Non Fungible Token (“NFT”) are notoriously illiquid, arguably more so than most traditional finance (“TradFi”) illiquid assets. Can we draw some insights from the traditional markets and apply this to NFTs?

This article will examine the different types of illiquid assets and related investment strategies, then overlay this with the NFT sector.

• Background

• Traditional Illiquid Assets

• Overlaying TradFi Concepts to NFTs

• Improving Market Liquidity

• Institutional Adoption

• Illiquid Asset Trading Strategies

• Closing Remarks (TL;DR)

An illiquid asset is typically traded in a low volume environment. There can be large discrepancies between the bid and ask price, from the buyers and sellers respectively. NFT traders that are willing to take on liquidity risk should be able to harvest a liquidity premium.

Generally speaking, the wider the bid/ask spread, the more illiquid the asset. The larger the required capital outlay and lack of divisibility, the less frequent the trading between market participants.

For example, real estate is one of the most illiquid asset classes. Fractional ownership can help with affordability, but we need space to live. So practically speaking, fractional ownership of a home can be challenging if several parties want to reside there. We’ve covered NFTs in the real estate market in another OriginsNFT article.

NFTs act in a similar “non fungible” capacity, with some more expensive 1/1 art (Fidenzas) or PFPs (Cryptopunks/BAYC) becoming fractionalized and owned by DAOs, thus alleviating liquidity issues.

The impact of adverse macro conditions on an illiquid asset class has exacerbated the downturn, as bidding dissipates and sell pressure increases. We will review tradFi markets with a view to developing how we approach the NFT asset class.

There are several illiquid assets within traditional finance (“tradFi”), including:

• Real Estate

• Automobiles

• Art, Antiques and Collectibles

• Private Equity or Stocks Traded Over The Counter (“OTC”)

• Some Debt Instruments

Typically one would allocate a lower portion of a portfolio to these types of assets, given the inherent liquidity risk. Refer to the portfolio allocation article for more information on allocating capital and risk management.

Illiquid assets tend to have wide bid-ask spreads, greater volatility, higher risk and associated risk premia.

Refer to the followingOriginsNFT article byBowse_Eth for more information on risk premia and an NFT expected returns framework.

When trading NFTs on Opensea, there are collection offers (bids) for 5-10% below the floor price. Many of these are bot offers automated at a set percentage below the floor price of the collection, so as to capitalize on holders needing liquidity.

The premium received for immediate liquidation is the “spread.” It’s effectively the same in traditional order books where the market maker takes the spread as their commission to provide liquidity. The buyer then takes on the risk associated with time to settle with a new buyer.

For instance, the BAYC collection has a 6% spread between the floor and best offer (bid).

It’s important to establish an NFT’s fair value to determine whether it is over- or undervalued at its current list price.

This chart from AB illustrates the approximate fair value of the NFT within a collection. The closer to the global bot bid, the quicker traders will be able to sell their NFT.

To caveat: the diagram is taken at a point in time and so is illustrated in a static environment. We should note that the floors and bids move relative to changes in demand.

The realizable price is unlikely to be linear, but generally as the trait increases in rarity its realizable value increases, albeit at a lesser rate. This is due to fewer investors having significant liquid funds available, hence these “grails” will take longer to find a willing buyer.

The liquidity for grails is improving with the introduction of lending protocols. There was a recent mega mutant acquisition for 1,000Ξ ($1.3m USD) using a lending protocol. If we were to chart NFT price against rarity we would see the following hockey stick:

Illiquid asset prices in tradFi are equally as impacted by the time taken to sell. The International Monetary Fund (“IMF”) has recently raised concerns over illiquid assets and the risk of amplifying the adverse macro conditions. Asset holders that need immediate liquidity may need to sell at huge discounts, causing cascading impacts on market price.

As a comparison the BoE has effectively taken on the role of the bot offer in the graph above. Those bot offers, or willing bids, soon get filled in the event of excessive sell pressure. The long duration gilts in the UK were heavily sold by pension funds, causing huge reductions in price. The BoE was required to step in to prevent a material risk to financial stability.

If there was no BoE (or bot offers in the context of NFTs) the fair value could effectively trend to zero with no liquidity. Ultimately“left holding the bag”. This is how the chart changes in the event of no bot offers:

We can view the impact on expected asset price (“EAP”) with reference to expected time (“ET”) to sale on this tradFi chart. Cash is assumed to be perfectly liquid, hence the vertical line. Asset A is more liquid than asset B given the steeper gradient.

