Knowledge that Transforms

This study investigates the seasonal pattern of aggregate insider trading to help distinguish between two competing explanations for the seasonal pattern of security returns. The first potential explanation examined is that the January effect arises from predictable changes in turn-of-the-year demand for securities. The second potential explanation examined is that the January effect represents compensation for the higher risk of trading against informed traders at the turn of the year.

Allan W. Kleidon, Bubbles, Fads and Stock Price Volatility Tests: A Partial Evaluation: 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. 656-660

In a recent article, Black 1 introduces a type of trading that he terms noise trading. He asserts that noise trading, which he defines as trading on noise as if it were information, must be a significant factor in securities markets. However, he does not provide an explanation of why any investors would rationally want to engage in noise trading. The goal of this paper is to provide such an explanation for one type of investor, managers of investment funds. As shown here, the incentive for a manager to engage in noise trading arises because of the positive signal that the level of the manager's trading provides about his or her ability to collect private information concerning current and potential investments. If the manager's compensation is directly related to investors' perceptions of his or her ability, the manager will then trade more frequently than is justified on the basis of his or her private information. In addition to providing this explanation for noise trading, the results of this analysis may also be useful for further empirical exploration of the relation between investment fund portfolio turnover and subsequent performance.

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.

This study shows that the competitive-equilibrium version of the APT may be extended to develop an exact model if idiosyncratic risks obey the Ross separating distribution. The results indicate that one only need add the market portfolio as an extra factor to the factor model in order to obtain an exact asset-pricing relation. Thus, this study presents an extension and integration of the CAPM and APT. The “empirical” APT is also generalized to allow for some factors to be omitted from the econometric model employed to test the theory. The developed model is extremely robust and may be reduced to the CAPM or expanded to approximate Ross's APT depending upon the number of omitted factors. Further, the importance of the market portfolio is shown to be a monotonic increasing function of the number of omitted factors. Finally, the study demonstrates that, in a finite economy, the pricing-error bound of the Ross APT in a correlated-residuals factor structure is an increasing function of the absolute value of market-residual beta, rather than the weight of the asset in the market portfolio as is the case of uncorrelated factor residuals. However, under the normality assumption, the pricing error becomes an extra component related to the market-portfolio factor, and the exact asset-pricing relation is once again obtained.

This paper introduces an estimator of stock price volatility that eliminates, at least asymptotically, the biases that are caused by the discreteness of observed stock prices. Assuming that the observed stock prices are continuously monitored, an estimator is constructed using the notion of how quickly the price changes rather than how much the price changes. It is shown that this estimator has desirable asymptotic properties, including consistency and asymptotic normality. Also, through a simulation study, the authors show that it outperforms natural estimators for the low- and middle-priced stocks. Furthermoret, he simulation study demonstratest hat the proposed estimator is robust to certain misspecifications in measuring the time between price changes.

I use weekly Treasury-bill rates with maturities of one to twenty-six weeks to examine the information in forward rates during the 1970s and 1980s. Forward rates contain better information about future changes in spot rates than the information captured by autoregressivea nd vector-autoregressivem odels. Forward rates also have considerable predictive power, which increased after October 1979 and remained strong after October 1982. The results show no necessary connection between interest rate predictability and the degree to which the Fed adheres to interest rate targeting.