This paper examines the multiperiod capital allocation problem of a corporate division that is subject to ex post financial scrutiny by the parent corporation based upon meeting a specified target rate of return on investment. Using a zero-order decision rule, a deterministic equivalent linear programming model is developed to solve for the division's optimal mix of productive assets and the maturity structure of its debt.

The pivotal role of earnings expectations in equity valuation and therefore in certain areas of business finance is widely recognized, yet there is little theoretical or empirical evidence as to the manner in which investors and other groups actually formulate their estimates of future earnings. The resulting necessity to utilize proxy or indirect measures of expected earnings specified largely according to the predispositions of the investigator has led to numerous difficulties in the testing of cost of capital propositions and models of equity valuation.1 This study is intended to supply a preliminary response to the question of how earnings expectations are determined by appraising the extrapolative component of a limited sample of short-term estimates of earnings per common share. More specifically, the issues are the extent to which the earnings estimates (1) are extrapolative in nature and (2) may be approximated by familiar, naive, extrapolative techniques. In this context, “extrapolative” simply means determined by application of a specified weighting scheme to prior observations in the time series.

An attempt was made in this paper to build time-series models with strong structural relationships with various maturity-bond, holding period yields. The relative strength of several groups of key macroeconomic variables with bond returns was specified and tested.

The most useful regressor with regard to short maturity issues was the monetary policy ratio which had a strong association with the dependent variable in the intermediate and short maturities. The rate of change of wholesale prices was also important in the explanation of capital market yields. The rate of change in the English bank rate added still to the explanatory power of the model, particularly in the longer maturities; the combination of these three variables produced the most effective model. The regression coefficients were frequently many times their respective standard errors, and the correlation for every equation for the final model was statistically significant.

As the authors, Donald L. Tuttle and William L. Wilbur, indicate in footnote 1 of their article, “A Multivariate Time-Series Investigation of Annual Returns on Highest Grade Corporate Bonds,” the equations tested in this paper seem to me to be ad hoc. The problem is not that I think the authors should have estimated a general equilibrium model of the economy, but rather that they have not provided a satisfactory explanation of the single equation they have tested. The use of a technique to choose among alternative variables on the basis of their ability to shrink the coefficient of multiple correlation could be taken as further evidence of the absence of a well-articulated theoretical relationship explaining annual returns on corporate bonds.

In “An Investigation of the Extrapolative Determinants of Short-Run Earnings Expectations,” Professor McEnally has presented a wide spectrum of research results dedicated to the following questions: Are short-run earnings estimates extrapolative and to what extent can these earnings estimates be approximated by familiar extrapolation techniques? His thesis is that expected future earnings are in part a function of prior earnings and that there is much to be learned by fitting a series of regressions or other forecasting models to historical data.

The purpose of this study is to analyze the behavior of the cost of equity capital in the commercial banks by looking at whether there exists an optimal composition of the bank “fund structure” that would maximize bank earnings through the minimization of its cost of funds. The analysis should give an approximate cut-off point for testing such projects as “checking plus,” checkless payment systems, etc.

There has developed over the last several years a large and growing literature on the relationship between local bank market structure and performance. Two characteristics of this development are particularly notable. First, a carefully structured microeconomic model of the banking firm is rarely used as the starting point of the analysis. Secondly, the possibility that local market structure may have a differential impact on bank performance in different activities seems to have escaped systematic investigation. Specifically, it is a contention of this study that an empirical investigation of structure and performance in banking must be grounded in the explicit development of a microeconomic model of the banking firm.

To assure that the commercial banking industry's performance serves the “convenience and needs” of the public, bank supervisory authorities have been vested with broad powers to alter the competitive environment in bank markets. While several criteria have been used to evaluate the effects of entry, merger, branching, and other changes in the allocation of bank resources, the results have been largely inconclusive. Since the regulatory authorities have pursued somewhat conflicting objectives in seeking a “failure-proof” system that is also “efficient,” there may be no single criterion for evaluation of bank behavior that is wholly consistent with the behavior predicted by the neoclassical theory of the firm.

The papers by Emery, “Risk, Return, and the Morphology of Commercial Banking,” and by Klein and Murphy, “The Pricing of Bank Deposits: A Theoretical and Empirical Analysis,” are impressive attempts to describe the rationale of banking behavior. They provide an interesting combination of contrast and similarity. Both derive a model of the individual bank—Emery credits Shull's descriptive model of multiple-product price discrimination, while Klein and Murphy assign no specific competitive category but provide rather a generalized mathematical construct. Emery then incorporates portfolio theory to determine the difference between the actual return on bank capital from an idealized rate. This difference, called rent, is posited as a measure of the extent to which the actual level of interbank competition differs from the ideal. Assuming that regulatory agencies aim to maximize competition and thus consumer welfare, the implication of positive or negative rent differentials is inefficient regulation.

Profeśsor Magen's paper, “Cost of Capital and Dividend Policies in Commercial Banks” is an attempt to measure and explain the effects of financial leverage on the cost of capital for commercial banks. The methodology used is that of linear multiple regression which is inappropriate with regard to his data.

Articles by Sharpe [1], Lintner [2], and Hastie [3] introduce concepts of systematic and unsystematic risk associated with portfolio rate of return. Defining risk as variation in portfolio return, such risk comprises two elements:

1. Systematic risk or variation, which is the covariation of portfolio rate of return with market rate of return.

2. Unsystematic risk or variation, which is the difference between total portfolio variation and systematic variation. Unsystematic variation is therefore variation due to attributes of individual securities.

