Crypto assets (especially those backed by legitimate early-stage projects) behave like startups with early liquidity. Most startup equity is extremely risky and illiquid for years; in crypto, similar risk profiles trade every second. Combine early-stage cash-flow uncertainty, high sensitivity to aggregate risk (beta), and fat-tailed idiosyncratic shocks with a pricing process that’s well-approximated (first pass) by lognormal dynamics. The result is high dispersion, large drawdowns, and a brutal selection process where many projects go to ~0. Diversification and disciplined rebalancing are not optional; they’re the whole game.
Alt-Coins = Startups with Early Liquidity
Traditional startups live in private markets. Prices are updated episodically (financing rounds), insiders gate supply, and information flow is sparse. In crypto, the same early-stage uncertainty trades in continuous time with public order books, 24/7 access, and reflexive flows. Unsurprisingly, the empirical vol is large:
Multiple studies and daily experience document order-of-magnitude higher volatility for Bitcoin than major FX pairs or alt-coins versus traditional equities. In addition, observed attrition is extreme. CoinGecko’s 2025 study finds >50% of all cryptocurrencies have already failed, with failure rates >70% for some issuance cohorts. This doesn’t mean that crypto is a bad investment, but rather, it is exactly what you’d expect if tokens are “public-market startup equity” with constant repricing and low barriers to issuance. Post-mortem evidence from the venture world — cash shortfalls, no PMF, competitive displacement — maps cleanly to token projects.
CAPM: High Expected Return ↔ High Beta & Risk
In the Sharpe–Lintner–Black Capital Asset Pricing Model (CAPM), the required (ex-ante) return scales linearly with market beta. Startups (and high-growth tokens) are effectively levered claims on systematic growth risk, so they should command high betas and therefore high required returns — before any idiosyncratic risks. The CAPM is not the whole truth (multi-factor models often fit better), but it is the right first principle: if a token’s cash-flow (or adoption) is pro-cyclical and option-like, beta will be high, and so will the cost of capital.
Empirically, crypto’s market factor is dominant in risk decompositions during stress; cross-sections often look like “beta + lottery-like idiosyncratic tails.” That mix drives both the high expected return hurdle and the wide dispersion investors observe. This is observable in classic empirical references such as Black–Jensen–Scholes time-series tests and later critiques/extensions.
Lognormality: High σ Punishes Long-Run Growth / Survivorship
A standard first-pass model for risky assets is Geometric Brownian Motion (GBM). Two critical implications follow immediately. First, the expected arithmetic return is μ, but the long-run geometric (log) growth rate is μ−(1/2)σ^2. For high-σ assets, this volatility drag is enormous; unless μ comfortably exceeds half of variance, wealth compounds poorly (or not at all) even if the average return looks attractive. For many altcoins, the implied μ never clears that bar.
The second implication is that there is significant right-skew / mass near zero for the price of the asset over the long-run. Lognormal distributions are positively skewed: a few big winners, many small outcomes clustered near zero. As σ rises, the probability mass near zero increases for any fixed μ and horizon. That’s the mathematical backbone of “most tokens go to ~0, a few go to the moon.” Put differently: high dispersion is not a bug of crypto; it’s the expected outcome of compounding under high σ with thin/uncertain cash-flow support and reflexive demand.
If you accept GBM-style dynamics and the startup analogy, then high σ + modest μ implies many paths to ruin, but a few paths towards stratospheric wealth.
Diversification + Rebalancing “Solves” Volatility Drag
For an equal-weight portfolio of N assets with common variance σ^2 and average pairwise correlation ρ, as N→∞, idiosyncratic risk diversifies and you’re left with systematic risk ρσ^2. In crypto, pairwise correlations rise in stress (like equities), so the floor ρ matters. But the first-order effect holds: breadth slashes idiosyncratic tails. Two practical corollaries are that
- Breadth beats “conviction” in winner-take-all domains when optimizing for the typical outcome. With fat right tails and heavy attrition, ex ante selection is hard; owning the distribution (subject to quality screens) is rational.
