IV. Conclusions In summary, we have proposed and studied an agent-based model of trading incorporating momentum investors, which provides an alternative approach for studying the impacts of momentum investors on market behavior. The model gives simulated time series of stock prices that carries some of the well-known stylized facts. Furthermore, we illustrated how real data sets could be used to constrain the model parameters, which in turn provided information on the behavior of momentum investors in different markets. References: [1] J. R. Wei, J. P. Huang, and P. M. Hui, “An agent-based model of stock markets incorporating momentum investors”, Physica A 392, (2013). [2] R. Cont and J.P. Bouchaud, “Herd behavior and aggregate fluctuations in financial markets”, Macroeconomic Dynamics 4,170 (2000). [3] J.D. Farmer, “Market force, ecology, and evolution”, Santa Fe Inst. Working Paper An agent-based model of stock markets incorporating momentum investors J. R. Wei*, J. P. Huang*, and P. M. Hui** * Department of Physics, Fudan University, Shanghai, China ** Department of Physics, The Chinese University of Hong Kong, Hong Kong, China It has been widely accepted that there exist investors who adopt momentum strategies in real stock markets. Understanding the momentum behavior is of both academic and practical importance. For this purpose, we propose and study a simple agent-based model of trading incorporating momentum investors and random investors. The model is able to reproduce some of the stylized facts observed in real markets. To illustrate how the model can be applied, we show that real market data can be used to constrain the model parameters, which in turn provide information on the behavior of momentum investors in different markets. I. Agent-based Model Hyp.1: Random investors Hyp.1: Random investors (N r ) Hyp.2: Price change is decided by excess demand [2,3] II. Simulation & analysis The model is used to reproduce the well-known stylized facts in real market, such as fat-tail behavior(Fig.1), weak long-term correlation and scaling behavior of kurtosis. III. Model’s application Sell Buy Decision-making Up 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 Left, simulated price series ( N r =100,  =3, λ =4); Right, PDF of returns. Fig.2 Contours showing constant (left) kurtosis κ and (right) probability Popp that two consecutive returns are of opposite signs in the α-λ space. Fig.3 Based on the monthly returns of the S&P500 index between 1950 and 2011, we find κ=5.45 which gives a constant-κ contour, while Popp=0.46 which gives a constant-Popp contour. These two contours intersect at a point corresponding to (α, λ) = (3.7, 2.5). Table 2 We have carried out similar analysis on the China stock market, the Hong Kong stock market and the Japan stock market. Our findings: (i)The similar values of λ indicate that the momentum investors react to strong and significant volatility. (ii) The value of α in the China market is higher than that in the other three mature markets, suggesting that there is a better opportunity for momentum strategies in the China market. This is reasonable within the viewpoint of momentum behavior being related to market inefficiency. As an emerging market, the China market is less efficient than the other three mature markets, thus providing more opportunities for momentum investors. Stock market  USA China Hong Kong Japan3.82.9