TY - GEN
T1 - Minority game data mining for stock market predictions
AU - Ma, Ying
AU - Li, Guanyi
AU - Dong, Yingsai
AU - Qin, Zengchang
PY - 2010
Y1 - 2010
N2 - The Minority Game (MG) is a simple model for understanding collective behavior of agents in an idealized situation for a finite resource. It has been regarded as an interesting complex dynamical disordered system from a statistical mechanics point of view. In previous work, we have investigated the problem of learning the agent behaviors in the minority game by assuming the existence of one "intelligent agent" who can learn from other agent behaviors. In this paper, we propose a framework called Minority Game Data Mining (MGDM), that assumes the collective data are generated from combining the behaviors of variant groups of agents following the minority games. We then apply this framework to time-series data analysis in the real-world. We test on a few stocks from the Chinese market and the US Dollar-RMB exchange rate. The experimental results suggest that the winning rate of the new model is statistically better than a random walk.
AB - The Minority Game (MG) is a simple model for understanding collective behavior of agents in an idealized situation for a finite resource. It has been regarded as an interesting complex dynamical disordered system from a statistical mechanics point of view. In previous work, we have investigated the problem of learning the agent behaviors in the minority game by assuming the existence of one "intelligent agent" who can learn from other agent behaviors. In this paper, we propose a framework called Minority Game Data Mining (MGDM), that assumes the collective data are generated from combining the behaviors of variant groups of agents following the minority games. We then apply this framework to time-series data analysis in the real-world. We test on a few stocks from the Chinese market and the US Dollar-RMB exchange rate. The experimental results suggest that the winning rate of the new model is statistically better than a random walk.
UR - https://www.scopus.com/pages/publications/77958507137
U2 - 10.1007/978-3-642-15420-1_15
DO - 10.1007/978-3-642-15420-1_15
M3 - 会议稿件
AN - SCOPUS:77958507137
SN - 3642154190
SN - 9783642154195
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 178
EP - 189
BT - Agents and Data Mining Interaction - 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010, Revised Selected Papers
T2 - 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010
Y2 - 11 May 2010 through 11 May 2010
ER -