November 3, 2012
Robin Greenwood and Andrei Shleifer, Harvard University and NBER
Greenwood and Shleifer analyze time-series of investor expectations of future stock market returns from five data sources between 1963 and 2011. All five measures of expectations are highly positively correlated with each other, as well as with past stock returns and with the level of the stock market. However, investor expectations are strongly negatively correlated with model-based expected returns. The authors reconcile the evidence by calibrating a simple behavioral model, in which fundamental traders require a premium to accommodate expectations shocks from extrapolative traders, but markets are not efficient.
Milo Bianchi, University Paris-Dauphine, and Philippe Jehiel, PSE and UCL
Bianchi and Jehiel consider a competitive financial market in which companies engage in strategic financial reporting, knowing that investors pay attention only to a finite number of aspects of firms' reports, and then extrapolate from their sample. They investigate the extent to which stock prices differ from fundamental values, assuming that companies must report all their activities but otherwise are free to disaggregate their reports as they wish. They show that no matter how many aspects investors are able to consider, a monopolist can induce a price of its stock bounded away from the fundamental. Besides, competition between companies may exacerbate stock mispricing.
Lauren Cohen, Harvard University and NBER, and Huaizhi Chen and Dong Lou, London School of Economics
Cohen, Chen, and Lou explore a new mechanism through which investors take correlated shortcuts. Specifically, the authors exploit a regulatory provision that governs firm classification into industries: a firm's industry classification will be determined by the segment that has the majority of sales. They find that investors rely too much on this primary-industry classification. Firms that are just above the industry classification cutoff have significantly higher betas and more sector mutual fund holdings and analyst coverage than nearly identical firms just below the cutoff. The authors then show that managers undertake specific actions so as to take advantage of the shortcuts. Firms around the discontinuity cut-off of 50 percent sales in both top segments are significantly more likely to have just over 50 percent of sales from the "favorable" industry. These firms barely over the cut-off have significantly lower profit margins and inventory growth rates than other firms, consistent with their slashing prices to achieve sales targets. Identical firms (same industries) but with compositions not near the cut-offs exhibit none of these behaviors. Furthermore, these same firms do not exhibit any different behavior in any other aspect of their business (for example, CapEx or R&D), which suggests that there is not a firm-wide shifting of focus. Finally, firms garner tangible benefits from switching into favorable industries, such as engaging in significantly more SEOs and M&A transactions.
Stefano Giglio, University of Chicago and NBER, and Kelly Shue, University of Chicago
As illustrated in the tale of "the dog that did not bark," the absence of news and the passage of time often contain information. Giglio and Shue test whether markets fully incorporate the information content of "no news" using the empirical context of mergers. Following the initial merger announcement, uncertainty relating to merger completion can take many months to be resolved. The authors find that the passage of time during this interim period is informative about the probability of merger completion. For example, once six months have passed after announcement, the probability that the merger will ever complete falls rapidly. The authors also show that the variation in hazard rates of completion during the 12 months after announcement strongly predicts returns. A strategy that invests in deals during event windows when completion hazard rates are high outperforms a strategy that invests in deals when completion hazard rates are low by 100 basis points per month. This is consistent with a limited attention model in which markets underreact to the information content of the passage of time. The authors also show that their findings cannot be explained by event time variation in systematic risk, downside risk, idiosyncratic risk, or other frictions. Finally, they show that the mispricing is concentrated in smaller and less liquid deals, suggesting that trading frictions limit arbitrage by more sophisticated investors.
Markus Brunnermeier, Princeton University and NBER; Alp Simsek, Harvard University and NBER; and Wei Xiong, Princeton University and NBER
Brunnermeier, Simsek, and Xiong propose a welfare criterion for economies in which agents have heterogeneously distorted beliefs. Instead of taking a stand on whose belief is correct, their criterion asserts an allocation to be belief-neutral inefficient if it is inefficient under any convex combination of agents' beliefs. While this criterion gives an incomplete ranking of social allocations, it can identify negative-sum speculation in a broad range of prominent models with distorted beliefs.
Harrison Hong, Princeton University and NBER, and David Sraer, Princeton University and NBER
Hong and Sraer provide a model for why high-beta assets are more prone to speculative overpricing than low-beta assets. When investors disagree about the common factor of cashflows, high-beta assets are more sensitive to this macro-disagreement and they experience a greater divergence of opinion about their payoffs. Short-sales constraints for some investors, such as retail mutual funds, result in high-beta assets being over priced. When aggregate disagreement is low, expected returns increase with beta because of risk sharing; when it is large, expected return initially increases but then decreases with beta. High-beta assets experience greater shorting from unconstrained arbitrageurs and more share turnover. Using measures of disagreement about stock earnings and economic uncertainty, the authors verify these predictions. A calibration exercise yields reasonable parameter values.