Resource allocation neural network in portfolio selection
Expert Systems with Applications: An International Journal
A double-stage genetic optimization algorithm for portfolio selection
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
The optimality of non-additive approaches for portfolio selection
Expert Systems with Applications: An International Journal
A hybrid stock selection model using genetic algorithms and support vector regression
Applied Soft Computing
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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We introduce an asset-allocation framework based on the active control of the value-at-risk of the portfolio. Within this framework, we compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behavior, in conjunction with a traditional mean-variance allocator for constructing the portfolio. The second paradigm uses the network to directly make the portfolio allocation decisions. We consider a method for performing soft input variable selection, and show its considerable utility. We use model combination (committee) methods to systematize the choice of hyperparameters during training. We show that committees using both paradigms are significantly outperforming the benchmark market performance