Standard theory predicts a positive relationship between risk and return, yet recent data show that housing returns vary positively with risk in some markets but negatively in others. This paper rationalizes these cross-market differences in the risk-return relationship for housing, and in so doing, explains the puzzling negative relationship. The paper shows that when the current house provides a hedge against the risk associated with the future housing consumption, households are willing to accept a lower return to compensate for risk, thus weakening the positive risk-return relationship. Further, in markets with less elastic housing supply and a growing population, hedging incentives can be sufficiently strong to make the relationship negative. The empirical analysis confirms these predictions, suggesting that hedging incentives, housing supply, and urban growth are indeed central to understanding the risk-return relationship for housing.

[Standard theory predicts a positive relationship between risk and return, yet recent data show that housing returns vary positively with risk in some markets but negatively in others. This paper rationalizes these cross-market differences in the risk-return relationship for housing, and in so doing, explains the puzzling negative relationship. The paper shows that when the current house provides a hedge against the risk associated with the future housing consumption, households are willing to accept a lower return to compensate for risk, thus weakening the positive risk-return relationship. Further, in markets with less elastic housing supply and a growing population, hedging incentives can be sufficiently strong to make the relationship negative. The empirical analysis confirms these predictions, suggesting that hedging incentives, housing supply, and urban growth are indeed central to understanding the risk-return relationship for housing.]

This article examines how price risk affects housing demand. It identifies two relevant channels: a financial risk effect that reduces demand, and a hedging effect that increases demand since current homes may hedge future housing costs. The latter dominates when hedging incentives are strong, namely when the likelihood of moving up the housing ladder is high and the tendency to move across markets is low. For households with weak hedging incentives, the article finds negative effects of price risk on the timing and size of home purchases, but positive effects for households with strong hedging incentives. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

[This article examines how price risk affects housing demand. It identifies two relevant channels: a financial risk effect that reduces demand, and a hedging effect that increases demand since current homes may hedge future housing costs. The latter dominates when hedging incentives are strong, namely when the likelihood of moving up the housing ladder is high and the tendency to move across markets is low. For households with weak hedging incentives, the article finds negative effects of price risk on the timing and size of home purchases, but positive effects for households with strong hedging incentives.]

[Recent winners have temporarily higher loadings than recent losers on the growth rate of industrial production. The loading spread derives mostly from the positive loadings of winners. The growth rate of industrial production is a priced risk factor in standard asset pricing tests. In many specifications, this macroeconomic risk factor explains more than half of momentum profits. We conclude that risk plays an important role in driving momentum profits.]

An empirical q-factor model consisting of the market factor, a size factor, an investment factor, and a profitability factor largely summarizes the cross section of average stock returns. A comprehensive examination of nearly 80 anomalies reveals that about one-half of the anomalies are insignificant in the broad cross section. More importantly, with a few exceptions, the q-factor model's performance is at least comparable to, and in many cases better than that of the Fama-French (1993) 3-factor model and the Carhart (1997) 4-factor model in capturing the remaining significant anomalies.

[A new methodology for equity valuation arises from the perspective of managers' supply of capital assets. Under q-theory, managers optimally adjust the supply of assets to changes in their market value. The first-order condition of investment then provides a valuation equation that infers asset prices from managers' costs of supplying the assets. This equation fits well the Tobin's q levels across many testing assets, including portfolios formed on q. With current investment-to-capital as the only input, the supply approach does not require cash flow forecasts or discount rate estimates, both of which are notoriously difficult to obtain in practice.]

[We investigate whether skilled hedge fund managers are more likely to pursue unique investment strategies that result in superior performance. We propose a measure of the distinctiveness of a fund's investment strategy based on historical fund return data. We call the measure the "Strategy Distinctiveness Index" (SDI). We document substantial cross-sectional variations as well as strong persistence in SDI. Our main result indicates that, on average, a higher SDI is associated with better subsequent performance. After adjusting for risk, funds in the highest SDI quintile outperform funds in the lowest quintile by 3.5% in the subsequent year.]

[Despite extensive disclosure requirements, mutual fund investors do not observe all actions of fund managers. We estimate the impact of unobserved actions on fund returns using the return gap--the difference between the reported fund return and the return on a portfolio that invests in the previously disclosed fund holdings. We document that unobserved actions of some funds persistently create value, while such actions of other funds destroy value. Our main result shows that the return gap predicts fund performance.]

[We construct firm-specific measures of expected equity returns using corporate bond yields, and replace standard ex post average returns with our expected-return measures in asset pricing tests. We find that the market beta is significantly priced in the cross section of expected returns. The expected size and value premiums are positive and countercyclical, but there is no evidence of positive expected momentum profits.]