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Computational Intelligence and Neuroscience
Volume 2017, Article ID 9580815, 18 pages
https://doi.org/10.1155/2017/9580815
Research Article

An Evolutionary Method for Financial Forecasting in Microscopic High-Speed Trading Environment

Dept. of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

Correspondence should be addressed to Chien-Feng Huang; wt.ude.kun@51gnauhfc

Received 27 August 2016; Accepted 11 January 2017; Published 20 February 2017

Academic Editor: Jorge Reyes

Copyright © 2017 Chien-Feng Huang and Hsu-Chih Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.