Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 47 , 01 December 2023
Open
Access | Article
Stock Price Prediction During the COVID-19 Pandemic Based on the GRU Algorithm
* Author to whom correspondence should be addressed.
Abstract
Stock price prediction during the Covid-19 pandemic has emerged as a significant research domain within the financial sector, giving rise to a multitude of neural network-based methods aimed at forecasting stock prices. This paper uses multiple machine learning models and analyzes the stock fund flows to attempt to learn and predict price trends from a dynamic perspective. The data used in this study includes the closing price, change rate, and fund flow of an A-share Pharmaceutical stock in the most recent 1000 trading days. The Mean Squared Error (MSE) is used as the model evaluation metric, and the model’s MSE can reach 0.013 after training. The predictions generated by the model exhibit a high degree of alignment with the actual price trends, indicating its accuracy in short-term price trend prognostication. These findings substantiate the efficacy of the model for stock price prediction during the pandemic period, thereby contributing to the body of knowledge within the field of financial forecasting.
Keywords
GRU, RNN, stock price prediction
References
1. Liu, T., Siegel, E., Shen, D. (2022) Deep learning and medical image analysis for COVID-19 diagnosis and prediction. Annual review of biomedical engineering, 24: 179-201.
2. Qiu, Y., Yang, Y., Lin, Z., et al. (2020) Improved denoising autoencoder for maritime image denoising and semantic segmentation of USV. China Communications, 17(3): 46-57.
3. D’Amato, V., Levantesi, S., Piscopo, G. (2022) Deep learning in predicting cryptocurrency volatility. Physica A: Statistical Mechanics and its Applications, 596: 127158.
4. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017) Attention is all you need. Advances in neural information processing systems, 30.
5. Akhil, S. & Purva, R. (2018) Application of LSTM, GRU and ICA for Stock Price Prediction Information and Communication Technology for Intelligent Systems pp 479–487
6. Ya, G., Rong. W., and Enmin, Z. (2021) Stock Prediction Based on Optimized LSTM and GRU Models Scientific Programming/2021/Article.
7. Sherstinsky, A. (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404: 132306.
8. Williams, G., Baxter, R., He, H., et al. (2022) A comparative study of RNN for outlier detection in data mining, 2002 IEEE International Conference on Data Mining, Proceedings. IEEE, 709-712.
9. Aston, Z., et al. (2021) Dive Into Deep Learning, arXiv:2106.11342
10. Dey, R., Salem, F. M. (2017) Gate-variants of gated recurrent unit (GRU) neural networks, 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE, 1597-1600.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International 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 Instruction).
- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-141-4
- ISBN (Online)
- 978-1-83558-142-1
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/47/20230370
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited