Abstract
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. 2005 Springer Science + Business Media, Inc.
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CITATION STYLE
Stoltz, G., & Lugosi, G. (2005). Internal regret in on-line portfolio selection. Machine Learning, 59(1–2), 125–159. https://doi.org/10.1007/s10994-005-0465-4
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