《计算机应用》唯一官方网站 ›› 2021, Vol. 41 ›› Issue (12): 3632-3636.DOI: 10.11772/j.issn.1001-9081.2021061006

• 第十八届中国机器学习会议(CCML 2021) • 上一篇    

结合公司财务报表数据的股票指数预测方法

王基厚, 林培光(), 周佳倩, 李庆涛, 张燕, 蹇木伟   

  1. 山东财经大学 计算机科学与技术学院,济南 250014
  • 收稿日期:2021-05-12 修回日期:2021-08-17 接受日期:2021-08-25 发布日期:2021-12-28 出版日期:2021-12-10
  • 通讯作者: 林培光
  • 作者简介:王基厚(1996—),男,山东济南人,硕士研究生,主要研究方向:机器学习、金融数据分析
    周佳倩(1996—),女,浙江台州人,硕士研究生,主要研究方向:机器学习、数据挖掘
    李庆涛(1995—),男,山东济宁人,硕士研究生,主要研究方向:机器学习、金融数据分析
    张燕(1980—),女,湖南澧县人,副教授,博士,主要研究方向:Web数据集成、机器学习、区块链
    蹇木伟(1982—),男,山东临沂人,教授,博士,CCF会员,主要研究方向:人脸识别、图像和视频处理、机器学习、计算机视觉。

Stock index forecasting method based on corporate financial statement data

Jihou WANG, Peiguang LIN(), Jiaqian ZHOU, Qingtao LI, Yan ZHANG, Muwei JIAN   

  1. School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan Shandong 250014,China
  • Received:2021-05-12 Revised:2021-08-17 Accepted:2021-08-25 Online:2021-12-28 Published:2021-12-10
  • Contact: Peiguang LIN
  • About author:WANG Jihou, born in 1996, M. S. candidate. His research interests include machine learning, financial data analysis.
    ZHOU Jiaqian, born in 1996, M. S. candidate. Her research interests include machine learning, data mining.
    LI Qingtao, born in 1995, M. S. candidate. His research interests include machine learning, financial data analysis.
    ZHANG Yan, born in 1980, Ph. D., associate professor. Her research interests include Web data integration, machine learning, blockchain.
    JIAN Muwei, born in 1982, Ph. D., professor. His research interests include human face recognition, image and video processing, machine learning, computer vision.

摘要:

股票市场参与者的所有市场活动综合影响着股票市场的变化,使股票市场的波动充满复杂性,也使得准确预测股票价格成为难题。在这些影响股市变化的活动中,财务披露是预测股票指数变化的一种吸引人的且具有潜在财务回报的手段。为了应对股票市场的复杂变化,提出一种结合公司披露的财务报表数据进行股票指数预测的方法。该方法首先对股票指数历史数据和公司财务报表数据进行预处理,主要是对公司财务报表数据生成的高维矩阵进行降维,然后用双通道的长短期记忆(LSTM)网络对归一化后的数据进行预测研究。在上证50指数和沪深300指数数据集上的实验结果表明,该方法的预测效果优于仅使用股票指数历史数据的预测效果。

关键词: 股票指数预测, 财务报表分析, 数据降维, 长短期记忆网络, 双通道

Abstract:

All market activities of stock market participants combine to affect stock market changes, making stock market volatility fraught with complexity and making accurate prediction of stock prices a challenge. Among these activities that affect stock market changes, financial disclosure is an attractive and potentially financially rewarding means of predicting stock indexe changes. In order to deal with the complex changes in the stock market, a method of stock index prediction was proposed that incorporates data from financial statements disclosed by corporates. Firstly, the stock index historical data and corporate financial statement data were preprocessed, and the main task is dimension reduction of the high-dimensional matrix generated from corporate financial statement data, and then the dual-channel Long Short-Term Memory (LSTM) network was used to forecast and research the normalized data. Experimental results on SSE 50 and CSI 300 Index datasets show that the prediction effect of the proposed method is better than that using only historical data of stock indexes.

Key words: stock index prediction, financial statement analysis, data dimension reduction, Long Short-Term Memory (LSTM) network, dual-channel

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