Index tracking with controlled number of assets using a hybrid heuristic combining genetic algorithm and non-linear programming

Sant’Anna, Leonardo Riegel; Filomena, Tiago Pascoal; Guedes, Pablo Cristini; Borenstein, Denis

doi:10.1007/s10479-016-2111-x

Cited by 36 publications

(29 citation statements)

References 28 publications

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“…In the remaining papers, metaheuristics such as evolutionary algorithms (cf. [1,6,17,21,27,28,29]) or local-search heuristics (cf. [18,22,32]) are proposed.…”

Section: Related Literaturementioning

confidence: 99%

Tracking and outperforming large stock-market indices

Gnägi

Strub

2020

Omega

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Highlights • We study the problem of tracking and outperforming large stock-market indices. • We compare linear and quadratic objective functions used in the literature. • We consider various real-life constraints that are relevant in practice. • We propose novel MIP formulations and novel matheuristics. • We find that the tracking error variance, a quadratic function, should be optimized.

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“…In the remaining papers, metaheuristics such as evolutionary algorithms (cf. [1,6,17,21,27,28,29]) or local-search heuristics (cf. [18,22,32]) are proposed.…”

Section: Related Literaturementioning

confidence: 99%

Tracking and outperforming large stock-market indices

Gnägi

Strub

2020

Omega

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“…The growing popularity of the enhanced index funds, experienced both in mature and emerging markets (Parvez and Sudhir 2005;Weng and Wang 2017), has pushed the academic community towards the design of quantitative tools to support investors. While the scientific literature on the IT is rather extensive [see, for example, Sant'Anna et al (2017) for a recent overview], the number of contributions on the EIT problem is lower but steadily increasing. The EIT problem was originally introduced by Beasley et al (2003) and an overview of the early literature can be found in Canakgoz and Beasley (2009).…”

Section: Related Literaturementioning

confidence: 99%

Enhanced indexation via chance constraints

Beraldi

Bruni

2020

Oper Res Int J

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The enhanced index tracking (EIT) represents a popular investment strategy designed to create a portfolio of assets that outperforms a benchmark, while bearing a limited additional risk. This paper analyzes the EIT problem by the chance constraints (CC) paradigm and proposes a formulation where the return of the tracking portfolio is imposed to overcome the benchmark with a high probability value. Besides the CC-based formulation, where the eventual shortage is controlled in probabilistic terms, the paper introduces a model based on the Integrated version of the CC. Here the negative deviation of the portfolio performance from the benchmark is measured and the corresponding expected value is limited to be lower than a given threshold. Extensive computational experiments are carried out on different set of benchmark instances. Both the proposed formulations suggest investment strategies that track very closely the benchmark over the out-of-sample horizon and often achieve better performance. When compared with other existing strategies, the empirical analysis reveals that no optimization model clearly dominates the others, even though the formulation based on the traditional form of the CC seems to be very competitive.

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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%

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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Index tracking with controlled number of assets using a hybrid heuristic combining genetic algorithm and non-linear programming

Cited by 36 publications

References 28 publications

Tracking and outperforming large stock-market indices

Tracking and outperforming large stock-market indices

Enhanced indexation via chance constraints

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

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