An Intelligent Model for Pairs Trading Using Genetic Algorithms

Chien‐Feng Huang · Chi-Jen Hsu · Chi‐Chung Chen · Bao Rong Chang · Chen-An Li

34 citations0/0 files storedComputational Intelligence and Neuroscience

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Evolutionary Algorithms and Applications

Abstract

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.

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1998Forecasting with artificial neural networks:2006Pairs Trading: Performance of a Relative-Value Arbitrage Rule 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 2008Statistical Arbitrage in the U.S. Equities Market 2005Pairs trading 2008A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting 2011A hybrid stock selection model using genetic algorithms and support vector regression 2014A causal feature selection algorithm for stock prediction modeling

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