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Poster
Efficient Online Portfolio with Logarithmic Regret
Haipeng Luo · Chen-Yu Wei · Kai Zheng
We study the decades-old problem of online portfolio management and propose the first algorithm with logarithmic regret that is not based on Cover's Universal Portfolio algorithm and admits much faster implementation. Specifically Universal Portfolio enjoys optimal regret $\mathcal{O}(N\ln T)$ for $N$ financial instruments over $T$ rounds, but requires log-concave sampling and has a large polynomial running time. Our algorithm, on the other hand, ensures a slightly larger but still logarithmic regret of $\mathcal{O}(N^2(\ln T)^4)$, and is based on the well-studied Online Mirror Descent framework with a novel regularizer that can be implemented via standard optimization methods in time $\mathcal{O}(TN^{2.5})$ per round. The regret of all other existing works is either polynomial in $T$ or has a potentially unbounded factor such as the inverse of the smallest price relative.
Author Information
Haipeng Luo (University of Southern California)
Chen-Yu Wei (University of Southern California)
Kai Zheng (Peking University)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Spotlight: Efficient Online Portfolio with Logarithmic Regret »
Wed. Dec 5th 03:35 -- 03:40 PM Room Room 220 CD
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