We find that the sign of the correlation between institutional ownership and volatility depends on the firm’s dividend policy: institutional ownership is negatively (positively) related to volatility among non-dividend (dividend) paying stocks. The empirical results are consistent with an interaction between institutional preference for low volatility and the tendency of higher levels of institutional ownership to increase volatility through their trading behavior. This result is robust to many control variables and possible endogeneity concerns. Supporting our conjecture that institutions herd on dividend signals we find that the correlation between turnover and institutional ownership is higher for dividend paying stocks, and that the positive correlation between turnover and institutional ownership is higher on dividend declaration days. Finally, we also find that the level of institutional ownership drops following an increase in volatility for both dividend payers and non-payers, and that volatility rises following increased institutional ownership for dividend paying stocks.

We establish general conditions under which younger investors should invest a larger proportion of their wealth in risky assets than older ones. In the finite horizon dynamic setting, we show that such phenomenon, known as ‘‘time diversification,” can occur in the presence of human wealth, guaranteed consumption, or mean-reverting stock returns. We formalize two alternative notions of time diversification commonly confounded in the literature. Analytic solutions are provided for both time-series and cross-sectional forms of time diversification. To our best knowledge, this paper is the first to solve in closed-form the hedging demand for a CARA investor with inter-temporal consumption and a finite horizon, facing mean-reverting expected returns. Our results indicate that horizon can have a significant effect on the portfolio demand of a CARA investor due to inter-temporal hedging.

This paper provides a method for testing for regime differences when regimes are long-lasting. Standard testing procedures are generally inappropriate because regime persistence causes a spurious regression problem – a problem that has led to incorrect inference in a broad range of studies involving regimes representing political, business, and seasonal cycles. The paper outlines analytically how standard estimators can be adjusted for regime dummy variable persistence. While the adjustments are helpful asymptotically, spurious regression remains a problem in small samples and must be addressed using simulation or bootstrap procedures. We provide a simulation procedure for testing hypotheses in situations where an independent variable in a time-series regression is a persistent regime dummy variable. We also develop a procedure for testing hypotheses in situations where the dependent variable has similar properties.