If we were to view investment research as a bottle of water, we might say the internet bought it close to full capacity, but now with social media and AI, it’s been carbonated, shaken, and let loose. Quality research is flying out the door — the issue now lies in discovering and differentiating it from the crap research we’re inundated with. It’s not easy, and these days there’s only one thing better than discovering a quality piece of research: discovering a quality author.

They’re not easy to come by, so when you do, take notes. That’s what I’ve been doing with the work of Michael Mauboussin — the prolific financial author — whose benefited me in more ways than this introduction has time for. Over the decades he’s covered virtually every investment topic imaginable, and since his repertoire is so large, many great pieces go under the radar. Today we will be discussing one of those underrated pieces, along with insights I’ve found in my own research. The topic is pattern recognition, and if you wish to read Mauboussin’s piece on it, it’s linked below:

Pattern Recognition — Opportunities and Limits.

Pattern recognition is something we can’t help but use everyday. It can help us and hurt us. You might see a story unfolding in the same way a previously successful investment had done in the past, allowing you to spot something before others catch on. Or, you might make an investment decision based on a pattern that doesn’t really exist — where you’ve found some illusory order among the chaos. You might see all stocks in a certain niche booming and recognise a pattern, thus buying into the next one you find. But in reality, the industry might be flat and those other stocks might be booming only by stealing customers from the business you just bought stock in. Uh oh…

I find pattern recognition such an interesting and under discussed topic because it clearly holds a lot of power; Buffett once said that his partner, Charlie Munger, was “the best 30-second mind in the world.. (who) sees the essence of everything before you can even finish the sentence.” Munger wasn’t born with this skill. He couldn’t always understand everything in 30 seconds all the time, he just had an expansive background knowledge — learnt over a long life — on virtually everything from which he would frame new information against. He spent 99 years learning and the result was a pattern recognising genius; he understood new things instantly because he saw in that new thing a million other things or events he already understood and knew, which allowed him to frame all of his previous knowledge on to the new thing, instantly. Outwardly it looked like genius, but really it was a lifetime of dedication to learning patterns and information. Munger’s the most glaring example of the power of pattern recognition. If we have any interest in following his footsteps, understanding where to use our pattern recognition and how to improve it would be a good place to start. With this in mind, Mauboussin’s piece on pattern recognition will be the skeleton of today’s article; I’ll then be adding flesh from insights uncovered in my own research.

Today we’re going to cover:

• What pattern recognition actually is,

• where it works,

• where it falls short, and

• how to improve it.

There’s heaps of value in this one, I hope you enjoy it.

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What is pattern recognition?

Do excuse the elementary question here, but starting with the basics allows us to build a much stronger understanding. Pattern recognition is about noticing that a current event may have already occurred in the past, and that the outcomes of that past event offer us insight into how today’s events will unfold.

We inadvertently use pattern recognition everyday thanks to a lifetime of memory we have access to. When you hear the elevator bell ring, memory tells you the doors are about to open. Pattern recognition can be synonymous with something called a heuristic. A heuristic is a kind of shortcut or mental rule of thumb we use to save ourselves thinking time; historically, this was crucial for humans. If you see a tiger approaching, you don’t wait to analyse the risks, you’d just run for your life. Tiger = danger was a good mental shortcut, and most mental shortcuts were pretty good for us, at least that was until we ticked off the bottom levels of Maslow’s hierarchy of needs. Our day to day troubles are far more nuanced now which has meant our heuristics are often oversimplifications, or even misinterpretations, of more complex situations. It’s the same with our pattern recognition skills. They’ve been critical for human survival (e.g. ten people in camp died last week after eating that strange fruit, I’ll probably not eat that) but now that survival is no longer our chief concern, we need to understand pattern recognition better as to not let it detriment us. Pattern recognition told real estate investors in the early 2000’s that house prices will always rise, and we all know how that turned out…

We have to look into the details of pattern recognition — specifically when it does and doesn’t work, and how to improve it — if we’re to make rational financial decisions.

Let’s start by looking at the situations in which Mauboussin notes pattern recognition does work.

When pattern recognition works

Pattern recognition most favours stable environments with clear and established feedback loops, like chess. Expert chess players can see an entire game within twenty seconds of looking at the board, this is because there’s only a few fixed outcomes that can occur, and there are objectively good and bad decisions. The more iterations of the board they see, the better prepared they are to see what might work best in the future. Mauboussin mentions the work of a psychologist named Robert Hogarth, who noted the distinction between “kind" and “wicked” environments. A kind environment favours pattern recognition, whereas wicked environments do not. Chess is a kind environment, and that’s all well and good for chess players, but what about us investors who have completely opposite parameters: unclear feedback loops, infinite and uncertain potential outcomes, and the quality of processes and their outcomes being hard to distinguish?

Investing is definitely a wicked environment for pattern recognition. But we know that some investors do successfully use it, so there must be some insight here. The issue lies specifically in two things: how much experience we have and how well we have utilised that experience, and in which areas we apply our pattern recognition.

Experience only matters if you take away what is valuable from it; a stock might fall because of an earnings miss, but if you conclude that it fell for some other incorrect reason, your experience is of no value. On top of that, if we use pattern recognition to try and predict the movement of events like interest rates, we’ll be out of luck because there are too many variables and unknowns in play; but if we use it to find interesting new investment opportunities, like Joel Greenblatt did when looking at spinoff companies (more on this coming), we’re in luck. So our two crucial ideas to understand how pattern recognition works are:

• Having knowledge of the underlying reality behind an event, AKA understanding why a pattern occurs, and

• finding realistic areas to apply that pattern recognition knowledge dependably.

Let’s discuss both.

Understanding reality

I’m going to keep this brief because I think the best way to understand reality in complex, blurry environments such as investing is to fully understand and implement second-level thinking. This is a term coined by Howard Marks and it refers to thinking beyond the superficial first-level; a second-level thinker is always asking, “and then what? And why?” to everything. When an event occurs, a second-level thinker asks why it happened and what that means, and then asks why again, and again, and again, and again…

If a stock releases earnings that have fallen 20%, fist-level thinkers might see it and want to sell immediately on the bad news, but a second-level thinker, seeking a better sense of reality, might see that expectations has earnings falling over 30%, therefore this is a beat and the stock will be likely to rise. They’ll hold on and outperform the superficial thinkers of the first-level.

Second-level thinkers get a real grasp on reality because they seek more answers constantly. If you dig deep enough you tend to hit something valuable, and second-level thinking is the act of constantly digging. When you do this you start to tie together causes and effects accurately, which become the basis of informed pattern recognition and ultimately decision making.

There are many ways to try to understand the facts of reality, but so far I haven’t found a single better way than the simple process that second-level thinking offers.

The next step: applying that knowledge effectively

Let’s say — theoretically — you took the time to figure out the underlying cause of why interest rates just fell 25bps, that’s great, but it doesn’t do anything for you, it won’t tell you where they’re going to go next — and that’s where all the money is made. So clearly there are better places than others when it comes to applying knowledge.

The situations where there are less variables in play, where the feedback loop is clearer, and where understanding the truth behind patterns can actually give you predictive power. These are the situations worth seeking.

I mentioned Joel Greenblatt’s work on spinoffs; this is a perfect situation to apply pattern recognition knowledge. If you’re unaware of Greenblatt’s method, he essentially noticed that when a company spins off a division, index funds and institutional holders are forced to sell the new shares regardless of price — whether it’s because of size constraints, industry constraints, wrong indexes, wrong mandates, or just not being what they signed up for. That indifferent selling depresses the price far below fair value, which Greenblatt saw and exploited consistently. He saw the true cause behind the pattern of depressed spinoff prices, he applied his knowledge of that pattern by investing in other spinoffs that matched his criteria, and he profited handsomely many times over.

If you can find the underlying cause behind an inefficiency that other investors haven’t noticed, well, you’ll probably be writing a book on it within a decade. Greenblatt saw a pattern with depressed spinoffs and he took the time to understand why that pattern was occurring. He didn’t stop until he found the true cause of the effect.

This is the insight. We always see patterns every minute of every day, but the money is made in figuring out the cause of those patterns before other people do, and making sure you can actually make money from it. Looking at patterns in the micro-economic environment (like special situations, executive actions, technical factors, or accounting nuances) is more likely to be fruitful than looking at patterns in the macroeconomic environment. When you go into the smaller ponds, there’s less variables in play, which means less randomness, which means a tighter grip on tying cause and effect together. This is why macro bets don’t work with pattern recognition — you’ll never know if your prediction was right for the right reasons, or wrong for the wrong reasons, and so on. There’s just too many variables and nothing to tell you what caused an event to happen after the fact.

So before trusting a pattern you see, ask how many variables are in play, how much predictive power the understanding of that pattern carries (AKA “can I actually bet on something with this knowledge?”), and how realistically you’ll be able to find out if you were wrong. Individuals who spot real patterns find ways to close feedback loops — it’s the only way to understand what is causing a pattern to occur.

Now that we have an idea of where and how it works, let’s steal Charlie Munger’s idea of inversion and look at all the ways in which pattern recognition fails; in avoiding these situations, we end up doing pretty well by default.

When pattern recognition fails

Unfortunately for us, the number of ways pattern recognition hurts us far outweighs the amount it helps us. Oddly enough, recognising this will increase the effectiveness with which you use pattern recognition... You’ve got to love the counterintuitiveness of investing — understanding our limits seems to always have a way of expanding them. That will always fascinate me.

As I mentioned before, the wicked environment we live doesn’t work hand in hand with pattern recognition; cause and effect is extremely hard to distinguish, which means our conclusions about the effects are often wrong. If we can’t draw accurate conclusions about cause and effect, we can’t make any dependable predictions about the future, which is what investing is all about.

And unfortunately an environment which is particularly punishing for pattern recognition is the complex adaptive system (of which stock markets are one). Mauboussin breaks down the definition of Complex Adaptive Systems into three parts, he noes that ““Complex” reflects lots of agents that interact. “Adaptive” means that agents learn and evolve to reflect changes in the environment. And “system” means that the whole that emerges has behaviors that cannot be readily explained by the agents alone. Ant colonies, cities, ecologies, economies, and stock markets are examples of complex adaptive systems.” You can think of agents interacting as investors bidding up and down prices, adaptive as investors updating their views as new information is revealed, and systems being the market.

In these systems cause and effect is blurred. With so much complexity, it becomes virtually impossible to tie an outcome to its input. There are too many variables. One of a complex system’s most important features is non-linearity, which is about how an input might lead to a disproportionate output — where something small can cause something big. Sometimes, a pattern you’ve noticed producing the same outcome can suddenly produce a wildly different outcome for no apparent reason; consider once more the real estate investors of the early 2000’s, they saw a pattern of increasing house prices and there was no obvious reason to expect this pattern to end, but the pattern crumbled under its own weight and there was no way to tell when it was going to crumble. Non-linearity is a highly underappreciated force which I have discussed in detail here.

My favourite example that shows how non-linear systems punish pattern recognition is best given by Nassim Taleb who gave us the Turkey Problem in his book The Black Swan.

In this example, Taleb’s turkey is fed every day for a thousand days. Each meal is confirming the evidence that humans are benevolent, so the pattern holds and the turkey’s confidence rises, peaking the very day before Thanksgiving where that the pattern breaks catastrophically... The turkey’s patterns were real, all three years of them, but in a non-linear system, the size of the sample tells you nothing about the kinds of risk sitting just past it. When it comes to markets, you might see a pattern holding up for years before it seems to fall apart for no apparent reason. Non-linear systems have a thing for reminding you that you can’t predict them. As you’d imagine, pattern recognition works until it doesn’t, and you can’t know when the pattern will break. Unfortunately, our predictions — which are often based on conclusions from patterns — are at the mercy of non-linearity.

The difficulties don’t stop there. Another issue lies in being able to effectively read the patterns we see. When we recieve new data, it doesn’t just arrive in the way it ought to be interpreted — that’s up to us to do — instead, it slots into whatever frameworks you already hold in your head. If you have one mental model that you use to view the world, you will torture new data to fit into that narrow view. The conclusions you draw from this will be just as tortured. Whereas, someone who has an expansive set of mental models is more likely to interpret new data against the correct frameworks, hence extracting the necessary information from it. Having a wide range of knowledge allows you to see real patterns instead of fabricating ones in your head to fit your world views.

Mauboussin offers another reason our pattern recognition fails us: extrapolation; extrapolation is right just often enough to be dangerous. Most trends do continue, most of the time, which is why it survives. But the cost of extrapolating is concentrated entirely into one moment: when it stops working. It might work for five years, but if you’ve bet everything on a trend that falls apart in two months, it doesn’t matter how long you were right for because you were so wrong in the end. It feels good to extrapolate 99% of the time, but it’s the 1% where most of the money is made — when betting on change.

The final flaw of pattern recognition that we’ll discuss is one that I’ve felt in my mind for so long but only now have seen it put into words, thanks to Mauboussin’s piece. It’s called Acquiescence. Mauboussins states that acquiescence occurs “when an individual realizes their intuition is misguided, (yet) they still act on it rather than correcting their error.…” Sometimes we know our choice is wrong yet continue to choose it. This is most commonly seen in relationships (like picking that partner you know isn’t good for you) but it’s also common in investing when we invest in stocks knowing we intuitively know we shouldn’t.

Even when we call out our pattern recognition’s faults before we make a choice, we still follow through with it… At one point we’ll all have done this, you probably saw a stock that was booming, or one that looked like it was bottoming, and you invested before you had all the facts because you didn’t want to miss out — you knew it was wrong, you felt it was wrong in your gut, but you did it anyway. It’s something we’ve all done, and it’s important to listen when we realise our intuition is flawed. No amount of FOMO should override our ability to make a rational decision. When you sense your intuition is wrong, listen to it.

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How to improve pattern recognition

It’s been a pretty depressing story so far, but hope is not completely lost. There are ways to improve this skill. Since the benefits of pattern recognition in investing are so sensitive, we have to sharpen up to get the best use out of it. Combined with Mauboussin’s ideas, I’ve offered a few ways we can improve pattern recognition; I’ll list them below and run through how we can implement each.

• Acknowledge which environments are wicked and kind.

• Understand that experience only matters when it’s learnt from and analysed.

• Keep a decision journal.

• Build a latticework of mental models.

Let’s quickly run through each.

Acknowledge which environments are wicked and kind

This is the step that everything else rests on. Being good at the game only matters if you’re playing the right one; knowing which games to play is the first step. When you are making a decision based on pattern recognition, you have to ask yourself what kind of environment this is; how friendly is this environment to patter recognition? Does it have clear feedback loops? Does it have few variables controlling outcomes? Can you get an accurate read on cause and effect? These are the important questions you must answer before making a decision that relies on pattern recognition.