Typically the longer we are willing to wait (ET), the higher expected asset price (EAP) we are likely to receive. This doesn’t, however, take into account volatile adverse market movements.

If an entire collection or asset class is adversely impacted by an event, then it may be better to liquidate at a loss rather than wait, as the spot price now may be worth more than the spot floor price in the future. The term would be to “cut your losses” so that the liquidity can be used for another opportunity rather than being tied up in an illiquid NFT.

Bot offers are typically flippers looking to “scalp” a profit, with the downside being the liquidity risk. This occurs in NFT trading whereby holders acquire the asset from an accepted bot bid with the intention of flipping them at the spot floor price.

However, as listings increase and sales reduce, panic sets in and undercutting begins causing a dramatic reduction in price. Without any traders bidding, the collection price slumps until someone with liquidity intervenes.

In TradFi this would be a cash rich entity or a central bank. NFT collections that have strong fundamentals could be rescued by institutions in the future, thus improving the liquidity in the market through more bid offers on a wider number of NFT collections, likely blue chips.

NFT market liquidity can be improved through:

• Accessibility of liquid funds;

• Increasing the accessibility to more market participants; and

• Reducing liquidity extraction.

@poof_eth performed eye opening research on available liquidity in the market, including liquidity extraction to date. He noted that there is currently 1.0m Ξ available, yet in 2022 2.2m Ξ had been extracted:

• 1.1m Ξ spent in NFT Mint fees

• 0.5m Ξ spent in project royalties

• 0.2m Ξ spent in market fees

• 0.4m Ξ spent in gas fees

So how do we increase the number of wallets, and or the amount of available Ether, MATIC, SOL etc. to trade in the markets? Let’s take a look.

• Lending/borrowing can provide liquidity to the market provided the interest payments can be met. This is less likely to be an effective solution in a hawkish environment where rates are rising. We’ve also included an article on short selling in the NFT market which provides increased liquidity.

• Fractional ownership / stock splits are other means of increasing the accessibility of an asset to a wider group of participants. It’s why crowdfunding using the internet has seen a rise. It’s also why TSLA performed a recent stock split. Fractionally owning expensive NFT assets means more market participants can own a piece of those expensive grails. It may have the adverse effects of artificially inflating blue chip NFT asset prices.

• There are more than 7 billion mobile phones globally. This is over 4x the number of desktop computers. Building infrastructure for NFT trading to be performed on mobile dramatically increases the market reach.

• Improving the user experience through more intuitive applications will bring in more users.

  • The GME saga brought in millions of retail investors through an intuitive trading platform in Robinhood.

  • Similarly, Coinbase implemented a “buy” button on their crypto app which made acquisition of BTC more readily available in 2013. Prior to this orders were manual, with Mt GOX being the prime exchange. This type of UX innovation will be what catalyzes faster adoption and hence more liquidity.

  • The OriginsNFT terminal intends to use data analysis to provide better NFT trading experiences.

• The types of participants in the market will change the depth of the market and amount of available funds. Increased institutional investors will likely increase the amount bot bids available, the depth of the market (“DOM”), and hence the liquidity available. These institutions will effectively take the bid/ask spread akin to traditional market makers.

• Reducing the costs of high fees through increased marketplace competition. It’s in marketplaces’ long term interest to preserve the NFT ecosystem, so reinvesting profits back into improving the quality of services and infrastructure will be beneficial.

• Royalties are a hot topic, but charging a fair price for the service or product is at the crux of an optimal solution.

• Gas costs can be reduced through contract optimization and minting mechanisms to prevent bottlenecks in block space. Use of layer 2s and even layer 3s are additional means of preserving capital of NFT market participants.

We have seen an influx of Venture Capital (“VC”) funding in 2022 as projects receive more financial backing, reducing execution risk and increasing the likelihood of delivering against roadmaps.

This provides the NFT space with some much needed verification, validation and legitimacy. There was a period when VC-backed collections would see huge volume pumps as NFT traders acquired these projects with “peace of mind” that there was an increased level of due diligence performed by the VC funds.

Market confidence plays a large part in NFT investors’ willingness to hold the NFT long term, as there’s a lower risk of collection trending to zero or rugging. We can draw a similar comparison to tradFi markets; when contagion sets in, bids are pulled and prices cascade as the depth of the market diminishes.

