Shortly after that article was published, I decided to look at another governance token to gauge its liquidity in relation to other, more liquid governance tokens. My aim for this study is to use these metrics to tell a comprehensive story on token liquidity.

$HOP token was used as the subject for this study, a token primarily issued for influencing the governance of bridging protocol: Hop. I attempted to compare its liquidity to some of its competitors: Stargate ($STG) and Across ($ACX), both bridging protocols.

In addition to the simple metrics I listed in my previous article, I also used cumulative volume and "slippage" as well (which I'd class as both complex and experimental).

Slippage here is simply the disparity between the USD value of tokens swapped-in and swapped-out. It seemed reasonable to me to simplify the slippage metric by using this calculation, this should capture user-facing "spread".

The results I observed for $HOP "slippage" were very surprising, and revealed how thin liquidity is for the token, particularly on Layer-2 chains.

Of the three bridging tokens which were studied, using the "simple" metrics, it's clear that $HOP is the least liquid: Volume is very low across chains,

turnover rate is lowest amongst the three,

and while it still has a higher cumulative volume than $ACX, the latter's cumulative volume is growing exponentially, while $HOP is growing linearly. $ACX will likely catch up soon at this rate (despite $HOP being first issued 6 months prior).

Despite this, however, $HOP is the most actively traded asset amongst the three tokens

But most trades are very small

Source for all these metrics.

It should be noted that these metrics are indeed tracking "active liquidity", which should be obvious by now to readers.

TVL, meanwhile, could be a bit misleading as a metric for liquidity depending on how it is calculated since most AMMs provide liquidity for a wide range of possible valuations (for Uni-V2-based AMMs the range is (0, ∞), for Uni-V3-based AMMs it's [a, b] for some {a, b} ∈ ℝ+).

Comparing TVL for full ranges can indeed be misleading, e.g. imagine observing the historical TVL of two tokens, one declining and the other increasing overtime to some value ε. This could possibly be interpreted in two ways:

1. The declining one is becoming less liquid overtime (i.e. active tvl is decreasing) and is not compensating for the declining liquid supply while the other is increasing its liquidity in active ranges.

2. The declining one is optimising it's TVL by removing liquidity/token supply in inactive ranges/ranges anticipated to never/unlikely to ever be reached (via collection), while the other token is becoming more "optimistic"/wasteful in its liquidity deployment

The reasons outlined seem too convoluted to be reliable if not specified by the presenter of this metric. Therefore, I believe that the metrics described so far should be more reliable than a vague interpretation of the TVL metric.

Note that this is purely conjecture, and I’d love to hear any debate against this line of reasoning. But examining what ranges of liquidity are being removed/added over time seems like a very interesting idea for future studies.

When you do compare TVL in this case, however, similar conclusions can be drawn

Source: TokenTerminal

This "liquidity ranking", $STG > $ACX > $HOP, holds when comparing their TVL.

This assertion holds when comparing their slippages as well... But this is where things start to become weeeeeird.

$ACX stands in the middle of the rest, once again, in the liquidity rankings. The slippage on Optimism (blue/purple line) will become relevant below.

All $STG liquidity is deployed on Ethereum Mainnet (primarily paired with $USDC on CurveFi), and again shows that it dominates $ACX in terms of slippage. It also dominates $HOP, but $HOP's slippage metric on Optimism and Arbitrum is very sus

Why is slippage so high on Arbitrum and Optimism? The TL;DR of it is that... $HOP is very illiquid on these L2s for the demand.

Here are some of the worst offenders

It seems like just about all of these contracts deployed on Optimism are unverified, but I will not speculate on what class of contracts they are.

Instead, I show that $HOP is indeed quite popular on L2s, in fact Optimism has overtaken Ethereum Mainnet as the chain where $HOP is most frequently traded for over a year now, and Arbitrum surpassed Ethereum Mainnet since March 2024

And $HOP is indeed way above average in slippage on its two most traded DEXs on Optimism (Velodrome) and Arbitrum (Camelot)

The reason for its slippage being above average may be topic of a later post, however I hope you had as much of a fun ride as I had making this post.

Although, I do feel a sense of dissatisfaction that I haven't solved the mystery, but I suppose I'll have to leave that for a later post.

