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Structural breaks in online investor sentiment: A note on the nonstationarity of financial chatter

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  • Ballinari, Daniele
  • Behrendt, Simon

Abstract

Given the increasing interest in investor sentiment derived from social media platforms, we address one overlooked question – are there structural breaks in online investor sentiment? We cast the problem of break-point estimation in the dynamics of the sentiment series as a model selection problem. Considering 360 stocks, we detect structural breaks in most of the respective online investor sentiment series. A return prediction exercise illustrates the economic significance of the detected structural breaks. Our results call into question the widespread practice of using online investor sentiment series without taking into account the nonstationarity induced by structural breaks.

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  • Ballinari, Daniele & Behrendt, Simon, 2020. "Structural breaks in online investor sentiment: A note on the nonstationarity of financial chatter," Finance Research Letters, Elsevier, vol. 35(C).
    • Handle: RePEc:eee:finlet:v:35:y:2020:i:c:s1544612319311821
    DOI: 10.1016/j.frl.2020.101479
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    Cited by:

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    2. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.

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    More about this item

    Keywords

    Investor sentiment; Structural breaks; Nonstationarity; Group Lasso;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G40 - Financial Economics - - Behavioral Finance - - - General

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