Title: Stock price prediction using historical data and news articles: a survey

Authors: Vijay Kumar Dwivedi; Manoj Madhava Gore

Addresses: Department of Computer Science and Engineering, MNNIT Allahabad, Prayagraj, UP 211004, India ' Department of Computer Science and Engineering, MNNIT Allahabad, Prayagraj, UP 211004, India

Abstract: Stock traders predict the price of a stock to maximise their trading profit in the stock market. Predicting a stock price is a complicated task as the price of a stock changes frequently and abruptly. The volatility of stock price is affected by various factors of social, economic, and political nature. The study of literature on stock price prediction reveals that existing models perform prediction by utilising datasets of historical data, news articles, or both. The predicted price is not always useful for stock traders for accomplishing their objectives. Numerous research have been done by various researchers to accurately predict the price of a stock in a consistent manner. However, there is still a scope of improvement in this area. This article reviews several approaches employed for prediction of stock price. The article also highlights the various open research issues and challenges, which may be helpful to the interested researchers.

Keywords: historical data; ensemble; machine learning; news article; stock price prediction; SPP.

DOI: 10.1504/IJCSYSE.2021.120289

International Journal of Computational Systems Engineering, 2021 Vol.6 No.4, pp.182 - 200

Received: 07 Dec 2020
Accepted: 22 Jul 2021
Published online: 13 Jan 2022 *

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