SOM2W and RBF Neural Network-Based Hybrid Models and Their Applications to New Share Pricing

Abstract

In order to obtain a reasonable method for new share pricing, new hybrid models based on self-organizing map with 2 winners (SOM2W) and radial basis function (RBF) neural network with characteristics of intelligence are proposed and applied to new share pricing in this paper. To enhance the dynamic competition and clustering capability of SOM2W network, and improve the precision of solutions, a tabu-mapping method is also used to avoid the same output node to be mapped by more than one input. Firstly, we use SOM2W model to clustering for stocks. The financial indexes reflecting the whole performance level of companies are used in the simulated experiments, so that the level of each stock can be confirmed. Then we use RBF neural network to simulate the system of the black box of stock to make a price for stocks. Experimental results show that the proposed hybrid models could provide a feasible approach and reference basis for new share pricing, which has potential applications in the financial field.