Bombay Stock Exchange of India

Abstract

Stock market nature is considered to be dynamic and susceptible to quick changes because it depends on various factors like share price, fundamental variables like P/E ratio, dividend yield etc. election results, rumors etc. Now a day's prediction is an important process which determines the future worth of a company. The successful prediction brings motivation and awareness in stock community as well as economic growth of the country. In past various theories and methods like Efficient Market Hypothesis (EMH), Random Walk Theory, fundamental and technical analyses have been proposed. These methods or combination of methods have not got as much success even yet because these methods are very complex and time consuming and performed well on short data. These days stock market users mostly rely on intelligent trading system which would be help them to predict share prices based on various situations and conditions. Data mining is a broad area and also supports various business intelligence techniques. It has mastery to raise various financial issues like buying/selling security, bond analysis, contract analyses etc. in this study various prediction techniques like linear regression, multiple regression, association rule mining, clustering, neural network have been proposed and their significant performances will be compared by Bombay Stock Exchange (BSE) data.