Abstract

This paper establishes a direct link between (anti) herding behavior in currency markets and investor sentiment, proxied by a social media based investor happiness index built on Twitter feed data. Our analysis of daily data for nine developed market currencies suggests that the foreign exchange market is generally characterized by strong anti-herding behavior. Utilizing the quantile-on-quantile (QQ) approach, developed by Sim and Zhou (2015 Sim, N., and H. Zhou. 2015. “Oil Prices, US Stock Return, and the Dependence between Their Quantiles.” Journal of Banking & Finance 55:1–8. doi:10.1016/j.jbankfin.2015.01.013[Crossref], [Web of Science ®] , [Google Scholar]), we show that the relationship between investor sentiment and anti-herding is in fact regime specific, with anti-herding behavior particularly prominent during states of extreme investor sentiment. The effect of sentiment on anti-herding is generally stronger in extreme bullish sentiment states, while average sentiment is associated with less severe anti-herding. The findings lend support to the behavioral factors for asset pricing models and suggest that real time investor sentiment signals can be utilized to monitor potential speculative activities in the currency market.

摘要

本文建立了货币市场中的（反）羊群行为与投资者情绪之间的直接联系，以建立在 Twitter 提要数据上的基于社交媒体的投资者幸福指数为代表。我们对九种发达市场货币的每日数据分析表明，外汇市场普遍具有强烈的反羊群行为特征。利用 Sim 和 Zhou（2015 年）开发的分位数对分位数 (QQ) 方法 Sim, N.和H. Zhou。2015 年。“石油价格、美国股票收益及其分位数之间的相关性。” 银行与金融杂志55：1 – 8。doi: 10.1016/j.jbankfin.2015.01.013 [Crossref], [Web of Science®]  , [Google Scholar])，我们表明投资者情绪与反羊群行为之间的关系实际上是特定于制度的，反羊群行为在极端投资者情绪状态下尤为突出。在极端看涨情绪状态下，情绪对反羊群效应的影响通常更强，而平均情绪与不太严重的反羊群效应相关。这些发现为资产定价模型的行为因素提供了支持，并表明可以利用实时投资者情绪信号来监控货币市场中潜在的投机活动。