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Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression

  • Jujie Wang EMAIL logo , Zhenzhen Zhuang , Dongming Gao , Yang Li and Liu Feng
Published/Copyright: May 30, 2022
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Studies in Nonlinear Dynamics & Econometrics
From the journal Studies in Nonlinear Dynamics & Econometrics Volume 27 Issue 3

Abstract

Stock price prediction has become a focal topic for relevant investors and scholars in these years. However, owning to the non-stationarity and complexity of stock price data, it is challenging to predict stock price accurately. This research develops a novel multi-scale nonlinear ensemble learning framework for stock price prediction, which consists of variational mode decomposition (VMD), evolutionary weighted support vector regression (EWSVR) and long short-term memory network (LSTM). The VMD is utilized to extract the basic features from an original stock price signal and eliminate the disturbance of illusive components. The EWSVR is utilized to predict each sub-signal with corresponding features, whose penalty weights are determined according to the time order and whose parameters are optimized by tree-structured Parzen estimator (TPE). The LSTM-based nonlinear ensemble learning paradigm is employed to integrate the predicted value of each sub-signal into the final prediction result of stock price. Four real prediction cases are utilized to test the proposed model. The proposed model’s prediction results of multiple evaluation metrics are significantly improved compared to other benchmark models both in stock market closing price forecasting.

Keywords: long short-term memory network; nonlinear ensemble; stock price prediction; tree-structured Parzen estimator; weighted support vector regression
Corresponding author: Jujie Wang, School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China, E-mail:

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 71971122 and 71501101

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research was supported by the National Natural Science Foundation of China (Grant No. 71971122 and 71501101).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2021-0096).

Received: 2021-11-02
Revised: 2022-05-05
Accepted: 2022-05-09
Published Online: 2022-05-30

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. On determination of the number of factors in an approximate factor model
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https://doi.org/10.1515/snde-2021-0096
Keywords for this article
long short-term memory network; nonlinear ensemble; stock price prediction; tree-structured Parzen estimator; weighted support vector regression

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. On determination of the number of factors in an approximate factor model
  4. Clean energy consumption and economic growth in China: a time-varying analysis
  5. Panel data models with two threshold variables
  6. What will drive global economic growth in the digital age?
  7. On the nonlinear relationships between shadow economy and the three pillars of sustainable development: new evidence from panel threshold analysis
  8. Tail behaviours of multiple-regime threshold AR models with heavy-tailed innovations
  9. Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression
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