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iTrade: A Mobile Data-Driven Stock Trading System with Concept Drift Adaptation

iTrade: A Mobile Data-Driven Stock Trading System with Concept Drift Adaptation

Yong Hu, Xiangzhou Zhang, Bin Feng, Kang Xie, Mei Liu
Copyright: © 2015 |Volume: 11 |Issue: 1 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781466675919|DOI: 10.4018/ijdwm.2015010104
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MLA

Hu, Yong, et al. "iTrade: A Mobile Data-Driven Stock Trading System with Concept Drift Adaptation." IJDWM vol.11, no.1 2015: pp.66-83. http://doi.org/10.4018/ijdwm.2015010104

APA

Hu, Y., Zhang, X., Feng, B., Xie, K., & Liu, M. (2015). iTrade: A Mobile Data-Driven Stock Trading System with Concept Drift Adaptation. International Journal of Data Warehousing and Mining (IJDWM), 11(1), 66-83. http://doi.org/10.4018/ijdwm.2015010104

Chicago

Hu, Yong, et al. "iTrade: A Mobile Data-Driven Stock Trading System with Concept Drift Adaptation," International Journal of Data Warehousing and Mining (IJDWM) 11, no.1: 66-83. http://doi.org/10.4018/ijdwm.2015010104

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Abstract

Among all investors in the Chinese stock market, more than 95% are non-professional individual investors. These individual investors are in great need of mobile apps that can provide professional and handy trading analysis and decision support everywhere. However, financial data is challenging to analyze because of its large-scale, non-linear and noisy characteristics in a varying stock environment. This paper develops a Mobile Data-Driven Stock Trading System (iTrade), which is a mobile app system based on Client-Server architecture and various data mining techniques. The iTrade is characterized by 1) a data-driven intelligent learning model, which can provide further insight compared to empirical technical analysis, 2) a concept drift adaptation process, which facilitates the model adaptation to market structure changes, and 3) a rigorous benchmark analysis, including the Buy-and-Hold strategy and the strategies of three world-famous master investors (e.g., Warren E. Buffett). Technologies used in iTrade include the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, Support Vector Machine (SVM) and risk-adjusted portfolio optimization. An application case of iTrade is presented, which is based on a seven-year (2005-2011) back-testing. Evaluation results indicated that iTrade could gain much higher cumulative return compared to the benchmark (Shanghai Composite Index). To the best of our knowledge, this is the first study and mobile app system that emphasizes and investigates the concept drift phenomenon in stock market, as well as the performance comparison between data-driven intelligent model and strategies of master investors.

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