IDEAS home Printed from
   My bibliography  Save this article

Mining corporate portfolio optimization model with company’s operational performance level and international risk


  • Achille N. Njike

    (McGill University)

  • Mustafa Kumral

    (McGill University)


The mineral industry has encountered severe price turbulence in the recent years. A new portfolio management strategy will help to actively deal with this turbulence, corporate mining organizations need to improve their decision-making processes associated with capital allocation to new proposed projects. The proposed approach will help mining corporates to improve their capital allocation strategies to new projects in such a way as to consider operational performance in the prioritization of business-related spending on capital projects. The problem is formulated as the minimization of the risk at the desired return under constraints of operational performance requirement of the project’s initiators product group. This optimization model is solved using MATLAB. The results show that, on top of the NPV criteria, the more you diversify the portfolio, the more you potentially increase the corporate portfolio return and the more you slightly increase the risk for correlated projects. These results also show that as the performance of the product group increases, the number of approved projects at the corporate level also increases.

Suggested Citation

  • Achille N. Njike & Mustafa Kumral, 2019. "Mining corporate portfolio optimization model with company’s operational performance level and international risk," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 32(3), pages 307-315, November.
  • Handle: RePEc:spr:minecn:v:32:y:2019:i:3:d:10.1007_s13563-019-00171-w
    DOI: 10.1007/s13563-019-00171-w

    Download full text from publisher

    File URL:
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL:
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Mathieu Sauvageau & Mustafa Kumral, 2018. "Cash flow at risk valuation of mining project using Monte Carlo simulations with stochastic processes calibrated on historical data," The Engineering Economist, Taylor & Francis Journals, vol. 63(3), pages 171-187, July.
    2. Mikael Collan & Jyrki Savolainen & Pasi Luukka, 2017. "Investigating the effect of price process selection on the value of a metal mining asset portfolio," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 30(2), pages 107-115, July.
    3. Robert T. Clemen & Gregory W. Fischer & Robert L. Winkler, 2000. "Assessing Dependence: Some Experimental Results," Management Science, INFORMS, vol. 46(8), pages 1100-1115, August.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Mugebe, P. & Kizil, M.S. & Yahyaei, M. & Low, R., 2023. "Foundation of a framework for evaluating the impact of mining technological innovation on a company's market value," Resources Policy, Elsevier, vol. 85(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tianyang Wang & James S. Dyer & Warren J. Hahn, 2017. "Sensitivity analysis of decision making under dependent uncertainties using copulas," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 117-139, November.
    2. Donald L. Keefer & Craig W. Kirkwood & James L. Corner, 2004. "Perspective on Decision Analysis Applications, 1990–2001," Decision Analysis, INFORMS, vol. 1(1), pages 4-22, March.
    3. Robert L. Winkler & Robert T. Clemen, 2004. "Multiple Experts vs. Multiple Methods: Combining Correlation Assessments," Decision Analysis, INFORMS, vol. 1(3), pages 167-176, September.
    4. Wang, Fan & Li, Heng & Dong, Chao & Ding, Lieyun, 2019. "Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    5. Herbert Hove & Frank Beichelt & Parmod K. Kapur, 2017. "Estimation of the Frank copula model for dependent competing risks in accelerated life testing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(4), pages 673-682, December.
    6. Paola Monari & Patrizia Agati, 2001. "Fiducial inference in combining expert judgements," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 10(1), pages 81-97, January.
    7. Ali E. Abbas & David V. Budescu & Yuhong (Rola) Gu, 2010. "Assessing Joint Distributions with Isoprobability Contours," Management Science, INFORMS, vol. 56(6), pages 997-1011, June.
    8. Kamel, Ahmed & Elwageeh, Mohamed & Bonduà, Stefano & Elkarmoty, Mohamed, 2023. "Evaluation of mining projects subjected to economic uncertainties using the Monte Carlo simulation and the binomial tree method: Case study in a phosphate mine in Egypt," Resources Policy, Elsevier, vol. 80(C).
    9. Ardian, Aldin & Kumral, Mustafa, 2020. "Incorporating stochastic correlations into mining project evaluation using the Jacobi process," Resources Policy, Elsevier, vol. 65(C).
    10. Tianyang Wang & James S. Dyer, 2012. "A Copulas-Based Approach to Modeling Dependence in Decision Trees," Operations Research, INFORMS, vol. 60(1), pages 225-242, February.
    11. Gillian Anderson & Lesley Walls & Matthew Revie & Euan Fenelon & Calum Storie, 2015. "Quantifying intra-organisational risks: An analysis of practice-theory tensions in probability elicitation to improve technical risk management in an energy utility," Journal of Risk and Reliability, , vol. 229(3), pages 171-180, June.
    12. J. Eric Bickel & James E. Smith, 2006. "Optimal Sequential Exploration: A Binary Learning Model," Decision Analysis, INFORMS, vol. 3(1), pages 16-32, March.
    13. Aldin Ardian & Mustafa Kumral, 2021. "Enhancing mine risk assessment through more accurate reproduction of correlations and interactions between uncertain variables," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(3), pages 411-425, October.
    14. James K. Hammitt & Yifan Zhang, 2013. "Combining Experts’ Judgments: Comparison of Algorithmic Methods Using Synthetic Data," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 109-120, January.
    15. Jesus Palomo & David Rios Insua & Fabrizio Ruggeri, 2007. "Modeling External Risks in Project Management," Risk Analysis, John Wiley & Sons, vol. 27(4), pages 961-978, August.
    16. Robin L. Dillon & Richard John & Detlof von Winterfeldt, 2002. "Assessment of Cost Uncertainties for Large Technology Projects: A Methodology and an Application," Interfaces, INFORMS, vol. 32(4), pages 52-66, August.
    17. Werner, Christoph & Bedford, Tim & Cooke, Roger M. & Hanea, Anca M. & Morales-Nápoles, Oswaldo, 2017. "Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions," European Journal of Operational Research, Elsevier, vol. 258(3), pages 801-819.
    18. Kim, Byung-Cheol, 2022. "Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment," European Journal of Operational Research, Elsevier, vol. 296(2), pages 679-695.
    19. Hanea, Anca & Morales Napoles, Oswaldo & Ababei, Dan, 2015. "Non-parametric Bayesian networks: Improving theory and reviewing applications," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 265-284.
    20. Marie-Sophie Denner & Louis Christian Püschel & Maximilian Röglinger, 2018. "How to Exploit the Digitalization Potential of Business Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(4), pages 331-349, August.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:minecn:v:32:y:2019:i:3:d:10.1007_s13563-019-00171-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.