If pension funds or other asset managers were to change their investment mandates to include a small portion of their portfolio in high-risk assets like NFTs, then this would have huge, favorable impacts on the liquidity of the NFT market. Refer to the OriginsNFT article on asset management for the background on the industry.

The total AUM of the asset management industry is in excess of $100 trillion USD, so if they were to allocate 0.1% of a portfolio to NFTs and crypto, then this would provide an influx of $100 billion of liquidity. As a comparison, the current liquidity in the space is estimated to be 1.4% of this amount ($1.4 billion USD or 1 million Ξ).

More institutional involvement will provide greater depth to the market. But with that comes sophistication through optimal algorithms, tools and increased market efficiency, making it more difficult for retail traders to win. The current market spreads for liquidity makes NFT trading so profitable; as spreads tighten, so will the opportunities for meaningful profit.

Nifty Blue is developing a pricing tool for NFTs. This will help define what the spot fair value of an NFT is at any given point. The blue paper examines pricing methodology for heavily-traded collections such as BAYC.

The paper makes the valid point that NFTs are by definition non fungible, so certain traits may command a price that deviates through a random walk. This tends to be readjusted in typical stock / crypto markets given the fungibility. Additionally, there will be NFTs that have been held since mint, so reduced historical sales data must be factored into one’s assessment of the model.

Currently unrealized p&l tools often scrape pricing data from the Opensea floor price, but floor price can be manipulated and is not an accurate representation of fair value, particularly for NFTs with rarer traits in a collection. Once pricing strategies have been optimized, we could see an increased opportunity for more easily assessing whether certain NFTs are under/over valued, thus enhancing “sniping” opportunities.

The NFT space is not heavily regulated. This means that many traders act on alpha that could arguably be construed as insider information in traditional equity markets. We’ve seen an overlap with Opensea and Coinbase employees being investigated for questionable practices. It’s important for traders to think about alternative strategies to implement in the absence of early information access.

Charting

Technical analysis uses charts and statistics to predict future price action. Larger collections are easier to chart given increased volume / sales to infer directional patterns. NFTs are highly sensitive to news announcements so it can be difficult to accurately predict price action. But in the absence of news and tracking of smart money, we can form investment thesis around certain projects.

Momentum Trading

Momentum trading is a frequent means of trading NFTs. It is, however, extremely risky given NFTs’ illiquid nature. As volume commences, scalpers may jump in and list slightly above the floor price in the hope that another trader will purchase the NFT. However, with royalties, gas and marketplace fees this can be counterproductive for any small price movements. At least with fungible token markets, fees are lower. Centralized exchanges such as Binance offer a 0.1% fee. This combined with the fungibility and increased volume makes technical analysis more effective in these markets.

Investing using fundamentals for businesses include looking at the profitability, revenue, assets, liabilities, and growth potential. This could be similar for NFT collections, which can be treated like early phase start-ups, though unlike with traditional businesses, many of the fundamental “metrics” we might look to are specific to NFTs as an asset class.

NFT Purpose

Look at the underlying purpose of the NFT. Art 1/1s are purely based on the collectors’ appeal and would be different characteristics to a gaming NFT which may look at the in-game properties of that asset. Value may accrue to in-game NFT assets based on the damage they do whereas PFP collections and art is predominantly the visual appeal or brand.

Utility

The PFP NFTs that token gate communication platforms, such as Discord, may have other intrinsic value (dual purpose). For instance, Alpha passes have this value proposition by offering quick and actionable information that members can profit from. Obtaining information early is directly correlated to more profitability in the NFT space given how sensitive the price action is for this asset class. Any inflow of volume can significantly impact prices.

The Edge (Over Institutional Investors)

As more institutions enter the space, obtaining this alpha will become more difficult without similar trading tools. The advantage many NFT traders have now is knowledge in the market. This can either be used in an advisory capacity to add an additional revenue stream or acquiring longer term collectable assets now will likely accrue value with lower risk. Understanding the fundamentals of a project or what gives it value now will likely be something that institutions do at a later stage when they peel back the history books on old collections. We’ve seen the likes of Pudgy Penguins be revitalized, so it is possible that other collections may also follow suit. particularly for collections that are fully on chain, can be acquired and restored with relative ease.

Macro

Fundamentals also look at the macro economic environment. The hawkish stance by the Federal Reserve is adversely impacting all risk on assets, including NFTs. Refer to the OriginsNFT macro article for more information on impacts on the NFT sector.

