Abstract
Every stock market investor wants to earn more profit from his/her investment. Investor tries different strategies to invest their money. Nowadays, many investors use computer algorithms based stock market prediction system to predict the future prices of stocks. Machine learning and artificial intelligence are one of the advanced and efficient techniques for stock price prediction. This paper will implement polynomial linear regression model and is compared with simple linear regression (SLR) machine learning model. The implementation and experimental results show that polynomial linear regression (PLR) model gives better prediction accuracy and results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang B, Gong Z-J, Yang W (2017) Stock market index prediction using deep neural network ensemble. In: 36th Chinese control conference, 26–28 July 2017
Nguyen TH, Shirai K (2015) Topic modeling based sentiment analysis on social media for stock market prediction. In: Proceedings of 7th international joint conference on natural language processing, Beijing, China, 26–31 July 2015, pp 1354–1364
Bhuriya D, Sharma A, Singh U (2017) Stock market prediction using linear regression. In: International conference on electronics, communication and aerospace technology ICECA 2017
Waqar M, Dawood H, Shahnawaz MB, Ghazanfar MA, Guo P (2017) Prediction of stock market by principal component analysis. In: 13th international conference on computational intelligence and security
Bommareddy SR, Reddy KSS, Kaushik P, Vinay Kumar KV, Hulipalled VR (2018) Predicting the stock price using linear regression. Int J Adv Res Comput Sci 9(3):81–85
Iacomin R (2015) Stock market prediction. In: Proceedings of 19th international conference on system theory, control and computing (ICSTCC), 14–16 Oct, Cheile Gradistei, Romania, pp 200–205
Shepal Y, Yatish B, Rahul K, Anis S (2018) Stock market prediction. Int J Res Eng Appl Manag. Special issue—iCreate
Ince H, Trafalis TB (2017) A hybrid forecasting model for stock market prediction. Econ Comput Econ Cybern Stud Res 51(3):263–280
Oliveira N, Cortez P, Areal N (2017) The impact of microblogging data for stock market prediction: using twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst Appl 73:125–144
Sun A, Lachanski M, Fabozzi FJ (2016) Trade the tweet: social media text mining and sparse matrix factorization for stock market prediction. Int Rev Fin Anal. https://doi.org/10.1016/j.irfa.2016.10.009
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Amrutphale, J., Rathore, P., Malviya, V. (2020). A Novel Approach for Stock Market Price Prediction Based on Polynomial Linear Regression. In: Shukla, R., Agrawal, J., Sharma, S., Chaudhari, N., Shukla, K. (eds) Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems, vol 100. Springer, Singapore. https://doi.org/10.1007/978-981-15-2071-6_13
Download citation
DOI: https://doi.org/10.1007/978-981-15-2071-6_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2070-9
Online ISBN: 978-981-15-2071-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)