Some recent empirical studies have concluded that the common stock investor can virtually eliminate diversifiable risk with a portfolio that contains a “small” number of separate common stock issues [5, 6, 10, 11, 13]. The conclusion has several important implications. One of the inherent limitations of a portfolio manager is his inability to evaluate an infinite number of securities. The seriousness of this problem is directly related to the risks associated with a “small” portfolio. The economic function of a mutual fund industry is to provide diversification and professional management. If it is assured that a “small” portfolio can virtually eliminate diversifiable risk, the necessity of these functions may be questioned. In addition, the strategy of concentration may be less “risky” than is commonly supposed. Finally, the modern portfolio models generally assume that portfolio additions are costless.

Markowitz [12] and Tobin [19] pioneered in the development of a portfolio selection model resting on the assumptions that the investor

1. Chooses among alternative investment opportunities solely on the basis of expected return (E) and standard deviation of return 〈σ〉, and

2. Prefers more expected return to less but will refuse to incur additional risk (measured by standard deviation) unless compensated by increased expected return.

Investment literature, particularly materials made available to investors by financial magazines, brokerage houses, and investment services, places a great deal of stress upon analysis of investment opportunities by industry groups. Examples are industry analyses, such as those of Forbes and Financial Analysts Journal, as well as the industry segregations of popular services such as Value Line, Standard & Poor's and Moody's.

Professor Tysseland's paper, “Further Tests of the Validity of the Industry Approach to Investment Analysis,” calls into question the validity of the industry approach to investment analysis. His examination of inter- and intra-industry investment results through time leads him to conclude that “the lack of homogeneity within industries is great and industry consistency in performance over time is unimpressive.” Furthermore, he states that a prospective investor who chooses investments based on past industry performance records is on tenuous grounds and that “the usual types of industry data, analyses and forecasts generally made available by financial magazines, brokerage houses, and investment services, may be of doubtful value.”

The number of empirical studies aimed at examination of the relationship between risk and return to securities portfolios has increased dramatically over the last five years. There are basically two features of Nancy Jacob's paper, “The Measurement of Systematic Risk for Securities and Portfolios: Some Empirical Results,” that I feel contribute significantly to this area of study. First, the empirical analysis is based on an axiomatic system of characterizing the securities investment decision; this departs substantially from the assumptions underlying the more familiar mean-variance approach. A comparison of the conclusions reached while empirically investigating different axiomatic systems of investor behavior may play an important role in the validation of positive models of capital markets. The second contribution stems from the depth of the study as it relates to the effects of changes in the observation interval, the horizon time, and the portfolio selection procedures. Determining the appropriate horizon time and observation interval is always a major problem, when working with historical price and dividend data. The author's grouping of data into one-, five-, and ten-year horizon periods - along with varying the observation interval over monthly, quarterly, and annual data - illuminates many of the problems associated with empirical studies using a fixed holding period and observation interval.

This paper has shown that the models developed to select common stock port-folios can be adapted to the selection of real estate portfolios and mixed asset portfolios. The concepts are all identical, and as long as return and risk can be quantified, the problems are soluble.

The portfolios identified using a small sample indicate that real estate portfolios can have more return and less risk than do common stock portfolios. When the two assets are combined, the real estate assets dominate the resultant portfolios. On an after-tax basis these results are more apparent. The local aspect of real estate versus the national aspect of common stocks is primarily responsible for these results.

Careful examination of the behavior of financial variables over time uncovers an important distinction: variables expressed in dollars, such as earnings per share, behave very differently from percentage changes in those same variables. Similarly, financial ratios, such as the price/earnings ratio, behave quite differently from percentage changes in financial ratios. Variables expressed in dollars and financial ratios appear comparatively stable and predictable over time. Successive values are fair approximations of one another. Percentage changes in dollar and financial ratio variables-i.e., growth variables on the other hand, tend to be erratic, volatile, and unpredictable over time. Successive values bear little relation to one another. This distinction between dollar and ratio variables, on the one hand, and percentage changes in these variables-i.e., growth variables-on the other, serves as a useful basis for a financial model.

Harry C. Friedman in his paper, “Real Estate Investment and Portfolio Theory,” has put his mind to an increasingly important area of financial concern, the relationship of real estate as a security in developing portfolio theory. Between now and the year 2000, it has been estimated that $1,500 billion will be spent on building and remodeling nonfarm housing. There will be an estimated $1,000 billion spent on commercial, industrial, and utility construction. In addition, $1,000 billion will probably be spent on public utilities and service institutions, plus $30 billion annually on community facilities. It is anticipated that each year in coming generations we will add to our existing inventory the equivalent of fifteen cities of 200,000 persons each. Predictions are that there will be a need for an additional 2 million dwelling units per year during the 1970's and that this need will climb steadily thereafter.

Murphy and Nelson in their article, “Random and Nonrandom Relationships Among Financial Variables: A Financial Model,” develop three postulates which deal with the temporal behavior of financial variables. They suggest that the three postulates constitute a useful financial model. The basis for the model is the distinction between dollar or ratio variables on the one hand and percentage change or growth variables on the other hand.