Given my past history of writing research exposing pump & dumps and China Frauds at Geoinvesting, I couldn’t wait for this one.

We dug into where Sean is seeing opportunity right now, what he’s avoiding, and what he’s learned the hard way running a long-short book for years.

and co-hosted alongside me. Both are definitely great stock pickers to follow. If you’d ever like to cohost a skull session with us, just let us know in the comments. Thank you.

Sean runs roughly 80–90% net long, so shorting is a tool he uses carefully, not a constant exposure. A lot of this conversation centered on discipline: the mechanics that make shorting fundamentally different from being long: borrow costs, timing risk, market cap and liquidity constraints, and how easily a short that looked like a winner on paper can actually have lost money once you account for everything.

We also worked through his read on the current bubble environment. He has clear views on quantum, where the first leg has already deflated and he’s recently re-opened positions on IonQ (IQNT) and Rigetti (RGTI). He also walked us through how he’s thinking about the AI supply chain: the optical, semiconductor, and memory names that supply the hyperscalers… where real revenue growth makes timing any unwind extremely difficult right now. The parallels to the 2022 EV bubble came up several times. One idea kept resurfacing through the whole conversation: shorting is almost entirely a timing game, and identifying overvalued companies is only step one.

Toward the end we touched briefly on the Andrew Left’s criminal conviction and what it might mean for both the short selling environment and long-side investors who write publicly about their positions. Right after my chat with Sean, we recorded an Expert Insights session with securities attorney John Sutter, where we cover that case in much more depth. Get alerted on the availability of this conversation by subscribing, for free, to this Substack;

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Sean also got into the secondary benefit of spending so much time studying the short side: occasionally a low-quality company sets up for an asymmetric squeeze that’s playable from the long side. Sean writes about both halves of that on his Substack. We talked through how he thinks about those setups, what makes some workable, and why most of them don’t fit his criteria.

Again, a special thanks to Lukas Milosic from Pixel Research and JEV from for joining the conversation and pushing on several of these topics.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and MS Microcaps LLC , home of the Microcap Quality Index (MSMqi)

Key Takeaways

• Shorting is almost entirely a timing game. Identifying overvalued companies is only step one. The hard part is finding the exact right moment when the market turns on them.

• Borrow costs can quietly destroy short returns. Many “winning” shorts actually lose money once you do the math, which is why Sean caps annualized borrow at 30% before he’ll take a position.

• The AI supply chain has real fundamental growth, which makes the timing of any short hard to call. Most names in the cycle won’t be shortable until at least 2027 or later, when hyperscaler spending growth eventually reverses.

• The quantum bubble has deflated once and is starting to inflate again. The 2022 EV bubble is the closest parallel… it trailed out over multiple years rather than crashing in weeks.

• Spending time studying shorts occasionally surfaces asymmetric long opportunities through squeezes, but they’re rare (one every couple of months) and involve low-quality companies, not high-conviction longs.

• The best squeeze candidates combine extended drawdowns (~90% over three years) with public name recognition. No-name microcaps rarely move enough to play.

• Activist short reports vary wildly in quality. Hindenburg-style research that engages directly with companies is closer to a real edge; many others are smash-and-grab. Even well-substantiated reports sometimes don’t move the stock at all.

Stocks Discussed

• IonQ (NYSE: IONQ): Quantum computing name Sean has recently re-opened a short position on, following the bubble’s initial deflation and a partial rally back.

• Rigetti Computing (Nasdaq: RGTI): Another quantum name in Sean’s current short basket, treated as part of the same industry bubble setup.

• Avis Budget Group (Nasdaq: CAR): Cited as an example of a recent short squeeze in a low-quality, low-float setup.

• Beyond Meat (Nasdaq: BYND): Discussed as another squeeze-prone name fitting the “down significantly with public recognition” profile.

• Virgin Galactic (NYSE: SPCE): Squeeze setup Sean wrote about and traded, though noted he was a day or two late on the move.

• Groupon (Nasdaq: GRPN): Used as the canonical example of a battered, well-recognized name that can run dramatically on small narrative shifts.

• Energous (Nasdaq: WATT): Example of a name with stubborn short interest that eventually pivoted into a real operating business.

• New Era Energy (Nasdaq: NUAI): Discussed as an example of a “fake” AI company, built around repurposing a small oil and gas footprint into an AI data center narrative.

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So for this Skull Session, I invited Dean, alongside Lukas Milosic, for a conversation that went well beyond stock pitches. We spent a lot of time digging into how Dean researches companies, builds conviction, uses AI tools, and thinks about identifying strong businesses before the they look pretty.

Dean comes from a chemical engineering background, but is not “spreadsheet-only way investor.” He doesn’t treat investing like a static exercise. Instead, he thinks in terms of systems that are constantly in motion: management decisions, customer behavior, competitive pressure, and broader industry shifts all interacting at the same time.

The core idea behind what he calls “Motor Investing” is really about tracking that movement, whether a business is gaining momentum or losing it before the financials fully reflect it.

A big part of our discussion also centered on how investing research itself is changing. Dean is already using AI tools for idea generation, screening filings, and mapping industries faster than traditional workflows allow. At the same time, we talked through the parts of the process that AI can’t replace… things like understanding management quality, deciding when site visits actually should occur, and learning how easily you can talk yourself into or out of an idea based on the wrong things.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and MS Microcaps LLC , home of the Microcap Quality Index (MSMqi)

Q&A with Dean Pernas

1. How did your engineering background shape the way you invest today?

Dean Pernas: “It’s your job as a chemical engineer to make sure things don’t deviate. You want static conditions, production consistency, quality consistency day in and day out. Whereas investing is very dynamic. Things are always changing, there’s tail risk involved, and you’re constantly adapting. I found being a babysitter of large plants or well sites pretty boring for me. I always loved investing, and my brother and I have been doing it for close to 20 years now. We just loved researching companies, researching industries, talking to management teams. It’s almost like a game of poker.

When the market crashed in 2020, we thought it was the right time to pursue investing full time. We also realized there was a complete lack of high-quality stock research that was backed by an actual audited track record. A lot of firms show you the two stocks that worked and ignore the other 98. We thought there was demand for transparent, high-quality research.”

2. Why are you selective with microcaps?

Dean Pernas: ”In microcaps you rely heavily on management execution and strategy, and we find that to be lacking severely in many cases. These businesses are often fragile. There’s significant key-man risk or customer concentration risk where if one thing breaks, the whole business can go belly up.

Unless the demand environment changes significantly, we typically stay away from microcap land. A lot of management teams are also overly promotional and salesy, and we don’t like that at all.”

3. What is “Motor Investing” and how is it different from “Moat Investing”?

Dean Pernas: “We codified our philosophy into something called ‘Motor Investing.’ Just like the motor drives the motion of a car, the motor in a company drives its future financials. The motor is the interplay between internal and external elements. Does the company have the right strategy? Is management executing? How are customers evolving? How are competitors evolving?

We like that analogy better than moat investing because Buffett’s moat analogy conveys something static… like there’s a moat and that’s it. We don’t think business quality is static. Moats can strengthen or weaken over time, and customers evolve too.

Our sweet spot is when the motor is getting stronger, the potential energy is building, but the financials don’t yet reflect it. That’s where we like to play.”

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4. How do you actually generate investment ideas today?

Dean Pernas: “We use both quantitative and qualitative processes. We run screens, but we also spend a huge amount of time reading industries and trends. Once we find a trend we have very high conviction in… for example AI penetration increasing and more time being spent on AI every day… then we look for companies that can benefit from that trend.

The process changes depending on the environment. The screens we used during COVID are different from what we use today. It’s dynamic.

We’ve also built AI tools internally. For example, if we’re looking for companies pursuing volume-led growth instead of price-led growth, we built an AI scraper that goes through every 10-K filing and identifies those companies for us.”

5. How should investors think about AI as part of their research process?

Dean Pernas: “AI is extremely helpful for quickly bringing yourself up to speed on an industry. It compresses the amount of time required to learn dramatically.

It also helps with idea generation. Sometimes you know an area is going to be important, but you don’t know which company is the best way to express that theme. AI can surface names you might never have found otherwise. That said, you have to be careful. ChatGPT sometimes just agrees with everything you say, so you need to pressure test your own ideas. You can’t treat it like holy text.

As investors, you have to adapt constantly. The second your methods become stale, that’s dangerous. AI is only getting better, so the more investors lean into understanding it, the better positioned they’ll be over time.”

6. What made Capstone Green Energy such an attractive opportunity?

Dean Pernas: “Capstone makes microturbines. Essentially very small turbines ranging from about 100 kilowatts up to 10 megawatts.

Historically, the industry was terrible. Almost everybody lost money. Capstone itself emerged from bankruptcy with about a billion dollars in NOLs. But then two things changed simultaneously. First, a new CEO came in and began making operational improvements, including bringing manufacturing more in-house. Gross margins improved dramatically.

Second, the demand environment completely changed because of AI. Data center power demand exploded, and the electrical grid became constrained. Companies increasingly needed off-grid power solutions. Capstone’s products suddenly became highly relevant because they could provide fast deployment compared to the years-long backlog for large turbines.

