In this episode of Tascha Labs podcast I talk about:

  1. A valuation framework to assess blockchain platforms
  2. Impact of token supply on price. 



Episode notes

1. A valuation framework to assess blockchain platforms

00:57 – What will determine the price of a blockchain native token?

Three main factors:

  1. Adoption & usage of the blockchain.
  2. Platform moat: How secure is it & whether it has been around for a long time? ⇒ Affect investors’ perception. Example: Bitcoin = digital gold, since it has been around since 2008.
  3. Crypto market condition: a proxy for adoption level of the digital market. More adoption ⇒ more liquidity, potential buyers in the market, and easier for the platform to be discovered. 

Put these factors together ⇒ construct an empirical model on how these things affect the valuation and market cap of different blockchains. Data was used mostly from Artemis

07:40 – How is this useful for investment?

  • Have a model to predict the market cap of that platform.
  • Can compare the predicted market cap with current ⇒ undervalued or overvalued relative to its peers.
  • Current model: only for 12 blockchains. 
  •  Future: Simple valuation tool to plug in the number of any platform. Estimate release date: around New Year.

09:30 – Results of the analysis

  • Overvalued platform: Polygon, Ethereum, BSC, Flow.
  • Undervalued platform: Near, Ripple/XRP, Avalanche, Bitcoin.
  • The rest (Optimism, Aptos, Algorand, Solana): difference 10% ⇒  Current values align with the prediction.

11:05 – On “overvaluation”, “undervaluation” and “predicted value” terminology

  • Overvaluation: The current market cap is above the predicted value.
  • Undervaluation: The current market cap is under the predicted value.
  • Predicted value: How much investors would have been willing to pay for a token given historical patterns.

14:15 – How this model can be used for any tokenized platforms

  • As long as the native token is involved in the user activity of that platform ⇒ positive relationship between:
    • The number of users,
    • The platform’s activity,
    • Value of the token.
  • More participants on a blockchain ⇒ more demand for the native token (Similar to why fiat money has value).

18:13 – Why do I only run the model with 12 blockchains?

  • Data is available.
  • This model can be used for other blockchains. Required data: Adoption level, active users, and transaction level of that blockchain.

19:51 – How is this model different from the cash flow-based model?

  • Cannot apply cash flow-based model (such as discounted cash flow model) for web3 platforms because:
    • token holders are not shareholders.
    • A blockchain token ≠ equity for most cases. Staking in Proof-of-Stake has some characteristics of equity.
    • Fundamental demands are driven by network effects: people actively use the chain and need to hold some amount of that token.

23:43 Regarding undervalued tokens – Is it because of high inflation?

  • This model is predicting the market cap, not the price.
  • Market cap = Price x Token supply.

24:51 – Do Solana’s transactions include voting transactions?

  • Already excluded voting transactions in this model.

25:26 – Regarding XRP result

  • This model is an empirical estimate based on historical patterns, not personal opinion.
  • Some people think XRP, or Near, or Avalanche is not undervalued. 
    • Is it possible that they are right? Yes. 
    • Is it statistically likely according to the model? No.
    • One may argue that price reflects the survival risk of the platform. But that should be already captured in platform-specific markup of the model.
  • If the current value is over the predicted market cap ⇒ The token is likely to underperform relative to the total crypto market and vice versa.

31:44 – What does higher markup mean?

  • Suppose chain A & chain B both have 500000 users. 
  • If chain A has a higher moat (more Lindy) and investors think it’s safer ⇒ higher valuation given the same usage and adoption level as chain B.
  • Example:
    • High markup: BTC & Eth – safest so far.
    • Low markup: Aptos & Optimism – newer chains without a long track record.

33:40 – Regarding people having different opinions

  • Different opinions make the market.
  • If everyone has the same opinions ⇒ Price will go to infinity or zero immediately.

2. Impact of token supply on price 

34:47 – How was it done?

  • Look at the top 1000 market cap tokens.
  • Range: 2020-2022.
  • Study the change in supply & price on an annual basis.

35:33 – Result of the analysis

  • Negative correlation: If the token’s supply increases ⇒ downward pressure on price.
  • The impact on price is not 1 to 1: 10% token supply change ⇒ 5% price change on average.
  • Token supply increase ⇒ could increase its market cap (Similar to stock splits in the stock market).
  • Supply reduction has a stronger impact on price compared to supply expansion.
  • Supply has more impact on price in the bear market because:
    • Lower liquidity.
    •  investors are more risk-averse.
    • Lower demand compared to the bull market.
  • The effect is the same whether the token has a max supply cap or not. 
    • Having a fixed supply cap may not be a good idea except for digital gold. 
    • Will write more about this.

44:12 – Is token supply a good metric when losing private key cases are not included? 

  • Unless you think this has an ongoing impact on the token supply, otherwise it won’t have a systemic effect on the model.

45:15 – Do NFTs have the same price mechanisms regarding the token supply versus price?

  • Not surprised if they have similar mechanisms.
  • Similar phenomena in stock ⇒ could see similar things in other assets if they are relatively liquid.
  • NFT is harder to measure due to less liquid.

46:15 – Is crypto over? Will policy change affect how assets are priced?

  • Is crypto over? No, not at all.
  • Policy change could affect the crypto price in the short term since cheap liquidity is gone but not forever.
  • We are only at the beginning of the tokenization revolution, decades-long cycle, which will change:
    • Business models.
    • How businesses and people make money.
    • Shareholders and company relationship.
    • Value distribution in society.

49:38 – Does this model take into account veTokenomics?

  • Yes. veTokenomics is just a way that projects encourage people to hold their tokens like staking in proof-of-stake blockchains.
  • Projects can strategically increase token supply to attract new users through incentives and avoid hurting long-term holders (ex: ve token model or stakers in POS) by giving part of the emission to them. 

52:53 – Can we split it into subcategories?

  • Yes, but this analysis only has around more than 700 tokens ⇒ limited data.

53:30 – How supply shrinkage affects price in the bull versus the bear market?

  • Didn’t look at it. 
  • Tascha’s guess: Less effect in the bear market. Reason: Harder to assess due to the smaller sample size.