1/ Financialized NFTs is an evolving space with huge potential. Yet, low liquidity and inadequate price discovery are key limitations to be tackled.
Here I explain its importance, bottlenecks, market landscape, and potential opportunities.
fundamentallabs.substack.com/p/financialize…
A thread🧵👀
2/ The importance of NFT financialization
Securitization has been a long-time practice in the field of finance.
Financialization transforms illiquid assets into tradable securities, which helps to channel cash flow to borrowers and fund significant economic development.
3/ Where is the market today?
@NFTfi issued $36 million worth of loans in May, while NFT transaction volume exceeded $3 billion.
The penetration rate of NFT credit market is slightly north of 1%
vs. 10% in traditional art lending and >50% in mortgage markets.
4/ NFTs suffer from low liquidity:
‣ Unique characteristics -> limited market
‣ High price -> low affordability
‣ Lack of utility -> speculation deters investors
Which leads to:
‣ High slippage
‣ Capital inefficiency
‣ Poor price discovery and valuation
5/ Current market landscape for NFT financialization
Categorized into...
‣ Marketplace and Aggregator -> facilitate transactions
‣ Lending and Rental -> improve capital efficiency and
explore yield-generating possibilities
‣ Liquidity and Valuation -> infra for trading
6/ Protocol deep-dive
LIQUIDITY POOLS
Liquidity is a key trading infra.
Abundant liquidity ->
+asset utilization rate
+user participation.
@NFTX_ regards the NFTs within the same vault fungible. The mechanism is suitable for floor items within the same collections.
7/ Deeper pool can maintain a stable price, and create a smooth trading experience.
Here is when @FloorDAO comes in...
With bond & rebase mechanisms by @OlympusDAO, @FloorDAO accumulates liquidity for NFTs, which are deployed in @NFTX_ to generate yield.
8/ FRACTIONALIZATION
@fractional_art operates the same way as @NFTX_/@NiftyMuseum.
The similarity comes from transforming ERC721 tokens into ERC20 tokens and the difference lies in the ability of yield generation.
9/ Why is fractionalization not a solution?
Fractionalization just transfers existing liquidity issues from NFTs to fractional NFTs, meaning: instead of having one illiquid NFT, you now have many illiquid ERC-20 tokens.
Good for community building, not so good for trading.
10/ LENDING
There are three mechanisms to facilitate NFT-as-collateral loans:
‣ Peer-to-peer lending
‣ Peer-to-pool lending
‣ Over-the-counter (OTC) lending
11/ Peer-to-peer lending
@NFTfi
The loaning process is similar to a loan marketplace, where the NFT owner selects the offer from several liquidity providers based on their terms.
12/ Peer-to-pool lending
The segment of NFT lending protocols utilizing the peer-to-pool mechanism has many strong players, including @BendDAO, @dropsnft, @XCarnival_Lab, @PineLoans, @GradientFinance, @JPEGd_69 and @PilgrimProtocol amongst others.
13/ COMPARISON: NFT LENDING
P2P
+long waits; suits long-tailed NFTs
-not scalable
P2Pool
+Instant loan
-blue chips only
OTC
+institutional scale
-KYC required; blue chips only
All with low LTV->low capital efficiency
14/ RENTING
NFT rental market is still young. As of June there are <100 NFTs for rent on @renftlabs, indicating room to grow.
Lack of PFP utility + guides providing in-game asset rental service = low rental demand
However, we expect new use-case NFTs to boost the growth.
15/ VALUATION AND APPRAISAL
Valuation and appraisal is a fundamental infrastructural component of any trading market and can be used across many NFT financialization use-cases.
Pricing mechanisms can be categorized into:
‣ oracle-based
‣ optimistic proof of stake pricing.
16/ @abacus_wtf uses optimistic proof-of-stake to create a liquid backed valuation system.
The value of the NFT equals the total value locked in the pool.
The valuation mechanism of Abacus is one of a kind and its effectiveness remains to be proven over time.
17/ There are several protocols experimenting on leveraging machine learning technology to predict NFT prices, such as @NFTbank_ai, @banksea_finance, and @UpshotHQ.
Data fed for training models can be categorized into three types:
‣ pricing data
‣ NFT metadata
‣ market data
18/ Determining valuation: Pricing oracles
In this method pricing is determined through on-chain data that is fed to the protocol using oracles. While this improves trustlessness, unexpected short-term upsides cannot be captured as this method is backwards-looking.
19/ Determining valuation: Machine learning based pricing oracle
This method also relies on pricing oracles for on-chain data, but also off-chain data and uses pattern recognition to provide more context to the data. This method needs more data to properly evaluate a collection.
20/ Determining valuation: Optimistic Proof of Stake pricing
Spot traders form a network of real-time human appraisers, whereas the value of the trading pool reflects the value of the NFT. While unique, this approach faces scalability challenges that are yet to be overcome.
21/ The development and experimentation of NFT financialization just started.
Despite all the progress in various verticals, we see some common challenges:
‣ Foundations waiting to be established
‣ Limited utility layers
22/ Here are some of my thoughts on where we can find opportunities in building this space.
‣ Segmenting NFTs into different categories with
different tranches of risk and return profiles
‣ Building risk profiles of borrowers
‣ Extending utility layers
23/ If you're working on building this space, have feedback on this piece, please reach out!
Special thanks to @0xPhillan for his support on this piece.
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