Stock Market Prediction using Machine Learning Concepts
Vidhyavani. A1, Deepak Adithya. K. N.2, Sateesh N.3, Vignesh Kannan4
1Vidhyavani, Assistant Professor, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
2Deepak Adithya.K.N., Batchelor of Technology, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
3Sateesh N, Batchelor of Technology, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
4Vignesh Kannan, Batchelor of Technology, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1050-1053 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6118048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Stock market plays a vital role in deciding the Economy of the company as well as the country, since some part of GDP of the country depends upon MNC’s present in the country. This paperwork is done in order to predict the future of the stock market using machine learning for accurate and profitable outcome. Stock market analysis is one of the toughest analysis since it not only depends upon the previous values of stock but also depends upon numerous other factors like sentiments of the company, projects undertaken by the company and many more. To predict stock market we used three algorithms. First is Least square support vector machine (LS-SVM) which will analyse the previous data of the stock market, second is Autoregressive moving average model (ARMA) for getting multiple predicted outcomes in terms of polynomial function, and the third is Particle swap optimization (PSO) for optimising the value which is obtained from Autoregressive moving average model. In accordance with the proposed system it is expected to get an accuracy of 25% – 30% in predicting the stock market. In future where earning money will be difficult but it still defines world’s view we can use this project to earn money through stock exchange and MNC’s. Investors can use this prediction to invest money on the Companies.
Keywords: Stock Market Analysis, Least Square Support Vector Machine (LS-SVM), Autoregressive Moving Average Model (ARMA), Particle Swap Optimization (PSO)
Scope of the Article: Machine Learning