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Daily expectations of returns index

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  • Gholampour, Vahid

Abstract

The paper introduces a daily index for expectations of returns based on tweets that express a directional prediction about the stock market index. I develop a dictionary that includes lexicon of traders to identify and classify opinionated tweets. The results show that (1) the Twitter Expectations of Returns Index (TERI) is positively correlated with weekly changes in net long position of investment managers, (2) expectations index of high followers accounts predicts stock market returns, and (3) private information is the primary source of return predictability.

Suggested Citation

  • Gholampour, Vahid, 2019. "Daily expectations of returns index," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 236-252.
    • Handle: RePEc:eee:empfin:v:54:y:2019:i:c:p:236-252
    DOI: 10.1016/j.jempfin.2019.10.004
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    References listed on IDEAS

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    1. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
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    4. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    5. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 09, European Central Bank.
    6. Robin Greenwood & Andrei Shleifer, 2014. "Expectations of Returns and Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 714-746.
    7. Brown, Gregory W. & Cliff, Michael T., 2004. "Investor sentiment and the near-term stock market," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 1-27, January.
    8. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    9. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    10. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 9, European Central Bank.
    11. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
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    Cited by:

    1. Prajwal Eachempati & Praveen Ranjan Srivastava, 2021. "Accounting for unadjusted news sentiment for asset pricing," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 13(3), pages 383-422, May.
    2. Steyn, Dimitri H. W. & Greyling, Talita & Rossouw, Stephanie & Mwamba, John M., 2020. "Sentiment, emotions and stock market predictability in developed and emerging markets," GLO Discussion Paper Series 502, Global Labor Organization (GLO).
    3. Michael Stiefel & Rémi Vivès, 2022. "‘Whatever it takes’ to change belief: evidence from Twitter," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(3), pages 715-747, August.
    4. Machus, Tobias & Mestel, Roland & Theissen, Erik, 2022. "Heroes, just for one day: The impact of Donald Trump’s tweets on stock prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).

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    More about this item

    Keywords

    Private information; Expectations of returns; Twitter; Stock market;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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