Warren Buffett once said that when memos from Howard Marks arrive in his mail, they are the first thing he opens and reads, and that he always learns something from them.

“When I see memos from Howard Marks in my mail, they're the first thing I open and read. I always learn something, and that goes double for his book.”

I understand the impulse. Over the years, I have noticed that Marks’s memos come in two flavors:

• Some revisit timeless principles about risk, psychology, and price, and reading them feels like returning to a favorite book.

• Others are dispatches from the current environment, in which Marks tells you what he sees in the market right now and what worries him about it.

Both are valuable. They are valuable in different ways, though. The first kind sharpens your framework, mental model database, and thinking. The second shows how to apply that mental toolbox to the real world, or it might confirm or challenge your views on the current environment and opportunity set.

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For this Blueprint I went back to the very beginning: thirteen memos written between 1990 and 1998, first at Trust Company of the West (TCW) and then at the newly founded Oaktree.

I expected a pleasant history lesson. What I got was a week of uncomfortable déjà vu, because some of the present-day, 2026 headlines I am reading alongside these memos these days could have been footnotes to them.

What Can a Memo From 1990 Possibly Tell Us About 2026?

A fair question. These memos predate the internet economy, index funds as a dominant force, unheard of zero interest rate environments (well, that’s been changing fast anyhow…), and anything resembling artificial intelligence.

Marks writes about convertible bond funds with a few hundred million dollars in assets and quotes economists’ forecasts for the yen. The hedge fund names he mentions belong to a different era. Read them as historical documents, though, and you miss the point.

Even back then, Marks was writing about mechanics: how losses compound, how forecasts are formed, how leverage turns small mistakes into fatal ones. Mechanics and timeless principles do not expire. The vocabulary changes. The machinery stays the same.

So in what follows I want to unpack three of those mechanics, each through a lens that feels current to me in October 2026.

Why Did My Worst Investment Teach Me More Than My Best?

The very first memo in the collection opens with an argument. A money manager, trailing the S&P 500 by more than eighteen percentage points over a year, defended himself with a line many investors would nod along to:

If you want to be in the top 5% of managers, you have to accept the risk of landing in the bottom 5% too.

Marks rejected that bargain.

His counterexample was a pension plan whose equity portfolio never ranked worse than the 47th percentile or better than the 27th in any single year, and yet over fourteen years it compounded into the 4th percentile. No spectacular years. No disasters either.

“the best foundation for above-average long term performance is an absence of disasters.”

I learned this lesson the expensive way. My position in Endor, a German tech hardware maker, ended in a permanent loss of capital, and what stung most was the opportunity cost, more than the number on the screen. A total loss does more than subtract from a portfolio. It removes capital that would otherwise have kept compounding for decades, and at the time it brought the compounding of my broader portfolio to a halt.

My winners over the years, some of which went up two times or more, did their job. They did not undo the damage in the way I once assumed they would.

The arithmetic is brutal and familiar: a 50% loss needs a 100% gain to break even, and a 100% loss needs a miracle.

That is why Marks frames the goal as avoiding the losers and letting the winners take care of themselves. It sounds defensive. It is in fact the most aggressive long-term strategy available, because it keeps the base intact for the compounding that does the heavy lifting.

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Buffett’s early partnership years illustrate this very well:

• Average annual return: 30.4% vs. Dow’s 8.6%

• Most importantly, not a single negative year (!), even when the market fell -15.6%

• Outperformed the index by more than 22% compounded annually

Downside protection paired with consistent alpha is how to get filthy rich.

Marks makes the same point in a sports metaphor I love. When he and his partners left TCW to found Oaktree in 1995, he contrasted Babe Ruth’s home-run credo with golfer Tom Kite, who had earned more career prize money than anyone in the history of the sport before ever winning a major.

Consistency over headlines. Batting average over the occasional home run.

There is a behavioral layer to this too, and Marks touches on it with an old poker adage about scared money never winning. I wrote about that dynamic recently, when the stakes rose, and a sharp one-day drop hit several members of our community. Read it here:

Is Forecasting Pointless, or Just Done Badly?

Marks’s 1993 memo on predictions is one of his most quoted for good reason. He builds his case like a proof. The value of a forecast equals the payoff from being right multiplied by the probability of being right.

The Expected Value of a Forecast = Value of Correct Forecast x Probability of Being Correct

Most forecasters have roughly average ability, and the consensus forecast is already embedded in prices, so being right along with everyone else earns you an average return.

Only a forecast that is both non-consensus and correct produces superior results. And those are rare for an obvious reason: outcomes cluster near the consensus most of the time.

“non-consensus forecasts are hard to make, hard to make correctly and hard to act on.”

Three years later, he tested the idea against the Wall Street Journal’s economist surveys he had stashed in his desk drawer. The consensus missed by about 15% on average. Simply assuming that rates and currencies would stay where they were missed by about 16%. The forecasts tracked the current level of each variable almost perfectly and anticipated none of the major turns.

The economists were extrapolating. Marks’s verdict: stay away from the macro future and focus on what is knowable.

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I agree with most of this, and I also think it needs an update for 2026. One of my favorite projects this year was building an “AI superforecaster,” grounded in the research of Philip Tetlock and Barbara Mellers on what separates the best forecasters – the superforecasters – from the rest.

Their findings do not contradict Marks. They explain why the forecasters he studied failed. Look again at those survey results. The economists anchored on the present and adjusted a little. In Tetlock’s language, they started with the inside view, the specifics of the current situation, and never looked outward. Superforecasters reverse that order. They anchor first on the outside view, the base rate of the relevant reference class, and only then adjust using case-specific information.

Sequence matters more than weighting. They establish what usually happens to situations of this kind before reasoning about this one. They look for other perspectives on how the question should have been framed in the first place. They aggregate across several models and reference classes. They express the result as a number, fifteen percent rather than “a small chance,” because a forecast in words cannot be scored, and a forecast that cannot be scored cannot improve you. Then they update, mostly in small steps, occasionally in a large one when something breaks, and they keep a record. That’s the TL;DR version of their approach.

