The Discoverable Market: Why Cards Aren't Like Stocks

Both sides studied the stock market. Nobody applied this to cards. Until now.
In this episode of Slabnomics, we introduce the Discoverable Market framework and make the case that the card market is neither efficient nor random. It's exploitable. The information exists. Most people just aren't using it.


✅ What the Efficient Market Hypothesis actually means
✅ The 3 conditions required for market efficiency
✅ Why collector behavioral errors aren't random

The card market is newly becoming a real market. The frameworks that explain its behavior are barely being applied. The information required to price these assets correctly is freely available — and widely ignored.

That's the definition of a discoverable market. The question is whether you want to do the work.

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Transcript

SPEAKER_00 0:13

There is a debate that's been going on in academic finance for over 50 years. No matter who has championed either side, neither has definitively won. The question, like the best questions always are in life, is simple. It asks, is it even possible to beat the market? One side says no. The stock bark is a total information game, and prices already reflect all information in the market. The other side says it doesn't matter if you can beat the market, because prices move randomly like a blindfolded monkey throwing darts at a dartboard. You can't predict them anyway. The weird thing is both sides end up giving you the same advice. Don't even try. Just buy the index fund at regular intervals and retire with your white picket fence. Now many people in this hobby may have heard of this debate applied to the stock market, but it's likely that few of you have thought about it whether it applies to the market for cards. But I've been thinking about it for some time, and today I'm ready to share the fruits of that thought and research with you all. Once you understand what I'm going to talk about, you will never look at a card price the same way again. Welcome to Slabnomics. In 1973, a Princeton economist named Burton Mackle published a book called A Random Walkdown Wall Street, and it became one of the best-selling finance books in history. The core argument is this stock prices move randomly, they don't trend in predictable ways, past prices tell you nothing about future prices, and because of that, the average investor cannot consistently outperform a simple index fund. That argument rests on a related theory called the efficient market hypothesis. This was formalized by Eugene Fama in 1970. The idea is that in a well-functioning market, prices reflect all available information. Everything known about a company, its earnings, its future prospects, its risks. These are already baked in. So there's no information you could find that would really give you an edge. Here's the part that may make your head spin a little. These two ideas are not the same thing, but they can lead to the same conclusion. If the market is efficient, it's because every piece of information gets absorbed instantly by millions of traders acting on it. And once information is absorbed, the only thing that moves prices is new information. And new information is by definition unknowable in advance. So, ergo, price changes look random. It's really almost philosophical at this point. The more efficient the market, the more random it has to appear. Now, this debate has been going on since before many of us were born. And the honest answer after five decades of research is this. It depends on the market. And that dependency is exactly where the card market comes in to introduce itself. Before we say whether a market is efficient, we have to understand what efficiency really needs to exist. Because efficiency doesn't just happen, it's not a default state. It's actually an outcome that emerges only when specific conditions are met. The research clearly defines three conditions, and all three of these have to be present for a market to approach anything close to informational efficiency. The first condition is liquidity. Liquidity is the lubricant of price discovery. In a liquid market, when new information arrives, traders can act on it instantly. They can buy or sell without moving the price by much. They can arbitrage away missed pricings before those mispricings persist. The New York Stock Exchange adjusts to new information faster than smaller exchanges, not because it's smarter per se, but because it's more liquid. Speed of adjustment is a function of ease of trading. Now, in an illiquid market, new information may exist for days or weeks before it gets reflected in a price. That lag is a structural inefficiency, and you can exploit it. The second condition for an efficient market is something called information symmetry. Markets price things correctly when everyone has roughly equal access to the relevant information. Now, that's never perfectly true, especially these days, which is why strong form efficiency, the version that says even insiders can't beat the market, has been empirically rejected consistently. Insiders trade on private information and they make money. The market catches up eventually, but there is a window. The final condition for an efficient market is rational participants who act on what they know. This is where behavioral finance enters into the picture. And this