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

fYear

2008

fDate

16-18 Dec. 2008

Firstpage

292

Lastpage

296

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/INNOVATIONS.2008.4781644

Filename

4781644