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How do you win at gambling? The easy answer is to not play, but the most profitable answer is to be the house. In this stock analysis piece, I wanted to take some time to look at key players in the financial plumbing sector, namely four exchanges (**CBOE**, **CME**, **ICE**, **NDAQ**), a market maker (**VIRT**), and two brokers (**IBKR**, **SCHW**).
# Section 0: Aren’t Some Brokers Missing?
You might notice that for brokers, I chose to leave out companies like **HOOD** and **BULL**. This is because my general thesis is that, a la recent SaaS fears, brokers that are effectively aesthetic overlays on top of some fairly weak financial infrastructure have very little real moat as more users start to look for API-native solutions with more flexibility and better offerings. It takes all of maybe 15 minutes to vibe out a fully functional trading terminal that links into Alpaca, and with that I can also access a real-time news stream, 10k data calls a minute, and competitive margin rates.
**HOOD** at least has a functional API and some name recognition, so in the short term it might be fine, but **BULL** is essentially dead in the water (pull up the all time price chart). The API has been literally unusable for years now, and their meager attempt at onboarding prediction markets feels like an anemic cargo cult-esque attempt at catching up with an industry that has already lapped them many times over. Regardless, neither brokerage is seriously used by non-retail, and there is only so much money to be made in catering to WSB apes, especially in the broader context of payment for order flow (PFOF) getting more strictly regulated over time. The US’ SEC has been flirting with ideas which would lower margins on PFOF, and the EU is implementing an outright ban of the practice by June 30th 2026.
In contrast, I consider **IBKR** and **SCHW** to be materially different because they each have fairly important moats: **IBKR** grants access to a huge range of markets that are inaccessible via other platforms and an API that is state of the art (even if an absolute pain in the ass to work with), while **SCHW** has an unbelievable amount of institutional momentum & legitimacy that no vibe coded front-end can replace in the near term. I believe that **SCHW** is a significantly weaker company than **IBKR**, however, due to its relatively higher reliance on PFOF and what I can only describe as an impressively slow pace of change (although granted, this could be viewed as a positive in terms of product stability).
# Section 1: What Affects Volume?
We first need to assess whether we really are in a period of continual trading growth, or if this is just a cyclical boom to be followed by a bust.
Options volume across the major public platforms (**CBOE** & **NDAQ**) should, in theory, tend to fluctuate with macroeconomic conditions, and while quarterly revenue should be a better dependent variable, volume is more useful due to its weekly frequency and standardized time periods across every ticker.
To select the most useful regressors, I used rolling random forests with permutation importance, which led to the conclusion that macro variables have modest but episodic explanatory power for weekly options volume growth; in other words, they don’t have high explanatory power for any options volume growth. With that being said, the four main statistically relevant FRED series were **VIXCLS**, **STLFSI4**, **DGS10**, and **BAMLH0A0HYM2**.
Interestingly, what I found was that even after controlling for macro variables, running a simple linear time-trend regression shows that options volume over the past two years has increased by about 0.2869% per week with high robustness (Newey-West and start date robustness tests all yielded effectively-zero p values); annualized, this is around 16% YOY growth. Granted, this is a very small sample, but because I ran a weekly test, I believe that this is still a useful result.
Unfortunately, I could not find any freely accessible futures data that’s structured like the OCC options data, but reports by **ICE** and **CME** both show around 12% YOY growth in futures trading, so I’m inclined to believe that the derivatives market as a whole is growing quite healthily even within a secular context.
# Section 2: Trend Continuation?
While it’s great and all that the derivatives market is expanding over time, we need to make sure that there’s evidence it will continue to do so. We already have quantitative data to back up this upward trend from section 1, so let’s do some qualitative discussion.
I believe that the current trend of financialization and speculation ([a great writeup can be found here](https://oldcoinbad.com/p/long-degeneracy)) will only accelerate as AI makes more accessible the vast reams of data and knowledge that humanity has accumulated at an ever more rapid pace. This post would be way too long if I were to delve into the reasons that I believe this to be the case, but in short, a combination of explosive prediction market growth (which exchanges will no doubt look to capitalize upon) and easier access to medium frequency trading by retail users thanks to the cost of entry into algorithmic trading falling sharply (as evidence, you can see huge growth in quant and algo related communities online) means that we will only see more trading volume over time.
