中国股市牛熊市的周期性转换特征——基于DMCPSO-HSMM模型  

The Cyclical Transition Characteristics of the Bull and Bear States in China’s Stock Market:Based on the DMCPSO-HSMM Model

作　　者：杨杰 冯芸[1] 杨豪 Yang Jie;Feng Yun;Yang Hao(Antai College of Economics and Management,Shanghai Jiao Tong University,Shanghai 200030;Business School,Nankai University,Tianjin 300071)

机构地区：[1]上海交通大学安泰经济与管理学院,上海200030 [2]南开大学商学院,天津300071

出　　处：《管理评论》2024年第11期3-13,共11页Management Review

基　　金：国家社会科学基金重大项目(23ZDA039)。

摘　　要：本文围绕中国股市市态的周期性转换,深入研究了沪深300指数收益率的时变分布特征。通过在标准粒子群算法中引入K-Means++聚类算法动态种群重组和混沌搜索策略提出了动态多种群混沌粒子群算法,并基于此对隐半马尔可夫模型的初值进行优化。实证分析发现,中国股市存在三种市场状态——熊市、牛市和震荡市,并且牛市基本总是跟随在熊市之后,而牛市之后市场有更大的概率转向震荡态势,震荡市和熊市分别在股市的“尖峰”和“厚尾”特征中扮演着关键角色。基于解码结果,利用粗粒化方法构建了模态转换网络并识别了关键性的枢纽模态,还进一步分析了大、中、小盘股的牛熊市协同性,大盘股和中小盘股间周期性地出现显著的两极分化特征。最后,本文提出了更加准确的样本外预测方法,并通过一个简易的择时交易策略证明了本文模型的实用价值。This paper studies the periodic transition of the state of China’s stock market and discusses the time-varying distribution characteristics of returns of CSI300 in depth.By introducing the dynamic population reorganization based on the K-means++clustering algorithm and the chaotic search strategy into the standard particle swarm optimization algorithm,a dynamic multi-population chaotic particle swarm optimization algorithm is proposed,and the initial values of hidden semi-Markov model are further optimized based on this algorithm.The empirical analysis shows that there exist three states in China’s stock market,namely the bear,bull,and volatile markets.A bull market generally follows a bear market,and after a bullish situation,the market has a greater probability of turning to a volatile situation.The volatile state and the bearish state play key roles in the leptokurtic and heavy-tailed characteristics of the stock market,respectively.Based on the decoding results,a mode transformation network is constructed using the coarse-grained method,and key hub modes are identified.Further analysis is conducted on the co-movement of bull and bear states of large-,medium-,and small-cap stocks.There is a significant cyclical polarization between large-cap and medium-or small-cap stocks.Finally,we propose a more accurate outof-sample forecasting method for the hidden semi-Markov model and prove the practical value of our model via a simple market timing strategy.

关 键 词：中国股票市场 粒子群算法 隐半马尔可夫模型 样本外预测 

分 类 号：F83[经济管理—金融学]