Strategic - Risk, Return and Technical Analysis of Stock Prices

11 Pages Posted: 12 Jun 2019

See all articles by Sidharth Rai

Sidharth Rai

Chandigarh University - Apex Institute of Technology

Muskan Bansal

Chandigarh University - Apex Institute of Technology

Dilbag Singh

Apex Institute Of Technology, Chandigarh University,Gharuan,Mohali,Punjab

Manjit Kaur

Chandigarh University - Apex Institute of Technology

Date Written: February 25, 2019

Abstract

Risks and Returns are the parameters for observation of stocks or mutual funds for investors and traders before investments are made. Similarly, Technical analysis is a study to analyze the movements of the stock prices on the basis of historical behavior of the near past. The movement of stock prices sometimes low or high depends not only on company businesses but also on company-related news, socio-political conditions, natural disasters, and economic changes. In this paper, a novel machine learning approach is designed and implemented to forecast the stock market prices. This proposed model contains three parts. Initially, knowledge is obtained by using the technical analysis. Thereafter, regimes are developed to contemplate hypothetical parameters like Gaussian distribution, and Particle Swarm Optimization (PSO) is used to train the machine learning model. Finally, the Support Vector Machine (SVM) is also used to predict the trend of stock market. Experimental analyses reveal that the proposed technique can efficiently monitor the stock prices. The regime-based algorithm could be deployed for building an application to help investors in making an idea for making a strategy before investing.

Suggested Citation

Rai, Sidharth and Bansal, Muskan and Singh, Dilbag and Kaur, Manjit, Strategic - Risk, Return and Technical Analysis of Stock Prices (February 25, 2019). Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur - India, February 26-28, 2019, Available at SSRN: https://ssrn.com/abstract=3356304 or http://dx.doi.org/10.2139/ssrn.3356304

Sidharth Rai (Contact Author)

Chandigarh University - Apex Institute of Technology ( email )

India

Muskan Bansal

Chandigarh University - Apex Institute of Technology ( email )

Dilbag Singh

Apex Institute Of Technology, Chandigarh University,Gharuan,Mohali,Punjab ( email )

India

Manjit Kaur

Chandigarh University - Apex Institute of Technology ( email )

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