Quantitative Valuation Models for Cryptocurrencies на сайте Nedvio

Unlike traditional assets, cryptocurrencies lack commonly accepted valuation models to estimate fair value. Prices are primarily driven by speculation. However, valuing cryptocurrencies based on quantifiable usage and adoption metrics provides a more objective basis than hype or technical analysis alone. Here we explore quantitative crypto valuation techniques.

Network Value to Transactions Ratio (NVT)

NVT evaluates a blockchain’s market cap relative to its transaction volume and utility. Lower NVT indicates undervaluation, meaning high volume compared to market cap. NVT is calculated as:

Market Cap / Daily Transaction Volume

Bitcoin has sustained an NVT between $20-$100 long-term. An NVT of $100+ typically signals overvaluation and heightened risk of price declines.

Equation of Exchange (MV = PQ)

This model calculates market cap (M) as:

M = PQ

Where P = price and Q = quantity of transactions. This frames market cap as directly tied to daily transaction activity on the blockchain. More transactions justify a higher valuation.

Metcalfe’s Law

Metcalfe’s Law states that a network’s value is proportional to the square of its users. This models crypto valuation as:

Market Cap = n2 * k

Where n is monthly active users and k is a proportionality constant. Platforms with more users have higher network effects and value.

Cost of Production Model

This model values a cryptocurrency based on the estimated cost to «produce» new coins through the mining process. The key variables include:

• Hardware Costs — Cost of mining equipment like high-powered GPUs or ASICs needed to mine coins. This is a significant capital expenditure.
• Energy Costs — The ongoing electricity costs for powering and cooling mining rigs. This makes up the bulk of operational mining expenses.
• Labor Costs — Salaries for staff needed to operate mining facilities and hardware.
• Number of Coins Mined Annually — The cryptocurrency’s issuance schedule dictates how many new coins are created and enter supply each year through mining.

These costs are projected forward using assumptions around hardware improvements, electricity prices, and mining difficulty increases. The present value of cumulative future costs and minted coins provides an estimated production value. Comparing this to market cap indicates if a crypto is over or undervalued.

Discount Cash Flow Analysis

Traditional DCF analysis can also value crypto companies like exchanges based on projected cash flows discounted back at an appropriate rate. The key projections involve:

• Revenue Growth — Projecting annual revenue growth rates based on factors like user adoption, trading volumes, and new products. Historical performance is considered.
• Operating Costs — Estimating annual operating costs from staff, servers, regulations, etc. These are subtracted from revenues.
• Discount Rate — Determining an appropriate discount rate based on the firm’s risk and cost of capital. A higher risk firm will have a higher discount rate.
• Terminal Value — Calculating estimated future valuation at the end of the projection period. This is discounted back.

The projected cash flows are discounted back annually at the discount rate and summed to determine a net present value — the estimated current valuation. This can indicate if a crypto company is under or overvalued.

The challenge is that assumptions and projections for early stage crypto companies can be highly speculative due to the nascency and volatility of the industry. But DCF provides a model for valuation based on established financial techniques.

Case Study: Valuing Ethereum

Ethereum can be valued via Metcalfe’s Law using monthly active Ethereum addresses as a proxy for network size. This produces a “fair value” market cap range for Ethereum based on historical trends.

Current active address data is applied to the model to derive an estimated valuation. Comparing this valuation estimate to the actual market cap provides an overvalued/undervalued assessment.

Challenges to Overcome

Crypto valuation modeling faces difficulties including:

1. Hard to quantify true network usage adjusted for wash trading.
2. Complex token economics with staking, burning, etc.
3. Lack of reliable data like active users or transaction costs.
4. Speculation and hype overwhelming fundamentals.
5. Rapidly evolving landscapes resisting modeling.

Conclusion

While imperfect, quantitative models provide an objective reference point for blockchain valuations anchored to usage and adoption data. As the industry matures, valuation techniques leveraging network effects, cash flows, and other frameworks may become more reliable. But intrinsic value modeling remains challenging due to crypto’s dynamic nature.