur. Phys. J. Spec. Top. 222, 401- 420. (10.1140/epjst/e2013-01849-4) · DOI

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Sorted by citations

Authors
Jazbec, M., Pàsztor, B., Faltings, F., Antulov-Fantulin, N., & Kolm, P. N.
Affiliations
  • ETH Zurich (CH)
  • Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
  • Courant Institute of Mathematical Sciences (US)
Corresponding author
  • Antulov-Fantulin, Nino
  • Kolm, Petter N.
ORCID
Venue
Royal Society Open Science
Publisher
The Royal Society
Published
2021-07
Volume
8
Issue
7
Pages
202321
ISSN
2054-5703 (ISSN-L)
DOI
10.1098/rsos.202321
PMC
PMC8316821
OpenAlex
W3093843538
Cited by
3
References
2
Citations by year
2026: 2 · 2024: 1
FWCI
0.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Royal Society Open Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • arXiv (Cornell University)
  • ETH Zürich Research Collection (cc-by)
Funders
Grant Agreement (871042)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
Keywords
information transmission, financial markets, web crawling, Common Crawl, news sentiment, S&P 500, stock market, information theory

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