Classification and identification of stocks using SOM and genetic algorithm based backpropagation neural network

Investment in stock market is one of the most popular type of investment. There are many conventional techniques being used and these include technical and fundamental analysis. The main aim of every investor is to earn maximum possible return on investments. The main issue with any approach is the proper weighting of criteria to obtain a list of stocks that are suitable for investments. This paper proposes an improved method for stock picking using self-organizing maps and genetic algorithm based backpropagation neural networks. The stock selected using self-organizing maps and genetic algorithm based backpropagation neural networks outperformed the BSE-30 Index by about 30.17% based on one and half month of stock data.