So you had a management team making the right operational changes combined with the best demand environment imaginable. That combination created the opportunity.”

7. What are some investing mistakes that fundamentally changed your process?

Dean Pernas: “One mistake was American Outdoor Brands during COVID. I thought outdoor activity trends would continue much longer than they actually did. In hindsight, I had no real predictive ability around long-term human behavior changes after COVID. Management also bought back stock aggressively at valuations they shouldn’t have, which made us more skeptical of buybacks in certain situations.

Another major lesson came from Endor, a German sim-racing company. Demand exploded during COVID, but management completely failed to scale operations fast enough. Then Chinese competitors entered the market and wiped them out. The CEO had too much control and not enough pushback internally. That taught us an important lesson about microcaps and founder-led businesses: if leadership becomes overly confident or insulated, things can deteriorate very quickly.

A lot of investing is separating true signal from noise. That’s much harder than most people realize.”

Key Takeaways

• Great investing often comes from identifying businesses whose future earnings power is improving before the market fully recognizes it.

• Dean Pernas’ “Motor Investing” framework focuses on strengthening business momentum rather than static concepts like traditional “moats.”

• Microcaps only become attractive when there is either a major external demand shift or unusually strong management execution.

• AI is rapidly changing the investing workflow by improving industry research, idea generation, filing analysis, and pattern recognition.

• Management meetings and site visits can provide insight, but they can also create false conviction if investors mistake noise for signal.

• Many SaaS businesses may ultimately benefit from AI adoption rather than be disrupted by it, particularly those with strong customer workflows and adaptability.

• Long-term portfolio performance is primarily driven by concentrated, high-conviction core positions rather than short-term catalyst trades.

  
  Stocks Discussed

• META Platforms (Nasdaq: META) : Discussed as an example of a mega-cap opportunity where market panic around TikTok, Apple privacy changes, and metaverse spending created a deeply undervalued setup.

• Capstone Green Energy (Nasdaq: CGRN): Microcap turnaround benefiting from AI-driven electricity demand, constrained power grids, and improving execution under new management.

• Sprout Social (Nasdaq: SPRT): SaaS company the market may be overly discounting despite AI potentially strengthening its long-term value proposition.

• Bandwidth (Nasdaq BAND): Software company benefiting from AI-related communications infrastructure and changing enterprise workflows.

• American Outdoor Brands (Nasdaq: AOUT): Used as a case study in misjudging post-COVID consumer behavior and overestimating the durability of pandemic-driven demand trends.

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One thing I really agreed with Lukas on was the importance of still starting with the numbers. He explained that when he looks at a company, he still opens the filings first and decides pretty quickly whether it’s even worth deeper research. I think that’s important because a lot of newer investors are starting to rely too heavily on AI before they’ve actually learned how to analyze companies on their own. Where I think AI becomes incredibly useful is on the qualitative side: understanding industries, learning new concepts quickly, exploring business models, and helping you think through ideas from multiple angles. I also talked about how I’m personally using AI almost like an investing partner now: challenging assumptions, testing frameworks, spotting inconsistencies in management commentary, and even helping identify potential catalysts or opportunities I may have otherwise overlooked.

We also spent a lot of time talking about whether AI will eventually destroy the investing edge, especially in microcaps and nanocaps. Both Lukas and I agreed that while AI is becoming an incredibly powerful tool, investing still remains deeply personal and judgment-driven. Lukas made the point that conviction itself is something investors still have to develop on their own through experience, mistakes, and independent thinking, not something an AI model can simply hand you. I agreed with that, but also talked about how AI can strengthen conviction when used the right way by helping investors explore industries faster, challenge assumptions, and think through situations from multiple angles. Ultimately, we both came back to the same core idea: the investors who combine strong manual research skills with AI tools intelligently will probably have the biggest advantage going forward.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and MS Microcaps LLC , home of the Microcap Quality Index (MSMqi).

Key Topics Discussed

• How AI is changing investment research

• Why AI should enhance, not replace, manual analysis

• The importance of reviewing financial statements yourself

• AI as a tool for qualitative research and business understanding

• Whether AI will destroy the investing edge in microcaps

• Building AI workflows around an existing investment process

• Conviction building in investing

• Using AI to challenge assumptions and management claims

Stocks Discussed

• RCM Technologies (RCMT): Engineering and staffing company with exposure to industrial and data center-related demand.

• ABVC BioPharma (ABVC): Small biotech name used as an example for analyzing sharp post-selloff situations with AI.

• Bandwidth Inc. (BAND): CPaaS/software communications company discussed in the context of SaaS durability under AI pressure.

• Cipher Mining (CIFR): Bitcoin mining company linked to broader AI/data center infrastructure themes.

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We also talked through the practical reality that short-termism isn’t inherently bad. Everyone wants returns quickly, but it becomes dangerous when it pushes investors into chasing low-quality stories, pump-and-dumps, or promotional themes simply because they appear to be moving. In my own experience, some of the best returns have come not from blindly “holding forever,” but from identifying pockets of explosive growth or clear catalysts before the market fully prices them in. Furthermore, long-term investing has its own traps. Investors can become lazy, using “I’m long term” as an excuse to ignore deteriorating execution or avoid making difficult sell decisions when management underdelivers.

One of the biggest themes from our conversation was the importance of staying adaptable. In the nano- and micro-cap world, there are opportunities on both the short- and long-term side, but only if you stay focused on the thesis and are willing to adjust as facts change. We also talked about how the investing landscape is evolving. More retail investors, better AI tools, and faster access to information are making it harder for hidden opportunities to stay hidden for long, which means finding catalysts and acting early matters more than ever. We wrapped up with Lukas breaking down his quick look into VersaBank, including its U.S. expansion, steady growth in its receivables financing business, and upside potential from newer fintech and AI-related initiatives.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and MS Microcaps LLC , home of the Microcap Quality Index (MSMqi).

Key Points Discussed

• Fixed Holding Period Rules Can Be Misleading: Every investment thesis has its own timeline

• Time Horizon Should Match the Opportunity: Some setups play out in months, others take years

• Short-Termism Isn’t the Real Issue: The problem is chasing hype over fundamentals

• Catalyst Investing Still Works: Big moves often come from spotting inflection points early

• Long-Term Investing Has Risks Too: Patience can turn into complacency if you’re not careful

• Management Execution Is Key: Continued execution may justify holding through volatility

• Nano Caps Remain Inefficient: Strong businesses can stay overlooked for long periods

• Conviction Must Be Balanced With Flexibility: Avoid both panic selling and overholding

• AI Is Changing Research Dynamics: Faster tools may shrink market inefficiencies over time

• Investor Psychology Creates Opportunity: Short-term narratives can lead to mispricing

Stocks Discussed

• VersaBank (TSE:VBNK): Discussed as a longer-term holding tied to U.S. expansion, receivables financing growth, and fintech/AI optionality.

• TSS Inc. (Nasdaq:TSSI): Referenced as an example of a painful long-term hold that ultimately worked after major volatility.

• KORU Medical Systems (Nasdaq: KRMD): Mentioned as an example of a catalyst/fundamental trigger investment.

• Ceco Environmental Corp (Nasdaq: CECO): Referenced as another example of a thesis-driven investment.

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Lukas started investing at a young age, but how quickly he evolved through different market environments. From experiencing the tailwinds of the 2020 bull market to adjusting through the 2022 downturn, he’s developed a flexible, framework-driven approach that blends growth, value, and special situations without boxing himself into any one style. He’s focused on finding mispriced opportunities, often around inflection points, while staying grounded in process, position sizing, and continuous self-reflection.

Lukas walks through how he sources ideas, why he prioritizes flexibility over rigid frameworks, and how journaling and self-awareness have shaped his development as an investor. We also get into his views on microcaps, portfolio concentration, and the balance between short-term opportunities and long-term conviction.

Thanks for reading! This post is public so feel free to share it to help us find more great investors to interview.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and MS Microcaps LLC , home of the Microcap Quality Index (MSMqi).

1. When did your investing journey really begin?
“So I started when I was 13, I think around that age. My brother, at that time, had an interest in the stock market… he sent me one YouTube video from some German finance guy that made educational content about how to value stocks. I found that topic intriguing, because I would say I was always a little bit more of a number and spreadsheet kind of guy. I could just spend the whole day looking at numbers and be satisfied with it. And yeah, it really just came from my own passion.”

2. What were your early investing experiences like?
It was really one of those periods where almost everything was going up… it didn’t really matter how speculative it was. I did fairly well during that time, but, I mean, who didn’t, right? Looking back, it was an amazing experience because I got to see firsthand what a strong bull market actually feels like. I wouldn’t say that I got all the lessons out of it, but at least I experienced it and became aware of it.”

3. How would you describe your investing philosophy today?
“I don’t like to box myself in. I’m not a quant guy. I just have a spreadsheet, I have my DCFs, I have my databases. But at the end of the day, what are all these approaches there for? They’re only there to give you a framework to find out whether a stock is overvalued, undervalued, or fair valued. So at the end of the day, you’re trying to find a mispricing… and why would I box myself into one specific category?”

4. Where do your best ideas come from?
“Sometimes it’s random, sometimes I find something on X, sometimes on Discord or a forum. Or you get into contact with somebody and you talk about stocks. But I also like to really screen a lot… either I test out certain screeners or sometimes I even go A to Z on stocks. I would say it’s really a mixture of everything, but most of the time I find something myself through a screener.”

5. What exactly are you screening for?
“I like to screen a lot for growth. I try to make the screener in a way where I screen for an inflection point in terms of either growth or revenue. Then I try to take into account whether the company is unprofitable and what levers it has to become profitable. I don’t like to buy things expensive… usually something below a 10 price-to-earnings ratio is appealing for me. But of course, that’s just a guideline.”

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6. How do you structure your portfolio and positions?
“I like to run a concentrated portfolio… let’s say three or four names becoming like 80% of the whole pie. I usually like to size by conviction because I’ve done a lot of research and taken into account as many factors as I can. I also like to build positions slowly and sometimes start with a tracking position. It’s always a little bit different when you have a stock in your portfolio compared to just having it on a watchlist.”

7. You’re active in microcaps. How do you think about mispricing and discovery in that space?
“I think every framework should have points where they converge in terms of agreeing that a particular stock is mispriced. And mispricing… it has a lot to do with discovery, in a sense. A lot of times, these companies are just not well understood or followed. So if you do the work, you can find something before the market fully recognizes it.”

8. You mentioned most mistakes trace back to mindset. What specifically tends to go wrong?
“Every time I made a mistake, I could sort of trace it back to the most simple fact… sometimes it’s a lack of research. But the other fact was also that I wasn’t open-minded enough. By thinking differentiated, I mean you really have to think in all possible cases: the bull case, the bear case, the base case. And if you don’t do that, you sort of miss what can actually happen.”

Key Interview Takeaways

Flexibility Over Labels: Lukas doesn’t identify as a value, growth, or momentum investor… he uses all frameworks as tools depending on the situation

Mispricing Is the Goal: Everything centers around finding stocks where price doesn’t reflect underlying value

Inflection Point Focus: Lukas screens for companies where growth, profitability, or sentiment is about to shift

Screening + Network: Ideas come from a mix of deep screening work and conversations with other investors

Concentrated Portfolio: Lukas typically holds 10 names, with top positions making up the majority of the portfolio

Conviction-Based Sizing: He sizes positions based on research depth and confidence, not equal weighting

Process Over Prediction: Emphasizes journaling, reviewing mistakes, and refining decision-making over time

Micro Over Macro: Focuses primarily on company-specific opportunities rather than macro forecasting

Stocks Discussed

Cloudflare (NYSE:NET): Early interest sparked by seeing widespread usage before fully understanding the business

Minnova (CVE:MCI): A speculative gold play where the thesis was driven by strong risk/reward and existing infrastructure

Enterprise Group (TSE:E): Screened idea that led to discovering overlapping investor ecosystems in Canadian microcaps

NTG Clarity (CVE:NCI): Another example of finding overlooked small caps through screening and research

Sezzle Inc (Nasdaq:SEZL): Highlighted as a higher market cap opportunity

AltynGold PLC (LON:ALTN): A gold producer with long-term upside

Xpel Inc (NASDAQ:XPEL): Referenced as an example of a microcap winner that can significantly impact portfolio returns

Biosyent Inc (CVE:RX): Another example of a successful small-cap that can drive outsized gains over time

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This Round Table with Lee Roach of , , and breaks down the opportunity set and risks associated with this Chapter 11. The conversation also reveals some really interesting lessons in the Chapter 11 process that you can use to evaluate bankruptcies.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and , home of the Microcap Quality Index (MSMqi).

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Through sharing ideas and discussing emerging opportunities like TSSI and TGEN, I’ve seen firsthand his ability to spot under-the-radar trends early and think through catalysts in a clear, structured way. Mike has built a reputation for deep research and focusing on asymmetric setups, often identifying mispriced narratives before they become widely recognized. In this 2025 “rewind” Skull Session , he walks through how he finds potential multibaggers, evaluates risk, and builds conviction.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and MS Microcaps LLC , home of the Microcap Quality Index (MSMqi).

1. What sparked your interest in investing, and how did your journey evolve?

“I really have always been interested in markets and investing. And really post graduation, I worked in investment banking in New York, pretty briefly. The impetus for that was just an interest in companies and valuing businesses, but I pretty quickly realized that I’m more of a self- directed person. I’m really interested in the kind of freedom to research what interests me at the time, or to hop on to something compelling.”

2. How did your early experience trading news shape your investing approach today?

“What it really showed me, and kind of gave me an education in, is what specifically moves markets and what specifically moves stocks. Because if every day you’re looking at all the news coming out and seeing how that affects things, and tracking how it affects things, you get a good sense of what really matters and what kind of things will be overlooked.”

3. How do you generate ideas today?

“A lot of what I do on my Substack and on my Twitter is I just try and notice larger trends. That’s something that’s been very, very successful with TSSI or TGEN, and I try and think through the beneficiaries, or I’ll just notice something that is maybe not a large trend, but something has changed in the world, and it’s been overlooked. And I’m happy to get into more examples of those specifics to kind of illustrate those points. But I guess I’ll just finish by saying the other thing I really try and do is connect with people and talk to people across Twitter.”

4. Why do you emphasize network and idea flow so much?

“I think something I want to emphasize to everyone on Twitter that is interested in this world is that you can monetize through charging money. That’s great, or you can try to derive alpha from simply having a really broad network and putting more and more stuff out there… What that results in is you talk to a lot of extremely smart people, and you get really good idea flow. And then there’s kind of a reciprocity there… it’s kind of easier to identify when an idea or situation is really compelling than it is to find those situations yourself or to, you know, wave through 1000s of ideas. It’s better to have ideas flowing to you.”

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5. How do you think about thematic investing?

“There’s first order beneficiaries. So, for something like GLP1s that might be, who is actually selling the drugs. Then, there’s second order beneficiaries that might be like a HIMSS or something like that, right? And of course, you can abstract more and more. I typically tend to focus on the second order beneficiaries of large trends. That’s something like data centers are getting built out… I’m more interested in the TSSI and TGENs. That’s like a second order beneficiary.”

6. What does your day-to-day process look like when sourcing opportunities?

“I would say that I do really two approaches, and I guess, are a bit contradictory. One is, I look for large themes that I think will play out… The other thing I do is more just like tracking the news and what’s been happening recently… And so this company, they were trading like they wouldn’t benefit at all… it’s really frequently the case that there is information arbitrage… And the way I source ideas is just through curiosity. I mean, when you come across an interesting account on Twitter… you really want to see all the names they’re mentioning and just look into it.”

7. Why do you focus on microcaps instead of large, well-covered stocks?

“I genuinely don’t have an edge on predicting what a company like META will do in the next 12 months. There’s there’s no chance. But I know for a fact that there are obviously compelling opportunities that very few other people are looking at. And even if they are looking at them, like a company under 100 million…typically, a larger fund… is simply too liquidity constrained to take a serious look.”

Key Interview Takeaways

• From Trading to Investing: Started with short-term, news-based trades, but has shifted toward longer-term, high-conviction ideas

• Idea Flow is the Edge: A lot of his best ideas come from conversations and his network, not screens

• Second-Order Thinking: Looks for the “hidden winners” of big trends, not the obvious ones everyone is already chasing

• Microcap Inefficiency: Focuses where bigger funds can’t play, which creates more opportunity

• Practical Research Approach: Spends time where it matters: filings, management calls, and talking to the right people

• Management Really Matters: Prefers teams that understand their problems and speak honestly about them

• Runs Concentrated: Keeps a small number of positions and sizes up when he has conviction

• Catalyst + Momentum: Wants a reason for the stock to move, and pays attention when momentum starts building

Stocks Discussed

• TSSI: A good example of a stock that lagged its peers, then quickly caught up once the numbers started to show up

• TGEN: A longer-term story where things are starting to come together, especially with partnerships helping validate the thesis

• LASR: Not getting much attention, but could benefit from increased defense spending in a meaningful way

• HNRG: A company most people have written off, but may have a second life tied to powering data centers

• PSIX: Brought up to show how another high performing stock in the data center was found using information arbitrage

• LFVN: Mentioned as a smaller, less obvious way to play the GLP-1 trend

Watch the video to learn more.

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I met David many years ago after he subscribed to GeoInvesting, and over time we developed a friendship.

As I got to know him better, it became clear he had a unique background, from managing money to building and selling businesses, and now launching the Land Value Alpha fund.

David has moved across public markets, private ventures, and real estate, and today he’s settled in on a strategy centered around land, infrastructure, and water rights.

In this conversation, we walk through that evolution and how he’s thinking about building value in an asset class that most investors don’t spend much time on and why he believes this is one of the most overlooked opportunities in investing today.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and MS Microcaps LLC , home of the Microcap Quality Index (MSMqi).

How did you first get into investing?

“I was a stock jockey starting at age 13, my grandfather taught me about the stock market. In my early teenage years, I was even trying to get on the phone with Kenneth Iverson from Nucor Steel, and I was an early investor, with my grandfather’s guidance, in Nucor Steel and International Mineral and Chemical.

