Since Markowitz [15, pp. 98–101] and Sharpe [19] developed the single-index market model (SIMM hereafter) it has received considerable research attention. Empirical tests have established the model's econometric significance [3, 12, 13] in partial equilibrim analysis. However, research into the relationship between the SIMM and its macroeconomic environment has been meager. It has been shown that the market factor changes intertemporally [13, 16, 18, 20]. However, whether these changes in the market factor and, more basically, changes in the macroeconomic situation affect the SIMM is unknown.

I have always considered it unfair for a discussant to criticize an author for writing a paper differently than he would have written it had he been addressing that topic. However, since the topic of the Brueggeman and Peiser paper is so important and the paper is so different from what I would have written, I am going to indulge myself for a moment.

In a recent article Lloyd and Shick [3] examined a two-index model of bank stock returns with interest rates as the extra-market source of covariance. Based on their findings, the authors were optimistic that the inclusion of an interest rate index would prove to be worthwhile in market model regressions. The purpose of this comment is to question their conclusions by pointing out some specific deficiencies concerning their data, the statistical tests, and their interpretation of the results.

In the June 1978 issue of this Journal, Herbst [2, p. 363] cautioned the reader to beware of a unique, real internal rate of return (IRR) because such a return is “an incorrect measure of the return on investment” for a mixed project. While it is true, as discussed by Teichroew, Robichek and Montalbano (TRM) [4], and now by Herbst, that a mixed project may have a unique IRR, several additional observations developed by TRM should be added to Herbst's discussion. These comments will clarify the implications of Herbst's paper for a decision maker who has a mixed project with a unique real IRR.

In the September 1975 issue of this Journal Ben Branch works out in detail an “optimal” strategy for an investor seeking to purchase or sell a security. The suggested strategy involves placing limit orders at specified prices and then waiting for the order to be filled. Hypothetical calculations indicate the magnitude of savings possible through use of the strategy. Ben Branch apparently accepts the standard random walk model and develops his theory around it. If one believes that the value of a stock is a constant and that prices fluctuate randomly, the treatment seems correct.

The Edelstein and Follain papers are not as different as they may appear at first. The two are linked through the income elasticity of housing. Follain is concerned with this measure explicitly, while Edelstein1s analysis hinges on the value he assumes for it. Both papers are also policy papers, and their conclusions are of immediate importance for ongoing policy decisions.

The composite measures of investment performance: the reward-to-variability index, by Sharpe ([29], [30]) and Lintner [23], and the reward-to-volatility index, by Treynor [33], were developed after Markowitz ([24], [25]) and Tobin [32] popularized the mean-variance framework of analyzing the problems of certain investments. Since these are ex ante measures they are not directly applicable to the evaluation of ex post performance. A theoretical basis for doing so has been provided by Jensen ([17], [18]) who also developed another composite performance measure, the predictability index. In practice, these composite measures have been found to have problems. Foremost, they have been observed to exhibit systematic biases. Various causes of the biases have been proposed. These are: the existence of unequal lending and borrowing rates, the failure to consider higher moments of return distributions, and the elusive “true” holding period.

Random stock returns result from irregular vibrations of a share's price through time. Divide any arbitrary time interval into two mutually exclusive and exhaustive sets. One set contains time periods when trading is formally open on an organized market such as the New York Stock Exchange (NYSE). Its complement contains closed trading time periods, i.e., when the NYSE is not open. Conventional theory assumes that the same return process operates over all periods in both sets. No allowance is made for possible differences in the return sequence between sets or among time periods within each set. There are reasons to assume that such differences may exist. For example, during a trading day, stock prices fluctuate as orders are executed. During nights, weekends, holidays, and holiday-weekends there are no transactions, but a share's value from close to open on the next trading day may still change to reflect revised expectations about a firm's productivity. In fact, capital changes and important news items are usually announced after the stock exchanges close.

