I publish two deep-dive investment research pieces every month for Macro Notes subscribers.
Each one represents 40-60 hours of work. Reading transcripts. Building financial models. Mapping competitive landscapes. Interviewing industry experts when I can. Digging through patent filings at 2 AM because something doesn’t add up.
People sometimes ask me: “How do you find these companies? How do you know what to research?”
The honest answer? I’ve spent years developing systematic processes that help me separate signal from noise. These aren’t secret formulas or proprietary data feeds. They’re disciplined research methodologies that anyone could use—but most investors won’t, because they require patience, curiosity, and a willingness to look where others aren’t looking.
Today, I’m pulling back the curtain. I’m going to share the exact ten processes I use to identify investment opportunities that the market has overlooked, mispriced, or simply hasn’t discovered yet.
Some of these led me to companies that returned 300%, 500%, even 1000%+ over the years. Others helped me avoid expensive mistakes that looked compelling on the surface but were traps underneath.
Let me walk you through how I actually do this work.
Process #1: Invisible Monopolies Analysis
Most investors obsess over Apple, Amazon, Tesla—the companies everyone knows and talks about.
I hunt for companies you’ve never heard of that control chokepoints nobody’s paying attention to.
Here’s how it works: I start with an industry experiencing structural growth. Let’s say electric vehicles. Everyone’s focused on Tesla, Rivian, Lucid—the car manufacturers themselves.
I go backward through the value chain.
Who supplies the critical components? Who makes the equipment that manufactures those components? Who owns the patents that everyone must license? I’m looking for companies with 60%+ market share in narrow, highly defensible niches. Bonus points if their product represents a tiny fraction of their customer’s total costs—because that makes price increases almost automatic.
Let me give you a real example.
Before ASML became a $300+ billion company that everyone in finance talks about, they were an obscure Dutch manufacturer of lithography equipment. But here’s what made them extraordinary: they were the only company on Earth capable of producing extreme ultraviolet (EUV) lithography machines.
Not Intel. Not Samsung. Not TSMC. None of them could manufacture cutting-edge semiconductors without ASML’s equipment.
ASML wasn’t flashy. They weren’t consumer-facing. They just happened to be absolutely, completely, 100% indispensable to the entire global semiconductor industry.
That’s an invisible monopoly.
Another example that I love: Verisign. They hold the exclusive contract to operate the .com and .net domain registries. Every single .com domain renewal—billions of dollars annually—flows through them. Near-zero competition. Minimal capital expenditure. It’s a toll bridge on the internet itself, hidden in plain sight.
When I find companies like this, I dig deeper. I model out their pricing power. I map their competitive landscape (or lack thereof). I figure out what could disrupt them.
Most of the time, the answer is: nothing can disrupt them. At least not for 5-10 years.
That’s when things get interesting.
Process #2: Technology Wave Mapping
Every major technological shift creates not just one winner, but an entire ecosystem of winners.
The problem? Most investors pile into the most obvious plays and ignore the rest of the value chain.
When I identify a genuine technological inflection point—not hype, but real adoption curves accelerating—I don’t just look at the headline companies. I map the entire ecosystem.
I break it into three layers:
Infrastructure Layer: The foundational technologies and platforms
Enablement Layer: The tools, services, and components that builders need
Application Layer: The end-user products and services
Here’s the insight that’s made me a lot of money over the years: the infrastructure and enablement layers often generate more consistent returns than the application layer, where competition is brutal and winner-take-all dynamics create lottery-like outcomes.
Let me show you what I mean.
During the smartphone revolution, everyone focused on handset manufacturers. Remember when Nokia was untouchable? Then BlackBerry? Then it was all about who could compete with the iPhone.
The real money wasn’t there.
The real money was in the ecosystem: ARM Holdings, whose chip designs ended up in every single smartphone. Corning, who made Gorilla Glass. Skyworks Solutions, making the wireless chips. Apple and Google, who controlled the operating systems.
Infrastructure and enablement. Not the flashy consumer products.
Fast forward to today with AI. Everyone’s obsessed with OpenAI, Anthropic, Google, the companies building the models themselves.
I’m looking at Nvidia (the GPUs), TSMC (manufacturing those GPUs), Vertiv (cooling systems for data centers that run AI), Constellation Energy (power supply for AI infrastructure).
These companies aren’t building ChatGPT. They’re building what makes ChatGPT possible.
When I map a technology wave, I literally draw it out. I create a visual representation of every layer, every dependency, every critical component. Then I start researching the companies that control the most important nodes in that network.