What was interesting about Nucor was that they paid performance bonuses to employees at every level, from the lowest to the highest, and obviously the stock did very well over decades.

So I was hooked on the stock market. When I got into college during the crash of ’87, I doubled down… and it worked out quite well. I ended up buying more of both International Mineral and Chemical and Nucor Steel.”

What did your early career look like after that?

“When I went to law school, I worked for the SEC in the Division of Enforcement. What was great about it was not only did I learn a lot, but I also learned that I didn’t want to be on that side of securities in any form.

So when I graduated from law school, I worked for Merrill Lynch in San Francisco. I had a very good experience there, and then after three years, I went off and started my first hedge fund, focused on small-cap and microcap stocks, searching for idiosyncratic returns. I ran that fund for the better part of seven years. It was a very strong-performing fund: 42% gross, 33% net audited returns over almost seven years.”

How did you transition from public markets into real assets?

“I went off to a startup company that I founded called Sector Base, which was later renamed Revere Data, and eventually it was sold to FactSet Data Systems in 2013. Over that period of time, I then got into doing reverse merger transactions… alternative ways of going public.

We had a bell curve of results. Some didn’t work, some were average returns, and some were exceedingly successful.

At that point, I said, ‘Well, I’m going to take a left turn and check out real estate.’ So, being a contrarian investor, I bought facilities in bad locations, in poor condition, with low occupancy, assets that no institution would touch, and then turned them around.”

What led you specifically to focus on land?

“So focusing on hard assets, I then decided to explore land. What happened was, in 2019, I started flying around the country, traveling by train, driving… really trying to find a place for the rest of my life that was a resort community or near a resort community.

After doing all that travel, in 2022 I found the Flathead Valley (Montana). I determined it was an incredible opportunity, not just from a personal lifestyle perspective, but also for investing… because it’s a very fast-growing community. And yet, at the same time, it still has modern conveniences.”

What insight led to your Land Value Alpha strategy?

“I met a large landowner, and I saw what he was doing on the land. He didn’t know anything about Wall Street or capital markets, but he knew how to buy land, and he knew how to use heavy equipment to build roads and develop infrastructure.

Through that observation, I realized there was an opportunity not just to buy land, but to conduct infrastructure work and create water rights. That would create an active layer of appreciation on top of the passive layer of simply buying land and waiting.”

How do you actually generate returns from land?

“People think when they buy land, you just hold it and that’s it. But there’s really an active component. We look at land as having three parts: passive appreciation, active appreciation, and a reset premium.

So building roads, installing power, drilling water wells… that active component is what drives returns well beyond the typical five to seven percent you might expect from passive appreciation alone. Then there’s also a reset premium that we gain over time as the land becomes more discovered.

If I had to estimate, I would say something like 20% to 25% of the return comes from the passive component, and roughly 60% to 70% comes from the active component.”

What role do water rights play in the strategy?

“There’s secular scarcity in the western United States… What a lot of people don’t do when they’re researching to purchase land is they’re not looking at water basins, they’re not looking at aquifers… We look at all that stuff, and we use that as an information advantage when buying land.

We project that for every dollar you invest in water infrastructure… you should get 4x your return… So water is not only critical for life and critical for the value of the property, but it’s quite an accelerant.”

What kind of returns are you targeting, and why is this asset class attractive?

“So we’re targeting 20% annual returns… we expect to generate 6x in terms of your return on capital in 10 years.

What I find incredible about this asset class is not only the returns are very compelling, but it’s very, very stable. So the volatility is very low, measured against every other asset class you could find.”

Closing Thoughts

What was interesting to me in this conversation is how much of David’s approach still reflects his roots in microcap investing, just applied to a completely different asset class.

He’s still looking for informational advantages, still focused on asymmetry, and still willing to go where others aren’t. The difference now is that instead of parsing filings or catalysts, he’s “underwriting” land, infrastructure, and water… and physically creating value rather than waiting for a management team to execute on their timeline.

It’s also a reminder that alpha doesn’t disappear, it just migrates. And sometimes, it shows up in places that investors who specialize in information arbitrage aren’t even looking, something we also continue to explore more deeply through conversations and research shared at the MS Cliffnotes Substack.

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Andrew is the author of Underlying Value Substack (@underlyingvalue on X).

The focus of our conversation last week was on deep value investing with a catalyst: hunting globally for small-cap mis-pricings (South Africa), realizing that a good business might still be “messy,” and capital allocation discipline.

We also dug into how to use and not use AI in stock research, and managing portfolios as conviction builds.

Skull Sessions is a collaboration with Geoinvesting.com, a full-stack microcap research platform and MS Microcaps LLC , home of the Microcap Quality Index (MSMqi).

The idea of “deep value with a catalyst” sounds simple, but it’s actually demanding. It forces you to think about timing, incentives, capital allocation, and whether you truly have an edge, not just a spreadsheet that looks attractive.

Microcap investing is a corner of the market where if something looks obvious, it might not actually be priced into the stock. With that being said, Andrew does not confine himself to microcaps, and one of the reasons I found this conversation beneficial.

Here are the key themes from our discussion, in Andrew’s words:

1. Can you tell us more about your background and career?

“My first job, (graduated 2009) was at Brown & Brown, which is this insurance brokerage based out of Daytona Beach, that would just buy brokerages, and they’d be paying two to three times, and they’d be recognized for it at 15 times earnings. So that’s kind of like right out of the gate… was my first exposure. Then I worked for Accenture in their business strategy practice for six and a half years. That was really mega fortune, 50 companies stepping in with more of a specific focus on executing different business divisions,. Microsoft, Cisco or Sprint are some of the examples of companies I worked at. After that and doing some m&a work, I went and jumped into Platinum Equity as an ops professional, where you’re really dealing with creating and executing a playbook, post acquisition of various companies, and you’re working with senior management to execute it. And then for the last five years, bought and operated a business, did a “bots” business with my wife. We operated it for five years, and then got out of that, sold it last year.”

2. How has your consulting and operational experience shaped your investing?

“I think you just, you try to get more context on what’s actually happening. You know, it’s easy to be a spreadsheet jockey and come up with all these numbers, but I think you got to really know the nuance on what’s happening within those companies. And especially when you’re going small cap, these are messy companies. A lot of them are just trying to tie things together. Some of the last things that they’re really worried about is how they put the polish on the IR deck, at least if they’re probably doing the right things. They’re actually focused on executing, they’re not too worried about what their investor focus is or how they’re engaging with with that cohort. Having exposure and having exposure across really large and medium and small cap has been a benefit, and really helps in talking with management and getting a better understanding of the business.”

3. What is your core investing framework?

“Deep value with Catalyst. Something that you have the value component there, but there’s a catalyst is where I like to focus on in any company that I’m investing in. There has to be a reason why it’s undervalued. And by and large, the market’s pretty efficient, and there are lots of smart people swimming around. So you really have to have some kind of contrarian edge, or contrarian take on, you know, why? Why you’re the lucky one to be able to stumble on on this security. But, yeah, it’s a deep value with the catalyst. But I’m not against a long term compounder that I think I can forecast into the future. And see what the growth and how that will result in a great investment opportunity in the current state.”

4. What role does capital allocation play in your decision-making?

“Yeah, absolutely. I think that that’s probably the most important thing I’m trying to understand… this company is producing X amount of cash. Where’s that cash gonna go? How’s it going to find its way back into my pocket? Or, how are you going to be able to grow this in a way that that makes more sense than putting that into my pocket?”

5. How do you use AI in your research process?

“AI is just great leverage… It’s kind of like having a high powered research analyst right next to you, and you’re able to get near instantaneous information. I use it on just standard prompts that kind of run in the background and send me reports, plus just using it in my research process throughout as I have questions… Maybe it comes down to what is critically important in any investment as well. You have to know what’s critically important for this to work out. And then you need to go and verify it yourself… It’s a great servant, but it’s a terrible master.”

6. What makes public markets uniquely attractive?

“That’s kind of why I went back from private to public markets… because you can change your mind. When you’re operating a private company, you might change your mind, but you sure as hell better be coming and figuring out how to rally a team to make this a more successful enterprise, because, if not, the whole thing’s going to be a house of cards that falls over the top of you. I think there are two beautiful things about public markets:
One is that you could change your mind, and unless something changes, you’re not terribly handicapped or hurt for new retail.
Two, it’s not a zero sum environment. It’s actually a positive sum environment to go out and talk to different investors and to share different ideas and to grow it together, because you’re incentivized to bring people on board, and hopefully the share price rises over time, and you’re able to benefit of that instead of in the private markets.”

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Skull Sessions is a collaboration with Geoinvesting, a full-stack microcap research platform and , home of the Microcap Quality Index (MSMqi).

Getting Started

Carlos is a statistician who began investing in 2022 with his first €1,000 saved from his first job in Germany. After losing about 30% he decided to reexamine his investing approach and started writing Python code in 2023 to develop algorithms for asset allocation, focusing on ETFs and similar assets.

In 2024, a friend introduced him to Portfolio123. He spent the first few months working his way through Portfolio123 and stock fundamentals. Even though his initial model was “terrible”, he quickly incorporated momentum into his model and achieved a 115% return in 2024.

