北京航空航天大学学报(社会科学版) ›› 2018, Vol. 31 ›› Issue (3): 55-60.DOI: 10.13766/j.bhsk.1008-2204.2016.0228

• 经济与管理 • 上一篇    下一篇

中国A股市场结构性突变研究

方兆本, 王传好   

  1. 中国科学技术大学 管理学院, 安徽 合肥 230026
  • 收稿日期:2016-06-15 出版日期:2018-05-25 发布日期:2018-05-25
  • 作者简介:方兆本(1945-),男,浙江金华人,教授,博士,研究方向为金融工程.
  • 基金资助:

    国家自然科学基金青年科学基金资助项目(71001095)

Research on Structural Change of China's A-share Market

FANG Zhaoben, WANG Chuanhao   

  1. School of Management, University of Science and Technology of China, Hefei Anhui 230026, China
  • Received:2016-06-15 Online:2018-05-25 Published:2018-05-25

摘要:

中国A股市场近几个月以来表现出非常大的波动性,经常出现千股跌停或者千股涨停的现象,能不能基于股市之前的历史信息提前发现涨跌拐点?尝试利用Relative unconstrained Least-Squares Importance Fitting(RuLSIF) 模型,以六维矩阵为样本,基于时间序列样本间的非参数密度函数比估计A股市场的结构性变点去分析中国股市的结构变点问题。实证结果表明,自从A股成立以来,2014年10月16日为市场结构突变最大点,即从该日开始,A股市场的市场结构已经发生本质的改变,和之前的市场结构具有很大的不同。模型基于样本内数据得出的样本外预测与实际走势较吻合,进而模型可以为实际投资中对阶段性投资策略的调整提供参考依据。

关键词: 中国股市, A股市场, 市场波动性, RuLSIF模型, 六维矩阵, 非参数密度函数比, 结构变点

Abstract:

In recent months, China's A share showed a very large volatility with more than 1 000 shares falling by their 10 percent daily limit or increasing by their daily 10 percent limit usually on the same day. Is it possible to find stock market structural change point in advance based on historical information? For each component, a certain RuLSIF model is built. Taking the six dimension matrix as the sample, the non parametric density function ratio of the time series sample is estimated to be the structural change point of A stock market. Empirical results show that since the establishment of A shares, October 16 2014 has been the market structure mutation points. That is, beginning from that day, A-share market structure has undergone fundamental changes, to be very different from the market structure before. The model based on the sample data obtained from the sample forecast and the actual trend is consistent, and then the model can provide reference for the adjustment of the investment strategy in the actual investment.

Key words: China's Share Market, A-share Market, volatility, RuLSIF model, six dimension matrix, non parametric density function ratio, structural change point

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