NFT liquidity problem may be solvable, CoinFund founder interprets the potential of price discovery mechanism


 76 total views

A capital-efficient price discovery mechanism will change the NFT and other non-current asset markets.

Written by: Jake Brukhman, CoinFund founder, author authorized by Chain Wen to translate and publish the Chinese version of this article. Compiler: Perry Wang

At present, most people who follow the non-homogeneous token NFT have noticed that this is a very liquid asset class, and everyone should agree that this situation may continue. However, it is possible that everyone has not seen that in the context of the encryption economic mechanism of the blockchain, “liquidity” is just a mechanism design problem that can be solved quickly.

In this article, I would like to explore in depth how to use the encrypted economic model to achieve “financialization” in the NFT field, improve its liquidity, and how to extend this technology to other illiquid assets over time.

Financialization of NFT asset classes uses NFT as loan collateral to realize the financialization of NFT

Looking back at the early stages of the blockchain field, it can take months or even years for fungible tokens (ERC20 category) to achieve significant liquidity. Token issuers list currency transactions on centralized exchanges, pay high fees and skip regulatory measures. But the market uses smart contracts and encrypted economic mechanisms to solve the liquidity problem of alternative products. Today, through the magic of liquidity mining and the originality of the automated market maker AMM, the time required for ERC20 tokens to obtain liquidity can be reduced to almost zero, and the daily transaction volume of the decentralized exchange DEX may exceed At the same time, the market value of decentralized financial DeFi reached 44 billion US dollars.

In contrast, even though 2020 has experienced a year in which the total GMV of NFT transactions has risen sharply, the prospect of selling any personal NFT on the secondary market today is still bleak. In my personal last article , I proposed to design NFT to follow “Mobile Intellectual Property IP”. As the scope of NFT covers more and more digital content, the mobile IP view implies a hypothetical premise that NFT itself is a new type of financial asset.

As the financial attributes of NFTs become stronger, they will require new types of exchanges, loan agreements, and derivatives. Therefore, I declare that price discovery is the next major issue in the NFT field. A capital-efficient price discovery mechanism can facilitate transactions faster, increase liquidity through tokenization, allow NFTs to be used as collateral without an order book, and create a large number of NFT-based derivatives. In other words, price discovery will make the financialization of NFT asset classes possible.

So how should this be implemented? As we have explained in this article, new price discovery mechanisms (especially appraisals) will create key innovations to solve the liquidity problem of NFTs and other non-current assets.

Understand the current price discovery mechanism

Today, there are only a few price discovery methods in the NFT field. First, understanding these mechanisms will help us form a framework for how to think about price discovery.

NFT liquidity problem may be solvable, CoinFund founder interprets the potential of price discovery mechanismSuperRare users’ sales and auction preferences (Source: Dune Analytics)

Sales mechanism. Rarible and most other NFT markets use sales methods, where valuations are made through open market public sales. As NFTs are traded through sales, the market will notice their historical prices and asset provenance. If there are no large market participants, this default mechanism will not provide much information about pricing, and market liquidity will be very poor at that time. As we will notice, selling bears the brunt of capital inefficiency: the main disadvantage is that for every dollar of valuation, someone actually has to pay a dollar to create it.

Auction mechanism. For better or worse, most market buyers and sellers actually prefer auctions to price and buy NFTs. ’s native gallery uses permanent auctions, as does SuperRare . Beeple’s 20 personal digital art works have created a legendary $3.5 million auction record, indicating that the auction is generally very active and can be famous. It is worth noting that auctions are very suitable for art sales. In art auctions, the intrinsic value of assets is often more subjective, and the price is given by the viewer. But from the perspective of the capital efficiency of the entire NFT asset, auction is the second best choice. Most of the blockchain auction mechanisms that have entered the production stage are not the Vickrey auction mechanism (second price sealed auction), and the latter will encourage participants to bid for its true value. Even the Vickrey auction mechanism will be less capital efficient than the sales mechanism: this requires bidders to lock up capital. (Some platforms even recommend that bidders be paid with DeFi proceeds based on the locked bid capital to reduce the impact of asset lock-in.) In the auction, for each dollar of valuation, this mechanism may require multiple times of US dollar bid funds.

Segmentation mechanism. The division of NFT, pioneered by the Niftex approach, is the first innovation in the field of NFT price discovery towards creating capital efficiency. Ark, Wrapped Punks, WG0, and have also contributed different split mechanisms to split one or more NFTs into an ERC20 token, which can generate liquidity in DEX or centralized exchanges. Anyone can buy any number of tokens to help increase the overall valuation of the NFT and reduce the valuation cost of a single user (but this mechanism does not necessarily lead to a reduction in the overall valuation cost). The segmentation mechanism also brings challenges related to stakeholder governance and the management of many ERC20 assets corresponding to the NFT universe.

Discussion of existing methods raises the following question: Is there a way to significantly improve the capital efficiency of the price discovery problem? We will introduce some of these methods in the next chapter.

Capital efficiency is about making price discovery disruptive

Based on the discussions so far, a basic framework for evaluating the price discovery mechanism is measuring its capital efficiency. Let us define P(x) as the discovery price of commodity x, and C(x) as the total cost expenditure required by the pricing participant. Then for a series 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, and the segmentation mechanism is E≥1. This article will skip the specific analysis for the time being. The question is, can we still do better than the split mechanism, the answer is yes.