- Rebalancing harvests volatility. In mean-reverting relative prices, periodic rebalancing sells partial winners/buys partial losers, converting volatility into a rebalancing premium, especially when constituents are high-σ and imperfectly correlated. This is a mechanical consequence of reducing the volatility of the portfolio under any short period while preserving the expected returns.
Crypto / Startup Vs. Public Equities Portfolios
VCs “spray and pray” because early-stage outcomes are wildly uncertain and extremely skewed: most startups fail or stall, and a tiny handful deliver nearly all the returns. You can’t reliably pick those few in advance—moats aren’t established, product-market fit is fragile, timing and distribution are chaotic, and one competitor can change everything. So the rational strategy is many small, independent bets, strict sizing, and only concentrate after real evidence appears.
Crypto magnifies this logic. Most tokens are startups with early liquidity—you’re repricing venture-like uncertainty every minute instead of every funding round. There are more ways to die and faster narrative swings, but also bigger right tails. That mix argues even more strongly for breadth, small initial tickets, hard risk caps, and rules-based rebalancing to harvest volatility and let a few winners carry the portfolio.
Public equities are different. The big winners already show moats, cash flows, and ecosystem lock-in; even the index is concentrated in durable compounders. Concentrating there can be reasonable because you’re betting on proven advantages with abundant information.
Putting this framework into practice, the immediate implications are:
- BTC / ETH / SOL: It’s reasonable to hold more concentrated positions here. They behave more like large-cap equities: deeper liquidity, broader adoption, clearer roadmaps, stronger network effects, and much better information flow. You can build a coherent, durable thesis (digital store-of-value/monetary premium; generalized settlement/computation layer; high-throughput consumer/app chain) and size accordingly. Think of these as your “large caps.”
- Altcoins (even the so-called blue chips): Treat these like a venture basket, not like your NVDA. Visibility is lower, moats are unproven, lifecycles are shorter, and idiosyncratic risk dominates. If you assume the market is not fully efficient (a fair assumption in newer sectors), market-cap weighting can overexpose you to yesterday’s narratives and underweight tomorrow’s winners. A more diversified, closer-to-equal-weight approach across a screened set can make sense: It avoids overconcentration in the few names the market already anointed; It lets the distribution work for you; when one name 5–10×’s, its weight naturally grows; the many small losers don’t sink the ship; With periodic rebalancing, you systematically trim partial winners and add to laggards, converting volatility into a steadier path of compounding.
Concluding Thoughts
In the end, the evidence points to a simple posture: be intentionally concentrated only where durable theses, scale effects, and information quality justify it (BTC/ETH/SOL), and treat everything else like a venture universe: broadly diversified, small starting sizes, strict risk caps, and relentlessly rules-based rebalancing.
The CAPM lens reminds us that high beta implied a high expected return while the lognormal lens reminds us that high volatility taxes geometric growth and pushes most outcomes toward the left tail; and variance math remind us that diversification and rebalancing is the only partial cure for a left-tailed distribution. These implications argue for humility, and letting the framework, rather than headlines, decide when a position grows, shrinks, or exits.
That’s the stance we’ve tried to encode at Freeport, not as a pitch but as infrastructure for our own investing: a way to operationalize breadth in the “startup-like” altcoin sleeve while allowing concentrated conviction in BTC/ETH/SOL to be sized thoughtfully closer to their market weightings.
In practice that means our weighting methods that don’t blindly mirror market cap in a sector where markets are likely inefficient; and we advise users to employ scheduled and band-based rebalancing that harvests volatility without over-trading.
If the math says most names won’t make it and a few will drive outcomes, our job isn’t to predict the few with certainty, it’s to own the distribution safely enough to survive long enough for those few to matter. If we can make rational investments processes easy to deploy and boringly consistent for our suers, then the right tail can do its job, and long-horizon allocators can own crypto’s upside without pretending it isn’t a venture asset class with a ticker.
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