What We’re Reading (Week Ending 25 February 2024) - 25 Feb 2024
Reading helps us learn about the world and it is a really important aspect of investing. The late Charlie Munger even went so far as to say that “I don’t think you can get to be a really good investor over a broad range without doing a massive amount of reading.” We (the co-founders of Compounder Fund) read widely across a range of topics, including investing, business, technology, and the world in general. We want to regularly share the best articles we’ve come across recently. Here they are (for the week ending 25 February 2024):
1. Wang Chuanfu: A Name Everyone in the West Should Know – Kevin Xu
Wang Chuanfu (王传福), the founder of BYD which just beat Tesla in global electric vehicles sales, is virtually unknown in the west. Even in China, he is only well-known in the business circle and has a low profile otherwise compared to the more flashy tech entrepreneur, Jack Ma, or the more cosmopolitan investor-turned-founder, Kaifu Lee.
Whether you think China’s mass production of EVs and other renewable energy products is a net-positive for dealing with climate change, or an evil “onslaught” on the west, BYD’s global impact is hard to ignore and cannot be wished away. Its batteries have been powering millions of cell phones long before it started making cars. Its EVs can be now seen on the streets of every Chinese city, and quite a few European and Latin American cities. Its battery-powered buses are transporting commuters in Hyderabad, Bogotá, and the Los Angeles International Airport. It is also making electric SkyRails (subway in the air) that may soon appear in São Paulo’s skyline. Oh, and it supplies batteries to Tesla too.
Wang Chuanfu, the pudgy-faced chemist-turned-entrepreneur, is the main, if not the sole, reason why BYD, which meant literally nothing when the company was incorporated in 1995, became BYD, which now means “Build Your Dreams.” The late Charlie Munger called him a “genius”. Yet, there is no comprehensive biography (that I’m aware of) about the man. (Musk, on the other hand, has at least three about him.)…
…It is difficult to describe just how poor Wang’s upbringing was and how much the cards were stacked against him to amount to anything. In fact, his plan was to get into a vocational high school, not university, because it was easier in the early 1980s in China to get a job with vocational training. But the year he applied was the same year that his mother passed away, so he was affected by the loss and didn’t get in. Instead, he ended up in a normal high school that inadvertently paved the path for him to eventually attend a university. Even though he could have dropped out, his older brother insisted on supporting him financially, so he could focus on studying, get into a university, and bring the whole family out of poverty.
As the story goes, because Wang had no guidance or tutelage from his parents or anyone else, he read a lot of books on his own and developed some early muscle as an independent thinker. He had no choice. He ended up going to Central South University of Technology in the neighboring province of Henan as a chemistry major. In his own telling, Wang did not even remember applying to this school. His first choice was the Hefei University of Technology in his home province to study wireless technology, because he liked playing with radios as a kid, but he didn’t get in…
…With a 250,000 RMB loan from a cousin who worked in finance, Wang incorporated BYD in Shenzhen in 1995, where nothing was built and anything was possible. Registering a similar company in Beijing would have been a huge hassle, but in Shenzhen as a pilot SEZ, it sometimes took as little time as one day to form a company. Thus, there were a ton of companies being incorporated. In a rush, Wang chose B(比) Y(亚) D(迪) – three random Chinese characters that meant nothing together – because it was a name that wasn’t used yet. He optimized the first character’s pinyin for being earlier in the English alphabet, so the name could be seen earlier at a trade show or conference. (Jack Ma picked Alibaba for the same reason.)
Back then, the global leaders in battery manufacturing were Japanese giants – Sanyo, Panasonic, Phillips. Sanyo, in particular, was the company Wang aspired to and wanted to beat. But BYD was poor and could not afford any of the advanced equipment or assembly lines that Japanese manufacturers were using. So Wang reverse-engineered the manufacturing process, broke it down into small pieces, then hired very cheap human labor – the only advantage China had at the time – to work on each of those pieces to build cheaper batteries by hand. It was the most literal implementation of “human as a cog in a machine.”
