How Herding and Delayed Information Create Momentum and Reversal in A-Shares
Summary
This study uses an agent-based model to examine how local imitation and slow information flow can jointly produce momentum and later reversal in China's A-share market. Investors hold varied beliefs about future prices, choose whether to buy, sell, or stay inactive, and adjust their actions in response to nearby traders. The model tests multiple lattice and random-network structures, while a separate diffusion process represents how information reaches investors over time.
Simulations associate stronger herding with clustered trades, larger price swings, and heavier-tailed returns. Faster diffusion brings prices toward signal-implied values sooner, while the interaction of diffusion and social reinforcement can cause overshooting followed by reversal. An empirical comparison with A-share data finds that a rolling tail-based herding measure, using a Johnson transformation, varies similarly to CSAD and LSV measures and rises during major disruptions. The document presents model simulations and indicator comparisons, not a trading strategy or evidence of live profitability; conclusions depend on the model's assumptions and the empirical indicator's construction.
Key ideas
- Local imitation can cluster trading and amplify price fluctuations.
- Delayed information diffusion can slow price adjustment.
- Combining information flow with social reinforcement can produce overshooting and reversal.
- A rolling tail-based herding indicator is compared with CSAD and LSV measures in A-shares.
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Full text
# Herding, Momentum, and Reversal in China's A-Share Market: An Agent-Based Network Model with Information Diffusion # Herding, Momentum, and Reversal in China's A-Share Market: An Agent-Based Network Model with Information Diffusion 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.
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.