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Predicting FTSE 100 returns and volatility using sentiment analysis

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  • Mark Johnman
  • Bruce James Vanstone
  • Adrian Gepp

Abstract

We investigate the statistical and economic effect of positive and negative sentiment on daily excess returns and volatility in the FTSE 100 index, using business news articles published by the Guardian Media Group between 01/01/2000 and 01/06/2016. The analysis indicates that while business news sentiment derived from articles aimed at retail traders does not influence excess returns in the FTSE 100 index, it does affect volatility, with negative sentiment increasing volatility and positive sentiment reducing it. Further, an ETF‐based trading strategy based on these findings is found to outperform the naïve buy‐and‐hold approach.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:acctfi:v:58:y:2018:i:s1:p:253-274
    DOI: 10.1111/acfi.12373
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    3. Zhijuan Chen & William T. Lin & Changfeng Ma & Kent Wang, 2020. "Are individual investors liquidity providers around earnings announcements? Evidence from an emerging market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3447-3475, December.
    4. Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 56-65.
    5. Basak, Gopal K. & Das, Pranab Kumar & Marjit, Sugata & Mukherjee, Debashis & Yang, Lei, 2023. "The British Stock Market, currencies, brexit, and media sentiments: A big data analysis," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    6. Gopal K. Basak & Pranab Kumar Das & Sugata Marjit & Debashis Mukherjee & Lei Yang, 2019. "British Stock Market, BREXIT and Media Sentiments - A Big Data Analysis," CESifo Working Paper Series 7760, CESifo.
    7. Pedro Manuel Nogueira Reis & Carlos Pinho, 2021. "A Reappraisal of the Causal Relationship between Sentiment Proxies and Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 22(4), pages 420-442, October.
    8. Yasheng Chen & Xian Huang & Zhuojun Wu, 2023. "From natural language to accounting entries using a natural language processing method," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 3781-3795, December.
    9. Weiguo Zhang & Xue Gong & Chao Wang & Xin Ye, 2021. "Predicting stock market volatility based on textual sentiment: A nonlinear analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1479-1500, December.
    10. Mariano González-Sánchez & M. Encina Morales de Vega, 2021. "Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
    11. Gong, Xue & Zhang, Weiguo & Wang, Junbo & Wang, Chao, 2022. "Investor sentiment and stock volatility: New evidence," International Review of Financial Analysis, Elsevier, vol. 80(C).

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