Machine Learning

, Volume 59, Issue 1–2, pp 125–159

Internal Regret in On-Line Portfolio Selection



This paper extends the game-theoretic notion of internal regret to the case of on-line potfolio selection problems. New sequential investment strategies are designed to minimize the cumulative internal regret for all possible market behaviors. Some of the introduced strategies, apart from achieving a small internal regret, achieve an accumulated wealth almost as large as that of the best constantly rebalanced portfolio. It is argued that the low-internal-regret property is related to stability and experiments on real stock exchange data demonstrate that the new strategies achieve better returns compared to some known algorithms.


individual sequences internal regret on-line investment universal Portfolio EG strategy 

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Département de Mathématiques et ApplicationsEcole Normale SupérieureParisFrance
  2. 2.Department of EconomicsPompeu Fabra UniversityBarcelonaSpain

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