Stock Price Prediction System

Authors

  • Prof. Sulochana Sonkamble  BE, Department of Computer Engineering, JSPM’s Rajarshi Shahu School of Engineering and Research, Pune, Maharashtra, India
  • Vaibhav Vyas  BE, Department of Computer Engineering, JSPM’s Rajarshi Shahu School of Engineering and Research, Pune, Maharashtra, India
  • Prathamesh Shimpi  BE, Department of Computer Engineering, JSPM’s Rajarshi Shahu School of Engineering and Research, Pune, Maharashtra, India
  • Aniket Mule  BE, Department of Computer Engineering, JSPM’s Rajarshi Shahu School of Engineering and Research, Pune, Maharashtra, India
  • Mihir Sonawane  

DOI:

https://doi.org//10.32628/CSEIT2390229

Keywords:

Principal component analysis, Linear regression, Root mean square error, r square value.

Abstract

To determine the future stock value of a company is the main purpose of stock price prediction there is a continuous change in the price of stocks which is affected by different industries and market conditions. The high dimensionality of data is a challenge for machine learning models because highly correlated dimensions/attributes may exert influence on precision of the model. PCA is used to reduce dimensionality to fit linear regression algorithm for future stock price prediction. Linear regression algorithm is used prior to and subsequent to implementation of Principal Component Analysis on the Tesla stock price data. Results manifest that production of machine learning models can be boosted by PCA, reducing the correlation and appropriate selection of principal components for high redundancy of data. Root mean square value and R-square value is used for assessment. Keywords: Principal component analysis, Linear regression, Root mean square error, r square value.

References

  1. Shah D, Isah H, Zulkernine F, “Predicting the effects of news sentiments on the stock market”, In2018 IEEE International Conference on Big Data (Big Data) 2018 Dec 10 (pp.4705-4708) IEEE.
  2. Mehtab S, Sen J, Dutta A, “Stock price prediction using machine learning and LSTM-based deep learning models. symposium on Machine Learning and Metaheuristics Algorithms, and Applications” , Springer, Singapore.2020 Oct 14 (pp. 88-106).
  3. NekoeiQachkanloo H, Ghojogh B, Pasand AS, Crowley M, “Artificial counselor system for stock investment”, InProceedings of the AAAI Conference on Artificial Intelligence 2019 Jul 17 (Vol. 33, No. 01, pp. 9558- 9564).
  4. Mehtab S, Sen J. A robust ,“Predictive model for stock price prediction using deep learning and natural language processing.” Available at SSRN 3502624. 2019 Dec 12.
  5. Chen S, Zhou C, “Stock prediction based on genetic algorithm feature selection and long short-term memory neural network”, IEEE Access. 2020 Dec 24;9:9066-72.
  6. R. Sathya, Prateek Kulkarni, Momin Nawaf Khalil, Shishir Chandra Nigam, “Stock Price Prediction using Reinforcement Learning and Feature Extraction”, IJRTE Volume-8 Issue-6, March 2020
  7. “Pattern Recognition in Stock Market”, D. Sorna Shanthi*1; T. Aarthi*2; A.K. Bhuvanesh*3; RA. Chooriya Prabha*4.2020.
  8. Chacón HD, Kesici E, Najafirad P, “Improving financial time series prediction accuracy using ensemble empirical mode decomposition and recurrent neural networks” ,IEEE Access. 2020 May 25;8:117133-45
  9. Yuan X, Yuan J, Jiang T, Ain QU, “ Integrated long-term stock selection models based on feature selection and machine learning algorithms for China stock market”, IEEE Access. 2020 Jan 24;8:22672-85.
  10. Ketter W, Collins J, Gini M, Gupta A, Schrater P, “A Computational Approach to Predicting Economic Regimes in Automated Exchanges”, 2005 Jan 1 (pp. 147-152).
  11. Li X, Li Y, Yang H, Yang L, Liu XY, “DP-LSTM: Differential privacy-inspired LSTM for stock prediction using financial news.”, arXiv preprint arXiv:1912.10806. 2019 Dec 20.
  12. Cho P, Park JH, Song JW., “Equity Research Report-Driven Investment Strategy in Korea Using Binary Classification on Stock Price Direction.” ,IEEE Access. 2021 Mar 22;9:46364-73.
  13. Blohm I, Riedl C, Füller J, Köroglu O, Leimeister JM, Krcmar H, “The effects of prediction market design and price elasticity on the trading performance of users: an experimental analysis.” , arXiv preprint arXiv:1204.3457. 2012 Apr 16.
  14. Chou JS, Truong DN, Le TL, “Interval forecasting of financial time series by accelerated particle swarmoptimized multi-output machine learning system.” ,IEEE Access. 2020 Jan 10;8:14798-808.
  15. Shen J, Shafiq MO, “Short-term stock market price trend prediction using a comprehensive deep learning system.”, Journal of Big Data. 2020 Dec;7(1):1-33

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-04-30

Issue

Volume 9, Issue 2, March-April-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. Sulochana Sonkamble, Vaibhav Vyas, Prathamesh Shimpi, Aniket Mule, Mihir Sonawane, " Stock Price Prediction System, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.273-277, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2390229