Three good ways to highlight this are to look at **IBKR**/**SCHW** and **VIRT**’s last earnings reports, and the listed year-over-year (YOY) figures. IBKR reported a 32% increase in customer accounts, 37% increase in customer equity, a 30% increase in daily average revenue trades (DARTs), and an astounding 40% rise in customer margin loans. SCHW had similarly strong figures, with a 31% increase in DARTs and a 34% increase in customer margin loans. On the market maker side, VIRT has had an excellent year as well. Their trading revenues increased 34% from 2024 to 2025, and the Normalized Adjusted EPS grew by an eyewatering 61%.
What do numbers from these three companies tell us? I believe that, overall, the most obvious takeaway is that there is little reason to believe that we will see any sort of imminent slowdown in trading volume within the short term. We already showed that YOY % growth in derivatives volume is in the double digits, brokers are reporting large increases in paid activity on their platforms and leverage via margin which boosts trading volume even more, and market makers that harvest income from volume clearly are doing well too.
# Section 3: Priced In?
The obvious next question is, given these strong growth figures, surely the market has already priced everything in right? The harsh reality is that the answer is probably yes, at least for the names that are closest to being pure-play exchange volume monetizers. **CBOE** and **CME**, the two largest exchanges for their respective derivatives classes, have seen relatively-astronomic YTD stock price increases, with CBOE being up 17.40% and CME being up 13.97% in a market where SPX is only up 1.28%, with CME seemingly shrugging off even massive concerns like technical outages that forced a trading stoppage on Feb. 25th. **NDAQ** and **ICE** are a little weaker, with ICE flat and NDAQ down 13.11% since year start, but NDAQ’s weakness is more likely to be a function of its majority reliance on analytics and corporate services than anything else. If we are to just focus on the pure exchanges, it seems like they are essentially money printers, and unfortunately we are too late to a party that began and will end without us. Or are we?
This is where the data forces a less emotional answer. I ran a macro-adjusted multiple framework using the four key FRED drivers that mattered for volume, namely **VIXCLS**, **STLFSI4**, **DGS10**, and **BAMLH0A0HYM2**, plus an equity risk premium proxy, and then evaluated valuation state using rolling regressions to avoid the regime-mixing problem that showed up in structural break tests. In plain terms, the question becomes: after controlling for the macro environment that naturally moves multiples around, which names look rich, which look fair, and which look cheap? On a 24-month rolling basis, the ordering is not subtle. **ICE** screens as cheap after macro adjustment, sitting around the 10th percentile of its rolling residual distribution, and it stays cheap even when conditioning on the current low-volatility regime. **NDAQ** and **SCHW** also screen on the cheap side in the low-20s. In contrast, **CBOE** sits closer to the upper half, and **CME** and **IBKR** push into the mid-80s, meaning they are expensive after macro adjustment over the recent regime window. That is already enough to reject the idea that everything is uniformly priced to perfection.
The second question is whether these residuals are just statistical noise. I stress-tested the framework against forward EPS noise by smoothing and winsorizing the estimate series, and the ranking barely moved under those reasonable perturbations. I also added an EV-based check once data coverage allowed it. That cross-check is broadly consistent for the key conclusion: **ICE** remains cheap-ish on EV/EBITDA residual percentiles, and **SCHW** looks especially cheap on that metric, while **CME** looks rich-ish. **NDAQ** is the one name that becomes less “cheap” on an EV basis than it appears on the P/E residual, which is consistent with the idea that its mixed business model makes single-denominator conclusions fragile. In other words, the signal is not purely an artifact of forward EPS estimate noise, and it is not entirely a P/E illusion either.
The final question is the only one that really matters to an investor: does this valuation-state signal have any predictive content, or is it just a narrative in numerical clothing? On pooled tests, higher residual valuation is associated with weaker subsequent returns and worse downside, and this survives multiple specifications, including shorter horizons and overlap adjustments. It is not perfectly uniform by ticker, but the direction is consistent enough to treat “rich after macro” as a real headwind rather than a cosmetic label. I also translated the signal into a simple, implementable portfolio rule and ran a long-only backtest that buys the cheapest subset and rebalances monthly. The unhedged version performs strongly even after conservative transaction costs, and walk-forward testing and leave-one-out checks suggest the result is not just one lucky name carrying the entire strategy. The hedged version is much weaker, which is an important caveat, because it implies that part of the realized performance is equity premia plus timing rather than pure market-neutral alpha, but that does not negate its usefulness for entry discipline.
