All that Glitters…

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One of the standout performers in 2025 has been gold, up 45% year-to-date (YTD) to record highs. Even better than gold have been gold mining stocks: The 8 biggest gold miners in the world have returned on average ~130% YTD. While precious metals mining represents less than 1% of the global stock market (an insignificant outlier), it’s far more significant in the South African context where it comprises roughly a fifth of the main stock index.

So far in 2025, the JSE Precious Metals Mining Index has returned 170% in ZAR, while the rest of the index has averaged single digits. For a local investor that’s notable. It also raises the question: Should we own these stocks? Should we expect recent outperformance to persist? To answer these questions, we’re going to analyse the 8 biggest gold miners in the world (including AngloGold Ashanti and Gold Fields) in the same way we analysed the ‘Magnificent Seven’ at the beginning of the year.

The following graphs show how the total return (green line) of each of these stocks has developed relative to its sustainable return (blue line), along with a projected range (dotted lines) for future sustainable return based on a range of analyst estimates (high/consensus/low). The relationship between these 2 lines is that the sustainable return ultimately determines the path which total return follows. That doesn’t mean that total return can’t diverge dramatically from sustainable return for an extended period. Rather, extreme divergences are the hallmark of irrational/bubble-like behaviour.

Note: To get a feel for how good sustainable return has been, don’t just look at the curve of the blue line, look at the scale of the Y-axis. The higher the number in the top left corner of the graph is, the better the sustainable return has been. For comparison, that number averaged ~165 in our ‘Magnificent Seven’ analysis.

Zijin Mining

Zijin (a Chinese company listed in Hong Kong) is the biggest gold miner in the world by market capitalization. While profit margins are highly cyclical, the company has managed to remain profitable for the last 15 years, thus delivering strong sustainable returns. As gold miners go, this is one of the few with decent fundamentals. The recent surge in share price (as depicted by the green line) leaves the company priced for poor long-term returns.

Newmont

Newmont is the 2nd biggest gold miner in the world. It has a poor track record of profitability. Its real sustainable return over 20 years has been only 2.8% pa. This stock is both poor quality and overpriced.

Agnico Eagle

Agnico Eagle has a better track record than Newmont, but isn’t a high quality business in terms of consistent profitability or high sustainable return. It’s also very expensive at current valuations.

Barrick

A very similar story to Newmont…

Wheaton Precious Metals

Similar to Agnico Eagle, perhaps slightly better. Far better than Newmont and Barrick. But still very expensive following the recent run up in the share price.

Franco-Nevada

Mostly profitable, reasonable long-term real sustainable return (7.8% pa), but, like the others, expensive at present.

AngloGold Ashanti

AngloGold is the biggest gold miner listed in South Africa. Unfortunately, its fundamentals are much like Newmont and Barrick. Real sustainable return over the last 16 years has been only 0.6% pa. Like any gold mining company, there are times when the share price shoots up, as it has in 2025, but there is little underpin from a quality perspective to suggest that such moves are sustainable over the long-term.

Gold Fields

Very similar to AngloGold…

 

From a fundamental quality perspective, the only company here that might compete for a place in a global portfolio would be Zijin Mining, bearing in mind that it operates in an emerging markets context. Agnico Eagle, Wheaton Precious Metals and Franco-Nevada are better quality than the rest, but not great compared to other investment options. Unfortunately, the South African gold miners have some of the worst fundamentals anywhere, which is why we don’t own them in local portfolios. This doesn’t mean that they never have their day in the sun – as is the case now – but it puts their strong 2025 returns into perspective.

Irrespective of their quality, all of these companies are very expensive at their current valuations. Their share prices are being driven by sentiment, linked to recent gold price movements. It’s interesting that the gold price has outperformed the sustainable return of most of these companies over the last 20 years. Truly, all that glitters is not gold…

AI: The Indexation of Everything

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Artificial intelligence is fast becoming the most significant technological development since the advent of the internet. Recent breakthroughs have brought AI out of obscurity and into the everyday lives of ordinary people everywhere. As with any such development, there are pros and cons. There are those who embrace this new reality, those who feel threatened by it, and those who proceed with caution.

