An Integrated Stock Market Forecasting Model Using Neural Networks

This thesis focuses on the development of a stock market forecasting model based on an Artificial Neural Network architecture. This study constructs a hybrid model utilizing various technical indicators, Elliott’s wave theory, sensitivity analysis and fuzzy logic. Initially a baseline network is constructed based on available literature. The baseline model is then improved by applying several useful information domains to the different models. Optimizations of the Neural Network models are performed by augmenting the network with useful information at every stage.