St. James Investment Company Value Investor’s First Quarter 2024 Letter

“You can’t predict. You can prepare.” -Howard Marks.

Meteorologist Edward Lorenz is best known for coining the term “butterfly effect,” the idea that a slight disturbance, like the flapping of a butterfly’s wings, can provoke enormous consequences. As told in the book “Chaos” in 1961, Dr. Lorenz ran weather simulations using a simple computer model.¹ In an attempt to replicate an already completed simulation, he started the second simulation in the middle, typing in numbers from the first batch run for the initial conditions. Because the computer program was the same, the weather patterns of the second simulation should have matched the first results. Instead, the two weather trajectories diverged on entirely separate paths.

Initially, Dr. Lorenz suspected a computer malfunction. But upon closer inspection, he realized he had not accurately entered the same initial conditions. The computer stored the numbers with a precision of six decimal places, but the printout truncated the numbers to three decimal places for space-saving purposes. He re-entered the truncated three decimal numbers. This minor discrepancy led to a complete alteration in the results. Dr. Lorenz realized that weather prediction was a mere illusion. A perfect forecast would necessitate an ideal model and flawless knowledge of wind, temperature, humidity, and other conditions worldwide at a single moment in time.

Dr. Lorenz was not the first to discover chaos theory. At the end of the nineteenth century, the mathematician Henri Poincaré showed that the gravitational interaction of as few as three bodies was hopelessly complex to calculate, even though the underlying motion equations seemed simple. Poincaré, one of the originators of the mathematics underlying chaos theory, worked on what is known as the “three-body problem.” In short, a three-body problem refers to a system solvable not through an elegant algorithm but only through brute-force computation. There is no closed-form solution to predict the future locations of three planetary bodies in a vacuum, like the Earth, the sun, and the moon. Edward Lorenz and Henri Poincaré reached their conclusions in a deterministic physical world. In contrast, economic and financial forecasts deal with a far more complex input-human emotions.

Sentiment is an unpredictable variable that can drive market outcomes. Financial markets exist to price the risk of capital efficiently and accurately. Rusty Guinn of Epsilon Theory saw financial markets mostly behave like two-body systems.² The interaction of Planet A (fundamental data) and Planet B (price) is often predictable. While information takes time to propagate, market participants also know the disturbing existence of a Planet C adds immense complexity. Planet C is far enough away that its gravity induces only short- and medium-term distortions in the relationship between fundamentals and prices. If one knew Planet A and B’s starting positions and velocities, one should be able to determine where prices should be within a margin of error…but this is often not the case.

Once upon a time, sound investing meant employing traditional security analysis to uncover the truth about Planet A and predict Planet B’s future location (price). Yet even the devoted Graham and Dodd fundamental disciple recognizes market periods where Planet C’s gravity influences the system in hard- to-understand ways. It is especially difficult to invest based on fundamentals relative to securities prices during bubbles and manias. Sir John Templeton, an influential twentieth-century American-born investor, noted, “The four most expensive words in the English language are ‘This time it’s different.'”

Although it is never different, something has changed in today’s markets. Artificial intelligence and cryptocurrencies cannot explain current valuations, and one suspects that financial markets may no longer serve as a mechanism for price discovery. Markets have morphed into a political utility, a tool to protect the wealth and stability of our political structures. Once meant to stabilize business cycles, monetary policies now function as tools to prop up financial asset prices while central bankers employ communication narratives to support political policies. Planet C’s gravity now dominates markets.

Murray Stahl, chairman of investment firm Horizon Kinetics, attempted to explain this morphosis and provided his timeline of events that influence today’s markets.³ Since 1980, Murray remarks that most investors have only experienced a disinflationary environment. The historical narrative credits the U.S. Federal Reserve Bank under Paul Volcker’s leadership in raising interest rates to an appropriately elevated level, slowing the economy enough that pricing pressures relented. Murray argues that the world of 1982, particularly the industrialized world as it then existed, benefited from a series of economic miracles that had nothing to do with the U.S. Federal Reserve.

With the Soviet Union on the verge of financial collapse during the 1980s, the country used its only source of external hard currency: commodities. The Soviets possessed every hard commodity, from oil and coal to diamonds; all they could do was sell them on the global market. When the Soviet Union ultimately collapsed, so did its economic circumstances. The country desperately needed cash, resulting in more pressure on the selling price of their commodities. After the Cold War, this previously unavailable supply flooded the market and broke the back of commodity price inflation.

Murray then cites the People’s Republic of China as another massive disinflationary force to shape the global economy. Although China could not offer commodities, it had a population of one billion. Rather than sell commodities, China offered the world its enormous low-cost labor pool. Like China, eventually Vietnam, Thailand, the Philippines, India, Pakistan, Indonesia, and Malaysia offered their low-cost work force. Labor in the industrialized world lost influence and wages collapsed.

