Results H. S. Zhang, J. R. Wei, and J. P. Huang Department of Physics, Fudan University, Shanghai , China Method Abstract Results Conclusion The predictability of Chinese market is lower than American market. We know that American market is a mature market but Chinese market is an emerging market. So I believe this result is caused by the Political system of China. In China, the politics makes great influence on stock market. The predictability of stock Index is lower than individual stocks. In a price-weighted index such as the Dow Jones Industrial Average, the price of each component stock is the only consideration when determining the value of the index. A capitalization-weighted such as S&P500 index and Shanghai Composite Index is a stock market index whose components are weighted according to the total market value of their outstanding shares. The weighted average operation of Index reduces the predictability. References: 1. R. Garvey and A. Murphy, Journal of Applied Finance 15,93 (2005). 2. H. L. Chen, N. Jegadeesh, and R. Wermers, Journal of Financial and Quantitative Analysis 35,343 (2000). 3. B. M. Barber, and T. Odean, J. Finance 55,773 (2000). 4. A. Gr ӧ nlund, I. G. Yi, and B. J. Kim, PLoS ONE 7,33960 (2012). Stock market provides a way for people to be rich. Everyone who invests in stock market wants to earn excess profit from it[1]. Various technical trading strategies have been being used for profit. But it is shown that even the experienced fund managers can’t ensure the excess returns[2], not to mention the individual investors[3].In 2012 In 2012, Grӧnlund et al. found that the local maxima in the profit landscape are spread in the form of a fractal structure, which means the stock market has low predictability by technical analysis[4]. We’ll use the similar way to compared Index and individual stocks between China and USA. The profit landscape method we’ll used to solve the data[4].The trade strategy depends on the log return R. If R>p/K, one sells a part of stocks. n(t) is the number of stock in the account at time t. K is the scale factor.If R<-q/K, one uses a part of cash to buy the stocks. m(t) is the number of cash in the account at time t. From the different p and q, we can have different final profit. Then we construct a two- dimensional parameter space T(p, q),which is a N×N matrix (N is the resolution of this space). In this space, the matrix element is satisfied. The fig1.is a profit landscape from S&P 500 with N=100.. With different resolution N, we can gain the relationship between N and M(the number of local maxima). If one wants to make the best strategy, he or she will find a strategy within a range of local maxima. It is certain that if the M don’t change with N, we will take M ∝ N 0. And if we choose the random series to do the above work, We will take M ∝ N 2. That means the predictability of value series is stronger when the power law exponent is smaller. We chose 30 USA and Chinese company stocks' daily price data during 1998 to At the same time, we also use daily price data by S&P 500 Index and The Shanghai Composite Index from 1998 to We first analyzed the index of China and USA. Fig 2.shows the number of local maxima M for index. One can see both S&P500 index and Shanghai Composite Index chart show power law distribution of M with exponent γ=1.42 and γ=1.48. Fig 3.shows the number of local maxima M for individual stocks. One can see both American stocks and Chinese stocks chart show power law distribution of M with exponent γ=1.55and γ=1.78. Above all, one can see that predictability of Chinese stock market is lower than American stock market. And it is harder for one to predict the index than individual stocks. Introduction We use one of the most basic trading strategy to make the profit landscape. Stocks are sold and bought if the log return is bigger than p/K and less than – q/K. The Local maxima in the two- dimensional N×N parameter space of p and q follows a power law distribution with N. The power law exponent value is between 0 and 2. One can see the predictability is better when the exponent value is smaller. So we use this way to compare the stock Index and individual stocks between China and USA.