Abstract :
Neural network, K-nearest neighbor, naive Bayesian classifier and genetic algorithm evolving classification rules are compared for their prediction accuracies on stock exchange index data. The method yielding the best result, nearest neighbor, is then refined and incorporated into a simple trading system achieving returns above index growth. The success of the method hints the plausibility of nonlinearities present in the index series and, as such, the scope for nonlinear modeling/prediction.
