The Silba "Story" Framework

A human approach to company analysis in an algorithmic world

The other day, I read this wonderful post by

Will Storr

Welcome to You Are a Story with Will Storr

It did struck a chord with me. It’s a great read.

It's a vivid story, that makes you suddenly see a tick, yes, a tick, in a completely different light. Within a few sentences you evolve a bit of empathy for that tick, the hero of the story. On how different the universe feels through his senses.

And just as the tick perceives only what its limited senses allow, we humans perceive reality through narrative frameworks. This isn't a limitation—it's our evolutionary advantage, our unique umwelt.

You know, I've been thinking about this interplay between stories and data for years, and it always strikes me that we've created this false dichotomy. As if, rationality belongs to numbers and emotion belongs to narrative.

To a degree, yes. But also, not really. And more importantly, that's not how our minds actually work. I will try to apply it to when an investor, or analyst, is trying to understand a company for the first time.

When I consider what a company truly is, I'm reminded of what Borges once suggested about the universe being a kind of library. (I love the concept, it makes you think in such a different way) Well, companies, in that sense, are libraries of human experiences, decisions, relationships, and cultural patterns that happen to generate financial statements as a byproduct. There’s just so many of those human interactions on a daily basis, that define what a company is.

Harari would recognise this immediately—what he calls "imagined realities." In his analysis, corporations are among humanity's most powerful fictions, entities that exist primarily in our collective imagination and shared stories. Yet these 'fictions' build real products, employ real people, and generate real financial statements.

Think about it - when I analyse Hornbach or X-FAB or any company I've written about, I'm not just collecting facts and numbers about them. Primarily, I am. But more importantly, I'm engaging with them as living entities with personalities, habits, and tendencies. This isn't anthropomorphising - it's recognising their true nature.

There's something deeply ironic about how we've come to privilege quantitative analysis in investing. We take these rich, complex human organisations and reduce them to numbers, then act surprised when our models fail to predict their behaviour. It's like trying to understand a novel by counting how many times each letter appears.

Why are we doing that then?

Two reasons I can see. First, with the advent of computers, we got immense power in calculations. And secondly, quantifying something gives you the appearance of objectivity - a single number that makes it easier to compare and to sort.

I think there's a psychological comfort in numbers. They create the illusion of control in an inherently uncertain domain. When you can say a company is trading at 12x earnings versus the industry average of 15x, you feel as though you've grasped something solid in a sea of ambiguity.

There's also an institutional aspect we shouldn't overlook. Imagine a portfolio manager explaining an investment decision to a committee. "The numbers indicate undervaluation" sounds more defensible than "I find the company's story compelling", right? The first approach transfers responsibility to the model; the second requires personal judgment. And if you ever worked in any of those institutions, you can recognise the risk and weight those two paths can include.

That said, as with every domain, some people saw things differently and tried to think outside the box. The pioneers of their fields. We've seen this pattern before - reducing a concept to a narrative rather than numbers and ratios. "Moat" is perfect example, to make the obligatory Buffett reference. It's not technically precise, but it's immediately graspable. There's no "number" to have a quantifiable moat score (I’m sure people tried to do that). But, most of us can easily apply it to any business they examine, we can try to visualise what the "moat" is.

Which brings me back to why I feel that traditional analysis is like trying to understand a person by only looking at their blood test results. Tons of data, very little understanding of the actual entity.

And that's why I created my "Story" approach - understanding the whole organism - its history, behaviours, environment, and potential futures. Because when you truly understand a company's story, you see opportunities others miss.

Our brains need structure to process complexity. This framework does just that - nine interconnected segments that map directly to how our minds naturally understand businesses.

Origins gives us foundation.

How do they make money provides function.

Numbers delivers evidence.

People show agency.

Competition & the Moat(?) reveals context.

Mr. Market offers external perspective.

Bear and Bull theses create tension.

And the "So what do we make of all that?" brings resolution and leaves the conviction to the reader.

Together, they form a mental model that sticks because it works with - not against - our cognitive architecture.

Let’s explore the 9 “Story” segments.

The Nine Segments

It was Warren Buffett himself, in one of his more direct moments, who provided the tersest case for thorough company analysis. "Risk," he said, "comes from not knowing what you're doing." Behind this folksy simplicity, we find the central challenge facing every investor:

how do you really understand a company before committing capital?

The nine-segment framework I've developed doesn't just answer this question — it reframes it entirely.

