东北大学学报（自然科学版）

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (8): 1201-1209.DOI: 10.12068/j.issn.1005-3026.2022.08.018

• 管理科学 • 上一篇    下一篇

中国投资者多角度舆情分析及其在股市预测中的作用

马源源1,2， 刘晏泽1,3， 刘呈隆1,2， 张甜洁1,2   

  1. (1. 东北大学 工商管理学院， 辽宁 沈阳110819； 2. 东北大学秦皇岛分校 经济学院， 河北 秦皇岛066004； 3. 东北大学秦皇岛分校 管理学院， 河北 秦皇岛066004)
  • 修回日期:2021-12-06 接受日期:2021-12-06 发布日期:2022-08-11
  • 通讯作者: 马源源
  • 作者简介:马源源(1983-)，女，辽宁沈阳人，东北大学副教授.
  • 基金资助:
    国家自然科学基金资助项目(71701036)； 中央高校基本科研业务费专项资金资助项目(N2123022).

Chinese Investors’ Multi-perspective Sentiment Analysis and Its Role in Stock Market Forecasting

MA Yuan-yuan1,2， LIU Yan-ze1,3， LIU Cheng-long1,2， ZHANG Tian-jie1,2   

  1. 1. School of Business Administration， Northeastern University， Shenyang 110819， China; 2. School of Economics， Northeastern University at Qinhuangdao， Qinhuangdao 066004， China; 3. School of Management， Northeastern University at Qinhuangdao， Qinhuangdao 066004， China.
  • Revised:2021-12-06 Accepted:2021-12-06 Published:2022-08-11
  • Contact: MA Yuan-yuan
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摘要： 股市中存在与投资者舆情有关的非理性现象，舆情与股市关系的量化研究对发掘股市规律和辅助投资预测具有重要意义.本文基于论坛中的投资者发言，创新性地建立CNN-TLDA混合模型对舆情进行多角度量化分析，从积极度和关注主题两方面探究投资者舆情和股市的相互影响关系，并基于长短时记忆(LSTM)网络对舆情在股市预测中的作用进行探讨.研究表明:中国股市投资者普遍悲观，投资者乐观度和关注主题都与股市高度相关.多角度舆情分析使预测误差下降至41%.研究成果能够辅助投资者的投资决策，也能为股市中个体投资者舆情的分析与利用提供科学参考.

关键词: 投资者舆情；卷积神经网络；LDA模型；长短时记忆网络；股市预测

Abstract: There are irrational phenomena related to investor sentiment in the stock market， and the quantitative study of investor sentiment and stock market is important for discovering stock market patterns and aiding investment forecasting. Based on the investor statements in forums， a CNN-TLDA hybrid model is innovatively built to quantify investor sentiment from multiple perspectives， and explore the interaction between investor sentiment and the stock market from both positive and topic. The roles of investor sentiment in forecasting are investigated based on LSTM (long short-term memory) network. It is shown that Chinese stock market investors are generally pessimistic， and both investor optimism and topics of interest are highly correlated with the stock market. The multi-perspective sentiment analysis reduces the prediction error to 41%. The results of the study can assist investors in their investment decisions and also provide a scientific reference for the analysis and utilization of individual investors′ sentiment in the stock market.

Key words: investor sentiment; CNN(convolutional neural network); LDA(latent Dirichlet allocation) model; LSTM(long short-term memory) network; stock market prediction

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