Once you’ve figured which environments surround you, isolate the specific parts of it that are structurally kind (recurring, mechanical, low variable situations like Greenblatt’s spinoffs) and concentrate your efforts there. Leave the wicked parts (like macro calls and rate forecasts) to humble, fallible models instead of gut feel.

Experience only matters when it’s learnt from and analysed

Every investor whose returns are extraordinary over 20-years has at least two decades of experience. But not all investors with two decades of experience are extraordinary. The important variable in becoming an expert isn’t just time, it’s whether your decisions were analysed and reviewed afterward and correctly attributed to skill, luck, or error. No one is an expert because of raw repetition. Without self reflection we build up the false confidence that experience leads to expertise, which can lead to even worse decisions down the road. You might now be wondering how we can learn from our experiences instaed of letting them go to waste?

By keeping a decision journal.

This is one of the best pieces of advice an investor will ever recieve — I know this from first hand experience. Feedback in investing is notoriously hard to get, no one tells you whether you got it wrong or right, not even an outcome truly does that. Writing a decision journal is the biggest step you’ll ever take in receiving genuinely effective feedback.

When you buy a stock, write out your thesis, or when you make any kind of prediction, write down your reasoning; most importantly you must have a date with which you think the prediction will come to fruition.

When that date comes, compare reality with how you thought it would unfold, see which events did and did not occur, ask why they didn’t occur, ask whether they ocurred for the reasons you predicted, ask why your reasoning was wrong but your prediction was right. Dissect your decision and analyse each component against the reality that unfolded.

You might think you can do this without the need for writing it all down. Believe me, this doesn’t work. You will either forget your reasoning, ignore it (especially if you were wrong), or worst of all, distort your reasoning to fit the reality that ocurred. You might have predicted a stock to rise because of multiple expansion, but if the multiple fell while earnings rocketed upward, you might have been right about the stocks rise, but you were wrong about why it rose. And that’s what’s important if you’re to bring forward anything valuable from the experience.

Writing a journal of your decisions and predictions will be one of the best things you ever do to become a better investor. Another fantastic thing you can do is to start building your own latticework of mental models. This concept, coined and popularised by Charlie Munger, is our final area of improvement. You’ll be glad to know that this doesn’t just improve pattern recognition, it improves your entire life.

Building a latticework of mental models:

I’ve discussed this topic in detail on a few occasions now, and while I’ll try to explain it briefly I strongly recommend taking the time to read the article linked below, it goes into detail about how to start your own latticework. It’ll be worth your time, I promise.

The Latticework of Mental Models: A Comprehensive Guide

A latticework of mental models is essentially a term that refers to holding a set of key ideas from multiple disciplines — like psychology, economics, physics, biology, statistics and so on — and cross referencing them when studying new problems or data (rather than relying on one dominant framework to force the entire world into). These models interconnect like a lattice, so new information gets checked against several models and angles at once, catching issues and details that any single model would miss on its own.

This is where I previously mentioned Charlie Munger’s ability to accurately analyse a situation in 30 seconds — he has so many ideas in his head that he can apply new information to the model that best suits it, which allows him to draw the most accurate conclusions.

The bottom line here is that a latticework lets you match a pattern to the kind of model that actually explains it, as opposed to torturing the evidence until it conforms to a narrow worldview. It’s difficult to summarise this topic because it gives birth to hundreds of wonderful areas of discussion. I’ve only scratched the surface here. I can’t recommend reading the full guide enough, It’s probably my favourite article I’ve written.

That’s today’s discussion come to a close. I hope you found it insightful. At the very least, I hope it made you think twice about how much you rely on pattern recognition and how flawed it really is in the world of investing when it isn’t used correctly. Hopefully this article will have helped you utilise your pattern recognition skills a little better.

Thank you for reading. I’ll see you next week with another article!

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Best,

The Intellectual Edge

Thumbnail: Agnes Martin — Starlight, 1963

References: Mauboussin, M.J. (2023) Pattern recognition: opportunities and limits. Consilient Observer. New York: Counterpoint Global, Morgan Stanley Investment Management. Available at: https://www.morganstanley.com/im/en-us/institutional-investor/insights/consilient-observer/pattern-recognition.html

I’ve now sent out ten articles covering his memos, and the result is a bit of a digital goldmine; we’ve traversed the dotcom bubble, the great financial crisis, and heaps more in between.

It’s been a great privilege to understand how one of the markets greatest minds has approached these obstacles. Marks’ memos are a gateway into his mind; we get to see the mental obstacles, the thought processes, and the decision making that’s made him as successful as he is today.

I knew he was great before this series begun, but now I understand why. His insights are invaluable and I hope this series has been as beneficial to you as it has been for me. If you haven’t kept up to date — don’t worry, each article can be understood independently from any other. But if you’re interested in reading more, I’ve linked a few of the best ones below.

• The Lessons of Howard Marks’ Memos pt.2 (’01-’05)

• The Lessons of Howard Marks’ Memos pt.3 (’01-’05)

• The Lessons of Howard Marks’ Memos Pt.3 (’06-’10)

• The Lessons of Howard Marks’ Memos Pt.1 (’11-’15)

In today’s article, we round off the 2011-2015 period. This has been my favourite one to write in the entire series so far. We cover the following topics:

• The role of luck in investing

• What is risk?

• Thoughts on liquidity

• Lessons from oil

• Why investing is hard, and why it matters to know it

• A few lessons from the world of sports.

It’s a great one, let’s dive in.

Read more

Over the years I’ve read many ‘great’ books that I didn’t connect with. Maybe I didn’t have the sufficient knowledge to appreciate their contents or I just wasn’t in the right head space — I’m not sure.

But once in every while the right book comes and you have the time and knowledge to read it in the way it deserves to be read. I’m currently reading one of those books, and I think it holds an explanation that is so prominent for investors that I’m writing this article before I’ve even finished it.

It has put into words — in such a succinct and beautiful way — my jumbled philosophy on what it means to be a successful investor. Oh, and the book has nothing to do with investing.

The book is called “The Beginning of Infinity”, by famed physicist David Deutsch. I picked it up when one of my personal heroes, Naval Ravikant, recommended it over 40 times in just a few years. He’s not big on endorsements so I stopped ignoring his recommendations and dove in.

I’m now just six chapters in and I’ve already been filled with ideas that have made me a considerably smarter person. The ideas have impacted how I view the world and have immediately flowed into my investment philosophy, creating an instant and drastic improvement in how I approach stock analysis. Any really good idea you come across won’t just benefit your investing, it is beneficial to every facet of your life.

I’ll run you through Deutsch’s idea today.

I hope — just like this is the right book at the right time for me — that this is the right article at the right time for you.

Here’s an overview of what we’ll cover

• Book overview

• Deutsch’s idea of a good explanation

• Becoming a fallibilist

• Observation is theory-laden

• Reject authority at all costs — create a tradition of criticism.

This article is only in relation to one chapter of the book. It’s a complete gold mine that deserves careful time and attention.

As you might infer from the overview, the chapter is all about the power and importance of good explanations. How boring, you might be thinking. You’d be mistaken. Take the time today to read this in its entirety before making any judgments. The opportunity cost of not doing so is monumental.

I’m annoyed at myself for having left this book on my shelf for so long. Don’t be like me.

Enough small talk, let’s get started.

A quick book overview

There’s a lot in this book that needs digesting. To lay a bit of foundation for you I’ll give a quick overview of some key ideas before jumping into our main discussion, which will be about chapter one.

As the book title suggests, Deutsch believes we are — and always will be — at the beginning of infinity, specifically infinite and unbounded knowledge growth. For every problem we solve, many more reveal themselves to us, all of which are solvable, which allows for endless knowledge creation.

He argues that anything is possible as long as it obeys the laws of physics. This bold view means that every single problem we have, and will ever have, is not a matter of possibility, but a matter of knowledge.

If physics allows us to genetically modify humans to possess a chameleon’s camouflage, then achieving this is simply a knowledge problem. There is nothing between us and that modification but knowledge. This world view immediately makes anything seem possible. It’s optimistic, but why not be optimistic?

It’s quite an inspiring idea, that everything allowed by the laws of physics is possible given the right knowledge. Literally anything. Deutsch views the universe not just as our home but our resource to utilise in achieving knowledge growth.

And since all problems are solvable, and every solved problem introduces many more problems to us, we are always at the beginning of infinity — the beginning of an infinitely long stretch of knowledge growth ahead of us.

That’s the main idea behind this book, though today we’ll be discussing something a bit more specific. Through its 18 chapters, Deutsch explains his ideas on societies, computing, optimism, philosophy, reality, infinity, and heaps more. The first chapter struck me immediately and I can’t help but think he went straight in with his most fundamental idea; it’s all about explanations and how science the constant search for better ways to explain reality. It’s not about prediction, it’s about understanding. You can make a prediction that Winter will come this year because the Gods must reinstate our gratitude for more pleasant weather for the rest of the year. The prediction of course will be correct, Winter will arrive. But the reason for Winter’s arrival is wrong, and that’s what matters. It’s the understanding of an event that holds the power because a good explanation tends to reach much further than the ‘thing’ it originally set out to explain. If I were to truly understand why Winter occurs every year (because of the Earth’s tilted axis), my explanation would also reach across and explain all other seasons, why other countries across the world have different seasons at different times, and even the seasons that might occur on other planets. That simple example — a genuinely good explanation — goes to show how far reaching and powerful good explanations are. They are both the root, and the forefront, of all knowledge growth.

This links to investing in enormous ways that might not be clear yet, so stick with me. Let’s first dig into what Deutsch constitutes as a good explanation.

Deutsch’s idea of a good explanation

A good explanation is our best available account of reality, it’s hard to vary, and it typically reaches further than the problem it aimed to solve.

That’s the fundamental idea, but a definition has no use unless we understand it, so I’m going to dive into these details and explain deeply what a good explanation really is. Let’s break it down.

• A good explanation is hard to vary. The explanation that Winter comes because it always comes in December is a bad one. Among many other reasons, it’s badness is thanks to how easy its variables are to change. If you live in England, it is Winter in December, but if you went to Australia, you’d need to swap out December for July to make the explanation stand. Or you might go further; if Winter comes because of a certain mythical God getting upset, one could change the God to any other and the explanation would still stand — whether Winter is caused by Demeter, Hades, or Persephone makes no difference. When the explanation still works even when variables are switched it’s because none of those variables were ever accounting for anything real. Weak explanations tend to last so long because of how easily their variables are changed — they tend to update and flex based on whatever happens. A good explanation doesn’t have much freedom. When it comes to Winter, once you explain it through axial tilt, you can’t swap that out for something else without the explanation collapsing; that’s because the detail plays a critical role in its account of reality.

  (when) underlying explanations are bad, they can easily accommodate the new experience without changing the substance of the explanation. Without a good explanatory theory, they can simply reinterpret the omens, pick a new date, and make essentially the same prediction. In such cases, testing one’s theory and abandoning it when it is refuted constitutes no progress towards understanding the world. If an explanation could easily explain anything in the given field, then it actually explains nothing.

  - Deutsch

• A good explanation accounts for reality, it’s not about prediction. Once you understand why Winter happens, you don’t need to predict it. The explanation of it accounts for the reality behind it, which means prediction — if it’s needed — is the bi-product of understanding, not the goal in and of itself. We can predict seasonal change because we understand why they occur. “Winter will come because it always comes at this time of year” is not an explanation, it’s a rule of thumb, and these can’t be trusted — as we’ll come to find out.

• Testing a prediction only matters once you already have a good explanation. This is because bad explanations can still past tests. You can still predict Winter to arrive in December with a bad explanation of why it’ll happen. Hence you can prove a bad explanation right with testing. With this in mind, testability is not the most important part of an explanation, it’s that it is hard to vary. Similarly, I can say that XYZ Corp will go up in price because my left ear felt a little hot this morning, it might go up, but it has nothing to do with my explanation; testing a prediction only matters when your explanation of reality is actually good.

• A good explanation has far reach. A good explanation doesn’t simply answer the question that it set out to solve. It reaches into problems beyond its original scope; a good explanation of Winter also explains the other three seasons, the seasons on different hemispheres and different planets and so forth. Everything in the universe follows the same physical laws, so when you find a truly good explanation for something, it tends to go pretty far. Understanding why gravity causes an apple to drop to the floor will also explain why the Moon orbits the Earth. That’s not a lucky coincidence, it’s understanding a crucial aspect of reality. A far reaching explanation is evidence that it’s accounting for something real rather than some local rule of thumb.

• A good explanation is the best we have, and it is bound for change. Soon we will be discussing something called fallibilism, which is basically about knowing you could be — even expecting to be — wrong all of the time. A good explanation is a placeholder until something better comes along. It being hard to vary doesn’t mean it’s complete, it means it’s the best account we have, until a better one displaces it. Deutsch’s loyalty to fallibilism is what stops a “hard to vary” explanation from becoming dogmatic. It once seemed that Winter coming because “it always comes at this time of year” was a hard to vary explanation — but that’s only because we had no knowledge to suggest otherwise; as we gained knowledge we realised it was a poor explanation and replaced it with better ones.

The best investors seek better explanations:

Average investors just spot patterns, they don’t bother to explain reality. They might say “This stock is cheap on a PE basis, and low PE stocks tend to outperform.” But that’s not an explanation, that’s a rule of thumb, which is something Deutsch doesn’t like. Rules of thumb are predictive theories whose content is based on experience, they are helpful, but not explanatory. A rule of thumb offers no real account of what it is that causes something to happen — in this case, what is actually causing a stock to be cheap and why it will outperform.

Rules of thumb work until they don’t, and when they fail nobody has an explanation because there was never an explanation underneath the prediction to begin with. Thinking a stock is going to outperform because of a low PE is the investor equivalent of “Winter comes because it always comes in December.” When it doesn’t outperform, you’re none the wiser and much poorer.

This is where good investors separate themselves. An investor with a real explanation for why a company is mispriced has an explanation that is hard to vary. If their thesis rests on a company experiencing some margin expansion from a new contract which the market has undervalued, they have a hard to vary explanation for that stocks cheapness. If they know that people aren’t appreciating the margin expansion, they can’t just swap it out for another factor like increased demand — without the factor they’ve uncovered, the thesis collapses, and that is because their explanation of the mispricing is hard to vary. It accounts for reality.

Most investors fabricate explanations of why a company is cheap — typically something like “because people don’t understand the business like I do”— and then go silent when the business stays cheap forever. The reason it stayed cheap was because their explanation didn’t account for reality. They might vary their explanation to account for their failure, they might then say that it’s all down to forced selling, or sector rotation, or one of any other million wrong reasons. You get the point. They had a weak, easy to vary explanation and the market proved how wrong it was by punishing them.