Price and volume are two of the more common data inputs used in quantitative analysis for mathematical models. This tends to involve high frequency trading using algorithms or complex models in tradFi.

Max Minting

NFT trading typically has lower volume and prices can involve wide spreads. The most suitable application is minting new NFT projects. Those projects that apply a stealth mint approach with no whitelist are exposed to botting tools that automate the minting process. Some projects implement barriers to this, for instance limiting the number of mints per wallet. However, several minting bots have already found workarounds by automatically funding dozens, if not hundreds, of individual wallets and executing the mint instructions. The term in the NFT space is a “max minter.” The trader will then list the new mints on the secondary market to recoup the cost and then hold a “moon bag” in case the project continues to excel.

Scripting Bots

Quant trading is not currently prevalent in the NFT space, but scripts can be written to execute transactions if certain criteria are met. For instance, if an NFT hits a certain price, then the bot could automatically snipe the asset.

More complex trading strategies, established on historical data, are likely to be deployed as more experienced institutional traders enter the ecosystem.

• Review the bid-ask spread. The wider it is, the more risk there is with the collection. It’s a reliable indicator.

• Establishing fair value will be essential to help calculate whether NFTs are under/over priced and will help with sniping. There will be differences in opinion on this given there is some subjectivity with art and given this asset’s non fungible nature.

• Liquidity extraction exacerbates the reduced volume and downturn. This will change as infrastructure is developed and contracts are optimized. Improving this will aid the long-term health of the NFT sector.

• Institutional adoption will change the landscape. Educate yourself to maintain an edge over institutional investors.

• Adapt strategies for trading. Alpha groups are not effective if the information is common knowledge. Current practices are questionable and may be regulated against if NFT markets are to align with tradFi restrictions on privileged information.

• Trading tools will help maintain an edge. Minting bots, sweeping tools and data analysis are your friends.

At OriginsNFT we leverage data-driven decision making, educational resources, and proprietary analytics to remain ahead of the curve with respect to blockchain tech and specifically NFTs. To find out more, please visit our website or Twitter.

To purchase a pass, please visit our Opensea page.

Non Fungible Token (“NFT”) are notoriously illiquid, arguably more so than most traditional finance (“TradFi”) illiquid assets. Can we draw some insights from the traditional markets and apply this to NFTs?

This article will examine the different types of illiquid assets and related investment strategies, then overlay this with the NFT sector.

• Background

• Traditional Illiquid Assets

• Overlaying TradFi Concepts to NFTs

• Improving Market Liquidity

• Institutional Adoption

• Illiquid Asset Trading Strategies

• Closing Remarks (TL;DR)

An illiquid asset is typically traded in a low volume environment. There can be large discrepancies between the bid and ask price, from the buyers and sellers respectively. NFT traders that are willing to take on liquidity risk should be able to harvest a liquidity premium.

Generally speaking, the wider the bid/ask spread, the more illiquid the asset. The larger the required capital outlay and lack of divisibility, the less frequent the trading between market participants.

For example, real estate is one of the most illiquid asset classes. Fractional ownership can help with affordability, but we need space to live. So practically speaking, fractional ownership of a home can be challenging if several parties want to reside there. We’ve covered NFTs in the real estate market in another OriginsNFT article.

NFTs act in a similar “non fungible” capacity, with some more expensive 1/1 art (Fidenzas) or PFPs (Cryptopunks/BAYC) becoming fractionalized and owned by DAOs, thus alleviating liquidity issues.

The impact of adverse macro conditions on an illiquid asset class has exacerbated the downturn, as bidding dissipates and sell pressure increases. We will review tradFi markets with a view to developing how we approach the NFT asset class.

There are several illiquid assets within traditional finance (“tradFi”), including:

• Real Estate

• Automobiles

• Art, Antiques and Collectibles

• Private Equity or Stocks Traded Over The Counter (“OTC”)

• Some Debt Instruments

Typically one would allocate a lower portion of a portfolio to these types of assets, given the inherent liquidity risk. Refer to the portfolio allocation article for more information on allocating capital and risk management.

Illiquid assets tend to have wide bid-ask spreads, greater volatility, higher risk and associated risk premia.

Refer to the followingOriginsNFT article byBowse_Eth for more information on risk premia and an NFT expected returns framework.

