Intuitively, a risk averter diversifies between two investments if there is some sort of negative interdependence. In [3], Samuelson gives the example of buying shares in a coal company and an ice company. It is of interest to characterize this concept of negative interdependence more sharply.

Certainly, the concept of skewness of returns and its role in the context of portfolio analysis has gained increasing attention in recent literature. Witness the studies by Alderfer and Bierman [1], Arditti [2, 3], Jean [4], and Simonson [5]. Each of these studies has treated skewness as the third moment of a series expansion—accordingly, skewness has been measured and interpreted as a logical extension of the traditional two-dimensional return-versus-standard deviation analysis of security evaluation.

The authors, Hodges and Schaefer, of the preceding paper [2], taking up where my own article [3] left off, have contributed to a better understanding of the geometric mean index of stock price relatives. Their basic point is that, if in any practical situation a portfolio were managed according to a policy of periodic reallocation, the wealth relative of the portfolio would not be approximated by the geometric index. This is demonstrated through simulation, using randomly generated price sequences as well as empirical data. In addition, they have presented a verbal characterization of the hypothetical portfolio policy whose wealth relative is measured by the mth-order power mean of price relatives discussed in my paper. This policy, as I had stated, is not an intuitively simple one like “maintain equal dollar amounts at all times” or “buy and hold.”

The FINSIM model provides a fundamental and analytical basis for security evaluation. The methodology presented includes the relevant economic and firm variables in an efficient computational scheme and is useful for:

1. reducing the analysts' judgments about the future to a specific stock price (the model described does not replace the analyst, rather it provides the analyst with a vehicle to determine the implications of his critical assumptions);

2. testing the probable impact of changed expectations concerning the firm and/or the level of the market on stock value;

3. getting at what “the markets” expectations must be to justify the current price;

4. determining the value of additional information (are results changed significantly to pay for the expense of refined estimates?);

5. determining the impact of alternative growth horizons on value; and

6. determining what the actual growth rate of total earnings must be to overcome the dilution effects of financing with external equity.

While the security analyst still faces the problems associated with decision making under uncertainty, the methodology presented facilitates the use of sensitivity analysis to study the implications of uncertain knowledge of parameters.

Professor Filante examines data on the patterns of trade and revenues on the Erie Canal during its first thirty-five years of operation.

This paper will take the following form:

a) a brief discussion of Erie Canal operations in the period 1825–1835, when neither rails nor other canals were competitors.

b) a discussion of the period 1835–1860 with specific attention turned to the sources and destinations of freight shipments, the composition of those shipments, and changes in the canal itself.

c) a discussion of rail-canal competition centering on the division of the market between the two along value of product and length of shipment criteria.

Arguing that the American bituminous coal industry suffered from “excessive competition,” this study traces the industry's repeated failures to control output or prices, whether by various kinds of trade associations, mergers, or by attempts to secure government sanctions for cooperation. Although an over-zealous Department of Justice must bear some responsibility for the industry's “sick” condition, Professor Graebner concludes, the fundamental problem lay in the basic economic conditions in the industry.