Portfolio risk reduction: Optimising selection of resource projects by application of financial industry techniques

Abstract

The resource industry seeks to forecast accurately the likely outcome of an exploration program ? in particular minimum and maximum and average size of a success; also the chance of achieving a success. Commonly such predictions are employed on a project-by-project basis. When a group of projects are combined in a portfolio, a higher level of evaluation is possible by quantifying the correlation coefficients among the projects. The outcome is that the portfolio total is not necessarily the sum of the individual parts. Combining the best individual projects usually does not produce the most efficient (lowest uncertainty) portfolio. Including some ?risky? projects (larger uncertainty) can lower the overall uncertainty of the portfolio. Ranking projects by common measures such as Expected Monetary Value or Expected NPV are not the best approach to building a portfolio. The approach in this paper draws on financial portfolio theory. This is routinely employed in the financial industry, which faces a similar challenge of forecasting outcomes (from investments). It is suggested that combining the better elements of resource and financial evaluations produce a more definitive prediction of the outcome of an exploration program. Analysing projects within a portfolio structure reduces the uncertainty in the range of deposit success-case sizes that may be encountered. It allows selection of the most efficient group of projects. This maximises the expected NPV value for the total portfolio, while at the same time minimising the uncertainty in the range of NPVs that could occur. Furthermore, if projects are selected with low correlation coefficients with each other (?diversification?), then the chance of obtaining one success is increased, compared to projects that are more positively correlated.