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A Feature Fusion Based Forecasting Model for Financial Time Series

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  • Zhiqiang Guo
  • Huaiqing Wang
  • Quan Liu
  • Jie Yang

Abstract

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models.

Suggested Citation

  • Zhiqiang Guo & Huaiqing Wang & Quan Liu & Jie Yang, 2014. "A Feature Fusion Based Forecasting Model for Financial Time Series," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
    • Handle: RePEc:plo:pone00:0101113
    DOI: 10.1371/journal.pone.0101113
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    References listed on IDEAS

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    1. Colin Lizieri & Stephen Satchell & Qi Zhang, 2007. "The Underlying Return‐Generating Factors for REIT Returns: An Application of Independent Component Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(4), pages 569-598, December.
    2. Lu, Chi-Jie & Wang, Yen-Wen, 2010. "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, Elsevier, vol. 128(2), pages 603-613, December.
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    Citations

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    Cited by:

    1. Linwei Li & Paul-Amaury Matt & Christian Heumann, 2022. "Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization," Papers 2204.12914, arXiv.org, revised May 2022.
    2. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    3. Jacinta Chan Phooi M’ng & Mohammadali Mehralizadeh, 2016. "Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-29, June.
    4. Wudi Wei & Junjun Jiang & Hao Liang & Lian Gao & Bingyu Liang & Jiegang Huang & Ning Zang & Yanyan Liao & Jun Yu & Jingzhen Lai & Fengxiang Qin & Jinming Su & Li Ye & Hui Chen, 2016. "Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-13, June.
    5. Zhiqiang Guo & Huaiqing Wang & Jie Yang & David J Miller, 2015. "A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-19, April.
    6. Luca Di Persio & Matteo Garbelli & Kai Wallbaum, 2021. "Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis," Risks, MDPI, vol. 9(2), pages 1-16, February.
    7. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    8. Mahla Nikou & Gholamreza Mansourfar & Jamshid Bagherzadeh, 2019. "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 164-174, October.
    9. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
    10. Manogna R L & Aswini Kumar Mishra, 2021. "Forecasting spot prices of agricultural commodities in India: Application of deep‐learning models," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 72-83, January.

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