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Cited by 247Stock Market Forecasting MethodsFinancial Markets and Investment StrategiesFinancial Analysis and Corporate Governance

A novel ensemble deep learning model for stock prediction based on stock prices and news

Open access
Li, Yang, Pan, Yi·2022·International Journal of Data Science and Analytics
DOI 10.1007/s41060-021-00279-9PMC PMC8446482
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Abstract

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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Crossref · 10.1007/s41060-021-00279-9

Cites

19 of 20 references verified0 citation errors · 0 not checkable

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  • The vanishing gradient and exploding gradient problems often need to be addressed in RNN-based architectures.

    “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

    The Methodology section on gated recurrent units says GRU gates address the vanishing-gradient problem in traditional RNNs. Our copy does not say that RNN-based architectures also need to address exploding gradients, so it supports only part of the cited claim. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Both LSTM and GRU have advantages due to their capability to eliminate the vanishing gradient problem and blend deep ensemble algorithms.

    “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

    The Methodology’s GRU section says its gates eliminate the RNN vanishing-gradient problem, and the Architecture overview describes a blending ensemble that combines LSTM and GRU. The copy does not explicitly make the vanishing-gradient claim for LSTM, so it supports only part of what the sentence attributes to the paper. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Blending ensemble (LSTM+GRU) combines two of the most-used improvements to RNN and solves the vanishing gradient problem; both handle long-term dependence well, combining them would improve forecasting, and overfitting can be reduced; it may require high computational power and time to train.

    “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

    Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • XG-Boost + LSTM, blending ensemble (LSTM + GRU), and ensemble techniques for sentiment analysis are proposed by the research community for financial instrument price forecasting.

    “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

  • Blending ensemble (LSTM+GRU) achieved MSE 186.32, MPA 99.65, precision 60%, Recall 75%, F1-Score 66.67%.

    “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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Cites

Flagged first, then by citations

2 of the 22 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

  1. [2]Choi, H.: Stock price correlation coefficient prediction with ARIMA-LSTM hybrid model. arXiv:1808.01560 (2018) · arXiv
  2. [7]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
  3. [14]Chung, J.,Çaglar, G., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555 (2014) · arXiv
  4. [16]Dietterich T Ensemble Learning, The Handbook of Brain Theory and Neural Networks 2002 2 Cambridge The MIT Press

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Related

Authors
Li, Y., & Pan, Y.
Affiliations
Georgia State University (US)
Corresponding author
Li, Yang
ORCID
Venue
International Journal of Data Science and Analytics
Publisher
Springer Science and Business Media LLC
Published
2021-09-17
Volume
13
Issue
2
Pages
139-149
ISSN
  • 2364-415X (ISSN-L)
  • 2364-4168
DOI
10.1007/s41060-021-00279-9
PMC
PMC8446482
OpenAlex
W3123815889
Cited by
247
References
22
Citations by year
2026: 26 · 2025: 60 · 2024: 67 · 2023: 62 · 2022: 25 · 2021: 4 · 2020: 1
FWCI
24.02
Type
journal article
Language
en
Open access
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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