Human experiments of forecasting the real-world CSI 300 Index and human dynamics in it Lu Liu and Jiping Huang Department of Physics and State Key Laboratory of Surface Physics, Fudan University, Shanghai 200433, China Abstract Results Result The movement of stock prices is a crucial problem in financial markets. Whether we could accurately predict the market trend become the core issue for investors. Therefore, we designed an online human experiment for the study of investors’ prediction. In our experiment, market participants can make predictions on the open and close prices of the CSI 300 Index in the real market, providing us opportunities to study the predicting ability and individual behaviors similar to the real market, which can be hardly investigated due to limited data. We found that although the number of players is far less than that of the real market, our predicting indices are quite similar as the real ones. Besides, we also found exciting results of both the individual and collect human dynamics in our experiments. (1) Experiment information There are overall 28 players participating our experiment, eleven of which have trading experiences, seven of which are female, and three of which are teachers. (3) Human dynamics 1) Individual dynamics We study the individual behaviors calculating the time interval between two consecutive predictions for each individual. 2) Collective dynamics We study the collective behaviors by calculating the time interval between two consecutive predictions received by the server. 3) Intraday pattern (a) (d) (b) (e) (c) (f) Fig. 2 The ranking of number of operations for 28 players. The most active one made 61 predictions and the least one made only one prediction. Fig. 4 The cumulative distribution function (CDF) of time intervals for (a)-(c) the most active player and (d)-(f) the second most active player. All figures are in log-log plot. Results Human experiment (2) Prediction results The following picture shows the prediction results of open and close price of CSI 300 for ten trading days. Our experiments are conducted online, where anyone could participate in predictions in anytime through their personal computer. People can predict the close price of index from 9:00 a.m. to 14:00 p.m. in every trading day. They could predict the open price in any other time. The experiment lasted for ten consecutive trading days. (a) (b) (c) (a) Fig. 5 The cumulative distribution function (CDF) of time intervals of (a) open price predictions, (b) close price predictions and (c) overall predictions. All figures are in log-log plot. (a) (b) (b) Fig. 6 The intraday pattern for (a) open and (b) close price predictions. The x-axis represents the timeline in 24 hours. The y-axis represents the number of predictions. Summary Fig. 3 The predicting results of (a) open prices and (b) close prices of the CSI 300 Index. The red round spots represent the real index, while the black square spots represent pure predicting results with straight lines representing error bars. Through human experiment of forecasting the CSI 300 Index, we found that the prediction results are quite similar to real markets with only limited number pf players. We also found the individual and collective human dynamics in our experiments. The intraday pattern shows that the human dynamics may result from deadline and the physiological cycle. Fig. 1 The homepage of the experiment website. The left column includes player’s prediction choices, prediction records and score, experiment results and experiment description. The right column shows the CSI 300 minute line and K line from Sina Finance, as well as the ranking of our experiment. Reference: [1] L. Liu, J. R. Wei, and J. P. Huang , “Scaling and volatility of breakouts and breakdowns in stock price dynamics “, PLoS One, volume 8, e82771 (2013) [2] L. Liu, J. R. Wei, H. S. Zhang, J. H. Xin, and J. P. Huang, “Statistical physics view of pitch fluctuations in the classical music from Bach to Chopin: Evidence for scaling“, PLoS One volume 8, e58710 (2013)