A measure of market share mobility is derived from an hypothesized model of underlying market share behavior. Explicit linkage of the two exposes some properties of the index and its implied behavioral assumptions and informs empirical work. Results for the U.K. newspaper industry offer some evidence of stable, overall mobility relationships, but the absence of long-run equilibrium relationships at the market share level suggests these may be misleading. Further work at the latter level seems likelier to advance understanding of both market share behavior and mobility, while care is required when supplementing concentration data with mobility statistics in antitrust cases.

Two classes of models for pricing interest rate contingent claims are compared. The first contains standard single factor models, while the second includes stochastic volatility variants of these. It is demonstrated that incorporating stochastic volatility significantly improves the ability of the models to fit movements in short term interest rates. The fundamental reason for this is that standard univariate models do not generate enough conditional heteroskedasticity. The models are also compared on the basis of bond option prices when each is constrained to fit a particular yield curve. The results suggest that stochastic volatility models will typically produce lower option values than corresponding single factor models, particularly for near-the-money to deep-out-of-the-money options. This is because estimated historical volatilities are significantly lower under stochastic volatility. When single factor models are constrained to the same level of volatility as implied by stochastic volatility models, in most cases the pricing differences become minor.

This paper introduces an improved procedure for estimating capital asset price indexes. We jointly estimate conventional hedonic and repeat sales models via maximum-likelihood procedures, thereby taking advantage of the unique features of the individual models and using all the data that are available. Our model captures depreciation within the repeat sales model and accounts for serial correlation in hedonic data. The improvement in precision obtained by estimating the joint model is illustrated by smaller standard errors and narrower interval estimates for the resulting price indexes. We also carry out a simulation experiment that shows estimation errors significantly smaller using the joint estimation technique than either of the individual models or the GLS estimator of Case and Quigley (1991).

This paper examines the benefits from communications made at corporate presentations to securities analysts. We examine whether firms benefit by increasing analyst following or by correcting mispricing, and whether analysts gain by acquiring information that improves the frequency or quality of their forecasts. The results show significant increases in analyst following, and significantly positive abnormal returns on the presentation date, with larger reactions observed for underpriced securities. Finally, although we find an increase in forecasting activity following the presentations, we find no evidence that analysts' post-presentation forecasts are less disperse, more accurate or less biased than their pre-presentation forecasts.

Estimates of the cost of equity for industries are imprecise. Standard errors of more than 3.0% per year are typical for both the CAPM and the three-factor model of Fama and French (1993). These large standard errors are the result of(i) uncertainty about true factor risk premiums and (ii) imp ecise estimates of the loadings of industries on the risk factors. Estimates of the cost of equity for firms and projects are surely even less precise.

This paper uses a unique financial instrument in the U.S. Treasury market to study the price behavior of the put option embedded in the November 2009 14 callable U.S. Treasury bond. We find that, beginning in August 1993, the estimated option value was persistently negative on nearly every day for the ensuing eight months. We show that the anomalous pricing behavior arose because the underlying callable bond became the cheapest to deliver issue against U.S. Treasury bond futures contracts. Hence, this paper provides direct evidence that derivative assets can significantly distort pricing in the primary asset market.

This paper develops a model of the optimal investment strategy for a firm confronted with a sequence of technological innovations. We incorporate many of the most important characteristics of real-world technology markets. For example, we permit innovations to be stochastic in their arrival times and their profitability. We also incorporate learning so that firms adopting current innovations become better able to benefit from future innovations. The model yields four distinct investment strategies. The model is then used to predict actual firm policy. These implications are discussed and compared with observed firm behavior.

This article compares the predictions of finite-shareholder models of conditional and unconditional takeover offers with the outcomes of laboratory experiments. In addition to differentiating between types of offers, the experimental designs span small and large firms as well as different levels of offer premiums. It is found that in unconditional offers to large groups of subjects (28–40), the symmetric Nash equilibrium predicts observed tendering frequencies quite accurately. For other experimental designs, the results are mixed. The analysis of shareholder tendering strategies from the experiment yields insights into (i) the effects of takeover offer designs, (ii) the appropriateness of finite-shareholder models for research, and (iii) the costs of free riding when shareholders are nonatomistic.

[A simple time-series market microstructure model is constructed within which existing models of spread components are reconciled. We show that existing models fail to decompose the spread into all its components. Two alternative extensions of the simple model are developed to identify all the components of the spread and to estimate the spread at which trades occur. The empirical results support the presence of a large order processing component and smaller, albeit significant, adverse selection and inventory components. The spread components differ significantly according to trade size and are also sensitive to assumptions about the relation between orders and trades.]

A simple time-series market microstructure model is constructed within which existing models of spread components are reconciled. We show that existing models fail to decompose the spread into all its components. Two alternative extensions of the simple model are developed to identify all the components of the spread and to estimate the spread at which trades occur. The empirical results support the presence of a large order processing component and smaller, albeit significant, adverse selection and inventory components. The spread components differ significantly according to trade size and are also sensitive to assumptions about the relation between orders and trades.