Application of Genetic Algorithms in Portfolio Optimization for the Oil and Gas Industry

Fichter, Daniel

doi:10.2523/62970-ms

Cited by 4 publications

(3 citation statements)

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“…However, investment risks are also increasing at the same time. In order to diversify the risk and increase exploration efficiency, many oil companies have begun to apply modern portfolio theory to guide the exploration portfolio decisionmaking (Brashear, Becker, and Gabriel 1999;Fichter 2000;Erdogan, Mudford, Chenoweth, Holeywell, and Jakubson 2001;Tyler and Mcvean 2001). In China, China National Petroleum Corporation has applied the modern portfolio theory to exploration projects and pre-drilling exploration investment decisionmaking (Luo and Yu 2000;Zhang et al 2006;Guo 2007;Chen et al 2008).…”

Section: Introductionmentioning

confidence: 98%

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

Guo

Zou

et al. 2012

International Journal of Systems Science

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Variance is substituted by semi-variance in Markowitz's portfolio selection model. For dynamic valuation on exploration and development projects, one period portfolio selection is extended to multi-period. In this article, a class of multi-period semi-variance exploration and development portfolio model is formulated originally. Besides, a hybrid genetic algorithm, which makes use of the position displacement strategy of the particle swarm optimiser as a mutation operation, is applied to solve the multi-period semi-variance model. For this class of portfolio model, numerical results show that the mode is effective and feasible.

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Section: Introductionmentioning

confidence: 98%

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

Guo

Zou

et al. 2012

International Journal of Systems Science

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“…The standard framework of the GA was developed by Holland (1975) and Michalewicz (1996). In the last ten years, substantial research effort has been applied to the investigation and development of Genetic Algorithms, such as Bauer (1994), Berger (1995), Fichter (2000 and Goldberg (1989). However, these works on the portfolio optimization model with Genetic Algorithms have been confined to the static models.…”

Section: Introductionmentioning

confidence: 99%

Improving Portfolio Efficiency: A Genetic Algorithm Approach

Yang

2006

Comput Econ

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“…To assist their search for the optimal portfolio or at least an efficient one, several optimisation methodologies have been proposed. Linear programming (LP) (April et al, 2003;Back, 2001;Beaujon et al, 2001;Lessard, 2003) and evolutionary algorithms (EA) (Fichter, 2000;Sarich, 2001;Deb et al, 2002;Medaglia et al, 2005) are methods well suited to identifying an efficient portfolio from a large set of alternatives. The efficient portfolio identified by these methods is optimal given a single metric and a specific set of constraints.…”

Section: Introductionmentioning

confidence: 99%

Creating Portfolio Insight by a Pratical Multi-Method Optimisation Approach

Willigers¹,

Weis²,

Majou³

2011

SPE Annual Technical Conference and Exhibition

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Effective portfolio management requires a comprehensive understanding of the tradeoffs between different portfolio choices. Given a set of constraints, portfolio optimisation techniques based on linear programming and genetic algorithms can be applied to identify an optimal portfolio. However, this optimal portfolio might not be the preferred portfolio. Decision makers have to understand the tradeoffs between generally conflicting objectives and constraints before one portfolio can be identified as the preferred option. Such assessment of the overall search space is not made using linear programming and genetic algorithms when used to identify a single best solution and many portfolio options will have been eliminated before an understanding of these alternatives has been developed. Markowitz's mean-variance approach and the traditional "rank and cut" approach are typically used to establish a relationship between a portfolio's value and its variance or associated development cost. Although these methods enable decision makers to compare and contrast different options, the optimisation is limited to the portfolio value measure and a single other metric. This latter limitation is overcome by the more recently developed portfolio filtering approach. This method is practical and transparent and allows for a quick development of strategic portfolio alternatives while considering a large number of portfolio attributes. Its main drawback is that the analysed set of portfolios generally represents a subset of the total search space. Hence, as the number of feasible portfolio options increases, so does the chance that the optimal portfolio is not present in the population of sampled portfolios. This paper presents a portfolio optimisation approach that combines linear programming, genetic algorithms and portfolio filtering. An analysis of a realistic upstream portfolio illustrates and validates the optimisation process by demonstrating how limitations of individual methods can be mitigated by combining them.

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Application of Genetic Algorithms in Portfolio Optimization for the Oil and Gas Industry

Cited by 4 publications

References 0 publications

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

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

Improving Portfolio Efficiency: A Genetic Algorithm Approach

Creating Portfolio Insight by a Pratical Multi-Method Optimisation Approach

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