Putting this together, the correct conclusion is not that the exchange complex is fully priced and therefore uninvestable. Instead, it is that the market has priced in the growth narrative most aggressively where the story is cleanest and easiest to underwrite, namely **CBOE** and **CME**, and that this shows up both in raw multiple percentiles and in macro-adjusted residuals. At the same time, there are still names where the market is not paying a premium relative to recent macro conditions, most notably **ICE**, and to a lesser extent **NDAQ** and **SCHW**, and this is consistent with independent “median multiple” style price targets that show minimal downside for **ICE** and meaningful upside for **SCHW**, while showing larger downside gaps for **CBOE**, **CME**, and especially **IBKR**. **VIRT** is missing from this because its valuation tends to be highly explainable by macro variables to a degree no other name here is, and its value thus fluctuates heavily depending on the regime we are in. I may do a deeper individual dive on **VIRT** in the future because it’s truly a fascinating company, but the tldr is that this is a fun (and very profitable) stock to play a medium-term mean reversion/scalping strategy on, not a buy-and-hold stock.
# Section 4: Is This Just Tech Beta?
One lingering concern is whether any of this is simply disguised technology exposure. After all, exchanges run electronic platforms, brokers are effectively software businesses, and financial infrastructure increasingly looks indistinguishable from enterprise tech. If the “cheap” signals are merely the result of a tech drawdown, then the investment case is far less interesting. To address this directly, I ran rolling 24- and 36-month correlations and betas against **XLK**, and then estimated a three-factor model controlling for **SPY** and **XLF** to isolate independent tech exposure.
The traditional exchanges, namely **CBOE**, **CME**, and **ICE**, show little to no structural tech beta. In fact, their 24-month correlations to **XLK** are mildly negative in the most recent window, and once **SPY** and **XLF** are controlled for, their independent tech exposure effectively disappears. These are not tech stocks in disguise, they are financial infrastructure assets whose economics are driven by volume, volatility, and margin structure rather than semiconductor cycles or AI enthusiasm.
**NDAQ** initially looks somewhat correlated to tech in raw rolling correlation, but that effect largely vanishes in the three-factor regression. Once broad market and financial sector exposure are accounted for, **NDAQ**’s independent **XLK** beta is approximately zero. In other words, **NDAQ** may trade alongside tech during risk-on periods, but its return profile is not fundamentally tech-driven. That supports the idea that its business model, while more diversified into data and analytics, is still anchored in financial infrastructure rather than pure technology cyclicality.
**IBKR** is the exception. Its rolling correlation and single-factor beta to **XLK** are meaningfully positive, indicating genuine tech adjacency in how it trades. However, even here, much of the exposure weakens once market beta is controlled for, suggesting that part of the apparent tech linkage is simply high-beta growth behavior. **SCHW** sits somewhere in between, retaining some tech-like characteristics even after factor controls, likely reflecting its retail platform and digital brokerage positioning. **VIRT**, interestingly, shows negligible or even negative independent tech exposure once controls are applied, reinforcing the conclusion that it is a volatility- and liquidity-cycle vehicle rather than a tech-cycle play.
The practical implication is important. The valuation signals we are observing, especially for **ICE**, **NDAQ**, and **SCHW**, are not simply artifacts of technology sector rotations. They persist after controlling for both market-wide and financial sector factors. When **ICE** screens cheap on a macro-adjusted basis, it is not because tech has sold off; it is because its multiple has compressed relative to its own recent regime and macro backdrop. Conversely, when **IBKR** or **CME** screen rich, it is not a byproduct of AI hype alone; it reflects genuine premium valuation within the financial infrastructure complex.
In short, this is not a hidden tech allocation story. The exchanges remain what they have always been: structurally advantaged toll collectors on financial activity. The dispersion we are observing is not sector rotation noise but differentiated valuation within a high-quality, secularly growing subset of the market.
# Section 5: What to Buy?
Great, so we’ve done the analysis and found that, broadly speaking, financial plumbing companies are undervalued at best and fairly valued at worst, largely uncorrelated with AI-related hype via first-order effects (although second-order effects like increased algorithmic trading or lowered cost-to-entry may still show up), and generally seem primed to grow robustly in the long term.
The next step is to translate all of that into an actual portfolio, and the important point is that this should not be done by vibes. If we take seriously the idea that valuation state matters, then we need a systematic method that concentrates capital in names that are cheap after macro adjustment, scales by risk, and refuses to allocate to rich names simply because we like the business. This is exactly what the residual framework is built to do.
