In my recent conversation with Quant investor, Yuval Taylor, had this to say about discretionary investing:
“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 Andreas Himmelreich and Kurtis Hemmerling 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 Andreas Himmelreich and Kurtis Hemmerling.
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.
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
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.
