Center for Social Complex Systems, Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan.
Graduate School of Artificial Intelligence and Science, Rikkyo University, Toshima-ku, Tokyo, Japan.
PLoS One. 2024 Apr 17;19(4):e0301462. doi: 10.1371/journal.pone.0301462. eCollection 2024.
Transactions in financial markets are not evenly spaced but can be concentrated within a short period of time. In this study, we investigated the factors that determine the transaction frequency in financial markets. Specifically, we employed the Hawkes process model to identify exogenous and endogenous forces governing transactions of individual stocks in the Tokyo Stock Exchange during the COVID-19 pandemic. To enhance the accuracy of our analysis, we introduced a novel EM algorithm for the estimation of exogenous and endogenous factors that specifically addresses the interdependence of the values of these factors over time. We detected a substantial change in the transaction frequency in response to policy change announcements. Moreover, there is significant heterogeneity in the transaction frequency among individual stocks. We also found a tendency where stocks with high market capitalization tend to significantly respond to external news, while their excitation relationship between transactions is weak. This suggests the capability of quantifying the market state from the viewpoint of the exogenous and endogenous factors generating transactions for various stocks.
金融市场中的交易并不是均匀分布的，而是可能集中在短时间内发生。在这项研究中，我们调查了决定金融市场交易频率的因素。具体来说，我们采用 Hawkes 过程模型来识别在 COVID-19 大流行期间影响东京证券交易所中单个股票交易的外生和内生力量。为了提高我们分析的准确性，我们引入了一种新的 EM 算法来估计外生和内生因素，该算法专门解决了这些因素随时间变化的相关性问题。我们检测到交易频率发生了显著变化，这是对政策变化公告的响应。此外，不同股票之间的交易频率存在显著的异质性。我们还发现，市值较高的股票往往对外界消息有显著的反应，而它们之间的交易激励关系较弱。这表明，从产生交易的外生和内生因素的角度来量化市场状态是可行的。
