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

Ameer Tamoor Khan (), Xinwei Cao () and Shuai Li ()
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Ameer Tamoor Khan: The Hong Kong Polytechnic University
Xinwei Cao: Jiangnan University
Shuai Li: Swansea University

Computational Economics, 2023, vol. 62, issue 4, No 2, 1413-1435

Abstract: 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.

Keywords: Beetle antennae search; Portfolio selection; Meta-heuristic algorithm; Quadratic interpolation; Stochastic algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10303-0

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