There is so much being written about AI (much of which is being written by AI!) that I don’t intend to reproduce here. AI could do that better than me. Rather, I want to share a different perspective that comes from the investment world: AI can be better understood through the lens of indexation. Or perhaps indexation can be better understood through the lens of AI? Regardless, the index fund is essentially a very primitive form of AI.

The Essence of Indexation

An index is a representation of the average investment portfolio within a certain universe of publicly traded assets. The S&P 500, for example, is an index of the 500 largest public companies in America. The JSE Top 40 is an index of the 40 largest public companies in South Africa. There are indices for different regions and industries. Some are quite narrowly defined, while others are broader. There are also different weighting methodologies like market capitalization or price. Some indices are simply equally weighted. More sophisticated weighting methodologies are based on company fundamentals – so called smart-Beta indices. But in essence, every index is an average of some sort.

For this reason, every index is reliant on the availability of public information. This information has come about as a result of the work (or activity) of more traditional investment managers (also called active managers). These companies employ research analysts and other professionals to analyse potential investments with a view to determining a fair price for those assets. These ‘fair’ prices are then communicated to the public domain through trading activity on public exchanges. The prevailing market prices, which are determined by supply and demand, represent the ‘average’ opinion of these active investors.

Indexation’s Free Rider Problem

Because this information is publicly available, other investors who aren’t eager to devote resources to their own determination of a fair price, are happy to simply track an index (or an average) of some subset of the overall market, thereby achieving average results without incurring costs. This is called indexation, or passive investment. The obvious advantage of passive investment is that it will, by definition, always do slightly better than the average active manager operating in the same space, after costs.

The disadvantage is less obvious: Passive investment presents a classic free rider problem, where people choose to enjoy the benefits of some good or service, without contributing to the cost thereof. Unchecked, this eventually leads to market failure. In the US, passive investing already accounts for more than half of the stock market capitalization, up from 20% in 2009. At what point does the price discovery mechanism break down? Perhaps it already has, as a wave of indiscriminate buyers has dominated the purchasing activity of the major stock index constituents for more than a decade…

AI as an Extension of Indexation

But let’s move on to AI. Many of its most popular applications today are nothing more than sophisticated forms of indexation being extended to other industries, particularly those which are based on intellectual property. Artwork is a clear example of this: Why pay an artist to draw an image of a cow if AI can produce a generic cow at a fraction of the cost? The same is true for music and video. AI is already being used to write entire novels.

But where does AI get its ‘inspiration’? From data that’s been shared on the internet. Not an exact copy, mind you. That would be plagiarism. But an average of sorts. There are various ‘weighting’ methodologies that can be specified, but ultimately what you end up with is some kind of average representation of work that’s already been done. If you plagiarise everyone, does that still count as plagiarism? What we have here is another classic free rider problem. It’s the indexation of everything.

Lessons from the Investment Industry

So how will this play out? We can’t know for sure, but there are some important lessons we can learn from the investment industry, which serves as a kind of primitive case study into the potential effects of AI on other markets:

For industries most susceptible to AI mimicry, the supply of low-cost products and services is going to skyrocket, putting pressure on the prices of non-AI participants. This has pros and cons. One of the plagues of the investment industry in the 80s and 90s was investment managers milking their clients for doing nothing more than tracking the average anyway. Indexation has gradually forced active managers to become increasingly active (i.e. different from the average) and more cost-efficient. Likewise, one of the positive effects of AI will likely be to expose overpaid copycats in other industries. In this sense, AI has the potential to enhance creativity and efficiency rather than to kill it.

