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Portfolio selection with qualitative input

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  • Chiarawongse, Anant
  • Kiatsupaibul, Seksan
  • Tirapat, Sunti
  • Roy, Benjamin Van

Abstract

We formulate a mean–variance portfolio selection problem that accommodates qualitative input about expected returns and provide an algorithm that solves the problem. This model and algorithm can be used, for example, when a portfolio manager determines that one industry will benefit more from a regulatory change than another but is unable to quantify the degree of difference. Qualitative views are expressed in terms of linear inequalities among expected returns. Our formulation builds on the Black–Litterman model for portfolio selection. The algorithm makes use of an adaptation of the hit-and-run method for Markov chain Monte Carlo simulation. We also present computational results that illustrate advantages of our approach over alternative heuristic methods for incorporating qualitative input.

Suggested Citation

  • Chiarawongse, Anant & Kiatsupaibul, Seksan & Tirapat, Sunti & Roy, Benjamin Van, 2012. "Portfolio selection with qualitative input," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 489-496.
    • Handle: RePEc:eee:jbfina:v:36:y:2012:i:2:p:489-496
    DOI: 10.1016/j.jbankfin.2011.08.005
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    Cited by:

    1. Kiatsupaibul, Seksan & J. Hayter, Anthony & Liu, Wei, 2017. "Rank constrained distribution and moment computations," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 229-242.
    2. Gulliksson, Mårten & Mazur, Stepan, 2019. "An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection," Working Papers 2019:3, Örebro University, School of Business.
    3. Harris, Richard D.F. & Stoja, Evarist & Tan, Linzhi, 2017. "The dynamic Black–Litterman approach to asset allocation," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1085-1096.
    4. Mårten Gulliksson & Stepan Mazur, 2020. "An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 773-794, December.
    5. Çela, Eranda & Hafner, Stephan & Mestel, Roland & Pferschy, Ulrich, 2021. "Mean-variance portfolio optimization based on ordinal information," Journal of Banking & Finance, Elsevier, vol. 122(C).
    6. Zaiwen Wen & Xianhua Peng & Xin Liu & Xiaoling Sun & Xiaodi Bai, 2013. "Asset Allocation under the Basel Accord Risk Measures," Papers 1308.1321, arXiv.org.
    7. Zhu, Shushang & Fan, Minjie & Li, Duan, 2014. "Portfolio management with robustness in both prediction and decision: A mixture model based learning approach," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 1-25.
    8. Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2024. "Randomized Control in Performance Analysis and Empirical Asset Pricing," Papers 2403.00009, arXiv.org.

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