In addition to the disinflationary impact of commodities and labor, major consumer brand companies found newly opened markets in 1990 and aggressively expanded within the “free world”. Global expansion ensued and new paths to growth were uncorked. McDonald’s could now open a restaurant in once closed Bucharest or Warsaw. Coupled with cheap labor and materials, these global tailwinds created enormous economic change. Diminishing inflationary pressures allowed central banks to reduce interest rates. Lower interest rates encouraged financial engineering and financial assets eventually replaced tangible assets as the preferred form of holding wealth.

One tailwind Murray neglected is the enormous benefit of the domestic shale revolution. Unlike elsewhere in the world, in the United States the landowner owns the hydrocarbon resources underneath their property. With access to investment capital, abundant industry expertise, and entrepreneurial zeal protected by the rule of law, the United States is a leading oil and gas producer. Few appreciate the importance of shale oil production, the only source of non-OPEC production growth over the past two decades. Between 2010 and 2020, U.S. shale oil and natural gas liquids production grew by 11.6 million barrels per day, more than Saudi Arabia’s production of 10.5 million barrels per day. Shale gas production grew an incredible 65 billion cubic feet per day. When converted to barrels of oil equivalent, U.S. shale gas added another 10.8 million barrels per day, comparable to a second Saudi Arabia.⁴

In just ten years, U.S. oil companies brought online double the equivalent of Saudi Arabia’s output, an incredible achievement in a world that depends on hydrocarbons to sustain a modern life and support continued economic productivity. Powered by low commodity prices, an enormous new labor pool, genuinely global markets, and the shale revolution, financial markets have enjoyed a period of generationally low interest rates. With decades of ever-lower interest rates, governments’ interest expense burden declined, providing governments with more money to spend.

This additive government spending padded economic growth. Corporations benefitted from these tailwinds that resulted in extraordinary global expansion in a wide variety of ways: cost-of-goods-sold profit margin expanded, labor cost decreased, and debt and equity funding cost improved. Markets appear convinced these tailwinds that have generated current record profit margins are the correct basis for projecting future cash flows for decades to come.

Corporate profit margins that once averaged 6% now average 10%. Should investors continue projecting future cash flows at current record profit margins?

Corporate profit margins that once averaged 6% now average 10%. Should investors continue projecting future cash flows at current record profit margins?

For value investors, the S&P 500 (SP500, SPX) has been nearly impossible to outperform over the past decade and, most certainly, the last year. The U.S. equity market has grown more concentrated, with the largest stocks outperforming. To outperform an index, logically, an investor must differ from the index. Typically, a value investor holds a bias against the largest stocks in their benchmark index, particularly investors who favor a concentrated portfolio. For most of history, biasing portfolios against the largest stocks has been rewarding, but it has been a disaster over the last decade, particularly last year.

Since 1957, the ten largest stocks in the S&P 500 index have underperformed an equal-weighted index of the remaining 490 stocks by 2.4% per year. The last decade notably deviated from this trend, with the largest ten stocks outperforming by a massive 4.9% per year on average. The continued, unrelenting outperformance of the largest companies has led to the S&P 500 becoming significantly more concentrated. The top seven names in the index now comprise 28%, up from 13% a decade earlier. Ten years ago, the index was more than twice as diversified. According to investment firm GMO, over any ten-year period, the market has never experienced a decline in diversification of the magnitude over the last decade.⁵

This less diversified market is undoubtedly more efficient in creating bubbles, which appear faster than ever but narrower in scope. The most recent mini-bubble centers around the obsession with artificial intelligence (‘AI’), benefiting a handful of stocks. Opinions differ in definition, but a bubble is when an asset no longer moves on fundamental information. The asset price increases as new buyers enter the market because “prices go up.” George Soros, a legendary hedge fund manager, observed this behavior in his theory of reflexivity. Financial asset prices are one of the few things where demand can increase as the price rises. Although a rising price should theoretically make the asset less attractive, rising prices reinforce buying propensity. This reflexive behavior lies at the heart of all bubbles.

Financial price reflexivity reduces the importance of fundamentals, as markets grow increasingly institutionalized with the dominance of passive investment vehicles. With State Street’s introduction of the first exchange-traded fund (S&P 500 Trust ETF or SPDR) in 1993, the “butterfly effect” has induced enormous future consequences as quantitative analysis is replacing fundamental analysis. A quantitative approach emphasizes and exploits the movement of liquidity among asset classes. With more money now managed quantitatively, there is an increase in momentum-chasing strategies. New ideas are quickly recognized and then morph into reflexive price-chasing mini-bubbles. The difficulty is when market participants can no longer differentiate whether the asset is a worthwhile investment for fundamental reasons and if it is going up because others are chasing price momentum.

With the world awash in too much cash, Wall Street eagerly develops new products and structures to accommodate market participants. The byproduct is less volatility, as prices steadily rise but ultimately lower future returns. Periods of high returns like 2023 and the first quarter of 2024 will intermittently appear yet are heavily reliant on government supported liquidity. On a fundamental basis, businesses can generate only so much profit in a global economy drowning in debt. Today’s extreme stock valuations are aided and abetted by the market structure and monetary policy factors. Market structures in the form of ETFs and other passive investment products direct money to the biggest stocks like NVIDIA Corporation (NVDA) without investors understanding what they are buying.

