Abstract
The advancement of information technology in the financial environments have been characterized by fast market-driven events that prompt flash decision making and actions issued by computer algorithms. As a result, today's markets experience intense activity in the highly dynamic environment where trading systems respond to others at a pace much faster than it would take for a human trader to make a decision. This new breed of technology involves the implementation of high-speed trading strategies that have generated significant portion of activity in the financial markets and thus presenteds researchers with a wealth of information not available in traditional low-frequency datasets. In this study, we aim at developing feasible computational intelligence methodologies, particularly genetic algorithms (GA), to shed light on high-speed trading research using the market microstructure price data. Our results show that the proposed GA-based model is able to improve the accuracy of the prediction for price movement on the microscopic level, and we expect this GA-based method to advance the current state of research for high-speed trading.
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