arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~

中国A股市场的羊群效应、动量与反转:基于信息扩散的 Agent 网络模型

Herding, Momentum, and Reversal in China's A-Share Market: An Agent-Based Network Model with Information Diffusion

Jiahao Weng

arXiv 2607.27063首次发表:更新:

AI 中文总结

本研究构建基于Agent的金融市场模型,结合局部羊群效应与信息扩散机制,解释中国A股市场的动量与反转现象,经实证验证相关机制的互补作用。

AI 中文摘要

本研究构建了一个基于 Agent 的金融市场模型,旨在通过局部羊群效应与延迟信息扩散的共同作用解释股票价格的动量与反转现象。投资者对下一期价格形成异质高斯信念,在买入、卖出和弃权(不执行)之间进行选择,并根据邻近投资者调整自身的行动概率。局部交互结构最初由冯·诺依曼格子和摩尔格子表示,后续为增强鲁棒性替换为 Erdős–Rényi 网络和 Watts–Strogatz 网络。独立的信息过程通过有限速度扩散机制更新投资者信念,使信息调整与行为模仿得以区分。模拟结果显示,更强的羊群效应会产生空间聚集的交易、更大的价格波动以及收益中更显著的超额峰度;更快的信息扩散缩短了价格趋近信号隐含价值所需的时间,而信息扩散与社会强化的结合会引发超调及后续反转。将模型应用于中国A股市场的实证分析中,研究人员将传统的CSAD、LSV指标与经Johnson SU变换后得到的滚动尾部羊群效应指标进行对比,发现这些指标呈现出相似的时间变化特征,且在重大市场动荡期间会上升。这些研究结果表明,信息延迟、局部社会强化以及羊群效应的最终衰减是动量与反转背后的互补机制。

英文摘要

This study develops an agent-based financial market model to explain stock-price momentum and reversal through the joint effects of local herding and delayed information diffusion. Investors form heterogeneous Gaussian beliefs about the next-period price, choose among buying, selling, and remaining inactive, and revise their action probabilities in response to neighboring investors. The local interaction structure is represented by von Neumann and Moore lattices and is later replaced by Erdős--Rényi and Watts--Strogatz networks for robustness. A separate information process updates investor beliefs through a finite-speed diffusion mechanism, allowing informational adjustment to be distinguished from behavioral imitation. The simulations show that stronger herding produces spatially clustered trading, larger price fluctuations, and more pronounced excess kurtosis in returns. Faster information diffusion reduces the time required for prices to approach the signal-implied value, whereas the combination of information diffusion and social reinforcement generates overshooting and subsequent reversal. An empirical application to China's A-share market compares conventional CSAD and LSV measures with a rolling tail-based herding indicator obtained after Johnson $S_U$ transformation. The indicators display similar time variation and rise during major market disruptions. These findings identify information delay, local social reinforcement, and the eventual decay of herding as complementary mechanisms behind momentum and reversal.

论文原文

arXiv 摘要页 · PDF 原文 · HTML 原文

↑