Layer2 Public Chain Token Valuation Model Analysis

Analysis of Layer2 Public Chain Token Valuation Model

Author: Yuxing Liao; Source: Author’s Twitter @0xlewisliao

This article aims to estimate the valuation of a Layer2 public chain token from the perspective of building a valuation model. To estimate the value of Layer2, we need to look at the current Layer2 from the perspective of Layer1. The following figure shows the market capitalization of mainstream Layer1 during the previous cycle and the current market capitalization.

I have summarized some of their characteristics:

• Cosmos, Polkadot V.S Layer2 Stacks Chapter (1) Technical Solution Overview
• Interview with Mysten Labs Product Director Why is Sui’s technology particularly suitable for enterprise services?
• Puffer Finance Research Report LSD Track, a technology-driven seed player with dual staking and dual rewards.

• “Exchange”: The alt Layer1 tokens that once reached the highest market capitalization were those tied to exchanges. When Solana lost the exchanges, its value returned to the same level as other ordinary Layer1 tokens, while BNB remained high.

• “Narrative”: Polygon, which embarked on the zk-EVM journey, has a trend of surpassing other non-exchange tied public chains in terms of current market capitalization.

• “Technology”: Polkadot, Avalanche, Polygon, and Cosmos are Alt Layer1 tokens with different technological solutions. Polkadot, which is recognized as having the best technology, had the highest market capitalization among several projects. • “Team”: Similarly, a rough judgment is that among the factors influencing market capitalization, exchange tying > narrative > technology ≈ team.

The valuation model I have established mainly analyzes through analogy. Assuming that Ethereum + Layer2 will be the absolute protagonist of the next cycle, we can reasonably assume: Layer2’s total market capitalization / Ethereum’s total market capitalization * α = Alt Layer1’s market capitalization in the previous cycle / Ethereum’s total market capitalization. The parameter α is between {0,+∞}, and its value is influenced by the value capture method of Layer2 and Alt Layer1, as well as the value transmission of Ethereum.

Due to the closer binding between Layer2 and Ethereum, the value of α is most likely greater than 1. Let’s assume α = 1, which belongs to a conservative estimate. Assuming that the price of Ethereum in the next bull market is $10,000, Ethereum’s market capitalization will reach 1194B. Based on the proportion, the total market capitalization of Layer2 is calculated as 738.4B, and there will be 1-2 leading Layer2 tokens.

1. The top-ranked Layer2 token accounts for 1/4 of the total market capitalization of Layer2, which is 185B.

2. The second-tier tokens account for approximately 1/8 of the total market capitalization of Layer2, which is 92B.

3. The third-tier tokens account for approximately 1/16 of the total market capitalization of Layer2, which is 46B. So, now we only need to judge based on the fundamentals to determine which tier the Layer2 token we want to estimate will be in, and the corresponding valuation at the peak of the next bull market.

This valuation model is also a thinking model, and it has three key assumptions, which are the flaws of this model. These assumptions are:

1. Ethereum + Layer2 will be the absolute protagonist of the next cycle, and: Layer2’s total market capitalization / Ethereum’s total market capitalization * α = Alt Layer1’s market capitalization in the previous cycle / Ethereum’s total market capitalization;

2. The price of Ethereum in the next bull market will be $10,000;

3. Layer2 is also distributed in tiers, with the proportions of different tiers and the previous cycle’s AltLayer1 being the same. The top-tier tokens account for 1/4 of the total market capitalization of Layer2, the second-tier tokens account for approximately 1/8 of the total market capitalization of Layer2, and the third-tier tokens account for approximately 1/16 of the total market capitalization of Layer2.

Realizing the assumptions of this mental model will help identify where deviations may occur in the model. When conducting specific analysis, adjustments can be made to the assumptions based on actual circumstances, ultimately leading to a more accurate estimate.

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