In Supermoney, author Adam Smith asked Buffett what advice he would give his younger self if he were to start all over again, to which he replied:
Buffett: […] if he were coming in and working with small sums of capital I’d tell him to do exactly what I did 40-odd years ago, which is to learn about every company in the United States that has publicly traded securities and that bank of knowledge will do him or her terrific good over time.
Smith: But there’s 27,000 public companies.
Buffett: Well, start with the A’s.
When I read this some years ago, it had a profound impact on how I approached research. Buffett’s advice might have to do with there being fewer options to scale research before the Internet so researching stocks meant flipping through print materials and physically requesting company reports, sometimes without luck. What Buffett mainly did when running his 50s and 60s partnership was flip through Moody’s manuals and ValueLine where each company’s key components spanned one page each.
This is a far cry from what’s possible in the Internet age. But while the Internet brought efficiencies, it also brought laziness. To find a shining investment, Buffett had already become familiar with every investment possibility in the near universe through book flipping and report reading. In their attempt to find a shining investment today, modern investors use stock screeners, easily accessed sell-side reports, charts, and an endless stream of other people’s opinions as to whether a stock is a buy or sell—all those short-cut activities that postpone rolling up the sleeves, doing the work yourself, and wrapping your arms around it. It’s easy to learn something about any business merely by existing on the Internet, subscribing to some Substacks, and scrolling Twitter, but the majority of such information is secondary. Sometimes it’s wrong. The result is that most investment decisions come about from real-world opinions and nicely-told narratives that form “your own” thesis without cross-examining your thought process before pushing the buy button.
When Buffett bought Precision Castparts, he ostensibly did so by snap decision—one that was a result of accumulated knowledge not only about the company over an x period but a latticework of businesses like it and businesses unlike it through decades. No alternative to hard work could have reached such a quick decision.
So there’s a paradox to stock picking. Studying every company in the universe can give you the breadth of knowledge and pattern recognition you need, but that ain’t enough. You also need a deep level of knowledge about the company you invest in to properly calibrate the risks and assess its value creation for more than a few years ahead of time. This requires you to know all you can about the business, its nature, strategy, and culture through tons of work and reading. Deep research is what you put your money on the line for. The rabbit hole is your friend. But the nature of deep research is that time runs away from you. When you decide to go deep into a potential investment, you dig into the filings, build your model, think a little, read some more, search the web, write down what you think, and suddenly it’s been days, maybe weeks, until you’ve finally concluded. Then you have to fight your sunk cost bias of not wanting to let go if your conclusion has been incohesive and all your work has been in vain.
When studying businesses, no work is wasted. Do the actual work (while not fooling yourself), and compounded knowledge will eventually do its magic to give you the occasional insight sometime in the future. But if deep research is all you do without looking at the whole universe of potential investments, missing the forest for the trees becomes a real risk to your stock-picking effort. Meanwhile, if all you do is study compounders off your list of Twitter follows, systematically scan for low P/Bs, or dive into the 52-week low list, you risk becoming Munger’s one-legged man. To approach investing through the lens of a business owner, you have to not only understand one type of company but the whole spectrum. You need the ability to instantly tranche businesses into good, bad, and everything in between. You need mental examples of how seemingly great financials can lead to business disasters (think Silicon Valley Bank). Otherwise, how do you determine your hurdle rate? How do you determine the average height of a population, let alone assess what a tall or short person looks like, if you haven’t measured each individual first?
You might say: “But Oliver, averages and shortcuts, that’s what ratios and multiples are for”, and you’d be right. That’s why screeners are popular. But you need to bring another element to the analysis other than looking at cold-hard figures. For every “ideal” ratio you feed your screener, you lose out on learning (and investment) opportunities. Take margins: what does the typical gross margin look like for a retailer with ostensible pricing power? 25-30%? Say you fed your screener with a floor of 20% gross margin amongst other filters. Then you’d miss out on Costco for the company’s lifetime. Consequently, you’d disregard the fact that Costco’s secret sauce (culture, cost paranoia, purchasing power, and scale economies shared with the customer) in the end originates from its puny 12% gross margin. Or if you take screens for minimum ROIC or FCF, there’s a good chance that Amazon would never have appeared. My favorite example of a faulty screener requires positive working capital, failing to include all those moaty companies that grow via negative cash conversion cycles.
So I don’t use screeners. Instead, I’ve taken my idea about stock picking being like book reading (skimming a lot, reading few, and only re-reading the best ones) to create a regular exercise I call:
Stock sprints
1-3x/week I spend a day blazing through a bunch of stocks, spending as little as 2 min and as much as 15 min on each. I pick a cluster of companies for each session, either within the same industry/ecosystem or a random bunch with different market caps and economics to vary the learning process. I go through them one by one, spending as little time as I need before boredom to maximize the number of stocks I can expose myself to in one session.
