An article I published on SA.
In a recent Sprott Money podcast, Eric Sprott discussed the merits of investing in high-cost producers during gold bull markets (https://www.sprottmoney.com/Blog/when-you-contemplate-negative-interest-rates-i-would-rather-own-gold.html). The intuition is simple, a high-cost producer may only produce $100/oz of cash at a $1,200/oz gold price. When prices jump to $1,300/oz, net income effectively doubles; why wouldn’t the share price jump with it? The math does not lie, but I thought it would be worthwhile to investigate the validity of this statement in the context of current market performance.
YTD, gold prices are up approximately 5% or $60/oz. That is a sizeable amount, enough to produce a material increase in profits for high-cost gold producers.
Let us take a look at Detour Gold as an example. In 2018, the company produced 610,000 oz of gold at an all-in-sustaining cost of $1,158/oz (https://www.detourgold.com/investors/news/press-release-details/2019/Detour-Gold-Reports-Fourth-Quarter-and-Year-End-2018-Financial-Results/default.aspx). With this production, cost, and a realized price of $1,268/oz, they produced adjusted net earnings of US$64.2M. At today’s gold prices, using last year’s production costs, we could expect earnings to increase by 65%. This statement is a simplification of reality as production costs are inherently variable as is production. The underlying notion, however, that low-cost producers have more leverage to the gold price is true (a small increase in gold prices can make these miners much more profitable).
So given the increase in gold prices, my hypothesis is that we should see a positive correlation between production costs and 2019 returns. Well, let us see what the data shows.
This YTD price return tornado shows some promising results. I see high-cost producers like Detour in the green, but there are many similar peers that are in the red.
This chart is much more useful. It plots 2019 YTD price returns against 2018 AISC per oz. The relationship, however, is the exact opposite of what I would expect. The lower the cost the producer, in general, the better they have done this year.
Before you start challenging this chart, I will acknowledge a few caveats. First, this is the GDX peer group. The silver price has been hammered this year, and some poor performing companies (Hecla) have a large exposure to the silver metal. There are also notable idiosyncratic factors that have resulted in poor (St. Barbara Feasibility study disappointment) and better than average performance.
The underlying premise, however, that high-cost producer will outperform in a rising gold environment appears to be less certain than the underlying logic would propose.
So how should you think about this information in the context of your investment framework? To start, the data suggests that jumping into high-cost producers does not make sense at the moment. Perhaps the gold price needs to consolidate before market participants rerate these companies. The data suggest that less risky, lower cost producers, may be the first to benefit (in terms of stock price) during a bull market.
In this case, Kirkland Lake stands out as the obvious choice; being the lowest cost producer in the GDX index.
Intuitively I’ve known that the gold mining industry doesn’t allocate capital well. I’ve touched on some of the return performance of royalty companies in the past. Their low return royalty model looks angelic compared to other actors. As I browse through company balance sheets I note the scars of the past (dramatically negative retained earnings). I’ve never really looked at this information in much detail. I know Kinross made some bad bets, I know Barrick lost its track with Pascua Lama. The full magnitude of the capital destruction, however, was way worse than I thought it would be.
Take a look at this snapshot. In 2013, this group -which is by no means comprehensive- wrote off over $24B in 2013.
Over this same time period, the group destroyed $70B worth of capital. Barrick alone tallied $30B.
If we look how much was written off, compared to these company’s current enterprise value the data shows the Kinross leads the pack. Between 2005 and 2018 the company wrote off $12B, which is 50% more than the current valuation…
Interesting stuff. Terrifying really. There are, however, a handful of companies that did pretty well during this time period. All of the streamers kept write-down % under 10%. Kirkland Lake was one of the only companies that survived the period without an issue.
“Pit Optimization” is a mysterious catch-all term used by mining engineers, financiers, and geologists. You input a bunch of economic variables into various Black-Box algorithms and with a click of a button, and 20-30 minutes, out spits a pit shell that is “optimized.” From this shell, you can tell the world how much metal is economic and then run some mine plans to figure out the economic reality for the project. My interest in digging into this topic a bit deeper was spurred by the realization that experienced industry leaders were treating the Nevada Net Proceeds Tax differently.
What is Whittle? What is Lerchs-Grossman?
Lerch-Grossman is the name of a modeling approach for solving open-pit optimization. It was developed in 1965 and implemented in the 1980’s by Jeff Whittle. The algorithm uses block dependencies to determine which blocks must be mined out as a group (i.e. if you mine G you must mine B, C, and D). The graphical relationship between blocks is determined by slope parameters (i.e. if you are using a very shallow slope than mining G may necessitate the mining of A through E).
Figure 1: Dependency Example (source: CONSTRUCTION ECONOMIC ORE BODY MODELS FOR OPEN PIT OPTIMIZATION, D. Whittle)
I like to think about optimization in a backwards fashion. Flip the topography upside down and whatever falls out is the optimized pit. Instead of gravity, you have revenue, this is the force propelling the ore out of the ground. In the opposite direction, let’s call it friction, we have costs; forces that go against the revenue generated by the ore. Like any physics equation, the fundamental forces need to be correct and well thought out.
