A Novel Approach to predict the Stock Price using LSTM and Linear Regression

Authors

  • Preetjot Kaur  Department of CSE, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India
  • Karan Marwaha  Department of CSE, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India
  • Keshav Kumar  Department of CSE, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India

DOI:

https://doi.org//10.32628/IJSRSET2411111

Keywords:

Stock Price Prediction, Machine Learning, Linear Regression, long short-term memory

Abstract

Stock price prediction is a challenging and crucial task in financial markets. Traditional methods often struggle to capture the complex patterns present in stock price movements. In this study, we propose a hybrid model combining Long Short-Term Memory (LSTM) and Linear Regression techniques to improve the accuracy and robustness of stock price predictions. We evaluate the performance of our hybrid model using historical stock price data and compare it with individual LSTM and linear regression models. The experiments demonstrate that the hybrid model outperforms the standalone models in terms of accuracy and robustness.

References

  1. Conrad, J., & Kaul, G. (1998). An anatomy of trading strategies. Review of Financial Studies, 11, 489–515
  2. Roondiwala, Murtaza, Harshal Patel, and Shraddha Varma. "Predicting stock prices using LSTM." International Journal of Science and Research (IJSR) 6.4 (2017): 1754-1756.
  3. Dai, Z., Zhou, H., Wen, F., & He, S. (2020a). Efficient predictability of stock return volatility: The role of stock market implied volatility. The North American Journal of Economics and Finance, 52, 101174.
  4. Dangl, T., & Halling, M. (2012). Predictive regressions with time-varying coefficients. Journal of Financial Economics, 106,
  5. Masoud, Najeb MH. (2017) “The impact of stock market performance upon economic growth.” International Journal of Economics and Financial Issues 3 (4) : 788–798.
  6. Mr. Amit B. Suthar, Ms. Hiral R. Patel, Dr. Satyen M. Parikh, “A Comparative Study on Financial Stock Market Prediction Models”, 2012.
  7. Vinod Mehta at el. , “Stock Price Prediction Using Regression And Artificial Neural Network”, 2017.
  8. Ryo Akita, Akira Yoshihara, Takashi Matsubara, Kuniaki Uehara, “Deep learning for stock prediction using numerical and textual information”, 2016.
  9. Oyeyemi, Elijah O., Lee-Anne McKinnell, and Allon WV Poole. (2007) “Neural network-based prediction techniques for global modeling of M (3000) F2 ionospheric parameter.” Advances in Space Research 39 (5) : 643-650.
  10. Zhang, G. Peter. (2003) “Time series forecasting using a hybrid ARIMA and neural network mode.” Neurocomputing 50 : 159-175.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2024-02-29

Issue

Volume 11, Issue 1, January-February-2024

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Preetjot Kaur, Karan Marwaha, Keshav Kumar, " A Novel Approach to predict the Stock Price using LSTM and Linear Regression, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 11, Issue 1, pp.118-123, January-February-2024. Available at doi : https://doi.org/10.32628/IJSRSET2411111