Abstract

Stock market prediction is essential and of great interest because success- ful prediction of stock prices may promise smart benets. These tasks are highly complicated and very dicult. Many researchers have made valiant attempts in data mining to devise an ecient system for stock market movement analysis. In this paper, we have developed an ecient approach to stock market prediction by employing fuzzy C-means clustering and articial neural network. This research has been encouraged by the need of predicting the stock market to facilitate the investors about buy and hold strategy and to make prot. Firstly, the original stock market data are converted into interpreted historical (nancial) data i.e. via technical indi-cators. Based on these technical indicators, datasets that are required for analysis are created. Subsequently, fuzzy-clustering technique is used to generate dierent training subsets. Subsequently, based on dierent training subsets, dierent ANN models are trained to formulate dierent base models. Finally, a meta-learner, fuzzy system module, is employed to predict the stock price. The results for the stock market prediction are validated through evaluation metrics, namely mean absolute deviation, mean square error, root mean square error, mean absolute percentage error used to estimate the forecasting accuracy in the stock market. Comparative analysis is carried out for single Neural Network (NN) and existing technique neu- ral. The obtained results show that the proposed approach produces better results than the other techniques in terms of accuracy.