A preference foundation for log mean-variance criteria in portfolio choice problems

A preference foundation for log mean-variance criteria in portfolio choice problems

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Article ID: iaor1995605
Country: Netherlands
Volume: 17
Issue: 5/6
Start Page Number: 887
End Page Number: 906
Publication Date: Sep 1993
Journal: Journal of Economic Dynamics and Control
Authors:
Keywords: portfolio analysis
Abstract:

The appropriate criterion for evaluating, and hence also for properly constructing, investment portfolios whose performance is governed by an infinite sequence of stochastic returns has long been a subject of controversy and fascination. A criterion based on the expected logarithm of one-period return is known to lead to exponential growth with the greatest exponent, almost surely; and hence this criterion is frequently proposed. A refinement has been to include the variance of the logarithm of return as well, but this has had no substantial theoretical justification. This paper shows that log mean-variance criteria follow naturally from elementary assumptions on an individual's preference relation for deterministic wealth sequences. As a first and fundamental step, it is shown that if a preference relation involves only the tail of a sequence, then that relation can be extended to stochastic wealth sequences by almost sure equality. It is not necessary to introduce a von Neumann-Morgenstern utility function or the associated axioms. It is then shown that if tail preferences can be described by a ‘simple’ utility function, one that is of the form equ1 where equ2 is wealth at period n, this utility must under suitable conditions be a function of the expected logarithm of return, independent of the functional form of equ3. Finally, ‘compound’ utility functions are introduced; and they are shown under suitable conditions to be functions of the expected value and variance of the logarithm of one-period return, again independently of the specific form of the underlying function. The infinite repetitions of the dynamic process essentially ‘hammer’ all utility functions into a log mean-variance form.

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