That last point lines up neatly with a complaint in Marks’s 1993 memo: nobody ever publishes a forecaster’s track record. Can you imagine hiring a baseball player without knowing his batting average? So my takeaway is a little more nuanced than “don’t forecast.” Ultimately, investing is all about forecasting. Marks is right that confident point predictions about rates, currencies, and GDP add little value. Tetlock and Mellers show that disciplined, probabilistic, base-rate-first estimates can be calibrated and improved. You can try. Just know which kind of forecasting you are doing.

"There is a danger of expecting the results of the future to be predicted from the past.” – John Maynard Keynes

Where Is the Lender This Time?

The memo I keep thinking about this week is the last one in the collection I looked at for this Blueprint.

“Genius Isn’t Enough,” written in October 1998, dissects the collapse of Long-Term Capital Management.

LTCM had Nobel laureates, a former vice chairman of the Federal Reserve and Salomon’s best bond traders. By Marks’s account, it also had roughly $4.6 billion of equity supporting around $150 billion of positions. When spreads moved the wrong way between August and late September 1998, about 90% of that equity disappeared. The individual trades were mostly sensible. The structure was not.

“Volatility + leverage = dynamite.”

Marks draws out a further lesson that I think matters even more today: the credit cycle tends to be far more volatile than the economy underneath it. Lenders sell money, the most commoditized product there is, so in good times they compete by accepting more risk, lower rates, and looser terms.

When conditions turn, all of that reverses at once.

“Look around the next time there’s a crisis; you’ll probably find a lender.”

Now look at the AI trade in October 2026. I have written before that I see several warning signs, and if I had to pinpoint what could stop the trade, it would be the financing structures that have grown more “creative” over time, with off-balance-sheet arrangements and companies financing their own customers. Coatue had this nice slide in its October 2026 deck:

Last week the Financial Times reported that OpenAI had told investors its annualized revenue was nearing $50 billion, roughly $20 billion below figures reported earlier, and tech stocks fell on the news.

Part of the gap is definitional. The two leading labs calculate annualized revenue differently, with Anthropic counting sales made by its cloud partners and OpenAI excluding them. Even so, a $20 billion revision that markets learned about secondhand says a lot about how much faith is being placed in numbers few people can verify.

Marks would recognize the pattern. He wrote about LTCM’s investors accepting 40% returns without understanding what the fund did.

Then came CoreWeave. On Wednesday, its unsecured bonds due in 2032 traded below 80 cents on the dollar, a level widely considered the threshold for a financially strained borrower.

The stock fell 15% that day after Chamath Palihapitiya raised the question of a private credit bubble with a screenshot of the bonds yielding 15%.

The bull case points to a revenue backlog of roughly $104 billion at the end of the second quarter. The bear case points to second-quarter capex of $6.42 billion against free cash flow of minus $5.74 billion, and to third-quarter interest expense guided at up to $940 million. A backlog only becomes cash if the customers can pay, and many of those customers are themselves raising money on projections.

Volatile revenue, heavy debt, interlinked counterparties. Marks wrote that recipe down in 1998.

Does that mean I know what happens next? No, and Marks would warn me against pretending I do. In a 1997 memo, quoted in “Who Knew?”, he argued that the next surprise would most likely be something nobody had anticipated, including himself.

A month later, the Asian financial crisis erupted. COVID was the same kind of event in our time. Black swans are unknowable by definition.

Fragility is not.

You do not need a medical degree to see that in a system this interconnected and this leveraged, it may not take much for something to break and for the ripple effects to travel far.

I cannot name the trigger. I can assess how exposed I am if one arrives. That is the knowable part.

Why I Turned Thirteen Memos Into Six Slides

Reading these memos back to back is a different experience from reading them one at a time over the years. Themes I had filed under separate headings, consistency, forecasting, psychology, cycles, and leverage, turn out to be one argument viewed from five angles.

So I distilled them into a visual architecture: a cover slide with the central thesis, followed by five modules on consistency over brilliance, the limits of forecasting, price and psychology, the pendulum of market cycles, and risk, leverage and hubris.

Each slide carries the key data points from the memos, short definitions for the terms that need them, and the historical context that makes a 1990s memo readable in 2026. I also flagged a few places where the historical record differs from Marks’s numbers.

You can access the Blueprint via the note I shared with the slide deck as image files a couple of days ago:

As a small thank-you during my parental leave month, I’m also making the PDF of this Blueprint free for everyone to download. For most past and future Blueprints, the PDF remains a perk for paying subscribers.

My suggestion: create a dedicated folder on your computer – something like “Investing Blueprints” – and save this one as your first entry.

Howard Marks' Memos from 1990-1998

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Over time, it becomes a compact reference library of the most important investing concepts, papers, and memos, ready whenever you want to revisit an idea.

Until next time, keep compounding.

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.”

In the current market environment of 2026, classic value investing – call it Buffett 1.0 investing – seems completely out of fashion.

Institutional investors spend billions chasing macroeconomic narratives, momentum trends, and high-flying technology multiples. Trump even began offering high-speed access to his market-moving announcements for $100,000 a month in August.

Institutional investors spend billions chasing macroeconomic narratives, momentum trends, and high-flying “AI-multiples.” Trump even began offering high-speed access to his market-moving announcements for $100,000 a month in August, and more than ten firms signed up, mostly high-frequency shops. Speed on a headline is what they are buying. The backdrop rewards it. Warsh's Fed raised rates on September 16, the ten-year yields around 5%, the top ten companies make up close to 40% of the S&P 500 against a 27% peak in 1999, and the five largest cloud providers have committed something like $700 billion of capex this year, increasingly on borrowed money.

And if market participants ever discuss Warren Buffett today, they almost exclusively study what I call Buffett 2.0. They analyze the legendary, late-stage strategist shaped by Charlie Munger – the investor who preferred deploying tens of billions into durable capital-light franchises with durable competitive moats, sometimes holding them for decades.

That mental model makes sense when managing a massive conglomerate like Berkshire Hathaway post-2000 (arguably way sooner already).

