This thesis investigates a new method for financial data simulation using novel neural network models developed by the author. Using two improved models for financial data simulation and prediction, the trigonometric polynomial higher order neural network group models have been developed. The theoretical principles of these improved models are presented and demonstrated in the thesis. It is the first attempt to use trigonometric polynomial high order neural network group models for financial data simulation. We could not find any references to using trigonometric polynomial high order neural network group models for financial data simulation in the extensive literature search conducted for this thesis, including a thorough Internet search on this topic. The author has developed a computer program, called 'THONG'. The program, running on X-windows, is based on the new neural network models developed, and also uses group models. This program allows users to apply in practice his new analysis and prediction method. The 'THONG' program is a user-friendly GUI system. All the steps of the operation in this system are easily controlled using a mouse. Both system operation and system mode can be viewed during the processing of data. THONG models have proven capable of handling high frequency, high order nonlinear and discontinuous data. The results of processing the experimental data using the THONG financial simulator are presented in the thesis. These results confirm that the THONG group models converge without difficulty, and are considerably more accurate than traditional neural network models. / Doctor of Philosophy (PhD)