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
Study on the Applicability of LSTM for Predicting Stock Price when Facing Extreme Events
* Author to whom correspondence should be addressed.
Abstract
The analysis of stock price fluctuations holds considerable significance in the field of economics, particularly given the present environment characterized by unpredictability and rapid changes. Previously, the long short-term memory (LSTM) model has been employed effectively in addressing time series problems, including stock market forecasting. However, in the current dynamic landscape, the ability of LSTM to adapt to volatile conditions and provide accurate predictions is an area that merits further investigation. This study gathers stock data from prominent and representative companies, namely Apple, Google, Amazon, and Microsoft, spanning from January 2012 to March 2023. Specifically, two significant events are examined: the impact of the Covid-19 outbreak on the US stock market on February 26, 2020, and the Russia-Ukraine conflict occurring on February 26, 2022. By dividing the stock data surrounding these events into training and test sets, this research aims to evaluate the differential performance of LSTM in scenarios where it possesses no prior knowledge of these events versus situations where it has already assimilated the influence exerted by them.
Keywords
LSTM, stock prediction, Covid-19, machine learning
References
1. Qiao, R., (2019) Stock prediction model based on neural network, Operations Research and Management Science, vol. 28, no. 10, pp. 132–140.
2. Lu, W., et al. (2020) A CNN-LSTM-based model to forecast stock prices. Complexity, 2020: 1-10.
3. Hochreiter, S., et al. (1997) Long short-term memory, MIT Press, vol. 9, no. 8, pp. 1735–1780.
4. Prasannavenkatesan, T. (2020) Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model for the State of Tamilnadu, India, Research Square Platform LLC
5. Wang, F., Tax, D. M. J. (2016) Survey on the attention based RNN model and its applications in computer vision. arXiv preprint arXiv:1601.06823.
6. Yu, Q., Wang, J., Jin, Z., et al. (2022) Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training. Biomedical Signal Processing and Control, 72: 103323.
7. Patel, J. L., Goyal, R. K. (2007) Applications of artificial neural networks in medical science. Current clinical pharmacology, 2(3): 217-226.
8. Ta, V. D., Liu, C. M., Tadesse, D. A. (2020) Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 10(2): 437.
9. Gupta, N., et al. (2019) Integration of textual cues for fifine grained image captioning using deep CNN and LSTM, Neural Computing and Applications, vol. 12, pp. 1–10.
10. Wang, X., et al. (2022) Research on Stock Forecasting Based on LSTM-ARIMA Model Chengdu Jincheng University, Chengdu Sichuan, Advances in Social Sciences, 11(7), 2843-2856.
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
- 10.54254/2754-1169/47/20230365
- 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