Stock Market Price Prediction using Cyclic Self-Organizing Hierarchical CMAC

This paper analyses stock market price prediction based on a hierarchical cerebellar model arithmetic controller (HCMAC) neural network. Applications using stock market price prediction tools are required to be adaptive to new incoming data as well as have fast learning capabilities. Current popular neural networks are based on the Multi Layer Perceptron (MLP) structure which has low memory consumption and has a fast processing speed however the performance of the MLP deteriorates as the network expands. An HCMAC structure uses a direct memory mapping technique which would perform consistently fast independent of size of network. The drawback is a huge amount of memory is required to perform direct mapping. This can be reduced by using self-organizing techniques to optimize the clusters during each training cycle. The accuracy of the output can be controlled based on the values set in the cyclic self-organizing module. The cyclic self-organizing HCMAC (CSOHCMAC) combines both the HCMAC and cyclic self-organizing modules to create a neural network model that would be robust and fast as well as flexible to adapt to changes