Can We Learn to Beat the Best Stock
AbstractA novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can "beat the market" and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1107.0036.
Date of creation: Jun 2011
Date of revision:
Publication status: Published in Journal Of Artificial Intelligence Research, Volume 21, pages 579-594, 2004
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-07-13 (All new papers)
- NEP-CMP-2011-07-13 (Computational Economics)
- NEP-FMK-2011-07-13 (Financial Markets)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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