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Linear programing models for portfolio optimization using a benchmark

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  • Seyoung Park
  • Hyunson Song
  • Sungchul Lee

Abstract

We consider the problem of constructing a perturbed portfolio by utilizing a benchmark portfolio. We propose two computationally efficient portfolio optimization models, the mean-absolute deviation risk and the Dantzig-type, which can be solved using linear programing. These portfolio models push the existing benchmark toward the efficient frontier through sparse and stable asset selection. We implement these models on two benchmarks, a market index and the equally-weighted portfolio. We carry out an extensive out-of-sample analysis with 11 empirical datasets and simulated data. The proposed portfolios outperform the benchmark portfolio in various performance measures, including the mean return and Sharpe ratio.

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  • Seyoung Park & Hyunson Song & Sungchul Lee, 2019. "Linear programing models for portfolio optimization using a benchmark," The European Journal of Finance, Taylor & Francis Journals, vol. 25(5), pages 435-457, March.
    • Handle: RePEc:taf:eurjfi:v:25:y:2019:i:5:p:435-457
    DOI: 10.1080/1351847X.2018.1536070
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    Cited by:

    1. Seyoung Park & Eun Ryung Lee & Sungchul Lee & Geonwoo Kim, 2019. "Dantzig Type Optimization Method with Applications to Portfolio Selection," Sustainability, MDPI, vol. 11(11), pages 1-32, June.
    2. Ziming Gao & Yuan Gao & Yi Hu & Zhengyong Jiang & Jionglong Su, 2020. "Application of Deep Q-Network in Portfolio Management," Papers 2003.06365, arXiv.org.

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