DEVELOPING AN ENHANCED PORTFOLIO TRADING SYSTEM USING K-MEANS AND GENETIC ALGORITHMS

Authors

  • Wonbin Ahn Yonsei University
  • Donghyun Cheong Yonsei University
  • Youngmin Kim Missouri University of Science and Technology
  • Kyong Joo Oh Yonsei University

DOI:

https://doi.org/10.23055/ijietap.2018.25.5.3688

Keywords:

An enhanced portfolio trading system, k-means clustering, Genetic algorithms, Investor information

Abstract

The objective of this study is to enhance the ability of an index fund strategy using k-means clustering and genetic algorithms. This study proposes a novel enhanced portfolio mechanism consisting of two phases. In the first phase, a subset of all the index shares is selected using k-means clustering based on investor information. In the second phase, a genetic algorithm is employed to search for the optimal stock weights in the selected clusters. In order to identify the usefulness of the proposed model, this study is compared with the conventional approach. For measuring trading performance, the tracking error, which a measure of how closely a portfolio follows the index as a benchmark, is evaluated. Furthermore, the information ratio is used to compare the performance of the proposed model in terms of the risk-adjusted return. An empirical study of the proposed model is simulated in the Korea stock exchange market.

Author Biographies

Wonbin Ahn, Yonsei University

Department of Industrial Engineering

Donghyun Cheong, Yonsei University

Department of Industrial Engineering

Youngmin Kim, Missouri University of Science and Technology

Department of Engineering Management and Systems Engineering

Kyong Joo Oh, Yonsei University

Department of Industrial Engineering

Published

2019-01-03

How to Cite

Ahn, W., Cheong, D., Kim, Y., & Oh, K. J. (2019). DEVELOPING AN ENHANCED PORTFOLIO TRADING SYSTEM USING K-MEANS AND GENETIC ALGORITHMS. International Journal of Industrial Engineering: Theory, Applications, and Practice, 25(5). https://doi.org/10.23055/ijietap.2018.25.5.3688