The market that nobody talks about

It’s been about 1 year and 9 months since I graduated college.

In college terms, that’s basically 4 semesters, right at the end of sophomore year. And honestly, not that much has changed. I’m still figuring out what I’m going to eat each day, what I’m going to wear to happy hour this weekend, when I’m going to do my laundry, and the apartment is somehow never fully clean.

What has changed is what’s taking up space in my brain.

In school, I studied computer science and took some econ classes. I knew what a stock was, could fumble my way through a DCF, and had a vague sense of what the Fed did. I thought I had a somewhat decent handle on “finance”. That’s funny.

There’s a whole other market

And it’s bigger than the equity market. The global bond market is roughly $145 trillion.¹ The global stock market (the one that most people watch) is around $127 trillion. But when people say “the market”, they almost always mean stocks.

Part of that is just visibility. You can look up a stock price in two seconds. Credit markets (bonds, loans, structured products) trade mostly over-the-counter, in large institutional blocks, with way less price transparency.

A lot of the people I’ve spoken with say credit markets are “behind” equities: less electronic, less efficient, slower to adopt new technology. And honestly, I can’t really disagree with that. Equity markets have had algorithmic trading and real-time price discovery for years. Fixed income, especially in more complex instruments, is still catching up.

But I’ve started to think that misses the point a little. The complexity is part of what makes it interesting. And right now, the space is moving fast: private credit has grown from under $1 trillion a decade ago to over $2 trillion today, attracting capital and talent at a crazy rate.² I think it’s an exciting time to be paying attention to it.

What trading in the credit market actually looks like

When most people picture financial markets, they usually picture something like the stock market: algorithms firing off thousands of trades per second, prices updating in real time, the whole thing humming along like a machine. High frequency trading, order books, millisecond execution. That’s the equity market.

Credit is a different world entirely.

The bond market is almost entirely institutional. The players are hedge funds, asset managers, insurance companies, pension funds (you get the idea). And when they trade, they’re not moving a few shares around. A typical trade might be $10 or $50 million (or more) of a single bond or tranche. And the way a lot of that gets negotiated? Bloomberg chat. Literally an instant message: this much notional, at this price, are you buying or selling?

The first time I really internalized this, I thought it was kind of insane. You have two institutional traders (one usually sitting at a big bank, think JPM, MS, GS), moving tens of millions of dollars of a highly structured credit instrument, and they’re essentially texting each other to work out the terms. It goes through fund administrators and clearing houses, so it’s properly regulated and settled, but still. There’s something very analog about it given the size and complexity of what’s actually being traded.

This is a big part of why credit markets haven’t gone the way of high frequency trading. Every bond is different. A share of Apple is a share of Apple, no matter who’s selling it. But every bond has its own maturity, coupon, seniority, covenants. You can’t just plug it into an algorithm and let it rip the way you can with equities. The pricing requires judgment, context, and a lot of back-and-forth.

That said, the quant side absolutely exists. Where I work, we use pretty sophisticated models to inform our view on price and size before a trade ever happens. The model tells you where something should trade. But then a human still opens a Bloomberg chat and negotiates. The model informs the trade, it doesn’t execute it. That gap between the quantitative framework and the human judgment call is actually one of the more interesting parts of the job.

The first concept I actually understood

Being honest, I always found corporate finance kind of boring. DCF models, comps, plugging numbers into a spreadsheet to get a valuation that’s mostly just your own assumptions anyway. It felt very monotonous and “why is everyone doing the same thing” and calling it analysis.

The first concept that really clicked for me was the relationship between interest rates and bond prices. When rates go up, bond prices go down. Inverse relationship, simple enough. But there’s a lot more going on than this.

Why does the Fed raise or cut rates? (I couldn’t have given a complete answer to this a year ago). Inflation and unemployment: these are the two mandates. When inflation runs hot, they raise short-term rates to cool things down. Higher rates make borrowing more expensive, people spend less, hiring slows, prices come down. When the economy weakens, they cut. That’s the basic loop.

When Powell speaks, he’s signaling where short-term rates are heading. But there’s a whole other end of the curve. The long end, the 10-year Treasury yield, is something the Fed doesn’t (and can’t) directly control. The market sets it. It’s essentially the collective bet of every bond investor about where growth and inflation are headed, sometimes years out.

The spread between the 2-year and 10-year yield is one of the oldest recession indicators out there. When short-term rates rise above long-term rates (an inverted yield curve, hello high school math) it’s historically a sign that the market thinks the economy is going to slow down. The fact that you can read that from two numbers and a simple graph on a screen is kind of wild when you think about it.

There’s a whole universe of structured products: CLOs, ABS, CMBS, CDS (yes they each have their own abbreviation, it’s a lot). Cash flows getting sliced into tranches, prepayment risk, convexity, credit events, jump-to-default risk. The math gets hard, fast.

Nobody really knows what comes next

And that’s kind of the point. One of the things I’ve come to appreciate about credit markets is how much uncertainty lives inside it.

Nobody has the full picture, everyone is working with incomplete information and making their best guess about a future that hasn’t happened yet. And right now, there are some genuinely open questions without clear answers. How will private credit hold up as AI reshapes the business models underlying a lot of these loans? Where do rates go from here? Do the assumptions baked into the last decade of lending still make sense?

I’ve been reading a lot, thinking a lot, and slowly starting to form my own views on some of this (although I have way more questions than views right now). I certainly don’t have any answers, but I’m paying attention.