The NFT liquidity dilemma may be solvable, CoinFund founder interprets the potential of price discovery mechanisms
A price discovery mechanism with capital efficiency will change the NFT and other illiquid asset markets.This article was published on ChainNews, authored by Jake Brukhman, founder of CoinFund, and compiled by Perry Wang.
Currently, most people tracking non-fungible tokens (NFTs) have noticed that this is an asset class with very poor liquidity, and everyone would likely agree that this situation may persist. However, it is possible that people have not seen that, in the context of blockchain's crypto-economic mechanisms, "liquidity" is merely a rapidly solvable mechanism design problem.
In this article, I want to delve into how to leverage crypto-economic models to achieve "financialization" in the NFT space, improve its liquidity, and how this technology can be extended to other illiquid asset classes over time.
Financialization of the NFT Asset Class
NFTfi.com uses NFTs as collateral for loans, thus achieving the financialization of NFTs
Looking back at the early stages of the blockchain space, fungible tokens (ERC20 category) could take months or even years to achieve significant liquidity.
Token issuers list their tokens on centralized exchanges, paying high fees and bypassing regulatory measures. However, the market has adopted smart contracts and crypto-economic mechanisms to address the liquidity issues of fungible products.
Today, through the magic of liquidity mining and the ingenuity of automated market makers (AMMs), the time required for ERC20 tokens to gain liquidity has been reduced to almost zero, with daily trading volumes on decentralized exchanges (DEXs) potentially exceeding $2 billion, while the market capitalization of decentralized finance (DeFi) reaches $44 billion.
In contrast, even after the dramatic increase in total sales (GMV) of NFTs in 2020, the prospects for selling any individual NFT on the secondary market today remain bleak. In my previous article, I proposed designing NFTs as "liquid intellectual property (IP)."
As the scope of NFTs encompasses more and more digital content, the liquid IP perspective implies an underlying assumption that NFTs themselves represent a new class of financial assets.
As the financial attributes of NFTs become stronger, they will require new types of exchanges, lending protocols, and derivatives. Therefore, I assert that price discovery is the next major issue in the NFT space.
A capital-efficient price discovery mechanism can facilitate transactions more quickly, enhance liquidity through tokenization, allow NFTs to be used as collateral without an order book, and create a plethora of NFT-based derivatives. In other words, price discovery will make the financialization of the NFT asset class possible.
So how should all this be implemented? As we elaborate in this article, new price discovery mechanisms (especially valuation appraisal) will create key innovations that address the liquidity issues of NFTs and other illiquid assets.
Understanding Current Price Discovery Mechanisms
Today, there are only a few price discovery methods in the NFT space. Understanding these mechanisms will help us form a framework for thinking about the price discovery issue.
Sales and auction preferences of SuperRare users (Source: Dune Analytics)
Sales Mechanism. Rarible and most other NFT markets use a sales method, where valuation is conducted through public market sales. As NFTs are traded through sales, the market takes note of their historical prices and asset provenance.
Without a large number of market participants, this default mechanism does not provide much information about pricing, resulting in poor market liquidity.
As we would notice, sales are primarily a capital-inefficient method: the main drawback is that for every dollar of valuation, someone must actually pay a dollar to create it.
Auction Mechanism. Whether good or bad, most market participants actually prefer to price and buy NFTs through auctions. The native gallery of Async.art adopts permanent auctions, as does SuperRare. Beeple's 20 individual digital artworks achieved a legendary auction result of $3.5 million, indicating that auctions are generally very active and can lead to fame.
It is worth noting that auctions are very suitable for art sales, where the intrinsic value of the asset is often more subjective, priced by connoisseurs. However, from the perspective of capital efficiency for the entire NFT asset class, auctions are a suboptimal choice.
Most blockchain auction mechanisms that have entered production are not Vickrey auction mechanisms (second-price sealed auctions), which incentivize participants to bid their true value. Even with Vickrey auction mechanisms, their capital efficiency will be worse than that of sales mechanisms: this requires bidders to lock up capital.
(Some platforms even suggest paying DeFi yields to bidders based on locked bid capital to mitigate the impact of asset locking.) In auctions, for every dollar of valuation, the mechanism may require multiple dollars of bidding funds.
Fractionalization Mechanism. The fractionalization of NFTs, pioneered by Niftex, is the first step innovation in the NFT price discovery space towards creating capital efficiency. Ark, Wrapped Punks, WG0, and NFTX.org have also contributed different fractionalization mechanisms, allowing one or more NFTs to be split into an ERC20 token, generating liquidity on DEXs or centralized exchanges.
Anyone can buy any amount of tokens to help enhance the overall valuation of the NFT, reducing the valuation cost for individual users (though this mechanism does not necessarily lead to a reduction in overall valuation costs).
The fractionalization mechanism also brings challenges related to stakeholder governance and the management of numerous ERC20 assets corresponding to the NFT universe.
The discussion of existing methods raises the question: are there capital-efficient methods that can significantly improve the price discovery issue? We will introduce some of these methods in the next section.
Capital Efficiency Makes Price Discovery Disruptive
Based on the discussion so far, a fundamental framework for evaluating price discovery mechanisms is measuring their capital efficiency. Let’s define P(x) as the discovery price of commodity x and C(x) as the total cost expenditure required by pricing participants.
