Investor Attention and Sentiment: Risk or Anomaly?

Abstract

Are stocks' varying sensitivities to changing investor attention and sentiment priced? Employing internet search-based proxies for both, I find novel results that are consistent with theory. Stocks that co-vary negatively with increased investor attention to the stock market outperform in the following months in a behavior consistent with a risk premium. The pricing of co-variation with investor sentiment depends on aggregate mispricing (Baker-Wurgler index), behaving like a risk premium when mispricing is low and like an anomaly when mispricing is high. Sensitivity to both sentiment and attention is strongly related to idiosyncratic volatility and limits to arbitrage: High absolute attention/sentiment loadings are associated with higher volatility, smaller size and other limits to arbitrage. However, the priced attention and sentiment components are clearly distinct from the idiosyncratic risk puzzle and stay significant when controlling for relevant pricing factors and company characteristics. Investor attention is both very robust and highly powerful in pricing a broad variety of test assets. On the other hand, investor sentiment's effect on performance is strongly related to return reversal/momentum and does not add much information on its own.

Investor attention, analysts coverage and idiosyncratic volatility puzzle: based on behavioral perspective

“…Investor attention (IA) is of great significance in theoretical analysis. A major problem encountered in empirical research is the lack of a direct measure of investor attention (Bucher, 2018). The indirect proxy variables that investors use in the empirical analysis include excess returns (Barber and Odean, 2008), transaction volume (Gervais and Kaniel, 2001;Barber and Odean, 2008), advertising cost of expenditure (Grullon et al, 2004), news and headlines (Yuan, 2015), etc.…”

Exploiting investor sentiment for portfolio optimization

“…Da et al (2015) show its predictive power for return reversals for 1-and 2-day horizons, and point out several advantages of search-query-based sentiment data over questionnaires: high-frequency data sets are easily available and Internet searches should reveal more personal information. Recently, Bucher (2017) finds that the return predictability of the FEARS index concerning a cross-section of stocks with different exposures to FEARS sentiment does not stem from its sentiment loading, but can be explained by return reversals and momentum.…”

“…Finally, Bucher (2017) remarks that the FEARS index is primarily interesting because of its cross-sectional features at daily and weekly horizons. We, therefore, do not suspect that it will be useful for our portfolio strategies addressing much longer horizons.…”