Neural network analysis and the characteristics of market sentiment in the financial markets

The properties of a modular neural network system designed to forecast movements of the long gilt futures contract are examined. The examination illustrates that diagnostic measures can effectively inform end-users of the system when appropriately to switch modules given the likelihood of the degradation of forecasts. The modelling is based upon periods of market sentiment, and the inductive data models encapsulated, within the modules of the neural network system, are investigated The models track, and also provide knowledge of underlying market characteristics in different periods of market sentiment. The research provides deductions concerning the (dis-)similarity of underlying characteristics in differing periods of market sentiment.