Scale forces giant capital bases into narrow corners.

Yet most investors overlook the fact that Warren Buffett’s legendary early performance was built on an entirely different foundation. Between 1957 and 1963, operating with under $10 million in partnership capital, he faced zero size penalties.

This was Buffett 1.0. He was an aggressive arbitrageur, a “balance sheet activist,” and a relentless and incredibly talented special situation investor.

While the finance world fixates on meme stocks, high-frequency trading algorithms, passive investing, or quality compounding, the playbook of early Buffett remains one of the most effective, under-studied avenues for generating asymmetric alpha.

Asset-based activism, corporate workouts, and severe balance sheet mispricings still deliver extraordinary results in today’s markets.

I recommend listening to my conversation with Jamie Halse and his activist approach in Japan.

To capture those returns, you must study the original partnership letters when his capital was nimble and his investment taxonomy was sharp.

Why did early capital allocation rely on three distinct engines?

To eliminate dependence on stock market advances, Buffett constructed a portfolio divided into three operational categories:

• Generals,

• Work-Outs, and

• Controls.

This tripartite architecture allowed him to generate superior returns while maintaining strict down-market capital preservation.

Generals represented classic undervalued securities where the partnership exercised no corporate control and had no fixed timeline for price realization. These positions carried massive individual margins of safety and generated substantial upside during rising markets. However, their market prices remained correlated with general market moves.

Work-Outs provided the defensive counterweight. These were corporate arbitrage commitments tied directly to specific corporate actions such as liquidations, mergers, spin-offs, and tender offers. The returns on Work-Outs depended on corporate schedules rather than market sentiment. To optimize capital efficiency, Buffett routinely used short sales to strip out broader equity market risk and deployed conservative bank leverage – capped strictly at 25% of net worth – solely to finance these predictable transactions.

“You will note on our yearend balance sheet (part of the audit you will receive) securities sold short totaling some $340,000. Most of this occurred in conjunction with a work-out entered into late in the year. In this case, we had very little competition for a period of time and were able to create a 10% or better profit (gross, not annualized) for a few months tie-up of money. The short sales eliminated the general market risk.“ – 1962 Letter

Controls formed the third pillar. When market prices remained depressed for extended periods, Buffett systematically accumulated dominant equity positions to force corporate change. Prolonged stock stagnation was welcomed because it lowered acquisition costs. Once control was secured, performance became completely insulated from Wall Street sentiment.

Decoupling portfolio performance from general market indexes required this structural variety. In static or declining markets, Work-Outs and Controls drove performance, allowing the overall partnership to compound capital while broad indexes faltered.

In case you missed it, I recently wrapped up my 126-idea series, in which I sorted the ideas found in the fund letters I track by:

1. Worst Performers: Quarter-to-Date (QTD)

2. Worst Performers: 3-Month Window

3. Worst Performers: 6-Month Window

How do you extract cash from non-performing assets?

The true genius of Buffett 1.0 lay in his appraisal discipline and aggressive balance sheet restructurings, best demonstrated by his investments in Sanborn Map Co. and Dempster Mill Manufacturing.

In 1958, Sanborn Map presented a glaring market inefficiency. The company’s stock traded at $45 per share, yet its underlying investment portfolio alone was worth $65 per share. Investors were pricing the core mapping business at a negative $20 valuation despite its historical stability. Board inertia caused this severe mispricing; nine directors owned just 46 shares combined and continuously slashed dividends while maintaining executive fees.

Buffett accumulated a 44% controlling stake alongside allied partners and engineered a tax-efficient corporate separation. Instead of liquidating the stock portfolio and incurring a massive corporate capital gains tax, he structured an SEC-approved exchange. Shareholders wishing to exit swapped 72% of Sanborn’s outstanding stock directly for underlying portfolio securities at fair value. The transaction eliminated over $1 million in prospective tax liabilities, unlocked immediate intrinsic value for exiting partners, and left the remaining business debt-free.

At Dempster Mill Manufacturing, Buffett demonstrated his quick-liquidation valuation framework. Ignoring optimistic book values, he applied severe haircuts to the balance sheet: 40% off inventory, 15% off receivables, and basic scrap values for physical equipment.

“We intend to use the same method or valuing our controlling interest in Dempster Mill Manufacturing at this yearend that we did at the end of last year. This involved applying various discounts to the balance sheet items to reflect my opinion as to what could be realized on a very prompt sale. Last year this involved a 40% discount on inventories, a 15% discount on receivables, estimated auction value of fixed assets, etc., which led to an approximate value or $35.00 per share”

He acquired majority control at an average price of $28 against a book value of $75 per share.

When original management failed to reduce operational drag, Buffett installed turnaround specialist Harry Bottle. Bottle slashed operating breakeven points in half and reduced excess inventory from $4.2 million down to under $1.0 million.

The extracted capital was funneled directly into a high-yielding securities portfolio managed by the partnership. By 1963, Dempster held $35 per share in liquid securities held inside a bank vault alongside $16 per share in productive manufacturing assets. The liquid assets alone exceeded the total acquisition cost of the entire company.

Blueprint of Buffett’s Early Partnership Days

To help sophisticated investors visualize these dense corporate finance mechanics, I distilled the core architectural frameworks from Warren Buffett’s 1957–1963 letters into a visual deck.

It breaks down the portfolio allocation models, governance turnarounds, and valuation frameworks into a clean structural format.

You can access the Blueprint via the note I shared with the slide deck as image files a couple of days ago (free to access for everyone):

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

Downloadable Blueprint PDF

The complete image series above is free for everyone to review. Paying subscribers can access and download the Blueprint as a PDF so you can store it – and all old and future Blueprints – nicely on your computer.

Until next time, keep compounding!

(PDF download link below)

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As I recently shared, between roughly 2019 and 2023, I wrote a three-volume investment framework that runs to about 2,100 pages. Thousands of hours went into it, and most of those hours were spent doing something that looks inefficient from the outside: reading hundreds of books, listening to thousands of podcasts and YouTube videos, and trying to decode what I liked and didn’t like and writing down what I had understood in my own words.

