Out of
Our Minds

Property Deleveraging

Disney and Charter clashed over a new cable agreement. Charter complained about the high fees it paid for ESPN, FX, and other Disney channels while Disney was funneling premium content to its own Disney+ streaming service. Eventually, Disney’s channels disappeared from Charter’s Spectrum TV service, and Spectrum stood firm: it was prepared to give up Disney’s networks, seemingly forever.

It didn’t come to that, though, and the terms of the agreement don’t sound monumental: Disney+ will now be an option for Charter subscribers for an additional fee. Disney gets new eyeballs, but hands over a chunk of its fees to Charter, which had faced a seemingly inevitable revenue decline from its cable business due to the rise of streaming. The agreement offers a reprieve from that.

However, the terms of the resolution show that Disney as a supplier had far less bargaining power with its customer than it had assumed. It was forced to make material concessions that will affect its transition to a full-streaming model. Make no mistake, Disney backed down, and that loss is going to hurt its bottom line in the long run. Moreover, if the world’s largest entertainment company couldn’t use its muscle to get what it wants, then all the other networks are going to face the same dilemma when their carriage agreements come up for renewal. Disney’s capitulation could signal a major shift in the entertainment industry.

For decades, “content is king” was the mantra of the media industry. While cable companies were the primary distributor of content made by studios, the ultimate pricing power was always in the hands of the studios, especially studios like Disney that owned must-see kids, sports, and movie content. A cable provider could not attract subscribers without these must-have channels. As a result, even though there would be heated negotiations over fees, ultimately cable providers would yield to the studios’ demands.

The studios’ leverage has been weakened by the very thing they are focused on: streaming. Streaming subscribers often cherry pick services, signing up for a short time if there’s a must-see program and then canceling. For Disney and the others, there is a fundamental mismatch between costs and revenue duration. Streaming platforms need a constant flow of new programming to limit subscriber churn, but that pushes operating costs up into the billions. Not all of them can afford it.

It’s possible Charter perceived this, or just felt like it had nothing to lose. It was willing to take a loss of subscribers upfront by terminating its Disney arrangement rather than bleed subscribers over a number of years if it didn’t challenge Disney, given either course would likely lead to the same terminal point, just in different time frames. Charter’s hard stand showed the flaw in the Disney plan.

The content king may not be dead but it definitely seems like he holds less power.

Ozempic being used as a substitute for a different medical treatment goes beyond Novo Nordisk selling the same drug under different names. Ozempic could also serve as a replacement for people contemplating bariatric surgery, since it stands to reason that anybody considering surgery would likely be willing to try a less-invasive treatment. Recently, Intuitive Surgical noted a slowdown in the growth of bariatric surgical procedures because of the increasing use of drugs such as Ozempic. “Some customers have indicated that they are seeing increased patient interest in weight-loss drugs,” Intuitive chief financial officer Jamie Samath said in July.

Given that obesity is the most important reversible factor contributing to sleep apnea, there’s a chance Ozempic and similar drugs could become a treatment for that, too. Eli Lilly, for instance, is running a study of its type 2 diabetes medication Mounjaro, which is in the same drug class as Ozempic/Wegovy, as a possible treatment for sleep apnea. Results are due in March 2024.

That could be a problem for a company like ResMed, which makes sleep-apnea treatments with its CPAP (continuous positive airway pressure) therapy. These are machines that force air through the patient’s nasal passages, enabling better sleep. For patients, the issue may go beyond just which treatment is more effective.

Cost will be a big factor. Medicare and private insurers cover Ozempic for its commercially approved use, diabetes, but not for weight loss or other off-label uses. That could be important because the cost of Ozempic can be considerable (without insurance, it can cost about $1,000 a month). For sleep apnea patients, ResMed products may end up delivering the same or better results at a much lower price.

ResMed CEO Michael Ferrell in fact seems to welcome the new attention, since sleep apnea tends to be an underdiagnosed condition. Ozempic and similar drugs could bring potential patients “into the funnel,” he said on a conference call, while issues like cost and side effects will limit any hit to sales caused by the drugs. “That’s good for us, too,” he said.

