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In search of alpha
(2015)
In this study we develop a trading strategy that exploits limited investor attention. Trading signals for US S&P 500 stocks stocks are derived from Google Search Volume data, taking a long position if investor attention for the corresponding security was abnormally low in the past week. Our strategy generates 19% average annual return and thereby outperforms a simple market buy-and-hold strategy. After controlling for the well-known risk factors, a significant alpha (abnormal return) of 10% p.a. remains. Returns are sufficiently large to cover transaction costs.
This study provides novel insights to the ongoing debate how market efficiency is challenged by investor behavior. Applying search engine data we find that retail investor attention can enhance market efficiency. High attention is associated with better incorporation of idiosyncratic stock information, which we interpret as improved pricing efficiency. This effect is even more pronounced in bullish markets. In bearish markets, however, retail investor attention leads to a deterioration of pricing efficiency, which might be explained with herding behavior. Our evidence holds for a broad sample of European and US stocks.
FMCG marketing and sales
(2015)
Meaningful metrics
(2015)
Modern pathfinders
(2015)