Multi-Winners SOMs and Their Applications to Stock Analysis

In order to enhance the dynamic competition and clustering capability of self-organizing map （SOM） neural network and improve the precision of solutions, multi-winners SOM models are proposed by extending numbers of winners based on an unsupervised SOM neural network and by improving the neighbor function and weight function in the network. In addition, a tabu-mapping method is proposed to avoid that the same output node is mapped by more than one input. The clustering analysis for the stock is used to examine the effectiveness of the proposed models. The financial indexes reflecting the whole performance level of companies are used in the simulated experiments, including earning per share （EPS）, net asset per share （NAPS）, return on equity （ROE）, cash flow per share （CAPS） and net profit. Simulation results show that the clustering effect of the SOM with 2 winners （SOM2W） is the best compared with those from the standard SOM and other proposed multi-winner SOMs. Further analysis for the experimental results also shows that the clustering performance of the SOM2W is superior to the standard SOM and those of other models. The proposed model could provide a feasible approach for analyzing and selecting stock, which has potential applications in the financial field.