International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

An Approach for Stock Price Forecast using Long Short Term Memory

  • K.A.Surya Rajeswar (Department of Management Studies, Hindustan Institute of Technology and Science) ;
  • Pon Ramalingam (Department of Management Studies, Hindustan Institute of Technology and Science) ;
  • Sudalaimuthu.T (Department of Computer Science and Engineering, Hindustan Institute of Technology and Science)
  • Received : 2023.04.05
  • Published : 2023.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

Keywords

References

  1. Althelaya, K. A., & El-Alfy, E.-S. M, (2018) Evaluation of bidirectional LSTM for short-and longterm stock market prediction. IEEE International Conference on Information and Communication Systems (ICICS), 71-78.
  2. Jiang. (2020). Applications of deep learning in stock market prediction: recent progress. Statistical Finance.
  3. Kang Zhanga, Guoqiang Zhonga, Junyu Donga, ShengkeWangaYongWanga, (2019). Stock Market Prediction Based on Generative Adversarial Network,Procedia Computer Science, 147, 400-406. https://doi.org/10.1016/j.procs.2019.01.256
  4. EhsanHoseinzade, Saman Haratizadeh. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129(1).
  5. P.Gandhmal, K.Kumar, D. (2019). "Systematic analysis and review of stock market prediction techniques", Computer Science and Review, 134.
  6. Zhang, Xi, Yunjia Zhang, Senzhang Wang, Yuntao Yao, Bin Xing Fang and Philip S. Yu(2018) "Improving stock market prediction via heterogeneous information fusion." Knowl. Based Syst. 143, 236-247. https://doi.org/10.1016/j.knosys.2017.12.025
  7. Nabipour, M., P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and Shahab S. 2020. "Deep Learning for Stock Market Prediction" Entropy 22, no. 8: 840.
  8. Chung, Hyejung, and Kyung-shik Shin. 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction" Sustainability 10, no. 10: 3765.
  9. Li, Xiaodong, Pangjing Wu and Wenpeng Wang,(2020) "Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong." Inf. Process. Manag. 57: 102212.5).
  10. Xingyu Zhou, Zhisong Pan, Guyu Hu, Siqi Tang, and Cheng Zhao (2018). "Stock Market Prediction on HighFrequency Data Using Generative Adversarial Nets. Computational Intelligence in Data-Driven Modelling and Its Engineering Applications.
  11. Pang, Xiong Wen, Yanqiang Zhou, Pan Wang, Weiwei Lin and Victor I. C. Chang(2018). "An innovative neural network approach for stock market prediction." The Journal of Supercomputing 76 : 2098-2118.
  12. Moghar, Adil, Hamiche, Mhamed (2020). Stock Market Prediction Using LSTM Recurrent Neural Network. Procedia Computer Science, 170, 1168-1173. https://doi.org/10.1016/j.procs.2020.03.049
  13. J. Lee, R. Kim, Y. Koh and J. Kang (2019), "Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network," in IEEE Access, vol. 7, pp. 167260-167277. https://doi.org/10.1109/ACCESS.2019.2953542
  14. Nam, Ki-Eun and Nohyoon Seong (2019), "Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market." Decis. Support Syst. 117: 100-112. https://doi.org/10.1016/j.dss.2018.11.004
  15. Ananthi, M. & Vijayakumar, K.. (2021), "Stock market analysis using candlestick regression and market trend prediction (CKRM)". Journal of Ambient Intelligence and Humanized Computing. 12.
  16. Wei Huang, Yoshiteru Nakamori, and Shou-Yang Wang. (2015), "Forecasting stock market movement direction with support vector machine". Comput. Oper. Res. 32, 10, 2513-2522. https://doi.org/10.1016/j.cor.2004.03.016
  17. Mohamed M. Mostafa, (2016) Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait. Expert Syst. Appl. 37, 9, 6302-6309. https://doi.org/10.1016/j.eswa.2010.02.091
  18. Hyun Joon Jung, Aggarwal J.K (2017), "A Binary Stock Event Model for stock trends forecasting: Forecasting stock trends via a simple and accurate approach with machine learning", 11th International Conference on Intelligent Systems Design and Applications (ISDA), Pages 714- 719, 2011.
  19. Jigar Patel, Sahil Shah, Priyank Thakkar , K Kotecha (2015),"Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques", Expert Systems with Applications, Volume 42, Issue 1, Pages 259-268. https://doi.org/10.1016/j.eswa.2014.07.040
  20. Shin-Fu Wu, Shie-Jue Lee(2015), "Employing local modeling in machine learning-based methods for timeseries prediction", Expert Systems with Applications, Volume 42, Issue 1, Pages 341-354. https://doi.org/10.1016/j.eswa.2014.07.032