Wang also flexed his chemistry training and caught up quickly in terms of battery technologies, from nickel-cadmium, to nickel-metal hydride, to lithium. BYD quickly caught up on all three types of batteries, while producing them at a fraction of the cost compared to its Japanese competitors. Its early investment in lithium-based batteries, along with Wang’s penchant to reverse engineer, would feature more prominently later in our story when BYD decided to make EVs…
…The company went public in 2002. That same year, Li Lu, the Tiananmen-protest-leader-turned-value-investor bought a stake with the money that Charlie Munger entrusted him to start Himalaya Capital…
…To Munger, investing in BYD in 2002 was akin to writing a VC check into an early stage startup – high probability of going to zero but with infinite upside.
Munger nonetheless admired Wang Chuanfu the person – someone he considered a “genius” with great engineering aptitude who works 70 hours a week. He would also soon learn of Wang’s independence and stubbornness, a trait that made his and Li Lu’s wager look like a terrible idea for a time, but set it on the path to become one of the best performing investments ever.
In January 2003, BYD bought a local carmaker called Qichuan Motors. Qichuan was so bad that the only worthwhile asset from that acquisition was the license it held, which BYD could now use to make its own cars.
Wang has had his eyes on the massive Chinese car market, and this was his way to move into it. His investors, however, were not pleased with this expansion. Li Lu, Munger, and just about everyone inside and outside the company opposed it. BYD’s stock price tanked by one-third during this acquisition.
But Wang didn’t care. For one reason or another, he acquired an immense confidence in his ability to reverse-engineer, vertically-integrate, then mass-produce just about anything. To learn how to make cars, he bought 50 or so second-hand cars from all the best foreign brands, took them apart, and learned how to make cars – a tale he has been fond of sharing in interviews since…
…Tesla was incorporated in July 2003, a few months after BYD bought Qichuan Motors. And Elon Musk would not come into the picture until February 2004, when he made an investment into Tesla’s Series A round using his PayPal-to-eBay acquisition winnings.
Technically, Wang was into making cars before Musk was.
Warren Buffett’s investment in BYD is a well-told story. Buying 225 million shares for $230 million dollars in 2008, when BYD was trading at barely more than $1, it is one of the best examples of Buffett’s “buy and hold” strategy working its magic. Buffett did not begin selling until 2023 – 15 years after his initial purchase. He is still holding more than half of his original stake, at the time of this writing.
However, there are two details to the Buffett-BYD love story that are less well-known and provide interesting colors to Wang Chuanfu’s personality.
First, Wang rejected Buffett’s initial overture to buy BYD, because the Oracle of Omaha wanted to buy a bigger stake than Wang was willing to give up. Despite the obvious benefits of capital infusion and stamp of approval from the greatest investor of all time, Wang stubbornly treated BYD like his baby, his kingdom, and his calling that couldn’t be so easily sold to the highest or most famous bidder. In the end, Buffett was only able to acquire about 10% of BYD…
…From 2009 to 2010, buoyed by Buffett’s investment and branding, Wang set BYD on an aggressive expansion path to make and sell as many cars as possible in China. Although Wang was an engineering and mass production savante, force-feeding BYD cars, which were not of the best quality nor had any brand premium at the time, turned out to be a terrible move. BYD had no problem pumping out tens of thousands of cheap cars. But Wang’s sales target – doubling year over year – forced its sales teams to in turn force dealerships across the country to take on more BYD inventories and higher sales targets of their own.
But not enough consumers wanted BYD cars. Demand overall was also weakening at the time when every country was, in one way or another, dealing with the aftermath of the Global Financial Crisis. Thus, major dealerships started rejecting BYD cars and severing relationships with the company in droves, from Sichuan, to Hunan, to Shandong, and beyond.