This makes it difficult for potential buyers, some of whom may want to participate in DAO governance (and governance over their owned protocols), to enter the market for these tokens.

In this article, I explore some measurements for liquidity which could be employed by DAOs to measure how liquid their governance tokens are in the secondary market.

I begin by introducing the most commonly used metrics, why some of these metrics can't yet be used for on-chain data, and then provide additional metrics which could be used in their absence.

Common Liquidity Measures

Here is a list of commonly used liquidity measures which can be easily calculated from Automated Market Making (which are typically used for on-chain asset exchanges) data:

• Volume: number of market participants and the size of their TXs

• Turnover Rate: rate of outstanding volume changing hands

• Average Trade Size: average cost of trade; ratio of daily volume to trade count

I was not able to compile the following measurements with on-chain data, and would love to find a source for doing so for future research:

• Market Depth: measures market breadth; the market's ability to settle large trades

• Bid v. Ask spread: the gap between the highest bid and lowest ask price on an order book

This is due to the fact that limit order book data does not exist for AMMs, central limit order books use a totally different mechanism for matching buyers to sellers.

This trading mechanism is not typically used on-chain due to discrepancies with transaction cost and throughput on L1 chain solutions compared to centralized central limit order book exchanges.

Alternative measures for liquidity need to be employed in their absence using price and volume data which is more readily available.

Market Efficiency Coefficient

Proposed by Hasbrouck and Schwartz (1988) [1], this is the ratio of the variance of long period returns and the product of the variance of short period returns by the number of short periods (e.g. days) used for this metric.

Values slightly below one denotes high liquidity levels, values significantly below one denotes low liquidity. This short period volatility could be due to (implicit) execution costs (e.g. spread, slippage), which this metric assumes.

This metric also builds on the intuition proposed by Amihund and Mendelson (1986) [2] that, ceteris paribus, bid-ask spread size is proportional to returns.

The Liquidity Index

Danyliv et. al (2014) [3] shows how suitable this metric is for measuring the impact of a price change relative to a given volume, aka price impact. For this, they propose the Liquidity Index (LIX).

They state that low liquidity assets would have a LIX of ~5, assets with high liquidity typically have a LIX of ~10.

Its main contribution is estimating how much volume is required to move the price of an asset by $1, calculated as 10 raised to the LIX value. This is useful for potential traders seeking a strategy for buying/selling an asset.

Other low-frequency estimators

A few studies were consulted for finding the best estimators for bid-ask spread to measure the tightness of crypto assets on-chain.

The best performing ones seem to be the Tobek (2016) [4] Volume over Volume estimator based on daily high/low price data (VoV_HL) and the Corwin & Shultz (2012) [5] spread estimator (CS) which is also based on high/low prices.

However, when testing these metrics with on-chain DEX data via Dune Analytics, DAO native tokens which seem to have vastly different liquidity levels were found to have CS and VoV_HL values very low and close to each other.

This suggests that the trading mechanism used may be inefficient in minimizing tx costs, or that these estimators are not well suited for AMM data. Further research will be required before these estimators can be used to measure liquidity.

Next Steps and Improvements

As mentioned earlier, the difficulty in measuring implicit tx costs using low frequency measures may be due to their ineptitude as a liquidity measure or inefficient trading mechanisms which make implicit tx costs high.

Next steps then would be to compare these measurements to daily price and volume data from central limit order book exchanges (e.g. via Kaiko data or dYdX trading data) and compare them to trading data from AMMs.

Conclusion

A lot of research has been conducted in finding ways to measure liquidity in financial markets using low frequency measurements.

If these measurements are accurate and can be applied to DAO native tokens, it gives potential buyers and sellers for these assets more information on how to strategize a trade or choose a trading mechanism, respectively.

Reducing the friction between buyers and sellers leads to increased liquidity for these assets. It is also key to promoting decentralization in the ownership of DAOs as the market for their native token is better able to attract a more diverse set of buyers.

So, do these tokens suffer from low liquidity, can this issue be remedied with the existing DeFi trading mechanisms? Or are we still waiting for the dawn of a new trading mechanism that can effectively reduce this friction?