When trading NFTs on Opensea, there are collection offers (bids) for 5-10% below the floor price. Many of these are bot offers automated at a set percentage below the floor price of the collection, so as to capitalize on holders needing liquidity.

The premium received for immediate liquidation is the “spread.” It’s effectively the same in traditional order books where the market maker takes the spread as their commission to provide liquidity. The buyer then takes on the risk associated with time to settle with a new buyer.

For instance, the BAYC collection has a 6% spread between the floor and best offer (bid).

It’s important to establish an NFT’s fair value to determine whether it is over- or undervalued at its current list price.

This chart from AB illustrates the approximate fair value of the NFT within a collection. The closer to the global bot bid, the quicker traders will be able to sell their NFT.

To caveat: the diagram is taken at a point in time and so is illustrated in a static environment. We should note that the floors and bids move relative to changes in demand.

The realizable price is unlikely to be linear, but generally as the trait increases in rarity its realizable value increases, albeit at a lesser rate. This is due to fewer investors having significant liquid funds available, hence these “grails” will take longer to find a willing buyer.

The liquidity for grails is improving with the introduction of lending protocols. There was a recent mega mutant acquisition for 1,000Ξ ($1.3m USD) using a lending protocol. If we were to chart NFT price against rarity we would see the following hockey stick:

Illiquid asset prices in tradFi are equally as impacted by the time taken to sell. The International Monetary Fund (“IMF”) has recently raised concerns over illiquid assets and the risk of amplifying the adverse macro conditions. Asset holders that need immediate liquidity may need to sell at huge discounts, causing cascading impacts on market price.

As a comparison the BoE has effectively taken on the role of the bot offer in the graph above. Those bot offers, or willing bids, soon get filled in the event of excessive sell pressure. The long duration gilts in the UK were heavily sold by pension funds, causing huge reductions in price. The BoE was required to step in to prevent a material risk to financial stability.

If there was no BoE (or bot offers in the context of NFTs) the fair value could effectively trend to zero with no liquidity. Ultimately“left holding the bag”. This is how the chart changes in the event of no bot offers:

We can view the impact on expected asset price (“EAP”) with reference to expected time (“ET”) to sale on this tradFi chart. Cash is assumed to be perfectly liquid, hence the vertical line. Asset A is more liquid than asset B given the steeper gradient.

Typically the longer we are willing to wait (ET), the higher expected asset price (EAP) we are likely to receive. This doesn’t, however, take into account volatile adverse market movements.

If an entire collection or asset class is adversely impacted by an event, then it may be better to liquidate at a loss rather than wait, as the spot price now may be worth more than the spot floor price in the future. The term would be to “cut your losses” so that the liquidity can be used for another opportunity rather than being tied up in an illiquid NFT.

Bot offers are typically flippers looking to “scalp” a profit, with the downside being the liquidity risk. This occurs in NFT trading whereby holders acquire the asset from an accepted bot bid with the intention of flipping them at the spot floor price.

However, as listings increase and sales reduce, panic sets in and undercutting begins causing a dramatic reduction in price. Without any traders bidding, the collection price slumps until someone with liquidity intervenes.

In TradFi this would be a cash rich entity or a central bank. NFT collections that have strong fundamentals could be rescued by institutions in the future, thus improving the liquidity in the market through more bid offers on a wider number of NFT collections, likely blue chips.

NFT market liquidity can be improved through:

• Accessibility of liquid funds;

• Increasing the accessibility to more market participants; and

• Reducing liquidity extraction.

@poof_eth performed eye opening research on available liquidity in the market, including liquidity extraction to date. He noted that there is currently 1.0m Ξ available, yet in 2022 2.2m Ξ had been extracted:

• 1.1m Ξ spent in NFT Mint fees

• 0.5m Ξ spent in project royalties

• 0.2m Ξ spent in market fees

• 0.4m Ξ spent in gas fees

So how do we increase the number of wallets, and or the amount of available Ether, MATIC, SOL etc. to trade in the markets? Let’s take a look.

• Lending/borrowing can provide liquidity to the market provided the interest payments can be met. This is less likely to be an effective solution in a hawkish environment where rates are rising. We’ve also included an article on short selling in the NFT market which provides increased liquidity.