By mid-2010, “Dealership Exodus Gate” was in full swing, BYD slashed its sales guidance, implemented mass layoffs, and Wang was humbled. He realized that treating dealers like minions, while making cars with no brand value was not going to work, even with Buffett’s blessing. Unlike batteries, which few consumers know of or care about the brand or manufacturer, cars are prized possessions that convey social status and prestige.
BYD had to become a brand, not just an efficient producer of cheap, affordable cars…
…Tesla first started selling EVs in China in 2014. It commanded brand premium, conveyed social status, and produced high-performing EVs with solid range – three things BYD did not have. Tesla’s were coveted by many, but affordable to only a few, due in large part to China’s high tariffs on foreign-made cars. This barrier gave BYD and other domestic EV makers some room to survive by continuously catering to low-end, cost-conscious consumers.
All that changed in 2019, when Tesla opened its Shanghai factory. Musk’s creations could now both be made and sold in China. This meant Tesla cars could avoid the tariffs and lower prices to compete with the likes of BYD. That year, BYD sold 20% less vehicles than the previous year. Its earnings fell by almost half. Wang Chuanfu was in survival mode again…
…To fix BYD EVs’ lack of range and improve safety concerns, Wang came up with a new design concept that became the Blade Battery – a new form factor that could pack more power density and release heat faster than the standard battery pack modules. BYD’s adaptive and vertically-integrated manufacturing line quickly churned out prototypes of Lithium Iron Phosphate (LFP) Blade Battery…
…By packing more LFP-composed power into Wang’s blade-shaped design, which allowed for more density and a larger surface area for cooling, the LFP Blade Battery achieved a nice middle ground that enabled longer range than conventional LFP block batteries, a bit less range than NMC batteries, with way less heat during an accident…
…By March 2020, Blade Battery started making its way into BYD EVs. From 2020 to 2022, BYD’s sales quadrupled. The same Blade Battery is now in Tesla’s Model Y…
…What Wang will face next in order to take BYD to the next level is a geopolitical problem that has been decades in the making. It will require more words, more finesse, and less inventive chemistry composition and hardcore engineering. It is probably not the kind of wheeling-and-dealing he is naturally good at. Then again, for a peasant kid orphaned as a teenager, he is not supposed to be naturally good at anything.
Whether he succeeds or not, Wang Chuanfu, is a name that everyone in the west should know. It’s long overdue.
2. The road to investing wisdom begins with ‘I don’t know’ – Chin Hui Leong
When it comes to buying stocks, investor and mutual fund manager Peter Lynch has a simple mantra: Invest in what you know. But what does it mean to know something? How do you gauge your knowledge and skills?
Businessman and investor Warren Buffett has a useful concept for this conundrum: your circle of competence. In layman’s terms, it refers to the range of topics and fields that you can understand well.
For instance, if you are a teacher, you will have a better understanding of the education system than most people. Likewise, if you are a restaurant owner, you will know the ins and outs of the food and beverage industry.
Here is what investors miss: Knowing what you are good at is just the beginning. The real challenge is to know your limits. You need to be honest about your weaknesses and avoid investing in areas you do not understand, Buffett says. In other words, you need to know what you do not know…
…It is better to admit early that you are out of your depth than to suffer months and years later from holding the wrong stocks. Even a winning stock will be useless if you lack the conviction to hold it…
…Ben Graham, the father of value investing, used a story to explain how the stock market works: he called it Mr Market. A friendly guy, Mr. Market always tells you the price of your shares every day. But there is a catch: He is also very emotional. He often gets too excited or too depressed, and gives you prices that are too high or too low.
The trick is to know when Mr Market is wrong. That is how you beat him at his own game. Then again, while Mr Market has mood swings, he is not dumb. Even Graham admits that Mr Market can get it right sometimes, giving you a fair price for your stock based on how the underlying business is doing and its prospects.
The trick, then, is to realise that while Mr Market is not stupid, he is impatient. In the short term, he will change the price of your stocks to reflect the prevailing business news.
