The Cyclical Transition Characteristics of the Bull and Bear States in China's Stock Market:Based on the DMCPSO-HSMM Model
This paper studies the periodic transition of the state of China's stock market and discusses the time-varying distribution char-acteristics of returns of CSI300 in depth.By introducing the dynamic population reorganization based on the K-means++clustering algo-rithm 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 algo-rithm.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 situ-ation.The volatile state and the bearish state play key roles in the leptokurtic and heavy-tailed characteristics of the stock market,re-spectively.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 out-of-sample forecasting method for the hidden semi-Markov model and prove the practical value of our model via a simple market timing strategy.
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