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Quantifying the effects of online bullishness on international financial markets

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  • Mao, Huina
  • Counts, Scott
  • Bollen, Johan

Abstract

Computational methods to gauge investor sentiment from commonly used online data sources that rely on machine learning classifiers and lexicons have shown considerable promise, but suffer from measurement and classification errors. In our work, we develop a simple, direct and unambiguous indicator of online investor sentiment, which is based on Twitter updates and Google search queries. We examine the predictive power of this new investor bullishness indicator for international stock markets. Our results indicate several striking regularities. First, changes in Twitter bullishness predict changes in Google bullishness, indicating that Twitter information precedes Google queries. Second, Twitter and Google bullishness are positively correlated to investor sentiment and lead established investor sentiment surveys. The former, in particular, is a more powerful predictor of changes in sentiment in the stock market than the latter. Third, we observe that high Twitter bullishness predicts increases in stock returns, with these then returning to their fundamental values. We believe that our results may support the investor sentiment hypothesis in behavioural finance. JEL Classification: C1, C12

Suggested Citation

  • Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 9, European Central Bank.
    • Handle: RePEc:ecb:ecbsps:20159
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    Cited by:

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    2. Gu, Chen & Kurov, Alexander, 2020. "Informational role of social media: Evidence from Twitter sentiment," Journal of Banking & Finance, Elsevier, vol. 121(C).
    3. Matteo Accornero & Mirko Moscatelli, 2018. "Listening to the buzz: social media sentiment and retail depositors' trust," Temi di discussione (Economic working papers) 1165, Bank of Italy, Economic Research and International Relations Area.
    4. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
    5. Angelico, Cristina & Marcucci, Juri & Miccoli, Marcello & Quarta, Filippo, 2022. "Can we measure inflation expectations using Twitter?," Journal of Econometrics, Elsevier, vol. 228(2), pages 259-277.
    6. Francesco Corea & Enrico Maria Cervellati, 2015. "The Power of Micro-Blogging: How to Use Twitter for Predicting the Stock Market," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 3(4), pages 1-7.
    7. Jing, Wei & Zhang, Xueyong, 2021. "Online social networks and corporate investment similarity," Journal of Corporate Finance, Elsevier, vol. 68(C).
    8. Gholampour, Vahid, 2019. "Daily expectations of returns index," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 236-252.
    9. Shah, Syed Faisal & Albaity, Mohamed, 2022. "The role of trust, investor sentiment, and uncertainty on bank stock return performance: Evidence from the MENA region," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    10. Gurdgiev, Constantin & O’Loughlin, Daniel, 2020. "Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    11. Mark Johnman & Bruce James Vanstone & Adrian Gepp, 2018. "Predicting FTSE 100 returns and volatility using sentiment analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 253-274, November.
    12. Teti, Emanuele & Dallocchio, Maurizio & Aniasi, Alberto, 2019. "The relationship between twitter and stock prices. Evidence from the US technology industry," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    13. Florian Röder & Andreas Walter, 2019. "What Drives Investment Flows Into Social Trading Portfolios?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 42(2), pages 383-411, July.
    14. Gholampour, Vahid & van Wincoop, Eric, 2019. "Exchange rate disconnect and private information: What can we learn from Euro-Dollar tweets?," Journal of International Economics, Elsevier, vol. 119(C), pages 111-132.
    15. Vahid Gholampour & Eric van Wincoop, 2017. "What can we Learn from Euro-Dollar Tweets?," NBER Working Papers 23293, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    big data; computational science; international financial markets; investor sentiment; social media;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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