Differences of Opinion and the Cross Section of Stock Returns

Diether, Karl B.; Malloy, Christopher J.; Scherbina, Anna

doi:10.1111/0022-1082.00490

Cited by 1,845 publications

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“…Anderson, Ghysels, and Juergens (2005) took the disagreement of forecasters about the future values of variables as an 2 indication of heterogeneity in the beliefs of agents and showed how the disagreement is priced in a heterogeneous agents model with micro-foundations. Diether, Malloy, and Scherbina (2002) rationalize their findings that higher dispersion stocks have lower future returns with arguments from the short-sale constraints literature, in particular Miller (1977). They argue dispersion proxies for differences of opinions among traders where only the most optimistic opinions are reflected, thereby driving up current prices.…”

supporting

confidence: 57%

“…They argue dispersion proxies for differences of opinions among traders where only the most optimistic opinions are reflected, thereby driving up current prices. Johnson (2004) offers an alternative explanation to the findings of Diether, Malloy, and Scherbina (2002). In his model, levered firms may be able to reduce the cost of capital by increasing idiosyncratic risk of earnings volatility and subsequently the dispersion of earnings forecasts.…”

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The impact of risk and uncertainty on expected returns☆

Anderson

Ghysels

Juergens

2009

Journal of Financial Economics

371

194

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We study asset pricing when agents face risk and uncertainty and empirically demonstrate that uncertainty has a substantial effect on asset prices. We measure risk with past volatility and uncertainty with the degree of disagreement of professional forecasters, attributing different weights to each forecaster. We run regressions representing the typical risk-return trade-off and augment these regressions with a measure of uncertainty. We find stronger empirical evidence for a uncertainty-return trade-off than for the traditional risk-return trade-off. We investigate the performance of a two-factor model with risk and uncertainty in the cross-section. * Evan Anderson: Northern Illinois University, Department of Economics, Zulauf 515, DeKalb, IL 60115, phone: (630) 450-0533, email: ewanderson@niu.edu. Eric Ghysels: Department of Finance, Kenan-Flagler Business School, and Department of Economics, University of North Carolina -Chapel Hill, Chapel Hill, NC 27599-3305, phone: (919) 966-5325, email: eghysels@unc.edu. Jennifer L. Juergens: Arizona State University, W.P. Carey School of Business, Department of Finance, Tempe, AZ 85287, phone: (480) 965-3131, email: Jennifer.Juergens@asu.edu. We would like to thank Lars Hansen, Peter Hansen, Chris Jones, Leonid Kogan, Jianjun Miao, Athanasios Orphanides, Simon Potter, Eric Renault, Robert Rich, Tom Sargent and Raman Uppal for very helpful comments. This paper was presented at Northern Illinois University, SAMSI Opening Workshop, 2006 EFA meetings, 2006 Winter Meetings of the Econometric Society, the SAMSI Conference on Model Uncertainty, 2006 NBER Summer Institute Asset Pricing Workshop, 2007 AFA meetings, UC Santa Cruz, University of Illinois at Urbana-Champaign, and University of Wisconsin-Madison. Early versions of the ideas in this paper were also presented at the Board of Governors, the FDIC, the Federal Reserve Bank of New York, Simon Fraser University and Stanford University. We thank seminar participants for helpful comments.Electronic copy of this paper is available at: http://ssrn.com/abstract=890621One of the most studied theoretical relationships in empirical finance is that the expected excess return of the market over a risk-free bond should vary positively and proportionally to the volatility of the market return. This risk-return trade-off is so fundamental that it could well be described as the "first fundamental law of finance." Merton (1973) derived this theoretical relationship in a continuous time model in which all agents have power preferences and hedging concerns are negligible, and it is sometimes referred to as Merton's ICAPM or simply the ICAPM.The empirical evidence for a risk-return trade-off is mixed. Many studies have run versions of the following regression:where r et+1 is the excess return of the market over a risk-free bond, γ is a risk aversion coefficient, and V t is (market) risk. 1 The goal has been to find a significantly positive γ coefficient that captures the trade-off between risk and return. Baillie and DeGennaro (199...

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The impact of risk and uncertainty on expected returns☆

Anderson

Ghysels

Juergens

2009

Journal of Financial Economics

371

194

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“…See Brennan and Subrahmanyam (1995); Aboody and Lev (2000); and Wu (2004). Diether, Malloy, and Scherbina (2002) show that the dispersion of the earnings forecasts by analysts is a measure of asymmetric information. We also find that companies issuing structured PIPEs have a significantly higher dispersion of earnings forecasts than companies issuing traditional PIPEs.…”

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confidence: 99%

Hedge Funds as Investors of Last Resort?