In the long run, the market won’t pay you for being right through luck or prediction, it pays you for having found the reality behind a situation. The investors who can better uncover what is genuinely going on in a business, and what the markets views are toward that business, will be rewarded very handsomely.

This relentless, journalistic-like truth seeking is a common trait among the best investors.

Becoming a fallibilist

Deutsch is a strong believer of fallibilism, which is the position that all knowledge — including our best explanations — is inherently uncertain, and that no explanation should ever be treated as final. This is not an admission of weakness, it's the almost optimistic belief that even your best current theory has room for improvement. Fallibilism ends up being the force behind knowledge growth. Without it we may become satisfied with incomplete, or wrong, explanations. This stagnation prevents infinite knowledge growth, whereas fallibilism is constantly encouraging it.

Fallibilists expect even their best and most fundamental explanations to contain misconceptions in addition to truth, and so they are predisposed to try to change them for the better.

- Deutsh, TBoI, p.8

The best investors are fallibists:

When you buy a stock, you are betting on an explanation, a belief of why X is worth more than it’s priced for. Most investors are lazy, once they own a stock they stick to the explanation they had when they bought it, even in the face of new evidence. They see all new data and base it against their own bad explanation, not objectively, as it should be seen. They end up trying to explain or rationalise contrary evidence instead of updating and changing their explanations. This end you up with a sunk cost. A position will end up being held for far longer than it deserves simply because the investor can not allow themselves to be wrong.

On the other hand, you have investors like Charlie Munger and Warren Buffett who are brutally objective, known to “rub their noses in their failures.”

Investors like these two come to expect their theses to have holes, they understand that their explanation of reality might be flawed, and instead of ignoring this, they seek new information constantly in order to change their views; the result is a bet that is in accordance with reality. They increased their chances of success because they never fooled themselves.

You might even see great investors make occasional radical portfolio switches, this is not because they’e irrational but because they take new information and greet it objectively instead of ignoring and rationalising it. If a thesis breaking piece of news comes — they don’t hang around and see what happens, they get out. Life jackets are limited when it comes to liquidity.

A great investor might spend years building conviction around a stock and still sell it in a week when the thesis breaks. This looks irrational to the uninformed, but they’re doing exactly what fallibilism suggests. It’s the investor who can’t let go of a stock who has abandoned fallibilism and is now protecting their belief — and their ego — instead of pursuing the truth.

Being open minded, agile, and objective when your beliefs are shattered in front of you is something the best investors can do. They’re humble enough to know they’re going to be wrong a lot of the time and they’re willing to jump ship when they realise it.

Observation is theory-laden

For thousands of generations, our ancestors looked up at the night sky and wondered what stars are – what they are made of, what makes them shine, what their relationship is with each other and with us – which was exactly the right thing to wonder about. And they were using eyes and brains anatomically indistinguishable from those of modern astronomers. But they discovered nothing about it. Much the same was true in every other field of knowledge. It was not for lack of trying, nor for lack of thinking. People observed the world. They tried to understand it – but almost entirely in vain. Occasionally they recognized simple patterns in the appearances. But when they tried to find out what was really there behind those appearances, they failed almost completely.

- Deutsch

So, why did our ancestors fail? As Deutsch said, it wasn’t for lack of trying; it was because they didn’t have the prior knowledge needed to understand what they were looking at. Just like anyone uninterested in Physics will walk past David Deutsch on the street uninterested and unaware of his brilliance — when we don’t have the necessary prior knowledge we only look at things in terms of what we already know, and since we don’t know everything, the conclusions we draw from our observations are fallible and likely wrong.

In other words, our observations are theory-laden.

All observations are, as Popper put it, theory-laden,* and hence fallible, as all our theories are. Consider the nerve signals reaching our brains from our sense organs. Far from providing direct or untainted access to reality, even they themselves are never experienced for what they really are – namely crackles of electrical activity…

We do not just see blue: we see a blue sky up there, far away. We do not just feel pain: we experience a headache, or a stomach ache. The brain attaches those interpretations – ‘head’, ‘stomach’ and ‘up there’ – to events that are in fact within the brain itself.

Our sense organs themselves, and all the interpretations that we consciously and unconsciously attach to their outputs, are notoriously fallible… So we perceive nothing as what it really is. It is all theoretical interpretation: conjecture.

- Deutsch

Here’s an example.

Let’s say you’re a doctor reading an X-ray; to most people, an X-ray looks like a grey smudge. To a radiologist, the same image contains a fracture or a shadow that shouldn't be there. Nothing about the physical image changes between the two viewers, the doctor and the novice see the same image; what's seen is entirely a function of the theory the viewer brings to it. A novice isn't seeing the fracture at all because they have no theory to make it visible as a fracture. But it is there.

That novice might conclude the X-ray is showing nothing, they’ll tell their friends X-rays are useless, and maybe even write books on how useless X-rays are. This conclusion has nothing to do with X-rays, it’s only reflective of the naivety the novice approached it with.

It truly makes you wonder at all the things we look at everyday — what are they trying to tell us that we don’t have the theory to understand yet? We could go one further, what do we currently believe so strongly that might be based on entirely wrong theories?

This idea is another pillar that supports fallibilism. Our theories are based on incomplete information, using our fallible sense organs, meaning our observations and conclusions — which are made using prior information and our senses — must be fallible too.

The best investors have better theories than you:

Much like the radiologist and the novice, the best investors see things the average investor can’t.

We all get the same information. The raw data is mostly identical. The difference lies in the theories that each investor brings to it. Two investors will look at the same business and make different conclusions, they might both be looking just as hard, and they might both be as smart as each other, they simply have different theories that they’re basing their analysis on.

Neither of them are seeing the picture for what it really is, they are just seeing it in the way that most suits all of their current frameworks. We’re all biased toward our own experiences.

The best investors see things as they are, and that isn’t because they’re better at observing, but because they have better, more useful theories that account for the reality of the situation. The investor who arrives at wrong explanations isn’t being stupid, they just don’t know how to see what they’re looking at. Bad conclusions in investing rarely come from bad information — we all use a lot of the same information after all — they come from good information run through the bad theory.

This comes very neatly into Warren Buffett’s idea: circle of competence. Sticking to what you know isn’t about a fear of being brave, it’s just the smartest thing you can do. When you stray from the kind of situations you know well, you run the risk of being the novice who can’t read the X-ray. With experience and understanding comes better theories and knowledge, and with this you give yourself the best chances of success because you have better theories to apply to a situation.

You either need to expand what you know or stick to what you know. If an investment succeeded that was, say, a small cap industrial company from France, you can’t just presume your next one will work out because you’re ‘on a roll.’ Different investments require different theories and knowledge. Good investors know that and they don’t fool themselves into thinking they know something they don’t. They know a few theories extremely well, and they apply those theories to the stocks that are best suited for them.

Reject authority at all costs — create a tradition of criticism.

Before the (17th century), it was generally believed that everything important that was knowable had already been discovered, and was enshrined in authoritative sources such as ancient writings and traditional assumptions. Some of those sources did contain some genuine knowledge, but it was entrenched in the form of dogmas along with many falsehoods. So the situation was that all the sources from which it was generally believed knowledge came actually knew very little, and were mistaken about most of the things that they claimed to know. And therefore progress depended on learning how to reject their authority…

What was needed for the sustained, rapid growth of knowledge was a tradition of criticism. Before the (17th century), that was a very rare sort of tradition: usually the whole point of a tradition was to keep things the same.

…

(we must reject authority) because if we adopt a theory on authority, that means that we would also have accepted a range of different theories on authority. And hence it also implies the need for a tradition of criticism.

- Deutsch

Authority is an interesting thing. I remember hearing somewhere, I can’t remember where, that all the laws you follow and the authority you seek approval from, are made by people no smarter or more informed or more wise than you. It’s there first go at life, just as it is yours. They don’t know what they’re doing just as much as you don’t.

It’s kind of like how when you’re a kid, adults seem as though they’re all knowing and all powerful, until you become an adult and you realise no one has a clue about anything. On a larger scale this still applies. No one really knows anything, and blindly accepting traditions, beliefs and theories on authority is a mistake similar to a kid thinking adults are omnipotent.

The best investors are lonely by nature, they seek no approval and question authority:

In investing, authority takes its form via the consensus. One of my favourite lines from Howard Marks is that there’s no such thing as a bargain that everyone knows about. If it were a bargain, and everyone knew, it would no longer exist… I have seen, on many occasions, hundreds of investors online preaching about how underpriced a stock is. These investors and their wide reach become the authority through which we base our own actions. However, logic in investing would suggest that if most investors are in agreement on something, that something has already been stretched and will now offer more risk than return.

Authority can take shape in other ways, too. Many investors buy stocks based on moves from managers they admire. While this can be a good source of inspiration, it’s a grave mistake to follow them blindly. In blindly copying the movements of great investors you’re actively avoiding the very traits that made those investors successful in the first place, namely independent thinking, a reluctance to copy anyone else, and a willingness to be completely alone.

When you copy someones position, and that position goes against you, you don’t know whether the thesis broke, whether it needs more time, or the person you borrowed it from was wrong the whole time. This is a dire, dire situation to be in; where you never actually understood the decision yourself. When you base a decision on authority, you’re then at the mercy of said authority until you exit the position. When you buy because so-and-so said you should, when do you sell? When do you add? What if they go radio-silent? What do you do then?

This constant desire to make investment decisions that are approved by figures of authority is a deep hole that most don’t escape.

Another point I’d like to reiterate on authority is this: if you are hearing an idea from anyone, especially someone who is widely listened to, you are hearing it late, meaning the return potential of that idea has probably been exhausted.

If any thesis is well-known and widely agreed upon, it’s either priced in or it’s wrong; meaning the authority you’re borrowing from is, at that exact moment, the least useful it’ll ever be. Real mispricings are lonely endeavours because they simply have to be lonely in order to exist. If it were not lonely, the market would have already closed the gap. Seeking approval and seeking excess return are almost mutually exclusive.

I’d like to finish this section with a caveat: none of this means ignoring what smart people think, and Deutsch doesn’t want us ignoring them either, he’s just arguing for a tradition of criticism. You can look at a great investors recent move and take the time to analyse it, try to break it, and truly understand it, before entering the idea for yourself.

All of today’s discussion will lead you to the fundamental importance of understanding. And you can’t copy understanding, you have to put in the work.

That’s all for today. I hope this article was a good read. It’s difficult to articulate yourself when your mind is racing from idea to idea. I hope it came across coherently, if it didn’t, do let me know and I can clarify any one of my thoughts for you.

My best piece of advice for you would be to read the book yourself, Deutsch is infinitely smarter — no pun intended — than me and he will articulate in seconds what I have tried to do in 15 minutes here.

I’ll finish with a line that brings together a few of today’s ideas: Begin your own tradition of criticism — seek better explanations that are hard to vary — and you will be rewarded handsomely.

Have a great Sunday and I’ll see you next week.

Best,

The Intellectual Edge

Thumbnail: David Deutsch — The Beginning of Infinity

References: Deutsch, D. (2011) The Beginning of Infinity: Explanations That Transform the World. London: Allen Lane.

This article is an argument to become a writer as well as a reader. Writing has been one of the things that’s contributed most to my growth as an investor and thinker. I don’t even mean writing publicly, sometimes writing publicly can be dangerous because you feel the compulsion to live up to old statements that you may have grown out of; or as Naval Ravikant would say, you start to feel like you have to live up to things in private that you said in public.

Whilst these negative social contracts aren’t always the case, it can be easy for words to become a tether that holds us back from growing and changing our minds. So public writing certainly has its benefits, otherwise I wouldn’t be sending this email every week… but private writing captures almost all of the benefits without the negatives.

In any case and in whichever form suits you, today’s article is an encouragement for you to begin writing.

Thinking without writing is like swimming against the current, you make progress, but any pause, break, or fatigue can often leave you lost or further back than where you begun; writing brings so much clarity to your mind that it immediately feels like you’re swimming with the current, you can actually reduce your physical exertion whilst moving forward even quicker because writing carries that weight and organises the thoughts that your mind works so hard to remember. Writing is the current that speeds up your growth as a thinker. This is a hill I will die on. That’s the crux of my piece today.

In the following, I’m going to explain the benefits of writing, how drastically it has benefited my thinking and investing, and most importantly, how you can think about starting today and in what format (spoiler: everyone can do it, you don’t need to be ‘good at writing’).

This is another week of qualitative insights — do let me know how you feel about the balance between qualitative and quantitative discussion. The insight often feels most tangible in quantitative pieces, however it’s my belief that insight is most powerful in the qualitative stuff. Maybe it’s so powerful because of the very fact it is less tangible, which makes it a little harder to grasp and a little more valuable when you do. Either way, let me know.

I’m going to split this into three parts; the first will be about why I started writing and the benefits I’ve personally experienced and can vouch for; the second part will be a more far reaching argument, separate from my somewhat parochial views and experiences, drawing ideas from a few smart individuals and institutions; the third will be how you can get started today.

I hope this article converts you to a writer today. If you give writing a good go for a few weeks you’d likely never stop. Let’s discuss why and how.

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So far, we’ve covered over 20 years of memos from Howard Marks; that’s over 800 pages of writing summarised and sent to your inboxes.

Marks’ memos are one of, if not the most valuable investment resource on the internet. This series has completely transformed me as an investor, and many of you reading too. No single article requires you to read any other for it to make sense, so today’s a good time to start if you haven’t already.

If you’re interested in any others in the series, you can check out a few recent ones below:

• The Lessons of Howard Marks’ Memos Pt.1 (’11-’15)

• The Lessons of Howard Marks’ Memos Pt.4 (’06-’10)

• The Lessons of Howard Marks’ Memos Pt.3 (’06-’10)

• The Lessons of Howard Marks’ Memos Pt.2 (’06-’10)

Do excuse this brief sales pitch, but I believe this series is worthy alone of a paid subscription. The insights in here might be worth half of your portfolio, let alone $160 a year. I’ve also put hundreds of hours of work into putting this all together so far, so your support really does mean a lot to me.

Thank you!

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Anywho, we’re all here for one thing: Marks’ memos; so let me stop talking and start summarising. As all other time periods have now proven, this one offers a wealth of fascinating ideas for us, from dealing with uncertainty to ideas on equity risk premiums, from cycles to the role of confidence, and heaps more. I shall reiterate, as I always do, that this series will change you as an investor. The effort put in here is dwarfed by the knowledge you’ll take away from this. If I had to quantify it, I’d say one ounce of effort is equal to ten ounces of reward. These memos are nothing short of excellent.

Happy reading!

