In bad times, uncertainty is high, so that investors find it more difficult to assess the prospects of the firms they invest in. Learning models suggest that in such times investors should, everything else equal, value informative signals such as analyst forecasts and recommendations more than...

The existing literature measures the contribution of analyst recommendation changes using average stock-price reactions. With such an approach, recommendation changes can have a significant impact even if no recommendation has a visible stock-price impact. Instead, we call a recommendation...

Not all stock recommendation changes are equal. In a sample constructed to minimize the impact of confounding news, relatively few analyst recommendation changes are influential in the sense that they impact investors' beliefs about a firm in a way that could be noticed in that firm's stock...

Because uncertainty is high in bad times, investors find it harder to assess firm prospects and, hence, should value analyst output more. However, higher uncertainty makes analysts' tasks harder so it is unclear if analyst output is more valuable in bad times. We find that, in bad times, analyst...

We propose a simple methodology to evaluate a large number of potential explanations for the negative relation between idiosyncratic volatility and subsequent stock returns (the idiosyncratic volatility puzzle). We find that surprisingly many existing explanations explain less than 10% of the...