Huang Chien-Feng, Li Hsu-Chih
Dept. of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan.
Comput Intell Neurosci. 2017;2017:9580815. doi: 10.1155/2017/9580815. Epub 2017 Feb 20.
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.
如今，信息技术在金融应用中的进步导致了快速的市场驱动事件，这些事件促使计算机算法进行快速决策和采取行动。因此，当今市场在高度动态的环境中经历着激烈的活动，交易系统对其他系统的响应速度比以往任何时候都要快得多。这种新型技术涉及高速交易策略的实施，这些策略在金融市场中产生了很大一部分活动，并为研究人员提供了传统低速交易环境中所没有的大量信息。在本研究中，我们旨在开发可行的计算智能方法，特别是遗传算法（GA），以便在微观层面上利用股票价格数据来阐明高速交易研究。我们的实证结果表明，所提出的基于GA的系统能够显著提高价格走势预测的准确性，并且我们期望这种基于GA的方法能够推动当前高速交易及其他相关金融应用的研究现状。
