iTrade: An Adaptive Risk-Adjusted Intelligent Stock Trading System from the Perspective of Concept Drift
Yong Qiang Hu, Bin Feng, Xiangzhou Zhang, Kang Liu, Kang Xie, Mei Liu · 2013
In China stock market, more than 95% are non-professional investors. Due to the lack of professional skill and the complexity of financial indicators and the varying investment environment, non-professional investors are in great need of a data mining-based intelligent stock trading decision-support system. Considering the existence of concept drift phenomenon, this study proposes an adaptive learning process with the Lasso algorithm-based feature selection. Moreover, we use support vector machine as stock market predictor for stock selection and a risk-adjusted method for portfolio optimization. Finally, a web-based Adaptive Risk-adjusted Intelligent Stock Trading System (iTrade) is established. The seven-year (2005-2011) back-testing shows that our system can generate much higher cumulative return than the benchmark (Shanghai Composite Index) in China stock market. Meanwhile, concept drift analysis of adaptive relevant variable discovery process has revealed contrasting historical trends between two selected industries. In conclusion, the iTrade is suitable for non-professional investors in portfolio management, following the varying stock market environment and providing effective guidance.