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A Survey and Comparison of Portfolio Selection Models**

Published online by Cambridge University Press:  19 October 2009

Extract

This article will concern itself with the various techniques for selecting portfolios of securities. It should be made clear at the outset that a good portfolio is not just an amalgamation of a number of “good” stocks and bonds. Rather, it is an integrated whole, each security complementing the others. Thus, the investment manager must consider both the characteristics of the individual securities and the relationships between those securities. Until recently there was no comprehensive theoretical framework for the analysis of the latter aspect of the portfolio problem. Intuitive judgment and experience were the guidelines used by investment managers.

Type
Research Article
Copyright
Copyright © School of Business Administration, University of Washington 1967

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References

1 Markowitz, Harry M., “Portfolio Selection,” The Journal of Finance, March, 1952, pp. 7791.Google Scholar

2 Farrar, Donald E., The Investment Decision Under Uncertainty. (Englewood Cliffs, N. J.: Prentice-Hall, Inc.), 1965.Google Scholar

3 Sharpe, William F., “A Simplified Model for Portfolio Selection,” Management Science, January, 1963, pp. 277293.Google Scholar

4 Cohen, Kalman J. and Pogue, Jerry A., “An Empirical Evaluation of Alternative Portfolio Selection Models,”A working paper of the Carnegie Institute of Technology,Pitts., Pa., October, 1966.Google Scholar

5 Markowitz, op. cit.

6 Markowitz, Harry M., “Portfolio Selection: Efficient Diversification of Investments, (New York: John Wiley & Sons, Inc.), 1959.Google Scholar

7 This selection requires that the investor determine the nature and shape of his risk-return utility function. The optimal portfolio can then be obtained by selecting that portfolio which lies at the point where the utility function is tangent to the portfolio possibility set.

8 While any measure of “return” can be employed by the model, the selection of a “good” measure is not a trivial problem.

9 Since the expected value of a sum of random variables is equal to the sum of the expected values of each of the random variables.

10 A convenient way of defining the covariance is Cijiσj Rij, where σi is the standard deviation of the ith variable and Rij is the correlation coefficient between the two variables.

11 For a suitable algorithm see: Hadley, George, Non-Linear and Dynamic Programming (Reading, Mass.: Addison-Wesley Publishing Co.), 1964.Google Scholar

12 Farrar, op. cit.

13 Cohen and Pogue, op. cit.

14 The semi-variance is defined as the expected value of the squared deviations to one side of the mean.

15 Sharpe, op. cit.

16 Cohen and Pogue, op. cit.

17 Available from the Standard Statistics Corporation.