Today’s Blueprint comes out of a small foundational piece of that project, pats of “Lesson 8.2.” of my framework. The companies that appear in it as examples are simply the businesses I was studying at the time. Slack was still a young growth case. Peloton was the vertical integration showcase, GameStop the fading incumbent, and the Switch OLED an open question about whether Nintendo would move to an iPhone-style hardware cadence.

I finished that work before generative AI existed, and I am glad about the timing. Writing the framework myself is what made the lessons stick.

I am publishing this particular Blueprint now because the market has made its lesson urgent. Sentiment is euphoric, and more importantly, the structure of price discovery has temporarily shifted in a direction that makes business analysis feel like a handicap.

Retail speculators carry leveraged single-name exposure. High-frequency shops and pod-based multi-managers work holding periods measured in weeks. Large institutional allocators have become functionally valuation-agnostic, because career risk punishes them far more severely for being absent from a rising name than for owning it at forty times sales. Robinhood built the interface that trained a generation to check a position the way you check a notification. I discussed these dynamics and some of the implications more thoroughly in the piece below (with which many investors resonated quite a lot).

Whether you can hold anything at all for more than a couple of weeks is now a real-world question for many “investors.”

Are you an owner or a renter?

My framework opens with a mindset shift. Pretend you are buying the entire company. Not a share. The whole thing, funded out of your own pocket, with no exit available for a decade.

Graham established the distinction in chapter eight of The Intelligent Investor, and it is the distinction between price and value. Look at what the exercise removes. When you own a business outright, nobody shows you a price every ninety seconds. Your information set collapses down to profits, returns on incremental capital, and competitive position, and those things move slowly, so your behavior moves slowly with them.

Reintroduce a live price quote, and you have handed yourself a second variable, far noisier than the first, that almost everyone reacts to.

One of Buffett’s best investments

The cleanest demonstration of what the ownership frame strips away is Buffett’s purchase of the Washington Post.

If you did not know, Warren Buffett's investment in The Washington Post Company was one of the most successful and famous investments of his career. By 1985, his initial $10.6 million stake had grown to $221 million, compounding at roughly 35% per year. Over 40 years, the investment grew to roughly $1 billion by 2013, representing a return of over 9,000%.

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Buffett did not calculate the stock’s beta. He ran no capital asset pricing model, checked no position on an efficient frontier, reviewed no average daily trading volume, and never asked whether the company belonged to an index. He valued the business and bought a piece of it at a substantial discount to that value. The full apparatus of modern portfolio theory was available to him, and he had no use for any of it, because none of it describes earning power.

On pro-rate stakes

There is a second-order point here that most investors miss. Public markets routinely price pro-rata stakes in excellent businesses at discounts you could never negotiate privately.

A strategic buyer taking out a whole company pays a control premium.

You, buying two percent of that same enterprise through an exchange on a panic day, get the discount instead.

Berkshire operates in both markets precisely because the public one is periodically the cheaper of the two. Your inability to acquire entire corporations costs you nothing. The temperament of the people you transact with, exploiting behavioral mistakes, leaning into behavioral edge setups … that is what pays you.

Then run the thought experiment in the lesson. Assume the market closes for ten years the moment you buy. No quotes, no marks. What would you need to believe about the business to be comfortable? Answer that honestly and you have defined your actual research agenda.

Can you say it in two sentences?

Assume you have accepted the ownership frame. How would you know whether you understand a business well enough to act as its owner?

My test: Describe the company along a handful of parameters (I use eight) in one or two dense, unambiguous sentences, specific enough that a layperson with no market exposure could repeat the business model back to you. Product, monetization, customer, geography, distribution. No jargon, no hedging.

Try it on something you own.

Bonus points for sharing it in the comments below!

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When I wrote the chapter in my book, I used Apple as the worked example, and the description took me considerably longer than the reading did. Two sentences had to carry the hardware suite, the software and services layer wrapped around it, the multi-channel distribution running from owned retail through third-party resellers, and a customer base split across middle- and higher-income consumers, small and mid-sized businesses, and educational institutions.

Every word in it does work, and cutting any of them changes what the business is.

The world-renowned multinational technology company Apple Inc. designs, manufactures, and markets high-end smartphones, personal computers, tablets, wearables, and accessories along with a variety of related software, services, peripherals, and networking solutions. Headquartered in Cupertino, California, Apple sells its products worldwide through its online stores, its retail stores, its direct sales force, third-party wholesalers, and resellers to middle to higher- income consumers, small and mid-sized businesses and educational institutions

Research of the University of Colorado

The reason the exercise works has a cognitive basis. Research out of the University of Colorado found that being made to teach a concept has a humbling effect, because articulation exposes the gaps that recognition conceals.

You read a sentence in a 10-K, it feels familiar, and familiarity passes for comprehension right up until you have to generate the sentence yourself.

That is overconfidence bias with an unusually cheap antidote. Teach it, and the holes announce themselves.

Which is why the input matters as much as the exercise. The parameters have to be filled from annual and quarterly filings, earnings call transcripts, and shareholder letters, not from a broker note or a thread summarizing a business on X.

It is also why I keep telling newer investors to start with a focused single-industry operator. A conglomerate makes you define all eight parameters four or five times over, across merged financials and a single management team, and that is a genuinely hard analytical task wearing the costume of an easy one.

Structure sets the ceiling on economics

Another parameter in the lesson is the one I find most underused. Consider the product profile, which sits on three continua rather than in three boxes.

• Goods against services, where services generally carry higher margins for the unglamorous reason that they tie up less physical capital.

• Consumable against non-consumable, where consumables produce recurring revenue because the customer runs out and has to buy again.

• Commodity against differentiated, which is the axis that determines whether you have pricing power at all. A commodity producer competes on cost, since every unit it makes is economically identical to a competitor’s unit. A differentiated producer gets to compete on something else, and branding alone can move a product a long way along that axis. You have a preferred brand of milk.