It’s tedious work. It takes hours. But it reveals opportunities that 99% of investors will never see because they’re too focused on the shiny applications at the top of the stack.
Process #3: Weak Signal Investigation
Markets are pretty efficient at processing obvious information.
They’re terrible at connecting dispersed weak signals into coherent patterns.
I’ve built a systematic approach to aggregate signals that, individually, seem meaningless but collectively reveal major shifts 6-18 months before they become consensus.
Here are the signal categories I monitor:
Patent filings: What are companies protecting that isn’t yet in their products?
Strategic hiring: Who’s recruiting talent from which competitors or adjacent industries?
Insider transactions: Are executives buying their own stock with personal money?
Regulatory changes: What’s being approved or restricted that will reshape industry dynamics?
Search trends: What are engineers, doctors, or industry professionals suddenly researching?
Supply chain shifts: What are manufacturers suddenly ordering in bulk?
Conference agendas: What topics are dominating industry gatherings?
The key is correlation across multiple signal types.
One data point is noise. Five data points pointing in the same direction? That’s a pattern.
Let me give you a concrete example that made me—and my subscribers—a lot of money.
In early 2019, I started noticing weird signals around weight-loss treatments:
Novo Nordisk was hiring cardiovascular researchers. That seemed odd for a company primarily known for diabetes treatments. Then I noticed they were filing patents for GLP-1 agonists with indications that went way beyond diabetes. Clinical trial recruitment was accelerating for obesity studies. And here’s the kicker: bariatric surgery centers in certain test markets were reporting declining patient volumes.
Individually? Each signal was forgettable, maybe even meaningless.
Together? They were forecasting what would become the Ozempic/Wegovy revolution—years before the mainstream media caught on, years before these drugs became household names.
Novo Nordisk and Eli Lilly have added hundreds of billions in market cap since then. But the opportunity was visible to anyone paying attention to the weak signals in 2019.
I track these signals systematically. I have spreadsheets. I have RSS feeds. I have Google alerts set up for oddly specific search terms that most people would never think to monitor.
It’s boring work. It’s often frustrating because 90% of weak signals lead nowhere.
But that other 10%? That’s where fortunes are made.
What’s Coming in the Premium Section
Those three processes have been responsible for some of my best investment ideas over the past five years.
But they’re just the beginning.
In the premium section below, I’m going to reveal seven more research processes that I use to identify opportunities the market hasn’t priced in yet:
Process #4: Contrarian Narrative Deconstruction — How I systematically challenge consensus views and find mispriced opportunities when everyone believes the same story. This process helped me identify a 400%+ return in commercial real estate when everyone said the sector was “dead forever.”
Process #5: Financial Forensics Deep Dive — The forensic accounting techniques I use to spot both value traps (companies that look healthy but aren’t) and hidden gems (companies that look troubled but are actually solid). This saved me from a near-certain disaster that fooled most of Wall Street.
Process #6: Confluence Mapping — How I identify sectors where multiple macro trends converge simultaneously, creating unstoppable structural tailwinds. This is how I spotted the weight-loss pharma opportunity 18 months before it became obvious.
Process #7: Disaggregation Analysis — My system for breaking down complex multi-division companies and finding situations where the sum of the parts is worth far more than the market price. I’ll show you exactly how I valued Alphabet’s segments separately and found 40%+ upside.
Process #8: Smart Money Tracking — How I monitor the moves of specialized institutional investors (not to copy them, but to reverse-engineer their thesis and use it as a research trigger). This led me to a small-cap pharma company that eventually got acquired at a 900% premium.
Process #9: Risk-Asymmetry Matrix — My framework for evaluating opportunities not by probability of success, but by risk-reward asymmetry. This is how I find situations where I can be wrong without catastrophe but being right changes everything. I’ll show you the exact math.
Process #10: Durable Competitive Advantage Investigation — How I empirically test whether a company actually has a moat or if management is just telling a good story. This includes four specific tests that separate real competitive advantages from narrative fluff.
These seven processes require more explanation, more examples, and more nuance than I can fit in a free newsletter. They’re the result of years of trial and error, expensive mistakes, and hard-won lessons.
But they work.
They’ve helped me identify companies that went on to return 300%, 500%, even 1000%+ over the years. They’ve helped me avoid disasters that looked compelling on the surface. And they’ve given me a systematic way to research markets that doesn’t rely on luck, tips, or hoping I’m smarter than everyone else.
If you’re serious about finding overlooked opportunities and doing the kind of deep research that actually moves the needle on your returns, these processes will change how you approach investing.
Let me show you exactly how they work.