Knowledge that Transforms

This paper directly tests the hypothesis that upstairs intermediation lowers adverse selection cost. We find upstairs market makers effectively screen out information‐motivated orders and execute large liquidity‐motivated orders at a lower cost than the downstairs market. Upstairs markets do not cannibalize or free ride off the downstairs market. In one‐quarter of the trades, the upstairs market offers price improvement over the limit orders available in the consolidated limit order book. Trades are more likely to be executed upstairs at times when liquidity is lower in the downstairs market.

The purpose of this article is to explain the spread between rates on corporate and government bonds. We show that expected default accounts for a surprisingly small fraction of the premium in corporate rates over treasuries. While state taxes explain a substantial portion of the difference, the remaining portion of the spread is closely related to the factors that we commonly accept as explaining risk premiums for common stocks. Both our time series and cross‐sectional tests support the existence of a risk premium on corporate bonds.

We study asset allocation when the conditional moments of returns are partly predictable. Rather than first model the return distribution and subsequently characterize the portfolio choice, we determine directly the dependence of the optimal portfolio weights on the predictive variables. We combine the predictors into a single index that best captures time variations in investment opportunities. This index helps investors determine which economic variables they should track and, more importantly, in what combination. We consider investors with both expected utility (mean variance and CRRA) and nonexpected utility (ambiguity aversion and prospect theory) objectives and characterize their market timing, horizon effects, and hedging demands.

This paper examines the effects of uncertainty about the stock return predictability on optimal dynamic portfolio choice in a continuous time setting for a longhorizon investor. Uncertainty about the predictive relation affects the optimal portfolio choice through dynamic learning, and leads to a state‐dependent relation between the optimal portfolio choice and the investment horizon. There is substantial market timing in the optimal hedge demands, which is caused by stochastic covariance between stock return and dynamic learning. The opportunity cost of ignoring predictability or learning is found to be quite substantial.

A long return history is useftil in estimating the current equity premium even if the historical distribution has experienced structural breaks. The long series helps not only if the timing of breaks is uncertain but also if one believes that large shifts in the premium are unlikely or that the premium is associated, in part, with volatility. Our framework incorporates these features along with a belief that prices are likely to move opposite to contemporaneous shifts in the premium. The estimated premium since 1834 fluctuates between four and six percent and exhibits its sharpest drop in the last decade.

This paper presents the model of a relationship between a venture capitalist and an entrepreneur engaged in the formation of a new firm. I assume that the entrepreneur derives private nonpecuniary benefits from having some control over the firm. I show that to separate the entrepreneur's value of control from the firm's expected payoff, the venture capitalist demands disproportionately higher control rights than the size of his equity investment. The entrepreneur is compensated for a greater loss of control through better terms of financing, ability to extract higher rents from asymmetric information, and improved risk sharing.

Existing studies of mutual fund market timing analyze monthly returns and find little evidence of timing ability. We show that daily tests are more powerful and that mutual funds exhibit significant timing ability more often in daily tests than in monthly tests. We construct a set of synthetic fund returns in order to control for spurious results. The daily timing coefficients of the majority of funds are significantly different from their synthetic counterparts. These results suggest that mutual funds may possess more timing ability than previously documented.

This paper analyzes mutual‐fund performance from an investor's perspective. We study the portfolio‐choice problem for a mean‐variance investor choosing among a risk‐free asset, index funds, and actively managed mutual funds. To solve this problem, we employ a Bayesian method of performance evaluation; a key innovation in our approach is the development of a flexible set of prior beliefs about managerial skill. We then apply our methodology to a sample of 1,437 mutual funds. We find that some extremely skeptical prior beliefs nevertheless lead to economically significant allocations to active managers.