The obvious risk, however, is that you don’t only end up exposing the copycats but also killing the creative engine which makes this kind of AI possible in the first place. Some believe that AI could eventually replace that creative engine. That’s a scary thought if it’s true, but I’m not convinced that AI can be any more creative than a passive investor can determine a fair price for a stock. Some would argue that accurate price discovery in public financial markets is already failing because of indexation, but no one really cares as long as markets keep rising. It’s only on the downside that the problem will become apparent. The same problem could easily extend to other markets over time, with even further reaching consequences. Free rider problems, if left unchecked, have a tendency to end the ride eventually.

Much of the potential to harness AI sustainably hinges on our ability to mitigate the inherent free rider problem. Time will tell whether we do a good job of it not. In the meantime, whether you love it or hate it, it looks like AI is here to stay.

Facing Uncertainty

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The MSCI AC World index declined 1.3% in USD in the 1st quarter of 2025, while the Rand strengthened 2.8% against the Dollar. Our own portfolios gained in the 1st quarter as the gap between US Megacaps and other international markets narrowed slightly. The real news however is what took place in the 1st week of the 2nd quarter where global stock markets fell 10% in the few days following the announcement of fresh tariffs from the US administration. The average ‘Magnificent Seven’ stock has declined 24% in 2025 (as of 7 April), more than double the global stock market. (Tesla has lost 42%.) The Rand also weakened 7.3% against the Dollar in the 1st week of April as concerns around local politics compounded the effect of international tensions.

So how should we respond to this sudden resurgence of market volatility? One approach would be to try and estimate the impact of tariffs on various countries, industries and businesses and then position accordingly. This approach is fraught given the inherent unpredictability of international tariff policies and their impact. The very nature of increased volatility is that it is associated with heightened uncertainty about the future.

Our preferred approach to facing uncertainty is to focus on robustness. A robust investment process is one that delivers satisfactory returns under a wide range of potential outcomes. What does this look like in practice?

1. Invest in fundamentally sound businesses, which are consistently profitable, cash generative and unencumbered by excessive debt. When fundamentals are tested by economic stress, these are the companies that will endure.

2. Avoid shares that are priced for perfection. During times of optimism, investors become accustomed to favorable outcomes and price stocks as though these outcomes are normal. They’re not normal. When outcomes inevitably disappoint, these stocks can face the steepest corrections.

3. Diversify your portfolio across different industries and geographic regions. This has always been a cornerstone of effective risk management. A portfolio focused on one specific niche is most at risk.

4. Don’t put yourself in a position where you become a forced seller of assets. This means having sufficient cash flow to meet near-term liabilities, as well as avoiding excessive financial leverage.

5. Remain disciplined. Don’t let market volatility derail you from your long-term financial plan. Resist the temptation to time the market. The investors who experience the most regret are those who sell out because of fear and fail to buy back again. The two reasons they fail to buy back again are: i) They got their timing wrong and they can’t stomach buying back at a higher price; ii) They got their timing right, but as the market continues to decline, fear is heightened rather than alleviated. The best time to buy is also the most uncomfortable time to do so. As Warren Buffett said, ‘Be fearful when others are greedy and greedy when others are fearful.’ This statement is true, but by its very truth also impossible for the majority to apply in practice. It’s much easier to predetermine a robust course of action and stick to it.

We’re committed to applying our investment process consistently in all market conditions. We’re also actively working through the potential opportunities presented by the recent market volatility to see where we can improve the quality and return profile of our portfolios.

The ‘Magnificent Seven’

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In his latest memo ‘On Bubble Watch’, renowned investor Howard Marks discussed the phenomenon known as the ‘Magnificent Seven’ (hereafter M7) – Apple, Microsoft, Amazon, Alphabet (Google), Meta (Facebook), Nvidia, and Tesla – the popular US tech giants which have driven much of the S&P500’s performance in recent years. It’s well worth the read. I’ll highlight some of his comments below:

“My early brush with a genuine bubble caused me to formulate some guiding principles that carried me through the next 50-odd years:

It’s not what you buy, it’s what you pay that counts.

Good investing doesn’t come from buying good things, but from buying things well.

