Fast universalisation of investment strategies

Karhan Akcoglu*, Petros Drineas, Ming-Yang Kao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

A universalization of a parameterized investment strategy is an online algorithm whose average daily performance approaches that of the strategy operating with the optimal parameters determined offline in hindsight. We present a general framework for universalizing investment strategies and discuss conditions under which investment strategies are universalizable. We present examples of common investment strategies that fit into our framework. The examples include both trading strategies that decide positions in individual stocks, and portfolio strategies that allocate wealth among multiple stocks. This work extends in a natural way Cover's universal portfolio work. We also discuss the runtime efficiency of universalization algorithms. While a straightforward implementation of our algorithms runs in time exponential in the number of parameters, we show that the efficient universal portfolio computation technique of Kalai and Vempala [Proceedings of the 41st Annual IEEE Symposium on Foundations of Computer Science, Redondo Beach, CA, 2000, pp. 486-491] involving the sampling of log-concave functions can be generalized to other classes of investment strategies, thus yielding provably good approximation algorithms in our framework.

Original languageEnglish (US)
Pages (from-to)1-22
Number of pages22
JournalSIAM Journal on Computing
Volume34
Issue number1
DOIs
StatePublished - 2005

Keywords

  • Computational finance
  • Constantly rebalanced portfolios
  • Investment strategies
  • Portfolio optimization
  • Portfolio strategies
  • Trading strategies
  • Universal portfolios

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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