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It’s my belief that, after a point, reading more investment books comes with a massive intellectual opportunity cost. In acquiring that tiny bit of extra financial knowledge, we miss out on acquiring heaps more valuable knowledge in other fields. I’ve been reluctant to write this article for some time now as I haven’t known quite how to tackle it, but I won’t know how to do it until I start, so this is the laying down of a few raw thoughts — do excuse me if I ramble.

The crux of today’s piece follows suit with a few other articles I’ve released on this publication — it’s that we ought to expand our reading outside the world of finance and how that can benefit us a lot more as investors than just reading investment books. This conclusion might change your entire trajectory as an investor, it certainly did for me. I highly recommend giving this piece a chance — even if you end up disagreeing — consider it an avenue justly explored.

Included in here is not just an argument against reading investment books, there’ll also be a lot of inspiration for you to begin your broader reading journey; we’ll cover various interesting academic fields and a few book recommendations to get you underway.

Let’s get into it.

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Julian is a keen individual investor, he’s been at it for a few years and he’s enjoyed good returns along the way. He loves researching stocks alongside his 9-5, where he works full time as a product designer.

Since he has a full time job, he hasn’t the time to undergo an A-Z trawl of a random European stock exchange on a Tuesday morning (as much as he, and we all, would love to). Considering this, he subscribes to various stock research newsletters to gather ideas, and following the newly accepted wisdom1 among investors, he uses a mixture of company websites and Claude AI to research those gathered ideas.

On Sunday mornings, Julian wakes up with some coffee to receive his weekly dose of investment ideas. There was one Sunday, however, that was slightly different.

He received his typical emails from his two favourite investment newsletters. But this week something intriguing occurred — they both pitched the same stock, let’s call it BTC Corp. Typically, he’d be overjoyed at this vote of confidence — “these two know what they’re doing” he’d tell himself — but that wasn’t the case this week. Unfortunately for Julian, one email presented a very compelling buy recommendation on BTC Corp, the other presented an equally compelling sell recommendation on the stock.

His two favourite places to find ideas were so confidently disagreeing with each other.

He keenly reads and believes their commentary to be correct, but of course in this case, one had to be right and one had to be wrong; this sparked an uncomfortable and confronting cognitive dissonance in him.

Any other week he’d have been lulled into their persuasive language, the attractive prospects, and of course the promise of profit that the email offered. But now knowing that one of these two investors had to be wrong, he approached them both with far more skepticism. When hope and promise was removed from the equation, he saw things a little more objectively.

And so he sat, sipping on his coffee, pondering over the dilemma of who was to be right. As any good ponder does, it struck Julian with an insight. He came to the realisation that had he only received one email, say, from the investor going long, he’d have also been long BTC Corp. In the same vein, had he only received the email from the short investor, he too would’ve been short BTC Corp. Much like a cosy fire goes with a good film, a harsh truth comes with a little wisdom. Julian remembered that a stock pitch is a product of the investors’ unique perspectives, knowledge, and biases. It’s not gospel, nor is it necessarily the same conclusion he might’ve arrived at given he’d undergone the same research process; it’s a game of opinion, he’d just stopped treating it as such; realising he had unintentionally outsourced all of his own thinking, he begun to second guess all of his investments. He noticed that he’d developed a few bad habits. When his companies released news, he wouldn’t go on the company website, he’d rely on people on the internet to do everything for him — he wanted all the important stuff packaged and sent straight over in a neat email. But now, reminded of a concept he knew well as a beginner, he understood every bullish argument for each of his stocks likely had an equally compelling bearish argument against it. In fooling himself into thinking he was doing his own research, he fooled himself into thinking he knew what he held. All he knew was someone else’s opinion on his holding, not his own — this made him rather portfolio-conscious. And with his new found self-consciousness, he went through each of his holdings and researched them for himself, all over again. He promised therein to use social media as a form of idea generation, not as a neat hack that allows one to stop thinking and researching.

And so Julian took the final sip of his Sunday morning coffee, maybe a few pennies poorer and a little sheepish, but a whole lot wiser — and definitely a better investor — for it.

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In the face of unlimited ideas

Across Substack, Twitter, and the vast number of dedicated stock research platforms, we are inundated with golden tickets, sure things, and early stage 100-baggers. It starts to feel like every stock is a once in a lifetime buying opportunity. Being barraged by so many ideas on a daily basis, it’s rather easy to forget to find any ideas of our own. Why trawl through an index when there’s 15 emails in your inbox, all promising retirement in three years?

Don’t get me wrong, there are lots accounts across media that have consistently proved themselves to be highly valuable and talented investors, but statistically speaking, only a few can be. When you find one, hold on tight… because there are hundreds of accounts claiming to have achieved above average returns for 10-years plus ( who definitely have not). We can’t forget that more than half of road users think they’re above average at driving…

Statistics have a fantastic way of unapologetically revealing the truth. Unfortunately, we can’t all beat the market.

In outsourcing our thinking, we take away something unique from an investment proposal: ourselves. You have knowledge and perspectives that others don’t have, and you give that up when you leave research up to someone else. As I’ve said, there are no doubt many talented investors on social media — I can vouch for a few off the top of my head — but it is the exception that proves the rule. Investing is hard and only a few are extraordinary. In listening to the ideas of the many, you’ll end up with average returns. Don’t forget that these people will have approached the idea with their unique skillset, and undoubtedly their skillset has gaps. The long investor missed out on the accounting flaw that the short investor knows very well, and the short investor missed the growth avenue that the long investor has spent years studying.

Each investor has his reason to position themselves the way they have done. If you’re to follow someone into something you have to be fully aware of their reasons, so much so that you are not following them into it at all, but rather investing in the same thing as them, the difference is subtle but important. When you take research seriously you stop following people into things and you start leveraging their knowledge to benefit your own. External research is supplementary to your own — it is not yours.

If there’s another reason today to encourage you to read that annual report yourself, it’ll be this:

Remember you’re buying from someone who wants to sell, or selling to someone who wants to buy. You’re making a bold claim that you disagree with the other person so much that youre willing to put your hard earnt money on it. Surely a bet like this deserves extremely careful thought and research, not just a few minutes reading another persons potentially one sided research? We’d like to think all available research is conducted with the highest level of vigour — but we can’t know it for sure — the only true way to know the quality of someone’s research is to also do it ourselves alongside them.

If you don’t research ideas for yourself, like our good friend Julian, everything becomes a matter of persuasion. A stock pitch is less about merit or inspiration and more about salesmanship. It’s like when you go to a mechanic with your rickety car that you know nothing about; with enough big and scary words you’ll be spending thousands on parts that don’t need replacing.

Without knowing what you’re doing, you become convinced not by the right thing, but by the first thing. If you read the bullish argument first, you’ll be skeptical of the upcoming bearish argument. Likewise, reading the bearish argument first, you’ll be far more likely to disagree with the bullish argument. This susceptibility to salesmanship will make your investment lifetime a dreary one. The only answer is to do your own research and compliment it with other people’s research. People are clever, make use of them, but don’t rely on them.

It’s more than likely that we’ve all been — or still are — investing like Julian. To become a better investor, it’s absolutely vital to break this spell of delusion — that we know what we’re invested in.

By all means, utilise the fantastic research that the platforms have to offer — you’d be silly not to — but don’t forget the most important thing, which is that you have made up your own opinions about a stock’s prospects and are not just borrowing someone else’s ideas. Only when it’s far too late will you realise how fragile borrowed opinions are. I say this from experience, too much of it. There is no kick-up-the-ass to research a stock like being 50% down on it… Don’t be that guy.

Know what you own, use research platforms as a supplement, not a crutch.

After all, is this not why we invest? It’s not just about money, it’s because we love doing it, we love the process, so let’s start doing it again.

This was short one today, but it’s a discussion that’s been on my mind for a while and I thought I’d write about it. I hope you still got a few insights as normal.

Have a great Sunday and I’ll see you next week.

Best,

The Intellectual Edge

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References:

Andy Warhol — Campbell’s Soup Cans (1962)

It’s a philosophy that our favourite investors, like Charlie Munger and Howard Marks, swore by. They believed in such similar ideas; Marks believes that in avoiding the losers, the winners will take care of themselves, and Munger believed that if you want to achieve something, find out all the things that will prevent you from achieving it, and avoid those things like hell.

Big losses ruin our prospect of beating the market, so we ought to avoid them like hell too.

These big losses come in many forms, but one of the most perverse is through financial foul play. By the time foul play comes to the spotlight, the stock price will have fallen so far that you won’t be able to find a buyer — especially in the small and micro cap space.

This is why understanding how to spot financial manipulation is absolutely vital. We see companies continuously following unsustainable accounting practices and shareholders suffering the brutal consequences — but there is always a sweet spot where the foul play can be seen in the numbers and has not yet been reflected in the share price.

The reason this sweet spot exists — where foul play is visible but not reflected in the price — is because most investors don’t know how to spot it; they don’t put in the work to read the reports and do the necessary digging.

If we want to sell a bad player before the market catches on to them, we absolutely have to understand how they go about manipulating the figures. And as previously mentioned, most of the investment game is to do with avoiding big losses. They really do kill you. That’s why this article exists, and if you’ve followed this publication for a while, you’ll know I’ve already shared a few articles on similar topics — mostly on how to understand financial statements, and a little bit on sniffing out foul play (Linked below).

• Becoming Financially Intelligent

• Understanding The Income Statement & Its Nuances

• The Balance Sheet Reveals The Most

• The cash flow statement: profit is not cash

• How (and why) your favourite companies deflate their earnings.

So instead of finding a new company today, I highly recommend you take the time to read these articles and ensure that all is well among your own holdings. The risk of being killed by a stock you own today is far more important than the opportunity of finding a winner tomorrow.

With all of that being said, today we’re going to dive into how and why companies manipulate their cash flows and how you can detect such shenanigans. The content of today’s article is primarily drawn from a great book, written by Howard Schilit, called “Financial Shenanigans.” It’s a fantastic read that I’ve mentioned many times before and I highly recommend picking up a copy for yourself. If you don’t have a copy, or you can’t get your hands on one, this is going to be indispensable to you.

Here’s the three cash flow shenanigans we’ll cover in this article:

• Shenanigan #1: Shifting Financing Cash Inflows to the Operating Section.

• Shenanigan #2: Moving Operating Cash Outflows to Other Sections.

• Shenanigan #3: Boosting Operating Cash Flow Using Unsustainable Activities.

You don’t need to be an expert to grasp any of this — it is suitable for most, if not all levels of knowledge. Each part will be explained as it comes.

This article is a summary of almost 50-pages of writing, so if you don’t have access to the book, this is a great time & money saver.

Let’s get into it.

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We now enter a brand new five-year period. As you’ll have come to expect, the amount of insight up for grabs here is hard to imagine.

The knowledge that Marks has generously imparted on us for the past few decades through his memos has become one of, if not the most useful investment resources available to the public.

I can’t express how brilliant it is. If you haven’t taken the time to read his memos, or this series, I recommend doing so above anything else.

If you’re interested in reading the previous five year installment, you can do so below:

• The Lessons of Howard Marks’ Memos Pt.1 (’06-’10)

• The Lessons of Howard Marks’ Memos Pt.2 (’06-’10)

• The Lessons of Howard Marks’ Memos Pt.3 (’06-’10)

• The Lessons of Howard Marks’ Memos Pt.4 (’06-’10)

In today’s article, we’re going to discuss regulation, the shortness of investor memory, how to build an investment philosophy as effective as Marks’, and the importance of understanding history in future success.

lets get into it

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In part one (read it here) we studied Michael Mauboussin’s early 2026 research paper studying market inefficiency, where we learnt about the following and how to use them to our advantage:

• Why is this important?

• Etorre’s Wisdom

• The Types of Inefficiency

• Behavioural Inefficiency

• Analytical Inefficiency

Today we continue the discussion on Mauboussin’s paper, adding some additional insight from a few other smart investors; here are today’s chapters:

• Informational Inefficiency

• Technical Inefficiency

• Dare To Be Great

We’ll understand how to look at informational inefficiencies in the age of information, how Howard Marks thinks about outperformance and finds inefficiencies for Oaktree, and how we can use all of the information to become better investors.

Let’s dive into it.

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For a long time markets were thought to be efficient and the brave few who proved this wrong — like Warren Buffett — would have to argue their case for many years before being listened to. And as the likes of Warren Buffett have shown, it would have been a mistake to believe the accepted wisdom of market efficiency.

The wisdom has changed now — markets are not efficient — but that still doesn’t mean we’re right to blindly accept it.

How often do you ask yourself why, or where, markets are inefficient? How do bargains arise and why do you know about them before others? What are thousands of investors missing that you aren’t? Have you asked these questions or have you presumed you know the answers?

Automatically believing any accepted wisdom is a mistake, even the idea that markets are inefficient. Believing anything without an understanding of what you believe is foolish. If you blindly believe in inefficient markets you’ll fall victim to the same mistakes as the efficient market-eers. Inefficiency is a broad term which applies in specific contexts, blindly believing in it is much like being told it’s going to rain at some point this evening and thinking the entire world is going to receive rain this evening.

This two-part series is going to give you the context in which inefficiency reveals itself. You’ll learn everything you need to know in understanding market inefficiency; why, how, where, when, and so forth. This won’t transform you into a young Warren Buffett, but it will prevent you from making a whole lot of big mistakes, which is worth a whole lot.

But first, why does this matter?

Why is this important?

As previously touched on, markets are not uniformly inefficient. They are inefficient in specific places, for specific reasons, and at specific times. The investor who doesn’t understand this distinction wanders into games they’re unequipped to win.

Consider the investor who buys a large, liquid, heavily followed stock convinced they’ve spotted something the market has missed. They’ve entered a fiercely competitive arena and bet they know something that thousands of smart professionals don’t. They’re competing against informed investors, they have no edge. The typical outcome is to hold onto a dud for far too long because they stubbornly hold onto the idea that they know something the market doesn’t. It’s far more likely the market knows something they don’t.

This is why it’s key to understand the landscape of inefficiency. Once we know why a mispricing exists, we can exploit it; institutional size constraints, ESG mandates, complexity aversion, forced selling and so on, these are not anomalies, they’re structural and repeatable.

The market is a brutal and unforgiving place, it’s no use in hoping to get lucky in a fair game when you can make the game unfair. I believe understanding and exploiting structural inefficiencies is the only way to make the game unfair. To do this we have to know how, where, and why the inefficiencies arise. That’s what this two-part series is for.

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Back in January, one of my favourite financial authors — Michael Mauboussin — released a fantastic paper called “Who Is On the Other Side?” It’s a guide to understanding market inefficiency. You can read it here, but if you don’t have the time to read all 51 pages, I’ll be taking us through the important parts, alongside insights from other talented investors like Howard Marks.