• Fractional ownership / stock splits are other means of increasing the accessibility of an asset to a wider group of participants. It’s why crowdfunding using the internet has seen a rise. It’s also why TSLA performed a recent stock split. Fractionally owning expensive NFT assets means more market participants can own a piece of those expensive grails. It may have the adverse effects of artificially inflating blue chip NFT asset prices.

• There are more than 7 billion mobile phones globally. This is over 4x the number of desktop computers. Building infrastructure for NFT trading to be performed on mobile dramatically increases the market reach.

• Improving the user experience through more intuitive applications will bring in more users.

  • The GME saga brought in millions of retail investors through an intuitive trading platform in Robinhood.

  • Similarly, Coinbase implemented a “buy” button on their crypto app which made acquisition of BTC more readily available in 2013. Prior to this orders were manual, with Mt GOX being the prime exchange. This type of UX innovation will be what catalyzes faster adoption and hence more liquidity.

  • The OriginsNFT terminal intends to use data analysis to provide better NFT trading experiences.

• The types of participants in the market will change the depth of the market and amount of available funds. Increased institutional investors will likely increase the amount bot bids available, the depth of the market (“DOM”), and hence the liquidity available. These institutions will effectively take the bid/ask spread akin to traditional market makers.

• Reducing the costs of high fees through increased marketplace competition. It’s in marketplaces’ long term interest to preserve the NFT ecosystem, so reinvesting profits back into improving the quality of services and infrastructure will be beneficial.

• Royalties are a hot topic, but charging a fair price for the service or product is at the crux of an optimal solution.

• Gas costs can be reduced through contract optimization and minting mechanisms to prevent bottlenecks in block space. Use of layer 2s and even layer 3s are additional means of preserving capital of NFT market participants.

We have seen an influx of Venture Capital (“VC”) funding in 2022 as projects receive more financial backing, reducing execution risk and increasing the likelihood of delivering against roadmaps.

This provides the NFT space with some much needed verification, validation and legitimacy. There was a period when VC-backed collections would see huge volume pumps as NFT traders acquired these projects with “peace of mind” that there was an increased level of due diligence performed by the VC funds.

Market confidence plays a large part in NFT investors’ willingness to hold the NFT long term, as there’s a lower risk of collection trending to zero or rugging. We can draw a similar comparison to tradFi markets; when contagion sets in, bids are pulled and prices cascade as the depth of the market diminishes.

If pension funds or other asset managers were to change their investment mandates to include a small portion of their portfolio in high-risk assets like NFTs, then this would have huge, favorable impacts on the liquidity of the NFT market. Refer to the OriginsNFT article on asset management for the background on the industry.

The total AUM of the asset management industry is in excess of $100 trillion USD, so if they were to allocate 0.1% of a portfolio to NFTs and crypto, then this would provide an influx of $100 billion of liquidity. As a comparison, the current liquidity in the space is estimated to be 1.4% of this amount ($1.4 billion USD or 1 million Ξ).

More institutional involvement will provide greater depth to the market. But with that comes sophistication through optimal algorithms, tools and increased market efficiency, making it more difficult for retail traders to win. The current market spreads for liquidity makes NFT trading so profitable; as spreads tighten, so will the opportunities for meaningful profit.

Nifty Blue is developing a pricing tool for NFTs. This will help define what the spot fair value of an NFT is at any given point. The blue paper examines pricing methodology for heavily-traded collections such as BAYC.

The paper makes the valid point that NFTs are by definition non fungible, so certain traits may command a price that deviates through a random walk. This tends to be readjusted in typical stock / crypto markets given the fungibility. Additionally, there will be NFTs that have been held since mint, so reduced historical sales data must be factored into one’s assessment of the model.

Currently unrealized p&l tools often scrape pricing data from the Opensea floor price, but floor price can be manipulated and is not an accurate representation of fair value, particularly for NFTs with rarer traits in a collection. Once pricing strategies have been optimized, we could see an increased opportunity for more easily assessing whether certain NFTs are under/over valued, thus enhancing “sniping” opportunities.

The NFT space is not heavily regulated. This means that many traders act on alpha that could arguably be construed as insider information in traditional equity markets. We’ve seen an overlap with Opensea and Coinbase employees being investigated for questionable practices. It’s important for traders to think about alternative strategies to implement in the absence of early information access.

Charting

Technical analysis uses charts and statistics to predict future price action. Larger collections are easier to chart given increased volume / sales to infer directional patterns. NFTs are highly sensitive to news announcements so it can be difficult to accurately predict price action. But in the absence of news and tracking of smart money, we can form investment thesis around certain projects.

