In this paper we propose the FL-SMIA model. This is a novel neural network model that combines the principles of the Functional Link Neural Network (FLNN) with the Selforganizing Multilayer Neural Network using the Immune Algorithm (SMIA). We describe the FL-SMIA architecture and operation and evaluate its predictive performance on different financial time series in comparison to other neural network models. The FL-SMIA model combines the higher-order inputs of the tensor-product FLNN, i.e. the products of raw input features, with the self-organizing hidden layer of SMIA that dynamically grows and adapts to the input vectors. The FL-SMIA has two advantages over other models. First that it can dynamically adapt to growing data with model that grows increasingly complex. Second, it keeps an explicit representation of the patterns it recognises in the data. Experimental results show that FL-SMIA improves performance as measured by annualised return in five-days-ahead and one-day-ahead prediction tasks for share prices and exchange rates over the SMIA networks alone and over standard multilayer perceptrons. It performs on the same level as the FLNN, sometimes better but not significantly so. The result that FLNN and FL-SMIA outperform multilayer models indicates that particularly the higher-order features contribute to the improved performance and motivate further research into mixed neural network architectures for financial time series prediction.
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