Over the long term, however, it is the business’ earnings growth that will determine the direction of the stock price…
…Here’s what I have noticed: Most investors do not like to admit that they need to diversify to lower their risk. They prefer to follow Buffett’s advice and put all their eggs in one basket. They would hold no more than five stocks at a time, sometimes even less.
Sadly, these same investors are just trading one flaw for another – ignorance for arrogance. Holding a few stock positions implies you have the rare ability to pick winners with atypical accuracy. Buffett, with his decades of experience, can make that claim. How about you?…
…Investor, hedge fund manager and author Seth Klarman said it best – that when you buy a stock, it is an arrogant act. You are saying you know more than the person selling the stock to you. That is arrogance.
There is no thin line between arrogance and confidence. They are both sides of the same coin. But here is the good news. You do not have to be stuck on one setting. You can be confident when you buy stocks. And then be humble after you buy the stock. You can commit to learning about the business over years, and earn your right to be confident.
3. What a Viral Post on Giraffes Says About China’s Fed-Up Investors – Li Yuan
It’s a perilous time for investors in China. Their main vehicle, so-called A shares of Chinese companies, fell more than 11 percent in 2023 and have continued their losses this year. Many investors have instead flocked to the exchange-traded funds that track foreign markets and that have been performing much better.
Putting money in stocks is inherently risky. But Chinese investors are experiencing something especially alarming: financial losses in the markets, declining home values and a government that doesn’t want any public discussion of what’s happening.
With their frustrations piling up, Chinese investors recently found a way to vent that wouldn’t be quickly censored. They started leaving comments on an innocuous post about giraffe conservation on the official Weibo social media account of the U.S. Embassy in China. They lamented the poor performance of their portfolios and revealed their broader despair, anger and frustration. The giraffe post has been liked nearly one million times since Feb. 2, much more than what the embassy’s Weibo posts usually get. Many of the comments also offered admiration for the United States, as well as unhappiness about their own country.
“The different stock markets’ performances reflect the distances between America and China in terms of national power, technology, humanity and sense of well-being,” a commenter wrote.
The comments demonstrate a growing loss of confidence by the Chinese public in the stock market, the country’s economic prospects and the Chinese Communist Party’s ability to govern…
…Another investor I spoke to, Leo, a portfolio manager at an asset management company in Beijing, has been investing in China’s stock markets for nearly a decade. In November, he started closing out his positions. Now, like Jacky, he is placing his bets on overseas markets.
Leo said he used to hope that China’s internet giants Alibaba and Tencent would become $1 trillion companies like Amazon, and that investors like him would benefit from their growth. “That dream was shattered” after the government cracked down on tech in 2020, he said. “I can only look to the overseas markets now.”
The American Embassy’s Weibo comments section once served as an online punching bag for nationalistic Chinese who blamed the United States for their country’s problems. Now it’s called the Western Wall of China’s A shares investors.
“Under the protection of the U.S. government,” wrote one commenter, “the giraffes are 10,000 times happier than the Chinese stock investors.”…
…A recent survey by the Canton Public Opinion Research Center offered a bleak picture from the southern city of Guangzhou, a metropolis of nearly 19 million people and a hub of technology, manufacturing and trade. In a 2023 survey of 1,000 residents, the center found that the city’s “economy and the society were confronted with unprecedented challenges and pressure.”
The research center’s report said residents’ assessment of the economy, because of unemployment and falling incomes, was as low as it was in 2015, when China’s markets tanked. Satisfaction with the growth of the private sector dropped below 30 percent, the lowest level since the question was first asked in 2008. Most residents said they didn’t expect their incomes to improve in 2024, and more than 20 percent said they believed they were “likely” to lose their jobs.
News coverage about the survey was censored, and the report can’t be found on the center’s website…
…Leo, who was born in Beijing in the mid-1980s, said he had grown up as a nationalistic “little pink.” The first crack in his confidence, he said, was in 2021 when the government went after internet companies. The second crack appeared when the government abruptly ended its “zero-Covid” policy in December 2022 without preparing the population with effective vaccines or medications. Then in late July, the markets and the private sector failed to respond to government measures to stimulate the economy.