Distribution then reallocates the economics between the party that makes the product and the party that reaches the customer. The travel data I pulled at the time showed online travel agencies taking 44 percent of bookings in 2018 against 41 percent the year before, while direct bookings on the accommodation provider’s own website fell from 35 percent to 27 percent.

Direct-to-consumer structures keep more of the margin and hand the operator control over placement and presentation, which a retailer-dependent brand never has.

Operational form is worth being aware of too. Holding companies like Berkshire function as capital allocation hubs sitting above decentralized subsidiaries. Franchisors license the system and the trademark and earn royalties against almost no capital, an extremely attractive return profile when the concept travels. Vertical integration secures either the input or the customer relationship, and GrafTech is the tidiest case in the lesson: it bought Seadrift Coke to lock up petroleum needle coke, the one material without which its graphite electrodes cannot be made. That insulation from supply shocks and intermediary margin rarely shows up in a screen.

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Horizontal integration buys scale on the same tier of the chain, as Capri did in assembling Michael Kors at roughly 4.45 billion dollars of sales alongside Versace at 900 million and Jimmy Choo at 650 million.

Two parameters people skip deserve a mention: Customer concentration decides how much bargaining power the business actually holds. A diversified base means one departing buyer dents revenue instead of breaking it, and it keeps customers from dictating price and quality terms.

Geography decides where the shocks come from.

Balanced demand, concentrated supply. You want to know that before the next disruption, not during it.

Life cycle bounds all of it. Startup, growth, expansion, maturity, decline.

The attrition numbers are worth restating because they are worse than most people assume: roughly 40 percent of new businesses survive past year four, and about a third make it through year seven. Mature enterprises invert the trade, offering dominant positions and cheap capital alongside saturation and bureaucratic drag.

Fading incumbents almost never pivot in time, because by the point management concedes the model is broken, the window has already closed. Then ask the growth question properly. Is the next expansion tied to the core, or is it an adjacency the company has no right to win?

What I would write differently in 2026

One perspective shared in that lesson needs an amendment. I wrote that we prefer industries with an ultra-slow rate of change, because slow change lowers disruption risk and spares you from owning a business that has to reinvent itself successfully and repeatedly.

As a default, it still holds.

My own style has drifted – for better or worse. I hold more exposure to businesses with a wider range of outcomes than Buffett ever wanted, and I lean more technology-adjacent than he did, because some of those companies compound intrinsic value at rates the slow-change universe structurally cannot reach.

The cost of that drift is specific. The low-rate-of-change filter used to do my risk management for me at the selection stage, before a single euro was committed.

Give it up and the work moves to position sizing.

Owner Mindset Blueprint

I rebuilt the chapter as a condensed visual blueprint, anchored on the five-core-pillar framing I have used since the beginning.

That framing comes straight out of the core pillars of the Buffett and Munger approach: understand the business, then the moat, then management, then the balance sheet, then the price.

The Blueprint is below. Print it, keep it next to whatever you are working on this week, and run your largest position through it before you add to anything.

Then share it with your investing peers.

Understanding Businesses

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Until next time, keep compounding.

René

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

Thinking about edge – and carefully distinguishing between edge and skill – has been one of my most transformative theoretical breakthroughs as an investor this year.

Much of this clarity emerged from private exchanges with Tiho Brkan, who consistently sharpened my perspective on how market efficiency operates in practice. Tiho is an avid reader of Michael Mauboussin, whose institutional research deeply shaped my personal evolution as an investor over the past couple of years.

I explored complementary dimensions of this topic earlier this year in The Paradox of Investment Skill: Why Exceptional Thinkers Fail in Crowded Games and How to Measure Investing Skill: A Practical Framework to Isolate Your True Alpha. I think reading both pieces will make you a better investor, learner, and thinker.

To locate true structural edge today, you have to look directly at how the investing landscape itself has shifted underneath our feet. Mauboussin's research illuminates a defining friction in modern market microstructure. Between 2006 and 2025, over three trillion dollars migrated out of active mutual funds directly into passive index vehicles.

Looking at market structure today in 2026, rules-based passive strategies control well over a third of U.S. public equities. These passive entities trade strictly to accommodate fund flows or index rebalancings. They never evaluate intrinsic value or discount anticipated cash flows. This creates massive price-agnostic buying and selling across global exchanges.

Active investors face a structural puzzle. Highly talented managers struggle to outperform because they compete against equally brilliant peers in crowded strategies, while broader market mechanics are increasingly driven by non-fundamental capital flows.

My personal process when analyzing institutional literature like Mauboussin and Dan Callahan’s research involves rigorous structural extraction. I sit down with these long-form papers, pencil in hand, reading through each section twice to strip away corporate boilerplate. First, I highlight the core mechanical drivers. Next, I map out the economic feedback loops on paper to visualize how capital flows interact with human behavior. Finally, translating dense academic research into these visual blueprints forces me to clarify my own mental models.

Can High Skill Deliver Alpha Without an Uninformed Counterparty?

Skill reflects internal analytical processing capacity. Edge relies on external market frictions.

In their foundational work that served as the foundation of this blueprint, Mauboussin and Callahan unpack the Grossman-Stiglitz paradox, demonstrating that financial markets can never achieve absolute efficiency. Perfect efficiency eliminates all economic incentives to gather information, yet efficient prices require informed traders to process data.

Markets must exist in an efficiently inefficient equilibrium. Active management requires exploitable mispricings to recoup the substantial costs of gathering data.

Tip: Access all of Mauboussin’s writing here

Superior analytical skill yields zero excess return if your trade counterparty possesses identical information, operates on the same time horizon, and faces identical structural constraints.

Edge demands an asymmetry.

It requires identifying who is taking the opposite side of your transaction and pinpointing their explicit motivation.

You must verify whether your counterparty is trading based on

• flawed extrapolation,

• forced regulatory mandates, or

• temporary liquidity needs?

Without a clear answer to that single question, perceived skill is merely intellectual vanity.

Where Do Structural Inefficiencies Appear in Modern Markets?