NFT liquidity problem may be solvable, CoinFund founder interprets the potential of price discovery mechanismNFTBank tracks and estimates virtual real estate, such as Decentraland plots

Price calculation. Auctions are very useful for subjectively valued goods, while the collectibles market usually sets prices based on scarcity and well-defined attributes. Certain assets, such as CryptoKitties and virtual real estate, may have prices that can be calculated by simple calculations. is one of the first start-ups to propose accurate machine learning models to predict the price of collectibles based on the past prices of similar or adjacent collectibles. Virtual real estate pricing may succumb to models that consider past sales and revenue generation in nearby communities. In this case, capital efficiency can be increased because we can consider the mechanism to have a fixed cost, that is, the cost of deploying the pricing algorithm c. Therefore, the cost will be amortized between price discovery, and its efficiency value E = P / c, E will become infinite as time goes by. Nevertheless, for a prosperous and subjective product such as Beeple artwork, whether machine learning can work well is still uncertain.

Expert network. To get a fixed price discovery cost, we don’t necessarily need to build a machine learning model. Imagine that we pay a fixed fee to five experts every time we set a price, who will provide us with insights into the fair value of the commodity market. As commodities appreciate, capital efficiency increases. This method can be evaluated using centralized services or motivated crowd networks. One issue that needs to be considered when using human experts is the issue of scale: do we really have enough experts to handle all the potential product quantities that may be generated in the NFT field? As we will see next, this method is best to be finalized as an on-chain oracle network, which will naturally encourage evaluators to participate in appraisal appraisals and effectively conduct evaluations.

Peer prediction oracle machine. The most exciting recent development of the oracle is that Upshot is the first to implement peer-to-peer prediction as an on-chain mechanism. Peer-to-peer prediction is a cooperative game that motivates participants to answer queries honestly without data feed or objective facts from other sources. Upshot recommends combining peer-to-peer forecasting with sorting algorithms to create capital-efficient price discovery for the NFT field. In such a small field, it is difficult to judge whether peer-to-peer predictions are fair, but in essence, the mechanism has nothing to do with objectively priced collections or subjectively priced artworks-oracles will report an informed consensus . Most importantly, Upshot has made some interesting improvements to the capital efficiency of appraisal today. First, the evaluation cost of this mechanism is shared among a large number of commodities, which is similar to price calculation. Secondly, the security margin of the agreement can be assessed based on future income: if some assessors are malicious or the quality of their work is not high, the agreement will actively reduce their future cash flow by not selecting these assessors to participate in the task. Punishing these evaluators by cutting their future cash flow instead of making them pay security fees in advance is a huge increase in capital efficiency throughout the entire encrypted economy protocol. Upshot’s peer-to-peer prediction will be the first on-chain mechanism widely applicable to NFT price discovery, and NFT pricing will be the first use case of the Upshot protocol when it is launched in 2021.

Derivatives-implied pricing. The NFT loans displayed by companies such as NFTfi , and the NFT index expected to be launched by , create another vector for NFT price discovery-the implicit pricing of derivatives with NFT as the underlying asset. Conventional derivatives, such as options to purchase NFTs in the future, or prediction market shares, will potentially generate NFT pricing problems, while entrusting the total cost of liquidity to other platforms or mechanisms. The current development in this field is very immature and will continue to develop in the next few years with the trend of NFT financialization.

NFT liquidity problem may be solvable, CoinFund founder interprets the potential of price discovery mechanismUpshot proposes to use a combination of peer-to-peer prediction and sorting algorithms to effectively explore NFT pricing

The impact of NFT liquidity

In general, a capital-efficient price discovery mechanism will have a profound impact on the liquidity of existing NFTs, which in turn will cover any non-current assets that can be packaged into on-chain NFTs. In the next few years, we are likely to see the entire appraisal ecosystem develop around the problem of price discovery. We will also see that some methods are more suitable for objectively priced commodities, while other methods are more suitable for subjectively priced commodities.

Here are some practical applications of price discovery:

  1. Creators will be able to create works, and the effective appraisal market will provide fluidity for the works in a fully automated manner. In this way, it will bring a completely disruptive mechanism to realize creativity, content and digital goods.
  2. The oracle-based pricing will be used to evaluate NFT positions and collections to discover new value.
  3. NFT’s “real-time valuation” can be used to create a lower limit, when the price is lower than the lower limit, the holder can automatically liquidate their assets. Neolastics proposes similar mechanisms, as well as other price discovery mechanisms that may be applicable 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, a loan agreement can set a clearing margin based on automated pricing. Or in more technical applications, Optimistic Rollup operators may use this mechanism to reduce the roll-off cost of Layer 2 NFT.
  5. Investors can make purchasing decisions faster and more efficiently at the recommended price.
  6. We can create a centralized NFT index backed by the security of appraisal, without the need for trust or collateral. This can create high efficiency for investors seeking to invest in the NFT field without the need to assess assets item by item.

I hope to hear your thoughts on this very attractive emerging field, please follow me on Twitter @jbrukh.

Source link:

Disclaimer: As a blockchain information platform, the articles published on this site only represent the author’s personal views, and have nothing to do with the position of ChainNews. The information, opinions, etc. in the article are for reference only, and are not intended as or regarded as actual investment advice.

Let’s block ads! (Why?)