The concept of multifactor portfolio efficiency plays a role in Merton's intertemporal CAPM (the ICAPM), like that of mean-variance efficiency in the Sharpe-Lintner CAPM. In the CAPM, the relation between the expected return on a security and its risk is just the condition on security weights that holds in any mean-variance-efficient portfolio, applied to the market portfolio M. The risk-return relation of the ICAPM is likewise just the application to M of the condition on security weights that produces ICAPM multifactor-efficient portfolios. The main testable implication of the CAPM is that equilibrium security prices require that M is mean-variance-efficient. The main testable implication of the ICAPM is that securities must be priced so that M is multifactor-efficient. As in the CAPM, building the ICAPM on multifactor efficiency exposes its simplicity and allows easy economic insights.

This paper investigates the relation between the form of compensation and the manager's decision horizon. It finds that while all-cash contracts induce managers to underinvest in the long term, all-stock contracts induce overinvestment in the long term. It shows that compensation contracts consisting of both cash and restricted stock can produce efficient investment, thereby providing a rationale for the existence of both cash and stock incentive schemes in executive compensation packages. This explains why the adoption of either type of incentive scheme results in a positive stock price reaction. In addition, the paper derives the following testable hypotheses: i) the proportion of the stock compensation is decreasing in the precision of the manager's ability and increasing in the precision of the firm's cash flows; ii) firms compensate their managers with proportionately more stock in profitable years and proportionately more cash in leaner years; and iii) the greater the growth opportunities, the higher the proportion of stock compensation.

In this study, we examine the impact of security analysts' monitoring and marketing functions on firms' market value. We postulate that security analysts' monitoring of corporate performance helps motivate managers, thus reducing the agency costs associated with the separation of ownership and control. We also argue that the information intermediary function provided by security analysts helps expand the breadth of investor cognizance. Consistent with these conjectures, this study finds that analyst following exerts a significant and positive impact on firms' market value. We also find evidence that security analysts have a stronger incentive to follow stocks of high quality companies, since such stocks arc easier to market. Hence, the security analysis activities appear to be determined, in part, by the marketing considerations of brokerage companies.

We model and examine the impact of information releases on market uncertainty as measured by the implied standard deviation (ISD) from option markets. Distinguishing between scheduled and unscheduled announcements, we hypothesize that since the timing, although not the content, of scheduled announcements is known a priori, the pre-release ISD will impound the anticipated impact of important releases on price volatility and that the ISD will normally decline post-release as this uncertainty is resolved. Conversely, we hypothesize that the unexpected high volatility caused by major unscheduled releases will cause market participants to adjust upward their estimates of likely volatility over the remaining life of the option resulting in an increase in the ISD. Our evidence supports both hypotheses. The ISDs that we consider are from the T-Bond, Eurodollar, and Deutschemark options markets. We examine scheduled macroeconomic news releases such as the employment report and the PPI. We also find that the observed tendency for the ISD to fall on Fridays and rise on Mondays is due to the weekday pattern of scheduled news releases.

Previous tests of stock index arbitrage models have rejected the no-arbitrage constraint imposed by these models. This paper provides a detailed analysis of actual S&P 500 arbitrage trades and directly relates these trades to the predictions of index arbitrage models. An analysis of arbitrage trades suggests that i) short-sale rules are unlikely to affect the cash-futures mispricing, ii) the opportunity cost of arbitrage funds exceeds the Treasury bill rate, and iii) the average price discrepancy captured by arbitrage trades is small. Tests of the models provide some support for a version of the arbitrage model that incorporates an early liquidation option. The ability of these models to explain arbitrage trades, however, is surprisingly low.

Existing research finds little evidence of overinvestment by successfully acquired targets. This paper shows how samples drawn from completed takeovers are biased against finding overinvestment and documents evidence of overinvestment in targets that use a highly leveraged transaction to avoid a takeover. The evidence also suggests that these restructurings create value by mitigating the targets' overinvestment problems.

This paper considers the pricing of multiclass commercial mortgage-backed securities. A contingent-claims pricing methodology that overcomes state variable dimensionality problems is developed to examine mortgage pools with many distinct underlying assets and whose loan cash flow values are subject to interest rate uncertainty. Security structure and the correlation structure of collateralizing assets within a pool are found to be important determinants of tranche price and required yield spread. By disentangling default loss risk from default-related call risk, we show it is possible that mezzanine investment classes may require lower yield spreads than higher priority investment classes. Of particular interest is the finding that reduced cash flow volatility obtained through pool diversification may actually decrease the value of the first-loss (junior) tranche. When examining the relationship of pool size and tranche value, we find that five to 10 distinct mortgages are required to realize most of the effects of asset diversification.

This paper derives and numerically simulates maximum likelihood estimators for the drift in several important diffusion price models. The time series convergence properties of these estimators are compared to those of standard estimators including the geometric and arithmetic means. Merton (1980) demonstrated that it is difficult to efficiently estimate the drift in a log-normal diffusion model. We qualify and strengthen his result by noting that his estimator is the maximum likelihood estimator and by applying our simulation results. However, we also demonstrate that it is possible to efficiently estimate the drift in other useful diffusion price models. In particular, by asking just how much time is needed in order for the maximum likelihood estimators of the drift in different diffusion processes to converge, these results qualify and quantify Black's (1993) statement that “we need such a long period to estimate the average that we have little hope of seeing changes in expected return."