Asymmetric Attention and Stock Returns
利用谷歌搜索量构建本地与非本地投资者注意力分配的非对称指标，发现该指标能预测股票收益，高非对称注意力股票月均超额收益32个基点，且信息摩擦越大的股票效果越强。
This paper constructs a new measure of attention allocation by local investors relative to nonlocals using aggregate search volume from Google. We first present a conceptual framework in which local investors optimally choose to focus their attention on local stocks when they receive private news, leading to an asymmetric allocation of attention between local and nonlocal investors. Consistent with the main prediction of this framework, we find that firms attracting abnormally high asymmetric attention from local relative to nonlocal investors earn higher returns. A portfolio that goes long in stocks with high asymmetric attention and short in stocks with low asymmetric attention has an alpha of 32 basis points per month. The results are stronger for stocks with a greater degree of information friction. The new measure of asymmetric attention allows one to infer the arrival of unobservable private information by observing investors’ attention allocation behavior. This paper was accepted by Karl Diether, finance.