The big-picture view suggests that Meta is building a hub for user-generated content (UGC) discovery and distribution that aims to go head-to-head with the likes of YouTube and TikTok. In the world of unlimited content, the power shifts from creators to distributors and curators. In this scenario, the ultimate constraint on demand for content is the totality of leisure man-hours; Meta’s goal is to optimize how to fill those hours with the most compelling content.

Generative AI could also help transform Meta’s main money-maker, its advertising business. The digital-ad market comprises roughly 70% of the overall advertising market, and is dominated by two firms, Meta and Alphabet. Finding a way to expand its market share has been a stumbling block for Meta. However, with these new AI tools, Meta may have found a way around that constraint. It may not be able to take much more share of the advertising market, but it could start taking share of the advertising-creation market. On the call, Zuckerberg specifically mentioned the strides made by its Advantage+ generative AI tools that allows advertisers to supply just an image, from which the AI will then develop an entire ad campaign. For small companies that can’t afford to hire an advertising agency, this could be a powerful alternative and a lucrative market for Meta. Its Advantage+ ad program has the potential to take share from creatives and even advertising agencies by customizing, altering, or outright creating content for advertising, rather than just targeting or measuring it.

ChatGPT, the chatbot that helped spark the market’s AI enthusiasm, is an important innovation because it can digest large amounts of text (hence the term large language model), communicate in natural (human) language, and generate sophisticated responses. Based on the Transformer architecture, a technique first introduced by Google in 2017, ChatGPT demonstrates the advances that have been made in AI that open the door to a wider set of business uses. But while natural language models recently produced epiphanies among lay chief executives and investors regarding AI, some tech companies were already investing in such capabilities and are being rewarded for that foresight. NVIDIA has been the biggest beneficiary this year in terms of its stock run and projected revenue gains; however, our other holdings, such as Adobe, Microsoft, Salesforce, ServiceNow, Synopsys, and TSMC, also appear among the possible beneficiaries. More companies—including, perhaps, some not yet in existence—will certainly join the ranks over time.

While it is still early, it’s evident that these companies see generative AI as transformative to their businesses and something upon which they can build new revenue models. Additionally, they are turning to AI to boost internal productivity, enhance existing customer offerings, and improve the quality and efficiency of customer interactions.

Most notably, Microsoft was able to gain an immediate leadership position in generative AI by making a US$10 billion investment in OpenAI, the company behind ChatGPT, earlier this year. Microsoft’s Bing search engine has since introduced ChatGPT into its web index data—a collection so large that it is rivaled by the dataset of only one other business in the world, Alphabet’s Google. Data are the feedstock of AI models, and an AI-enhanced search engine trained on so much data may attract more users to Bing, allowing Microsoft to sell more ads on the service. Microsoft is also adding generative AI to other products, including the Azure cloud service, enabling business customers who use Azure to easily integrate OpenAI models to glean more insights from their data and automate functions such as certain IT tasks. These added capabilities should motivate more businesses to migrate their data to the cloud and make Azure more competitive with Amazon.com’s AWS and Google Cloud.

The beneficiaries of demand for generative AI aren’t limited to traditional IT-sector companies. Data centers used to train AI models require up to ten times more power than typical data centers, thus requiring more-powerful equipment and backup power. Given the amount of heat they generate, new liquid-cooling solutions will be needed as well. This creates an opportunity for Schneider Electric, which has been developing innovative data-center equipment solutions for many years.

In the meantime, NVIDIA has emerged as the unrivaled global leader in providing the technologies at the center of the AI arms race. Due to an explosion of demand related to generative AI and LLMs from across its customer base, NVIDIA projects that data-center revenue for its fiscal second quarter ending in July will surge to US$11 billion. Not only is that more than double last quarter’s total, but the forecast also shattered the average analyst estimate that called for about US$7 billion.

Our investments in NVIDIA and Schneider are reflective of how we are thinking through the many unknowns and approaching portfolio structure in this environment. Through our fundamental framework, we can appreciate the broad excitement for AI, but we also remain conscious of valuations and thoughtful about diversification, recognizing that it’s unlikely anyone can predict today the biggest long-term winners.

