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zbMATH Open
Article . 1980
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Mathematics of Operations Research
Article . 1980 . Peer-reviewed
Data sources: Crossref
https://doi.org/10.1142/978981...
Part of book or chapter of book . 2011 . Peer-reviewed
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DBLP
Article . 2017
Data sources: DBLP
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Competitive Optimality of Logarithmic Investment

Competitive optimality of logarithmic investment
Authors: Robert M. Bell; Thomas M. Cover;

Competitive Optimality of Logarithmic Investment

Abstract

Consider the two-person zero-sum game in which two investors are each allowed to invest in a market with stocks (X1, X2, …, Xm) ∼ F, where Xi ≥ 0. Each investor has one unit of capital. The goal is to achieve more money than one’s opponent. Allowable portfolio strategies are random investment policies B ∈ Rm, B ≥ 0, E ∑ mi = 1Bi = 1. The payoff to player 1 for policy B1 vs. B2 is P {Bt1 X ≥ Bt2 X}. The optimal policy is shown to be B* = Ub*, where U is a random variable uniformly distributed on [0, 2], and b* maximizes E ln bt X over b ≥ 0, ∑ bi = 1. Curiously, this competitively optimal investment policy b* is the same policy that achieves the maximum possible growth rate of capital in repeated independent investments (Breiman [Breiman, L. 1961. Optimal gambling systems for favorable games. Fourth Berkeley Symposium. 1 65–78.] and Kelly [Kelly, J. 1956. A new interpretation of information rate. Bell System Tech. J. 917–926.]). Thus the immediate goal of outperforming another investor is perfectly compatible with maximizing the asymptotic rate of return.

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Keywords

two investors, asymptotic rate of return, competitive optimality, 2-person games, Operations research and management science, two-person zero-sum game, random investment policies, logarithmic investment, Other game-theoretic models, portfolio selection, optimal policy

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
97
Top 10%
Top 1%
Average
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