Stock Price Prediction using Machine Learning and Swarm Intelligence

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Background and Objectives

Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem.

Methods

In this paper, a novel machine learning approach, which works in two phases, is introduced to predict the price of a stock in the next day based on the information extracted from the past 26 days. In the first phase of the method, an automatic clustering algorithm clusters the data points into different clusters, and in the second phase a hybrid regression model, which is a combination of particle swarm optimization and support vector regression, is trained for each cluster. In this hybrid method, particle swarm optimization algorithm is used for parameter tuning and feature selection.

Results

The accuracy of the proposed method has been measured by 5 companies’ datasets, which are active in the Tehran Stock Exchange market, through 5 different metrics. On average, the proposed method has shown 82.6% accuracy in predicting stock price in 1-day ahead.

Conclusion

The achieved results demonstrate the capability of the method in detecting the sudden jumps in the price of a stock. The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

Language:
English
Published:
Journal of Electrical and Computer Engineering Innovations, Volume:8 Issue: 1, Winter-Spring 2020
Pages:
31 to 40
https://www.magiran.com/p2238991