is where it gets very interesting because the research on this is not what most people expect. Most people assume that irrational investors are going to make random errors. Emotional decisions that go in unpredictable directions, let's say. Some might buy too high, some might sell too low, and it all washes out in aggregate, right? That's not what the research shows. The errors of retail investors are not random. They're actually correlated. They move together, they cluster around the same psychological triggers. Things like herding, anchoring, FOMO, confirmation bias. These are not individual quirks. They're actually systematic predictable patterns that appear across every retail-dominated market ever studied. And correlated errors are something very different from random ones. Correlated errors are predictable. Predictable errors are exploitable. And hold that thought because we're going to come back to it later. Let's run card markets through this framework as a diagnostic. We'll start with liquidity. The New York Stock Exchange trades around$25 to$30 billion in transactions per day. The currencies, Forex market, handles about$7.5 trillion in daily transactions. The high-end card market, cards above$5,000, has 82 sales in the past 24 hours at the time of this recording. In liquid markets, miss prices are arbitraged away almost instantly. In the card market, though, a mispricing can persist for months, for years, because the mechanism that corrects prices in efficient markets, the ability to quickly execute trades, barely exists there. Now let's look at the second thing, information symmetry. You can pull up card ladder, you can pull up gemrate, and you can search a card, any card that's been released. Did you know that there's a function on Gemrate called the look back tool where you can see what the pop count was on a card at a specific date back to 2021? This means that you can go to the highest sale of a card in COVID, see what the population of the card was at that time, and compare it with recent sales. This empowered comparison gives the pop that currently is there, and this lets the high sale be informed by more information than just looking at a price. Here's a practical example. I want to find out if a rookie LeBron James tops chrome refractor in a PSA 10 is expensive compared to COVID. How do I do that? Well, one way you can, you can look up the last sale price, which in this case is$37,500 a couple months ago. You can see that the population is 180 of those PSA 10s. Now you can go further. Wow, that's seven times the price it is now, right? But we're missing a key component here. How many of these existed back then in 2021? What was the pop count back then? The crazy thing is, Gemrate gives us the ability to look that up. The population on that day was 171, only nine less than today. And that's in almost 300 refractors submitted since that time. Population control. So the supply has remained at the same level over five years, and it's worth 14% of what it once was? How many of the people buying and selling that card have done that simple research? My experience across this hobby tells me very few. Most participants are buying on feel. They're buying on recency. Maybe on what they saw in a YouTube video or a podcast. Maybe just what a player did the past weekend or the last couple days. Now, mind you, this isn't really a criticism. This is a structural observation. It means the information required to price these assets correctly is publicly available. Card ladder is 20 bucks a month. Gemrate is free, but most participants are not using them as tools to craft their investing thesis, the way that they could be used. This means that prices are not incorporating available information. That further means that this market, the card market we love, is structurally, definitionally not efficient. And then there's participant rationality. Now we've covered some psychological biases on the show to avoid FOMO, recency bias, anchoring, sunk cost fallacy, identity theory. But here's what I want to add today because this is the layer that the research reveals we haven't really discussed in depth before. These biases don't move people randomly, they move people together in the same direction at the same time. Researchers call this the retail habitat. Retail investors across every market ever studied consistently gravitate towards the same type of asset. Hard to value, no clear fundamental anchor, high narrative dependence, long time horizons. These are assets where the correct price is genuinely unclear, so sentiment fills the gap. Tell me that's not a description of the card market most of the time. Now, this next section is the part that I want to spend some real time on. In 2021, we watched every segment of the card market rise together. Rookies, veterans, common players, rare players, basketball, soccer, football, baseball, everything went up nearly simultaneously. Now that's not what efficient markets do, though. Efficient markets price individual assets based on individual fundamentals. Sure, you can have bull runs, but you're going to have windows of demand. Different assets are thus going to respond differently to different information. What we actually saw in 2021 was a retail demand wave. New entrants were flooding the market. Starting at accessible price points, they built confidence, they moved up market, and they chased what