Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12–14, 2024, Ningbo, China

Research Article

Multi-layer Feature Fusion method based on Convolutional Neural Network for Stock Trend Forecasting

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  • @INPROCEEDINGS{10.4108/eai.12-1-2024.2347222,
        author={Mengyang  Liu and Mingyan  Xu and Wen  Jiang and Feng  Zhao and Yan  Jiang and Yong  Zhang},
        title={Multi-layer Feature Fusion method based on Convolutional Neural Network for Stock Trend Forecasting},
        proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12--14, 2024, Ningbo, China},
        publisher={EAI},
        proceedings_a={BDEDM},
        year={2024},
        month={6},
        keywords={technical indicators feature fusion convolutional neural network stock trend forecastin},
        doi={10.4108/eai.12-1-2024.2347222}
    }
    
  • Mengyang Liu
    Mingyan Xu
    Wen Jiang
    Feng Zhao
    Yan Jiang
    Yong Zhang
    Year: 2024
    Multi-layer Feature Fusion method based on Convolutional Neural Network for Stock Trend Forecasting
    BDEDM
    EAI
    DOI: 10.4108/eai.12-1-2024.2347222
Mengyang Liu1, Mingyan Xu1, Wen Jiang1, Feng Zhao1, Yan Jiang1, Yong Zhang1,*
  • 1: Shandong Technology and Business University
*Contact email: 304043687@qq.com

Abstract

Stock trend prediction remains a crucial area of research in the financial domain, where the stock market is characterized by a plethora of indicators that describe various stock features. A key challenge lies in effectively capturing the interrelationships among these features and extracting them comprehensively from stock data. To tackle this, a novel multi-layer feature fusion method based on convolutional neural networks is proposed in this paper, which transforms the time series prediction problem into an image classification problem. Specifically, the representative indicators of stock features are first selected, and then the indicator data are generated into a two-dimensional matrix with different indicators and different times as two dimensions. Finally, the multi-layer feature fusion convolutional neural network model is constructed to classify the stock trends, in which the shallow convolutional features and deep convolutional features of this network are fused. Experiments on real data show that the proposed method can be more effective in fully capturing stock features to the extent of improving the prediction accuracy.