“My first model was terrible. It was actually, as a normal person would say, a piece of garbage. Pretty, pretty bad. But then after the second month, I got the first great model that I developed, and I was lucky enough that it was momentum. And 2024 was a pretty bullish rally, so I was lucky, and I got 115% in 2024.”

Throughout the conversation, you will notice that Carlos is big on adapting his models to changing circumstances. He calls the success he has had with these moves “lucky” but I am not buying that. Carlos is clearly talented and loves what he does.

From Investor to Teacher to Fund

In 2025, Carlos successfully tweaked his model from momentum to a low volatility model just before the big March Trump tariff induced draw-down. This helped him achieve a 41% return in 2025.

In May 2025, a friend of his that we should all know by now, (@Quant_Kurtis) , convinced him to launch a Portfolio123 (P123) coaching program and start consulting.

Carlos expected only four to five clients, but since May 2025, he has had around 40 clients, most of whom are retail investors, but many are also institutional investors or ultra-high net worth individuals.

What is next for Carlos? In 2026, he, Kurtis, and Jeffrey Hamm launched the York Tech Capital Partners Fund. You can join their waiting list here.

His Strategy

Carlos’ approach to quant investing centers on developing simple, focused systems and mixing them to benefit from rebalancing, utilizing the uncorrelation of assets. His methodology revolves around common characteristics in stocks that explain the difference in returns. He discussed factors like:

• Momentum: Driven by the fear of missing out (FOMO) when a stock is rising.

• Low Volatility: States that low-volatility companies tend to outperform high-volatility companies because they “lose less”.

• Value: Involves buying “cheap” companies and selling expensive ones, assuming that cheaper companies have a larger margin of safety and higher upside potential.

• Quality: Based on the fundamentals of the company (e.g., competitive advantage) and is translated into three main components: profitability, safety, and growth. This concept was developed in a 2019 paper called “Quality Minus Junk” by Clifford S. Asness, Andrea Frazzini, and Lasse Heje Pedersen of AQR Capital Management.

Carlos really stressed that he likes “betting” on probabilities rather than using judgment, which can interject behavioral biases. The overriding message from Carlos was that his process starts with avoiding risk, followed by picking the best stocks and then battling some risks inside the strategy.

He also touched on the use of AI and machine learning in quant investing. The biggest challenge he sees is that machine learning models differ from traditional models in that they lack prior information and learn from patterns in the data provided. He sees the best use of AI as mixing it with traditional systems, as the correlation between the two is often very low, and AI can find opportunities that humans might not consider.

Carlos admitted that he needs to improve his knowledge in machine learning for trading.

By the way, if you want to delve into quant investing and AI, Andreas Himmelreich (@GfI_Himmelreich) is knee deep in this stuff.

Andreas was actually a Skull Session guest last year. You can watch that here.

Can The Quants Maintain Their Alpha?

One thing I was really curious about is if the quant community is growing, especially due to the growing presence of quants on twitter posting some amazing returns and studies. I find it fascinating that a systematic approach to investing can produce so many great quant investors, without strategies diluting “each other.”

And then, I’m thinking… great… now, I have to battle the quants to get a first mover advantage!

So far, Carlos does not see the growing quant interest as posing a risk to “quant alpha.”

Carlos broke down a growing quant community into three types of newbies: those curious about finance, fundamental traders trying to avoid behavioral biases, and those looking for “quick money”.

Some of you may not know this, but between 2010 and 2016, I was heavily engaged in short selling and fraud research at Geoinvesting, where we were uncovering and exposing pump & dumps and fraudulent China-based companies that had gone public in the U.S. through reverse mergers.

In total, we exposed 22 pump & dumps and dozens of china stock shams, culminating in 12 China based stocks trading in the U.S. getting halted and delisted.

You can read more about my history on this in a small reflection article I wrote in 2016, called The Evolution Of China Based Fraud, as well as in a 2017 documentary called the China Hustle (financed by Mark Cuban).

, the first ever intern at Geoinvesting back in 2006, joined me as co-host for this Skull Session.

Edwin runs The Bear Cave and Sunday’s Idea Brunch Substacks.

You can read a Q&A session Edwin had with me on his Sunday’s Idea Brunch here, an opportunity I am incredibly appreciative of.

Edwin doesn’t actively run a short book himself, but many serious investors read his work to understand where hidden risk may be building.

He makes it clear that being a short seller or short activist demands that you need to look beyond the obvious and not naturally trust the narrative inside earnings calls, investor decks, and consensus models.

That’s why he spends time in places the crowd might ignore: SEC Freedom Of Information Act (FOIA) logs, audit partner records, obscure interviews, comment sections, and regulator complaint databases.

Consumer complaints, in particular, can reveal cracks long before financials do. Two companies can show similar growth and margins, but if one has a pattern of billing disputes, cancellation friction, or questionable practices, that friction eventually shows up in churn, pricing pressure, or regulatory scrutiny.

I’ll literally go chronologically through the last three years of reviews… I don’t care about the average reviews, and there’s a lot of frivolous complaints. But when you do this a lot, one out of every 20 times you’re going to see real issues. For example:

• “I bought what all the former sales people were saying”

• “I feel like I need to lie about the product’s capabilities”

• “All our partners get upset that our product doesn’t live up to their expectations”. That’s a huge nugget of information” - Edwin Dorsey

Edwin’s New Tools

If you think it could be potentially useful to know if short sellers might have ammunition to discredit a stock you own… or if you want to avoid investing in stocks that might be prone to being pump and dumps or running into regulatory risks, you might be interested to know that Edwin has even built public tools to track stock promotion activity and regulatory breadcrumbs:

• StopNasdaqChinaFraud.com: which tracks WhatsApp pump-and-dump chatter

• StockPromotionTracker.com: which surfaces paid stock promotions

• FOIAsearch.com: which flags regulatory breadcrumbs
  

In small and micro caps, promotion risk and governance issues are particularly important to be aware of… not just from a short selling strategy point of view, but also from the perspective of being a long only investor.

On the thematic side, Edwin is focused on businesses on the wrong end of change, particularly around AI. In hindsight, this is pretty relevant right now, with software stocks getting crushed over similar fears.

For a more current take on the software vs. AI debate, you can listen to my Skull Session with Ben Brostoff I published a few days ago, here.

Edwin was early on Chegg (CHGG), arguing that AI tools permanently impair its core value proposition. He has also looked at call centers and workflow-heavy businesses that may struggle if automation meaningfully reduces labor needs.

Cautious On Signet

A detailed example Edwin walked through to get a glimpse of his investigatory process was Signet (SIG), owner of Kay, Zales, and Jared. His thesis centers on lab-grown diamonds.

He argues that lab-grown diamonds are chemically identical to natural diamonds, far cheaper to produce, and increasingly accepted by younger consumers. At the same time, prices for both natural and lab-grown stones have been trending lower.

Signet holds billions in natural diamond inventory. If prices continue to decline, Edwin believes the issue isn’t just margin compression. It becomes inventory and balance sheet risk.

He compared it to aluminum, which was once considered precious before production scaled and prices collapsed. The prestige disappeared when supply expanded. His view is that diamonds could face a similar structural shift.

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Bullish In Rave

What I didn’t realize before my conversation with Edwin is that even though his Bearcave newsletter frequently covers billion-dollar market-cap short ideas, he prefers to actually invest in microcaps with his own money.

One bullish idea he discussed was Rave Restaurant Group (RAVE), a small pizza franchisor showing early signs of stabilization under an experienced CEO, Brandon Solano, who was key to helping to turnaround $DPZ years ago.

With a clean balance sheet and improving same-store sales, he sees the potential for a long-term upward re-rating if execution holds.

Interestingly, RAVE is in my microcap quality index at my Substack, Microcap Investing Cliff Notes.

Anyways… I think you’re going to really like my conversation with Edwin, especially if you run a Substack and would like to be inspired by someone who’s had tremendous success on the platform. You can tell that Edward likes what he does.

Also, if you’re considering becoming a short seller, this Skull Session is for you.

is a software engineer and author of the Substack. Our Skull Session conversation circled back to one idea: Using what you’ve learned in your everyday life to uncover edges before others.

The Peter Lynch parallels just keep popping up in these Skull Sessions ( like in my recent chats with and ).

Speaking of Peter Lynch, I just republished key points from a vintage master class by Peter Lynch I found on Youtube. You can read it on the Microcap Investing Cliff Notes Substack. I had previously published it on GeoInvesting several years ago.

Microcap Investing Cliff Notes

Peter Lynch ‘Stock Shop’ Master Class Breakdown

Read more

5 months ago · 10 likes · 2 comments · Maj Soueidan and GeoInvesting

Anyways, I didn’t know my conversation with Ben was going to lead into a chat about software stocks, but when he brought up his software background, I thought it would be a great idea to get his take on what’s going on with the recent correction in software stocks.

The pullback is based on fear that AI is going to commoditize and reduce the total addressable market for some companies.

That discussion addressed how changes in software pricing, usage-based models, and automation may affect long-term margins and competitive positioning.

