太阳成集团tyc539(集团)有限公司

摘要：在本文中，我们分析了股票市场流动性与从社交媒体平台StockTwits和Twitter获得的实时情绪测度之间的关系。线性回归分析表明，极端情绪对应于较高的流动性需求和较低的供给，负面情绪对需求和供给的影响远大于积极情绪。日内事件研究显示，当看涨和看跌情绪分别达到极端水平时，繁荣和恐慌结束。在极端情绪之后，价格更加趋于均值并且价差变小。为了量化这些影响的大小，我们对市场中性均值回归策略进行了一次历史模拟，该策略使用社会媒体信息来确定其投资组合的分配。结果表明，流动性的需求和供给受到投资者情绪的影响，并且能够将交易费用保持在最低水平的做市商，可以利用社交媒体中的极端乐观和悲观情绪作为动量和均值回归收益的实时晴雨表，进而从中获利。

Momentum, Mean-Reversion, and Social Media: Evidence from StockTwits and Twitter

Shreyash Agrawal（Massachusetts Institute of Technology (MIT)）

Pablo Azar（Massachusetts Institute of Technology (MIT)）

Andrew W. Lo（Massachusetts Institute of Technology (MIT)）

Taranjit Singh（Massachusetts Institute of Technology (MIT)）

ABSTRACT

We analyze the relation between stock market liquidity and real-time measures of sentiment obtained from the social-media platforms StockTwits and Twitter. Linear regression analysis shows that extreme sentiment corresponds to higher demand and lower supply of liquidity, with negative sentiment having a much larger effect on demand and supply than positive sentiment. An 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, we conduct a historical simulation of a market-neutral mean-reversion strategy that uses social-media information to determine its portfolio allocations. Our results suggest that the demand 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.

原文链接：http://jpm.iijournals.com/content/44/7/85

翻译：黄涛

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