There’s no asset so good that it can’t become overpriced and thus dangerous, and there are few assets so bad that they can’t get cheap enough to be a bargain.”

“When something is on the pedestal of popularity, the risk of a decline is high.”

In the last 2 years, the average return of the M7 was 269% vs the S&P500’s 58%. Nvidia (+820%) skews that number somewhat: The median M7 stock ‘only’ returned 161%, still multiples ahead of the S&P500. It’s not unusual to find a group of outliers (typically identified after-the-fact), but what is unusual is that these 7 outliers are the 7 biggest listed companies in the world (by market capitalization), at the time of writing. When the outliers make up a third of the most popular index in the world, they cease to be outliers in the normal sense of the word.

These are undoubtedly some of the best businesses in the world, but the question is: Should we expect them to persistently outperform everything else? Should we be chasing them because of their superior returns? What about risk? And what do these stocks look like at an individual level?

As you consider the following data, keep Howard Marks’ comments above in mind.

At an aggregate level, the median price/earnings ratio (PE) of the M7 is 37x, while the median PE of the rest of the S&P500 is 22x. The long-term average PE of the S&P500 has been around 19x. The M7 trades at a premium of nearly 70% to the rest of the index, and nearly double the S&P500 long-term average. Individually, the M7 PE ratios are: Apple – 37x, Microsoft – 35x, Amazon – 45x, Alphabet – 25x, Meta – 27x, Nvidia – 53x, Tesla – 192x.

It’s worth noting that Nvidia – the star of the M7 – currently has net profit margins of ~55%, owing to insatiable demand for its powerful AI chips. This is double their long-term average, and double their peers. Margins like this attract competition, which eventually leads to lower margins. That’s a significant long-term risk that the market doesn’t seem to be concerned about at present.

The following graphs show how the total return (green line) of each of these stocks has developed relative to its sustainable return (blue line), along with a projected range (dotted lines) for future sustainable return based on a range of analyst estimates (high/consensus/low). The relationship between these 2 lines is that the sustainable return ultimately determines the path which total return follows. That doesn’t mean that total return can’t diverge dramatically from sustainable return for an extended period. Rather, extreme divergences are the hallmark of irrational/bubble-like behavior.

Apple:

Apple is well-ahead of its sustainable return, though not in classic bubble territory. Growth expectations are lower than the peer group, which suggests that Apple is priced for disappointing long-term returns.

Microsoft:

Microsoft is also well-ahead of its sustainable return, though growth is expected to be strong in coming years. Relative to consensus growth expectations, Microsoft is more reasonably priced than Apple.

Amazon:

Amazon is reasonably priced, though growth expectations have moderated relative to the past decade.

Alphabet (Google):

Alphabet is trading in-line with its sustainable return, with strong growth prospects. Google is the largest holding in most of our portfolios.

Meta (Facebook):

Meta is a very similar story to Alphabet. This is also one of our largest holdings.

Nvidia:

Nvidia has experienced dramatic growth in the last 2 years, but it’s total return has far outstripped its sustainable return. While still falling short of classic bubble extremes, the divergence between total return and sustainable return is very high and suggests disappointing future returns under all but the most optimistic scenarios.

Tesla:

Tesla exhibited typical bubble-like behavior between 2020 and 2022. We’ve seen a resurgence of this in 2024.

 

The ‘Magnificent Seven’ have dominated the investment landscape in recent years. They have an air of invincibility about them, and there’s a prevailing sense that their prices can only ever go up. But this isn’t the case: As recently as 2022, when the S&P500 declined 18%, the median M7 stock lost 50%. Regardless of how good the underlying business is, if the stock price becomes detached from sustainable return, it creates a very real risk of loss. This isn’t the case for every M7 constituent though. Rather than considering the group as a whole, it makes sense to assess the merits of each stock individually, as we do with the rest of our investible universe. This is why we hold large positions in Alphabet and Meta, but not the rest.