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A Utility Theoretic Basis for “Generalized” Mean-Coefficient of Variation (MCV) Analysis

Published online by Cambridge University Press:  06 April 2009

Extract

The coefficient of variation (CV) in investment returns is often presented in introductory finance texts as a measure of project risk [7, 9, 13, 15]. Curiously, the resulting mean-coefficient of variation (MCV) efficiency criterion is usually casually proposed as an alternative to the more widely recommended mean-standard deviation (MSD) definition of efficiency, as if MCV possessed an obvious intuitive appeal for some investors or some investment situations. The pervasiveness of references to CV as a risk measure is perplexing in light of the absence of utility theoretic underpinnings, especially by contrast with the substantial theoretical effort underlying the MSD notion of efficiency [3, 6, 8, 10, 11, 12].

Type
Research Article
Copyright
Copyright © School of Business Administration, University of Washington 1981

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References

REFERENCES

[1]Arrow, Kenneth J. “The Theory of Risk Aversion.” Essays in the Theory of Risk–Bearing, Chapter 3. Chicago: Markham Publishing Co. (1971).Google Scholar
[2]Baumol, William J. “An Expected Gain–Confidence Limit Criterion.” Management Science (10 1963), pp. 174182.CrossRefGoogle Scholar
[3]Hanoch, G., and Levy, H.. “The Efficiency Analysis of Choices Involving Risk.” Review of Economic Studies (1969), pp. 335346.Google Scholar
[4]Friend, I., and Blume, M. E.. “The Demand for Risky Assets.” American Economic Review, Vol. 65, No. 5 (12 1975), pp. 900922.Google Scholar
[5]Graves, Philip E.Relative Risk Aversion: Increasing or Decreasing.” Journal of Financial and Quantitative Analysis, Vol. 14 (06 1979), pp. 205214.CrossRefGoogle Scholar
[6]Levy, H., and Markowitz, H. M.. “Approximating Expected Utility by a Function of Mean and Variation.” American Economic Review, Vol. 69, No. 3 (06 1979), pp. 308317.Google Scholar
[7]Levy, H., and Sarnat, M.. Capital Investment and Financial Decisions. Englewood Cliffs, N.J.: Prentice–Hall, Inc. (1978).Google Scholar
[8]Markowitz, Harry. “Portfolio Selection.” Journal of Finance (03 1952), pp. 7791.Google Scholar
[9]Osteryoung, Jerome S.Capital Budgeting: Long–Term Asset Selection. Columbus, Ohio: Grid, Inc. (1974).Google Scholar
[10]Samuelson, Paul A.The Fundamental Approximation Theorem of Portfolio Analysis in Terms of Means, Variances, and Higher Moments.” The Review of Economic Studies, Vol. 37 (1970), pp. 537542.CrossRefGoogle Scholar
[11]Tobin, J. “Liquidity Preference as Behavior toward Risk.” Review of Economic Studies (1958), pp. 6586.Google Scholar
[12]Tsiang, S. C.The Rationale of the Mean–Standard Deviation Analysis, Skewness Preference, and the Demand for Money.” American Economic Review, Vol. 62 (06 1972), pp. 354371.Google Scholar
[13]VanHorne, James C.Financial Management and Policy, 4th ed.Englewood Cliffs, N.J.: Prentice–Hall, Inc. (1977).Google Scholar
[14]Wachowicz, J. M. Jr, and Shrieves, R. E.. “An Argument for ‘Generalized’ Mean–Coefficient of Variation (MCV) Analysis.” Financial Management, Vol. 9, No. 4 (Winter 1980), pp. 5158.CrossRefGoogle Scholar
[15]Weston, J. F., and Brigham, E. F.. Managerial Finance, 6th ed.Hinsdale, Ill.: The Dryden Press (1978).Google Scholar