Momentum, Mean-Reversion, and Social Media: Evidence from StockTwits and Twitter
研究了社交媒体情绪与股票市场流动性的关系，发现极端情绪会导致流动性需求上升、供给下降，且负面情绪影响更大；极端情绪后价格更易均值回归，市场中性策略可据此获利。
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. <b>TOPICS:</b>Security analysis and valuation, statistical methods