IV. Conclusions Model analyzing based on kurtosis diagram and Hurst exponent diagram suggests that the percentage of momentum investors in Chinese stock market is between 33% and 44%, which is consistent with the empirical research reported by Pan et al [5]. This work shows an optional method to study the investors’ behavior in real stock market via agent-based model analyzing. References: [1] R. Cont, J.P. Bouchaud, Herd behavior and aggregate fluctuations in financial markets, Macroeconomic Dynamics 4,170(2000). [2] J.D. Farmer, Market force, ecology, and evolution, Santa Fe Inst. Working Paper [3] Karin Dahmen and James P. Sethna, Phys. Rev. B 53, 14872–14905 (1996). [4] H. L. Chen, N. Jegadeesh, and R. Wermers, Journal of Financial and Quantitative Analysis 35(2000), [5] D. Pan, D. H. Shi, and M. Cao, The Journal of World Economy 11(2003), (in Chinese). How many momentum investors are there in stock market: Answer from agent-based model J. R. Wei and J. P. Huang Department of Physics, Fudan University, Shanghai, , China The fact that many investors in stock market adopt momentum strategy has been widely accepted. To analyze the behavior of momentum investors, we build an agent-based model, in which agents are divided into random investors and investors who trade using momentum strategy with an action threshold. We tested our model by reproducing the well-known stylized facts of stock price return. Model analyzing gives the percentage of momentum investors in Chinese stock market, which is consistent with previous empirical research. This work suggests a method to study the momentum investors’ behavior in real stock market via agent-based model analyzing. I. Agent-based Model Hyp.1: Random investors ; Hyp.1: Random investors (N r ) ; Hyp.2: Price change is decided by excess demand: II. Result 1- Stylized facts The model is used to reproduce the well-known stylized facts in real market, such as fat-tail behavior(Fig.2), long-term correlation and scaling behavior of kurtosis(Fig.3). III. Result 2- momentum investors Sell Buy Decision-making Up Fig.2, PDF of return (  =15 rounds). σ ∝  ^0.58 Fit ： x Hyp.3: Momentum investors; Hyp.3: Momentum investors (N m ) ; Hyp.4: Action threshold λ(λ>0) for momentum investors; Situation at time t-1Action at time t D[t-1]/σ > λBuy a unit of stock D[t-1]/σ < -λSell a unit of stock  D[t-1]/σ  < λ remain inactive Market trend Table 1: How a momentum investor trade? σ=. Fig.1, Simulated price series ( N r =101, N m =30, λ =0.23, rounds). Fig.3, Left, long-term correlation,  versus  ; Right, scaling behavior of kurtosis, kurtosis versus . Fig.4 Contour of kurtosis in parameter space of N m /N r and λ (  =15 rounds). Fig.5 Contour of Hurst exponent in parameter space of N m /N r and λ (  =15 rounds). For monthly return, empirical study shows that kurtosis is larger than 5, and Hurst exponent is larger than 0.6. Therefore we can locate the available area in parameter space.