other people were chasing. All of them were anchored to the same recent prices, and all of them were responding to the same social media narratives. Then in 2022, nearly everything fell together. In 2025, the recovery was not uniform, but concentrated primarily in the high-end market, as I've talked in some of my podcasts. I would say that the market is now beginning to price things more correctly. But even now, low-end pricing is still heavily driven by attention and narrative rather than fundamentals. Here's what behavioral research says about this pattern. Retail participants don't allocate capital based on comprehensive analysis of available information. They allocate based on what's grabbing their attention. Things like extreme price moves, social media volume, viral moments. The research shows that retail buying follows the attention, not the data. And here's a critical piece on top of that. When retail investors make mistakes in these environments, they don't make independent mistakes that cancel each other out. They make the same mistake at the same time. They all herd into the same overpriced asset, they all hold the same bag, and then they all panic sell at the same bottom. That pattern is really predictable. Not perfectly, otherwise, I'd own an island, and not with precision timing, that's for sure, but the shape of it is knowable, and the mechanism is pretty understood. If you know that mechanism, you can position yourself before the crowd and after the crowd, depending on what you're trying to accomplish. Now, let's bring in a different body of evidence, shall we? Because card markets aren't the only illiquid retail influenced, hard-to-value alternative asset class that researchers have studied. Fine art has been studied extensively, things like fine wine, classic cars, luxury watches, and the findings are consistent enough that I think they're directly applicable here. If you want to see more, check out my Instagram where I did a whole big post carousel about this. First, the uncomfortable truth, broad exposure to these asset classes, say just buying art or wine or collectibles as a category, produces very mediocre risk-adjusted returns. When you properly account for survivorship bias, that only winners get resold, transaction friction, and storage costs, the aggregate performance of art or wine is really not that impressive. The returns that get reported are inflated by selective data, you see. This is also a parallel worth noting for cards. The card market does not produce consistent aggregate alpha. Broad exposure to modern Prism, for example, is not a reliable investment strategy. Don't just go buy singles of every player of last year's Prism. The market is littered with cards that went up in 2021 and never came back. But here's the finding that changes everything. In every illiquid alternative market studied, a subset of participants consistently generates alpha. This can't be by luck, because the statistical persistence of the returns is too strong for luck. These guys generate alpha because of their structural positioning. The researchers found three sources of that structural edge. The first is information asymmetry resolution. These big words just mean in these markets, the seller typically knows more about the asset than the buyer, the condition, the authenticity, or the demand for that specific item, say. Experts who can verify quality and identify assets that the broader market has mispriced as lemons, which are low quality items priced at average because the buyer can't really tell the difference. These kind of experts consistently acquire undervalued assets. They're not really guessing, they're able to filter through all of the other assets, the noise. The second source to get structural edge is something called network centrality. This was confirmed specifically in art auction research. Participants with more connections to other informed buyers and sellers acquired assets at lower prices. It's not because they were cheating at things, it's because their network gave them access to better information about real demand. In a word, they knew what the market actually valued while others really didn't. The final source for us here to find that edge is momentum premium in markets with continuous pricing information. This finding from the luxury watch research specifically is something I want to isolate for a moment because it's directly applicable to what we do here. Researchers found a documented monthly return premium in the luxury watch market for watches with continuous visible pricing data, watches that were priced frequently, that had recent sales, that the market could track with regularity. The premium for those was 1.25% per month. Now, watches with infrequent pricing, opaque markets and low visibility, this premium collapsed from 1.25% to only 0.09%. The alpha didn't come from the watches themselves. It came from the informational environment around them. Now think about how we can apply this to cards. Think about flagship sets, blue chip players, cards with regular sales data on card ladder. Cards where the market has continuous visibility versus thin market parallels on obscure players with two comps in maybe 12 months. The research is telling you something that Hobby rarely says out loud. Liquidity and information visibility are not just conveniences, they're actually structural determinants of whether