The Effects of Twitter Sentiment on Stock Price Returns.
The Effects of Twitter Sentiment on Stock Price Returns.
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DOI：
10.1371/journal.pone.0138441
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Mozetič I
中科院分区：
文献类型：
--
作者：
Ranco G;Aleksovski D;Caldarelli G;Grčar M;Mozetič I
社交媒体越来越多地反映和影响其他复杂系统的行为。本文研究了知名微博平台Twitter与金融市场的关系。特别是，我们考虑在15个月的时间里，Twitter的交易量和对道琼斯工业平均指数（DJIA）的30家股票公司的情绪。我们发现在整个时间段内相应的时间序列之间存在相对较低的皮尔逊相关性和格兰杰因果关系。然而，我们发现在推特交易量峰值期间，推特情绪与异常收益之间存在显著的依赖关系。这不仅适用于预期的Twitter数量峰值（例如，季度公告），也适用于不太明显事件对应的峰值。我们将经济学和金融学中著名的“事件研究”应用于Twitter数据的分析，从而将这一过程正式化。该过程允许自动识别事件作为Twitter数量峰值，计算在这些峰值时tweet中表达的流行情绪（积极或消极），最后应用“事件研究”方法将它们与股票回报联系起来。我们证明了Twitter峰值的情绪极性暗示了累积异常收益的方向。累积异常收益的数量相对较低（约为1-2%），但在事件发生后的几天内，这种依赖性在统计上是显著的。
Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the “event study” methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.