In-depth analysis of Celestia token distribution and unlocking: Is the value underestimated?
Author: An Ape's Prologue
Compiled by: Deep Tide TechFlow
The highly anticipated Celestia governance token------ TIA has been launched, immediately attracting market attention, with over half of the token supply held internally. This uneven distribution has led to a limited circulation in the market, raising many questions: Is it undervalued? Let's explore:
The token distribution in the above image is striking: 53.2% is allocated to internal holders, while only 7.4% is allocated to the community, highlighting a clear imbalance from the start.
As for the unlocking plan, the community's tokens are fully unlocked at launch, providing a certain level of initial liquidity. In contrast, the internal holders' share will be locked in a two-year linear unlocking plan after 33% is unlocked at the end of the first year.
The research and development tokens follow a similar plan, with 25% unlocked in the first year, and the remainder linearly unlocked over three years after twelve months.
Although 25% of these funds have already been unlocked, equivalent to 67 million tokens, they are primarily expected to remain in the foundation's funding account, staying away from the public market in the short term, reducing overall selling pressure.
Despite TIA's fully diluted value (FDV) being high, reaching a market cap of $333 million, similar to $SUI's market cap after a 70% price drop. Considering the limited liquidity of the R&D tokens, the effective market cap is only $175 million, suggesting that TIA may be undervalued compared to its peers.
In comparison, TIA is also smaller in market size: its FDV is only half of $SUI's and one-third of $APT's. At launch, $SUI's valuation was six times that of $TIA, reaching $13 billion at that time. However, it is worth noting that market conditions now are significantly different from a few months ago, with trading volume and liquidity noticeably reduced, which may explain the discrepancies in these comparative data.