An Intelligent Model for Pairs Trading Using Genetic Algorithms.
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.
Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan.
Chien-Feng
CF
Huang
Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan.
Chi-Jen
CJ
Hsu
Department of Electrical Engineering, National Chiayi University, Chiayi City 60004, Taiwan.
Chi-Chung
CC
Chen
Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan.
Bao Rong
BR
Chang
Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan.
Chen-An
CA
Li
D000465:Algorithms; D001185:Artificial Intelligence; D007449:Investments / Q000191:economics / Q000706:statistics & numerical data*; D008957:Models, Genetic