Commonly defined, a market is efficient if prices always fully reflect available information. That market might be viewed as consisting of two major segments: an information market and pricing mechanism. The efficiency has been amply documented elsewhere. The information market, however, should be afforded increased attention. In particular, the efficiency of the information market may vary across securities and with respect to particular securities, across time. Stated another way, the degree of imperfection in the information market may vary across securities and across time, resulting in a relative efficiency phenomenon. The presence of such a phenomenon would offer research opportunities yielding a greater understanding of the functioning of the information market and the pricing of securities.

The need for an empirical measure of the relative value of financial theories becomes more important as we increase their number and sophistication. Although there are several methods of determining value, here we emphasize relative predictive power. According to this single criterion, a theory is insignificant if it fails to increase our present ability to predict future events. We recognize, however, that a theory can be useful if it merely increases our understanding of real events, or if it is based upon realistic assumptions.

The properties and characteristics of financial ratios have received considerable attention in recent years with interest primarily focused on determining the predictive ability of financial ratios and related financial data. Principal areas of investigation have included the prediction of corporate bond ratings [13, 20, 23, 34], and the anticipation of financial impairment [1, 2, 3, 5, 6, 7, 18, 19, 29, 32, 33, 35]. Related studies have examined the characteristics of merged firms [25, 28], the differencesin financial ratio averages among industries [9, 10], whether firms seek to adjust their financial ratios toward industry averages [15], the relationship between accounting-determined and market-determined risk measures [4, 8, 24], and the influence of financial ratios on analysts' judgments about impending bankruptcy [14, 17]. The general conclusion to emerge from these various research efforts is that a number of financial ratios have predictive and descriptive utility when properly employed.

The purpose of this paper is to provide evidence on the following question: Are there more banking offices available per person to furnish consumer and business services in branch banking states than in unit banking states? This question is a central part of a broader issue of what limitations should be placed on the ability of individual banks to branch. Indeed, in a recent review of the literature dealing with the branching question, and prepared for the Senate Banking Committee (McIntyre Committee), Guttentag [8] stated: “One of the most pervasive arguments for branch banking is that branch banks provide more office facilities than unit banking.” Yet the available evidence on the question is sparse and existing research contains methodological difficulties which make the findings of questionable value.

Increasing attention has been focused, as of late, on the relatively low rate of rental housing starts and the increase in apartment conversions to condominium ownership. By some estimates, additions to owner–occupied housing stock since 1970 have occurred at twice the rate of addition to the rental stock, a pattern that has caused concern to some policymakers.

This paper is concerned with costs of capital in mathematical programming formulations of the problem of capital budgeting under capital rationing. It shows that there is a serious error in the method outlined by previous authors for converting the shadow prices from the solution of the dual into measures of the firm's marginal internal opportunity rates. In addition to demonstrating that the traditional approach leads to erroneous and nonsensical results, this paper presents a correct procedure for determining these rates.

There is a considerable body of empirical research in accounting devoted to the analysis of relationships between accounting numbers and security prices. Very roughly, this body of research may be classified into three different categories: (i) share price valuation models and the determination of market equity values; (ii) the measurement of “unexpected earnings” and their contemporaneous association with security returns; (iii) the forecasting of future security returns. The selection and definition of accounting numbers in most of these types of studies have, by and large, been quite heuristic. The accounting variables are usually selected with little consideration given to their empirical time-series behavior; more important appears to be their intrinsic economic connotations. The approaches can thus be thought of as stipulating the existence of “real” economic variables, e.g., real income for a period, and then using numbers of published accounting statements as estimates of the real variables. The errors in estimates of the true variables are then often minimized by the use of aggregation procedures and the diversification effects of such procedures. The postulating of real economic variables has another methodological advantage: it permits the use of comparative statics analysis of corporate behavior and its effect on equity risk and return. For example, Hamada [7], among others, has shown that leverage affects risk in the usually hypothesized manner but this analytical result depends on the assumption that leverage and earnings are real and unambiguous economic variables without “measurement” errors.

The purpose of this study is to build and test a statistical model for the dynamic estimation of portfolio Betas. Of particular interest is the quality of Beta estimates obtainable from relatively small samples of daily return data. Also of particular interest is an assessment of the relationship between the quality of these estimates and the degree of portfolio diversification.