Mauboussin and Callahan organize market inefficiencies into four distinct domains through the BAIT taxonomy – Behavioral, Analytical, Informational, and Technical.

• Behavioral inefficiencies stem from overextrapolation and correlated crowd beliefs.

• Analytical inefficiencies center on time arbitrage and narrative transitions.

• Informational inefficiencies arise from limited investor attention and task complexity.

• Technical inefficiencies develop through forced selling and leverage liquidations.

Examining these categories reveals how institutional market microstructure has evolved.

Fundamental long-only funds now account for less than fifteen percent of daily equity trading volume, down sharply over the past decade. Multi-manager pod shops leverage capital four to five times over to harvest short-term quarterly earnings variance, while retail trading accounts for one-fifth of daily volume.

Context matters. Capital moves fast.

Dear compounders,

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Why join the community, you may ask? Our library is fast approaching 70 comprehensive deep dives, providing institutional-level research on some of the world’s most fascinating businesses. Most recently, we’ve dissected companies like Reddit, Pinduoduo, Mercado Libre, Grab Holdings, Tencent Music, CTS Eventim, Dino Polska, Toast, Workiva, Amadeuts IT, Fair Isaac, and Topicus.com.

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• Complete Access: Every deep dive in our library (65+ and counting). The most thorough analyses you will be able to get your hands on (including downloadable models).

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• Slide Decks: Every deep dive now comes with a companion slide deck. It’s the whole argument in compressed form – the hypothesis, the business, the competitive position, the valuation, and the case against – for the days when you don’t have an hour to spare but still want the shape of the thing. Paid subscribers get both, the long piece and the deck.

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Consider a striking market anomaly that routinely plays out across equity markets: established mega-cap corporations with stable operating businesses frequently see their stock prices swing by 100% or more over a twelve-to-eighteen-month window.

Fundamental enterprise value does not move that fast. Wal-Mart, Boeing, and Disney do not double or halve their intrinsic earning power in a single calendar year.

Yet institutional market participants continuously trade these equities at wildly divergent valuations.

That’s your opportunity.

I've been re-reading through Joel Greenblatt's class notes from his lectures (well, the notes on lessons #1-#7). I highly recommend reading them yourself if you haven’t already. Or if you have a limited amount of time, maybe my visual blueprint attached further below is a valuable alternative.

Anyway … Looking back at Joel Greenblatt’s legendary Columbia Business School lectures delivered two decades ago, the core friction in capital markets described above remains unchanged. Today, high-frequency algorithms and automated execution algorithms dominate trading venues, but human emotion still dictates asset allocation. Institutional money managers remain chained to benchmark tracking errors, quarterly career pressures, and macro forecasting noise. These structural rigidities create systemic mispricings. Short-term market prices diverge from intrinsic values, offering disciplined capital allocators an enduring edge.

My process for analyzing these class notes as a basis for today’s blueprint involved working through the first seven classes (#1-#7) of Greenblatt’s "Special Situation Investing" course taught at Columbia Business School, fifty-five pages of raw, unvarnished transcripts.

I dissected the exchanges between Joel Greenblatt, value fund manager Richard Pzena, and independent investor Bruce Newberg. My objective was to strip away historical market backdrop context and isolate the pure corporate finance mechanics. I focused on translating raw classroom dialogue into an actionable mental model framework, visualized as a blueprint.

Why Does Capital Structure Distort Traditional Valuation Ratios?

Topics covered include how conventional valuation metrics, which are still heavily relied on in 2026, like Price-to-Earnings (P/E) and Price-to-Sales (P/S) obscure economic reality because they ignore corporate capital structures and differential tax regimes. A company trading at a deceptively low P/E of 5x might carry significant debt liabilities, making its total enterprise acquisition cost far higher than an unlevered peer trading at 10x earnings.

To evaluate pre-tax operating yield accurately, institutional allocators must evaluate Pre-Tax Operating Profit (EBIT) relative to Enterprise Value (EV = Market Capitalization + Net Debt). Enterprise Value represents the true cost of acquiring the entire business outright. Comparing EBIT to EV establishes an apples-to-apples earnings yield across varying debt loads.

Measuring business quality requires an equally rigorous treatment of capital. Return on Invested Capital (ROIC) must be calculated as EBIT divided by Tangible Capital, defined specifically as Net Working Capital (NWC) plus Net Fixed Assets (NFA). Goodwill must be removed from the capital base entirely. Goodwill represents historical, sunk acquisition prices paid in the past, rather than the ongoing tangible capital required to run the operating business today.

Furthermore, cash flow analysis requires scrubbing raw EBITDA. Depreciation represents actual physical asset decay. Investors must isolate Maintenance Capital Expenditures (MCX) – the spending required strictly to preserve existing operational capabilities – from expansionary growth expenditures. True normalized operating cash flow equals EBITDA minus MCX. Comparing this normalized pre-tax yield against a conservative hurdle rate anchored by 10-year Treasury yields (utilizing a minimum 6% baseline floor) establishes an objective hurdle for capital deployment.

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Can Corporate Profitability Escape the Laws of Competitive Gravity?

The second core framework centers on corporate mean reversion. When grouping equities by Return on Equity (ROE), high-profitability companies experience deteriorating returns over a five-year cycle as competitors enter lucrative sectors to capture excess profits. Conversely, low-profitability companies undergo capacity rationalization, cost restructurings, management changes, and industry consolidation that drive returns back up toward the 10% to 12% corporate average.

Market participants systematically fail to account for mean reversion. Analysts extrapolate existing operational trends infinitely into the future, overvaluing temporary market darlings and severely punishing distressed businesses. Value creation occurs when a business experiences operational deterioration and falls off its historical growth trajectory. The equity price collapses as market participants panic.

The critical skill lies in distinguishing between temporary operational setbacks and permanent capital erosion. When a fundamentally sound business with high tangible ROIC enters a temporary stabilization trough, the risk-reward ratio becomes asymmetrical. Investors do not need to time the exact bottom. Buying during business stabilization, while allowing a two-to-three-year window for corporate rationalization and market recognition, routinely yields exceptional returns.