The idea is to gain efficiency to get the most important components of a company in the least amount of time, preferably being fed those components in the same way for each company to develop pattern recognition. ValueLine is an example of that since the more ValueLine pages you glance over, the quicker you will be to glance over the next. You can, of course, pay for ValueLine (it’s digital now although you can still get the classic print), but there are some other nice (and partly free) sources that I use instead. Here are three:
- FinChat: My go-to. “Owner mode” allows you to hide all stock prices to look at the business like an owner.
- QuickFS: An equally concise source of financials that’s as simple as it should be.
- Koyfin: Great for charting and tabling fundamentals against each other.
As I pace through companies, I have fun with it by treating it as a game. Sometimes, I look at the financials first without looking at the name and see if I can guess what kind of industry it’s in. Other times, I try to memorize the economics and how they have developed over time, make a note to revisit the company in x time, and then see if I get better at making numbers stick. Sometimes, I visit the website and read its history. Sometimes, I may read just the shareholder letter in the annual report and relate it to the numbers. Other times, I study the footnotes in the annual report first, since reading those alone can often tell you more about how the company operates than reading an entire case study on the company. The key idea is to switch up how I study companies while anchoring to the financials as displayed in FinChat, QuickFS, or ValueLine, and while keeping the study short and succinct.
Each study ignites a series of questions in my mind to understand what’s going on behind the numbers. In doing so, I think of myself as a skeptical journalist. Here are three starting questions:
- “What’s in the book? What’s invested in the business, and what are the returns?”
- “Does it look like a good business? Is it getting better as it grows?”
- “Does it look cheap? If yes, what am I missing?”
Which may lead to more granular why-why-why-kinda questions like:
- “Why did the company generate huge returns on capital in one year and low single digits in the next?”
- “Did they do any major acquisitions in the meantime?”
- “Does variable RoC mean there’s no durable competitive advantage?”
- “Would taking the average misrepresent reality, and why?”
- “Could this variability provide an opportunity in how the market may price the stock?”
Just like skimming a book equals less retention, you need to make an effort during a stock sprint to make things stick. Even though I might only spend a few minutes on each company, I make sure to write my thoughts down, even though they may later turn out to be ridiculous and the company may be way outside of my circle of competence. X% of the stuff I write down is going to be speculation to get the brain going, and I may never look at the company again. But I might, and that’s why I write it down in real-time. That way I can remember my first speculations as I later learn more about the business (either in passing, reading a random article, or deliberately). I can couple new inputs with what I wrote down previously, sparking a compounded learning curve.
I keep tabs on this in Obsidian, a bi-directional note-taking system. The thing about a bi-directional note-taking system, as opposed to a hierarchical one, is that it allows you to “rediscover” information because it automatically creates relationships between related notes. Say I made a note on Texas Instruments six months ago, reading: “Texas Instruments is a cyclical slow-grower (or non-grower) but perhaps has a nice moat since it doesn’t do what leading-edge semicos do. The majority of the business is analog, a different part of the value chain with different economics and competition. It spends shockingly little on R&D for a semico.” I then make the word “Texas Instruments” a hyperlink, which creates a page for Texas Instruments in my database. Now, say that six months later, I read a long-form article mentioning Texas Instruments in passing, which holds a nugget of information that I save in my notes, saying, “Texas Instrument’s ability to generate revenue with minuscule R&D and S&M lies in its customer relationships. Meanwhile, its analog chips are sticky, selling for 50 cents to a couple of bucks/piece with hundreds of thousands of SKUs”. So I save the phrase with the inference that there’s little chance of competition taking a meaningful piece of the business, and that little note (or “node”) may be buried within a bunch of other stuff I saved from the article. Then a month later, say I read the annual report, make a bunch of highlights including how the company generates huge margins and short payback period on each fab built together with some nice unit economics, and then export those notes to Readwise, which then imports those to Obisidan. Now, if I were to look at the Texas Instruments page in the database, all ‘nodes’ concerning the company would automatically be there, excavated from wherever they were buried in the database. It sounds confusing, but it’s simple, automatic, and valuable.
Diving deeper
Once I look at a company during a stock sprint and think “hey, this might be interesting to explore further”, I add it to my research kanban, which is my “annual report reading list” from where deeper research originates. Before I do more research, I ask myself the following question: “Can I make any qualified guess about the terminal value of the company?”. This is a nice question to ask since it uncovers:
- Whether the business is predictable.
- Whether I understand its place in its ecosystem.
- Whether I grasp the company’s value creation, not only in the recent past by the proof of its numbers, but also in the long run.
Now, if I determine that there’s an inch of a question mark on the company’s terminal value, I know I’m intrigued for the sole purpose of learning and expanding my circle of competence, not investing in the stock. This is also the remedy to sunk cost bias, which has its way of forcing you to find an insight that isn’t there. If you know beforehand that you study businesses to learn, dashed with a healthy dose of humility, you can let the occasional insight come to you without forcing it. And if you decide to leave it, you just head on to the next one.
