Adaptive supervised learning decision networks for traders and portfolios

We propose an adaptive supervised learning decision network for portfolio management which learns the best past investment decision directly instead of making a good prediction first and then making an investment decision based on the prediction. Without any extra effort, this network can be realized directly by any existing adaptive supervised learning neural networks. We propose to use an extended normalized radial basis function (ENRBF) network with matched competitive learning (MCL). We demonstrate with experimental results that the proposed approach can bring in appreciable profit on trading in the foreign exchange market