We present new evidence on the distribution of the ex ante risk premium based on a multi-year survey of Chief Financial Officers (CFOs) of U.S. corporations. Currently, we have responses from surveys conducted from the second quarter of 2000 through the third quarter of 2001. The results in this paper will be augmented as future surveys become available. We find direct evidence that the one-year risk premium is highly variable through time and 10-year expected risk premium is stable. In particular, after periods of negative returns, CFOs significantly reduce their one-year market forecasts, disagreement (volatility) increases and returns distributions are more skewed to the left. We also examine the relation between ex ante returns and ex ante volatility. The relation between the one-year expected risk premium and expected risk is negative. However, our research points to the importance of horizon. We find a significantly positive relation between expected return and expected risk at the 10-year horizon. _______________________________________________________________________________ *Draft: November 30, 2001. Corresponding author, Telephone: +1 919.660.7768, Fax: +1 919.660.8030, E-mail address: cam.harvey@duke.edu. We thank the Financial Executives International (FEI) executives who took the time to fill out the surveys. We thank Alon Brav, Magnus Dahlquist, Ron Gallant, Jim Smith, Paul Soderlind and Bob Winkler for their helpful comments and the participants at the NBER Corporate Finance Summer Workshop. Krishnamoorthy Narasimhan provided research assistance. This research is partially sponsored by FEI but the opinions expressed in the paper are those of the authors and do not necessarily represent the views of FEI. Graham acknowledges financial support from the Alfr...

Security analysts tend to bias stock recommendations upward, particularly if they are affiliated with the underwriter. We analyze how investors account for such distortions. Using the NYSE Trades and Quotations database, we find that large traders adjust their trading response downward: they exert buy pressure following strong buy recommendations, no reaction to buy recommendations, and selling pressure following hold recommendations. This “discounting” is even more pronounced when the analyst has an underwriter affiliation. Small traders, instead, follow recommendations literally. They exert positive pressure following both buy and strong buy recommendations and zero pressure following hold recommendations. We discuss possible explanations for the differences in trading response, including informa-tion costs and investor naiveté.

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This paper examines the value of sell-side analysts to firms by evaluating the long-term consequences of losing all analyst coverage for periods of at least one year. Our findings are consistent with the hypothesis that analysts add value to a firm by maintaining investor recognition for that firm‟s stock. In particular, we find that, in the years after the loss of coverage, sample firms experience a decrease in trading volume, stock liquidity, and institutional ownership, while their operating prospects are similar to their covered peers. Analysis of delisting rates indicates that sample firms are significantly more likely to delist than their covered peers, which are control firms matched on the propensity for bankruptcy and the potential for generating brokerage revenue. We find similar results when we examine a subsample of firms that lose all analyst coverage following an exogenous shock. Our results provide insight into the reasons why firms place so much

helpful comments and the participants at the NBER Corporate Finance Summer Workshop. Krishnamoorthy Narasimhan provided research assistance. This research is partially sponsored by FEI but the opinions expressed in the paper are those of the authors and do not necessarily represent the views of FEI. Graham acknowledges financial support from the Alfred P. Sloan Research Foundation. The views expressed herein

Abstract. This study analyzes the accuracy of forecasted target prices which are disclosed by leading investment banks within their analysts ’ reports on German stocks for the period from 2002 to 2004. We compute a measure for target price forecast accuracy that evaluates the ability of analysts to exactly forecast the ex-ante (unknown) 12-months stock price. Overall, the target price forecasting accuracy is 73.64%. Our main contribution is to determine factors that explain this target price accuracy. When focusing on analyst-specific differences, target price forecasts that highly deviate from current stock prices (since analysts are overly optimistic) are, ex-post, less accurate. On the contrary, target price forecasts issued by analysts who also provide a detailed rationale in their report are marginally more accurate compared to less thoroughly researched reports. With respect to firm-specific factors, analysts are more successful in forecasting 12-months stock prices for large companies. However, analysts do worse when forecasting target prices for highly volatile and risky stocks. Finally, we show that analysts working for highly reputable banks are more successful when issuing positive target price forecasts compared to the average analyst. Potential conflicts of interests between analyst and covered company, however, do not bias forecast accuracy.

We thank Jay Ritter for generously providing us with his IPO database for the project and for his comments. We also thank Paul Irvine for helpful comments. Corresponding author.

Asset pricing models predict relations between assets ’ expected rates of return and their risks or behavioral attributes. However, almost all tests of these models use realized return as a proxy for expected return. This paper extracts proxies for the market’s estimate of expected return using Value Line analysts ’ forecasts (for approximately 3,800 stocks over 27 years), and tests the relation between these ex-ante expected return estimates and factors proposed in the literature to explain them. Consistent with the predictions of the CAPM, we find that market beta is positively related to expected returns. As with tests that employ ex-post realized returns, we find that expected return is negatively related to the firm’s market value of equity. Contrary to the implication of Fama and French (1993) and others, we find that high book-to-market firms are not expected to earn higher

Analysts ’ Dividend Forecasts and Dividend Signaling This study complements existing research on the information content of dividends by focusing on the use of dividend expectations. We derive a measure of unexpected dividend changes, called dividend surprises, based on Value Line forecasts. Our results highlight a potentially serious sample misclassification arising from the extensively used naïve dividend change method. Classifications of unexpected changes in dividends using dividend surprises result in stock price reactions and earnings changes that are consistent with the implications of dividend signaling models. Also, the approach followed in this paper permits the analysis of a significantly “forgotten ” sample in previous event studies: firms announcing no dividend changes in which investors (analysts) are expecting a change. We find that no change in dividends often reflects a negative dividend surprise and is indeed associated with negative stock price reaction and negative earnings changes. We provide evidence that the failure to find a relationship between dividend changes and future earning changes may be due to measurement error arising from misclassification of dividend changes. One implication of this study for future research is that empirical tests of dividend signaling models should incorporate dividend forecasts. Analysts ’ Dividend Forecasts and Dividend Signaling 2 Dividend signaling models developed in Bhattacharya (1979), Miller and Rock (1985), and John

Economics (BUTE) for providing research scholar grant for this project. The financial support provided