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CoinFund founder: NFT's liquidity problem can be solved through price discovery

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An efficient price discovery mechanism in the capital market will revolutionize the NFT and other illiquid asset markets

Original title: "Appraisal games and the NFT liquidity problem"

Original author: Jake Brukhman, founder of CoinFund

Compiler: 0x13, Rhythm BlockBeats

Now almost everyone who pays attention to NFT is well aware of its poor liquidity, and people seem to think that NFT should be As such, there is no solution to this problem. However, in the field of encryption, in the encrypted economic system of the blockchain, the problem of "liquidity" can be solved through mechanism design. In this article, I will delve into whether the liquidity of the NFT space can be improved by financializing it, and extend this approach to other illiquid assets over time.

The Financialization of the NFT Asset Class

NFTfi.com financializes NFT as collateral for loans

In the early stages of blockchain technology development, the liquidity of ERC-20 tokens will take months or even years to become sufficient. Token issuers will scramble to list on centralized trading platforms, pay high listing fees, and try to avoid supervision. The market will apply smart contracts and encrypted economic mechanisms to solve the liquidity problem of homogeneous tokens. Today, with the genius of liquidity mining and the birth of an automated market maker mechanism, ERC-20 tokens can obtain sufficient liquidity almost immediately, and DEX has pushed the market value of DeFi to 440 billion US dollars, and the daily trading volume is as high as More than $2 billion.

In contrast, although the total turnover of NFTs has risen sharply in 2020, NFTs are now sold in the secondary market is still very difficult. I once mentioned that NFT can be regarded as "fluid intellectual property", because the scope of NFT covers more and more digital content. This view of fluid intellectual property actually has an implicit assumption that NFT itself It is a new financial asset class.

As NFTs become increasingly financialized, they will also require new types of trading platforms, lending protocols, and derivatives Taste. Therefore, I think NFT price discovery will be the next big problem to solve. Efficient price discovery mechanisms in capital markets will enable participants to trade more quickly, increase liquidity through tokenization, allow NFTs to be used as collateral without order books, and create a wealth of NFT-backed tokens. Derivatives. In other words, price discovery will enable the financialization of the NFT asset class.

So how to realize the price discovery of NFT? Again, as we articulate in this article, new price discovery mechanisms—particularly valuation mechanisms—will usher in unprecedented innovations that will address the challenges of NFTs and other illiquid assets. Liquidity issues.

How to understand current price discovery

There are only a few ways to discover the price of NFTs today. A solid understanding of these mechanisms first will help to build a framework in our minds and to better think about the price discovery problem.

Comparison of SuperRare user sales and auction preferences (data source: Dune Analytics)

1. Pricing through historical transactions

In the trading method used by Rarible and most other NFT marketplaces, valuations are determined by trading in the open market. When NFTs are traded, the market records the historical price and provenance of the asset. If the number of market participants is low, then this default mechanism will not obtain much effective pricing information, and the liquidity of the market is also very poor. As we have seen, the transaction efficiency of NFT is extremely low, mainly because the valuation of NFT must be recognized by someone and willing to bid to buy it.

2. Price through auction

Sellers and buyers in most markets currently prefer to buy and price NFT through auctions.

Async.art uses perpetual auctions in their website galleries, as does SuperRare. The $3.5 million auction at Beeple proved that, while still in its infancy, auctions are thriving enough. It is worth mentioning that auctions are very helpful for the sale of art, because the intrinsic value of NFT assets tends to be more subjective, and more people are on the sidelines.

However, from the perspective of capital market efficiency, selling NFT by auction is a suboptimal solution. Most auction mechanisms on the blockchain do not use Vickery auction, and Vickery auction is the auction method that can best reflect the value of the collection in the buyer's mind (Rhythm note: Vickery auction is a second-price sealed bid auction .Bidders submit bids without knowing the bids of others. The highest bidder wins the bid, but only pays the next highest bid.)

They are much less capital market efficient coin sales because they require bidders to lock up capital. In order to solve this problem, some platforms even propose to pay DeFi proceeds to bidders who lock up bidding capital in an attempt to solve this problem. The value of each bid at an auction can be much higher than it appears on the books.

3. Fragmented pricing

Fragmentation of NFT is a very innovative work that Niftex is doing. This is the first innovation in the direction of improving the efficiency of capital markets in the context of NFT price discovery.

As with projects like Ark, Wrapped Punk, WG0, and NFTX, fragmentation can split one or more NFTs into ERC-20 tokens and can be traded on DEX or centralized trading platforms. In this way, anyone can buy any number of tokens to help establish the overall valuation of NFT, reducing the valuation cost of a single user (but the cost of the entire mechanism will not necessarily be reduced). With the fragmentation of NFT, users also need to face the challenge of stakeholder governance and management of ERC-20 asset appreciation.

A discussion of existing approaches prompts us to consider whether there is a way to significantly improve the efficiency of capital markets for the price discovery problem . We describe some of these methods below.

