Detection of Mutual Exciting Structure in Stock Price Trend Dynamics
We investigated a comprehensive analysis of the mutual exciting mechanism for the dynamic of stock price trends. A multi-dimensional Hawkes-model-based approach was proposed to capture the mutual exciting activities, which take the form of point processes induced by dual moving average crossovers. W...
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2021
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oai:doaj.org-article:14f57880a50c46d2a474c5de9cef38522021-11-25T17:29:27ZDetection of Mutual Exciting Structure in Stock Price Trend Dynamics10.3390/e231114111099-4300https://doaj.org/article/14f57880a50c46d2a474c5de9cef38522021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1411https://doaj.org/toc/1099-4300We investigated a comprehensive analysis of the mutual exciting mechanism for the dynamic of stock price trends. A multi-dimensional Hawkes-model-based approach was proposed to capture the mutual exciting activities, which take the form of point processes induced by dual moving average crossovers. We first performed statistical measurements for the crossover event sequence, introducing the distribution of the inter-event times of dual moving average crossovers and the correlations of local variation (LV), which is often used in spike train analysis. It was demonstrated that the crossover dynamics in most stock sectors are generally more regular than a standard Poisson process, and the correlation between variations is ubiquitous. In this sense, the proposed model allowed us to identify some asymmetric cross-excitations, and a mutually exciting structure of stock sectors could be characterized by mutual excitation correlations obtained from the kernel matrix of our model. Using simulations, we were able to substantiate that a burst of the dual moving average crossovers in one sector increases the intensity of burst both in the same sector (self-excitation) as well as in other sectors (cross-excitation), generating episodes of highly clustered burst across the market. Furthermore, based on our finding, an algorithmic pair trading strategy was developed and backtesting results on real market data showed that the mutual excitation mechanism might be profitable for stock trading.Shangzhe LiXin JiangJunran WuLin TongKe XuMDPI AGarticleself- and mutually exciting processesHawkes processstock price trend dynamicsScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1411, p 1411 (2021) |
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self- and mutually exciting processes Hawkes process stock price trend dynamics Science Q Astrophysics QB460-466 Physics QC1-999 |
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self- and mutually exciting processes Hawkes process stock price trend dynamics Science Q Astrophysics QB460-466 Physics QC1-999 Shangzhe Li Xin Jiang Junran Wu Lin Tong Ke Xu Detection of Mutual Exciting Structure in Stock Price Trend Dynamics |
| description |
We investigated a comprehensive analysis of the mutual exciting mechanism for the dynamic of stock price trends. A multi-dimensional Hawkes-model-based approach was proposed to capture the mutual exciting activities, which take the form of point processes induced by dual moving average crossovers. We first performed statistical measurements for the crossover event sequence, introducing the distribution of the inter-event times of dual moving average crossovers and the correlations of local variation (LV), which is often used in spike train analysis. It was demonstrated that the crossover dynamics in most stock sectors are generally more regular than a standard Poisson process, and the correlation between variations is ubiquitous. In this sense, the proposed model allowed us to identify some asymmetric cross-excitations, and a mutually exciting structure of stock sectors could be characterized by mutual excitation correlations obtained from the kernel matrix of our model. Using simulations, we were able to substantiate that a burst of the dual moving average crossovers in one sector increases the intensity of burst both in the same sector (self-excitation) as well as in other sectors (cross-excitation), generating episodes of highly clustered burst across the market. Furthermore, based on our finding, an algorithmic pair trading strategy was developed and backtesting results on real market data showed that the mutual excitation mechanism might be profitable for stock trading. |
| format |
article |
| author |
Shangzhe Li Xin Jiang Junran Wu Lin Tong Ke Xu |
| author_facet |
Shangzhe Li Xin Jiang Junran Wu Lin Tong Ke Xu |
| author_sort |
Shangzhe Li |
| title |
Detection of Mutual Exciting Structure in Stock Price Trend Dynamics |
| title_short |
Detection of Mutual Exciting Structure in Stock Price Trend Dynamics |
| title_full |
Detection of Mutual Exciting Structure in Stock Price Trend Dynamics |
| title_fullStr |
Detection of Mutual Exciting Structure in Stock Price Trend Dynamics |
| title_full_unstemmed |
Detection of Mutual Exciting Structure in Stock Price Trend Dynamics |
| title_sort |
detection of mutual exciting structure in stock price trend dynamics |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/14f57880a50c46d2a474c5de9cef3852 |
| work_keys_str_mv |
AT shangzheli detectionofmutualexcitingstructureinstockpricetrenddynamics AT xinjiang detectionofmutualexcitingstructureinstockpricetrenddynamics AT junranwu detectionofmutualexcitingstructureinstockpricetrenddynamics AT lintong detectionofmutualexcitingstructureinstockpricetrenddynamics AT kexu detectionofmutualexcitingstructureinstockpricetrenddynamics |
| _version_ |
1718412269885325312 |