Knowledge that Transforms

To test the major prediction of a signalling hypothesis-that the market price is m onotonic in the signal-the price response to the signal must be measu red. Since a signal is an outcome of a rational decision rule of the signaller, the market can infer the true type of the signaller from t he signal. This necessitates estimation of the price response to the signal, conditional on the rational decision rule. Thus, the empirica l models (e.g., event studies in corporate finance) that estimate the market price responses to signals without conditioning on the ration al decision rules are misspecified if viewed as tests of the predicti on of a signalling hypothesis. This paper builds a generalized econom etric model with two possible discrete signals, derives the rational decision rules, presents a simple estimator of the price response to a signal, and illustrates its use in testing a recently expounded hyp othesis that firms signal their true value by forcing or not forcing an outstanding convertible bond.

In this paper, we extend earlier work on hedging models so that uncertainty about both deposit supply and loan demand is incorporated as well as random rates of return on loans and CD's. Our model suggests that the optimal forward position is the sum of three ratios that should be estimated simultaneously. Using bank‐specific data, the optimal hedge ratios are estimated in both the pre‐deregulation and deregulation subperiods. Our results show that previous studies of bank hedging with interest rate futures have greatly overstated (a) the volume of short futures positions that banks should take and (b) the degree of homogeneity of optimal hedge ratios across the banking system. Similarly, deregulation has not uniformly affected the interest rate risk borne by different institutions.

Lawrence Harris, Predicting Contemporary Volume with Historic Volume at Differential Price Levels: Evidence Supporting the Disposition Effect: Discussion, The Journal of Finance, Vol. 43, No. 3, Papers and Proceedings of the Forty-Seventh Annual Meeting of the American Finance Association, Chicago, Illinois, December 28-30, 1987 (Jul., 1988), pp. 698-699

The authors investigate the term structure of interest rates when the underlying state variables and production technologies follow the jump-diffusion processes. Even in some cases where the traditional expectations theory about the term structure is consistent with general equilibrium under diffusion processes, the traditional theory is not consistent under jump-diffusion processes. It is shown that bond prices are strictly higher under jump risks than otherwise and that consumers with logarithmic utility functions will develop hedge portfolios in the presence of jump diffusion.

Commonly, equilibrium security prices are modeled by continuous‐state stochastic processes, while observed prices are rounded into discrete units. This paper models the rounding mechanism and examines the probabilistic structure of the resultant rounded process. We provide accurate and simple estimates of the inflation in estimated variance and kurtosis induced by ignoring rounding. In particular, the maximum‐likelihood estimate of security price volatility using rounded prices is developed, and a simulation analysis is performed to examine the small‐sample properties of this estimator. For many practical applications, a simple correction for rounding becomes available.

This study examines effects of pertinent features of hospital capital payment policies on hospital capital structure decisions in a one-period stochastic, value-maximization model. Separate models are developed for for-profit and not-for-profit hospitals. Hospital debt-to-assets ratios are analyzed empirically using a cross-section of data from the American Hospital Association. Although the effect on capital structure of hospital reliance on cost-based reimbursement cannot be signed theoretically, in both for-profit and not-for-profit cases, a higher cost-based share leads to higher leverage. Factors associated with high bankruptcy risk (e.g., earnings volatility) cause hospitals to take on less debt.