Universal constant rebalanced portfolios with switching

Suleyman S. Kozat, Andrew Carl Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we consider online (sequential) portfolio selection in a competitive algorithm framework. We construct a sequential algorithm for portfolio investment that asymptotically achieves the wealth of the best piecewise constant rebalanced portfolio tuned to the underlying individual sequence of price relative vectors. Without knowledge of the investment duration, the algorithm can perform as well as the best investment algorithm that can choose both the partitioning of the sequence of the price relative vectors as well as the best constant rebalanced portfolio within each segment based on knowledge of the sequence of price relative vectors in advance. We use a transition diagram similar to that in [1] to compete with an exponential number of switching investment strategies, using only linear complexity in the data length for combination. The regret with respect to the best piecewise constant strategy is at most O(ln(n)) in the exponent, where n is the investment duration. This method is also extended in [2] to switching among a finite collection of candidate algorithms, including the case where such transitions are represented by an arbitrary side-information sequence.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
DOIs
StatePublished - Aug 6 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Adaptive signal processing
  • Bayes procedures
  • Finance

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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