Capital market efficiency is a subversive factor for price discovery

< p line="ZJZy">Based on our discussions so far, a basic framework for judging the merits of price discovery mechanisms is to measure their capital market efficiency. Let us define P(x) to be the discovery price of good x and C(x) to be the total costs that participants need to pay for pricing it. Then, we can roughly define the price discovery efficiency of a mechanism as E=P/C. This is the efficiency on a specific set of assets. In this framework, the efficiency of the sales mechanism is always E=1, the efficiency of the auction mechanism is E≤1, and fragmentation deserves a more detailed analysis here, E≥ 1. The question is whether we can do better than fragmentation, and the answer is yes.

NFTBank tracks and estimates virtual real estate such as Decentraland parcels

1. Price calculation

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While auctions are great for items of high subjective value, the collectibles market is often priced by scarcity and well-defined attributes. Some of these assets, like CryptoKitties and virtual real estate, may be priced with simple calculations.

NFTBank.ai was one of the first startups to come up with an accurate machine learning model for or the pricing of adjacent collectibles to predict the price of a collectible. Pricing for virtual real estate may depend on models that take into account past sales and potential income generation in neighboring areas.

In this case, capital market efficiency will be greatly improved, because we can think of the mechanism as having a deployment pricing algorithm The fixed cost of c. Therefore, the cost will be amortized in price discovery and the efficiency E=P/c will go to infinity over time. Still, it remains to be seen whether machine learning works well for exuberant, high-subjective-value commodities, such as Beeple's artwork.

2. Expert Network

To achieve a fixed cost of price discovery, we don't necessarily need to build a machine learning model.

Imagine that every time we set a price, we pay a fixed amount of money to five experts who provide us with information on An opinion on the fair market value of the commodity. As commodities appreciate, capital market efficiency increases. This approach could use a centralized service, or an incentivized network of humans to create the assessment. One concern with using human experts is scale: do we really have enough experts to handle the full potential volume of merchandise that could be generated in the NFT space?

As we shall see next, the best form of this approach may be translated into an on-chain oracle Machine network, it will naturally incentivize agents to play and participate in identification, and conduct identification efficiently.

3. Peer prediction oracle

The most exciting recent development in oracles is the pioneering implementation of peer predictions as an on-chain mechanism by Upshot.

Peer prediction is a cooperative game that incentivizes participants to answer questions honestly without the need for data feeds or Other sources of objective facts. Upshot proposes to apply peer predictions, coupled with sorting algorithms, to create capital-efficient price discovery for NFTs.

It is difficult to do justice to peer forecasts in this petite field, but this mechanism is essentially, Agnostic between objectively priced collectibles or subjectively priced works of art - oracles learn to agree.

Most importantly, Upshot makes several interesting improvements to capital market efficiencies identified today.

First, the valuation cost of this mechanism is amortized over a large number of goods, similar to price calculation. Second, the margin of safety of the protocol can be evaluated from future income: if some appraisers are malicious or have poor performance, the protocol will actively reduce their future cash flow and not select them for appraisal tasks. Penalizing agents by cutting their future cash flow rather than making them pay for security up front is a big capital market efficiency gain across cryptoeconomic protocols.

Upshot's peer prediction will be the first on-chain mechanism widely applicable to NFT price discovery, NFT pricing will be The first application launched by the Upshot protocol in 2021.

4. Implied pricing of derivatives

NFT lending services offered by companies like NFTfi, and NFT indices as envisioned by NFTX.org, create another vehicle for NFT price discovery – implicit pricing of derivatives with immutable assets as underlyings . Customized derivatives, such as the right to purchase NFTs in the future or share of forecasted markets, could potentially price NFTs while delegating the cost of liquidity aggregation to other platforms or mechanisms. This field is still in its early stages, and as the NFT financialization trend continues, it will develop to a greater extent in the next few years.

Upshot proposes to use peer forecasts plus popular algorithms to efficiently price NFT

The impact of NFT liquidity

In general, the potential of the capital market efficient price discovery mechanism for existing NFT liquidity, and extending to any chain that can be expressed as Both illiquid assets and non-variable assets have a profound impact. We are likely to see an entire ecosystem of valuation methods develop around the price discovery problem in the next few years. We have also seen that some methods are more suitable for objectively priced commodities, and some methods are more suitable for subjectively priced commodities.

Here are some practical applications of price discovery.

1. Creators will be able to produce a work, and an efficient appraisal market will provide The piece provides liquidity. So this will be a completely disruptive mechanism for monetizing ideas, content and digital goods.

2. Portfolios and collectibles of NFT will be valued using oracle-based pricing to discover new value.

3. The "instant pricing" of NFT can be used to establish a lower limit, and holders can realize their assets at any time. Neolastics proposes a similar mechanism, and other price discovery mechanisms may apply to a wider range of commodities.

4. Any application that uses NFT as collateral can rely on on-chain pricing to control risk. For example, lending protocols could set liquidation margins based on automated pricing. Or, in more technical applications, optimistic rollup may use this mechanism to reduce the "roll-off" cost of Layer2 NFT.

5. Investors can make purchase decisions faster and more efficiently through suggested prices.

6. We can create a decentralized NFT index, based on the security of the evaluation method rather than trust or collateral come to support. This can provide significant efficiency gains for investors seeking exposure to the NFT space but who are unwilling to evaluate assets one by one.