Interestingly, the conversation led me to revisit a software company I had bought in 2019 that turned into a 500% multibagger. I sold most of it, but kept a small portion since I really like the company and management’s capital allocation policies throughout time.

I am so glad I talked to Ben, bringing this stock back in focus, helping me to see that the company should actually benefit from AI. So, I decided to write about the company again. If you’re curious, you read the update here and see why I added to the Microcap Quality Index (MSMqi).

This is just one example of why I am loving these Skull Sessions and so appreciative that investors and experts like Ben take the time to chat with me.

I’m confident that the insights from my conversation with Ben will help us to become more informed investors on the software vs. AI topic and sharpen our overall investment process.

A Little More About Ben

Ben spent years in enterprise software at Klaviyo (email marketing), building billing systems. That exposure gives him a unique vantage point… he can see how pricing and product decisions translate into revenue and cash flow before it ever shows up on a balance sheet. Today, when he studies microcaps, that experience lets him assess business risk beyond the reported numbers and spot opportunities investors might overlook.

Software vs. AI

Ben explained that the software shakeup is not just AI hype. Traditional seat-based pricing models are under pressure. Tools like Claude and GitHub Copilot are leveling the playing field for small teams, which raises questions for others like DocuSign, and even giants like Salesforce. The irony? Small, agile companies may benefit most from the AI productivity boost, while the big names are stuck wrestling with bloated cost structures, inflexible pricing and shrinking total addressable markets.

Ben isn’t writing off software. He’s still looking for hidden opportunities, particularly in cybersecurity, where AI agents are creating entirely new product categories.

Companies like Cloudflare, which even showed up in Claude’s source code, are on his radar.

A Look Into Ben’s Research Process

On the idea sourcing side, Ben blends Peter Lynch-style observation with boots-on-the-ground networking and studying a company’s capital structure. He doesn’t shy away from preferred stocks or corporate bonds, especially when equity valuations make the risk/reward unattractive. He’ll even invest in municipal debt.

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And yes, he’s still looking at microcaps. One name he’s excited about now: Cizzle Brands Corporation (OTCQB:CZZLF) (NEO:CZZL), a Canadian sports drink company aiming to rival BioSteel.

They just acquired a bottling plant that Ben feels could generate >C$20M in EBITDA if management’s estimates hold. It’s early, risky, and a messy capital-structure play, but Ben’s pitch was intriguing.

The lessons from Ben’s approach are simple: you can use your experience to creates an investment edge, patience and diligence protect your sleep, and opportunities show up in unexpected places. And I bet we could all benefit from some less stock induced sleepless nights!

Enjoy!

“The analogy is like a used car. You don’t just look under the hood, you don’t just look at the blue book value, you don’t only look at what the previous owner did. You don’t look only at product reviews, and you don’t compare the price only to, I don’t know, the price of a new car. I mean, you look at so many different things, so many different things go into your decision of buying a used car. And I felt like that’s how you should deal with stocks. That’s how discretionary investors, good discretionary investors, value investors, deal with stocks. And I felt like that, that as an algorithmic investor, someone who has a real preference for algorithms, someone who, who cannot make a decision without, without looking at an algorithm. You know, I felt like that. That was the way to go. So the thing that portfolio 123, offered me, one thing that they offered me, which no one else has, no one else had, was ranking systems.” - Yuval Taylor

It’s funny, because in my April 9, 2025 Skull Session with and Andreas had said:

“I really have to say that the best ideas I got actually from discretionary traders like you…getting the inflection point right, I think this is really, really important.” - Andreas Himmelreich

To be honest, I had to google what discretionary meant in relation to these statements. Yeah, I’m not ashamed to admit that my reading comprehension skills are subpar, at best, but that’s our little secret 🤫.

Here’s what Google had to say:

“In investing, calling yourself a discretionary investor means you rely primarily on human judgment and qualitative analysis, not fixed rules or rigid formulas, to make decisions.”

I’ve always found that understanding “how” a business works and how spotting change before it shows up in the numbers can give me a great edge, especially since I didn’t have a strong financial background when I first fell in love with investing… and naturally, smaller cap stocks are where the best information inefficiency lives.

I also don’t have Rain Main math skills… so I knew I was really fucked, unless I developed some skills that didn’t involve a calculator or an abacus.

Needless to say, emphasizing the qualitative aspects of an investment is the path I went all in on, early in my investing journey…. reading press releases, CEO shareholder letters… interviewing CEOs, and attending special Town-hall CEO pitch sessions in Philadelphia when the Philly stock exchange existed.

But the qualitative edge isn’t free. Drawdowns, emotional decisions, and moments where risk could have been reduced earlier are the price you pay.

Even if you think qualitative investing can give you an edge over an already strong alpha generating quantitative strategy, is it worth some of the pain?

Over the last few years, I’ve had the pleasure of interacting with several quantitative investors who are producing incredible alpha… to the point where it has me thinking extra hard about this conundrum.

Quantitative investing is pushing me to re-examine my investment process. I want to understand how certain aspects of a systematic, quantitative approach can add structure, especially around risk and my behavioral bias, without breaking what already works for me.

That’s what led me to my 2025 conversation with and .

Related Skull Session

In Investor Insights #02, I spoke with Yuval Taylor, a quantitative investor who has built a disciplined, rules-based process while remaining deeply aware of the limits of data.

That conversation provides useful context for the discussion here.

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How This Collaboration Started

I originally connected with Kurtis through Twitter. Kurtis then mentioned that Andreas should also be part of the conversation. Both are active on Portfolio123 and atop leaderboards in small-cap and microcap strategies.

My approach has always been very manual. My team and I follow a large universe of companies, and spend most of our time narrowing that list down to the handful that truly stand out.

It’s really no different than what Andreas and Kurtis do, but they do it from a purely quantitative perspective.

By the way, it’s important to understand that even though I’m searching for qualitative clues, I don’t abandon quantitative factors as part of my analysis. Of course, in the end, it comes down to the numbers.

The question I keep coming back to is simple:

“Can I preserve what I do and improve upon it through a more rigorous quantitative approach?

From what Andreas and Kurtis showed me early on, the answer might be yes.

Two Different Paths Into Quant

Andreas doesn’t come from finance. He spent nearly 30 years in IT consulting and management, eventually serving as CEO of a company that grew meaningfully under his leadership. At 50, he walked away… not for financial reasons, but to spend the rest of his career doing work he actually enjoyed.

That led him into systematic investing and eventually to Portfolio123, where he now works closely with investors building and refining quant strategies.

Kurtis’ path was different. He was exposed to markets early, lived through crashes, and eventually found his edge by testing ideas instead of guessing. His work today reflects that experience.

Where Quant Could Actually Help Me

I know what I’m decent at:

• Identifying business inflections

• Understanding qualitative edge

• Spotting earnings acceleration early and high probability turnarounds

I also know where I struggle:

• Removing emotion

• Managing exposure during macro resets

• Knowing when to step aside and breathe

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Quant as a Filter, Not a Black Box

One idea Andreas shared was how he narrows down the universe.

Even after filtering for quality, tracking thousands of stocks is still overwhelming. Can quant analysis narrow that down to a smaller group, without losing the traits I care about, so discretionary analysis gets sharper?

A good quant system should be one that:

• Removes obvious losers

• Highlights hidden strength

• Flags risk early

• Challenges my bias

Where Qualitative Still Wins

We talked a lot about momentum and turnarounds. Improving margins before revenue shows it. Balance sheets are stabilizing quietly. Higher margin backlogs are growing under the radar.

I walked them through a real example ($CSBR), where the first clue was a single sentence buried in an earnings press release. That led me to an earnings transcript, and eventually a key disclosure hidden in an SEC filing about a large, high margin contract win.

The stock rose from around $4 to $11.

That’s information arbitrage at its core.

When Quant and Qualitative Align

Obviously I presumed a quant might miss the CSBR set-up. However, Andreas and Curtis showed me how highly CSBR ranked before the news broke.

So how could I use that? I could look at some of the stocks that are highly ranked by a Quant to potentially start my qualitative research.

How I See This Working

This is the framework forming in my head:

• Quant narrows the universe
  Focus research where the odds are better.

• Qualitative builds conviction
  Press releases, transcripts, filings, management.

• Price confirms timing
  When rank stays high and price moves, pay attention.

• Sizing reflects confidence
  Bet big where conviction is highest.

In practice, it could be a way for me to get more focused and to be more deliberate about when to act and when not to.

Actually, if we’re being real here, TSSI was a boring company until the data center theme found it. Boring stocks can work really well when they get paired with new trends that reignite growth or if you’re able to understand the cycles that some boring companies go through.

That brings us to our next Investor Insight Skull Session. Actually, strongly recommending that I should set this one up.🙏

Based in Montreal, is an equity research firm focused on overlooked global small and mid-cap stocks, founded by Jeremie Boyer and Leo-Pierre Trudel. The tracking index they publish for their subscriber is up around 65%, since they launched the index in July 2025.

I highly suggest that you visit their Substack.

Leo and Jeremy are both in their early 20s, but don’t let that fool you. Their background, process, and early results suggest a level of discipline and curiosity you don’t always see this early in the game.