With this explanation in mind, let’s think about a tricky situation. Below you’ll see excerpts from two technical reports. Both open-pit mines located in Nevada. One accounts for the State’s Net Proceeds Tax, one does not. So who is right here?
Well, the calculation of the net proceeds tax looks like you can deduct just about every expense that occurs during mining so the tax is more of a net profit royalty. Thinking about the flipped topo, the net proceeds tax only applies to blocks that actually generate a profit. So, as my beautiful the drawing shows, the tax applies after the blocks have “fallen out.” The tax applies to a component of the profit, so there is no way that there can be enough force to push the blocks back up. Therefore, I just can’t see why the Net Proceeds Tax would be included in the determination of the ultimate resource. Looks like Mine 2 has it right.
Figure 2: NNPT Calc, Source: https://nevadataxpayers.org/wp-content/uploads/2016/10/minerals-tax-2007-08.pdf
In playing devil’s advocate, I wonder if the tax should be used in the determination of interim pit phases? Imagine you have two phases, one high profit, one low profit. Does this change your thinking? It still shouldn’t matter. As long as the second phase is above breakeven, it should be mined.
Ok, so for now, I think the case is closed. NNPT and NPI royalties should not be included as cost factors in open pit optimization.
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The writer is a mining engineer trying to get by in the world of finance.
Whenever possible we’ll maximize the use of data in the hopes that we bring a unique perspective and rigor in mining analysis.
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I think it’s fair to say that we’ve all grown accustomed to miners overpromising and underdelivering. Whenever a new project is coming online there is a palpable uneasiness that is detectable in analysts, management, and investors. Will the project hit the guidance throughput? Mining Rate? Recovery?
I don’t want to group all projects into the underdeliver bucket but there is certainly a large percentage that not only fail to deliver on ramp-up projections but also in ultimate capacity. The chart below shows actual vs. projected mining rates for a large project that was recently constructed. Now I get that management can be overly optimistic and ramp-ups can be challenging. You’ll notice that the year 1 tonnage is way off of the projection but, by year 2 and 3, the company has closed much of the gap. What I’m more interested in is the levelling off of production and the inability to ever achieve expectations regarding ultimate mining rates.
Management will often highlight specific variables that are performing worse than expected. Shovel downs are decreasing availability. Pioneering is impacting productivity. A large weather storm reduced operating time. These are all possibilities. What I’m interested in, however, is why production falls short of budget when all variables are as expected.
But how can this be? How can production be less than expected when all inputs are as expected? Well, I think that the companies contracted out to perform mining studies underestimate volatility’s role in determining production rates.
Let’s look at a simple example:
The capacity table shows one shovel that is paired with three trucks. You’ll notice that truck and shovel capacity is fairly balanced, both around 25K tons mined per shift. You could expand the time period to encompass a year and this is how most feasibility studies determine equipment requirements. Now they would use variable parameters for each of these factors based on the specific conditions in the mine during the period (haul distance) and planned maintenance.
Ok, nothing wrong here, right?
These assumptions grossly underestimate the influence randomness within each variable. Fluctuations in, say, availability have asymmetric impacts on the production of the mine. -10% one day and +10% the next does not average out to zero impact on the mine. It averages something less.
This histogram of shift-by-shift availability reflects the average that is shown in the table (77%). This histogram reflects the outcome of 730 simulations (1 year’s worth of shifts) of another probability density function that is based on actual data.
These availability values were used to calculate shift production for an entire year. The chart below shows the mine’s production as a function of shovel availability. You’ll notice that tons mined per shift increases linearly as shovel availability is increased -the mine is shovel limited-. Once availability exceeds 70%, however, the mine is limited by truck capacity.
This is the root of the issue. Mine production falls short when shovel availability is lower than average and cannot sufficiently increase production when availability is higher than average. This asymmetry makes it effectively impossible for feasibility parameters to be achieved.
Average simulated production is barely over 20K tons per shift. A far cry (-18.5%) from the 25K tons per shift that would be predicted by the study. Coincidentally, this is the same delta between actual and planned tonnage in Y5 of the earlier chart.
Mines are fragile. They are hurt by volatility and cannot make up production on the positive side because rates are capped by a new bottleneck. It’s my opinion that this static mindset, when it comes to production scheduling, is the root cause of lots of the industry’s issues.
So what do we take away from this very simplified example?
Well, it pays to have buffer (excess) capacity. Miners with small equipment fleets are the most susceptible to volatile operating parameters as they don’t have other units to average out the shift-by-shift outcomes. In general, a static approach to forecasting will overestimate long term production.
So I woke up this morning thinking about gold producer price to NAV5% discounts/premiums. I’ve struggled to understand the intuition behind being priced at a premium to NAV5% as this implies that the appropriate discount rate for the stream of cash flows is less than 5%. How can this be given that gold mining is inherently risky. Arguably more so than the typical business in the S&P 500.