Leo’s change is remarkable. He said local Beijing residents like him and the people with whom he had gone to high school were among the stoutest supporters of the Communist Party’s rule because they benefited from the city’s expansion and the country’s growth.
When a group of Leo’s classmates met up in June, he said, they couldn’t believe that two of them, a couple, were migrating to Canada…
…He said the big problems that had made him flee remained unsolved: the imploding real estate sector, enormous local government debts and a fast-aging population.
He said that he wanted the government to loosen its grip on private enterprise and disband Communist Party branches that had proliferated inside companies, and that he wanted the private sector to start to invest again. Until then, he will keep his money out of China’s markets.
And what investing advice would he give to his families and friends? “Run as fast as you can,” he said, “even at a loss.”
4. Rohit Krishnan — Demystifying AI – Jim O’Shaughnessy and Rohit Krishnan
Jim O’Shaughnessy: This large language model says, and he’s speaking to you or it is speaking to you. “In your description of AI as a fuzzy processor, you acknowledge a level of unpredictability in AI behavior. How would you balance the need for predictable AI systems with the inherent uncertainty of their fuzzy outputs in critical applications?” …
…Rohit Krishnan: So with an LLM, the fact that it’s a fuzzy processor means that you can now use it in a lot of different places where you could not have used an AI or any kind of software before, because it can effectively be a replacement for parts of different jobs that people might actually be doing. However, the problem is that, if you or I as fuzzy processors are used in those places, we can be tested. We can be evaluated. If I’m hiring someone for a job, I know that they’re not perfectly predictable. However, I can talk to them and get a sense of how unpredictable they are, and how they would actually deal with different situations, and monitor those in different ways, and ask for previous employers or references, or interview them, and create basically this cone of uncertainty, if you will. I can bound it, so I know that they’re not completely crazy. I know that they will do some things, but it’ll be within the bound of error.
Rohit Krishnan: So with LLMs and fuzzy processors, we are at the early stages for that. The inherent fuzziness is problematic only because you cannot depend on when and how it is actually likely to be fuzzy, that it might end up going in any kind of random direction. So for us, to be able to use it in any actual real-life situation, especially in critical applications, we would need to have a whole lot more confidence in how precisely it works. We would need to not it’s in the internals, in the specific nodes and weights and stuff like that. We already know it, but it’s slightly unhelpful. It’s like doing, I don’t know, neuroscience to predict behaviorism. I don’t think it is hugely valuable in and of itself. However, we do need to bound its behavior in some sense so that we know it cannot go completely off the rails when you’re trying to use it.
Rohit Krishnan: Even with that, I mean, we are speaking, what, after the latest Boeing disaster, not that long after? So when you talk about complex systems where large number of parts actually interact with each other, the possibility of something going wrong always exists. So the way we solve it in real life is by having stringent QA and multiple checks and huge levels of evaluations and large amounts of redundancies. And the exact same principle applies for fuzzy processes as well, where the only way to make a fuzzy processor function in a critical system is by having large number of evaluations so that it can bound it, creating enough structure around it so that even if it does something weird or crazy, you can actually cut off those particular probability branches of the tree, and you can direct it towards something, and having large amount of redundancies so that you can actually ensure that the output that is coming from it is effectively usable, so that even if it does something crazy or stupid, the errors are not continuously compounded over a period of time.
