Dynamic index tracking via multi-objective evolutionary algorithm

Chiam, Swee Chiang; Tan, Kang; Mamun, Abdullah Al

doi:10.1016/j.asoc.2013.01.021

Cited by 31 publications

(18 citation statements)

References 39 publications

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“…We also propose the similarity ratio to calculate the degree of diversification of the constrained frontier with respect to the mean-variance frontier, which may be particularly relevant for the decision maker. The combinatorial problem of finding the optimal frontier constrained to assets is solved by using a genetic algorithm, as proposed in a wide range of recent studies (Ruiz-Torrubiano and Suárez, 2009;Woodside-Oriakhi et al, 2011;Anagnostopoulos and Mamanis, 2011;Chiam et al, 2013;Sant'Anna et al, 2016).…”

Section: Woodside-oriakhi Et Almentioning

confidence: 99%

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A similarity measure for the cardinality constrained frontier in the mean–variance optimization model

Guijarro

2018

Journal of the Operational Research Society

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This paper proposes a new measure to find the cardinality constrained frontier in the mean-variance portfolio optimisation problem. In previous research, assets belonging to the cardinality constrained portfolio change according to the desired level of expected return, so that the cardinality constraint can actually be violated if the fund manager wants to satisfy clients with different return requirements. We introduce a perceptual approach in the mean-variance cardinality constrained portfolio optimization problem by considering a novel similarity measure, which compares the cardinality constrained frontier with the unconstrained mean-variance frontier. We assume that the closer the cardinality constrained frontier to the mean-variance frontier, the more appealing it is for the decision maker. This makes the assets included in the portfolio invariant to any specific level of return, through focusing not on the optimal portfolio but on the optimal frontier.

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Section: Woodside-oriakhi Et Almentioning

confidence: 99%

“…However, the current practice is for brokers to apply a fixed component of the cost, regardless of the volume traded, plus a variable proportional of the volume. Under this premise, Chiam et al (2013) characterize the most common transaction cost functions in the literature:…”

Section: Transaction and Tax Costsmentioning

confidence: 99%

A similarity measure for the cardinality constrained frontier in the mean–variance optimization model

Guijarro

2018

Journal of the Operational Research Society

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“…Note here that since index tracking (so L = 1) has been extensively considered in the literature (e.g. see [3,10,11,13,16,24,29,30] for recent work) we focus here only on cases where L = 1, so portfolios of assets aiming to give a return different from that of the benchmark index.…”

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confidence: 99%

An optimisation approach to constructing an exchange-traded fund

2014

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In this paper we consider the problem of deciding the portfolio of assets that should underlie an exchange-traded fund (ETF). We formulate this problem as a mixed-integer nonlinear program. We consider ETFs which have positive leverage with respect to their benchmark index and ETFs which have negative leverage (inverse, short, ETFs). Our formulation is a flexible one that incorporates decisions as to both long and short positions in assets, as well as including rebalancing and transaction cost. Computational results are given for problems, derived from universes defined by S&P international equity indices, involving up to 1,200 assets.

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“…Chiam et al [13] propose a bi-objective formulation for the index tracking problem in a rebalancing context. The two goals to be simultaneously minimized are the tracking error and the total amount of transaction costs paid.…”

Section: Enhanced Index Tracking and Multi-objective Portfolio Optimimentioning

confidence: 99%

A heuristic framework for the bi-objective enhanced index tracking problem

Filippi

Guastaroba

Speranza

2016

Omega

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The index tracking problem is the problem of determining a portfolio of assets whose performance replicates, as closely as possible, that of a financial market index chosen as benchmark. In the enhanced index tracking problem the portfolio is expected to outperform the benchmark with minimal additional risk. In this paper, we study the bi-objective enhanced index tracking problem where two competing objectives, i.e., the expected excess return of the portfolio over the benchmark and the tracking error, are taken into consideration. A bi-objective Mixed Integer Linear Programming formulation for the problem is proposed. Computational results on a set of benchmark instances are given, along with a detailed out-of-sample analysis of the performance of the optimal portfolios selected by the proposed model. Then, a heuristic procedure is designed to build an approximation of the set of Pareto optimal solutions. We test the proposed procedure on a reference set of Pareto optimal solutions. Computational results show that the procedure is significantly faster than the exact computation and provides an extremely accurate approximation.

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Dynamic index tracking via multi-objective evolutionary algorithm

Cited by 31 publications

References 39 publications

A similarity measure for the cardinality constrained frontier in the mean–variance optimization model

A similarity measure for the cardinality constrained frontier in the mean–variance optimization model

An optimisation approach to constructing an exchange-traded fund

A heuristic framework for the bi-objective enhanced index tracking problem

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