Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM)

A challenging and daunting task for financial investors is determining stock market timing - when to buy, sell and the future price of a stock. This challenge is due to the complexity of the stock market. New methods have emerged that increase the accuracy of stock prediction. Examples of these methods are fuzzy logic, neural network and hybridized methods such as hybrid Kohonen self organizing map (SOM), adaptive neuro-fuzzy inference system (ANFIS) etc. This paper presents a number of methods used to predict the stock price of the day. These methods are backpropagation, Kohonen SOM, and a hybrid Kohonen SOM. The results show that the difference in error of the hybrid Kohonen SOM is significantly reduced compared to the other methods used. Hence, the results suggest that the hybrid Kohonen SOM is a better predictor compared to Kohonen SOM and backpropagation