The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility
By Simon Behrendt, Alexander Schmidt. Journal of Banking & Finance, 2018
117 citations recorded by the source corpus.
Taking an intraday perspective, we study the dynamics of individual-level stock return volatility, measured by absolute 5-minute returns, and Twitter sentiment and activity. After accounting for the intraday periodicity in absolute returns, we discover some statistically significant co-movements of intraday volatility and information from stock-related Tweets for all constituents of the Dow Jones Industrial Average. However, economically, the effects are of negligible magnitude and out-of-sample forecast performance is not improved when including Twitter sentiment and activity as exogenous variables. From a practical point of view, we find that high-frequency Twitter information is not particularly useful for highly active investors with access to such data for intraday volatility assessment and forecasting when considering individual-level stocks.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/7fe6293af240627500985a751bfe31c5850350bd
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Citations in the corpus
- Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, unknown): “5 Liu et al (2015) show that it is difficult to beat the standard 5-minute realized variance when forecasting volatility and the use of the 5-minute frequency is consistent with other studies such as Sévi (2014); Wen (2016), Behrendt and Schmidt (2018).”
- Social Media Sentiment in International Stock Returns and Trading Activity (2020, background): “Behrendt and Schmidt (2018) examine the relation between individual-level stock return volatility measured by absolute 5-min returns and Twitter sentiment for DJIA constituents and show that there are significant feedback effects of return volatility to Twitter sentiment.”