The findings of this study indicate, contrary to the recent claims by Aaron [1, 2], among others, that the real estate tax on residential properties is regressive. Using a data base from a sample of household observations for FHA–HUD 203–Program single–family market sales for counties contained in five major United States Standard Metropolitan Statistical Areas (SMSAs), the statistical analyses suggest that the degree of observed regressivity varies significantly across counties and is the result of two forces. First, in many counties, the poor and de facto inequitable assessment practices are the principal causes for property tax regressivity. Second, in some counties that exhibit relatively equitable and uniform assessment practices, the end product appears to be an income regressive property tax. Hence, the property tax in these counties may be intrinsically (slightly) regressive. However, in general, if there were uniform administration of the property tax, it would be (slightly) progressive.

”Poor people cause poor housing.” This statement is the basis for one of the major policy proposals to eradicate poor housing and neighborhoods that plague some of our urban areas. If the motto is correct, then poor housing and poor neighborhoods can be eliminated by providing direct support for the demand for housing by low-income households. The role of government, under such a policy, is to funnel its resources to households in the form of demand subsidies. Examples of such demand-side programs are housing allowances and the Section 8 existing housing program.

The theoretical desirability of stochastic dominance (SD) as a decision rule is well established [1, 3, 4, 7, and 11]. However, implementation of SD as a decision rule has been hindered seriously by the lack of an optimal search algorithm [8]. An optimal search algorithm is desirable since it takes the distribution of returns for a group of assets and determines the optimal proportion of each asset which should be combined to provide efficient combinations. For example, for a given expected value (variance) the mean-variance (EV) algorithm builds the portfolio with the smallest (largest) variance (expected value). The EV algorithm determines which assets should be combined and the proportion of the total investment that should be invested in each asset. An analogous algorithm does not exist for SD.

Traditionally, the problem of portfolio choice from risky assets has been solved by considering each asset as a probability distribution of future returns. Depending on the approach used to perform efficiency analysis, knowledge about the asset's probability distribution can be from summary to complete. Thus the mean-variance (EV) model of Markowitz [9] utilizes the first two moments of the distribution, whereas the stochastic dominance (SD) approach [3] employs the entire probability function.

In a past issue of the Journal of Financial and Quantitative Analysis, Norstrπm [7] has presented a very simple sufficient condition for detecting whether a given pattern of cash flows over time has a unique nonnegative internal rate of return. Nor strum's condition is now widely cited in the literature and included in stock computer routines for analyses using the internal rate of return. See, e.g., de Faro [5] and Newnan [6].

In the literature dealing with bond price volatility, there have been two divergent approaches. On the one hand, theoretical papers have looked at bond price volatility in the instantaneous framework of the calculus. Using the derivative of bond price (P) with respect to yield to maturity (y), it has been shown that volatility is linearly related to this derivative (dP/dy). (See [10].)

In a recent paper Lloyd and Shick (LS) [4] report empirical results of tests of Stone's [7] two-factor model. Based on a sample of 60 banks and the 30 Dow Jones stocks, LS conclude that their findings generally support Stone's model. That is, an “interest rate risk” proxy appears to explain an additional portion of the variability of the sampled security returns over and above the variability due to an equity market proxy.

Since the formulation of the portfolio selection problem by Markowitz [12] as a parametric quadratic programming problem, considerable effort has been devoted to obtaining operational portfolio management models. Research has involved: (1) characterizing the return generating process in terms of index models; (2) specifying special-purpose algorithms such as the critical-line method of Markowitz [13] or the solution procedure of Jucker and de Faro [11]; (3) using linear programming formulations to approximate solutions to the nonlinear programming problems such as Sharpe [20, 22] and Stone [25]; and (4) converting portfolio selection models into portfolio management models designed to revise an existing protfolio subject to transaction costs using heuristics such as Smith [24] or revision formulations such as Pogue [16, 17] and Stone and Reback [27].

This paper presents a hedonic index of residential services fit to 1975 data for the St. Joseph County, Indiana, rental housing market. The work it reports is part of a larger research effort, the Housing Assistance Supply Experiment (HASE), being conducted for the U.S. Department of Housing and Urban Development.

In an important article in 1971, Fisher and Weil [4] demonstrated that it is possible to immunize a portfolio of default-free coupon bonds against unexpected interest rate changes so that at the end of the planning period the investor will realize at least the return expected at purchase. Immunization may be achieved by constructing a portfolio whose average duration is equal to the length of the investor's planning period. The computation of duration that produces immunization is dependent on the nature of the assumed stochastic interest rate shocks. Fisher and Weil derive the duration that will produce immunization for additive shifts in the yield curve under instantaneous compounding, e.g., g(t) + λ where g(t) is the instantaneous interest rate at time t and λ is a random shift parameter.