Momentum Trading

Momentum trading is a frequent means of trading NFTs. It is, however, extremely risky given NFTs’ illiquid nature. As volume commences, scalpers may jump in and list slightly above the floor price in the hope that another trader will purchase the NFT. However, with royalties, gas and marketplace fees this can be counterproductive for any small price movements. At least with fungible token markets, fees are lower. Centralized exchanges such as Binance offer a 0.1% fee. This combined with the fungibility and increased volume makes technical analysis more effective in these markets.

Investing using fundamentals for businesses include looking at the profitability, revenue, assets, liabilities, and growth potential. This could be similar for NFT collections, which can be treated like early phase start-ups, though unlike with traditional businesses, many of the fundamental “metrics” we might look to are specific to NFTs as an asset class.

NFT Purpose

Look at the underlying purpose of the NFT. Art 1/1s are purely based on the collectors’ appeal and would be different characteristics to a gaming NFT which may look at the in-game properties of that asset. Value may accrue to in-game NFT assets based on the damage they do whereas PFP collections and art is predominantly the visual appeal or brand.

Utility

The PFP NFTs that token gate communication platforms, such as Discord, may have other intrinsic value (dual purpose). For instance, Alpha passes have this value proposition by offering quick and actionable information that members can profit from. Obtaining information early is directly correlated to more profitability in the NFT space given how sensitive the price action is for this asset class. Any inflow of volume can significantly impact prices.

The Edge (Over Institutional Investors)

As more institutions enter the space, obtaining this alpha will become more difficult without similar trading tools. The advantage many NFT traders have now is knowledge in the market. This can either be used in an advisory capacity to add an additional revenue stream or acquiring longer term collectable assets now will likely accrue value with lower risk. Understanding the fundamentals of a project or what gives it value now will likely be something that institutions do at a later stage when they peel back the history books on old collections. We’ve seen the likes of Pudgy Penguins be revitalized, so it is possible that other collections may also follow suit. particularly for collections that are fully on chain, can be acquired and restored with relative ease.

Macro

Fundamentals also look at the macro economic environment. The hawkish stance by the Federal Reserve is adversely impacting all risk on assets, including NFTs. Refer to the OriginsNFT macro article for more information on impacts on the NFT sector.

Price and volume are two of the more common data inputs used in quantitative analysis for mathematical models. This tends to involve high frequency trading using algorithms or complex models in tradFi.

Max Minting

NFT trading typically has lower volume and prices can involve wide spreads. The most suitable application is minting new NFT projects. Those projects that apply a stealth mint approach with no whitelist are exposed to botting tools that automate the minting process. Some projects implement barriers to this, for instance limiting the number of mints per wallet. However, several minting bots have already found workarounds by automatically funding dozens, if not hundreds, of individual wallets and executing the mint instructions. The term in the NFT space is a “max minter.” The trader will then list the new mints on the secondary market to recoup the cost and then hold a “moon bag” in case the project continues to excel.

Scripting Bots

Quant trading is not currently prevalent in the NFT space, but scripts can be written to execute transactions if certain criteria are met. For instance, if an NFT hits a certain price, then the bot could automatically snipe the asset.

More complex trading strategies, established on historical data, are likely to be deployed as more experienced institutional traders enter the ecosystem.

• Review the bid-ask spread. The wider it is, the more risk there is with the collection. It’s a reliable indicator.

• Establishing fair value will be essential to help calculate whether NFTs are under/over priced and will help with sniping. There will be differences in opinion on this given there is some subjectivity with art and given this asset’s non fungible nature.

• Liquidity extraction exacerbates the reduced volume and downturn. This will change as infrastructure is developed and contracts are optimized. Improving this will aid the long-term health of the NFT sector.

• Institutional adoption will change the landscape. Educate yourself to maintain an edge over institutional investors.

• Adapt strategies for trading. Alpha groups are not effective if the information is common knowledge. Current practices are questionable and may be regulated against if NFT markets are to align with tradFi restrictions on privileged information.

• Trading tools will help maintain an edge. Minting bots, sweeping tools and data analysis are your friends.

At OriginsNFT we leverage data-driven decision making, educational resources, and proprietary analytics to remain ahead of the curve with respect to blockchain tech and specifically NFTs. To find out more, please visit our website or Twitter.

To purchase a pass, please visit our Opensea page.