Rohit Krishnan: It’s like that… I don’t know whether this is apocryphal, but I remember hearing this story about Elon where they were trying to send computers up along with the Starlink satellites. And obviously, radiationshielded computers are very heavy and highly expensive. And radiation shielding is important because bit flips are more common when there is higher levels of radiation that actually hits once they’re above the atmosphere. And I think his solution, or the solution is one of his engineers in that particular apocryphal story, was to send three, and they would just vote, because chances of all three getting hit simultaneously are much lower. That’s a way to use redundancy to solve for unpredictability. And I feel like a similar kind of thesis has to exist with respect to LLMs as well…
… Rohit Krishnan: I think I’ve written about a couple of these things before, which is that, at a sufficient degree of complexity, highly deterministic systems can also show highly indeterministic outcomes. I am by no means the first person. It’s a common trope in pretty much anything to do with chaos theory or even things like sand piles and grains at a point of avalanche and cascade. And there’s a bunch of these questions which are, in my opinion, more feasible to see happen than to predict how it will happen because prediction requires you to effectively run the experiment, so to speak, and I’m fascinated by that.
Rohit Krishnan: So I think, in some sense, we in normal conversations quite often complicate indeterministic with random, or unpredictable with random, and they’re two different kind of processes. I mean, there is the common argument that people make against, things like free will, is like, everything is a physical phenomena. Physical phenomena, given a sufficiently powerful computer, might actually be able to get simulated, and therefore, you might be able to predict it. And it’s one of those things when, logically, it might hold true if and only if the computer that is predicting it did not need to actually run the simulation in order to predict it. And if it did, then from the perspective of the people being simulated, us in this instance, the outcome will still end up looking indeterministic, unpredictable, even though, theoretically, everything was as preordained.
Rohit Krishnan: I know this has vexed and driven more people mad than me, but I think there is a core kernel of truth here that just because you can’t create beautiful analog equations to predict the behavior of a particular piece of software, physical phenomena, whatever, does not mean that that is random. It just means that at a certain degree of complexity, there are way more permutations and combinations of how things can go wrong than there is feasible for us to, I don’t know, conceivably identify. And as we said in the previous, the only way to solve it is by having sufficient amount of QA and redundancy and bound the system so that you can actually be relatively sure that it does what you want it to do.
Rohit Krishnan: I mean, stock markets are a perfect example of this. I mean, the flash crash is my favorite example of this. It’s not an intended behavior of the system, but it is one chaotic outcome that could have happened. And how do you stop it? You don’t stop it by stopping each individual trader analyzing each one. You stop it at the macro level saying, “If it falls a little this much, we cut it off,” which is a macro behavior that then controls the micro behavior of each individual algo, which takes that into account. And even if it does hit, we mean that the worst-case scenario is bounded.
Jim O’Shaughnessy: And you also covered that in your book because you posit that we could have a so-called flash crash of AI. And why don’t you tell our listeners a little bit about your solution for that?…
…Rohit Krishnan: The only way to guard against it is at the macro level. You can’t go solution by solution and say, “Unless we can perfectly predict the outcome of this particular system, we will let it go off and do what it wants to do,” because if you could perfectly predict the outcome of the system, we didn’t really need the system in the first place. It’s arguing against the premise of the question in the first place.
Rohit Krishnan: The only way to guard against it is at the macro level. You can’t go solution by solution and say, “Unless we can perfectly predict the outcome of this particular system, we will let it go off and do what it wants to do,” because if you could perfectly predict the outcome of the system, we didn’t really need the system in the first place. It’s arguing against the premise of the question in the first place.
Rohit Krishnan: We will have to do something similar on the AI front as well, where if you don’t want it to do certain outcomes in a particular system, we have to go from outcome first rather than sort of algo first. You’re not going to prevent that by, I don’t know, bounding the number of flops, because even with the lower number of flops, we can find enough ways for it to screw us up, assuming there’s enough number of them that actually interact with each other. But the only way to stop that is step up a layer of aggregation, actually stop it from creating the chaos that we don’t actually want it to do…
…Rohit Krishnan: Oh, I’ll tell you one of the funny things that I’ve been working on. I created a bit of an evaluation suite for a bunch of LLMs for various reasons. And I ran it against a bunch of the Chinese LLMs because I could. I mean, there’s no reason to. So then the interesting things that come out from that is that they’re really good, first of all, I should say that. However, they’re also clearly slanted in what they’re actually allowed to say.