alpha, which is the return above market average, is even accessible for that asset. Alright, now there's one more layer to this, and it's the one that I find most intellectually honest. A finance professor at MIT named Andrew Lowe developed what he called the adaptive market hypothesis. It's his answer to the 50-year-old efficient market hypothesis debate. I think it's the most accurate framework I've encountered for understanding what actually is happening in card markets. His argument is this markets are not mechanical systems, they're biological ones. So they don't behave like physics, they behave more like evolutionary vehicles. Now, in stable environments with many sophisticated participants competing for the same information, markets become highly efficient. Strategies get competed away, alpha then disappears. The random walk becomes a real description of what you're looking at. But when the environment changes fast, when new catalysts arrive, when new participants flood in, when the rules shift, then all the old heuristics fail. The ecosystem isn't able to adapt quickly. And in that gap between the old assumptions and the new reality, there's significant mispricing. And this is the exploitable inefficiency, Andrew says. The card market completed a full market cycle between 2019 and 2024. You had the COVID expansion, the 2022 consolidation into a correction that lasted into 2024, and then the 2025 recovery. The participants who lived through that cycle were more sophisticated than they were at the start. But the overall market is still so early stage. There's no equivalent of the SEC here. There's no equivalent of institutional research departments pricing these assets every day. Most participants are operating on heuristics formed during a two-year demand shock that threw everything out of the loop. Now, when you look at it this way, the current moment is not really confusing. It's one of the most legible moments in this market's history. The sophisticated capital has legitimized the high-end card market. Low end has lagged for a time, but as of May 2025, it's finally starting to climb back up, increasing 21% over the last 11 months. The environment thus changed from COVID, and most don't realize that while low end has moved up well, the high end of the market has moved up three times its counterpart, 62% in that timeframe. Now, I want to give you a framework that you can carry out of this episode. Every asset market you encounter falls into one of three categories. First category is efficient markets. Markets where information is reflected in prices quickly and reliably, where arbitragers are active, where liquidity is deep, something like the large cap stock market. It's not perfectly efficient, but it's efficient enough that consistently beating it means either extraordinary skill or extraordinary luck, and most professionals can't beat it over a decade. The second category is random markets, markets where price movements genuinely have no predictable structure, where the randomness is not a byproduct of efficiency, but of chaos. Thin markets with no fundamental anchor, where price discovery is so broken that there's no signal really through the noise. A lot of crypto markets fall under this umbrella. The third category is what I'm gonna call discoverable markets. These are markets that are neither efficient nor random. Markets where prices do not reflect available information, not because the information doesn't exist, but because most participants just aren't processing it. Markets where mispricings persist, not because they can't be arbitraged away, but because the arbitrage mechanism is slow and imperfect. Markets where participant behavior is predictable enough that structural patterns can be identified and acted on. I think card markets are in this third category. The information that would price these assets correctly exists right now. You have things like gem rates, you have population reports, you have supply structures, you have sales velocity and historical demand flow. It's all available, but most people aren't using it. They're doing things like anchoring to prices from years ago, they're chasing players who just had a big game, they're herding into the same assets at the same time for the same behavioral reasons that have been documented in retail-dominated markets across every asset class studied. That gap between the information that exists and the information that's being used is not just random noise. It's a structural feature. And structural features persist long enough to act on. The adaptive market hypothesis also tells us that successful strategies get competed away as participants adapt. If enough people start running supply structure analysis, gem rate overlays, and demand flow models, the edge is going to compress and the market is going to get efficient. But we're not there yet, guys. Not even close. Let's finish up with something concrete. If card markets are discoverable, then the behaviors that make sense in efficient markets are wrong here. And the behaviors that make sense in truly random markets are also wrong here. In an efficient market, it makes sense to buy diversified exposure. Just own a little bit of everything. It's index fund logic. In a discoverable market, diversified exposure means owning a lot of things you've done no work on. I'm sure you've heard not to put all your eggs in one