In the first three months of 2023, P&G raised prices a whopping 10% year over year, following a series of large price hikes implemented throughout 2022:

By “not doing macro,” he means that we try not to allow our judgments about macroeconomic variables—GDP growth, inflation, and real interest rates—or geopolitical events to dictate our views on individual companies. By “macro does us,” he means that when the market’s risk tolerance and underlying assumptions change because of unexpected shifts in the macroeconomic environment, the consequential price movements can dominate a portfolio’s periodic absolute and relative returns. Although the injury may be only temporary, it is hard to avoid getting swept up in the general fervor. That’s a problem if it leads to reflexive and hasty reactions. It is precisely to avoid getting whipsawed in this way that we devote much of our efforts to restraining our inherent behavioral biases. But even with the sturdiest of behavioral guardrails designed to curb our responsiveness, the sudden jump in portfolio volatility and tracking error feels no less jarring.

Our investment approach centers on analysis of the prospects for specific companies and the industries in which they operate. As a result, the portfolios we construct are a mosaic of company-centric views, with the final picture coming into focus only after all of the pieces are assembled. Sometimes our bottom-up investment process leads us to sidestep systemic issues. In the years before the global financial crisis, for instance, we became disenchanted with the traditional banking industry. We didn’t like how the increased price transparency that came with the migration of services online diminished banks’ bargaining power over their borrowers and depositors, or how rising levels of consumer debt portended that growth could be weaker, and rivalry and risk-taking fiercer. That was enough to lead us largely to steer clear of banking stocks. Although in hindsight our portfolio positioning appeared to anticipate the subsequent dislocations, in fact we had no overarching view on systemic financial stability.

There is no question it would be nice to have clear foresight on GDP, inflation, and real rates. Like it or not, economic growth is the lifeblood of industrial economies, and, despite its ever-shifting relationship to equity returns, is closely associated with aggregate earnings. Similarly, inflation and real rates are both barometers and agents of economic transformation that always could and frequently do alter the path of economic growth. And there is strong reason to believe that macro-level dislocations are likely to be an order of magnitude greater than the mispricings that occur at the security level. Given the periodic importance of such dislocations, this raises the question: Why don’t we attempt to shape our portfolios more explicitly by directly forecasting economic variables or geopolitical events? The question is particularly vexing given the current importance of the inflation outlook for equities.

Tetlock’s conclusion was that expert predictions about geopolitical crises were no better than guesses. The only contribution that expertise seemed consistently to confer was a perverse boost in confidence regarding one’s (ineffective) forecasts.

The standard response typically trotted out is that forecasting is exceptionally hard, or as the Danish physicist Niels Bohr is alleged to have quipped, “Prediction is very difficult, especially about the future.” Nowhere is this more true than with geopolitical events, which by all accounts appear to defy anyone’s ability to anticipate them with anything approaching consistency. The political scientist Philip Tetlock tackled this issue head-on in a multidecade study described in his 2015 book, Superforecasting: The Art and Science of Prediction. Tetlock’s conclusion was that expert predictions about geopolitical crises were no better than guesses. What’s more, the only contribution that expertise seemed consistently to confer was a perverse boost in confidence regarding one’s (ineffective) forecasts.

The record for macroeconomic forecasting is not quite as wretched; at least there are frameworks and models on which to hang one’s thinking. But it’s still one of those endeavors where you’re doing very well if you’re right a little more often than you’re wrong. Even so, it is not as though the ground-level forecasting of cashflows, business prospects, and competitive forces is easy. So perhaps the real question is why we consider the latter sensible but the former a fool’s errand, at least for fundamental equity investors such as us.

The answer in large part comes down to the size of the opportunity set, or the number of times you get to apply your investing edge. Even the most skilled forecasters, whatever their forecasting game, have but the tiniest of edges and so the surest way to increase their chances of success is to apply that minute edge as many times as possible. In a global investment universe, there are roughly 8,000 equity securities, each operating in its own industry and geography with their own sets of return drivers, compared with a relative handful of forecastable macroeconomic variables. Given equal forecasting skill, you are going to have a far higher likelihood of some overall success by applying that skill across many securities rather than over a few economic statistics. Even allowing for the fact that not every security’s return is entirely idiosyncratic, there are still far more independent and durable drivers of individual security returns than there are of macroeconomic trends, which may allow you to get the micro right without so much as taking a swing at the macro.

