Abstract
This paper explores the investors’ feedback to the price change by modelling the price-related dynamics of trading intensity. A component decomposition duration modeling approach, called the component autoregressive conditional duration (CACD) model, is proposed to capture the variation of trading intensity across time intervals between price change events. Based on the CACD model, an empirical analysis is carried out on the Chinese stock market that covers different market statuses. The empirical results suggest that the CACD model can capture the price-related dynamics of trading intensity, which supports the existence of the feedback effect and is robust across different market statuses. The authors also study how the investors react to the price change by examining the driven factors of the price-related dynamics of trading intensity. The authors find that the trading can be triggered by the fast rise in the price level and the high trading volume. Besides, investors are more sensitive to the price change direction in the sideways market than in the upward or downward markets.
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The authors thank LU Fengbin, XU Dawei and other seminar participants at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, for valuable comments.
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This research was supported by the National Science Foundation of China under Grant Nos. 71201161 and 71671183.
This paper was recommended for publication by Editor ZHANG Xun.
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Huang, Z., Han, A. & Wang, S. Component ACD Model and Its Application in Studying the Price-Related Feedback Effect in Investor Trading Behaviors in Chinese Stock Market. J Syst Sci Complex 31, 677–695 (2018). https://doi.org/10.1007/s11424-017-6111-y
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DOI: https://doi.org/10.1007/s11424-017-6111-y