Abstract
The minimization of the costs related to portfolio insurance is a very important investment strategy. In this article, by adding the transaction costs to the classical minimum cost portfolio insurance (MCPI) problem, we define and study the MCPI under transaction costs (MCPITC) problem as a nonlinear programming (NLP) problem. In this way, the MCPI problem becomes more realistic. Since such NLP problems are commonly solved by heuristics, we use the Beetle Antennae Search (BAS) algorithm to provide a solution to the MCPITC problem. Numerical experiments and computer simulations in real-world data sets confirm that our approach is an excellent alternative to other evolutionary computation algorithms.
Highlights
Investment costs and fees can have a significant effect on the average returns on portfolios, over the longer term
The MCPI under transaction costs (MCPITC) is an nonlinear programming (NLP) problem, we approach it with an altered acceptation of the Beetle Antennae Search (BAS) algorithm and we compare the results with the results of some of the best evolutionary computation algorithms at present
Given a portfolio θθ = [2,10,2,1]T, a floor φφ = 580 and αα = 3000, ζζ = 5 in (17), we present the findings in Fig. 1 where: 1) Fig. 1a displays the payoff of the portfolio ηη, which is XX ⋅ ηη, created by BAS, Shuffled Frog Leaping Algorithm (SFLA), Firefly Algorithm (FA) and Genetic Algorithm (GA)
Summary
Investment costs and fees can have a significant effect on the average returns on portfolios, over the longer term. The MCPITC is an NLP problem, we approach it with an altered acceptation of the Beetle Antennae Search (BAS) algorithm and we compare the results with the results of some of the best evolutionary computation algorithms at present. These algorithms are the Shuffled Frog Leaping Algorithm (SFLA) from [3], the standard Firefly Algorithm (FA) from [4] and the Genetic Algorithm (GA) MATLAB function. In [7], a constrained portfolio optimization problem is tackled by an altered version of BAS, named Quantum Beetle Antennae Search (QBAS).
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