A class of multi-period semi-variance portfolio for petroleum exploration and development

Guo, Qiulin; Li, Jianzhong; Zou, Caineng; Guo, Yaqiong; Yan, Wei

doi:10.1080/00207721.2011.555011

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…In addition, Yu, Wang, and Lai (2008) introduced a novel neural network-based mean-varianceskewness model for portfolio selection. Besides, past works include Perold (1984), Crama and Schyns (2003), Huang (2007), Lin and Liu (2008), Guo, Li, Zou, Guo, and Yan (2012), Suganya and Vijayalakshmi Pai (2012), Yan (2012), etc. These researches help the invertors a lot in selecting an optimal portfolio.…”

Section: Introductionmentioning

confidence: 98%

Uncertain programming models for portfolio selection with uncertain returns

Zhang

Peng

2014

International Journal of Systems Science

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In an indeterminacy economic environment, experts' knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.

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Section: Introductionmentioning

confidence: 98%

Uncertain programming models for portfolio selection with uncertain returns

Zhang

Peng

2014

International Journal of Systems Science

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“…(a) In the Markowitz portfolio model, the risk dispersion problem with the upstream field of the oil & gas industry is simulated and decided. The optimal portfolio is consistent with the minimum risk under the determined return or the maximum return under the determined risk (Guo et al, 2012;Markowitz, 1952). (b) Conditional value at risk (CVaR) is the most frequently used risk measure in current risk management practice.…”

Section: Introductionmentioning

confidence: 99%

A class of nonlinear multi-objective mixed integer programming model for oil and gas portfolio and algorithm solution

Yan

2023

Energy Exploration & Exploitation

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This paper is concerned with the oil & gas assets portfolio. A multi-objective portfolio model of oil & gas assets is studied from two perspectives—scale and revenue. Considering the nonlinear and integer constraints in the model, a class of oil & gas assets portfolio model of nonlinear multi-objective mixed integer programming is established. The weight of the multi-objective is solved by the support vector machine model. A hybrid genetic algorithm, which uses the position displacement strategy of the particle swarm optimizer as a mutation operation, is applied to the optimization model. Finally, two examples are applied to verify the effectiveness of the model and algorithm.

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“…The Markowitz portfolio model is used to simulate and determine the risk dispersion problem in the upstream field of the oil and gas industry. The optimal portfolio is determined based on either the minimum risk for a given return or the maximum return for a given risk 1,2 The Boston Consulting Group Matrix (BCG Matrix) is a tool that categorizes strategic business units into four distinct categories, providing a visual representation of their performance.…”

Section: Introductionmentioning

confidence: 99%

Modeling and solving a multi‐objective optimal portfolio of upstream oil and gas assets

Yan

2023

Optim Control Appl Methods

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This paper focuses on optimizing project investments in oil and gas companies. It proposes a multi‐objective method for investing in oil and gas assets, considering factors such as scale and efficiency. The model takes into account the presence of nonlinear equations and integer constraints, and establishes a nonlinear multi‐objective mixed integer programming portfolio model for oil and gas. The weights of multiple objectives are determined using support vector machines. The optimization model incorporates the displacement transfer concept of particle swarm optimizer and the mutation operation of genetic algorithm using the transfer strategy of Gaussian particle swarm. The effectiveness of the model and algorithm is demonstrated through two examples.

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A class of multi-period semi-variance portfolio for petroleum exploration and development

Cited by 14 publications

References 22 publications

Uncertain programming models for portfolio selection with uncertain returns

Uncertain programming models for portfolio selection with uncertain returns

A class of nonlinear multi-objective mixed integer programming model for oil and gas portfolio and algorithm solution

Modeling and solving a multi‐objective optimal portfolio of upstream oil and gas assets

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