Sequential optimizing investing strategy with neural networks
AbstractIn this paper we propose an investing strategy based on neural network models combined with ideas from game-theoretic probability of Shafer and Vovk. Our proposed strategy uses parameter values of a neural network with the best performance until the previous round (trading day) for deciding the investment in the current round. We compare performance of our proposed strategy with various strategies including a strategy based on supervised neural network models and show that our procedure is competitive with other strategies.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1002.2265.
Date of creation: Feb 2010
Date of revision:
Publication status: Published in Expert Systems with Applications 38 (2011) 12991-12998
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-02-27 (All new papers)
- NEP-CMP-2010-02-27 (Computational Economics)
- NEP-CSE-2010-02-27 (Economics of Strategic Management)
- NEP-GTH-2010-02-27 (Game Theory)
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