Then, for a range of asset sets, we can broadly define the price discovery efficiency of a certain mechanism as E = P / C. In this framework, the efficiency of the sales mechanism is always E = 1, the efficiency of the auction mechanism is E ≤ 1, while we consider the efficiency of the fractionalization mechanism to be E ≥ 1, and we will temporarily skip specific analysis in this article. The question is, can we do better than the fractionalization mechanism? The answer is yes.
NFTBank tracks and estimates virtual real estate, such as Decentraland plots
Price Calculation. Auctions are very useful for subjectively valued goods, while the collectibles market typically prices based on scarcity and clearly defined attributes. Certain assets, such as CryptoKitties and virtual real estate, may have prices that can be derived from simple calculations.
NFTBank.ai is one of the earliest startups to propose accurate machine learning models to predict collectible prices based on past prices of similar or adjacent collectibles. Virtual real estate pricing may yield to models that consider past sales and revenues of nearby communities.
In this case, capital efficiency increases because we can consider the mechanism to have fixed costs, namely the cost c of deploying the pricing algorithm. Thus, costs will be shared among price discoveries, with efficiency value E = P / c, and over time E will approach infinity.
Nonetheless, it remains to be seen whether machine learning can perform well for prosperous and subjectively valued goods like Beeple's artworks.
Expert Networks. To achieve fixed price discovery costs, we do not necessarily need to build machine learning models. Imagine that each time we price, we pay a fixed fee to five experts who provide us with insights into the fair market value of the goods.
As the goods appreciate, capital efficiency improves. This approach can be evaluated using centralized services or incentivized crowd networks. One issue to consider when using human experts is the scalability problem: do we really have enough experts to handle the potential number of goods that may arise in the NFT space?
As we will see next, this approach is best finalized as an on-chain oracle network, which naturally incentivizes evaluators to participate in valuation appraisals and effectively conduct assessments.
Peer Prediction Oracles. The most exciting recent development in oracle technology is the implementation of peer prediction as an on-chain mechanism pioneered by Upshot. Peer prediction is a cooperative game that incentivizes participants to answer queries honestly without relying on data feeds or other sources of objective facts.
Upshot proposes combining peer prediction with ranking algorithms to create capital-efficient price discovery in the NFT space. In such a narrow field, it is difficult to judge whether peer prediction is fair, but fundamentally, the mechanism is unrelated to objectively priced collectibles or subjectively priced artworks— the oracle will report a well-founded consensus.
Most importantly, Upshot has made some interesting improvements to the capital efficiency of today's valuation appraisals. First, the evaluation costs of the mechanism are spread across a large number of goods, similar to price calculation.
Second, the safety margin of the protocol can be assessed based on future revenues: if certain evaluators are malicious or of poor quality, the protocol actively reduces its future cash flows by not selecting these evaluators to participate in tasks.
By punishing these evaluators by cutting their future cash flows rather than requiring them to prepay safety fees, this represents a significant improvement in capital efficiency across the entire crypto-economic protocol. Upshot's peer prediction will be the first widely applicable on-chain mechanism for NFT price discovery, with NFT pricing being the first use case when the Upshot protocol launches in 2021.
Derivatives—Implied Pricing. NFT loans showcased by companies like NFTfi, as well as the anticipated NFT index from NFTX.org, create another vector for NFT price discovery—implied pricing of derivatives with NFTs as the underlying asset.
Conventional derivatives, such as options for future purchases of NFTs or shares in prediction markets, will potentially generate NFT pricing issues while delegating the total cost of liquidity to other platforms or mechanisms. This field is currently very nascent and will continue to evolve over the coming years with the trend of NFT financialization.
Upshot proposes combining peer prediction and ranking algorithms to effectively explore NFT pricing
The Impact of NFT Liquidity
Overall, capital-efficient price discovery mechanisms will have a profound impact on the liquidity of existing NFTs, extending to any illiquid assets that can be packaged into on-chain NFTs.
In the coming years, we are likely to see an entire ecosystem of valuation appraisals develop around the price discovery issue. We will also see that some methods are better suited for objectively priced goods, while others are more suitable for subjectively priced goods.
Here are some practical applications of price discovery:
- Creators will be able to create works, and an effective valuation appraisal market will provide liquidity for these works in a fully automated manner. This will bring about a radically disruptive mechanism that monetizes creativity, content, and digital goods.
- Oracle-based pricing will be used to assess NFT positions and collections to discover new value.
- "Instant valuation" of NFTs can be used to create a lower limit, where if the price falls below this limit, holders can automatically liquidate their assets. Neolastics proposed a similar mechanism, along with other price discovery mechanisms that may apply to a broader range of goods.
- Any application using NFTs as collateral can rely on on-chain pricing to control risk. For example, lending protocols can set liquidation margins based on automated pricing. Or in more technical applications, Optimistic Rollup operators may use this mechanism to reduce roll-off costs for layer 2 NFTs.
- Investors can make purchasing decisions faster and more effectively at suggested prices.
- We can create a decentralized NFT index supported by the security of valuation appraisals, without the need for trusts or collateral. This can create high efficiency for investors seeking to invest in the NFT space without the need to evaluate assets item by item.
I would love to hear your thoughts on this exciting emerging field; please follow me on Twitter @jbrukh.
Source link: medium.com
