Stock Market Trend Prediction Model Using Data Mining Techniques

Oyelade Iyinoluwa* and Adewale Olumide Sunday

Received: November 26, 2019;   Published: December 12, 2019

DOI: 10.32474/CTCSA.2019.01.000122

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Abstract

Stock market prediction is essential and of great interest because successful prediction of stock prices may promise smart benefits. These tasks are highly complicated and very difficult. Many researchers have made valiant attempts in data mining to devise an efficient system for stock market movement analysis. This research has developed an efficient approach to stock market trend prediction by employing Frequent Pattern growth and Fuzzy C-means clustering algorithms. This research has been encouraged by the need of predicting the stock market to facilitate investors about when to buy, sell or hold a stock in order to make profit. Firstly, the original stock market data were converted into interpreted historical (financial) data via technical indicators. Based on these technical indicators, datasets that are required for analysis was created. Subsequently, Frequent Pattern Growth algorithm was used to generate frequent patterns. Based on these frequent patterns, Fuzzy C-means clustering technique was used to formulate the prediction model. Finally, a classification technique, K-Nearest Neighbor classifier was employed to predict the stock market trends. The results from the stock market trend prediction were validated through Hit ratio evaluation metric to estimate the prediction accuracy. Comparative analysis was carried out for the proposed model and a neural network model was used to benchmark the proposed model. The obtained results showed that proposed model produced better results than the neural network model in terms of accuracy. This paper has provided a novel approach which combines FP-Growth, Fuzzy C-means and K-Nearest Neighbor algorithms for stock market trend prediction.

Keywords: Fuzzy C-means Algorithm; FP-Growth Algorithm; K-Nearest Neighbor Algorithm; Stock Market Prediction

Abstract| Introduction| Related Works| Methodology of the Proposed Model| Model Architecture| Data Preprocessing| Frequent Pattern Mining| Fuzzy C-Means Clustering Algorithm| Model Implementation| Result and Discussion| Performance Evaluation| Conclusion| Future Work| References|