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With the easy access to share information and data, 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 companies' financial 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, a Data Mining Approach is 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 data mining techniques for classification and prediction purposes. Further, in conclusion, we can safely say that our stock selection and trading strategy outperformed the Australian Ordinary index.