- Bitcoin’s unique structure, sitting at the intersection of money, commodity, and network, renders traditional valuation frameworks difficult to implement.
- We explore multiple valuation models spanning price, production, and investor positioning, each offering a distinct lens through which market conditions can be evaluated across different dimensions of value.
- By standardising these signals across time horizons and integrating them into a weighted, percentile-based composite, we construct a measure of valuation that identifies structural extremes and cyclical positioning.
The Bitcoin Problem
Bitcoin occupies a structurally unique position within the broader asset landscape. It represents the first digital commodity to achieve durable, global adoption without reliance on a central issuer, affording it properties fundamentally distinct from traditional asset classes.
The network is maintained through a decentralized system of nodes and miners. Nodes verify transactions, while miners order them into blocks, secure the protocol, and introduce new coin supply according to a deterministic issuance schedule.
This design produces a bearer asset, like Bitcoin, that can be transferred directly between users on its own network without intermediaries or held and traded on exchanges where continuous, global price discovery occurs.
This structure renders Bitcoin superficially comparable to existing asset classes, yet mechanistically distinct from all of them. It produces no cash flows, has no underlying issuer, and is not tied to any single economy or sovereign credit profile.
As a result, standard valuation frameworks, including discounted cash flow analysis, earnings multiples, and yield-based models, do not apply.
Instead, Bitcoin sits at the intersection of being money, a commodity, and a distributed network, a combination of properties that resists categorisation within conventional modelling approaches. It is precisely this structural ambiguity that makes robust valuation difficult, as no single framework fully captures the range of dynamics that drive price across market cycles.
In this article, we explore a range of valuation approaches and develop a composite framework for assessing Bitcoin's value across time.
What is Value?
To begin, we must first define what value actually means. From the perspective of Austrian economics, value is a subjective property that exists in the mind of the individual uating it, rather than an intrinsic characteristic of the asset itself.
In practice, valuation frameworks attempt to approximate a notion of a consensus fair value through the lens of a chosen reference point. Rather than being directly observed, fair value is constructed relative to something that is known, typically historical price behaviour or other measurable benchmarks. This provides a basis against which current conditions can be compared, allowing assets to be described as overvalued, undervalued, or in equilibrium relative to that reference.
The choice of reference point is therefore critical. Value can be assessed across multiple dimensions: price, network activity, capital flows, investor positioning, and more. Each perspective offers a different lens through which market conditions can be interpreted, and no single dimension fully captures the asset's underlying dynamics.
Valuation therefore becomes a multidimensional problem. Different frameworks emphasise different signals, and their conclusions may diverge depending on the chosen reference point. A comprehensive approach requires integrating these perspectives, rather than relying on any single metric in isolation.
Valuation and Pricing Models
Having established that value is both relative and multidimensional, we now turn to some of the specific frameworks used to approximate it in practice. Each model captures a different aspect of Bitcoin's market structure.
From an analytical perspective, Bitcoin valuation frameworks are not fundamentally novel, and share clear similarities with traditional equity valuation approaches. In equities, analysts typically compare price or market capitalisation to a fundamental metric such as earnings, forming ratios like price-to-earnings. In Bitcoin, the structure is analogous, with price or market capitalisation used in the numerator and compared against a relevant fundamental metric in the denominator.
The key distinction between traditional equities and Bitcoin lies not in the valuation methodology itself, but in the nature of the underlying fundamentals. The metrics used to characterize Bitcoin can differ significantly from those used in equity analysis, which can introduce a layer of complexity for new investors. Nevertheless, as outlined above, the overarching structure and logic of valuation frameworks remain largely consistent across both domains.
Technical Indicators: The Mayer Multiple
The Mayer Multiple is a widely used valuation indicator that compares the current spot price to its 200-day moving average, providing a simple measure of trend extension. It is often interpreted as a boundary between expansionary and contractionary market regimes, with elevated values signalling overheated conditions and depressed values indicating potential undervaluation.
Despite its simplicity, the model is powerful precisely because it relies solely on price. This allows for consistent application across assets and avoids dependence on more complex or noisy inputs. Price itself is a highly informative signal. It represents the aggregation of all available information, expectations, and positioning at a given point in time.
Notably, the 50d, 365d and 200w moving averages are also commonly used equivalents. However, the 200d moving average remains the a widely adopted variant.
