This study explores how sentiment and attention variables, like Google searches and StockTwits messages, predict stock market volatility. It finds that these variables improve volatility forecasts, especially in the short term, with investor attention being the strongest predictor. While the improvements are modest, they are most effective during high-activity or unexpected news events. Further research is needed to examine non-linear effects and impacts across sectors or higher-frequency data.
The GameStop saga, fueled by Reddit users in r/wallstreetbets like "Roaring Kitty", saw retail investors driving up stock prices in a coordinated effort to squeeze short sellers. This highlighted the power of social media in shifting market dynamics and influencing institutional investors. Hedge funds and asset managers are now reassessing strategies to mitigate risks from social media-driven events. This data is being analyzed to understand the scope of such manipulation.
This study finds that media pessimism predicts short-term downward price pressure followed by a reversion to fundamentals. It links extreme sentiment with increased trading volume, especially in small stocks. The paper suggests that media influences liquidity and sentiment more than it reflects new information, and cautions against the profitability of sentiment-based trading due to transaction costs.
This paper explores how Reuters news sentiment for 87 companies over 27 quarters correlates with market shifts. It includes visualizations like sentiment heatmaps and relationship graphs, highlighting media sentiment’s role in stock price volatility. The study offers foundational insights but lacks the interactive elements we aim to implement.
This article provides evidence that media coverage volume correlates with trading activity and investor behavior. It uses static visualizations—such as scatter plots and time-series charts—to show how news mentions relate to stock volatility. While informative, its lack of interactive elements offers a clear opportunity for enhancement in our own visualizations.