The mining industry is actively searching for better quantitative methods of assessing project uncertainty, managing the effects of uncertainty resolution, and the impact it has on project value.
These efforts are seen in the internal investigations being conducted at most major mining companies, the proliferation of commercial Monte Carlo software packages (used to simulate and analyze project risk) and their application to economic modelling, and the growing number of professional development workshops and courses on the subject. However, for all this effort, there is still no firm consensus on the best procedures for valuing and managing project uncertainty.
One framework, (originally proposed by University of Alberta adjunct professor David Laughton) structures this investigation into two dimensions. The first focuses on the modelling techniques used to create a description of project structure, such as production profile, capital and operating costs, flexibility, taxes and financing, with models measuring the probability of the most important sources of project uncertainty.
The least complex model in this dimension is a conventional spreadsheet cash flow model built around a single production plan and one-point forecasts of uncertain project variables — for instance, mineral prices. Other quantitative techniques in order of increasing sophistication are scenario analysis, Monte Carlo simulation, decision trees, and numerical algorithms using decision trees and simulation.
There appears to be broad consensus in the industry that numerical valuation techniques must move beyond simplistic cash flow models that rely on one-point forecasts and single production plans. The adoption of increasingly complex cash flow modelling techniques, and the cost of accompanying training programs, is justified by the additional insights gained from recognizing the non-linear cash flow value effects of taxes, creditor payments, operating flexibility and unpredictable foreign exchange rates. Conventional spreadsheet cash flow models simply do not recognize the complex interactions occurring in the project environment, and as a result, can potentially provide misleading conclusions.
The other dimension of quantitative uncertainty assessment focuses on how to calculate an adjustment that is appropriate for the particular timing and uncertainty characteristics of each project’s cash flow. This is an important aspect of cash flow modelling because, as students of engineering economics and finance learn very early in their studies, most investors are risk averse and require compensation through a risk-and-time adjustment to induce them to defer immediate consumption and subject their capital to future investment uncertainty. The adjustment process, also known as discounting, allows investors to convert the sum of annual expected net cash flows (revenues less operating costs, taxes, financing, and capital) into a projected economic value, called a net present value (NPV), so that the value influence of cash flow timing and uncertainty are recognized.
One very problematic aspect of the NPV calculation is calculating a risk adjustment. This is troublesome because cash flow uncertainty varies in a dynamic and non-linear manner over a project’s life. Cash flow uncertainty variability is a result of changes in deposit quality (for example, the shift to low-grade ore from high-grade ore), operating strategy (developing marginal satellite resources versus early closure), financing, taxes, and the uncertainty characteristics of project variables such as mineral price and grade. This variability causes difficulties for the valuation process because the risk adjustment used for each cash flow must reconcile the cash flow’s uncertainty characteristics with investor risk aversion to this uncertainty. Part of a mine valuation professional’s responsibilities is to choose a risk-and-time adjustment method during the valuation process that is an acceptable tradeoff between simplifying a complex problem and recognizing its important elements in a realistic but reduced valuation model.
Discounted cash flow
The predominant risk-and-time adjustment method in the mining industry is discounted cash flow (DCF) whereby a risk-and-time adjustment is calculated with a constant discount rate. This rate incorporates a component for the time value of money and another component reflecting the aggregate risk associated with uncertainty in the project or corporate net cash flows. The advantages of this approach include its ease of use and wide acceptance in industry. However, an important limitation of using a constant DCF discount rate is that it produces a set of risk adjustments consistent with a constant (linear) increase in net cash flow uncertainty. The DCF approach can be adapted to recognize the non-linear variation of net cash flow uncertainty through the use of decision theory and utility functions. However, the mining industry has shown no interest in exploring the techniques required for this modification.
Real options
An alternative risk-and-time adjustment method that is causing much debate in the mining industry is real options (RO). This approach does not seek to summarize the risk effects of project uncertainty within a single aggregate measure. There are several versions of RO currently being tried in industry. One method being experimented with by some mining companies and industry analysts involves the Black-Scholes call option pricing formula. This formula is often found in rudimentary RO texts as a pedagogical tool to explain the relationship between financial options and the exercise of management flexibility. Unfortunately, in this author’s opinion, the usefulness of the Black-Scholes formula in professional valuations is limited because its implied structure of project cash flow uncertainty and flexibility is often a serious misrepresentation of the actual project valuation environment. RO valuations based on the Black-Scholes formula should be treated with skepticism.
