ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (8): 1769-1778.doi: 10.7544/issn1000-1239.2020.20200494

所属专题: 2020数据挖掘与知识发现专题

• 人工智能 • 上一篇    

SCONV:一种基于情感分析的金融市场趋势预测方法

林培光,周佳倩,温玉莲   

  1. (山东财经大学计算机科学与技术学院 济南 250014) (llpwgh@163.com)
  • 出版日期: 2020-08-01
  • 基金资助: 
    国家自然科学基金项目(61802230)

SCONV: A Financial Market Trend Forecast Method Based on Emotional Analysis

Lin Peiguang, Zhou Jiaqian, Wen Yulian   

  1. (School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014)
  • Online: 2020-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China(61802230).

摘要: 股票市场是国家经济发展的重要组成部分,也是与我们日常生活息息相关的一个市场,股民的情绪一定程度上可以作为影响股票价格的因素之一.提出一种基于ConvLstm(convolutional long short term memory)的股票情感分析价格预测的深度学习模型SCONV(semantic convolutional).该模型通过爬取股民评价,使用LSTM(long short term memory)模型并通过word2vec,进行情感分析,提取情感向量,并得出每一日的情感权重.随后将每日股价分别与对应前1日、前3日均值、前一周均值的情感权重与股票价格一起放入ConvLstm中进行训练,再使用叠加的一层LSTM来增加准确率,并在ConvLstm与增加的LSTM之间增加dropout层,来避免过拟合.实验数据采用了3年左右阿里巴巴(BABA.us)、1.5年左右平安银行(000001.sh)、5个月左右格力电器(000651.sz),实验结果表明:相比一些传统模型,SCONV在较小的样本集上依旧可以更好地预测股票价格的走势.

关键词: 人工智能, 长短期记忆神经网络, 卷积, 股价预测, 深度学习

Abstract: The stock market plays a critical role in the economic development of countries, and it is also a market closely related to our daily life. The sentiment of shareholders may be judged as one of the factors affecting the stock price. This paper proposes a deep learning model of stock sentiment analysis price prediction based on convolution long short-term memory, named semantic convolution (SCONV). The model utilizes long short-term memory model and word2vec to analyze the emotion, extracts emotion vector, and to calculate the emotion weight of each day. Then we put the corresponding weights of the daily stock prices respectively to the average of the previous day, the previous three days, and the average of the previous week, together with the stock price into the ConvLstm. There is a dropout between ConvLstm and the increased LSTM to avoid over-fitting. In this paper, BABA.us, 000001.sh, 000651.sz are used as experimental data. BABA.us about 3 years, 000001.sh about 1.5 years and 000651.sz about 5 months are respectively implemented in the experiment. Compared with traditional models, the experimental results show that SCONV is still able to predict more precisely the trend of the stock price on a smaller sample set.

Key words: artificial intelligence, long-term and short-term memory neural network, convolution, stock price forecast, deep learning

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