How Do Non-Economic Sellers Create Asymmetric Bargains?

The most fertile hunting ground for asymmetric value exists in special situations where market participants face structural mandates to sell equities regardless of price. Corporate spin-offs, index removals, and tax-loss harvesting force institutional funds to liquidate positions for non-economic reasons. When an index fund dumps an equity position because the company was deleted from a benchmark, the selling pressure is purely mechanical. Price separates entirely from value.

Similarly, capital structure complexity creates mispriced optionality. When MIPS Technologies traded at $1.22 per share following an index deletion, the firm held $2.20 per share in net cash, placing the business at a negative Enterprise Value of negative $30 million to $40 million. Investors willing to analyze non-recourse debt structures, unbundle consolidated balance sheets, and evaluate asset coverage can structure positions where downside risk is strictly capped while operational upside remains open-ended.

Why Did I Translate These Lectures Into a Visual Blueprint Deck?

Raw corporate finance lectures and transcripts are dense, text-heavy, and difficult to digest quickly during active market hours. To execute these frameworks effectively in modern markets, visual clarity is paramount.

High-density visual architecture forces structured thinking, isolating core formulas, capital structure mechanics, and special situation decision trees into clear conceptual blueprints.

I converted these foundational lessons into a 6-part visual presentation deck. This visual deck breaks down Greenblatt’s formulas, capital structure adjustments, mean reversion curves, and special situation case studies into precise, easy-to-scan diagrams. It serves as a visual reference manual for institutional fundamental analysis.

Joel Greenblatt Classes 1-7

10.8MB ∙ PDF file

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Until next time, keep compounding.

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

In late 2002, Estée Lauder announced a deliberate decision to reallocate capital into brand-building initiatives designed to strengthen its long-term pricing power and revenue trajectory.

The institutional reaction was immediate and punitive. Wall Street dumped the stock, driving a single-day decline of 15% because the additional marketing spend depressed next quarter’s reported earnings per share.

Around the same time, trading volume in Xerox implied that institutional money managers owned the stock for an average duration of less than four months.

Wall Street demands immediate gratification. That’s largely true today. And it was true twenty years ago when Nick Sleep started his investment partnership.

The modern asset management ecosystem already operated on hyper-compressed time horizons back then, where short-term price volatility is consistently mistaken for structural risk.

Professional fund managers frantically trade around quarterly data points, driven by job security concerns and index tracking mandates. This mechanical skittishness creates massive friction for traditional allocators. It also creates an extraordinary competitive advantage for those willing to look past the 18-month institutional horizon.

Deconstructing the Early Nomad Letters

Today, I spent my day systematically analyzing the early partner letters of the Nomad Investment Partnership spanning 2001 to 2004. My goal was straightforward. I wanted to extract the core mental models that transformed a small, unconventional London firm into one of the most remarkable compounding vehicles of our era. Nick Sleep and Qais Zakaria ran a true investment partnership as opposed to the standard fee-harvesting hedge funds you encounter all too frequently.

They built their edge on rigorous fundamental analysis, low portfolio turnover, and absolute return discipline.

Stripping away the daily noise of market commentary requires patience. It requires a willingness to re-examine received corporate finance wisdom.

My investing philosophy today is largely influenced by Sleep’s teaching. I even have a self-made book version of their letters, which I probably read five times or more.

When quarterly drawdowns hit my concentrated positions, I look straight through the market quote. I focus entirely on underlying business economics. If the ultimate destination of the firm remains intact – a concept Sleep formalized in his letters – price drops are welcome gifts. Drawdowns present prime buying opportunities.

Conversely, poor management behavior drives an immediate pass. I walk away from executive teams executing stock buybacks at cyclical tops or hesitating when high-return reinvestment opportunities emerge. Excessive compensation packages without genuine insider alignment reveal flawed incentives. I distrust corporate boards issuing dividends simply because industry peers do so.

Per-share capital discipline is truly rare.

My current portfolio reflects this deliberate time-horizon arbitrage. Positions like Tiger Brokers and Ashtead Technology face temporary operational headwinds, yet their multi-year trajectories remain completely sound. The market penalizes near-term uncertainty. I gladly take the other side of that trade.

Structural Frameworks for the Patient Allocator

The early Nomad letters establish several foundational mental models that remain vital for sophisticated investors.

The first framework centers on time horizon arbitrage through the equity yield curve. Borrowing concepts from fixed income analytics, the equity yield curve plots expected investment returns against holding duration. Up to 18 months, according to Sleep, equity markets display reasonable efficiency. Beyond two years, the curve steepens dramatically, offering potential returns reaching 35% annualized. Institutional fund managers rarely venture into this duration zone because average mutual fund holding periods hover around eleven months. They prize immediate liquidity over multi-year business compounding. You capture this return spread by holding quality assets far past the wall of institutional myopia.

The second mental model deconstructs business quality through scale efficiencies shared. Conventional executive teams treat growing scale as an opportunity to widen gross margins and extract immediate profits. Costco Wholesale inverted this incentive structure entirely. Management capped gross markups at 14% on branded goods and 15% on private labels. Every unit cost reduction achieved through rising purchasing scale gets returned directly to the consumer via lower shelf prices. The customer reciprocates with radical brand loyalty and $45 annual membership subscriptions. Sales density surges to industry-leading levels. High sales volume drives further purchasing scale. The competitive moat deepens automatically as the company grows. It operates as a perpetual motion machine.

The third model addresses mathematical underwriting and per-share discipline. Sleep approaches position sizing by targeting entry prices at fifty cents on the dollar of appraised intrinsic value, assuming a baseline 10% annual growth in underlying business value. If management allocates capital wisely, a dollar of intrinsic value compounds to $1.62 over five years, yielding a 26% annualized return from a fifty-cent entry point. Even if half of your investment decisions prove completely flat due to capital allocation errors, the aggregate portfolio still yields an annualized return around 13%. Math protects downside. Disciplined entry prices absorb analytical errors.

