This paper outlines a data mining approach to analysis and prediction of the trend of stock prices. The approach consists of three steps, namely partitioning, analysis and prediction. A modification of the commonly used k-means clustering algorithm is used to partition stock price time series data. After data partition, linear regression is used to analyse the trend within each cluster. The results of the linear regression are then used for trend prediction for windowed time series data. The approach is efficient and effective at predicting forward trends of stock prices. Using our trend prediction methodology, we propose a trading strategy TTP (Trading based on Trend Prediction). Some preliminary results of applying TTP to stock trading are reported.

This paper provides a simple documentation of the artificial stock market software developed by the AI-EC6N Research Center.

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We investigate the dynamics of trader behaviors using an agent-based genetic programming system to simulate double-auction markets. The objective of this study is two-fold. First, we seek to evaluate how, if any, the difference in trader rationality/intelligence influences trading behavior. Second, besides rationality, we also analyze how, if any, the co-evolution between two learnable traders impacts their trading behaviors. We have found that traders with different degrees of rationality may exhibit different behavior depending on the type of market they are in. When the market has a profit zone to explore, the more intelligent trader demonstrates more intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more profitable transactions than when there was only one learnable buyer in the market. We have analyzed the trading strategies and found the learning behaviors are very similar to humans in decision-making. We plan to conduct human subject experiments to validate these results in the near future.