Title :
Classification and identification of stocks using SOM and genetic algorithm based backpropagation neural network
Author :
Khan, Asif Ullah ; Bandopadhyaya, T.K. ; Sharma, Sudhir
Author_Institution :
Dept. of Comput. Sci. & Eng., All Saints Coll. of Technol., Bhopal
Abstract :
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.
Keywords :
backpropagation; genetic algorithms; investment; pattern classification; self-organising feature maps; stock markets; SOM; backpropagation neural network; genetic algorithm; investment; self-organizing map; stock classification; stock identification; stock market; stock picking; Algorithm design and analysis; Backpropagation; Companies; Economic forecasting; Environmental economics; Genetic algorithms; Investments; Neural networks; Self organizing feature maps; Stock markets;
Conference_Titel :
Innovations in Information Technology, 2008. IIT 2008. International Conference on
Conference_Location :
Al Ain
Print_ISBN :
978-1-4244-3396-4
Electronic_ISBN :
978-1-4244-3397-1
DOI :
10.1109/INNOVATIONS.2008.4781644