The contents of today’s article are as follows:

• Etorre’s Wisdom

• The Types of Inefficiency

• Behavioural Inefficiency

• Analytical Inefficiency

There’s a lot of information coming up so this is going to be a two parter; the second part will be released next week where we discuss:

• Informational Inefficiency

• Technical Inefficiency

• Dare To Be Great

Let’s begin!

Firstly, as investors we ought to believe in both inefficient markets and efficient markets. For anyone to make dependable profit, markets must make mistakes and eventually come to realise its mistake. We rely on it to be an imperfect system that eventually corrects its imperfections.

The market corrects itself in a few ways, two of them being:

• The wisdom of crowds (only under certain conditions…), and

• the presence of some rational investors who bring price closer to value.

The wisdom of crowds is a heavily debated topic as it can be relied on to both rationalise and irrationalise prices; the crowd is both brilliant and delusional, which is why — as Mauboussin states — the wisdom of crowds only works under the following conditions: a system requires “a diversity of views, a mechanism to aggregate those views, and proper incentives”1 in order to price assets efficiently.

If these criteria are broken, price setting becomes irrational. In any bubble you’ll see the diversity of views break down; think about attitudes towards tech in the late 90’s, real estate in the early to mid 2000’s, or quality companies in the 60’s. The common denominator among them all is a consensus belief, no one challenged each other and everyone had the same views. A consensus belief will always occur when the diversity of thought is broken, and that’s when you’ll see some of the craziest prices — both high and low. The most dependable red flag for any investment is when everyone agrees on it. Diversity is critical.

Regardless of its occasional flaws, the market tries its best to be efficient — and it’s pretty damn good at it. If there’s an excess return available, investors will chase it until it’s no longer available. The market is a system that seeks to eliminate excess returns, in fact the existence of a high return is the very thing that prevents it from continuing. To better explain that statement I’m going to turn to Howard Marks, one of the most brilliant investors of our time. In his 2002 memo, “Etorre’s Wisdom”2, he explains how both high and low returns will always be destined for regression.

Etorre’s Wisdom

There are few ideas as consistently frustrating as Etorre’s Observation. Put simply, Etorre’s Observation is that ‘the other line always move faster.’

Whilst driving in 2002, Howard Marks’ son asked a simple question: “Dad, why do you always have to drive in the slow lane? Why don’t you switch to that one; it’s moving faster?” Howard’s answer to this question ended up forming the basis of one his most brilliant — and underappreciated — memos.

He analogised markets and investor behaviour into a crowded highway.

When driving on a crowded highway, we’re often frustrated by how fast the cars in the adjacent lane seem to travel. We move over, only for that lane to slow down and the one we left to speed up. Sometimes someone tries to weave their way through each lane, but it never really amounts to much, you often smugly meet them at the next traffic light; the net result is often zero, they just take on more risk by driving recklessly in the process.

This is the function of any efficient system: it works to reduce outperformance. Everyone sees the fast lane, they switch to it, and that makes it the slow lane. It is the behaviour of the collective that alters the environment.

To make good time on the road in spite of the efficiency, you have to find the inefficient roads; the roads others won’t travel; the route with hazards that others are scared of, once you’ve made sure you can drive around them of course; the lesser known back-roads; the industrial park that feels like a dead end; the roads with ugly scenery people don’t want to see; it is their lack of popularity that makes them fast and that is precisely why they offer high prospective returns.

This is why it’s important to know the roads and to know your fellow drivers.

• Who is overtaking you?

• Who are you taking road space from and why are they giving it up?

• What kind of drivers are in your lane?

• How can you find roads that are off the beaten path?

• How can you drive better than others?

• Is there a shortcut that they don’t know about?

• Is there a road only you are equipped to drive?

• Is this road crowded by drivers better than you?

• Can you find somewhere that you’re the best driver?

These are the kind of questions that lead to a deep understanding of how to actually make better time on a crowded highway — without relying on luck. It’s the same with markets, of course. When we understand the where, why, and how, markets come to be inefficient we’re far better equipped to navigate it with intention. Not luck.

It’s critical to every investor and I believe it to still be an overlooked topic.

Leaving highways and coming back to markets, let’s explore how and where we can uncover dependable inefficiency, with the help of Mauboussin and co.

The Types of Inefficiency

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We’ve navigated the Great Financial Crisis and are now coming out the other side. The insights uncovered in this period of time have been invaluable and if you haven’t read the previous articles I’d highly encourage you to do so. I’ll link a few below.

• The Lessons of Howard Marks’ Memos Pt.1 (’06-’10)

• The Lessons of Howard Marks’ Memos Pt.2 (’06-’10)

• The Lessons of Howard Marks’ Memos Pt.3 (’06-’10)

Today we cover a few critical investment topics, a few lessons from the GFC, and a whole lot of other ideas that we’d be better investors for knowing. If you’re short on time today, give these three memos a read first:

• Touchstones

• Hemlines

• All That Glitters

If you have the extra time, your patience will be rewarded; every memo has some gold inside.

Let’s begin.

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Investing in a stock requires a degree of trust that I think we don’t give full credit for; we sign no contracts and we presume good intentions, but how often do we consciously think about how we’re entrusting our hard earned capital with a management team we’ve likely never met, on the other side of the world?

We don’t truly know their incentives, morals, and so forth. But it’s a risk we take, and unfortunately financial fraud is a constant danger. Without knowing how to detect it, the money that’s stolen with a pen is likely to be ours.

A critical pillar of our investment thesis will lay in the financials, and if those financials are manipulated or plain wrong, our analysis will follow suit. This problem is amplified in quantitative investing when screening for a business — where the sole dictator of a prospective investment is company data.

If you have a quantitative edge to your investment strategy, be that a screening process or a data driven ranking process, this post will be critical. I read a chapter from a great book recommended to me by a friend, and it has insight I felt should be shared. The book is called Quantitative Value — by Tobias Carlisle and Wes Gray — and in it they propose three measures to detect and remove manipulators from our screens and analyses.

Today we’ll discuss all three, but before we do, it’s critical to understand a few fundamentals behind earnings manipulation.

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This theory is of course flawed, and as great investors have argued — and proved — markets are not efficient, in fact they can be extremely inefficient. Over the decades the market opinion has again begun to change; it’s now accepted that, for various systematic reasons, outperformance is possible.

However, I can’t help but feel this belief has swung so far to the other side of the pendulum that people now believe inefficiency is constant, and it’s everywhere. With our heroes like Buffett, Greenblatt, Munger and so on preaching market inefficiency, we now presume markets are so inefficient that every stock we purchase must be mispriced.

Every move that goes against us is Mr. Market miscalculating. We use inefficiency as a way to protect our egos when we suffer large losses; surely being down 40% is a market miscalculation, surely I could not make such a mistake? Right…? Whilst markets are inefficient, I think we’ve taken the statement so far in that we presume everything is inefficient.

Hence I think a discussion on market efficiency is due. You may disagree with this article, and that disagreement is exactly what makes a healthy market. This is not an attack on anyone’s strategy or philosophy, it’s more of a note to myself that I now want to formalise through public discussion.

Today, we’re going to navigate a few main ideas.

• Market efficiency,

• how to think about inefficiency,

• where to look for mispricings, and

• how hard investing is.

It’s all too easy to underestimate the markets ability to discount the future. It’s a well-oiled machine most of the time, and we often don’t give it the credit it deserves. This article is my own reminder of how to think about efficiency, and how to find inefficiencies among it all.

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Etorre’s Observation

Back in 2002, Howard Marks wrote a memo that’s gone completely under the radar. No one talks about it. In this memo lies the best analogy of market (in)efficiency I’ve ever come across.

The memo is called “Etorre’s Wisdom”, and it’s fantastic.

One very frustrating law of life is known as Etorre’s Observation: the idea that the other line moves faster. And as Marks drove along a highway back in 2002, his son questioned, “Dad, why do you always have to drive in the slow lane? Why don’t you switch to that one; it’s moving faster?”

The conversation that resulted from this question sparked something utterly brilliant: the analogy of the crowded highway.

When driving on a crowded highway, we’re often frustrated by how fast the cars on the adjacent lane seem to travel. We then move over, only for the lane to slow down, then the lane we left begins to speed up. Sometimes someone tries to weave their way through each lane, but we know it never really amounts to much at all; I always have a smug smile arriving next to them at the nearest stop.

The Crowded Highway

The driving experiences on a busy highway are a perfect explanation of the efficiency of markets. We all want to get from A to B quickly and safely. Some go extra slow, some go very fast without a care. But for the most part, we want to be reasonably safe and reasonably fast. Often, we notice the other lane is moving much faster than ours, but we know that switching lanes won’t result in much net improvement.

This is where the genius of Marks’ analogy comes in. If all else remained the same and you could switch to the fast lane, you would reap the rewards. But this doesn’t happen. This is because everyone sees the fast lane, everyone switches to it, and that makes it the slow lane. It’s the behaviour of the collective that alter the environment. It’s a dynamic system that is made by the decisions of the drivers — what works on a given day will entirely depend on the drivers around you at any given moment. When people flock to the fast lane, they slow it down, hence the slow lane naturally becomes the fast lane. This is the function of any efficient system — it works to reduce outperformance. An efficient highway works to equalise the speed across the various lanes, rendering ‘lane-picking’ an ineffective method. It is this process that equity markets follow to eliminate excess returns. Investors chase the hot stock, which makes brings down its prospective return…

Marks notes that a lesser appreciated investment concept is how past returns influence behaviour, which in turn influences future returns.

It’s easy to see what has been outperforming — to see massive outperformance in a certain area — you also know it won’t last forever, so you know you better than to dive in head first. What requires more skill, however, is knowing what’s going to outperform before it starts outperforming. In essence, it’s hard to correctly switch lanes before it’s clear which one is going to be faster. Or as Marks puts it:

The highway mirrors markets because “when people switch to the better performing group, their buying bids up the prices of those securities. That bidding-up prolongs the outperformance somewhat, but it also reduces the prospective return and increases the probability of a correction.

…

At the same time the switchers will sell worse-performing securities to finance their move into the hot group. That will lower the prices of the laggards, and at some point they’ll be so cheap that they become destined to outperform.”

When people forget the cyclicality, they’re presuming the fast lane will be fast forever; for investors, this is in forgetting that 1) stocks bid up to high prices means a crash is inevitable, and 2) ignored stocks that are sold-off will inevitably become primed for massive acceleration.

There is no rule that tells us when each lane is going to change speed. The only rule is that nothing works forever, and nothing doesn’t work forever; every area of the market has it’s time in the sun, if enough people leave the slow lane it eventually has the perfect set up to become the fast lane again. In fact, it is only by becoming the slow lane that it can ever become the fast lane again. The two speeds are reliant on eachother.

But what about the talented lane-weavers?

Once in a while you get a driver who can weave through lanes effortlessly, beating traffic without breaking a sweat. They seem to be at the right place at the right time. Naturally, other drivers see this and try to copy them.

And this is where Marks’ analogy grows in brilliance: copying them is futile. It is of no use to us to know which lane has been going the fastest, to be fast we have to know which lane is about to go the fastest, before other drivers figure it out. We must see the future better than others. We know this is hard, but the majority will always believe they are the few.

Most drivers that try weaving through the lanes will occasionally end up going faster, and occasionally slower. The net result is often zero, they just take on more risk by driving recklessly in the process.

In spite of the mass of investors that believe they can see the future, markets — just like crowded highways — are pretty efficient. In fact, for every great driver there is a poor driver who made the wrong choice; these poor drivers end up leaving the slow lane just as it’s about to speed up, joining the fast lane just as it’s about to slow down. In effect, buying high and selling low.

How to make good time in spite of the efficiency

There’s one major question that comes from this: if there isn’t a way to get from A to B in a dependably fast way, is there no way to win at all?

The key word in the analogy just explained is “crowded.” It’s thanks to it’s crowded nature that every opportunity for outperformance is sniffed out quickly — the moment a gap forms, it is filled.

With this in mind, you have to find the inefficient roads,

• the roads others won’t travel,

• using the road that their cars can’t fit down,

• taking the route that has a few hazards on it that others are scared of, once you’ve made sure you can drive around them,

• taking the lesser known back-roads,

• the industrial park that feels like a dead-end,

• using the roads with ugly scenery people don’t want to see,

• or even driving at night, where others prefer daylight.

Every way to make good time on the road is analogous to finding an inefficiency in the stock market. You might find an inefficiency through looking in these kinds of places:

• Industries with negative sentiment like tobacco, oil, or defense contractors that ESG investors avoid.

• Micro-cap stocks too small for institutional investors whose fund size prevents them from taking positions.

• Distressed debt or bankruptcy situations where understanding the legal process gives you edge over investors scared by the complexity.

• Foreign markets or exchanges with less analyst coverage.

• Companies with messy financials, complicated structures, or recent scandals that deter investors.

• Investing during market panics when others are paralysed by fear, or holding positions through volatility when others need immediate liquidity.

One way or another, these methods do their best at capitalising on a market inefficiency — whether psychological or systematic.

Playing in inefficient markets is akin to playing unfair games. It’s the trick to achieving superior returns without too much risk. As Howard says, “a shortcut that everyone knows about is an absolute oxymoron… The route that’s little known, unattractive or out of favor might not be the one that’s most popular or least controversial. But it’s the one that’s most likely to help you come out ahead.”

Remember, it’s lack of popularity is the very reason that it has high prospective returns.

There isn’t a no-risk-fast-lane

I’m now extending Howard’s analogy to explain a few additional critical ideas.

If you’re on a crowded highway and someone tells you there’s a lane that is going fast, there’s no risk, and that it’s going to get faster — you just have to act fast. In other words, when someone offers you a free lunch, you should ask yourself these kinds of questions:

• Why is the lane going to continue being fast?

• If it’s going to continue being fast, why are other drivers not switching?

• Is it not true that me joining the lane would only lead to it becoming slower at some point?

• If it’s true, why is anyone telling me about it?

• If I knew there was a free-fast-lane, would I preach about it or would I just switch over and drive like hell? I’d shut up and drive.

Going fast will always come with risk, no matter what anyone says. Anytime you see a no-risk-fast-lane, you have to be extremely skeptical. Free lunches are never free, you just receive the bill at a time you don’t expect…

Learning to respect (in)efficiency

All too often, we dehumanise the market. It’s some mechanical system that occasionally slips up. It’s made up of people. For the most part, these are smart people.

In order for excess return to exist, people have to be making mistakes. You have to understand that in every investment you make, you are saying the insight you have is superior to the entire collective of smart individuals working together to create the going price. You are saying “hey guys, I don’t care how long you’ve been investing for, how many thousands of you there are, or how models you’ve made on this; you’ve got it wrong, I’ve got it right.”