Rohit Krishnan: If you ask it any questions about things around geopolitics, it’s like hackles get raised a little bit, and it says specific things. If you ask it questions about economics, its hackles get raised. If you ask about politics, of course, sometimes it just refuses to answer. Don’t even mention Tiananmen Square. It is fascinating to see that it has created an actually useful tool, which is it does coding really well. And you ask it to create ASCII art of a dinosaur, it does pretty well. You ask it to name, I don’t know, planets in reverse order with different whatever, different languages for each, it does the things that you would want it to do. But it also means you cannot put it into production anywhere you need any of that judgment.
Rohit Krishnan: So you cannot use it in a financial services institution because, guess what, if you’re making an investment decision, you cannot be influenced by things that were hard-coded into you. So similarly, the only way you’re going to be convinced about which ones you are most happy using are by ease of use and latency. It has to be easy to use in front of you, fast, et cetera, et cetera. But also, you can trust the advice coming from it. If I’m thinking about investing in something, I’m not going to call my friend up from Beijing to ask their opinion on a public line. Because there’s a set of information that comes back which is clearly biased. I would ask somebody that I trust and that is the benefit here…
…Rohit Krishnan: I don’t think you are wrong. I think the only caveat or perhaps addition that I would make is centaur models work best in areas which are not directly entirely competitive with the same things that the AIs do. Unless you find joy in doing it, because then it’s a self-fulfilling kind of prophecy.
Rohit Krishnan: To me, currently, and at least for the immediate future, AI is best used in areas where you can either automate part of your own job and yourself and also use it together with you in order to make your ultimate goal better. It’s just like any tech. We are all centaurs already. We live most of our lives on digital technology connected with other human beings. We are part of some weird form of a hive mind, and we are all cyborgs. This is a fact.
Rohit Krishnan: Then the question is, how much more integration would you like in different facets so that you can actually perform some of these things better? And the answer is all of them. Now, there might be some things where, guess what? If you like drawing for fun, you’re probably still going to drawing for fun, despite the fact that if you do want to make a profession out of it, there are some things that the AI will be able to do much better.
Rohit Krishnan: And you as somebody who actually understands it and can use it better and knows the intricacies of drawing will be able to direct it and make use of it in ways that me, as somebody who doesn’t, can’t. Your knowledge and education in doing that particular thing translates to how much better you can actually do something. It’s like giving yourself a boost. Everyone gets a boost kind of question.
5. Big Risks: Catastrophic Risk in Investing and Business – Aswath Damodaran
There are a multitude of factors that can give rise to catastrophic risk, and it is worth highlighting them, and examining the variations that you will observe across different catastrophic risk. Put simply, a volcanic eruption, a global pandemic, a hack of a company’s database and the death of a key CEO are all catastrophic events, but they differ on three dimensions:
- Source: I started this post with a mention of a volcano eruption in Iceland put an Icelandic business at risk, and natural disasters can still be a major factor determining the success or failure of businesses. It is true that there are insurance products available to protect against some of these risks, at least in some parts of the world, and that may allow companies in Florida (California) to live through the risks from hurricanes (earthquakes), albeit at a cost. Human beings add to nature’s catastrophes with wars and terrorism wreaking havoc not just on human lives, but also on businesses that are in their crosshairs. As I noted in my post on country risk, it is difficult, and sometimes impossible, to build and preserve a business, when you operate in a part of the world where violence surrounds you. In some cases, a change in regulatory or tax law can put the business model for a company or many company at risk. I confess that the line between whether nature or man is to blame for some catastrophes is a gray one and to illustrate, consider the COVID crisis in 2020. Even if you believe you know the origins of COVID (a lab leak or a natural zoonotic spillover), it is undeniable that the choices made by governments and people exacerbated its consequences.
