This study examines the complex process of the transfer of technology in the early stages of industrialization. The manner and timing of the selective acquisition of portions of Britain's new textile technology by entrepreneurs in the Philadelphia region were determined by the subtle interplay of market forces, technological constraints, and active efforts to encourage or discourage the transfer.

Professors Alberts and Archer provide a valuable addition to our understanding of capital markets and how resources are allocated to firms of different asset sizes. Their hypothesis is that the cost of equity capital to smaller firms is higher than it is to larger industrial firms. They test their hypothesis by analyzing the variability of returns of 658 industrial firms and attempt to determine whether variability of return is inversely related to asset size. The authors assume that for all firms Ke must equal the sum of the risk-free rate of interest and a risk premium, when risk is defined by four different measures. Two measures of risk use ex post rates of return on book value and two measures of risk employ ex post rates of return on market value. In the first two cases, risk is defined as variability of the firm alone and, in the second two cases, risk is defined as the firm's variability incorporated with the variability of a market portfolio of securities.

Professor Stevens has attempted to determine whether or not a consistent financial basis for merger exists as measured by premerger financial characteristics of the acquired firms. He suggests that results of his study are useful in identifying merger motives and in relating such motives to a general framework for analysis of merger movements.

Professor Huntsman's paper is a welcome addition to the growing literature on portfolio theory. Traditional mean-variance analysis, in spite of its obvious simplicity and its ability to explain portfolio diversification, has come under increasing attack, both for the theoretical weaknesses underlying the technique and for its failure to recognize that investors manifestly prefer returns that are positively skewed to those that are not.

Major points covered by Paul H. Cootner in his discussion of “The Prediction of Systematic and Specific Risk in Common Stocks” by Barr Rosenberg and Walter McKibben have been incorporated by the authors in the revised version of their paper.

Since this comment should be primarily addressed to Professors Bicksler's and Thorp's own research and results, I will not consider those pages that contain the authors' interpretation of prior studies.

Professors Bicksler and Thorp study the short-run properties of the optimal growth model via Monte Carlo simulation. This is an interesting idea because of the mathematical difficulty of the problem.

The paper presented by Professors Marcis and Smith (M-S), analyzing the determinants of the demand for cash and short-term Treasury obligations held by U. S. manufacturing corporations, is praiseworthy. The authors have made an interesting application of a seemingly unrelated regression (SUR) technique developed by Arnold Zellner [3] in estimating demand functions jointly for each of the liquid assets of corporations belonging to nine asset size categories. Nonetheless, I have some reservations about the implications of the model employed in their present study and the reliability of their results. Some of my reservations concern the theoretical foundation of their model itself, while others are related to their methodology and estimation techniques.

The theory of efficient capital markets indicates that the prices in an efficient market fully reflect all available information. In much of the literature on efficient markets the term fully reflect is made operational with the assumption that the conditions for market equilibrium can be expressed as expected returns. Fama suggests that most expected return theories can be expressed in the following manner:

(1)

where — adopting Fama's notation — E is the expected value operator; Pjt is the price of security j at time t; Pj,t+1 is its price at t+1; is the one-period percentage return (Pj,t+1|Pjt); φt is a general symbol to represent whatever set of information is assumed to be fully reflected in the price at time t; and the tildes indicate that Pj,t+1 and rj,t+1 are random variables at t.

Over the years there has been a growing interest in the over-the-counter (OTC) trading of exchange-listed securities (known as the third market). Although the third market has flourished and its advantages have been expounded, it has not been possible to compare accurately the third market with organized exchanges because of an incomplete quotation system. On April 5, 1971, the National Association of Security Dealers Automatic Quotation (NASDAQ) system began including bid-and-ask quotations for 30 stocks listed on the New York Stock Exchange (NYSE); see Table 1.

It has been a pleasure and a worthwhile experience to serve as President of the Western Finance Association (WFA) and to have had the opportunity to work with its officers and committee chairmen over this past year. In the brief history of the Association, our organization has come a long way in becoming firmly established due to the strong interest of its members and to the dedicated support of its officers and working committees. I wish to take a few moments to publicly acknowledge the services of a few who have given vital support to the activities of the Association during the past twelve months.

Professors Nielsen and Melicher (N-M) have conducted well an interesting study of merger premiums as related to various measures of synergy connected with those mergers. Their study is another in a growing body of literature concerned with the merger phenomenon which increased substantially during the sixties and has continued into this decade. In order to provide an evaluation of their study, I shall consider their choice of research design and their analysis of research findings.

Louis Bachelier would be pleased with the findings reported in John T. Emery's paper, even though Bachelier wrote in 1900 before there was any popular support for technical analysis. Considering technical analysis historically, the Dow Theory was the first popular technical approach, although Charles H. Dow, editor of The Wall Street Journal at about the time of Bachelier's writing, did not consider his theory a forecasting method. Later William P. Hamilton began to forecast with Dow's Theory, and then in 1932 Robert Rhea's publication of The Dow Theory popularized this technical approach. Earlier Bachelier had struck the first blow of an obviously continuing quest to execute the technical security analysts. (A technical security analyst, often called a chartest, develops esoteric charts or computer printouts which he hopes will allow him to make better than average returns in the stock market.) In the United States serious economic and statistical testing of technical analysis did not begin until the early 1950s; these academic tests continue today. Test results support the efficient capital market theory or, put more bluntly, technical analysis does not lead to greater than average profits in the stock market. On the other hand, perhaps technical analysis does work, but no statistical method used in testing has uncovered this fact. In short, perhaps our statistical tools are not sophisticated enough to disclose the relation between stock price and “daily market indicators.”