An alternative RO method is the Brennan-Schwartz method whereby the underlying sources of project cash flow uncertainty are identified and then each uncertainty is risk-adjusted individually using Black-Scholes-Merton techniques. Each risk adjustment is based on financial market information where possible and, failing that, using finance theory and professional judgment. The risk-adjusted models of uncertainty are then filtered through project structure to produce a risk-adjusted net cash flow. RO NPV is calculated by discounting each risk-adjusted net cash flow for time and then summing the resulting cash flow present values.
There are two main advantages to the RO approach. First, it is usually easier to identify and justify a risk adjustment for an individual project uncertainty than to determine an aggregate risk adjustment for the net cash flow stream that recognizes the complex interaction of project structure and uncertainty. The second advantage of RO is its ability to reflect the non-linear variation of net cash flow uncertainty over time in the implied structure of net cash flow risk adjustments resulting from applying an individual risk-adjustment to each source of uncertainty. The conventional DCF method is unable to match this because it uses an aggregate constant discount rate to calculate the risk-and-time adjustments applied to the net cash flow stream.
The primary disadvantage of the RO approach is that it has only recently started to move from academic interest to practical application in the mining industry. Most valuation professionals and corporations are unfamiliar with its application and are initially uncomfortable with some of its implications. There are also significant organizational costs associated with introducing the RO approach into business. These barriers should not prevent organizations involved with the mining industry from investigating whether the benefits of RO are greater than the costs associated with its introduction, especially given the ad-hoc modi
fications being made in industry to the conventional DCF approach to circumvent its limitations.
There are many situations where the judicious use of simulation and advanced finance theory can improve mining project valuation results. Some of these are as follows:
* Differentiating between competing project designs: Two or more competing designs are often proposed for the same project that differ by the amount of upfront capital, the level of unit operating costs, and the ability to manage uncertainty with operational flexibility. These designs frequently display important differences in cash flow uncertainty characteristics that should be recognized in the valuation process. For example, a design with low upfront capital costs may have high unit operating costs while a competing design may have high capital costs and low unit operating costs. Models incorporating simulation and advanced finance theory can explicitly recognize the lower net cash flow uncertainty of the second design and its impact on risk adjustments.
* Explaining the lack of gold project risk premium: Gold projects are increasingly valued using low discount rates in an effort to explain the difference between transaction values and DCF valuations completed with more traditional discount rates. The RO approach can explicitly demonstrate that gold projects with high or mid-level profit margins should have their cash flows risk-adjusted with low-risk premiums because it recognizes the low correlation between spot gold price movements and financial market uncertainty. The DCF method can implicitly support these lower risk premiums through analyzing the relationship between gold company share prices and financial markets.
* Valuing the interaction between equity and non-equity project participants: Project cash flows are divided between equity, creditors, the government, and other project stakeholders according to the financing terms of their participation. The uncertainty characteristics of each participant cash flow stream can vary markedly, given that payouts can be capped or obligations limited, and equity actions can affect the payouts to non-equity participants. Advanced simulation using RO or DCF methods can help explain the tradeoffs in setting the terms of sliding-scale royalties or the interest rate premium associiated with project financing.
There is renewed interest across the mining industry for improving the valuation process by using models that provide a more realistic representation of the project environment. These improvements are being provided with simulations that combine advanced finance theory with detailed descriptions of project uncertainty and the interaction of this uncertainty with project structure. The current debate regarding the DCF and RO methods of risk adjustment is also on-going and will likely continue until the RO method and its implications are more fully understood. However, there are sections of the mining industry that resolutely refuse to even recognize the existence of these new valuation innovations in the hopes that yet another review of the current valuation status quo will find some hidden improvement in valuation and risk analysis. This reticence should decline as industry experience with valuation simulation increases and the benefits of these new valuation techniques become apparent.
The author is the director of financial services (Mining and Metals), with AMEC Americas and adjunct professor, Department of Mining and Metallurgy, at the University of Laval. AMEC is an international project management, design and services company.
Be the first to comment on "Improving mine valuation"