Discovering stock price prediction rules using rough sets
The use of computational intelligence systems such as neural networks, fuzzy set, genetic algorithins, etc., for stock market predictions has been widely established. This paper presents a generic stock pricing prediction model based on a rough set approach. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. The rough set reduction technique is applied to find all the reducts of the data which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. Using a data set consisting of the daily movements of a stock traded in Kuwait Stock Exchange, a preliminary assessment indicates that rough sets are shown to be applicable and is an effective tool to achieve this goal. For comparison, the results obtained using the rough set approach were compared to that of the neural networks algorithm and it was shown that the Rough set approach has a higher overall accuracy rate and generates more compact and fewer rules than the neural networks.
stock price prediction rough set analysis rule generation data mining rough reducts