Edward Miller's basic point (see “Comment” in this issue) may be put rather briefly: If market prices follow the random walk model precisely (I assume he refers to the semi-strong form of the efficient market hypothesis), there are no gains to be made from a trading strategy which involves waiting for more attractive prices. In a fully efficient market the price is always in line with the available information and thus never becomes either more or less attractive than the relevant information set would allow.

This paper develops an exact theoretical test of the presence or absence of a filter effect for a portfolio of securities and a general number of different filter sizes. It is a natural development from Praetz [8], which obtained exact expressions for the mean and variance of rates of return of the investment strategies under filter tests assuming the underlying stochastic process is a random walk. These expressions showed that expected returns from filter strategies are, in fact, less than the return from a buy-andhold alternative with which filter returns are usually compared.

In a recent article [1], Beedles suggests that the valuation process for cash outflows (or negative benefits using his terminology) is, in some sense, different from the valuation process for cash inflows. This result, however, is not consistent with the assumption of perfect capital markets. Any cash outflow from one firm represents a cash inflow to some other firm(s) or investor(s). Consequently, any difference in the valuation processes for cash outflows and cash inflows will create profitable arbitrage possibilities.

Academic attention has increasingly been focused on the operation of security markets. This is largely due to the impetus provided by the Institutional Investor Study (see U.S. Securities and Exchange Commission [35]), by the Securities Act Amendments of 1975 whereby Congress mandated the development of a national market system (NMS), and by the expanding computer technology of the 1970s. Not surprisingly, much of the attention has focused on the role of dealers and stock exchange specialists as market makers. The literature has generally viewed these market makers as suppliers of immediacy to ordinary traders, and has taken the bid-ask spread to be the price they impose for the provision of this service.

The concept of a relationship between assumed risk and realized return is intuitively pleasing and has become widely accepted in the field of finance. Until recently this acceptance was anchored largely in what Hirschleifer [11] has called the “notorious fact” that stocks yield more in the long run than bonds and Hickman's finding [10] (since challenged by Fraine [7]) that over the years 1900–1943 the average ex post yield on publicly issued corporate debt was higher the lower the initial quality rating. However, with the advent of the capital asset pricing model of Sharpe [21], Lintner [16], and Mossin [19] the risk-return tradeoff concept has grown in importance and scope. The capital asset pricing model itself has weathered the years well, but has been theoretically and empirically revised, extended, and otherwise altered. Little remains of the original formulation except the proposition that in equilibrium more risk leads to more return--where “risk” for common stocks now means the nondiversified component as measured by the “Beta” coefficient of return volatility vis-à-vis the general market. As Modigliani and Pogue [18] observe after a review of the “more important” empirical tests of the capital asset pricing model: “Obviously, we cannot claim that the CAPM is absolutely right. On the other hand, the empirical tests do support the view that beta is a useful risk measure and that high beta stocks tend to be priced so as to yield correspondingly high rates of return.”

During the late 1960s and throughout the 1970s, a myriad of tests of the two-parameter capital asset pricing model (hereafter CAPM) have been executed and reported in the literature. Relatively recently, much attention has been focused on the asymmetry—skewness—of realized asset, portfolio, and market return distributions. The intent of the present effort is to report the results of an investigation of the asymmetry of one of these variables—the returns of the market portfolio.

According to the current state of knowledge in finance, the expected rate of return adjusted for risk is independent of the stock price. The basic proposition of the capital asset pricing model (CAPM) is that the expected rate of return for each security is a function of the “risk” of that security, and that this risk is measured by the contribution of the security to the variability of the market portfolio. The implication of the CAPM is that knowing the price of a security perse will add nothing to predicting its expected rate of return.

The first hypothesis underlying this study is that successive transactions exhibit systematic patterns. These patterns will be studied to better understand (1) the processes by which transactions are arranged, (2) the costs of transacting, and (3) the statistical characteristics of the reported transactions prices and their relationship to the market equilibrium. This study will not directly consider market efficiency, though its results may eventually lead to more searching and meaningful studies of market efficiency.