Using the 24-month rolling residual model as the “current regime” valuation-state signal, the ranking is pretty unambiguous. **ICE** is the clear standout, with a residual percentile of 10.2 and a strongly negative residual level. **SCHW** and **NDAQ** sit in the low-20s, which still qualifies as cheap in a regime-aware sense. Everything else is either neutral-to-rich or outright expensive on this framework. **CBOE** sits around the 69th residual percentile, **VIRT** around the 67th, and **CME** and **IBKR** are both in the mid-80s. In other words, if the goal is to buy undervalued or fairly valued plumbing companies, these latter names do not qualify right now, even if otherwise excellent.
Once we enforce a simple discipline of allocating only when the value score is positive, the portfolio naturally collapses down to three names. **ICE** receives the largest weight because it has the strongest value score and relatively moderate volatility. In the risk-scaled allocation, **ICE** takes roughly **44.9%**, **SCHW** takes roughly **32.8%**, and **NDAQ** takes roughly **22.3%**. These weights are proportional to the valuation signal strength divided by 24-month realized volatility, which prevents the portfolio from being dominated by the noisiest name and naturally sizes up the cleanest opportunity.
From a market exposure perspective, this portfolio is not doing anything reckless. The implied **SPY** betas for **ICE**, **SCHW**, and **NDAQ** are roughly 0.60, 0.88, and 1.01 respectively, which aggregates to a portfolio beta around the high-0.7 range. That is meaningfully less than the market, but still gives you equity participation. There is no need to hold cash for beta control in this configuration, and there is no need to include richer names purely for diversification, because doing so would dilute the valuation edge while adding only marginal reduction in volatility.
So the “what to buy” answer based on the data is not to buy everything in the theme, but to buy the subset that is actually offering valuation slack today. Concretely, that means **ICE** as the core exchange exposure, **NDAQ** as a cheaper, more diversified infrastructure name that is still fundamentally tied to trading and market structure, and **SCHW** as the most attractive broker-side expression of the trend, albeit with idiosyncratic rate and balance-sheet risk that exchanges do not have. Conversely, **CBOE** and **CME** remain investable long-term franchises, but the framework says they are priced up in the current regime and therefore should be treated as “wait for a better entry” rather than immediate buys. **IBKR** is the most clearly stretched name in the set, and **VIRT** is not cheap either, making it more suitable as a tactical macro-regime trade than as a core allocation at current valuations, as stated before.
# Section 6: Key Takeaways
Let’s say you’re too lazy to read all of this. What is the real meat here?
First, financial plumbing remains one of the cleanest ways to position for secular growth in trading activity without making directional bets on any single asset class. Exchanges, brokers, and market makers all monetize volume, volatility, and participation. The empirical evidence suggests derivatives activity has been compounding at double-digit rates, with options volume rising roughly 0.2869% per week over the past two years (\~16% annualized), and futures activity growing around 12% YOY based on reported figures.
Second, the growth narrative is real, but it is not uniformly priced. A 24-month rolling macro-adjusted residual framework shows meaningful separation between names. **ICE** sits near the 10th percentile of its residual distribution, **SCHW** and **NDAQ** in the low-20s, while **CBOE** (\~69th), **VIRT** (\~67th), **CME** and **IBKR** (mid-80s) screen rich relative to their own recent regimes. That dispersion matters, because historical tests suggest that higher residual valuations are associated with weaker forward returns and worse downside.
Third, allocating our portfolio intelligently naturally concentrates capital in **ICE** (\~44.9%), **SCHW** (\~32.8%), and **NDAQ** (\~22.3%). The resulting aggregate beta in the high-0.7 range provides equity participation without full market exposure. Importantly, richer names are excluded not because they are bad businesses, but because they do not currently compensate for their valuation risk. One additional benefit of this allocation is that we are essentially allocating across all of the major bases: we have a futures exchange, a broker, and a hybrid options exchange/financial services provider; in essence, we’ve covered all the major themes.
Finally, it’s important to realize that this is **not** a hidden technology bet. Rolling correlations and multi-factor regressions show that traditional exchanges do not carry meaningful independent **XLK** beta once **SPY** and **XLF** are controlled for. The cheap signals in **ICE**, **NDAQ**, and **SCHW** are not artifacts of AI rotations, but rather reflect relative multiple compression within financial infrastructure itself.