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Article Dans Une Revue Machine Learning Année : 2005

Internal regret in on-line portfolio selection

Résumé

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.
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Dates et versions

hal-00007535 , version 1 (15-07-2005)

Identifiants

  • HAL Id : hal-00007535 , version 1

Citer

Gilles Stoltz, Gabor Lugosi. Internal regret in on-line portfolio selection. Machine Learning, 2005, 59, pp.125-159. ⟨hal-00007535⟩
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