As the AI frenzy grips Wall Street, Nvidia sits at the epicenter, with a current market value of $2.3 trillion. To put this hype into perspective, Nvidia constitutes 5.1% of the S&P 500 Index, while the energy sector accounts for only 3.9%. Meanwhile, the top ten energy companies generated annual revenue eighteen times greater than Nvidia’s trailing sales.⁶ Of course, AI supporters might argue there is no measurable upside limit to the future potential of artificial intelligence usage or implementation throughout the world economy. The contrarian might say there is better value in the energy companies unless Americans no longer wish to enjoy the comforts of modern life.

“Vast swaths of the United States are at risk of running short of power as electricity-hungry data centers and clean-technology factories proliferate around the country, leaving utilities and regulators grasping for credible plans to expand the nation’s creaking power grid,” read a recent headline.⁷ In Georgia, demand for industrial power is surging to record highs, with the projection of new electricity use for the next decade now seventeen times what it was only recently. A significant factor behind the surging demand is artificial intelligence, which is driving the construction of large warehouses of computing infrastructure that require exponentially more power than traditional data centers. AI demands massive cloud computing, further pressuring the nation’s aging electrical grid – the network of transmission lines and power stations that move electricity around the country.

Few appreciate how much energy AI consumes. Each AI server uses five to nine times more power than traditional and requires ten times more cooling. A single Nvidia graphics processing unit (GPU) consumes the same energy as a typical U.S. home⁸ and Nvidia’s new semiconductor chips will only increase this requirement. American power demand has held steady for thirty years, but the AI boom is ushering in a surge. This shift, coupled with ‘clean energy’ programs, will undoubtedly challenge the country’s sixty-year-old grid.

Generative artificial intelligence may eventually transform many industries and the way people work. However, OpenAI chief executive Sam Altman is reportedly talking to investors about an artificial intelligence chip project that may require raising as much as $7 trillion. This amount equals the combined gross domestic product of Germany and the United Kingdom. A project seeking trillions reflects the market’s euphoria over AI, the Nvidia chips that power it, and Microsoft’s cloud-based servers that host it.

The historical parallel of record-high AI-related valuations is reminiscent of the boom and bust during the dot-com bubble era when price dominated fundamentals. However, the market has decided that Microsoft’s market value (7.1%) should exceed the combined index weights of the entire energy and utility sectors (6.2%), two industries critical to the very existence of Microsoft and Nvidia. A passage from Benjamin Graham’s book The Intelligent Investor explains the speculator’s mindset:

Most investors, I think, use market price as the signal of whether they are right or wrong. If you buy and the price goes up, you’re “right.” If you buy and the price goes down, you’re “wrong.”

Momentum attracts speculators, who may buy Nvidia and AI-related stocks due to soaring stock prices. Although the speculator believes price indicates whether one is right or wrong, using only price provides an incomplete and muddled picture. In the short run, a fluctuating stock price offers no validation. If price is the only feedback mechanism, one might have to wait months or years to determine whether they were right. Instead, Benjamin Graham focused on “data and reasoning.” If an asset goes up, did it go up for the reasons one expected, based on the evidence one gathered? Being intellectually honest about the valid reasons for price appreciation may be the most challenging aspect of investing.

Distinguishing between the probability of an event and the actual outcome is another challenge. A high probability does not equate with certainty. Markets are too unpredictable and too complex. A weather forecast for a 70% chance of rain is highly probable, but it does not mean it will rain. The outcome is still uncertain, and the same principle applies to investing. Based on one’s analysis, the investor might decide that an investment has a high probability of success, but there are always variables that could shift the outcome. Understanding this difference is critical to successful long-term investing. The most likely events can fail to materialize; conversely, low probability but catastrophic events can and do occur.

A thoughtful investor must prepare for various outcomes, including those that seem unlikely. In preparation, the investor should construct a portfolio that can withstand different scenarios, particularly unexpected ones. As Warren Buffett understands, financial markets will never unfold as predicted, and one should never underestimate risk. In Berkshire Hathaway’s 2023 letter to shareholders, Buffett wrote:

“Your company also holds a cash and U.S. Treasury bill position far in excess of what conventional wisdom deems necessary. During the 2008 panic, Berkshire generated cash from operations and did not rely in any manner on commercial paper, bank lines or debt markets. We did not predict the time of an economic paralysis, but we were always prepared for one.”

Because investors cannot predict the future, they must prepare, which means building a portfolio that can withstand the unknown. It also means keeping ‘dry powder’ when the current opportunity set is limited. In the words of Charlie Munger, Warren Buffett’s late business partner, “I didn’t get rich by buying stocks at a high price-earnings multiple in the midst of crazy speculative booms, and I’m not going to change.” For ninety-nine years, Charlie Munger never predicted; he prepared.

With kind regards,

St. James Investment Company

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Editor’s Note: The summary bullets for this article were chosen by Seeking Alpha editors.