Sentiment, social media and meme stock return predictability

What the paper says

Purpose The purpose of this paper is to investigate how investor sentiment surrounding “meme stocks” predicts subsequent returns over varying horizons. We construct two value-weighted meme-stock indices via textual analysis of Reddit's r/WallStreetBets and develop three sentiment measures–Google search volume, Bloomberg Twitter sentiment and Bloomberg news sentiment–each rescaled to a 0–100 range. Employing univariate and multivariate regressions with lags from one to fourteen days, we demonstrate that Google search sentiment forecasts returns over 3–7 days, Bloomberg news sentiment over 7–14 days, and Bloomberg Twitter sentiment primarily over one trading day, thereby illuminating platform-specific information dissemination dynamics. Design/methodology/approach We identify meme stocks by extracting ticker mentions from Reddit's r/WallStreetBets and construct monthly and semi-annual value-weighted indices. We build three sentiment measures–Google Trends, Bloomberg Twitter and Bloomberg news–each scaled to 0–100 and transformed via the Abnormal Search Volume Index. Using daily data from January 2021 to December 2022, we estimate univariate and multivariate regressions with sentiment lags of 1–14 days. To assess robustness, we include control variables (term spread, 14-day volatility and 14-day volume) and compare predictive power across horizons and platforms. Findings Google search sentiment significantly predicts meme stock returns at 3–7 days horizons. Bloomberg news sentiment forecasts returns over 7–14 days horizons, whereas Bloomberg Twitter sentiment only predicts one-day returns. These relationships remain robust after controlling for term spread, 14-day volatility and 14-day volume. Multivariate regressions show that combining sentiment measures improves short- and medium-term return forecasts. The differing forecast horizons reflect platform-specific information speeds and user profiles, highlighting the need to match sentiment sources to the desired prediction horizon. Practical implications Practitioners can align trading strategies with platform-specific sentiment: day traders should monitor Bloomberg Twitter sentiment for intraday signals, swing traders can use Google search sentiment for 3–7 days horizons, and position traders may rely on Bloomberg news sentiment for 7–14 days insights. Integrating multi-platform sentiment measures into algorithmic models enhances forecast accuracy and enables dynamic position sizing. Risk managers can improve volatility forecasts by incorporating sentiment-driven shocks, while portfolio managers can refine asset allocation by calibrating exposure based on real-time sentiment shifts. Fintech platforms can offer tailored sentiment dashboards to support investor decision-making. Originality/value Originality lies in constructing two novel meme-stock indices based on Reddit r/WallStreetBets discussions and integrating three distinct sentiment measures–Google Trends, Bloomberg Twitter and Bloomberg news–within a unified predictive framework. By systematically comparing their forecasting power across 1–14 days horizons, we reveal platform-specific dissemination dynamics and user behavior effects on return predictability. This approach advances the literature on investor sentiment by bridging social media, search activity and news analysis, offering practitioners actionable insights on selecting appropriate sentiment sources for different investment horizons.