Even if you were one of the few hyper-skilled and hyper-accurate macro forecasters, a portfolio of stocks would be a poor way to capitalize on views about inflation or economic growth. Although there’s a relationship between the macroeconomy and stock returns, that relationship is neither simple nor determinate. In practical terms, stocks are a terribly inefficient way to express a view on macroeconomic variables. Better to bet on currencies, yields curves, and commodity prices directly, all of which are far more closely tethered to the outlook for growth, inflation, and real rates.

It’s not just that there are better, more precise, and more levered ways to express macroeconomic views. It’s also that trying to do so with stocks risks erasing the hard-won company-level insights that are the linchpin of our portfolios.

And it’s not just that there are better, more precise, and more levered ways to express such views. It’s also that trying to do so with stocks risks erasing the hard-won company-level insights that are the linchpin of our portfolios. All the companies in which we invest have track records of successfully generating cash and reinvesting it wisely. In many cases these companies have survived wars, recessions, pandemics, inflation, deflation, and geopolitical shocks. Sacrificing those financially valuable fundamental attributes in a most likely vain attempt to time a particular economic cycle not only presupposes a preternatural ability to tie economic outcomes to individual security returns but also risks the long-term health of the portfolio.

We don’t do macro, so by default we allow macro to do us. There are, though, ways in which we can protect against developments that result in sudden changes in risk aversion. One is to diversify—events that damage the outlook in one industry or part of the world may have no impact, or even a beneficial one, on stock prices elsewhere. That said, diversification cannot work during times of systemic crisis, when correlations between geographies, industries, sectors, and individual securities converge. That’s where our reliance on a company’s strength comes in. Two hallmarks of a company’s quality are the ability of its management to prepare for a wide range of outcomes and whether it has the financial strength to survive the worst possible operating conditions.

Although we can’t estimate the probability of market-moving events, we can think about the magnitude and range of potential outcomes so we may more fully understand our exposures and ensure we are sufficiently diversified to protect against them. For example, before Russia’s invasion of Ukraine, many people thought about a range of outcomes that included war versus no war or disruption to energy supplies. But, given prior Western responses, few considered the potential for sanctions that would freeze all Russian assets and render them worthless, at least for the time being. Now, as we think about the financial market implications if China were to invade Taiwan, we must consider the possibility that Chinese assets could be similarly impaired.

So, what do we do about it? We certainly aren’t going to try to parse Chinese troop movements or overturn our investment theses on the dozens of companies, not only in China but also throughout the global supply chain, that could be impacted by what at this point must still be considered a very low-probability event. On the other hand, thinking long and hard about the potential risks to supply lines, revenues, or the corporate structures of portfolio companies and what further levels of diversification might be in order is very much in our wheelhouse.

As policymakers picked up where they had left off, they found themselves facing stakes heightened by the pandemic: stagnating incomes, weak consumer confidence, and a growing demographic crisis as birthrates continue to decline. These challenges may have accentuated their top priorities, ones that have been repeatedly highlighted in official policy statements over the last few years: innovation, rule of law, culture, the environment, and social harmony.

The fact is that ever since Deng Xiaoping initiated the initial series of capitalist overhauls in the 1980s, China has undergone multiple periods of reform. These changes cut a wide swath across economic activity and drastically curtailed certain targeted sectors. They were painful in their time, creating mass unemployment and fueling social discontent. Ultimately, they laid the groundwork and helped sustain several decades of nearly uninterrupted growth.

Previous reforms were far less visible to foreign observers because they barely touched the companies widely held by global investors at the time. For example, the coordinated supply-side reforms of 2015, undertaken in part to reduce chronic pollution, shuttered roughly one-fifth of China’s steel capacity (equivalent to Japan’s entire steel output) in under two years. Air quality improved dramatically, while bankruptcies almost tripled as many marginal producers were killed off. But not many foreign investors owned marginal steel producers, preferring to own faster growing companies such as Alibaba and New Oriental. Likewise, the anti-corruption campaigns that began in 2013 may have ushered in a more sustainable business environment, but they were terrible for liquor makers, whose products had become popular high-priced gifts to lubricate business deals and lobbying efforts. Kweichow Moutai, producer of its fiery namesake liquor, saw its sales growth plummet in 2014 and 2015, but the company was not nearly as widely owned externally as Tencent is today.

