Improving the genetic-algorithm-optimized wavelet neural network for stock market prediction

This paper improves stock market prediction based on genetic algorithms (GA) and wavelet neural networks (WNN) and reports significantly better accuracies compared to existing approaches to stock market prediction, including the hierarchical GA (HGA) WNN. Specifically, we added information such as trading volume as inputs and we used the Morlet wavelet function instead of Morlet-Gaussian wavelet function in our prediction model. We also employed a smaller number of hidden nodes in WNN compared to other research work. The prediction system is tested using Shenzhen Composite Index data.