That feels extreme, but that’s effectively what we say when we click buy.

In another of Marks’ memos, “Whodunit?”, he mentions how investors who presume excess return is readily available often fail to ask a few fundamental questions:

• Why should a free lunch exist despite the presence of thousands of investors who’re ready and willing to bid up the price of anything that’s too cheap?

• Why is the seller of the asset willing to part with it at a price from which it’ll give me an excessive return? Do I really know more about the asset than he does?

• If it’s such a great proposition, why hasn’t someone else snapped it up?

• If the return appears so generous in proportion to the risk, might I be overlooking a hidden risk?

Similar to the questions our previous highway driver should be asking, these questions are so simple and so often ignored. Remember that it is the function of a market to eliminate excess return. This is fundamental to my investment philosophy and it brings a much needed element of skepticism to every opportunity that looks too good to be true.

Investing is hard.

Charlie Munger once said “(Investing) is not supposed to be easy. Anyone who finds it easy is stupid.”

Investors work tirelessly to find bargain opportunities, and it is this tireless work that drives the bargain opportunities out of the market. Investing involves substantial nuance, understanding, experience, and complexity; to claim it’s easy is quite frankly stupid. As I keep reiterating, it is the function of the market to drive out excess return, to think you can be handed it on a plate is wrong.

I remember Buffett once saying something like this: if you’ve been at the poker table for 30 minutes and you still aren’t sure who the patsy is, you’re the patsy.

Similarly, if you’re buying stock and are wondering who’s on the wrong end of the deal, you probably are. The best investors only open positions in which they know they have some sort of advantage which gives them the clarity of knowing they’re on the right side of the deal.

In 2015 Howard Marks gave us another quality memo. It’s called “It’s Not Easy.” In this memo he explains the counter-intuitive approach to looking at ideas that everyone likes. Marks notes that people don’t understand the fundamental process by which opportunities come to have high return potential, they also don’t understand that the very notion of popularity behind an investment is likely to eliminate its profit potential.

He uses the example of that one stock everyone seems to agree is a great opportunity. But by definition, it simply cannot be so.

• If everyone likes it, it’s probably because it has been doing well. Most people seem to think outstanding performance presages outstanding future performance. Actually, it’s more likely that outstanding performance to date has borrowed from the future and thus presages sub-par performance from here on out.

• If everyone likes it, it’s likely that the price has risen to reflect a level of adulation from which relatively little further appreciation is likely.

• If everyone likes it, it’s likely the area has been mined too thoroughly — and has seen too much capital flow it — for many bargains to remain.

• If everyone likes it, there’s significant risk that prices will fall if the crowd changes its collective mind and moves for the exit.

• There’s no such thing as a bargain that everyone knows about. If everyone has realised something’s a bargain, they would have bid up the price to eliminate said bargain.

• Successful investment is about buying something lower than it should be, and by definition this is typically found in ideas where people can not see the merit, hence undervaluing the proposition.

Ultimately you have to see things other investors don’t or can’t, and you have to have the market agree with you about your conclusion in reasonable speed. This takes heaps and heaps of hard work to achieve.

When we talk about exceptional investment returns, it is the exceptions that prove the rule. The returns are exceptional because the investors that achieve them are the exception. They are the 1 in 1000 that smash the market. They are called exceptional returns for a reason, they are an exception.

This article is not meant to be pessimistic, only realistic. It’s easy to get carried away as an active investor and revisiting the topic of efficiency every now and then is, in my opinion, a critical thing to do to remain humble.

Anthony Bolton once gave the wise advice to never underestimate the markets ability to discount the future. I live by this advice. The moment you think the market is stupid is the moment you’re about to do something stupid.

The market does makes plenty of mistakes, but you have to give it the respect it deserves if you’re to capitalise on those mistakes.

Thank you for reading, I hope you enjoyed this article.

I’ll see you at the same time next week.

Best,

The Intellectual Edge

Thumbnail: Author, 2025

References: https://www.oaktreecapital.com/docs/default-source/memos/the-complete-collection.pdf?sfvrsn=58102966_3

Memo’s used as research for today’s article:

- Realist’s Creed

- Whodunit?

- Etorre’s Wisdom

- It’s Not Easy

I love investing for the vast intellectual stimulation it offers. On Monday I’m learning about computer algorithms, on Wednesday complex systems, and on Friday I’m back to value investing. No day looks the same, there’s always something new and interesting to be studied.

To any curious mind, investing has to be one of the most fulfilling professions. And my obsession with it now flows into all aspects of my life. I find myself formulating analogies of how a mundane experience in daily life is analogous to a complex investment insight.

A few months ago I was struck with such an experience, and my inspiration to discuss it has now also struck. The experience gave me an insight into hidden risks.

The hidden risks of puddle-crossing.

I live in a rural area where dog walking paths aren’t paved. In fact, the paths are not made with gravel, stone, wood-chips, or any such material. The paths are made by fellow walkers; the discrete patch of grass that’s been suppressed by the walkers before you — you’ll have to trust that’s a path.

There’s not many directional signs, I’m typically in some kind of woodland hoping that — at some point — I’ll emerge from it near human civilisation, let alone my car. And a few months ago, on one of these walks, I was met with a giant puddle. One of those puddles that makes you contemplate turning around… And in the middle of this puddle were two small, foot-sized islands breaching the water’s surface. To anyone familiar with this scenario, you’ll know the only way to get through this without losing your foot to the water is to hope this mysterious looking island will support your weight and grant you safe passage.

The problem here was that I had two options — two islands.

One island was slightly lower, it had a layer of water covering it and was made up by a few rocks. I knew these stones could take my weight, and I knew I’d only get a little wet treading on them.

But the other island was different, it was grass. This grass breached the surface easily over 10cm. But it’s thick grass, how long was it? How far down does it go? I wasn’t sure.

Hence my dilemma: should I tread on the stones and get a little wet, but definitely not drenched — or tread on the grass, potentially keeping completely dry, or getting completely wet.

In investment terms: should I take the lower profit potential which has lower risk, or a much greater profit potential which has risks I’m unaware of?

With some walking-induced endorphins flowing through my brain, I was feeling optimistic; I chose the grass. I stretched my foot across the puddle and placed my foot on the grass. In the blink of an eye my foot was entirely submerged. Drenched. I proceeded on my walk with a dripping-wet shoe, a very cold foot, and a brain no longer full of endorphins.

The positive of this story is that it gave me an interesting investment insight to ponder on.

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Optimism bias and the misinterpretation of risk.

Optimism bias describes the tendency to overestimate the likelihood of positive outcomes and underestimate the likelihood of negative outcomes. When we can’t fully see something, or we don’t fully understand it, we naturally overestimate the probability that things will turn out great. When we purchase stock in a company we presume it’s going to be profitable in spite of all the risks we may have unwittingly taken on.

We are a hopeful species. Given one chance, we’d rather take the chance chance of winning 50% or losing 50% than accept a guaranteed 5% loss. You can’t win a prize if you don’t at least buy a ticket, right?

This optimism serves the survivors well. We don’t hear the stories of all the optimists who didn’t receive those great outcomes. We only hear about the stories of success, which adds even more fuel to the optimistic fire.

Going back to our puddle-crossing; I took on risk I wasn’t aware of instead of a risk I was aware of. I was optimistic when I should’ve been objective. The big mistakes are made when we make risky decisions on the basis of misinformed risk assessment. This is the critical point: misinterpreting risk leads to bets that don’t correspond with the level of risk you’re taking on. We take large bets on those that should be small ones, and even small bets on those that should be large.

One of the best examples can be found in the Great Financial Crisis.

In the buildup to the GFC, banks invested in extremely high-risk assets (CDOs packed with subprime mortgages) as though they were low-risk assets. They didn’t know what they owned. They were blind to the risk they were taking so they thought they weren’t taking any at all. They loaded up and used massive leverage believing they were being prudent. This is why it’s the hidden risks that are the really dangerous ones — our bets don’t allow for them. This links to one of my favourite definitions of risk is this: “risk is what’s left over after you think you’ve thought of everything.”

If you take on huge risk but make it a small part of your portfolio with no leverage, your actions are in line with the risk being taken on. But when you have risks you’re unaware of, the size of your investment becomes disproportionate with the risk being taken. This opens up the opportunity for losses that you’re not prepared for, and when you’ve misjudged the downside of an investment you can suffer losses far greater than you’d ever thought possible.

This is why the risk of an asset isn’t the most important aspect of investment risk — what matters more is how well you understand it and how you proceed to invest in it. If you invest in a safe asset with massive amounts of leverage, it doesn’t take much for the investment to fail (think Long-term capital management). Whereas if you invested in a risky asset with no leverage and a small allocation, you could easily survive volatility, capital loss, etc. The bottom line lies in fully understanding the risk level of the asset, because that decides the size of the investment and the leverage you may put on it.

During my walk, the puddle itself was not the source of risk, the risk was in how I chose to cross it. A smarter, more experienced, risk-aware walker would’ve crossed the puddle dry. It is the same in investing. When you know the risk you’re taking and you approach it wisely, almost any asset becomes investible. I’m reminded of Howard Marks: “there is no such thing as good or bad assets, only good or bad prices.” He made his billions from an asset which seems very risky on face value: junk bonds. His success comes from understanding risk, not avoiding it or fooling himself into thinking he knows certainties.

I might put a spin on his famous phrase and say “there is no such thing as high or low risk assets, only a high or low risk methods of investment.”

I know that isn’t a bullet proof line, but it illustrates the idea. A more elegant way of putting it is:

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There is no free lunch…

If there was a hall-of-fame of investment mistakes, the belief of the free lunch would be near the top. Our misled optimism leads us to believe in the constant recurrence of the free lunch — the idea that entails no risk which can only go up.

I love Howard Marks’ investment philosophy, and one of my biggest takeaways from studying almost 20-years of his ideas is to do with ‘the free lunch,’ rather its inexistence. The fact that something appears to be a sure thing is probably the largest indicator you have of it not being a sure thing. When everyone agrees there’s no risk, that’s when risk is highest. I’ve come to realise you’re never taking on no risk, you’re just taking on risks you haven’t thought about.

Supply and demand explains why there’s golden ticket; when everyone loves something, they’ve already bought it — no buyers are left which means no one can push the price higher.

But when everyone hates something, no one wants it. If there aren’t any buyers, the price is as low as it can be. When even a few people change their minds, there’s no competition for shares and the price can move fast.

That analogy perfectly explains why disliked ideas offer higher returns at lower risk. The free lunch actually offers lower returns at higher risk.

Or in Howard’s words:

“What’s clear to the broad consensus of investors is almost always wrong. First, most people don’t understand the process through which something comes to have outstanding moneymaking potential. And second, the very coalescing of popular opinion behind an investment tends to eliminate its profit potential.”

Check out this excerpt from his 2015 memo, “It’s Not Easy”, it disproves the existence of the free lunch.

He goes on to say:

“So the things with the most obvious merit become the things that everyone likes. They’re also likely to be the things that are most hotly pursued and most highly priced, and thus least promising and most treacherous.”

The very fact that people like an idea will lead to optimistic estimates being baked into the price, leaving less room for upside and more room for disappointment. It is fundamental to understand that investing is counter-intuitive, and the most loved ideas are often the most dangerous, whereas the most hated often offer the most profit. This dynamic is now ingrained into my mind, I understand it down to its core, and it has changed me as an investor.

This entire thought process stemmed from my failed attempt to cross a puddle. The world is constantly offering us such interesting lessons. We just need to open our eyes and see them. It is a wonderful skill to learn, to become receptive to the beauties and the lessons of the world, they are in constant supply.

Thank you for reading.

I hope you found this valuable.

Best,

The Intellectual Edge

Thumbnail: Puddles in the woods painting by Julian Lovegrove.

References:

https://www.sciencedirect.com/topics/neuroscience/optimism-bias

One reason investors will continue to use this oxymoronic phrase is thanks to not taking the time to fully understand nonlinearity. Nonlinearity is where an input and its output are disproportionate; where a small change might cause a huge effect or a massive effort can produce barely any result. In financial markets, nonlinearity is a powerful force.

It’s thanks to nonlinearity that markets are virtually impossible to predict. You’ll find out exactly why in this article.

You see nonlinearity occur multiple times a day and you hear about it all the time. Have you heard of the saying: “it was the last straw that broke the camel’s back.” This is an expression of nonlinearity. A camel can take masses of straw without any struggle, but it’s just one straw that eventually caused the catastrophic failure. Small change, massive impact.

Another example we see everyday is shown in Physics. As kids we learnt in our science classes about ‘phase transitions.’ But if you were like me, you probably weren’t too interested at the time. To refresh our memory, a phase transition describes the transformation of matter among states like solid, liquid, and gas, influenced by changes in temperature and pressure.1 Think about melting, freezing, or boiling. These are all phase transitions.

Consider water at 99°C, it’s still liquid at this temperature, but at 100°C it becomes steam; that single degree causes a fundamental change in the structure and behavior of the water. The change in state is disproportionate to the temperature increase. It had the same incremental one-degree increase 99 times before and no such drastic change occurred. That’s nonlinearity.

It’s a fascinating topic which should be integral to every investors thinking. In today’s article we’re going to cover four main areas of discussion:

• Why inductive reasoning fails in nonlinear systems

• How to think about prediction in unpredictable markets

• Using sandpiles to understand how markets reach critical states

• Preparation > prediction.

Each section covers ideas that any curious mind will enjoy reading. This one took a lot of research to put together, I hope you enjoy it.

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The death of inductive reasoning.

The title of this article asked us what turkeys, camels, dams, and sand piles have in common. As you may have guessed, nonlinearity is the common feature.

• Damns can take millions of gallons of water, but it only takes one gallon too many to break the entire thing.

• Camels, “it was the last straw that broke the camel’s back…”

• Sand piles are toppled by one too many grains of sand, despite millions being placed before. (more on sand piles later)

But what about turkeys, where’s the nonlinearity? Let’s take a small detour and visit one of my favourite authors, Nassim Taleb.

Taleb’s turkey problem

In Taleb’s mindblowing book, “The Black Swan,” he presents us with the turkey problem.

Imagine that a turkey is fed every day for 1,000 days. Each day the turkey further reinforces its belief that humans are good and provide food. Its confidence grows with each feeding.

Then, the day before Thanksgiving, the turkey is killed. The 1,001st day doesn’t resemble the previous 1,000 days at all. It’s a nonlinear transition from being fed to being dinner. This is why the life of a turkey is nonlinear, and this follows onto why this section is called “The death of inductive reasoning.”

