Download pdf
Research on Stock Price Forecast of General Electric Based on Mixed CNN-LSTM Model
- 1 School of Computer Science, Hubei University, Wuhan, China
- 2 School of Computer Science, Hubei University, Wuhan, China
- 3 Heinz College, Carnegie Mellon University, Pittsburgh, USA
- 4 Information School, University of Washington, USA
* Author to whom correspondence should be addressed.
Abstract
Accurate stock price prediction is crucial for investors and financial institutions, yet the complexity of the stock market makes it highly challenging. This study aims to construct an effective model to enhance the prediction ability of General Electric's stock price trend. The CNN - LSTM model is adopted, combining the feature extraction ability of CNN with the long - term dependency handling ability of LSTM, and the Adam optimizer is used to adjust the parameters. In the data preparation stage, historical trading data of General Electric's stock is collected. After cleaning, handling missing values, and feature engineering, features with strong correlations to the closing price are selected and dimensionality reduction is performed. During model training, the data is divided into training, validation, and testing sets in a ratio of 7:2:1. The Stochastic Gradient Descent algorithm is used with a dynamic learning rate adjustment and L2 regularization, and the Mean Squared Error is used as the loss function, evaluated by variance, R - squared score, and maximum error. Experimental results show that the model loss decreases steadily, and the predicted values align well with the actual values, providing a powerful tool for investment decisions. However, the model's performance in real - time and extreme market conditions remains to be tested, and future improvements could consider incorporating more data sources.
Keywords
Stock price prediction, CNN-LSTM model, Feature engineering, Stochastic Gradient Descent
[1]. Lu W, Li J, Li Y, et al. A CNN‐LSTM‐based model to forecast stock prices[J]. Complexity, 2020, 2020(1): 6622927.
[2]. Liu S, Zhang C, Ma J. CNN-LSTM neural network model for quantitative strategy analysis in stock markets[C]//Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II 24. Springer International Publishing, 2017: 198-206.
[3]. Mehtab S, Sen J. Stock price prediction using CNN and LSTM-based deep learning models[C]//2020 International Conference on Decision Aid Sciences and Application (DASA). IEEE, 2020: 447-453.
[4]. Baek H. A cnn-lstm stock prediction model based on genetic algorithm optimization[J]. Asia-Pacific Financial Markets, 2024, 31(2): 205-220.
[5]. Singh P, Jha M, Sharaf M, et al. Harnessing a hybrid CNN-LSTM model for portfolio performance: A case study on stock selection and optimization[J]. Ieee Access, 2023, 11: 104000-104015.
[6]. Lu W, Li J, Wang J, et al. A CNN-BiLSTM-AM method for stock price prediction[J]. Neural Computing and Applications, 2021, 33(10): 4741-4753.
[7]. Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270.
[8]. Greff K, Srivastava R K, Koutník J, et al. LSTM: A search space odyssey[J]. IEEE transactions on neural networks and learning systems, 2016, 28(10): 2222-2232.
[9]. He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2961-2969.
[10]. Alzubaidi L, Zhang J, Humaidi A J, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions[J]. Journal of big Data, 2021, 8: 1-74.
Cite this article
Hu,Z.;Shen,B.;Hu,Y.;Zhao,C. (2025). Research on Stock Price Forecast of General Electric Based on Mixed CNN-LSTM Model. Applied and Computational Engineering,108,202-208.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).