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A novel ensemble deep learning model for stock prediction based on stock prices and news
Open accessAbstract
In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. One of the most popular and complex deep learning in finance topics is future stock prediction. The difficulty that causes the future stock forecast is that there are too many different factors that affect the amplitude and frequency of the rise and fall of stocks at the same time. Some of the company-specific factors that can affect the share price like news releases on earnings and profits, future estimated earnings, the announcement of dividends, introduction of a new product or a product recall, secure a new large contract, employee layoffs, a major change of management, anticipated takeover or merger, and accounting errors or scandals. Furthermore, these factors are only company factors, and other factors affect the future trend of stocks, such as industry performance, investor sentiment, and economic factors. This paper proposes a novel deep learning approach to predict future stock movement. The model employs a blending ensemble learning method to combine two recurrent neural networks, followed by a fully connected neural network. In our research, we use the S&P 500 Index as our test case. Our experiments show that our blending ensemble deep learning model outperforms the best existing prediction model substantially using the same dataset, reducing the mean-squared error from 438.94 to 186.32, a 57.55% reduction, increasing precision rate by 40%, recall by 50%, F 1-score by 44.78%, and movement direction accuracy by 33.34%, respectively. The purpose of this work is to explain our design philosophy and show that ensemble deep learning technologies can truly predict future stock price trends more effectively and can better assist investors in making the right investment decision than other traditional methods.
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“But the vanishing gradient and exploding gradient problems often need to be addressed in RNN-based architectures (Li and Pan 2021;Zhu 2020).”
From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Li and Pan 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Both LSTM and GRU have their advantages due to their capability to eliminate the vanishing gradient problem and blend deep ensemble algorithms (Li and Pan 2021).”
From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Li and Pan 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Blending ensemble (LSTM+GRU) is a combination of two to of the most-used improvements to RNN and solves the vanishing gradient problem.Both of them can handle long-term dependence well, and combining them would improve forecasting; also, overfitting can be reduced.It may require high computational power and time to train both LSTM and GRU (Li and Pan 2021).”
From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Li and Pan 2021
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“XG-Boost + LSTM, blending ensemble (LSTM + GRU) (Li and Pan 2021), and ensemble techniques for sentiment analysis (Gite et al. 2023) are proposed by the research community for financial instrument price forecasting.”
From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Li and Pan 2021
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“The blending ensemble algorithm, which consists of a modified version of RNN, i.e., LSTM and GRU, has the following evaluation parameters: MSE = 186.32,MPA = 99.65,precision = 60%, Recall = 75%, F1-Score = 66.67% (Li and Pan 2021).”
From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Li and Pan 2021
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Citations
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Research works printed but not linked: 5
- Choi, H.: Stock price correlation coefficient prediction with ARIMA-LSTM hybrid model. arXiv:1808.01560 (2018) · arXiv
- Li, X., Li, Y., Yang, H., Yang, L., Liu, X.: DP-LSTM: differential privacy-inspired LSTM for stock prediction using financial news. arXiv:1912.10806 (2019) · arXiv
- Kirlić, A., Orhan, Z.: Measuring human and Vader performance on sentiment analysis. Invent. J. Res. Technol. Eng. Manag. (IJRTEM) 1, 42–46 (2017)
- Chung, J.,Çaglar, G., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555 (2014) · arXiv
- Dietterich, T.: Ensemble Learning, The Handbook of Brain Theory and Neural Networks, 2nd edn. The MIT Press, Cambridge (2002)
Cited by
Sorted by citations
Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023) · International Journal of Financial Studies · doi:10.3390/ijfs11030094 · Cited by 262
“The model showed the lowest RMSE value of 186.32, a precision of 60%, and an F1-score of 66.47 (Li and Pan 2021).”
BackgroundMethodInfluentialA Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model
Zaheer, S., Anjum, N., Hussain, S., Algarni, A. D., Iqbal, J., Bourouis, S., & Ullah, S. S. (2023) · Mathematics · doi:10.3390/math11030590 · Cited by 190
Ashtiani, M. N., & Raahemi, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119509 · Cited by 185
Rane, N. L., Choudhary, S. P., & Rane, J. (2024) · Studies in medical and health sciences. · doi:10.48185/smhs.v1i2.1225 · Cited by 150
Barjasteh, A., Ghafouri, S. H., & Hashemi, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107340 · Cited by 110
Metadata
- Authors
- Li, Y., & Pan, Y.
- Affiliations
- Georgia State University (US)
- Corresponding author
- Li, Yang
- Venue
- International Journal of Data Science and Analytics
- Published
- 2021-09-17
- Volume
- 13
- Issue
- 2
- Pages
- 139-149
- ISSN
- 2364-415X (ISSN-L)
- 2364-4168
- PMC
- PMC8446482
- OpenAlex
- W3123815889
- Cited by
- 247
- References
- 22
- Citations by year
- 2026: 26 · 2025: 60 · 2024: 67 · 2023: 62 · 2022: 25 · 2021: 4
- FWCI
- 24.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- International Journal of Data Science and Analytics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
- Keywords
- stock price prediction, ensemble deep learning, recurrent neural networks, business news, S&P 500, investor sentiment, investment decision support
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