The valuation of cryptocurrencies and tokens has been an ever challenging topic in the blockchain space. Numerous methods and formulas have been proposed. However, in my opinion, nothing has yet emerged as being generally accepted or a de-facto standard, in the same way the widely accepted “earnings-per-share” (EPS) is the common metric traditional financial markets adhere to. 

Before jumping to devising models and equations, I think we need to have good visibility on the basic units of “input data” that could be used to then construct such formulas or later develop quantitative methods. 

Last year, I enumerated a number of Blockchain Metrics for quantifying usage. It was a step in the right direction, but here I’m organizing 9 metrics as the essential ones on top of which valuation frameworks could be constructed. Here, I have focused on bringing visibility to few metrics that have potential characteristics of being absolute in the sense of clarity, and therefore, they could carry little ambiguity when being published.

I believe that the industry needs precise data points that cannot be challenged (similar to the EPS analogy). For example, the number of public shares a company floats or issues is known and can be trusted as the real number. This is why the EPS number is trusted and cannot be debated. The EPS multiples that analysts decide to project for a given stock to derive market capitalization is a subjective number that is chosen later. 

That is why, for the blockchain sector, as a starting point, we need fewer, but more essential data points, not a panoply of metrics with no head or tail. Furthermore, the language around the metrics should be clear, non-technical, and easily understandable. 

The following is my attempt to list the essential data points organized under the PIB moniker, referencing the following descriptive sub-categories: Pulse, Impact, Breadth. 

In the field of quantification methods, there are inputs and outputs. There is causal activity and there are resulting effects. These metrics I’m proposing are in the “input” and “causal” categories, which means they can be used to in a variety of analysis methods to derive resulting outputs and effects.

The metrics in this category pertain to the dynamics behind the network’s operations. 

The nodes are an essential native unit for blockchains. 
What is the number of nodes that are servicing the underlying peer to peer network?

Decentralization is a key attribute, and it can be argued that a more even distribution of ownership is better for the future health of the network.
What is the ownership distribution of the nodes in operations? 

Although public blockchains (and many dApps) are not known for their transaction speeds, how quickly are transactions finalized is an important factor that indicates the actual throughput of a blockchain or app.

In this category, we look at the financial and economic aspects behind the cryptocurrency. 

Here, we need to make a clear distinction between currency-to-currency transactions (C2C) where the user is just exchanging one cryptocurrency for another (I1a), versus on-chain transactions that are used for a given utility, eg. earning/spending, gas costs, staking costs, etc. (this excludes the native earnings from mining or minting activity (I1b). Here, I1 = I1a + I1b. 

These are the revenues from mining, minting, or rewards related distributions.

What is the fiat-denominated value of current holdings in all issued wallets? See B3 to also include the segmentation between user vs. non-user wallets. Here, I3 = I3a (users) + I3b (non-users).

Breadth pertains to the available token/cryptocurrency. It is closest to the number of shares (float, restricted, issued) in public stocks.

What is the total number of tokens/cryptocurrency units in circulation? (both in the hands of users or investors)

What is the number of units that are either un-vested, un-minted or not yet issued? (publishing a vesting schedule is useful)

A distinction should be made between the wallets being held by users versus non-users. Users include app end-users or developers that need to use the token or underlying cryptocurrency to run their programs. Here, B3 = B3a (users) + B3b (non-users).

It is my opinion that we need to start with the above metrics before constructing valuation models. Once these numbers are available, any analyst can decide what equations to fit them in, what multiples or weights to give them, and how to construct various methods to derive meaningful comparative metrics. These can apply for blockchain protocols, networks and applications. 

I encourage crypto projects, blockchains, analysts and data sites (I provided a list to some of them in this blog post, Where Are All the Decentralized Applications) to help make that data easily available so that we don’t have to spend time finding it. It is the responsibility of each issuer to make sure their data is visible and easily measurable with integrity. 

Of course there are other metrics, but I believe these are the ones where a differentiated analysis can be built upon. Almost everything else could be a derivative of the above numbers. Many other blockchain/protocol metrics are table stakes or vanity metrics not worthy of comparative studies.

In a subsequent post, I’m going to propose some meaningful derivative ratios to consider, in the same way that the EPS is a meaningful ratio and reference point. As usual, I welcome suggestions on what these ratios should be, and any feedback on the proposed PIB framework.

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