Much of the focus of late has been on one policy priority: common prosperity. Redolent of China’s collectivist past (the term was first used by Mao in 1950), the phrase frightens some foreign investors who are unsure which companies’ prosperity will be sacrificed at the altar of the commons. Yet policymakers have been clear: their focus is on growing middle-class disposable income, not “robbing the rich to help the poor,” according to Han Wenxiu, executive deputy director of the General Office of the Central Financial and Economic Affairs Commission. This overt aversion to a European-style welfare model may seem contradictory for a party that still pays lip service to its Marxist roots. But the reality is that China systematically underinvested in education, health care, and other social spending—especially in rural areas—as it sought to catch up economically with more developed economies. Until now, policymakers have done little in the way of redistribution; indirect taxes, which generally serve to widen income inequality, still represent two-thirds of fiscal revenue. With China coming into its own, we should expect its practices to converge with those in more advanced economies, including some form of income and wealth redistribution.

To my mind, these regulations are reminiscent of the US Progressive Era of the late 19th and early 20th centuries, epitomized by Theodore Roosevelt’s Square Deal.

In practice, the government’s targets for common prosperity—judging from recent policies and the detailed roadmap for its first pilot program in Zhejiang, the richest province in China and home to Alibaba—are education, health care, and housing. In these pivotal areas, structural impediments have exacerbated inequalities over time, producing a set of challenges that would be very familiar, for example, to residents of California. One of the more draconian national policy shifts, which recently consigned much of the private after-school tutoring business to the non-profit sector, does not go as far as South Korea’s complete ban of private tutoring in the 1980s.¹ In each country, the reforms were designed to ease the burden on parents who spend up to thousands of dollars each month coaching their children on how to pass exams. (To put this cost in perspective, the Chinese city with the highest average annual per capita disposable income in 2020 was Shanghai at $11,000.) Likewise, China’s recent online regulations covering antitrust, data security, and the safety of minors are similar to the concerns of consumer advocates everywhere.

To my mind, these regulations are reminiscent of the US Progressive Era of the late 19th and early 20th centuries, epitomized by Theodore Roosevelt’s Square Deal. It was not an easy time to invest and was marked by muscular antitrust interventions, the inception of a progressive income tax, and the appearance of the first federal consumer and environmental protections. Certain industries faced a permanently higher level of regulation with which they had been unfamiliar. But many companies thrived, and the reforms arguably laid the foundation for a century of growth that shaped the American economy into the largest in the world today, home to the largest number of globally competitive companies.

Structural changes of this magnitude will inevitably shake up competitive forces, buffeting the outlook for growth and strength of free cash flow generation for many businesses—but not all of them in negative ways. If China’s reforms succeed in improving middle-class disposable income while opening more opportunities for more people and still ensuring that the country remains a meritocracy, the government will have set the stage for more sustainable end demand for many industries. It’s a tall order, but one notable advantage enjoyed by Chinese policymakers today is the benefit of a century of hindsight observing which policies worked—and which did not—in the countries that have tried them.

The fact that filtering information is the long-term investor’s task, of course, doesn’t prevent most from feverishly pursuing every bit of information they can about the companies they follow. Egged on by the 24/7 news cycle, internet chat rooms, and by data providers to know their companies in as much depth as possible, investors often seek to become experts in every aspect of each company’s business. It’s dubious whether this helps them make better estimates of what a company’s shares are worth. Instead, the evidence suggests that, beyond a certain amount, each extra piece of information has zero marginal impact on forecast accuracy. The impact remains positive, however, on the investor’s confidence in their forecast.

The evidence suggests that, beyond a certain amount, each extra piece of information has zero marginal impact on forecast accuracy. The impact remains positive, however, on the investor’s confidence in their forecast.

In one unpublished study, described in Winning Decisions by J. Edward Russo and Paul J. H. Schoemaker, people who handicap horse races were asked to predict, based on increasing amounts of information, which horse would win a race. They found that, beyond a very limited amount of data, forecast accuracy did not improve with more data, but the handicappers’ confidence in their forecasts continued to grow with each extra bit of information.

Knowing more makes you feel better, in other words, but doesn’t help your decision-making. In investing, feelings and actual results arguably are even more disconnected. After all, feeling good about a decision is rarely a help in successful investing—to have a good outcome, an investment view must be different from that of most other investors, and standing apart from the herd almost always leads to discomfort.