Get to Know the Manager & the Strategy

Benjamin Graham, the father of value investing, once wrote that in the short run, the market is a voting machine.

But in the long run, it's a weighing machine.

That single sentence is the foundation of how I invest at Kehlet Capital.

In the short run, prices reflect whatever the market is voting for — sentiment, narrative, headlines, a bad quarter, a tweet.

In the long run, they generally reflect business performance.

If a company compounds earnings at 15% a year for a decade, the stock price tends to follow.

That belief drives almost everything I do at Kehlet Capital.

That's why I study the business, not the chart.

That's also why I'm willing to hold positions through volatility, because short-term prices don't always reflect long-term reality.

And that's why I invest patiently.

Compounding takes time.

Some of the best returns in public markets have been generated by investors who were willing to wait — years, sometimes decades — for the weighing machine to do its work.

Simply put, the strategy is to try to find the next Netflix or Google before it becomes a household name.

I call these moonshots.

It's like venture capital for the public market — where the goal is to find a small number of exceptional winners, and hold them for the long term.

I pursue this goal in four ways:

First, I search for opportunities in the microcap space, because many of the best long-term winners were once small, under-followed businesses.

Second, I hold a concentrated portfolio, because great ideas are rare and holding too many positions can dilute the impact of my best ideas.

Third, I focus on what I consider high-quality businesses - companies with durable competitive advantages, significant long-term growth opportunity, and exceptional management. Because these are the businesses most likely to compound over the long term.

Fourth, I hold stocks for the long term — because compounding takes time.

And that's the strategy I run at Kehlet Capital.

My process is a funnel.

I start with a universe of about 1,500 publicly-traded U.S. microcap companies.

From there, I filter out the parts of the market where I'm unlikely to have an edge — early-stage biotech and biopharma, banks, and insurance companies.

I also filter out commoditized industries like REITs, where exceptional businesses are hard to find.

What's left is a smaller universe of businesses I can actually research.

From there, I work through three levels of research.

Level one is a high-level review.

I use my Qualitative Research App — a tool I built that uses AI to digest company filings, investor presentations, and earnings transcripts.

The goal is to quickly decide whether the business is worth a deeper look.

Level two is verification.

If a business looks interesting at level one, I move on to questions that the company's own materials can't answer.

I read industry reports. I talk to customers. I have conversations with management. I visit facilities.

This is the slow, unglamorous, boots-on-the-ground part of the process.

Level three is valuation.

If a business survives the first two levels, I use my Financial Analysis App — another tool I built with AI — to model the business, estimate an expected return over the next five years, and set a price target.

Only then will a business make it into the portfolio.

That's how I find the 5 to 15 businesses I own.

The primary way I source ideas is by going down the list, one at a time, of all the companies in my database.

As Peter Lynch famously said — the person that turns over the most rocks wins the game.

But ideas can come from anywhere.

A conversation. An investing publication. Or an industry report.

So I try to cast a wide net and always be on the lookout.

That's how I find ideas.

I would argue that the most important metric in investing is return on invested capital, or ROIC for short.

I think Charlie Munger put it best when he said: over the long run, the return you earn on a stock will roughly equal the return on capital the underlying business earns.

So, if a business earns 6% on capital for 40 years a shareholder will likely get somewhere close to a 6% annualized return — even if they bought the stock cheaply.

But a business that compounds at 18% will return something very different.

So when I evaluate a business, the first question I ask is: Can it achieve high returns on capital over time?

And I believe sustained high ROIC comes down to qualitative factors — namely, the strength of a business's competitive advantage.

I think about this in two parts.

The first is bargaining power — basically, does the company have real pricing power?

That depends on whether customers have alternatives, how costly it would be to switch, and how sensitive they are to price.

The second part is barriers to entry.

That is, bargaining power isn't worth much if it can be competed away.

So I look for signs of sustainable moats — things like network effects, switching costs, efficient scale, or intangible assets like a strong brand or intellectual property.

To work through all of this efficiently, I use a custom-built Qualitative Research App to digest filings, transcripts, and industry materials with the help of AI.

But to truly understand a business, it's important to get off the computer and into the real world.

That's where boots-on-the-ground research comes in.

It's about the slow, unglamorous work —

like flying to New York to attend a shareholder meeting for a company that doesn't do earnings calls,

or visiting a local police station to test a non-lethal restraining device,

or going to an industry conference to talk to customers, suppliers, and competitors.

And that's how I try to understand a business.

Beyond competitive advantage, I look at two more things.

First, is growth opportunity.

To compound capital, great businesses need opportunities to reinvest.

So I look for companies addressing large and growing markets.

Second, is management.

Every company experiences obstacles over time.

The strongest companies address problems head on and adapt to tackle challenges.

In my experience, outstanding management is usually what separates the businesses that compound for decades from those that don't.

That means asking questions like do they operate with integrity? are they passionate about the business and aligned with shareholders? Do they have strong business acumen, and do they have a track record of success?

If a business passes all of these criteria — sustainable competitive advantages, meaningful growth opportunity, and exceptional management — the next step is to merge the narrative with the numbers and try to estimate the company's intrinsic value.

And that's how I evaluate ideas.

At the most basic level, the value of a business is the present value of its future cash flows.

A company is worth the money you can get back from it — minus some discount based on your desired rate of return.

