Disagreement and return predictability of stock portfolios

Yu, Jialin

doi:10.1016/j.jfineco.2010.08.004

Cited by 170 publications

(74 citation statements)

References 55 publications

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“…Our findings are consistent with Diether, Malloy, and Scherbina () and Yu (), who find that dispersion of earnings forecasts predicts low returns in the cross section and for the market return in the time series, respectively, as predicted in models with disagreement and short‐sales constraints. Our particular focus is on the theory and the empirics of the Security Market Line as a function of aggregate disagreement.…”

supporting

confidence: 92%

“…In Panel B, we use

β \times value

‐weights to define aggregate disagreement. In Panel C, disagreement is the “top‐down” measure of market disagreement used in Yu (), which is calculated as the standard deviation of analyst forecasts of annual S&P 500 earnings, scaled by the average forecast on S&P 500 earnings. In Panel D, disagreement is the first principal component of the monthly cross‐sectional standard deviation of forecasts on GDP, IP, corporate profit, and the unemployment rate in the SPF and is taken from Li and Li ().…”

Section: Empirical Analysismentioning

confidence: 99%

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Speculative Betas

Hong¹,

Sraer

2016

The Journal of Finance

248

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The risk and return trade‐off, the cornerstone of modern asset pricing theory, is often of the wrong sign. Our explanation is that high‐beta assets are prone to speculative overpricing. When investors disagree about the stock market's prospects, high‐beta assets are more sensitive to this aggregate disagreement, experience greater divergence of opinion about their payoffs, and are overpriced due to short‐sales constraints. When aggregate disagreement is low, the Security Market Line is upward‐sloping due to risk‐sharing. When it is high, expected returns can actually decrease with beta. We confirm our theory using a measure of disagreement about stock market earnings.

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supporting

confidence: 92%

“…In Panel B, we use

β \times value

Section: Empirical Analysismentioning

confidence: 99%

Speculative Betas

Hong¹,

Sraer

2016

The Journal of Finance

248

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“…According to Park (2005), the dispersion in expectations among market analysts has predictive power for future stock returns: higher dispersion predicts lower stock returns. Similarly, Yu (2011) uses the Institutional Brokers' Estimate System (I/B/E/S) database on analyst forecast and finds that the ex post market return is negatively related to the bottom-up disagreement.…”

Section: Disagreement and Risk-premiamentioning

confidence: 99%

Disagreement and asset prices

Carlin

Longstaff

Matoba

2014

Journal of Financial Economics

179

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How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns, return volatility, and trading volume in the mortgagebacked security market. We find that increased disagreement is associated with higher expected returns, higher return volatility, and larger trading volume. These results imply that there is a positive risk premium for disagreement in asset prices. We also show that volatility in and of itself does not lead to higher trading volume. Rather, it is only when disagreement arises in the market that higher uncertainty is associated with more trading. Finally, we are able to distinguish empirically between two competing hypotheses regarding how information in markets gets incorporated into asset prices. We find that sophisticated investors appear to update their beliefs through a rational expectations mechanism when disagreement arises. * We would like to thank

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“…Both are noisy measures of divergence in investors' opinions. Although analysts' forecast dispersion appears to be a better proxy for divergence in investors' opinions (Basak, 2005;Diether et al, 2002;Moeller, Schlingemann, & Stulz, 2007;Verardo, 2009;Yu, 2011), there are still two potential problems with this measure. First, not all investors will make a decision according to the forecasts of analysts; analysts' forecast dispersion merely represents the differences in beliefs between professional investors.…”

Section: Measurement Of Divergence In Investor's Opinionsmentioning

confidence: 99%