Brophy¹,

Ouimet²,

Sialm³

2006

Rev. Financ. Stud.

190

163

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Hedge funds have become important investors in public companies raising equity privately. Hedge funds tend to finance companies that have poor fundamentals and pronounced information asymmetries. To compensate for these shortcomings, hedge funds protect themselves by requiring substantial discounts, negotiating repricing rights, and entering into short positions of the underlying stocks. We find that companies that obtain financing from hedge funds significantly underperform companies that obtain financing from other investors during the following two years. We argue that hedge funds are investors of last resort and provide funding for companies that are otherwise constrained from raising equity capital. (JEL G14, G23, G32)Hedge funds have recently become an important source of funding for public companies raising equity privately. Financing young companies with severe information asymmetries is an important investment strategy for some hedge funds. Since 1995, hedge funds have participated in more than 50% of the private placements of equity securities and have contributed about one-quarter of the capital raised in such equity issuances, a total investment that has exceeded the contributions of other investor classes. This paper sheds light on the role of hedge funds in such private placements.In perfect financial markets it should be irrelevant whether a firm obtains funding from hedge funds or from other investors. To investigate whether the identity of the investors matters, we use a unique dataset that

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“…More recently, several authors have relied on proxies for short-sale constraints or demand (Chen, Hong, and Stein (2002) -breadth of ownership, Diether, Malloy, and Scherbina (2002), Nagel (2004) -institutional ownership, Lamont (2004) -firm's actions to impede short-selling), and even the actual cost of borrowing stock (D'Avolio (2002), Cohen, Diether, and Malloy (2005),…”

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Can Short-sellers Predict Returns? Daily Evidence

Diether¹,

Lee

Werner

2005

SSRN Journal

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We test whether short-sellers in Nasdaq-listed stocks are able to predict future returns based on new SEC-mandated data for the first six months of 2005. There is a tremendous amount of short-term trading strategies involving short-sales during the sample: Short-sales represent 27 percent of Nasdaq share volume while monthly short-interest is about 3.1 percent of shares outstanding (5.5 days to cover). Short-sellers are on average contrarian -they sell short following positive returns. Increasing short-sales predict future negative returns, and the predictive power comes primarily from small trades. A trading strategy based on daily short-selling activity generates significant returns, but incurs costs large enough to wipe out any profits. * All three authors are at the Fisher College of Business, The Ohio State University. We are grateful for comments from Leslie Boni, Rudi Fahlenbrach, Frank Hatheway, David Musto, René Stulz, and seminar participants at the Ohio State University, the NBER Market Microstructure Group, and the University of Georgia. We thank Nasdaq Economic Research for data. All errors are our own.Many market observers accuse short-sellers of destabilizing markets by selling stocks (they do not even own) when prices are already trending downward, exacerbating the negative momentum. Issuers and journalists often characterize short-sellers' activities as immoral, unethical and downright un-American. 1 Academics and traders instead argue that short-sellers stabilize security prices by selling stocks when prices exceed fundamental values, thus helping correct market overreaction. Short-term over-reaction could be caused by impediments to short-selling, as high costs of executing short-sales may result in stock prices reflecting the opinions of optimistic investors only (Miller (1977)). 2 Some researchers have even argued that costly short-selling was one of the culprits behind the stock market bubble of the late 1990s (e.g., Ofek and Richardson (2003)).In this study, we use the SEC mandated tick-by-tick short-sale data for 2,815 Nasdaq-listed stocks for the period January 3, 2005 to June 30, 2005 to test whether short-sellers are contrarian or momentum traders and whether they are able to predict future returns. We test these hypotheses by studying the link between short-selling activity and future returns and how short-sellers react to past returns on a daily level.The literature on short-selling is growing rapidly. Most previous studies have used monthly stock-specific short interest data (e.g., Figlewski and Webb (1993), Figlewski (1981), Dechow, Hutton, Meulbroek, and Sloan (2001), Desai et al (2002), Asquith, Pathak, and Ritter (2005), and Singal and Xu (2005)). There are three important problems with using monthly short interest data.First, the monthly reporting frequency does not permit researchers to study short-term trading strategies. Second, monthly short interest data does not distinguish between the short interest of dealers (who are exempt from short-sale restrictions) from th...

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Differences of Opinion and the Cross Section of Stock Returns

Cited by 1,845 publications

References 47 publications

The impact of risk and uncertainty on expected returns☆

The impact of risk and uncertainty on expected returns☆

Hedge Funds as Investors of Last Resort?

Can Short-sellers Predict Returns? Daily Evidence

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