Well there are a couple of explanations for this pricing behavior:
- Analyst NAV assessments underestimate the future
stream of cash flows
- This is possible but I doubt this is the answer as any future resource conversion is going to occur so far in to the future that it would be discounted to oblivion
- Analysts use a flat price deck when creating the
- You could make the case that a nominal price deck should be used but this change would be offset by the use of a nominal discount rate; probably not the answer.
- NAV5% is not the correct discount rate to be
used for specific gold producers.
- I think this is the underlying logic driving the price to NAV logic.
In the gold space, small companies typically trade at a discount to their NAV. Generally, discount to NAV is negatively correlated with size; smaller company, larger discount. The intuition behind this equilibrium makes sense as larger companies are more diversified (operationally and jurisdictionally). Senior producers generally trade at a premium to NAV and streaming companies trade at an even higher premium.
Being priced at a premium is a challenging concept to understand. At the extreme, this can imply that the purchase of the security will provide cash flows that are less than the purchase price. This is very similar to negative interest rates. Unlike negative interest rates, however, the pricing of these securities is not manipulated by quantitative easing and central banks. The “rational” investor is doing the pricing.
So how can a discount rate of zero (or negative) be justified. The capital asset pricing model states that the equity risk premium is the risk free rate + beta * equity risk premium. The current T-bill rate is around 1% so this implies that the beta of these investments must be zero or slightly negative. This train of thought led me to start looking into beta and the correlation of gold to the broader economy.
So what is beta? I like to think of it as a leverage factor for stock returns. A beta of 1.25 means that the stock will move 125% of the move of the general market. Beta can also be thought of as “risk” factor. A high beta implies high risk -higher volatility- and investors will require a higher rate of return for an investment in the security.
If we look at the beta of monthly returns from 1973 to the end of 2018 we see that gold has exhibited a negative beta with respect to the S&P500. Conversely, copper, oil, and silver have positive betas. These relationships make sense as these other commodities are more related to economic growth and, in turn, the S&P500.
The beta values are misleading as they understate gold’s performance
Because, as you’ll see in this chart, gold generally hovers around a beta of zero during periods of relative calm and spikes in other occasions (2008).
This chart that gold acts as insurance during calamity, increasing in value by more than the S&P falls.
This post is a continuation of yesterday’s dive into the relationship between company-wide grade and cash flow from operations. Spoiler alert, there was no relationship. This fact isn’t surprising given the multitude of other factors that impact a company’s cash flow from operations.
I decided to get a little more granular and look at this relationship on an asset basis.
The chart shows that increased production correlates with lower AISC, an expected result given economies of scale. Large scale project
If we compare AISC for open pit operations vs. processed grades we see the trend that we were looking for! Increased grades
Similarly with UG operations, higher grade and lower opex.
Hope you find this interesting.
The purpose of this analysis is to answer the question: “Is Grade King?”
I’ve heard this line often and am interested in correlating mined grade to cash flow from operations to see if there is any relationship.
I’ll start with a broad assessment, ignoring the obvious impact of mine type (UG/OP).
The chart shows a couple of interesting points. Pretium and Kirkland Lake are really in a league of their own in terms of reserve grade for producers with >200K oz per annum of production.
The chart isn’t that useful with these higher reserve grade operators. Let’s take them out.
The chart, with a 5 g/t cap looks as follows:
Moreover, unfortunately, there really isn’t any relationship between reserve grade and CFO/oz (darn). This isn’t that surprising as reserve grades aren’t mined grades and there are countless other factors that impact the profitability of an operation. I was hoping that grade would be king-enough to prove some relationship.
This investigation will require further analysis. While we’re at it. Let’s look at 2018 production.
The chart shows that, as expected, more gold production = more CFO. It’s interesting to note which operators fall above or below the trend line. Kirkland Lake, Newcrest, and Sibanye stand out as a strong performers while Anglo Gold Ashanti is a laggard.
The last chart we’ll look at is a comparison between 2018 CFO per oz produced vs. current enterprise value per ounce. Now we wouldn’t expect CFO to directly translate to enterprise value but I think that this chart is interesting because it provides commentary regarding how much reserve value is going to be captured by the company (or at least the market’s perception of it). The shading of the dots reflects the comparison between these two metrics.
The variation in this metric is very impressive. Take New Gold for example, in 2018 the CFO per oz was close $550 while the company’s enterprise value per reserve ounce is only $100/oz. As mentioned, there are innumerable other items to consider (asset quality, assets not in production, CFI, capital structure).
Not really sure how to conclude this. Relating grade to cash flow is going to be tricky.
Most of the miners have released 2018 production results and reserve and resource updates. I thought it would be interesting to see which properties added or lost the most ounces during the year.
Out the list there are a
- Cerro Blanco
- Camino Rojo
- Block 14
- Eskay Creek
- Valentine Lake
Would be interesting to look at the corresponding jump in share price of the single asset owners.