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Unraveling the relationship between social moods and the stock market: Evidence from the United Kingdom

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  • Saurabh, Samant
  • Dey, Kushankur

Abstract

This study elucidates plausible relationships between various social moods-dimension and the stock market. To study this phenomenon, we consider a twitter-based data consisting of various moods-dimensions of people residing in the United Kingdom and FTSE 100 index data from January 1, 2010, to October 31, 2014. The findings report that social moods-dimension significantly impacts the market return at the aggregate level. The prediction accuracy measured by the artificial neural network is comparably higher than that of other models, such as support vector machine, discriminant analysis, and decision tree. We also observe that a specific moods-dimension named happy significantly improves the prediction of the index return. While there is no causality between aggregate social moods and the index return, we find bi-directional causality between happy mood-dimension and the market return. However, the intensity of causality from the index return to happy moods is profound. The novelty of our research lies in methodological augmentation and its congruence compared with previous empirical studies.

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  • Saurabh, Samant & Dey, Kushankur, 2020. "Unraveling the relationship between social moods and the stock market: Evidence from the United Kingdom," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    • Handle: RePEc:eee:beexfi:v:26:y:2020:i:c:s2214635019302163
    DOI: 10.1016/j.jbef.2020.100300
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    Cited by:

    1. Mohammad K. Elshqirat, 2024. "Investors’ Happiness and Stock Market," International Business Research, Canadian Center of Science and Education, vol. 17(2), pages 1-23, April.
    2. Yingying Xu & Jichang Zhao, 2022. "Can sentiments on macroeconomic news explain stock returns? Evidence form social network data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2073-2088, April.
    3. Steven Buigut & Burcu Kapar, 2021. "COVID-19 Cases, Media Attention and Social Mood," International Journal of Economics and Financial Issues, Econjournals, vol. 11(4), pages 66-72.
    4. Li, Xiao, 2020. "When financial literacy meets textual analysis: A conceptual review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    5. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
    6. Goodell, John W. & Kumar, Satish & Rao, Purnima & Verma, Shubhangi, 2023. "Emotions and stock market anomalies: A systematic review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    7. Hasso, Tim & Pelster, Matthias & Breitmayer, Bastian, 2020. "Terror attacks and individual investor behavior: Evidence from the 2015–2017 European terror attacks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    8. Humaira Asad & Iqra Toqeer & Khalid Mahmood, 2021. "A qualitative phenomenological exploration of social mood and investors’ risk tolerance in an emerging economy," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 14(1), pages 189-211, August.
    9. Mehdi Zolfaghari & Bahram Sahabi, 2021. "The impact of oil price and exchange rate on momentum strategy profits in stock market: evidence from oil-rich developing countries," Review of Managerial Science, Springer, vol. 15(7), pages 1981-2023, October.
    10. Jung, Sang Hoon & Jeong, Yong Jin, 2021. "Examining stock markets and societal mood using Internet memes," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    11. 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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