Application of data mining techniques in stock markets: A survey

Ehsan Hajizadeh*, Hamed Davari Ardakani and Jamal Shahrabi

Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran.

*Corresponding author. E-mail: hajizadeh.ehsan@gmail.com,

ehsanhajizadeh@aut.ac.ir .

Accepted 5 July, 2010

 Abstract

One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potential significant benefits of solving these problems motivated extensive research for years. The research in data mining has gained a high attraction due to the importance of its applications and the increasing generation information. This paper provides an overview of application of data mining techniques such as decision tree, neural network, association rules, factor analysis and etc in stock markets. Also, this paper reveals progressive applications in addition to existing gap and less considered area and determines the future works for researchers.

Key words: Stock market, data mining, decision tree, neural network, clustering, association rules, factor analysis, time series.