Valuation frameworks and market cap predictions for 12 mainstream public chains: Which are overvalued or undervalued?

Author: Tascha Labs

Compiled by: DeFi Dao

At any given point in time, you can think of the valuation of blockchain platforms as being determined by three aspects, including:

Adoption and usage

Platform moat

Cryptocurrency market conditions

How do these three factors affect the valuation/market cap of Tokens?

1. Adoption and usage:

This is the fundamental valuation driver. Users need a chain's native Token to pay transaction fees. More active users mean more people need to hold at least a certain amount of the aforementioned Token, leading to higher Token demand and driving prices up.

This is a mechanical relationship, unrelated to speculative demand or expectations of price appreciation. Therefore, this is a long-term valuation driver.

2. Platform moat:

This affects investors' risk assessment of the platform. If a chain is considered to have a significant moat—say, it has been around for a long time and has a strong community—then more people will view it as a safer bet.

Whether this perception is correct is another question. The key is that the view of a larger moat and lower risk allows the Token to command a premium, all else being equal. I refer to this as "platform-specific premium" (later, you will see what the premiums of various chains refer to).

3. Cryptocurrency market conditions:

Investor sentiment—such as risk appetite—will affect the demand for all Tokens. The overall level of cryptocurrency adoption is also relevant. Without the context of the overall cryptocurrency market, the valuation of a single Token is meaningless.

We can measure a chain's adoption and usage using active addresses or transaction counts, while using the total market cap of cryptocurrencies to represent market conditions. This gives us:

Valuation of a chain = a1 * (active addresses or transaction counts) + a2 * (total crypto market cap) + (platform-specific premium)

The chart below shows the actual market cap (logarithmic) of 12 major blockchains and the model-predicted market cap. These two models use active addresses and transaction counts to measure the chain's adoption.

About the terminology

I refer to periods where the actual market cap (green line in the chart below) is above the predicted valuation (red and blue lines in the chart below) as "overvalued periods," and conversely as "undervalued."

These are awkward terms because saying something is overvalued or undervalued implies that there is an objective, fixed true value somewhere.

The reality is that all valuations are relative. A chain's valuation today can only be assessed based on its past and the overall crypto context.

(This is like your physical location—a seemingly real and reliable concept, but actually just relative to the position on Earth.)

Here, "overvalued" really means that considering the current levels of adoption and the state of the crypto market, the current market cap is higher than what investors have historically valued the platform at.

However, this is just verbal expression. So we will continue to use "overvalued" and "undervalued."

Bitcoin

Ethereum

BSC

Ripple

Solana

Polygon

Avalanche

Algorand

Near

Flow

Optimism

Aptos

Below is the ranking of the gap between actual market cap and estimated market cap as of two weeks ago, arranged by chain. Similarly, a positive gap = overvalued, a negative gap = undervalued.

The results show that the most overvalued chains currently are:

Polygon

Ethereum

BSC

Flow

The most undervalued chains are:

Near

Ripple

Avalanche

Bitcoin

The rest—Optimism, Aptos, Algorand, Solana—are all < 10% small gaps. I believe these are more or less within the margin of error.

I know these results will strike a nerve with some people. If your favorite chain is not in the category you want to see, there are a few things to note:

First, these are empirically estimated results. I have not imposed any personal opinions. So there is no need to shoot the messenger.

Second, there are a thousand reasons that could explain why these results do not apply to the "current situation"—the so-called "this time is different" phenomenon.

For example, you might say chain xyz is priced high now because the Token supply is decreasing, or the market is pricing in higher growth prospects due to major projects coming online soon. Or chain XXZ is priced low because it is effectively dead or about to die.

Are these valid reasons? Certainly.

But regardless of how valid the reasons are, remember that historically, periods of overvaluation are often followed by Tokens performing poorly in the overall market in the subsequent year. The relationship between overvaluation and poor performance is very strong.

Can the case you have in mind really be an exception? Is it statistically possible?

Third, while these valuation gaps often imply regression, overvalued or undervalued periods generally last 6 months to a year, but there is no way to accurately predict how long they will last.

Just because a chain looks undervalued today does not mean it will self-correct tomorrow, and vice versa. The typical "value investor" trap is buying something that looks undervalued, only for it to remain undervalued indefinitely.

When you believe something is undervalued, a better approach is to keep it on your radar. But only buy when you see some signs indicating that the trend may be turning.

Finally, here is an estimate of the platform-specific premiums. Again, these measure a platform's moat or perceived risk level. If a chain is considered to have a larger moat, i.e., more likely to continue to survive and develop, then it can command a higher price at the same levels of adoption and usage.

Among these premiums, there are some prominent patterns. You're smart. I believe you can figure out what they are.

Finally, while these valuations are based on data from 12 public blockchains, the same framework can be used to evaluate other blockchains. In fact, any tokenized project with network effect potential, such as gaming platforms, can utilize this framework.