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Using Quadratic Interpolated Beetle Antennae Search for Higher Dimensional Portfolio Selection Under Cardinality Constraints

Author

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  • Ameer Tamoor Khan

    (The Hong Kong Polytechnic University)

  • Xinwei Cao

    (Jiangnan University)

  • Shuai Li

    (Swansea University)

Abstract

In this paper, we presented a Quadratic Interpolated Beetle Antennae Search (QIBAS), a variant of the Beetle Antennae Search (BAS) algorithm to solve the higher dimensional portfolio selection problem. The computational efficiency of BAS and its probabilistic global convergence made it viable to solve real-world optimization-based problems. Despite its numerous application, it is less accurate, not scalable, and its performance deteriorates as the dimension of the problem increases. To overcome this, QIBAS integrates BAS with the robust approximation of quadratic interpolation. We employed QIBAS to a well-known finance problem known as Portfolio Selection as a testbed. Traditionally, the portfolio problem is modeled as a convex optimization problem, which is efficient to solve but inaccurate. The cardinality constrained model with higher dimensional stock data includes stringent real-world constraints. It is more accurate but computationally challenging and not tractable, making it a perfect candidate to test QIBAS. The primary goal is to minimize the risk and maximize the profit while selecting the portfolio. We included up to 250 companies in simulation and compared the results with BAS and two state-of-the-art swarm metaheuristic algorithms, i.e., Particle Swarm Optimization and Genetic algorithm. The results showed the promising performance of QIBAS in comparison with other algorithms.

Suggested Citation

  • Ameer Tamoor Khan & Xinwei Cao & Shuai Li, 2023. "Using Quadratic Interpolated Beetle Antennae Search for Higher Dimensional Portfolio Selection Under Cardinality Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1413-1435, December.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:4:d:10.1007_s10614-022-10303-0
    DOI: 10.1007/s10614-022-10303-0
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    References listed on IDEAS

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    1. Rula Hani Salman AlHalaseh & Aminul Islam & Rosni Bakar, 2019. "An Extended Stochastic Goal Mixed Integer Programming for Optimal Portfolio Selection in the Amman Stock Exchange," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 10(2), pages 36-51, April.
    2. Olivier Ledoit & Michael Wolf, 2017. "Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4349-4388.
    3. Gianluca De Nard & Olivier Ledoit & Michael Wolf, 2021. "Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly [Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 19(2), pages 236-257.
    4. Xingyu Yang & Jin’an He & Hong Lin & Yong Zhang, 2020. "Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 231-251, January.
    5. Martin R. Young, 1998. "A Minimax Portfolio Selection Rule with Linear Programming Solution," Management Science, INFORMS, vol. 44(5), pages 673-683, May.
    6. Elton, Edwin J. & Gruber, Martin J. & Padberg, Manfred W., 1977. "Simple Rules for Optimal Portfolio Selection: The Multi Group Case," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(3), pages 329-345, September.
    7. Yong-Jun Liu & Wei-Guo Zhang, 2019. "Possibilistic Moment Models for Multi-period Portfolio Selection with Fuzzy Returns," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1657-1686, April.
    8. M. H. A. Davis & A. R. Norman, 1990. "Portfolio Selection with Transaction Costs," Mathematics of Operations Research, INFORMS, vol. 15(4), pages 676-713, November.
    9. Chao Gong & Chunhui Xu & Ji Wang, 2018. "An Efficient Adaptive Real Coded Genetic Algorithm to Solve the Portfolio Choice Problem Under Cumulative Prospect Theory," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 227-252, June.
    10. Yi-Ting Chen & Edward W. Sun & Min-Teh Yu, 2018. "Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 653-684, August.
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