The turkey used inductive reasoning — drawing general conclusions from specific past patterns — to predict its future. “For the past 1000 days”, it said, “the humans have been good to me, therefore humans are good.”

This line of reasoning arrived it at a poor conclusion. The underlying idea that “all days have been good so far, therefore all days must be good” is wrong. The problem lies of course in nonlinear systems, where inductive reasoning is often redundant. Let’s go back to our water example, inductive reasoning says at 100°C water will remain liquid because the change from 1°C to 99°C didn’t change it’s state; inductive reasoning says the dam will survive the extra gallon because it survived all the previously added gallons; inductive reasoning says the camel can take the extra straw. You get the point.

We can observe a million white swans, but that doesn’t prove all swans are white. The next one could be black. And in situations where we have incomplete information — like financial markets — our observations do not represent the full range of possibilities. Our estimate of probability is going to be wrong when we are unaware of every possibility. This is what makes inductive reasoning so hard. In a way, nonlinearity punishes experience. The more data you have confirming a pattern, the more confident you become, but as your confidence grows you get closer and closer to the ‘phase transition.’ You become most confident that water will never become steam at the very moment it’s about to transition. To those who don’t take the time to understand nonlinearity, it is the point at which they’re most confident that they’re most in danger of being wrong.

Another reason inductive reason is almost redundant is because a nonlinear collapse doesn’t give signs, it occurs very quickly without warning. A market crash might occur in a matter of hours, an avalanche in a sandpile occurs instantly. The things that a system has coped with for many years without struggle will be the same things that destroy it, making prediction virtually impossible. Markets can cope with leverage until they can’t. Water can cope with heat until it can’t. The turkey was fed for a thousand days, but it only took one day to disprove its theory that humans are good.

So, we have these two key features of a nonlinear system:

• Unpredictability - You can’t know which specific straw, grain, step, degree, or droplet will be the one to cause catastrophe.

• Immediate failure - The system doesn’t degrade gradually; it fails suddenly.

Combining unpredictability with immediate failures makes the entire thing pretty scary; how might we think of rational reasoning and prediction in inherently unpredictable systems? Let’s look into it.

Predicting the unpredictable

If X doesn’t hurt you, but you know X will kill you, how do you know when you’ll die? There’s no answer to this that I’m aware of. We can’t know, which is why tail events are so hard to predict; the causes of catastrophe are innocent 95% of the time. The camel was fine with the straw until the straw broke its back. It’s impossible to know which piece of straw is going to be the one that deals the fatal blow.

As with most things, some perspective goes a long way. In The Black Swan, Taleb suggests that the dangers of nonlinearity are only relative to expectations. In other words, nonlinearity is most dangerous when it is forgotten — when you stop expecting the straw to break the camels back.

To the person who no longer expects massive outcomes from small inputs, any nonlinear outcome will hit them like a freight train; however, someone who is expecting a nonlinear outcome will at least have escaped the tracks before the train comes.

This is also why people feel most safe when it’s most risky. If the camel has taken on 1000 straws so far, you think you’ve found an invincible camel and you might feel safe enough to bring another thousand straws on board. But in reality, each straw increases the probability of failure; the person who doesn’t think this way sees each additional straw that doesn’t break the camels back as a reduction in risk, “it coped with 1000, it can cope with another 1000!”

This is a catastrophic error. A system coping with a factor for a long time does not mean that factor can’t damage it at some point.

Simply acknowledging this helps us; we will never predict a tree will grow to the sky, we will never predict a camel to survive a million straws, we will never predict permanence. An appreciation for cyclicality will become a fundamental part of our philosophy. Change is the only constant.

In complex adaptive systems, prediction is laughably difficult. Let’s entertain the idea we have a correct prediction. According to the brilliant complex systems researcher, Didier Sornette, there’ll be three main outcomes:

• Nobody believes the prediction which was then futile and, assuming that the prediction was correct, the market crashes. One may consider this as a victory for the “predictors” but… this would only be considered by some critics just another “lucky one” without any statistical significance.

• Everybody believes the warning, which causes panic and the market crashes as consequence. The prediction hence seems self-fulfilling and the success is attributed more to the panic effect than to a real predictive power.

• Sufficiently many investors believe that the prediction may be correct, investors make reasonable adjustments and the steam goes off the bubble. The prediction hence disproves itself.

(Sornette — 20032)

In two out of three scenarios, your prediction still hurts, you lose money in a crash. And in the third prediction you look plain wrong; after all, it’d be a long shot to say your correct prediction prevented the prediction from being correct… You could then say every prediction you ever made was correct.

This is why complex systems are hard to predict. Anything you do or say will have an impact on the system which might negate what you’re predicting… it’s… complex…

We’re going to pick up prediction again later on. I want to take a moment to discuss a concept that illustrates how inevitable market crashes are, and how predictably unpredictable they are. So, let’s briefly discuss sand piles — you gotta trust me on this one.

Per Bak’s Sandpile

Earlier I mentioned that we’d be discussing sand piles in more detail. I can imagine your first question is “why?”

Funnily enough, sand piles are much more like financial markets than you might think. The similarities are demonstrated through something called Per Bak’s Sandpile.

Imagine dropping grains of sand onto a table, one at a time, building a pile. At first, each grain adds to the pile with no problem. The pile grows taller and steeper. Eventually, the pile reaches a critical state where it’s as steep as it can be while still holding together. It looks stable to the person adding the sand, but it’s actually maximally unstable.

Now, when one more grain is added (identical to all the others), it triggers an avalanche. The avalanche might be small or large, we cannot predict its size, nor can we know when the pile has hit its most critical state. It will be a grain the same as any other that causes the avalanche. It might send a few tumbling, or the entire thing.

This sandpile represents Bak’s theory called self-organised criticality: where nonlinear systems are naturally heading toward a critical state at all times. Sandpiles are always heading toward an avalanche and markets are always headed toward a crash. These systems are the enemies of their own design.

I’ve read through the research so you don’t have to, and here’s a few fascinating ideas we ought to know.

1. Nonlinearity: Large complex systems can experience catastrophic breakdowns from trivial triggers. Bak says ”systems as large and as complicated as (the stock market) can break down not only under the force of a mighty blow but also at the drop of a pin.”

2. Natural evolution to critical states: “Large interactive systems perpetually organize themselves to a critical state in which a minor event starts a chain reaction that can lead to a catastrophe.” For example, one small town bank experiencing withdrawals might cause a world wide run on the banks.

3. Same mechanism for all event sizes: “The mechanism that leads to minor events is the same one that leads to major events.” There’s no special explanation needed for catastrophes and minor disruptions; they emerge from identical dynamics. “When a grain of sand is added to a pile in the critical state, it can start an avalanche of any size, including a ‘catastrophic’ event.” I find this particularly fascinating.

4. Never in equilibrium: “Composite systems never reach equilibrium but instead evolve from one metastable state to the next.” The system is always in transition, never at rest, just like a pendulum is always in swing.

5. Unpredictability of large events: “An observer who studies a specific area of a pile can easily identify the mechanisms that cause sand to fall, and he or she can even predict whether avalanches will occur in the near future. To a local observer, large avalanches would remain unpredictable, however, because they are a consequence of the total history of the entire pile.” In other words, you might be able to predict a catastrophic failure in a small sector, or a stock, but to predict one at scale is infinitely more difficult.

6. Inevitable despite being unpredictable: We can’t know when catastrophe will strike, all we can know is that it will strike.

7. Robustness of critical state: “In general the critical state is robust with respect to any small change in the rules for the system.” Bak tested whether wet sand or dry sand would behave differently based on altered properties, even testing snow — either way, the system evolved to criticality. The wet sand and snow models still evolve to critical states; in other words, interventions don’t prevent the system’s natural evolution toward criticality.

Sand piles and stock markets.

You can probably imagine the similarities already, but I will make a couple of them crystal clear below.

1. Self-Organised Criticality

Markets naturally evolve toward critical states without any external coordination. Just as sand grains naturally pile up to instability, markets naturally evolve toward instability through:

• Increasing leverage

• Crowded trades

• Herding behavior

• Declining diversity of opinion

• Rising valuations

Nobody plans this. It emerges from individual investors acting on their own local information.

2. Nonlinearity

One grain (identical to millions before it) causes massive avalanche. In markets, the examples are plentiful. The most famous would be the October 1987 crash (Black Monday), there was no single cause that justified a 22% single-day drop, it kind of just happened.

The cause is small compared to the effect because the system was already at a critical state; it only needs a tiny nudge to push it over the edge.

3. Unpredictability

Just like you can’t predict which grain will cause an avalanche or how large it will be, you can’t predict when a crash will occur, what will trigger it, or how severe it will be.

All you can know about the system is that it’s in a critical state (high valuations, leverage, crowding) and that a crash is inevitable eventually.

4. Risk is invisible.

Just as the sandpile looks stable grain after grain, markets look safe day after day, until suddenly they’re not. Risk builds silently as the system approaches criticality, in fact it is as risk rises that investors feel risk is lowering, which naturally raises risk even further. The end result is inevitable: risk has been taken on that investors didn’t know they were taking on.

You can’t predict, you can prepare.

I mentioned we’d touch on prediction again — and in 2001, Howard Marks wrote a memo called “You can’t predict. You can prepare.”3 In this memo, he answers the major question that has risen from today’s article: how can we think about a future which is entirely unpredictable?

Luckily for us, Howard believes we don’t need to predict anything in order to succeed. The key lies in knowing where you are today and what that implies about the future.

The best path forward is not to know what’s going to happen, but to understand the nature of cycles and what that says about current valuations and behaviour. In other words, study cycles and the common denominators among cyclical tops and bottoms. It’s better to know where we are in the cycle than to guess the future. Marks’ advice is simple. For instance, when the market falls substantially, be more willing to invest in cyclical companies; when growth is prosperous, trim those investments. Always remember, trimming holdings early “beats the heck out of doing it too late.”

It really is as simple as that. You don’t need to know the future to succeed in it, you have to know where we are and what that says about the future. Does the current consensus think the cycle has been tamed, is this time really different? It usually isn’t. Does the consensus think the good times will last forever? Do they think the world is going to end?

Understanding what the consensus is saying about the future is the key. You don’t have to know the future.

Six years later, in 2007, we were blessed with more answers to today’s questions. This time the answers come from Michael Mauboussin. He wrote an article called “Fat Tails and Nonlinearity.”4 In this article he proposes three central points in understanding and dealing with nonlinear events in financial markets. Here are the three central points:

1. Don’t assume good times will continue forever: Just because something has been going up doesn’t mean it will keep going up. Success makes people overconfident and blind to risk. Look for evidence that disproves your thesis, not evidence that confirms it. Inductive reasoning is not the answer.

2. When everyone’s doing the same thing, danger is invisible: If a large group of investors are all making money the same way (e.g. everyone buying AI stocks), the market becomes fragile. When this crowd unwinds, it won’t happen gradually, it’ll be sudden and violent. A lack of diversity in markets is a sign of a critical state.

3. Small inputs cause massive outputs at critical points: When the market reaches a fragile state, tiny events cause huge crashes. These critical points are built into how markets work and they get worse when there’s lots of leverage or hype around a certain sector or strategy. While we don’t know when the crash will happen, the tells of a fragile market will be present.

A lot of research was involved in today’s article, I hope you found it as valuable as I did. Nonlinearity is one of those topics that you really can’t afford to not know. Especially as most of the money that’s made or lost in markets is thanks to nonlinear events.

As a wise man once said, nearly everything happens within two standard deviations, but nearly everything interesting happens outside of two standard deviations.

Thank you for reading. Have a great week and I’ll see you next Sunday!

Best,

The Intellectual Edge

Thumbnail: Christina's World — Andrew Wyeth, 1948

Understanding the thoughts of a successful credit investor during the largest credit crisis the world has seen is only going to be insightful.

I’ve said it many times, but I’ll say this once more; this has been easily the best thing I’ve ever done for my investment process. No matter how I word it, my compliments would still be understated. This has been revolutionary for me and I hope you feel the same if you’ve been reading along with me.

Following on from the previous articles, we’re covering Marks’ thoughts in the depths of the financial crisis. The insights he shared in this time are invaluable. Some topics include:

• Volatility and leverage

• Understanding short & long cycles

• Risk

• Not-knowing

• The long-term view

This is some of his greatest work yet. Remember this is not something you need to read all the way through. You can pick and choose the memos you want to read, if you were to choose just a few today, I’d recommend these four:

1. The Tide Goes Out

2. Nobody Knows

3. The Limits to Negativism

4. Volatility + Leverage = Dynamite

Read more

Some fishermen who had gone fishing were very worried about the fact that they’d caught nothing for a long time. Sitting in their boat, they wallowed in dejection.

Just at that moment a tunny-fish, who was being chased, attempted to save himself and, with a loud thump, jumped accidentally into their boat. They seized him and took him back to their village where they sold him.

The villagers thought these fisherman had great talent in catching a tunny-fish; they were celebrated as fishing legends. The fishermen did not confess it was randomness that caught the fish.

After all, who cares how you caught it when it’ll sell just the same…

That’s my twist on one of Aesop’s fables, and it’ll have given you an idea of where today’s article is going. Many investors are no different from those fisherman; they catch a big winner and report eye-watering returns, leaving out how they caught the winner and the risks taken in catching it.

Aesop’s fable immediately sparked my curiosity. It made me dive into researching how we should think about luck, skill, risk, outcomes, process, and everything in between. I’m glad to say the research was more than worth it. This article is the polished formulation of many hours of study, and it’s broken down into four sections:

1. Luck, skill, and Taleb’s Alternative Histories

2. Analysing processes and outcomes, courtesy of Graham & Dodd

3. How to think about risk, with Howard Marks

4. Risk is not a function of quality

By the end of this exploration, we’ll better clarify the difference between luck and skill, between processes and their outcomes, we’ll appreciate randomness, and most importantly we’ll have a more critical understanding of what risk is and how we can move forward as a better investor.

This would not be posted if I didn’t think it had the potential to help your investment process. I hope you find reading it as valuable as I did writing it.

Luck, skill, and Taleb’s Alternative Histories

“What skill denies us, chance gives us freely.”

— Aesop

The investment world is dedicated wholly to outcomes. Much more energy is spent looking at outcomes than the decisions made in creating those outcomes. We reward results and close our eyes to the processes that are responsible for them.

This is a massive flaw; it fundamentally skews how we view luck and skill. This article is going to clear up some of the fog surrounding luck and skill.

One of the best ways to think about it all is through Nassim Taleb’s concept of alternative histories which he explains in his book ‘The Black Swan.’