Commodity Cost of Production
Whilst Bitcoin exhibits a blend of characteristics, it retains many properties of a commodity. In commodity theory, price often finds a floor near the marginal cost of production. In Bitcoin's case, this corresponds to the capital (CAPEX) and operational (OPEX) expenditures incurred by miners competing for the right to append blocks and earn issuance rewards.
However, production costs vary significantly across participants, depending on factors such as energy pricing, hardware efficiency, and scale. This makes it difficult to define a single, uniform cost basis across the network.
One approach to this problem is the Difficulty Regression Model, introduced by Checkmate. This model estimates an all-in sustaining cost of production for the average miner by using network difficulty as a proxy. Difficulty can be viewed as the aggregate expression of mining competition, compressing a wide range of inputs including energy costs, hardware, and operational efficiency into a single observable variable, much like price itself reflects the aggregation of market forces.
A key limitation, however, is that the relationship is not stable over time. Structural changes in the mining industry require the model to be periodically recalibrated, making it less robust as a long-term, cycle-invariant valuation framework.
On-chain Positioning
Unlike traditional assets, where transactions are largely opaque, the Bitcoin ledger is fully transparent, with all transactions publicly observable, including the price at which coins last moved.
From this, we can construct metrics such as Realised Price, which represents the average acquisition price of all coins based on their last on-chain movement. Comparing the current market price to this aggregate cost basis allows us to derive ratios such as MVRV, which measure the level of unrealised profit or loss across the network.
This provides a fundamentally different lens on valuation, one rooted not in price alone, but in the positioning of investors. When a large share of supply sits in profit, the market tends toward overvaluation, while widespread losses are often associated with undervaluation.
This approach aligns closely with the subjective theory of value. Each participant's individual cost basis reflects their own valuation, and when aggregated across the network, forms a collective measure of market positioning.
Importantly, this class of analysis is unique to transparent blockchains. In traditional equity markets, equivalent information is fragmented and typically held at the exchange or custodian level, making it inaccessible for comprehensive valuation analysis.
The True Market Mean builds on this framework by estimating the average acquisition price of active investors only, excluding supply deemed lost or dormant such as early miner and Satoshi-era coins. This produces a more representative gauge of economically relevant capital and can be interpreted as a threshold separating macro bullish and bearish regimes.
Alongside this, the Short-Term Holder Cost Basis (STH-CB) is a derived variant of the Realised Price that captures the average purchase price of newer market entrants, typically coins aged less than 155 days. This 155-day threshold is derived from observed spending behaviour, where coins younger than this age are statistically more likely to be spent and remain active in the market.
These holders therefore exhibit the characteristics of newer market participants, tending to be more reactive and highly sensitive to price fluctuations, with behaviour closely tied to profitability. As a result, STH-CB has historically acted as a boundary for local bull and bear conditions, as marginal demand oscillates around profitability.
Creating a Composite
As discussed, valuation is fundamentally a function of reference, measuring how far a metric has deviated from a chosen benchmark, and over what time horizon that deviation is assessed.
Among the dimensions explored, price and investor positioning most closely reflect the aggregation of individual preferences expressed through market activity and therefore provide the most reliable reference points for assessing value. As such, we use these to form the foundation of the composite framework.
To integrate these dimensions, we standardise each input across time using Z-scores and uate them over multiple rolling windows, ranging from short-term to full-cycle horizons. These standardised series are then transformed into expanding percentiles, allowing each metric to be expressed relative to its own historical distribution. We then apply a weighting scheme across time, placing greater emphasis on longer horizons to anchor the model in cycle-level structure, while retaining shorter-term inputs as a secondary influence.
By combining these weighted percentile signals across both time horizons and valuation dimensions, we collapse the valuation surface into a single, interpretable measure, one that reflects not only where Bitcoin is trading, but how extreme that position is relative to its own history.
Bottom Line:
Bitcoin can be expressed in absolute terms through price, but its valuation is inherently relative, defined by how current conditions compare to prior states across time and market structure. In this work, we explored multiple valuation frameworks spanning price, production, and investor positioning, each offering a different lens through which value can be interpreted.
By combining price and positioning across multiple time horizons, this framework provides a systematic way to assess where the market sits within its cycle and how extreme current conditions are, unifying these perspectives into a single measure of relative value.
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