Knowledge that Transforms

This paper re‐examines the effects of nominal contracts on the relationship between unanticipated inflation and an individual stock's rate of return. This study differs in three main ways from previous research. First, announced inflation data are used to examine the effects of unanticipated inflation. Second, a different specification is used to obtain more efficient estimates. Third, additional nominal contracts are considered. The empirical results indicate that time‐varying firm characteristics related to inflation predominately determine the effect of unanticipated inflation on a stock's rate of return. A firm's debt‐equity ratio appears to be particularly important in determining the response.

In this paper, we focus on the optimal demand for futures contracts by an investor with a logarithmic utility function who attempts to hedge a nontraded cash position. When the analysis is conducted in the “cash‐commodity‐price” space, we show that the value function associated with the Bernoulli investor program is not additively separable, thus suggesting that this investor hedges against shifts in the opportunity set as represented by the commodity price. By establishing the equivalence between the cash formulation of the problem and the wealth formulation, we are able to analyze the problem in the “wealth‐commodity‐price” space. In this space, we show the additive separability of the value function when the futures settlement price process is perfectly locally correlated with the commodity price process. The demand for futures in this instance is composed of (a) a mean‐variance term and (b) a minimum‐variance component that is a classic feature of models with nontraded assets. Since the first‐best (nonmyopic) optimum is attained, however, the deviation from a mean‐variance demand should not be interpreted as the expression of a nonmyopic behavior but rather as an attempt to restore a first‐best optimum. On the other hand, when the correlation between the futures price and the underlying commodity price is imperfect, in general, the value function does not separate additively, the first‐best solution cannot be attained, and the optimal futures trading strategy involves a hedging term against shifts in the opportunity set.

The speed and path of adjustment in stocks to the degree of earnings surprise in their quarterly announcements are studied using price‐volume transactions data. A differential price‐adjustmentp rocess was observed,w ith stocks having large,p ositive earnings surprises experiencing a faster adjustment compared with those stocks with negative earnings surprises. Volume, transaction frequency, and size were found to be directly related to the absolute degree of surprise,b ut very favorablee arnings‐surprises tocks experienced initially a large number of smaller trades while stocks with large unfavorable earnings surprises had relatively fewer transactions but higher volume per trade.

Closed‐end funds' discounts contain information in the sense that they can be used to construct portfolios that earn returns exceeding those predicted by the two‐factor capital asset pricing model. The precise nature of the information contained in a discount is not clear, however. This paper provides evidence that the information contained in a discount is an incomplete prediction of the fund's likelihood of being open‐ended profitably.

To test the major prediction of a signalling hypothesis‐that the market price is monotonic in the signal‐the price response to the signal must be measured. 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 the signal. This necessitates estimation of the price response to the signal, conditional on the rational decision rule. Thus, the empirical models (e.g., event studies in corporate finance) that estimate the market price responses to signals without conditioning on the rational decision rules are misspecified if viewed as tests of the prediction of a signalling hypothesis. This paper builds a generalized econometric 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 hypothesis that firms signal their true value by forcing or not forcing an outstanding convertible bond.

This is a summary and interpretation of some of the literature on stock price volatility that was stimulated by Leroy and Porter 28 and Shiller 40. It appears that neither small-sample bias, rational bubbles nor some standard models for expected returns adequately explain stock price volatility. This suggests a role for some nonstandard models for expected returns. One possibility is a “fads” model in which noise trading by naive investors is important. At present, however, there is little direct evidence that such fads play a significant role in stock price determination.

This paper shows that bank runs can be modeled as an equilibrium phenomenon. We demonstrate that some aspects of the intuitive “story” that bank runs start with fears of insolvency of banks can be rigorously modeled. If individuals observe long “lines” at the bank, they correctly infer that there is a possibility that the bank is about to fail and precipitate a bank run. However, bank runs occur even when no one has any adverse information. Extra market constraints such as suspension of convertibility can prevent bank runs and result in superior allocations.

This paper presents empirical tests of market rationality using data from the point spread betting market on National Football League games. Data from this market avoid many common pitfalls of tests of rationality in conventional financial markets. The authors test for rationality with two types of tests, statistical and economic. Results of the tests reveal that the statistical tests cannot reject market rationality while the economic tests do reject market rationality.