An alternative history refers to that fact we have our observed reality — all of the things that are actually occurring — and then we have all the things that could’ve occurred but didn’t. Like the time you arrived late to that interview, in an alternative history the train wasn’t delayed and you arrived on time. Alternative histories explains why the quality of a process can’t be judged solely on its outcome. Outcomes are subject to randomness which unfairly reflects the quality of a process.

Let’s explain this with our earlier tale of fisherman and their tunny-fish.

In the observed reality, a tunny-fish randomly jumps into the fishermen’s boat while fleeing danger. They sell it and become legends. But take a moment to consider all the equally likely realities where the fish jumps two feet left and misses the boat entirely, or maybe the fish isn’t in danger and doesn’t need to jump at all. In 99% of cases, the fishermen catch nothing and come back to shore as failures. The fishermen’s process wasn’t any different between the successful timeline and the failed timelines, which proves randomness determined the outcome.

Unfortunately, we only see the one history that happened. And the villagers — blind to the process that caught the fish — will attribute their success to skill regardless of process. But if you could rewind that day a thousand times the fishermen would catch nothing in 999 versions. Their success is not repeatable across alternative histories, AKA they got lucky.

Compare this to a genuinely skilled fisherman who studies waters, understands behaviour, sets nets strategically and knows how to put a tunny-fish in danger. Play that scenario a thousand times over and he catches the fish in most versions. He has a genuinely sound process which works in spite of random events. AKA he has skill.

So we have a skillful fisherman and a lucky group of fishermen; one is clearly more valuable than the other, but they both come back to shore with the same tunny-fish… The fish is worth the same to the villagers no matter how it was caught; the outcome looks identical whether it came from skill or luck.

They’re identical but they couldn’t be more different, which is why it’s critical to think about what would happen in unobserved timelines; you have to understand the processes used in catching the fish, only then will you know who’s lucky and who’s skillful. That’s the key in separating luck and skill.

Another way to separate the two is time. Give them all a few years and the skilled fisherman will have come home from most trips with a fish because he has a quality, repeatable process. The lucky fishermen will come back empty handed most times, living off the fame of the previously caught tunny-fish, hoping they will get lucky again soon.

But there’s one more thing, and this is the real kicker: the lucky fishermen know it’s luck but they don’t say it.

Instead, they go on to write books about the “technique that allowed them to catch a tunny-fish without using any nets” and how “you can learn this skill too, all you need to do is buy their course.” Sound familiar?

The investment world is no different. Lucky investors are praised for their genius every single day. Without taking the time to understand the processes used in acquiring the returns, the outcome is a useless piece of information to us. Just like the fishermen.

Alternative histories forces you to view outcomes as just one of many possibilities; it forces you to better appreciate the chances of the outcomes happening again if the scenario were to be repeated over and over.

As I touched on just a moment ago, time is a great separator of luck and skill; one way to think about it is as time passes, more and more realities materialise. This is equal to having more sample periods, which allows the law of large numbers to come into play. The law of large numbers says that as you run more trials, observed outcomes converge toward the underlying probability.

Applied to investing, as more time passes and more decisions are made, you’re essentially sampling more alternative histories. Random luck gets averaged out and the level of skill is revealed. Those with no skill will be revealed, and those with skill will be revealed.

Someone achieving 40% returns for 2 years could easily be like the lucky fisherman. Two years is a snippet of the full range of potential realities in the market. But 20 years of high returns? This is a different story, 20 years spans market cycles; you’ve sampled so many alternative histories that luck has been given every opportunity to regress to the mean. If someone still outperforms after 20 years, they’re not the lucky fishermen, they’re the ones who actually know how to fish.

That’s how you separate luck from skill, you look at processes, and you look at how long the process has been in action. This brings me onto the next avenue of research: understanding what a good process looks like, and how to recognise one when you see it.

P.s. (Check out this note if you want to see Howard Marks’ views on alternative histories.)

Analysing processes and outcomes, courtesy of Graham & Dodd

Where the previous section defined process and time as being the things that separate luck and skill, this section is dedicated to understanding what a good process looks like and how to recognise one when you see it.

To do this, we’re going to go to the most brilliant financial mind there is. The best description of luck, skill, and processes I’ve seen is in an article titled “The Superinvestors of Graham and Dodddsville,” this renowned piece was written by Warren Buffett in 1984, and it’s just as powerful today.

At the time, investors achieving high returns consistently were being passed off as lucky. There was no way skill was the reason for their success; given enough investors, it’s statistically likely that a few are going to have Buffett-like careers by sheer chance. And after studying Nassim Taleb and his typewriting monkeys, this argument seems fair. It is inevitable that a few investors are going to have outsized returns simply by luck.

But Warren Buffett of course crafted a brilliant counter-argument. He laid it out his argument in “The Superinvestors of Graham and Dodddsville.” In this article he debunks the idea that luck is the explanation for his (and others’ like him) success in the stock market.

Buffett begins by asking us to imagine a national coin-flipping contest where all 225 million Americans wake up tomorrow morning and wager a dollar on calling a coin flip correctly. Those who call correctly win a dollar from those who called wrong, and each day the losers drop out while the stakes build as all previous winnings are put on the line. After ten flips on ten mornings, approximately 220,000 people will have correctly called ten flips in a row, each winning a little over $1,000.

This group will probably start getting puffed up about their success; at cocktail parties they’ll occasionally admit to attractive members of the opposite sex what marvelous insights they bring to the field of coin flipping. After another ten days, there will be 215 people who have successfully called twenty flips in a row, turning one dollar into over a million. They’ll travel around the country giving seminars on efficient coin-flipping and selling advice.

Some business school professor will point out that if 225 million orangutans had engaged in the same contest, you’d expect to find 215 orangutans with the same perfect record, it’s simply a matter of probability.

However, Buffett argues, imagine if you found that 40 of these successful orangutans all came from a particular zoo in Omaha. You’d be pretty sure you were on to something; you’d probably go down to the zoo and ask the zookeeper what he’s feeding them, what they’re doing differently. Similarly, if a disproportionate number of successful investors came from a very small intellectual village Buffett called “Graham-and-Doddsville,” that concentration of winners simply cannot be explained by chance and points to a shared intellectual process that produces genuine skill rather than random luck.

This group of investors — Munger, Schloss, Buffett, Ruane, etc. — shared the same intellectual patriarch: Benjamin Graham. The framework that Graham laid out is simple: the search for discrepancies between price and value. The nine investors Buffett mentions all applied Graham’s process in their own ways, in different places, at different times. But Buffett found a few common traits among them. Here’s a fwe characteristics of the process that made the superinvestors:

• They think most important investment variables are price and value.

• They approach investment with a business-owner mindset.

• They ignore beta, CAPM, covariance and other technical ideas.

• They work extremely hard in searching for new ideas, they do not need to be fed ideas from connections in Wall St.

• They are insistent on only purchasing with a significant margin of safety.

• And of course, they are disciples of Benjamin Graham.

After having read Buffett’s article, I’ve come up with a few insights that will help us better understand how to spot a good process and how not be fooled by an outcome.

1. A simple intellectual framework, applied in complex ways:

Every superinvestor could boil their process into two words: price and value. It’s so simple, there’s what you pay and what you get. It’s genius is in its simplicity. A simple principle like this allows what I’d call “riffing.” Investors can take this idea of price and value and apply it in their own way; for instance, Schloss focused on net-nets and Munger focused on exceptional companies. They had different applications but used the same principle: the price must be lower than its value.

As time passes, we all come to find out that complex strategies depend on specific conditions persisting, and they break when those conditions change. But price and value works in any environment. As long as businesses have value and markets sometimes misprice them, it works. A simple, time-tested principle is typically at the core of any successful process.

2. Clustering is the hallmark of a good process:

The geographic clustering — the 40 orangutans from one Omaha zoo — is key. These clusters reveal causation, which is something outcomes outcomes alone cannot reveal. This gives us a heuristic for finding good processes; look for non-random clusters in the inputs; for instance, do all successful micro-cap investors follow a certain investment philosophy? Do they invest in a certain market? In certain types of business? These clusters tell us a lot of information as to whether a process has some sort of intellectual grounds. Essentially, if successful investors disproportionately come from the same intellectual tradition, use the same principles, or share the same mentor, that clustering is the result of an outstanding process.

3. Find people that are public with their process before the outcome materialises. It’s too easy to use outcomes and fit a process to it after-the-fact:

The coin-flippers writing books about coin-flipping shows us how spectacular outcomes allow people to create narratives after the fact. The lucky winners will convince themselves they had a process when they had none. They will join all the dots after to make sense of it. But in evaluating investment success, we must be skeptical of process claims that emerge after the success. A genuine process will exist before the outcome, while a fraudulent process will be invented to explain the outcome; a successful investment should be successful for the reasons proposed in the investment pitch beforehand. If some random event occurs and makes the stock fly, the investor will feel very smart despite the outcome having nothing to do with their process.

4. How long have they been doing it, and is it the same process?

The coin-flippers got lucky for 20 flips; some Graham-and-Doddsville investors outperformed for 30+ years. Time will reveal the quality of a process. This is the law of large numbers, you need to see something implemented for decades to fully appreciate the process that creates the returns.

The bottom line of all of this is that outcomes are useless without examining the process behind them. When someone shows you great investment returns, you’ve learned almost nothing. Only by examining their decision-making framework, consistent application, discipline, approach to risk, long-term success during stress, etc… can you begin to separate signal from noise. And going back to Buffett, he concluded that his group of exceptional individuals succeeded as a consequence of their commitment to the principles of value investing. Buffett argued that value investing is a lower risk method to achieving higher returns. I’ve always been intrigued by that word, ‘risk’. It’s an ambiguous term, some believe it can be measured, some believe it can’t, a risk for one person isn’t risky to another. It’s a complex subject to navigate — but there are a few helpful ways to view it.

How to think about risk, with Howard Marks

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It’s one of those “right book right time” situations. The book was born in 20th century Switzerland. This is a country blessed with no minerals, no oil, no coal, and no hospitable farmland. Yet somehow, the Swiss are among the most affluent in the world.

How did they do it? Through wise investment and speculation.

The author’s father was a prominent banker and successful investor. Naturally he wanted to know what made him so successful, but his father never articulated his philosophy into words, hence this book was forged (with the help of a group of Switzerland’s most successful bankers and investors).

The book lays out a tested speculative philosophy — a collection of rules that have “incalculable value.” But,

“they are not just a philosophy of speculation; they are guideposts for successful living… If you study (these rules) with the diligence they deserve, they can enable you to win more of your bets than you ever thought possible.”

The approach differs from that of the legends like Buffett, and that’s where its value lies; it’s unconventional, even controversial. And to dismiss an opposing perspective on the basis that it contradicts our own is a catastrophic lapse in judgment.

This is one of those articles where I believe — if we were to create a monetary value for its impact on our decision making — it’s worth the subscription alone; one smart decision to buy or sell thanks to these ideas might save you many years worth of subscription value.

Let’s dive into one of my favourite books.

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He worked at Fidelity with the likes of Peter Lynch, who said one of the greatest compliments he could receive is to be associated with Anthony Bolton.

In Bolton’s highly underrated book, ‘Investing Against The Tide’, he laid out his framework for how he approaches periods of under-performance. Under-performance is inevitable, especially in such volatile times as we are in now. No strategy will work all the time and while we’d love to avoid underperformance altogether, we can’t — we must instead learn how to cope with it better than others.

Bolton’s advice on coping with a few bad years is extremely valuable. Sometimes the difference between good and great is not the ability to risk higher returns, but the ability to navigate the losing years more effectively.

Being a contrarian, Anthony had to have a particularly strong mental fortitude, much stronger than others. This advice is the result of a very long and successful career which had many moments worry and despair; like every other investor, he under-performed many times, but once he figured out how to deal with it he improved massively. The introspection and subsequent improvement that a few bad years brings can often be worth their cost and more.

In spite of these times, Anthony whooped the market in the long-run.

Here’s his advice on what to do when you’re underperforming:

• Don’t be too stubborn about your views, but don’t lose all your conviction. Ideally, your conviction level should be around 50% level (where 0% equals no conviction and 100% means you are so convinced you would never change your view). Maintain conviction but keep your flexibility.

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• Don’t box yourself into a corner with your own views (e.g. everyone knows you hate mining shares to such an extent that you find it very difficult ever to buy them back). Always allow yourself an exit and an entry.

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• Listen to advice from others about why you are not doing well and with an open mind. Seek views from colleagues about what you are doing wrong. You must be prepared to take criticism.

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• It is essential to keep an open mind to other points of view, particularly views on your main holdings that conflict with your own. You should never feel that you are the complete expert on a particular stock and no-one else’s view counts. Evaluate honestly why you believe they are wrong and you are right. Know what ‘wrong’ looks like for you investments, i.e. why they might not succeed.

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• Check whether your views are firmly agreed with the consensus and therefore more risky.

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• Don’t give up on your principles. Don’t try something very different that you don’t believe in.

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• Put down on paper your ‘start from scratch’ portfolio and see how that differs from your own. A ‘start from scratch’ portfolio is one where if you had a blank slate today, what would you invest in? What would it look like? Ideally your portfolio always looks a lot like your ‘start from scratch’ portfolio.

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• Put down on paper your worst investments over the past six or twelve months and an honest explanation of why they went wrong. What are the lessons from this? What are the common denominators? Think more about the downside risk of your positions.

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• Work out whether you are deciding how your day is spent or letting events and others dictate your calendar. You should always allocate the bulk of your working days to tasks/events that you choose.

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• Make sure that you are spending enough time on looking for new ideas rather than just monitoring what you own already. A portfolio with very few new holdings can become stale.

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• If you don’t use it, try using technical analysis as a cross-check for your fundamental views.

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• As well as examining your individual bets look at the shape of your portfolio and check that it reflects your conviction levels. Are your strongest conviction bets large enough? What are the characteristics of the tail of your portfolio? Check your portfolio for unintended bets.

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• Don’t put a defensive shell around yourself, becoming fatalistic that there is nothing you can do to improve things and cut yourself off from colleagues and clients.

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• Finally, when it’s going well again don’t forget the bad days and believe that you can walk on water — you can’t; I couldn’t; no-one can.

I hope that if you’re currently undergoing a period of low returns, this list was useful to you. And if not — it’s still worth keeping it handy because unfortunately bad days will come and you’ll want to know how to deal with them effectively. I found this list really useful when I first read it and I refer to it a lot when things aren’t going my way.

This is a much shorter article, but it packs no less value than a longer one — hopefully just a little easier to read!!

Thanks a lot for reading. Remember it is darkest before dawn, good times always follow after bad. There isn’t a great manager out there who didn’t under-perform a few times.

Sincerely,

The Intellectual Edge

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References: Investing Against The Tide, by Anthony Bolton - 2009