While traditional analysis often resembles an accountant's ledger, this approach constructs a narrative that reveals the company's soul as well as its balance sheet.

Let's examine the architecture of these segments:

Origins: DNA Doesn't Lie

A company's founding story is more than historical trivia; it's the genetic code that influences every decision for decades. When we learn that OVHcloud began with a Polish immigrant family operating from a borrowed basement — developing proprietary water-cooling systems out of necessity rather than innovation — we glimpse a bootstrap mentality that explains their current capital allocation strategies.

This section reveals the corporate equivalent of what psychologists call "formative experiences." Tech companies founded by engineers approach problems differently than those started by marketers. Financial firms built by traders think differently than those established by risk managers. DNA doesn't lie, even when PowerPoint presentations do. And numbers can definitely not help you here. You need to grok the story. The origins story.

How Do They Make Money: Following the Cash

Ask most investors to explain how a company makes money, and you'll get the business school version: product, market, value proposition. Ask again, but with genuine curiosity, ask "ok yes, but what do they really sell?" and then you will get a much better answer: the actual economic machinery driving returns. Or you might get a blank stare.

For example, when Baader Bank's revenue breakdown shows negative net commission income, we immediately understand something fundamental that glossy annual reports obscure. What looks like an accounting anomaly, is actually a structural flaw in their business model. And that’s why one needs to understand the essence of what this company relies on, to make money.

Baader Bank makes money by inserting itself into financial flows—capturing small slices of enormous transaction volumes, monetising unused banking capacity, and collecting interest on other people's money.

The value is in distinguishing between fundamentally sound businesses facing temporary challenges versus fundamentally flawed businesses experiencing temporary success. Industry jargon calls this "unit economics." I prefer a simpler term: reality.

Numbers: Where Narratives Face Judgment

Numbers are where corporate storytelling meets mathematical truth. For investors seeking understanding rather than confirmation, this section serves as a judicial proceeding where management claims face cross-examination by financial facts.

The key here is to understand what the numbers are telling us, while carefully observing trends, ratios and relative comparisons.

Numbers also expose the paradoxes that require deeper investigation. For example, when Energiekontor's segment profitability shows Power Generation delivering 85.6% of profits from 53.6% of revenue while Project Development contributes just 4.8% of profits from 43.1% of revenue, we're not looking at a temporary aberration. We're watching a fundamental transformation in real-time.

Numbers ground the narrative, and they give credibility and accuracy to it.

People: The Human Element

Companies aren't abstract entities. They're collections of human beings making decisions under constraints with incomplete information. Understanding who these people are — and how they work together — often explains corporate behaviour better than any strategic framework or financial model.

When Energiekontor's management explains, "We have no rigid organisational form, rather there is a constant experimentation with it," we glimpse an adaptive capability that wouldn't appear in any organisational chart. When the Klaba family increases their OVHcloud ownership from 68% to 81%, we're learning something profound about both their conviction and the governance implications of concentrated control.

As any intelligence analyst will tell you, knowing who people are — their backgrounds, incentives, relationships, and beliefs — offers more predictive power than any stated corporate strategy. After all, it's not companies that make decisions; it's people. And who are their customers? Yes, more people.

Competition & the Moat(?): Reality Check

The deliberate question mark in "Moat(?)" isn't typographical uncertainty; it's healthy skepticism about competitive advantages that often prove as durable as sandcastles at high tide.

This section does more than identify competitors. It serves a purpose to examine how competition actually works in this specific industry.

When OVHcloud faces hyperscalers who invest "€4 billion quarterly in European infrastructure" versus OVHcloud's "€993 million annual revenue," we're not just talking about scale differences. We're identifying a fundamental constraint on their strategic options, regardless of execution quality.

By applying frameworks like Hamilton Helmer's "7 Powers," this section distinguishes between genuine competitive advantages and temporary outperformance. It identifies the specific metrics that signal competitive health — the canaries in the coal mine that will show trouble before it appears in financial statements.

Mr. Market: The Psychology of Pricing

Named after Benjamin Graham's metaphor for market volatility, this section treats stock price not as a scorecard but as information about investor perception and expectation. After all, that’s what the market is really.

When Baader Bank's shares surge 568% during pandemic trading volatility, followed by subsequent declines as profits normalise, we're learning about both market dynamics and the cyclical nature of their business. The price chart isn't a dry historical record; it's psychological evidence above all. It’s a vector of sentiment and fundamental performance.

