Using a novel dataset containing the forecasts of both buy-side and sell-side analysts, and individual investors, we find that crowdsourced earnings forecasts are more accurate than expert forecasts of sell-side analysts. Examining the economic mechanisms that generate superior crowd forecasts,...

The paper finds that firms' exposure to temperature changes predicts stock returns. We use the sensitivity of stock returns to abnormal temperature changes to measure firm-level climate sensitivity. Stocks with higher climate sensitivity forecast lower stock returns. A trading strategy that...

We study the forecasting behavior of minority sell-side equity analysts. Distinct from the impact of cultural and geographic diversity, we demonstrate that, although minority analyst forecasts have lower accuracy, consistency in their forecasts generates stronger correlation between the...

This study examines whether sell-side equity analysts help the market assimilate information contained in global climate change. Using a new measure of firm sensitivity to climate change, we show that analysts located in states where firms exhibit greater sensitivity to abnormal temperature...

We show that the personal traits of analysts, as revealed by their political donations, influence their forecasting behavior and stock prices. Analysts who contribute primarily to the Republican Party adopt a more conservative forecasting style. Their earnings forecast revisions are less likely...

We find that forecast revisions by analysts with more favorable surnames elicit stronger market reactions. The effect is stronger among firms with lower institutional ownership and for analysts with non-American first names. Following the 9/11 terrorist attacks, and France and Germany's...

We use a new disclosure-based approach to show that value-relevant information about publicly-traded firms is geographically distributed within the United States and the market is slow in aggregating this information. Firm fundamentals such as earnings and cash flows can be predicted using the...