That's the principle behind a discounted cash flow analysis, or DCF.

Simple in theory.

But in practice, a DCF requires assumptions about future sales, profit margins, tax rates, capital requirements, market size, growth rates — and the costs of debt and equity.

Each assumption stacks on the others.

Which means any DCF estimate is largely conjecture.

In my opinion, a simpler and more robust approach comes from Bruce Greenwald and Judd Kahn's book Competition Demystified.

They argue that valuation should be thought of in three layers of increasing uncertainty.

The first layer is net asset value — what the business is worth based on its balance sheet alone.

Assets minus liabilities. No assumptions about the future.

This is the foundation — and the lowest reasonable price for any business.

The second layer is earnings power value — the cash flows the business can sustain at its current level of profitability, forever, with no growth.

Less certain than the balance sheet, but more certain than a multi-decade DCF.

The third layer adds growth — but only when the business has real competitive advantages.

Growth in the hands of a weak business destroys value.

Growth in the hands of a strong one compounds it.

Together, these three layers give me a more grounded view of what a business is actually worth — without pretending I can predict the next thirty years.

To work through all of this efficiently, I use a custom-built Financial Analysis App — a tool I built with AI.

It pulls financial data directly from SEC filings, standardizes it across companies, and lets me model businesses the way I actually think about them.

The result is a range of intrinsic values, not a single point estimate.

And when the market gives me a price meaningfully below that range, that's when I act.

And that's how I value companies.

In my opinion, position sizing is part art, part science.

I used to approach it mathematically — using a combination of formulas based on the Kelly Criterion, mean-variance optimization, and my estimate of intrinsic value.

But over time, I've realized that simple is better.

Short-term prices are unpredictable.

And the position that looks cheapest isn't always the one that performs the best.

So today, my general philosophy is to equal-weight each position in the portfolio and rebalance them at regular intervals.

The result is a system that's less precise but hopefully more accurate since it leaves open the possibility for any stock to lead the portfolio and acknowledges my inability to predict short-term price movements.

That's how I size positions.

It's easy to become attached to the stocks you own. So, my goal is to take the emotion out of the selling decision.

That's why I follow a clear framework.

For trims and additions, periodic rebalancing does the work automatically.

I exit a position outright in two scenarios.

First, if the thesis has changed.

It could be a change in management, a deteriorated moat, an accounting concern.

But if the reason I bought is no longer valid, I'll exit the position.

Second, if I find a better opportunity.

If I come across a business I believe has more upside, I'll free up capital from a less compelling name.

Though circumstances sometimes dictate an exit, my ideal holding period is forever.

That's how I think about selling.

When I built Kehlet Capital, I had one purpose in mind: to maximize long-term returns for my clients.

Everything about the firm flows from that — the principles I follow, the strategy I run, the way I do my work, and how I align with clients.

The principles are straightforward.

Fish where the fish are. Concentrate on the best ideas. Stay within my circle of competence. Focus on high-quality businesses. And take a long-term view.

The strategy follows from those.

I'm searching for what I call moonshots — businesses with the potential to compound for decades.

The process is rigorous.

I look for businesses that can reinvest capital at high rates of return, and I verify what I find through deep, real-world research.

And the incentives are simple.

Most of my liquid net worth is invested alongside my clients.

When the portfolio does well, I do well.

When it doesn't, I share in that too.

That's the firm I built.

I've been fascinated by the stock market since I was a kid.

I still remember coming home from school in eighth grade, logging onto AOL, and finding chat rooms where people argued about the Yahoo IPO.

That fascination never left.

But I viewed investing as a hobby. So I decided to study engineering as an undergrad.

After college, I worked as a mechanical engineer for about six years.

It was a great education in how businesses actually operate from the inside — how products get built, how operations work, how decisions get made on the ground.

To this day, I think that experience shapes how I evaluate businesses.

Engineers are trained to be methodical, to test assumptions, and to be suspicious of conclusions that don't survive contact with reality.

And I think those habits transfer well to investment research.

But while working as an engineer, my love for investing never faded. I spent evenings and weekends reading about investing.

I traveled to Omaha for the Berkshire Hathaway annual meetings.

And I researched individual stocks in my spare time.

Eventually it became clear that I needed to make investing my career.

So I went back to school for an MBA at the McCombs School of Business at the University of Texas.

While there, I was selected for the MBA Investment Fund — a student-run fund that actually manages real money for the university endowment.

That gave me my first hands-on experience with portfolio management and opened doors to investment-focused coursework I wouldn't have had access to otherwise.

I also earned the CFA charter along the way.

After business school, I joined Bares Capital Management, a microcap focused investment firm in Austin, as an Equity Research Analyst.

It was a tremendous opportunity.

I got to do exactly what I loved — research small companies — while learning from a well-respected microcap investor.

But my time there ultimately ended.

And I had a choice.

I could find another job with a new firm.

Or I could bet on myself, start my own firm, and invest the way I believed it should be done.

I had modest savings, a small group of family and friends who were willing to back me, and an extraordinarily supportive wife.

I chose the second path. And Kehlet Capital Management was the result.

Simply put, the firm exists because I believe deeply in the philosophy, strategy, and process.

And I built it around alignment with my clients — because I believe that's the best foundation for long-term success.

And that's how Kehlet Capital came to be.