Multi-period mean-semi-entropy portfolio management with transaction costs and bankruptcy control

Zhou, Jiandong; Li, Xiang

doi:10.1007/s12652-020-02053-4

Cited by 5 publications

(6 citation statements)

References 54 publications

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“…Zhou and Li presented a Genetic Algorithm (GA) to tackle a multi-period mean-semi-entropy portfolio optimisation model (hypothetical dataset) [47]. The returns of assets were described using fuzzy variables.…”

Section: ) Population-based Metaheuristic Algorithms (P-mh) A: Geneti...mentioning

confidence: 99%

“…Cardinality, floor and ceiling, and transaction costs constraints were integrated to the problem [46]. Transaction consts and bankruptcy constraints were included in [47]. Turnover, floor and ceiling, and cardinality constraints were integrated to the problem [30].…”

Section: Research Limitations (Challenges)mentioning

confidence: 99%

“…In [44], only 6 stocks (first example) and 20 stocks (second example) from Shanghai Stock Exchange were utilized to examine the proposed methodology. In [47], only 5 assets was used to illustrate the efficiency of the proposed methodology. In [35], the proposed methodology was only tested up to 40 stocks.…”

Section: Research Limitations (Challenges)mentioning

confidence: 99%

“…[45] Genetic Algorithm (GA) [46] Genetic Algorithm (GA) [47] Modified Version of Non-dominated Sorting Genetic Algorithm II (MNSGA-II) [48] Improved Quantum-Behaved Particle Swarm Optimisation Algorithm (LQPSO) [49] Artificial Bee Colony (ABC)…”

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confidence: 99%

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Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

et al. 2023

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This survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorised as Metaheuristic, Mathematical Optimisation, Hybrid Approaches, Matheuristic and Machine Learning. The datasets (benchmark, real-world, and hypothetical) utilised in portfolio optimisation research are provided. The state-of-the-art methodologies for benchmark datasets are presented accordingly. Population-based metaheuristics are the most preferred techniques among researchers in addressing the POP. Hybrid approaches is an emerging trend (2018 onwards). The OR-Library is the most widely used benchmark dataset for researchers to compare their methodologies in addressing POP. The research challenges and opportunities are discussed. The summarisation of the published papers in this survey provides an insight to researchers in identifying emerging trends and gaps in this research area.

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Section: ) Population-based Metaheuristic Algorithms (P-mh) A: Geneti...mentioning

confidence: 99%

Section: Research Limitations (Challenges)mentioning

confidence: 99%

Section: Research Limitations (Challenges)mentioning

confidence: 99%

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confidence: 99%

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Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

et al. 2023

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“…Generally, after having characterized the return of each investment object as a random or fuzzy variable, most of the portfolio selection frameworks mentioned above are constructed by the optimization model. However, owing to the nonlinearity of these moments, it is difficult to find the analytic solution of these optimization-based portfolio selection models [7], [24]- [26]. Consequently, only some optimization algorithms such as genetic algorithm (GA) [27], differential evolution algorithm (DE) [28], or particle swarm optimization algorithm (PSO) [29] can be used to solve these models, but it leads to a slow speed in the model-solving process and an unstable solving result [30].…”

Section: Introductionmentioning

confidence: 99%

Decision-Making Model to Portfolio Selection Using Analytic Hierarchy Process (AHP) With Expert Knowledge

Zhao

Jia

2021

IEEE Access

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On the premise of ensuring profits, how to give a relatively dispersed portfolio selection result reasonably and rapidly is a challenging problem in both theory and practice. Although the use of optimization models to make decision has been shown to be an essential approach towards portfolio selection, there still has an acute need for developing a knowledge-based expert model for portfolio selection so that this model can achieve better performance in reliability and real time, especially in leading more distributed investments. In this paper, a knowledge-based expert model is proposed for portfolio selection with the aid of analytic hierarchy process (AHP) and fuzzy sets. In the proposed model, the expert knowledge which can reflect the investment attitude and experience of different investors is mainly integrated into the criterion layer and represented by a reciprocal matrix, and the scheme layer is abstracted to a strictly consistent matrix by comparing and analyzing the state characteristics of investment objects. In order to characterize the state characteristics of investment objects under fuzzy environment, their corresponding time series data are quantified as fuzzy variables in advance. Experiments involving synthetic and real-world data demonstrate that the proposed model produces better performance than other typical portfolio selection models and gives more distributed investments.INDEX TERMS Decision-making, portfolio selection, analytic hierarchy process (AHP), consistency, expert knowledge.

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Multi-period mean–variance portfolio selection with real constraints based on machine learning

Cui,

Zhang

2024

Int. J. Mach. Learn. & Cyber.

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Multi-period mean-semi-entropy portfolio management with transaction costs and bankruptcy control

Cited by 5 publications

References 54 publications

Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

Decision-Making Model to Portfolio Selection Using Analytic Hierarchy Process (AHP) With Expert Knowledge

Multi-period mean–variance portfolio selection with real constraints based on machine learning

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