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Text and Context: Language Analytics in Finance

Author

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  • Das, Sanjiv Ranjan

Abstract

This monograph surveys the technology and empirics of text analytics in finance. I present various tools of information extraction and basic text analytics. I survey a range of techniques of classification and predictive analytics, and metrics used to assess the performance of text analytics algorithms. I then review the literature on text mining and predictive analytics in finance, and its connection to networks, covering a wide range of text sources such as blogs, news, web posts, corporate filings, etc. I end with textual content presenting forecasts and predictions about future directions.

Suggested Citation

  • Das, Sanjiv Ranjan, 2014. "Text and Context: Language Analytics in Finance," Foundations and Trends(R) in Finance, now publishers, vol. 8(3), pages 145-261, November.
    • Handle: RePEc:now:fntfin:0500000045
    DOI: 10.1561/0500000045
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    Citations

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    Cited by:

    1. Ioanna Kountouri & Eleftherios Manousakis & Andrianos E. Tsekrekos, 2019. "Latent semantic analysis of corporate social responsibility reports (with an application to Hellenic firms)," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 16(1), pages 1-19, March.
    2. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    3. Lauren Cohen & Christopher Malloy & Quoc Nguyen, 2020. "Lazy Prices," Journal of Finance, American Finance Association, vol. 75(3), pages 1371-1415, June.
    4. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    5. Faccini, Renato & Matin, Rastin & Skiadopoulos, George, 2023. "Dissecting climate risks: Are they reflected in stock prices?," Journal of Banking & Finance, Elsevier, vol. 155(C).
    6. Lauren Cohen & Christopher Malloy & Quoc Nguyen, 2018. "Lazy Prices," NBER Working Papers 25084, National Bureau of Economic Research, Inc.
    7. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    8. Rob Bauer & Dirk Broeders & Annick van Ool, 2023. "Walk the green talk? A textual analysis of pension funds’ disclosures of sustainable investing," Working Papers 770, DNB.
    9. Michael Lachanski & Steven Pav, 2017. "Shy of the Character Limit: "Twitter Mood Predicts the Stock Market" Revisited," Econ Journal Watch, Econ Journal Watch, vol. 14(3), pages 302–345-3, September.
    10. Christian Leuz & Peter D. Wysocki, 2016. "The Economics of Disclosure and Financial Reporting Regulation: Evidence and Suggestions for Future Research," Journal of Accounting Research, Wiley Blackwell, vol. 54(2), pages 525-622, May.
    11. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.
    12. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    13. Ongsakul, Viput & Chatjuthamard, Pattanaporn & Jiraporn, Pornsit & Chaivisuttangkun, Sirithida, 2021. "Corporate integrity and hostile takeover threats: Evidence from machine learning and “CEO luck”," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).

    More about this item

    Keywords

    Text analytics; Text mining; Financial analysis;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General

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