Stochastic Nonstationary Optimization for Finding Universal Portfolios

Alexei Gaivoronski () and Fabio Stella ()

Annals of Operations Research, 2000, vol. 100, issue 1, 165-188

Abstract: We apply ideas from stochastic optimization for defining universal portfolios. Universal portfolios are that class of portfolios which are constructed directly from the available observations of the stocks behavior without any assumptions about their statistical properties. Cover [7] has shown that one can construct such portfolio using only observations of the past stock prices which generates the same asymptotic wealth growth as the best constant rebalanced portfolio which is constructed with the full knowledge of the future stock market behavior. In this paper we construct universal portfolios using a different set of ideas drawn from nonstationary stochastic optimization. Our portfolios yield the same asymptotic growth of wealth as the best constant rebalanced portfolio constructed with the perfect knowledge of the future and they are less demanding computationally compared to previously known universal portfolios. We also present computational evidence using New York Stock Exchange data which shows, among other things, superior performance of portfolios which explicitly take into account possible nonstationary market behavior. Copyright Kluwer Academic Publishers 2000

Keywords: constant rebalanced portfolios; optimal growth; stochastic programming; nonstationary optimization (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (16)

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DOI: 10.1023/A:1019271201970

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