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Stock price forecasting based on LLE-BP neural network model

Author

Listed:
  • Yu, Zhuoxi
  • Qin, Lu
  • Chen, Yunjing
  • Parmar, Milan Deepak

Abstract

Most of the factors affecting stock prices have data redundancy and nonlinear characteristics. Classical linear mapping dimensional reduction methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) cannot get good results for nonlinear problems. In​ this paper, a local linear embedding dimensional reduction algorithm (LLE) is selected to reduce the dimension of the factors affecting the stock price. The data after dimensional reduction is used as the new input variable of Back Propagation (BP) neural network to realize the stock price prediction. The prediction results are compared with the BP neural network model, PCA-BP model, and the traditional ARIMA (3,1,1) model. The results show that LLE-BP neural network model has higher prediction accuracy in stock price prediction, and it is an effective and feasible stock price prediction method.

Suggested Citation

  • Yu, Zhuoxi & Qin, Lu & Chen, Yunjing & Parmar, Milan Deepak, 2020. "Stock price forecasting based on LLE-BP neural network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    • Handle: RePEc:eee:phsmap:v:553:y:2020:i:c:s0378437120300376
    DOI: 10.1016/j.physa.2020.124197
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    Citations

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    Cited by:

    1. Hongjie Yi & Ke Zhang & Kun Ma & Lijian Zhou & Futong Tang, 2022. "Prediction of Natural Rubber Customs Declaration Price Based on Wavelet Decomposition and GA-BP Neural Network Group," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    2. Zeyu Wang & Yue Deng, 2022. "Optimizing Financial Engineering Time Indicator Using Bionics Computation Algorithm and Neural Network Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1755-1772, April.
    3. Dushmanta Kumar Padhi & Neelamadhab Padhy & Akash Kumar Bhoi & Jana Shafi & Muhammad Fazal Ijaz, 2021. "A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators," Mathematics, MDPI, vol. 9(21), pages 1-31, October.
    4. Huang, Wenyang & Gao, Tianxiao & Hao, Yun & Wang, Xiuqing, 2023. "Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices," Energy Economics, Elsevier, vol. 127(PA).
    5. Huei-Wen Teng & Yu-Hsien Li, 2023. "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, vol. 5(1), pages 149-182, March.
    6. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
    7. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.

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