Knowledge that Transforms

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Corporate investment in an economy without a complete set of contingent claims markets has the characteristic of a public good in the sense that the stockholders’ consumption planscannot be separated from, but depend on, the specific investment plans of the firms. Drèze [4] has shown that a constrained Pareto optimal (CPO) allocation of investment in a stock market economy must satisfy a generalization of the Samuelson [24] condition for efficient production of public goods: the investment plan should maximize a weighted sum of the stockholders’ personal valuations of future output minus current input cost. However, except for those special cases in which CPO investment plans are unanimously supported by stockholders (see [17], [20], and [2]), the theory of the firm in incomplete markets lacks a suitable maximization criterion. Although the Drèze-Samuelson condition is a most appealing candidate, it is not unanimously preferred by stockholders, each of whom prefers that his or her own valuation of future output receives all the weight in the investment decision. Furthermore, the application of the Drèze-Samuelson condition depends on the correct revelation of stockholders’ preferences, which, in the absence of special inducements, cannot be expected from economic agents.

Normatively oriented approaches to decision making under uncertainty occupy a major portion of the literature of decision analysis. Among the proposed approaches, the Von Neumann-Morgenstern expected utility (EU) theory has been the most prominent model of rational choice despite some controversies that surround this model with respect to its descriptive ability as well as the plausibility of utility assessment in actual decision situations [6], [10]. Although most applications of the EU model have been concerned with decisions involving monetary outcomes, its scope is not limited to any particular class of consequences. The basic appeal of the model is attributable partly to its generality in this regard, and, more importantly, to the apparent reasonableness of its preferential axioms.

The advent of negotiated brokerage commissions on May 1, 1975, (Mayday) made it possible in principle for small investors as well as large institutions to bargain over the charges they are assessed for executing common stock trades. While the popular business press has speculated both that the actual incidence of negotiation by individuals is infrequent and that the net impact of the new regime has been to raise individuals' trading costs, empirical evidence has been sparse. The purpose of the paper is to examine these hypotheses, using a data base consisting of the actual common stock transactions records of a sample of some 8,000 accounts of a large retail brokerage firm, covering the years 1970 through 1979. The frequency and magnitude of commission-rate discounts from the posted post-Mayday schedules will be identified, the net impact on trading costs assessed, and the factors that appear to "explain" who gets a discount analyzed. Prior to Mayday, the only segment of transactions costs requiring investigation was the dealer mark-up or spread since, with the knowledge of price and volume, agency commissions were invariant. Studies on the spread were accomplished by Demsetz, Tinic, and Tinic and West to name but a few. It was only necessary to simulate transactions as an individuals' attributes had no effect on commissions. This however, is no longer the case. With the possibility of discounts from stated commissions, the data must include the pattern and frequency of transacting by individuals, the exchange or market on which the transaction occurred, and various other investor specific attributes. Thus, actual transactions across markets and over time are required. This is the first study to meet these criteria.

The traditional Capital Asset Pricing Model (CAPM) provides a foundation for the estimation of systematic risk that has been applied extensively in studies of investment performance, market efficiency, predictive models, and capital budgeting, to name only a few. Lee [10] considered a special case of nonlinearities occurring in the estimation of systematic risk within the context of the investment horizon problem. His findings, based on a limited sample, provided significant methodological implications for the estimation process and have received wide readership through republication of the study in a readings text [6].

Anjan V. Thakor, Richard Callaway, Costly Information Production Equilibria in the Bank Credit Market with Applications to Credit Rationing, The Journal of Financial and Quantitative Analysis, Vol. 18, No. 2 (Jun., 1983), pp. 229-256

In deciding among these estimators for a particular application, gains in efficiency must be weighed against additional computational effort. On these grounds, there would seem to be good reason in most circumstances for preferring the wrap-around overlap estimator to the simple unbiased one. The gain in efficiency is achieved at a small cost in additional computation, and, in addition, no data need be discarded if the sample size (T) does not happen to be an integral multiple of N. In going beyond the wrap-around estimator to the combinatorial one, however, the decision is less clear. The gain in efficiency is relatively small and the number of return relatives to be computed may become quite large. For example, with T=30 and N=10, the last parameter set for which computations are made in Table 1, evaluation of the combinatorial estimator requires computation of (= approximately 3×108) ten-period return relatives. In many applications, this additional effort is probably not worthwhile. Significantly, the ordinary overlap estimator seems to fare worst, probably due to its asymmetric use of the data, and, therefore, should not be used.It also should be noted that when relatively large amounts of data are available, the simple unbiased estimator will in most cases perform quite adequately. A common application in financial research involves computing annual stock returns from monthly data. Representative relative efficiency computations using μ = 1.01, σ = .05, and N = 12 are given in panel B of Table 1 for T = 60, 120, and 360 months. The incremental improvement of the wrap-around estimator relative to the simple one is quite small. Figures for the combinatorial estimator are not presented because the number of permutations would be prohibitively large.

William Stein, Roger Pfaffenberger, P. C. Kumar, On the Estimation Risk in First-Order Stochastic Dominance: A Note, The Journal of Financial and Quantitative Analysis, Vol. 18, No. 4 (Dec., 1983), pp. 471-476