basket. In a random market, though, no amount of analysis helps. The price tomorrow is independent of everything that happened before. In a discoverable market, analysis is the product. The work is the edge. So what does this look like in practice? The lemon problem is real in card markets. Most buyers cannot tell the difference between a well-priced card and a mispriced one without doing the work. Gem rate relative to submission volume, population relative to demand, supply structure of all of the parallels for that card, something like set level liquidity. Most buyers skip all of this and they just price into an average thing. They pay market price for lemons and apples alike. You can separate out the apples from the lemons, you can buy what those cards are worth on a long-term horizon to an informed buyer. That's the foundation of the edge that the alternative asset research confirmed in Art Wine and Watches alike. And it works the same way here with cards. Behavioral clustering is real in card markets. When a player goes viral after a big game, you can predict with reasonable confidence that card prices are going to move beyond what the underlying fundamentals justify. The crowd moves together in one direction with predictable timing relative to the trigger event. The question is whether you want to be in front of that move or behind it. The information visibility premium is real in card markets. The research showed that the momentum premium in watches existed almost entirely for assets with continuous pricing information. The equivalent in cards is flagship sets, blue chip players, cards that trade regularly enough that card letter as recent comps. So if you're operating in thin markets with no pricing visibility, you're not getting paid for your sophistication. You're just taking on more risk. I'm going to be direct about what this framework does not do. It does not give you precise timing. Knowing that a market is mispriced and knowing exactly when it corrects are two very different things. The behavioral research is clear that retail-driven mispricings can persist for years. This is because the mechanism that forces correction is complicated and takes a while to grind to a halt. The framework does not protect you from being wrong about underlying assets. Supply structure can be favorable, gem rates can be healthy, demand flow can be pointing in the right direction, and the player can still get injured, or retire, or have a bad season. The market being discoverable does mean that your analysis matters, but it doesn't mean that your analysis is always correct. And it doesn't mean the edge is permanent. If this show and the work that I do behind it succeeds in raising the analytical baseline of this hobby, then over time the market will become more efficient. The missed pricings are going to be smaller, the windows will be shorter, and that's exactly what's happening in a mature market. The 2025 card market is more efficient than the 2020 card market was. The 2030 card market is going to be more efficient than it was in 2025. Here's why I know though. Right now, today, in April 2026, the card market is newly becoming a real market. The frameworks that explain its behavior are barely being applied by the people transacting in it every day. The information required to price these assets correctly is freely available, yet widely ignored. That is the definition of a discoverable market. The question is not whether the edge exists, the research says it does. The question is whether you want to do the work required to find that edge. I want to leave you with the idea that started this whole episode. Burton Melkiel spent decades arguing that you can't beat the market. Eugene Fama spent a career building the case that prices already know everything. And 50 years later, the smartest people in finance still can't fully settle it. That's because they were studying one of the most efficient markets in human history. A market with dozens of billions of dollars in daily volume and centuries of documented cycles. It has continuous real-time pricing, and it has institutional participants running models that would make most of us feel dizzy. And even in that market, they couldn't get to a clean answer. Card markets have none of these features. They have participants who are learning in real time. They have pricing data measured in individual transactions per day. They have information gaps wide enough to drive a truck through. But the random walk argument assumes the price reflects nothing predictable. The efficient market argument assumes it already reflects everything relevant. The card market does neither. It reflects what the average motivator buyer thinks on the day of the sale based on the information they happen to have access to at that time. That's the gap. That's always been the gap. The collectors who understand that are operating in a discoverable market. They do the work, they build the frameworks, they resist the behavioral patterns that drive the crowd. Those are the investors who compound their investments. The people who buy on feel, the people that follow the crowd or treat this as just a lottery, they're not irrational, but they're running a strategy designed for a random market in a market that rewards discovery. On Slabnomics, we're here to build the discoverable edge. So as always, keep building, and I will talk to you later.