Universal portfolio algorithms in realistic-outcome markets

Ami Tavory, Meir Feder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Universal portfolio algorithms find investment strategies competitive against any CRP (constant rebalanced portfolio) for each and every market sequence. This work studies the problem of competitiveness over a subset of realistic, non-pathological, market sequences observed in many settings, e.g., high-frequency trading. Competitive investment in this setting will be shown to be more an extension of the easier universal 0-1 loss problem than of universal gambling (or coding). Analysis of realism-agnostic investment algorithms will show that they perform much better on in-hindsight realistic sequences than previously demonstrated. We suggest that this implies that the study of realistic universal portfolio algorithms must involve a comparison to a stronger adversary than the CRP adversary: an adversary that rebalances a portfolio often enough to avoid pathological sequences, but not so frequently that transaction costs dominate.

Original languageEnglish
Title of host publication2010 IEEE Information Theory Workshop, ITW 2010 - Proceedings
StatePublished - 2010
Event2010 IEEE Information Theory Workshop, ITW 2010 - Dublin, Ireland
Duration: 30 Aug 20103 Sep 2010

Publication series

Name2010 IEEE Information Theory Workshop, ITW 2010 - Proceedings


Conference2010 IEEE Information Theory Workshop, ITW 2010


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