Stock index tracking by Pareto efficient genetic algorithm

Ni, He; Wang, Yongqiao

doi:10.1016/j.asoc.2013.08.012

Cited by 32 publications

(14 citation statements)

References 28 publications

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“…The problem of selecting stocks to be included in the tracking portfolio is NP-hard [41]. Many heuristic algorithms have been developed to identify practical solutions that are close to the global optimum: Gilli and Kellezi [42] presented a threshold-accepting heuristic algorithm demonstrating that it constitutes an efficient optimization technique for index tracking problems with a small number of stocks in the benchmark index; Beasley et al [43] presented an evolutionary heuristic algorithm for the solution of the index tracking problem by including a constraint limiting the number of stocks, as well as transaction costs; Ruiz-Torrubiano and Suárez [41] proposed a hybrid strategy that combines an evolutionary algorithm with quadratic programming; and Ni and Wang [44] proposed a heuristic-searching approach that is based on a hybrid genetic algorithm with a self-adaptive evolving mechanism.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…Proposed west selection Proposed north selection Lobo et al [14] DeMiguel et al [15] Brodie et al [16] Xing et al [18] Roll [24] Jorion [25] Shen et al [26] Ruiz-Torrubiano and Suárez [41] Gilli and Kellezi [42] Beasley et al [43] Ni and Wang [44] Hodder, Jackwerth, and Kolokolova [28] Kuosmanen [29] Luedtke [30] Bruni et al [31] Fábián et al [32] Guastaroba and Speranza [33]…”

Section: Optimization Parameter Selection Performances Return Risk Spmentioning

confidence: 99%

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Dantzig Type Optimization Method with Applications to Portfolio Selection

Park

Lee

et al. 2019

Sustainability

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This paper investigates a novel optimization problem motivated by sparse, sustainable and stable portfolio selection. The existing benchmark portfolio via the Dantzig type optimization is used to construct a sparse, sustainable and stable portfolio. Based on the formulations, this paper proposes two portfolio selection methods, west and north portfolio selection, and investigates their empirical properties. Numerical results presented for 12 datasets and various simulated data show that the west selection can reduce risk, and the north selection may outperform the benchmark as to risk-adjusted returns (based on, e.g., information ratio and Sharpe ratio).

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Section: Literature Reviewsmentioning

confidence: 99%

Section: Optimization Parameter Selection Performances Return Risk Spmentioning

confidence: 99%

Dantzig Type Optimization Method with Applications to Portfolio Selection

Park

Lee

et al. 2019

Sustainability

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“…Ni & Wang [35] also made use of genetic algorithms. The mathematical model they proposed is based on a hybrid genetic algorithm with a self-adaptive evolving mechanism.…”

Section: Recent Literature On Partial Index Trackingmentioning

confidence: 99%

“…The index tracking problem with cardinality constraint has been extensively analyzed in the literature (Beasley et al [7]; Ni and Wang [35]; Filippi et al [19]; Ruiz-Torrubiano and Suárez [37]; Sant'Anna et al [38]; Mezali and Beasley [34]; Canakgoz and Beasley [9]; Li et al [27]), as has been the mean-variance model with cardinality constraint (Lwin and Qu [31]; Woodside-Oriakhi et al [43]; Streichert et al [40]; Chang et al [11]; Aouni et al [4]; Cesarone et al [10]; Maringer and Kellerer [32]). Some of these papers approach the problem from a traditional optimization perspective, but the most recent studies deal with the problem using heuristics to find solutions in a reasonable computing time which are closed to the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics

García

Guijarro

Oliver

2017

Neural Comput & Applic

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The aim of this study was to compare the performance of the well-known genetic algorithms and tabu search heuristics with the financial problem of the partial tracking of a stock market index. Although the weights of each stock in a tracking portfolio can be efficiently determined by means of quadratic programming, identifying the appropriate stocks to include in the portfolio is an NP-hard problem which can only be addressed by heuristics. Seven real-world indexes were used to compare the above techniques and results were obtained for different tracking portfolio cardinalities. The results show that tabu search performs more efficiently with both real and artificial indexes. In general, the tracking portfolios obtained performed well in both in-sample and out-of-sample periods, so that these heuristics can be considered as appropriate solutions to the problem of tracking an index by means of a small subset of stocks.

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“…In recent years, some metaheuristic-based research has been devoted to the study of Finance (Ni & Wang, 2013). One specific area of interest is portfolio selection, where different types of metaheuristics have been employed in the optimization process (Metaxiotis & Liagkouras, 2012), both under single objective and multiobjective perspectives.…”

Section: Introductionmentioning

confidence: 99%

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

Macedo

Godinho

Alves

2017

Expert Systems with Applications

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Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established Technical Analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs-Non-dominated Sorting Genetic Algorithm II (NSGA II) and Strength Pareto Evolutionary Algorithm 2 (SPEA 2)within portfolio optimization. In addition, when used with four TA based strategies-Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), Contrarian Bollinger Bands (CBB) and Bollinger Bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy.

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Stock index tracking by Pareto efficient genetic algorithm

Cited by 32 publications

References 28 publications

Dantzig Type Optimization Method with Applications to Portfolio Selection

Dantzig Type Optimization Method with Applications to Portfolio Selection

Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

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