[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 change influential only if it affects the stock price of the affected firm visibly. We show that only 12% of recommendation changes are influential. Recommendation changes are more likely to be influential if they are from leader, star, previously influential analysts, issued away from consensus, accompanied by earnings forecasts, and issued on growth, small, high institutional ownership, or high forecast dispersion firms.]

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). Surprisingly, we find that many existing explanations explain less than 10% of the puzzle. On the other hand, explanations based on investors’ lottery preferences and market frictions show some promise in explaining the puzzle. Together, all existing explanations account for 29–54% of the puzzle in individual stocks and 78–84% of the puzzle in idiosyncratic volatility-sorted portfolios. Our methodology can be applied to evaluate competing explanations for other asset pricing anomalies.

We find that analysts who issue more accurate earnings forecasts also issue more profitable stock recommendations. The average factor-adjusted return associated with the recommendations of analysts in the highest accuracy quintile exceeds the corresponding return for analysts in the lowest accuracy quintile by 1.27% per month. Our findings provide indirect empirical support for valuation models in the accounting and finance literatures (e.g., Ohlson, 1995) that emphasize the role of future earnings in predicting stock price movements. Our results also suggest that imperfectly efficient markets reward information gatherers, such as security analysts, for their costly activities in generating superior earnings forecasts.

ABSTRACT 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 whether analyst output is more valuable in bad times. We find that in bad times, analyst revisions have a larger stock‐price impact, earnings forecast errors per unit of uncertainty fall, and analyst reports are more frequent and longer. The increased impact of analysts is also more pronounced for harder‐to‐value firms. These results are consistent with analysts working harder and investors relying more on analysts in bad times.

Using unique real estate data that allow for accurately measured capital gains, we examine whether sell propensities depend on the magnitude of a seller’s capital gain. We find that short-term sell propensities are flat over losses and increasing in gains. Consistent with their higher sell propensities, selling prices are lower for properties with larger gains. Large-sized short-term stock investments also have sell propensities that are flat over losses and increasing in gains, although the sell propensities of typical-sized short-term stock investments are V-shaped. Our findings provide empirical support for theories of realization utility.