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An Intelligent Model for Pairs Trading Using Genetic Algorithms.

Chien-Feng Huang1, Chi-Jen Hsu1, Chi-Chung Chen2

  • 1Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan.

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Summary
This summary is machine-generated.

This study introduces a novel genetic algorithm (GA) for pairs trading, outperforming traditional statistical methods. The GA approach offers a robust solution for financial arbitrage opportunities in dynamic markets.

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Area of Science:

  • Computational Finance
  • Computational Intelligence
  • Algorithmic Trading

Background:

  • Pairs trading, a strategy for financial arbitrage, traditionally relies on statistical methods like regression.
  • Computational intelligence (CI) offers advanced techniques for complex financial applications.
  • Existing methods face challenges in dynamic financial markets.

Purpose of the Study:

  • To develop and evaluate a novel methodology for pairs trading using genetic algorithms (GA).
  • To demonstrate the effectiveness of GA in identifying and exploiting arbitrage opportunities.
  • To provide a robust computational intelligence solution for practical investment.

Main Methods:

  • Implementation of a novel pairs trading strategy utilizing genetic algorithms (GA).
  • Comparison of GA-based models against traditional benchmark methods.
  • Analysis of model robustness in dynamic financial market conditions.

Main Results:

  • GA-based models significantly outperformed the benchmark methods.
  • The proposed GA methodology demonstrated robustness in handling dynamic market characteristics.
  • Successful identification and exploitation of arbitrage opportunities through GA.

Conclusions:

  • Genetic algorithms provide a powerful and effective approach to pairs trading.
  • The proposed GA method advances computational intelligence in finance.
  • This approach offers a practical and robust solution for investment and arbitrage.