One major problem in applying neural networks to financial applications is the large number of features involved. The feature set is large because we simply do not know which of the given features may be useful to the system and include rather than risk throwing away a potentially useful one. In practice, training with the full set of features usually introduces unnecessary complexity and often degraded prediction performance. An important contribution of this paper is to focus the attention of neural network researchers on the need for a systematic feature preprocessing methodology for the purpose of improving predictability. The approach taken in this paper is to select subsets of the full feature sets that improve the prediction. We discuss two different feature subset selection algorithms : the Penalty selection algorithm and the feature elimination algorithm. We explain the criterion MisMatch we use to evaluate a feature set. Both of the proposed algorithms are described using this criterion. Improved accuracies are obtained with both of the subsets compared to using all the features on the DM-US exchange rate Tuesday return prediction task. We describe current evaluation of the proposed techniques for actual trading.
Feature Reduction, Feature Ranking, Financial Forecast
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