This section helps readers distinguish between price movements driven by fundamental changes versus those reflecting sentiment shifts. It reveals whether current prices reflect optimism or pessimism and whether investor expectations are likely to be exceeded or disappointed.

Think of it as the financial equivalent of mind-reading — not of the company's executives, but of the collective intelligence (and occasional madness) of markets.

Bear Thesis: The Case Against

The Bear Thesis could be just listing risks. The way I see it, it's more about constructing the strongest possible case against the company. Rather than presenting sanitised concerns, it asks: what would the smartest short-seller on Wall Street say about this business? How can you use all the available information, facts and numbers to reason how things can go wrong for this company?

This structured skepticism forces you to confront potential flaws you might otherwise overlook due to confirmation bias. It connects separate weaknesses into a coherent narrative explaining how and why the company might fail.

The value comes not from pessimism for its own sake but from creating a mental model for monitoring specific indicators that would validate or invalidate the bear case over time.

In investment as in geopolitics, anticipating threats requires understanding them first.

Bull Thesis: The Path Forward

The Bull Thesis articulates not just why the company might succeed, but how it could create substantial value beyond current expectations. It identifies potential catalysts, hidden assets, or market misperceptions that could drive outperformance.

When Baader Bank's bull thesis suggests it could be "Europe's Charles Schwab moment," it's creating a conceptual framework for understanding how significant value creation might occur beyond current financial projections.

By presenting optimistic arguments with the same analytical rigor applied to bearish ones, this section provides intellectual balance. It helps readers identify specific metrics and developments that would validate the bullish case over time.

Like the bear thesis, it serves as a map for future developments rather than a static judgment.

So What Do We Make of All This?: Synthesis

The concluding section moves beyond summary to synthesis, integrating contradictory evidence into a nuanced perspective. Rather than providing simplistic answers, it identifies the fundamental tensions and paradoxes that will determine the company's fate.

As humans, we have this special ability to hold fundamentally conflicting thoughts in our head. Cognitive dissonance as a psychologist would say. This is where a machine would struggle to reason. And yet, we can somehow do it. Granted, each one of us can read the same story and end up with slightly, or greatly, different conclusions. And that’s why I stay away from concluding one way or the other.

I strongly believe that conviction for an investor or analyst, has to come from within.

It has to be an elaborate combination of those conflicting thoughts and arguments that the story efficiently presents. And I know that every one of us does it differently and that’s the unique edge someone can have. Use it. Embrace it. Improve it.

Also, this section often adopts a more philosophical tone, examining what the company represents beyond its specific products or services. It helps you see both the forest and the trees, connecting company analysis to larger questions about industry evolution, technological change, or economic shifts.

The Framework's Strategic Value

In a world drowning in financial data but starving for insight, this nine-segment approach stands as a counterpoint to the madness of modern financial analysis.

Rather than reducing companies to spreadsheet cells or simplistic buy/sell recommendations, as I have seen numerous times across the web, the "story" creates a comprehensive understanding that respects complexity without surrendering to it.

For time-constrained investors, this structure delivers what might be called intellectual efficiency. In just 25-30 minutes, it provides not just facts but a mental model for understanding how a company works, why it exists, and what will determine its success or failure.

Ask yourself: in an age of algorithmic trading and artificial intelligence, what advantage remains for human investors? Not data processing speed or pattern recognition — computers beat us soundly there. Instead, our edge is understanding context, narrative, and human behaviour. This framework weaponises those uniquely human capabilities.

Which brings us back to Buffett's admonition about risk coming from ignorance. The nine-segment framework, the story, doesn't eliminate investment risk. But by illuminating a company from multiple angles — historical, financial, organisational, competitive, and psychological — it replaces the darkness of uncertainty with the clarity of calculated risk-taking.

In today's markets, that might be the only sustainable advantage left.

This is the Silba "Story" framework, it has helped me a lot so far to identify opportunities, I simply wouldn’t otherwise.

I will keep iterating, experimenting and improving it. And I welcome any criticism, suggestions, or experiences you've had applying similar frameworks.

This isn't a static model handed down from on high, but a living analytical approach that improves through collective refinement.

What company have you analysed recently that defied traditional metrics? Which segment of this framework do you find most revealing? The conversation about how we understand companies is ultimately more valuable than any single framework — even this one.

Antonis @

Silba