I mapped the 2001-2004 early Nomad principles into a clean 6-slide visual Blueprint deck below. This architecture translates Nick Sleep’s early letters into an institutional reference framework that you can deploy directly within your own research process.

Blueprint: 2001-2004 Nomad Letters

9.15MB ∙ PDF file

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Until next time, keep compounding.

Disclaimer:

The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

Great research without distribution is a stranded asset. Over three to four years – if I recall correctly, between 2019 and 2023 –, I dedicated thousands of hours to building a rigorous, three-volume investment framework spanning roughly 2,100 pages.

I built it because I wanted an unshakeable system for evaluating businesses, understanding competitive dynamics, and allocating capital effectively.

Yet, experience teaches a clear lesson about publishing: even institutional-grade analysis, as my deep dives and market commentary, needs a clear distribution mechanism to create actual value for readers.

In $100M Offers, Alex Hormozi made a point that applies just as directly to equity research as it does to traditional businesses. He noted that if you put a hot dog stand in front of a starving crowd at 2 AM, you can sell terrible hot dogs and still make a fortune – because the crowd dictates initial success, not the product alone.

"If you put a hot dog stand in front of a starving crowd at 2 AM, you could sell terrible hot dogs and still make a fortune. It's not about the hot dog; it's about the crowd."

He followed that up in $100M Leads with an equally sharp reality check:

"You can have the best product in the world, but if nobody knows about it, you’ll still go out of business."

I am confident in the quality of my research. Very confident. I know the sheer volume of labor and market experience required to produce high-level fundamental analysis, and I don’t see many others with a similar value proposition out there.

If you don’t want to ever miss a new “Visual Blueprint,” make sure you are subscribed and on the email list!

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But product quality only dictates long-term reader retention. It does not solve the initial hurdle of attention.

To bridge that gap and make complex ideas vastly more accessible, I am introducing a new format to the publication: “Blueprints.”

Four Pillars for the Modern Investor

Blueprints are visual slide decks designed to condense heavy corporate finance concepts, valuation mechanics, and strategy frameworks into clean reference sheets.

Rather than replacing my long-form deep dives, they act as concentrated architecture for your investment process.

I am structuring this format around four core pillars:

• Summaries of essential investing books. The classic texts by figures like Seth Klarman, Warren Buffett, and Philip Fisher contain timeless wisdom, but their core mental models can easily get lost in dense prose. Visual blueprints bring those core frameworks back into sharp focus.

• Direct modules pulled from my proprietary investment framework. Distilling these core modules – attached below are two examples I already created (my systematic mental models for where to deploy capital geographically + specific tools from my idea generation toolkit) – allows me to share the most valuable insights from my three-volume set over time. It is a deep reservoir of material that will fuel content for years.

• The works of Michael Mauboussin. If I were forced to choose only one contemporary writer on stock picking to read for the rest of my life, it would be Mauboussin without hesitation. Periodically forcing myself to systematically reread and synthesize his essays on return on invested capital, expectations investing, and competitive advantages keeps my own edge sharp.

• General investing, market, and corporate finance concepts. This covers everything else.

On Algorithmic Lotteries & The Beauty of a Newsletter

Social media algorithms are notoriously noisy. They constantly push creators toward clickbait, macro-panic, or apocalyptic bear thesis content designed to generate outrage rather than clarity.

I refuse to play that game.

Substack built its own social feed, Notes, to help creators distribute work. Yet, social feeds remain black boxes. Unpredictable.

The numbers prove it. When I shared my visual summary of Michael Mauboussin’s paper on Competitive Advantage Periods on X, it became my most bookmarked post to date.

A short while later, my breakdown of Seth Klarman’s Margin of Safety pulled in nearly 300 bookmarks on the platform.

Investors clearly found value in these visual frameworks. But when I posted those identical visual blueprints to Substack Notes, the best-performing piece gathered roughly seven likes.

I just checked … Six.

I’m not even sure how many of you actually got to see any one of these slide series decks? It requires you to open the Substack Feed in the first place, which I’m not sure how many readers actually regularly do.

My hunch is very few.

If you do find value in the PDFs below, please let me know via the comments. This keeps me motivated to keep creating these Blueprints.

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So that discrepancy exposes a massive flaw in relying on discovery feeds. Algorithms change overnight. It’s a bit of a lottery. Reach disappears without warning. Building a publishing strategy around algorithmic luck makes zero sense when the underlying goal is delivering rigorous, institutional-grade analysis.

I prefer direct distribution.

That’s why I plan to distribute these "Blueprints” as regular posts too. And by publishing these Blueprints as standalone Substack pieces, I intend to route every issue directly to your inbox. You receive the complete essay, a slide deck that is easily digestible in five minutes and hopefully adds some value too, alongside a downloadable PDF version of it.

This format lets you take ownership of the material. You can store the PDFs on your desktop, file them by topic, and build a permanent reference library over time.

I may eventually curate these decks into a single master archive or restrict certain downloadable PDFs to paid subscribers, but my immediate focus is making sure you get direct access to these frameworks, which is why at the bottom of this post, I have attached downloadable PDF files for all the visual Blueprints created so far.

If you find real utility in these decks, share this post with fellow investors or forward it to someone who values high-quality research.

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PS: I also added a new tab to the homepage for the new format!

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

From my Framework

IDEA GENERATION SERIES: Where Good Ideas Come From

Idea Generation I

8.85MB ∙ PDF file

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MARKET SELECTION PLAYBOOK: Where to Invest – The Intelligent Investor’s Geographic & Sector Selection Playbook

Where to Invest

11.1MB ∙ PDF file

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SETH KLARMAN: Margin of Safety – Capital Preservation in an Irrational Market.

Margin of Safety

10.9MB ∙ PDF file

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LAWRENCE A. CUNNINGHAM, TORKELL T. EIDE & PATRICK HARGREAVES: Quality Investing – Owning the Best Companies for the Long Term

Quality Investing

8.31MB ∙ PDF file

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MICHAEL MAUBOUSSIN: Competitive Advantage Period: The Neglected Value Driver

CAP Period

8.64MB ∙ PDF file

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Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.