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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

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    References listed on IDEAS

    1. 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.
    2. 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.
    3. 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.
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