基于投资者情感倾向的P2P市场成交量预测模型

计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 60-65.

基于投资者情感倾向的P2P市场成交量预测模型

张帅¹, 傅湘玲¹, 后羿²

北京邮电大学软件学院北京100876¹;
中国华融资产管理股份有限公司上海自贸试验区分公司上海200002²

出版日期:2019-06-14 发布日期:2019-07-02
通讯作者: 傅湘玲(1975-),女,博士,副教授,主要研究方向为文本挖掘、情感分析等,E-mail:fuxiangling@bupt.edu.cn
作者简介:张帅(1993-),男,硕士,主要研究方向为自然语言处理、机器学习;后羿(1989-),男,硕士,工程师,主要研究方向为金融大数据。
基金资助:
本文受国家自然科学基金(91546121),国家重点研发计划(2017YFB0803300),国家社会科学基金的重大研究课题(16ZDA055)资助。

Prediction Model of P2P Trading Volume Based on Investor Sentiment

ZHANG Shuai¹, FU Xiang-ling¹, HOU Yi²

School of Software,Beijing University of Posts and Telecommunications,Beijing 100876,China¹;
China Huarong Asset Management Co.Ltd.,Shanghai Pilot Free Trade Zone Branch,Shanghai 200002,China²

Online:2019-06-14 Published:2019-07-02

摘要/Abstract

摘要： 当前对Peer-to-Peer市场成交量的研究多种多样,但是常见方法只考虑了将投资者信息和市场信息作为特征,未考虑投资者情感变化与市场的关系。研究显示投资者的情感会对投资者的决策和行为产生深刻的影响。为此,以金融理论为基础,文中提出了基于投资者情感倾向预测P2P市场成交量的方法。首先以网贷之家的文本评论数据为研究对象,利用TextCNN模型对文本进行情感分类,得出情感倾向变化的时间序列,达到度量投资者情感变化趋势的目的;然后,通过格兰杰因果检验和皮尔逊相关系数验证投资者情感时间序列与成交量指数之间的关系;最终使用基于长短期记忆网络的预测模型预测Peer-to-Peer市场的成交量。实验结果表明,将情感特征加入到成交量预测模型能显著提高模型的预测能力。

关键词: P２P借贷, 情感分类, 深度学习, 自然语言处理

Abstract: There are many kinds of studies on the trading volume of Peer-to-Peer market.However,the common me-thods only take investor and market information as characteristics,and donot consider the relationship between investor sentiment changes and the market.The research shows that investors’ sentiments have a profound impact on their investment decisions and behaviors.Therefore,according to the financial theory,this paper proposed a method to predict the trading volume of Peer-to-Peer market based on investor’s sentimental tendency.Firstly,the comments of WangDaiZhiJia is taken as the research object and applied TextCNN model for sentiment classification.The time series of sentiment tendency is obtained,so as to achieve the purpose of measuring the trend of investor sentiment.Secondly,it verifies the relationship between investor’s emotion time series and trading volume index through Granger causality test and Pearson correlation coefficient.Finally,a predictive model based on long short term memory network is employed to predict the trading volume of the Peer-to-Peer market.The experimental results show that by adding sentimental features to the trading volume prediction model,the predictive ability of the model is improved significantly.

Key words: Deep learning, Natural language processing, Peer-to-Peer lending, Sentiment classification

中图分类号:

TP301

张帅, 傅湘玲, 后羿. 基于投资者情感倾向的P2P市场成交量预测模型[J]. 计算机科学, 2019, 46(6A): 60-65. https://doi.org/

ZHANG Shuai, FU Xiang-ling, HOU Yi. Prediction Model of P2P Trading Volume Based on Investor Sentiment[J]. Computer Science, 2019, 46(6A): 60-65. https://doi.org/

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