They’re spending lots of time where the crowd isn’t looking: boring industries, beaten-down sectors, messy balance sheets, special situations and misunderstood geographies.

Two Different Backgrounds, One Shared Mindset

Leo and Jeremy came to investing from different angles, which is part of what makes their partnership work.

Leo began his career on the buy side at small-cap focused firms like Tonus Capital and Pembroke Management, before spending time at PineStone Asset Management, a global large-cap manager overseeing tens of billions in assets. His background focuses heavily toward deep fundamental analysis, valuation, and free cash flow modeling across industrials, healthcare, consumer, and technology.

Jeremy, on the other hand, cut his teeth in the micro-cap world, including time around Rivemont Micro Cap Fund, managed by . Jeremy’s strengths show up in commodities, shipping, energy, and cyclicals… areas where sentiment, timing, and industry structure might be more relevant to a successful investment vs. the “numbers.”

They met about a year ago, connected through direct outreach and shared interests, and quickly realized their skills were complementary. When Jeremy generates ideas and themes, Leo stress-tests them with modeling, valuation work, and downside analysis. Each challenges the other, which they see as essential.

Generalists

Aurelion doesn’t pigeonhole itself into one sector. Instead, they operate as generalists with a clear rule: if they can understand the business and identify a mispricing, they’re willing to do the work.

That’s why their research spans industries many investors avoid: shipping, chemicals, insurance, banking, defense, and commodities. These are often inefficient, cyclical, and emotionally driven, which creates opportunity.

They run a concentrated index, typically around 10 equally weighted positions, that reflects their highest-conviction ideas. Every stock in the index is owned personally. There’s no trimming or partial exits. A name is either in or out.

Cash Flow First, Stories Second

One of the strongest themes throughout the conversation was their focus on free cash flow, especially in industries where reported earnings can be misleading.

In sectors like chemicals, shipping, or insurance, earnings can swing wildly due to accounting, cycles, or temporary pressures. Cash flow tells a cleaner story. It shows whether a business can survive, self-fund, and eventually compound.

They’re not running rigid screens or forcing companies into predefined boxes. Each situation is evaluated on its own terms. Sometimes they’ll invest when the numbers look ugly, and in other instances they’ll sell or ignore stocks that look cheap, when other indicators are telling them something otherwise.

Finding Inflection Points Before Sentiment Turns

Rather than waiting for consensus to improve, Aurelion tries to enter when things still feel uncomfortable.

In chemicals, for example, they’re currently looking past negative headlines and focusing on pricing trends, supply dynamics, and normalized margins. In shipping, they pay close attention to spot rates and cycle timing rather than headline multiples that often look “cheap” right before stocks collapse.

They acknowledge that timing matters in cyclical industries. Even great businesses can destroy capital if entered at the wrong point in the cycle. Their edge comes from combining bottom-up company work with a realistic view of where an industry sits.

Some of you that invest in cyclical stocks understand this. A low P/E is not necessarily the best time to invest because it may mean the company is potentially already benefiting from a cycle that might be nearing a top. The time to buy these cyclical stocks is often when their P/Es are high, before the inflection turn comes.

Boots on the Ground Still Matter

One thing I loved was how much value Jeremy and Leo place on firsthand research.

When possible, they attend investor conferences, meet management teams, and visit facilities. In at least one case, a site visit completely changed their confidence level in a turnaround story. Seeing unused capacity, completed infrastructure, and operational readiness helped confirm that management’s plan wasn’t just hype.

They also believe physical visits matter even more in small and micro-cap stocks.

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Peter Lynch Still Works

Not all insight comes from models and meetings.

Jeremy shared how one of his strongest early ideas came from simply noticing a product everywhere, Guru Organic Energy ($GURU.TO).

Friends were drinking it. He observed that stores were stocking it and that demand was spreading organically, without heavy marketing.

That observation led to deeper research, conviction, and ultimately a multi-bagger as the company improved margins, exited a costly distribution relationship, and moved toward profitability.

Learning from Being Wrong

They were also candid about mistakes.

Jeremy discussed a trucking company investment where a dividend cut, something he believed would strengthen the balance sheet, ended up crushing the stock as income-focused investors fled.

Even though the business later recovered, the experience reinforced an important lesson: the market’s expectations matter, even when you think management is making the “right” decision.

Understanding how other investors are positioned can be just as important as understanding the company itself.

The Bigger Picture

Leo and Jeremy are building a process around patience, curiosity, and doing work others won’t. They’re comfortable looking wrong before they’re right. They focus on cash flow, cycles, and real-world validation and willing to admit when something doesn’t play out as expected.

That combination, especially applied to overlooked parts of the market, is where some great investing edges tend to form.

“I’m helping with due diligence. So I use my expertise and my consultancy, right? And then I help some investors that are emerging into space tech, and I also connect them with some other, let’s say specific niche experts, that can help them with even, I would say, deeper due diligence for their cases.

Because when it comes to space technology, it’s always very new. It’s a lot of, let’s say, research and development. And, you know, there is a lot of uncertainty if that kind of technology will actually succeed or not, right? So there is, like, a big need of very specific, let’s say, university experts, that can actually de-risk this kind of technology and help investors reduce the risk.” - Matej Pratkovic

Matej’s started his career at PwC, digging through financial statements and learning how companies present themselves to investors. From there, he moved into sell-side analysis at a European bank, working with institutional investors, before eventually launching his own consultancy focused on simplifying complex financial processes through automation and AI.

Even though it wasn’t his intent, he eventually became attracted to space after being exposed to the industry through his work experience and seeing the investment dollars being spent.

I couldn’t wait to talk to Matej because I own some shares of and am tracking very closely. Both stocks have exposure to the space industry, and are in a microcap quality index my team has been building since 2022 at .

Why Space Is Still Small, But No Longer Fringe

One thing Matej emphasized early on is that space is still a relatively small industry. But it’s changing fast. For decades, space was almost entirely government-funded. That’s no longer true.

What caught his attention was capital flow. He pointed out that ten years ago, there were only a handful of venture funds focused on space. Today, there are hundreds. That doesn’t guarantee success, but it tells you where some smart capital is starting to look.

At GeoInvesting, our team likes to pay attention early signals. Obviously, you don’t invest just because money is flowing in, but ignoring it completely can be a mistake. Capital tends to sniff out future demand before earnings show up.

Why Private Markets Matter, Even If You Invest Public Stocks

Most of Matej’s research lives in the private markets. That might sound irrelevant to public investors, but it isn’t. In fact, I’d argue it could be essential. However, it’s probably a full-time job in itself.

Public space companies are limited in number. Private companies can show you where the industry is heading long before it appears in public company earnings reports. Matej uses public company disclosures as anchors, then works backward to understand what private companies might eventually look like when they mature.

In general, understanding how private and public companies intersect to gain a first mover advantage and become a better investor is definitely on my bucket list.

Matej only invests in public companies, but he helps investors with due diligence on private deals, often bringing in technical experts to assess whether a technology is viable or just a good story. I’ll probably be taking him up on this “offer.”

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Rocket Lab and the Reality of High Valuations

We spent some time discussing Rocket Lab, one of the better-known public space companies. Matej sees it as one of the few credible launch providers outside of SpaceX, with improving revenue, increasing launch cadence, and a growing satellite manufacturing business.

Of course, the valuation question comes up immediately. These stocks don’t look cheap by traditional metrics. I asked him directly whether that concerns him.

He understands that valuations are relevant. However, if a company has a product the market genuinely needs, and demand is growing, he believes high price-to-sales ratios may eventually normalize through growth.

That doesn’t mean every expensive space stock works. It means investors need to separate hype from reality.

The Picks-and-Shovels Angle Still Applies

One area I personally gravitate toward is the boring side of exciting industries. Components. Infrastructure. Picks and shovels. You’ll see this in a lot of stocks that we profile at .

I asked Matej whether he sees opportunity there too. His answer was yes, but mostly in private markets for now. He gave the example of a European company that quietly became a major supplier of space components after starting small, eventually becoming a regret for investors who dismissed space as uninvestable.

This lines up with how we’ve approached other boring companies that get reborn from finding new growth avenues in exciting industries, like multibaggers such as QBAK and so many others. All three stocks reside in the Microcap Quality Index (MSMqi) platform.

Satellites, Data, and Why ‘Materials’ Are More More Than You Think

As you would expect, recurring theme in our discussion was satellites. Weather monitoring, earth observation, communications, data collection. These are already revenue-generating services, not “science experiments.”

What I found particularly interesting was the discussion we had on materials. Temperature extremes, radiation, and equipment lifespan constraints create real engineering challenges in space. A company that extends satellite lifespan from seven years to ten years to more… gains an immediate competitive advantage.

This is the kind of detail investors might overlook and why I love talking to experts in their field(s). Just like that, I will now be more aware and get a little excited if i hear about a public company solving “wear and tear” problems in space.

Data Centers and Pharma in Space Sound Crazy, Until They Don’t

We also talked about some of the more futuristic ideas, like data centers in space and pharmaceutical research in microgravity.

