Sequential optimizing investing strategy with neural networks
In 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.
|Date of creation:||Feb 2010|
|Date of revision:|
|Publication status:||Published in Expert Systems with Applications 38 (2011) 12991-12998|
|Contact details of provider:|| Web page: http://arxiv.org/|
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