计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 143-148.

• 智能计算 • 上一篇    下一篇

基于外汇舆情的人民币汇率波动预测研究

成舟1, 余峥1, 过弋2,3,4, 王志宏2   

  1. (中汇信息技术(上海)有限公司 上海201203)1;
    (华东理工大学信息科学与工程学院 上海200237)2;
    (大数据流通与交易技术国家工程实验室 上海200237)3;
    (石河子大学信息科学与技术学院 新疆 石河子832003)4
  • 出版日期:2019-11-10 发布日期:2019-11-20
  • 通讯作者: 成舟(1989-),男,硕士,工程师,主要研究方向为自然语言处理、机器学习,E-mail:chengzhou_zh@chinamoney.com。
  • 基金资助:
    本文受国家自然科学基金项目(61462073)资助。

Research on Volatility Forecasting of RMB Exchange Rate Based on Public Opinion

CHENG Zhou1, YU Zheng1, GUO Yi2,3,4, WANG Zhi-hong2   

  1. (R&D Dept.II,CFETS Information Technology (Shanghai) Co.,Ltd,Shanghai 201203,China)1;
    (School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)2;
    (Business Intelligence and Visualization Research Center,National Engineering Laboratory for Big Data Distribution and Exchange Technologies,Shanghai 200237,China)3;
    (School of Information Science and Technology,Shihezi University,Shihezi,Xinjiang 832003,China)4
  • Online:2019-11-10 Published:2019-11-20

摘要: 舆情与金融市场波动之间的联系,对金融市场的监控、分析和异常发现有着重要的作用。外汇市场中,由于舆情的多样性和人民币汇率变化的复杂性,更好地量化舆情对汇率的影响对于实现人民币汇率的监测和分析有着重要的现实意义。首先对外汇舆情数据进行噪声过滤、分词等预处理,并基于汇率领域知识构建人民币汇率波动预测的特征,然后综合舆情的时效性和领域专家的知识设计了一种新的舆情对人民币汇率的影响力模型,并在此基础上实现了人民币汇率波动预测模型。实验结果表明,文中设计与实现的预测模型可以有效地对人民币汇率进行波动预测。

关键词: 汇率波动预测, 人民币汇率, 外汇市场, 舆情监测

Abstract: Public opinion has an impact on financial volatility,which plays an essential role in monitoring,analysis and anomaly detection for financial market.Due to the diversity of public opinion and the complexity of RMB exchange rate,how to quantify the impact of public opinion in a better manner has an important industrial significance for realizing the monitoring and analysis of the RMB exchange rate.This paper firstly performed pre-processing for public news of the foreign market,such as noise filtering,word segmentation.Meanwhile,it constructed a series of features for volatility forecasting of RMB exchange rate based on the domain knowledge of foreign exchange rate.Moreover,a novel influence model was proposed to represent the relationship between public opinion and RMB exchange rate.Finally,the volatility forecasting model is realized for RMB exchange rate on the real dataset.The experimental results testify that the proposed method can effectively forecast the volatility of RMB exchange rate.

Key words: Exchange rate volatility forecasting, Foreign market, Public opinion monitoring, RMB exchange rate

中图分类号: 

  • TP181
[1]李小文.汇率变化的经济影响分析[J].现代交际,2011(4):138-138.
[2]谢赤,王丽平.基于MS-MIDAS模型的人民币汇率预测研究[EB/OL].http://www.paper.ed-u.cn/releasepaper/content/201706-153.
[3]孟建钊.基于压缩感知的汇率预测与粗糙集加权的聚类研究[D].广州:华南理工大学,2017.
[4]王晴,朱家明.KNN算法在汇率预测中的应用及改进[J].兰州文理学院学报(自然科学版),2017,31(3):27-31.
[5]钱倩倩.基于向量自回归模型的人民币汇率预测研究[D].北京:北京外国语大学,2015.
[6]YANG M X,GAO Z,FINANCE S O.The empirical analysis of RMB-USD exchange rate forecasting based on ARMA model[J].Journal of Science of Teachers College & University,2018(4).
[7]LUO X.The Research of the Fluctuation Rules of USD/RMB Exchange Rate Series Based on GARCH Model[J].Application of Statistics & Management,2009,28(2):295-300.
[8]朱可飞.基于小波分析的人民币汇率预测方法研究[D].杭州:浙江工商大学,2014.
[9]SHIN T,HAN I.Optimal Signal Multi-Resolution by GeneticAlgorithms to Support Artificial Neural Network Models for Financial Forecasting[C]∥International Conference on Information Intelligence and Systems,1999.IEEE,2000:586-593.
[10]CAO D Z,PANG S L,BAI Y H.Forecasting exchange rate using support vector machines[C]∥International Conference on Machine Learning and Cybernetics.IEEE,2005:3448-34526.
[11]HUANG W,LAI K K,NAKAMORI Y,et al.Forecasting foreign exchange rates with artificial neural networks:a review[J].International Journal of Information Technology & Decision Making,2004,3(1):145-165.
[12]MAJHI R,PANDA G,SAHOO G.Efficient prediction of ex-change rates with low complexity artificial neural network mo-dels[J].Expert Systems with Applications,2009,36(1):181-189.
[13]王晴,朱家明.KNN算法在汇率预测中的应用及改进[J].兰州文理学院学报(自然科学版),2017,31(3):27-31.
[14]WUTHRICH B,PERMUNETILLEKE D,LEUNG S,et al Daily prediction of major stock indices from textual www data[J].HKIE Transaction,1998,5:151-166.
[15]LAVRENKO V,T SCHMILlM,LAWRIE D,et al.Mining of concurrent text and time series [C]∥KDD-2000 workshop on Text Mining.2000:37-44.
[16]赵丽丽,赵茜倩,杨娟,等,财经新闻对中国股价影响的定量分析[J].山东大学学报,2012,47:70-75.
[17]金雪军,祝宇,杨晓兰.网络媒体对股票市场的影响——以东方财富网股吧为例的实证研究[J].新闻与传播研究,2013(12):36-51.
[18]佟瑞鹏,谢贝贝,安宇.黑天鹅事件定义及分类的探讨[J].中国公共安全(学术版),2017(2).
[19]赵妍妍,秦兵,刘挺.文本情感分析[J].软件学报,2010,21(8):1834-1848.
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