Abstract—With an easy access to share information and data nowadays, many investors worldwide are interested in predicting stock prices. The prediction of stock prices using data mining techniques applied to technical variables has been widely researched but not much research to date has been done in applying data mining techniques to both technical and fundamental information. This paper is based on a personal approach to stock selection, using both technical and fundamental information. In this paper we construct a framework that enables us to make class predictions about industrial stock performances. In order to have a systemized approach for the selection of stocks and a high likelihood of the performance of the stock price increasing, several analytical techniques are applied. A trading strategy is also designed and the performance of the stocks evaluated. Our two goals are to validate our stock selection methodology and to determine whether our trading strategy allows us to outperform the Australian market. Simulation results show that our selected stock portfolios outperform the Australian All-Ordinaries Index. Our findings justify the use of analytics for classification and prediction purposes. Further, in conclusion, we can safely say that our stock selection and trading strategy outperformed the Australian Ordinary index.

Index Terms—stock price prediction, stock selection, stock market, analytics, decision trees, neural networks, logistic regression, trading strategy  

Cite: Carol Hargreaves and Yi Hao, "Prediction of Stock Performance Using Analytical Techniques," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 2, pp.136-142, May 2013. doi:10.4304/jetwi.5.2.136-142