Abstract
The advancement of information technology in financial applications nowadays have led to 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 much faster pace than before. This new breed of technology involves the implementation of high-speed trading strategies which generate significant portion of activity in the financial markets and present researchers with a wealth of information not available in traditional low-speed trading environments. In this study, we aim at developing feasible computational intelligence methodologies, particularly genetic algorithms (GA), to shed light on high-speed trading research using price data of stocks on the microscopic level. Our empirical results show that the proposed GA-based system is able to improve the accuracy of the prediction significantly for price movement, and we expect this GA-based methodology to advance the current state of research for high-speed trading and other relevant financial applications.
Highlights
The advances of information technology and big data research in finance have led to an ever increasing pace to marketdriven events and information that prompt decision-making and actions by computerized high-speed trading strategies
The datasets are made available to the public by the Taiwan Stock Exchange (TWSE) where the transaction prices, five best bid and ask quotes, and trading volumes are used to examine the performance of the systems
The datasets were extracted from two periods of time: (1) Sept./15/2015 through Nov./03/2015, during which time the value of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TSEC weighted index) went up from 8259.99 to 8713.19, and that is a period of time the broad stock market is achieving positive gain; (2) Dec./10/2015 through Jan./21/2016, during which time the TSEC weighted index went down from 8216.17 to 7664.01, the broad stock market achieving negative gain
Summary
The advances of information technology and big data research in finance have led to an ever increasing pace to marketdriven events and information that prompt decision-making and actions by computerized high-speed trading strategies. The net result is that today’s markets experience intense activity in the highly dynamic environment of seconds or only a little fraction of seconds, where trading algorithms respond to others at a pace much faster than it would take for human traders to blink. This new breed of trading technology and platform involves the implementation of lowlatency, high-speed trading strategies and has resulted in remarkable portion of activities in the financial markets [1]. The technologies of HFT have been diffusing into the financial markets worldwide, including Asia [3]
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