RT Journal Article SR Electronic T1 Momentum, Mean-Reversion, and Social Media: Evidence from StockTwits and Twitter JF The Journal of Portfolio Management FD Institutional Investor Journals SP 85 OP 95 DO 10.3905/jpm.2018.44.7.085 VO 44 IS 7 A1 Shreyash Agrawal A1 Pablo D. Azar A1 Andrew W. Lo A1 Taranjit Singh YR 2018 UL https://pm-research.com/content/44/7/85.abstract AB In this article, the authors analyze the relation between stock market liquidity and real-time measures of sentiment obtained from the social-media platforms StockTwits and Twitter. The authors find that extreme sentiment corresponds to higher demand for and lower supply of liquidity, with negative sentiment having a much larger effect on demand and supply than positive sentiment. Their intraday event study shows that booms and panics end when bullish and bearish sentiment reach extreme levels, respectively. After extreme sentiment, prices become more mean-reverting and spreads narrow. To quantify the magnitudes of these effects, the authors conduct a historical simulation of a market-neutral mean-reversion strategy that uses social-media information to determine its portfolio allocations. These results suggest that the demand for and supply of liquidity are influenced by investor sentiment and that market makers who can keep their transaction costs to a minimum are able to profit by using extreme bullish and bearish emotions in social media as a real-time barometer for the end of momentum and a return to mean reversion.TOPICS: Security analysis and valuation, statistical methods