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    A sentiment analysis approach to the prediction of market volatility

    Deveikyte, Justina and Geman, Hélyette and Piccari, Carlo and Provetti, Alessandro (2022) A sentiment analysis approach to the prediction of market volatility. Frontiers in Artificial Intelligence 5 , ISSN 2624-8212.

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    Abstract

    Prediction and quantification of future volatility and returns play an important role in financial modeling, both in portfolio optimisation and risk management. Natural language processing today allows one to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. We found that there is evidence of correlation between sentiment and stock market movements. Moreover, the sentiment captured from news headlines could be used as a signal to predict market returns; we also found that the same does not apply for volatility. However, for the sentiment found in Twitter comments we obtained, in a surprising finding, a correlation coefficient of -0.7 (p < 0.05), which indicates a strong negative correlation between negative sentiment captured from the tweets on a given day and the volatility observed the next day. It is important to keep in mind that stock volatility rises greatly when the market collapses but not symmetrically so when it goes up (the so-called leverage effect). We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modeling, based on Latent Dirichlet Allocation, in order to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modeling even on modest (essentially personal) architecture our classifier achieved a directional prediction accuracy for volatility of 63%.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Sentiment analysis, Twitter, News analytics, Volatility, Topic modeling (LDA)
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Research Centres and Institutes: Data Analytics, Birkbeck Institute for
    Depositing User: Alessandro Provetti
    Date Deposited: 04 Jan 2023 06:12
    Last Modified: 09 Aug 2023 12:54
    URI: https://eprints.bbk.ac.uk/id/eprint/50293

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