Portfolio selection and online learning

Tatsiana Levina, Glenn Shafer

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This paper studies a new strategy for selecting portfolios in the stock market. The strategy is inspired by two streams of previous work: (1) work on universalization of strategies for portfolio selection, which began with Thomas Cover's work on constant rebalanced portfolios, published in 1991,4 and (2) more general work on universalization of online algorithms, 17,21,23,30 especially Vladimir Vovk's work on the aggregating algorithm and Markov switching strategies.32 The proposed investment strategy achieves asymptotically the same exponential rate of growth as the portfolio that turns out to be best expost in the long run and does not require any underlying statistical assumptions on the nature of the stock market.

Original languageEnglish (US)
Pages (from-to)437-473
Number of pages37
JournalInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
Volume16
Issue number4
DOIs
StatePublished - Aug 2008

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Artificial Intelligence

Keywords

  • Investment strategies
  • Online learning
  • Portfolio selection
  • Universal portfolios

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