- Locus of Damage: Some catastrophes created limited damage, perhaps isolated to a single business, but others can create damage that extends across a sector geographies or the entire economy. The reason that the volcano eruptions in Iceland are not creating market tremors is because the damage is likely to be isolated to the businesses, like Blue Lagoon, in the path of the lava, and more generally to Iceland, an astonishingly beautiful country, but one with a small economic footprint. An earthquake in California will affect a far bigger swath of companies, partly because the state is home to the fifth largest economy in the world, and the pandemic in 2020 caused an economic shutdown that had consequences across all business, and was catastrophic for the hospitality and travel businesses.
- Likelihood: There is a third dimension on which catastrophic risks can vary, and that is in terms of likelihood of occurrence. Most catastrophic risks are low-probability events, but those low probabilities can become high likelihood events, with the passage of time. Going back to the stories that I started this post with, Iceland has always had volcanos, as have other parts of the world, and until recently, the likelihood that those volcanos would become active was low. In a similar vein, pandemics have always been with us, with a history of wreaking havoc, but in the last few decades, with the advance of medical science, we assumed that they would stay contained. In both cases, the probabilities shifted dramatically, and with it, the expected consequences.
Business owners can try to insulate themselves from catastrophic risk, but as we will see in the next sections those protections may not exist, and even if they do, they may not be complete. In fact, as the probabilities of catastrophic risk increase, it will become more and more difficult to protect yourself against the risk…
…When looking at how the market prices in the expectation of a catstrophe occurring and its consequences, both these human emotions play out, as the overpricing of businesses that face catastrophic risk, when it is low probability and distant, and the underpricing of these same businesses when catastrophic risk looms large.
To see this process at work, consider again how the market initially reacted to the COVID crisis in terms of repricing companies that were at the heart of the crisis. Between February 14, 2020 and March 23, 2020, when fear peaked, the sectors most exposed to the pandemic (hospitality, airlines) saw a decimation in their market prices, during that period.
With catastrophic risk that are company-specific, you see the same phenomenon play out. The market capitalization of many young pharmaceutical company have been wiped out by the failure of blockbuster drug, in trials. PG&E, the utility company that provides power to large portions of California saw its stock price halved after wildfires swept through California, and investors worried about the culpability of the company in starting them.
The most fascinating twist on how markets deal with risks that are existential is their pricing of fossil fuel companies over the last two decades, as concerns about climate change have taken center stage, with fossil fuels becoming the arch villain. The expectation that many impact investors had, at least early in this game, was that relentless pressure from regulators and backlash from consumers and investors would reduce the demand for oil, reducing the profitability and expected lives of fossil fuel companies.
While fossil fuel pricing multiples have gone up and down, I have computed the average on both in the 2000-2010 period and again in the 2011-2023 period. If the latter period is the one of enlightenment, at least on climate change, with warnings of climate change accompanied by trillions of dollars invested in combating it, it is striking how little impact it has had on how markets, and investors in the aggregate, view fossil fuel companies. In fact, there is evidence that the business pressure on fossil fuel companies has become less over time, with fossil fuel stocks rebounding in the last three years, and fossil fuel companies increasing investments and acquisitions in the fossil fuel space.
Impact investors would point to this as evidence of the market being in denial, and they may be right, but market participants may point back at impact investing, and argue that the markets may be reflecting an unpleasant reality which is that despite all of the talk of climate change being an existential problem, we are just as dependent on fossil fuels today, as we were a decade or two decades ago:
Don’t get me wrong! It is possible, perhaps even likely, that investors are not pricing in climate change not just in fossil fuel stocks, and that there is pain awaiting them down the road. It is also possible that at least in this case, that the market’s assessment that doomsday is not imminent and that humanity will survive climate change, as it has other existential crises in the past.
Disclaimer: None of the information or analysis presented is intended to form the basis for any offer or recommendation. We currently have a vested interest in Amazon, Tencent, and Tesla. Holdings are subject to change at any time.
