Downloads

https://doi.org/10.62836/iaet.v2i1.162
Li, Z. ., Yu, H., Xu, J., Liu, J., & Mo, Y. (2023). Stock Market Analysis and Prediction Using LSTM: A Case Study on Technology Stocks. Innovations in Applied Engineering and Technology, 2(1), 1–6. https://doi.org/10.62836/iaet.v2i1.162
Volume 2, Issue 1

Stock Market Analysis and Prediction Using LSTM: A Case Study on Technology Stocks

This research explores the application of Long Short-Term Memory (LSTM) networks for stock market analysis and prediction, focusing on four major technology stocks: Apple Inc. (AAPL), Google LLC (GOOG), Microsoft Corporation (MSFT), and Amazon.com Inc. (AMZN). Historical stock price data from Yahoo Finance spanning from January 1, 2012, to the present is utilized. The study aims to develop and evaluate an LSTM-based prediction model for forecasting future stock prices. The LSTM model consists of two LSTM layers with 128 and 64 units, respectively, followed by two dense layers. The model is trained using the Adam optimizer and mean squared error (MSE) loss function. Evaluation of the model is done using the root mean squared error (RMSE) metric. The results demonstrate the potential of LSTM models in capturing complex patterns in stock price movements and making reasonably accurate predictions.

stock market analysis; stock price prediction; long short-term memory (lstm); machine learning; financial analysis; time series data

References

  1. Liu S, Liao G, Ding Y. Stock Transaction Prediction Modeling and Analysis Based on LSTM. In Proceedings of the 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), Wuhan, China, 18–22 August 2018. DOI: https://doi.org/10.1109/ICIEA.2018.8398183
  2. Borovkova S, Tsiamas I. An Ensemble of LSTM Neural Networks for High‐Frequency Stock Market Classification. Journal of Forecasting 2019; 38(6): 600–619. DOI: https://doi.org/10.1002/for.2585
  3. Baek Y, Kim HY. ModAugNet: A New Forecasting Framework for Stock Market Index Value With an Overfitting Prevention LSTM Module and a Prediction LSTM Module. Expert Systems with Applications, 2018; 113: 457–480. DOI: https://doi.org/10.1016/j.eswa.2018.07.019
  4. Eapen J, Bein D, Verma A. Novel Deep Learning Model With CNN and Bi-Directional LSTM for Improved Stock Market Index Prediction. In Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 7–9 January 2019. DOI: https://doi.org/10.1109/CCWC.2019.8666592
  5. Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP. Stock Price Prediction Using LSTM, RNN and CNN-Sliding Window Model. In Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 13–16 September 2017. DOI: https://doi.org/10.1109/ICACCI.2017.8126078
  6. Nelson DM, Pereira AC, de Oliveira RA. Stock Market’S Price Movement Prediction With LSTM Neural Networks. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14–19 May 2017. DOI: https://doi.org/10.1109/IJCNN.2017.7966019
  7. Sherstinsky A. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network. Physica D: Nonlinear Phenomena 2020; 404: 132306. DOI: https://doi.org/10.1016/j.physd.2019.132306
  8. Nabipour M, Nayyeri P, Jabani H, Mosavi A, Salwana E, Shahab S. Deep Learning for Stock Market Prediction. Entropy 2020; 22(8): 840. DOI: https://doi.org/10.3390/e22080840
  9. Bhandari HN, Rimal B, Pokhrel NR, Rimal R, Dahal KR, Khatri RKC. Predicting Stock Market Index Using LSTM. Machine Learning with Applications 2020; 9: 100320. DOI: https://doi.org/10.1016/j.mlwa.2022.100320
  10. Althelaya KA, El-Alfy E-SM, Mohammed S. Evaluation of Bidirectional LSTM for Short-and Long-Term Stock Market Prediction. In Proceedings of the 2018 9th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 3–5 April 2018. DOI: https://doi.org/10.1109/IACS.2018.8355458
  11. Moghar A, Hamiche M. Stock Market Prediction Using LSTM Recurrent Neural Network. Procedia Computer Science 2020; 170: 1168–1173. DOI: https://doi.org/10.1016/j.procs.2020.03.049
  12. Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J. LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems2017; 28(10): 2222–2232. DOI: https://doi.org/10.1109/TNNLS.2016.2582924

Supporting Agencies

  1. Funding: Not applicable.