On data centers, Matej was realistic. Cooling and energy remain major challenges. “There’s no air in space.” Heat dissipation is expensive and inefficient. The technology isn’t ready yet, but companies are already working on it.

With regards to pharma in space, microgravity allows molecules and proteins to form differently than on Earth. That opens new research pathways, potentially accelerating drug discovery. Companies like Axiom Space (private) are building infrastructure to support this, including plans for commercial space stations as the ISS eventually retires.

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Commercial Versus Government Demand Isn’t Either-Or

Another important point Matej made is that most space companies serve both commercial and government customers. Governments are often the largest buyers, but commercial demand is growing.

Private capital tends to focus on commercialization, while government contracts provide scale and stability. Companies that can navigate both worlds tend to survive longer.

This dual-track demand model is something investors need to understand before assigning simplistic narratives to space stocks.

Why I Found This Conversation Useful

What I appreciated about this Skull Session is that Matej didn’t oversell the future. He acknowledged uncertainty, long timelines, and the real technical hurdles that still exist with investing in space.

I’ve always tried to approach emerging industries with a tempered curiosity. This came from my dad lending me a bit of his skeptical nature. But three decades of investing has also taught me that you can’t always be too skeptical. Too much skepticism can keep you from making some reasonable bets that could really pay off. It also could lead to you to not being patient enough to hold a stock through the ebbs and flows that companies will typically go through on a MultiBagger journey.

History has taught me that painful lesson. Space technology probably deserves the same treatment.

Like anything in investing, there will be winners and there will be losers. The key to navigating risk is these early industry opportunities is understanding where real value might be created and who is smartly building toward it.

If you want to dig deeper into space from a business and investing lens, work at is worth following.

And as always, we’ll keep filtering these themes through an opportunity vs. risk-awareness lens for our community. Hopefully we’ll gain valuable insights before the crowd shows up.

A special thanks to quant investor, Kurtis Hemmerling, for recommending that I “must” invite Yuval be a guest on our Skull Sessions. You can follow Kurtis on “X” at Quant_Kurtis, his website at yorktechpartners and at Portfolio 123.

About Portfolio 123: A financial software company that provides investment research tools to create, backtest and launch rule based portfolio strategies.

Our core principles include a meticulous attention to detail, relentless innovation, delivering a reliable service, and always listening to our customers.

Over the last few years, I’ve really become interested in talking to quantitative investors about how they extract alpha by not having to even “care” about the manual labor that a qualitative strategy requires. As an investor who thrives on trying to find alpha in the qualitative pieces of a company’s story, I guess I’m both jealous and envious that the quants are showing us that mouthwatering investment returns can be achieved through a systematic approach that’s purely based on the “numbers.”

Yuval didn’t come out of finance or Wall Street. He spent decades in publishing, only turning serious attention to financial markets later in life. Like many investors, he learned the hard way👉 Mistakes, drawdowns, and frustration forced him to rethink how decisions actually get made when real money is on the line.

His experience helped shape his quantitative framework designed to reduce behavioral errors.

“So it really clicked for me back in 2015 and it worked. I just.. I made so much money those first few years I was, you know, making, I think I made 60% one year and 50% the next… 2019 was a bit of a tough time for me… But 2020 I did really well again. So I... altogether. Over these 10 years, I had, I have a compound annual growth rate of 40% so I made, I made a lot of money. I was able to retired.” - Yuval Taylor

A Quant Who Understands The Limitations of Data

Yuval’s process relies on ranking systems with more than 200 factors, spanning quality, value, growth, stability, accrual behavior, and fraud risk. But he’s also very clear about the limitations of data.

He openly acknowledges that some turnaround situations simply can’t be captured cleanly by numbers, especially early on. That humility might not be shared in some quant circles and was refreshing to hear, since my team relies heavily on gaining edges through qualitative analysis and information arbitrage.

As Yuval put it, he has a deep respect for skilled discretionary investors. Not everything they do can be duplicated in a model, and pretending otherwise is a mistake.

Why “Middling” Metrics Matter More Than Perfect Ones

One area where Yuval’s thinking really stood out was his focus on moderation rather than extremes. Instead of chasing the highest growth or most explosive margin expansion, his system often rewards companies sitting in the 70th or 80th percentile. These businesses tend to be more sustainable and less prone to mean reversion. Interestingly, this is exactly what I did in my early days of investing if I was referencing The Investor’s Business Rankings.

He pays close attention to operating income growth, cash flow return on assets, and something that doesn’t get enough attention in most quant frameworks: the stability of a company’s cash conversion cycle. That focus helps reveal high-quality names that might otherwise fly under traditional screens.

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When Numbers and Narrative Intersect

Our conversation naturally drifted toward turnarounds, an area where quant and qualitative investing often overlap. Yuval looks for improving sales trends and operating income as early signals, but he also understands that numbers don’t tell the whole story. This idea resonated with me, since I’m a big fan of gaining a first move advantage.

We discussed how this reminded me of ACFN, which was added to GeoInvesting’s Focus Model Portfolio in September 2024 at $9.40, peaking at a return of 251%. Throughout that run, GEO consistently warned that the stock faced a potential air pocket if a major telecom contract wasn’t renewed or replaced. When that risk materialized and quarterly EPS came in far below expectations, the stock dropped sharply.

Investors who understood the business had options. Those who relied purely on extrapolated numbers didn’t. That’s the line Yuval draws as well: data is powerful, but context is important.

“I’m not like this quant who says, oh, all you discretionary investors, you’ve got your heads up your asses, you know, I’m not like that. I have a great admiration for what good qualitative investors do… I don’t think it can be duplicated.” - Yuval Taylor

Shared Ground in Underfollowed Names

The discussion touched on several names familiar to the GeoInvesting and Microcap Investing Cliff Notes communities. Yuval mentioned holding or previously owning stocks like Power Solutions International (PSIX), a turnaround we both flagged early, Conrad Industries (CNRD), one of his larger positions, D-Box Technologies (DBOXF), and Hammond Power Solutions (HMDPF), which he rode from roughly $10 to over $40.

There was clear alignment in how we think about these types of companies. Long operating histories. Limited coverage. Businesses that look boring until something starts to change.

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Risk And Hedging

Yuval was candid about mistakes earlier in his career, particularly around options and currency exposure in Europe. Those experiences reshaped how he thinks about risk today.

Instead of shorting, he now uses put options as asymmetric hedges, along with a newer custom hedge designed for environments where factor behavior breaks down. He’s realistic about the trade-offs. Hedges cost money in calm markets, but they matter when volatility returns.

That realism stood out. Many investors probably treat risk management as an afterthought, not a core part of the process.

Fraud Awareness Forged by Experience

Another moment that resonated was Yuval’s discussion of fraud detection. He shared how getting burned in $TIOG, eventually exposed by Hindenburg Research, pushed him to rethink how manipulation shows up in financial statements.

That experience led him to adapt the Beneish M-Score, looking not just for traditional red flags but also for suspicious “too good” signals. Given our own background uncovering fraudulent microcaps, this part of the conversation hit close to home.

Why Boring Still Works

We ended the conversation where my conversations often end: with an appreciation for boring, under-followed companies that have been around for decades. Businesses where margins, earnings, or cash flow might finally be turning after years of neglect.

Yuval believes there’s real value in being early to those stories, before attention shows up. I tend to agree.

For investors interested in the intersection of data-driven discipline and deep small-cap hunting, the full conversation is well worth your time.

Investor Insight Skull Sessions are deep discussions with investors to learn about their investing journey, investing process and favorite ideas.

I think what will resonate with a lot of readers is how unglamorous and grounded Lee’s path into investing has been. He didn’t come up through Wall Street or finance school. He found his way in through history books, the Great Recession, and eventually a thrift-store copy of The Intelligent Investor. That curiosity slowly turned into a disciplined value framework shaped by real-world scars: early losses in hype-driven sectors like green energy and cannabis, followed by a shift toward businesses he could actually understand.

Industrial companies, asset-heavy balance sheets, boring operations are where he sees his edge. He’s very clear about what he avoids (biotech, speculative tech, anything he can’t value with confidence) and equally clear about why writing matters: public feedback forces rigor, exposes bad assumptions, and sharpens conviction. That humility, knowing what you don’t know, is something many investors never develop.

Even though Lee likes investing in smaller companies, he’s comfortable moving outside that box. For example he’s owed stocks like Dollar General Corporation (NYSE:DG) and United Natural Foods, Inc. (NYSE:UNFI).

As a microcap investor who craves information arbitrage, what I really liked about Lee’s approach is how catalyst-driven it is and that he’s constantly hunting where the market is overreacting: missed filings due to auditor changes, cybersecurity incidents, failed acquisitions, regulatory overhangs: situations where the business is intact but sentiment is broken.

He’s not chasing momentum and likes to separate temporary noise from permanent impairment.

His process is straightforward, but not easy: screen relentlessly, focus on enterprise value and net asset value, wait for something to “snap back,” and sell when the original thesis plays out, not because the stock is up.

There’s also a refreshing transparency here. Lee shares his portfolio moves, tracks his results, and openly talks about volatility and mistakes. We chatted about small-cap industrials, legacy media as a deregulation play, and even distressed bonds as a potential strategy.