References

[1] J. Hasbrouck and R. A. Schwartz, “Liquidity and execution costs in equity markets,” The Journal of Portfolio Management, vol. 14, no. 3, pp. 10–16, Apr. 1988. doi:10.3905/jpm.1988.409160

[2] Y. Amihud and H. Mendelson, “Asset pricing and the bid-ask spread,” Journal of Financial Economics, vol. 17, no. 2, pp. 223–249, Dec. 1986. doi:10.1016/0304-405x(86)90065-6

[3] O. Danyliv, B. Bland, and D. Nicholass, “Convenient liquidity measure for financial markets,” SSRN Electronic Journal, Dec. 2014. doi:10.2139/ssrn.2385914

[4] K. Y. Fong, C. W. Holden, and O. Tobek, “Are volatility over volume liquidity proxies useful for global or US research?,” SSRN Electronic Journal, Jun. 2017. doi:10.2139/ssrn.2989367

[5] S. A. CORWIN and P. SCHULTZ, “A simple way to estimate bid‐ask spreads from daily high and low prices,” The Journal of Finance, vol. 67, no. 2, pp. 719–760, Mar. 2012. doi:10.1111/j.1540-6261.2012.01729.x

The idea he proposed was to build a fan based community using nfts followed by allow[ing] fans to have more perks within the projects we execute. Pretty simple idea, matter of fact, him centering the idea around the community is something I can 100% get behind. The contrary, sucks.

A Boring Dystopia

Here is an example of an NFT project that sucks: Bored Apes. Why? Because BAYC arguably contributes nothing positive to the Crypto space, and arguably destroys the space’s reputation with the attention it receives and the consequence of this attention to potential, and current, stakeholders.

In other words, Yuga Labs does not care about the community. The community only became important to them after significant backlash from the public. Crypto, and any Crypto project, should have the community first, not secondary.

Defining “Practical“ NFTs

Although it might be easy and, dare I say, fun to make fun of BAYC and its token holders, it’s not really the focus of this post. Really, I just wanted to highlight the idea of NFTs done wrong and how we can make it right. I’m sure by now you’re more or less convinced that an NFT project will likely suck if the community is not the primary concern for the project.

Still, can a community-centered Crypto project work in Saint Lucia? Will people want to learn to set up a metamask, seek and purchase Native Tokens (e.g. ETH), and pay gas to interact with a “somewhat promising” project that provides an alternative to some already feasible, existing infrastructure? Hard mode: can the alternative be fun, positive, and may even be profitable to the community?

Well, the conclusion I got from that WhatsApp group exchange was that an On-Chain ticketing system solution can facilitate those needs. To be more specific, an NFT minting contract that mints “tickets“. Tickets that can be identified by their tier (VIP or not) and the event it was issued for.

Given these guarantees, some very obvious extensions can be developed to further extend and supplement this idea of “On-Chain event organising“. On the other hand, that may just be enough to issue tickets for the next Vice fete. Who knows.

You can maybe already think about how perks can be obtained by “ticket holders“. For certain events, ticket holders can gain perks specific to the event the ticket NFT is bound to, and VIPs may get more perks or even more customizable perks due to their status.

Given how easy it is to mint NFTs in this model (a model where NFTs are minted openly by the public; NO PRE-MINTS) a rate limiting system needs to also be set in the contracts. In other words, a ticket buyer should only be able to buy a certain number of tickets for each specified period (e.g. 10 tickets every hour). This is all so that ticket issuance is fair, for all.

The community should also benefit from this fundraising as well, thus I think that in order for this venture to work, the community should be an entity, that benefits from each mint.

Mint fees are standard in NFT projects. Artists, admins, marketers… everyone that worked on a NFT project deserves a piece of the pie for creating a multi-billion dollar project. What’s so bad in letting the community take a (significant) piece of the pie too? Each mint should give the community at most 25% of the mint fee to a DAO treasury.

Why “at most” 25%?

Because 25% is already madness, and I would be surprised if I still have an audience at this point after the statement I made above. At first glance, it looks too generous. The community can do a lot of good with that money, however. For example, fund some local causes.

Hurricane season is here again in the Caribbean, I’m sure the community wouldn’t be against using that money for persons whose homes would be/are affected greatly by our volatile weather. The 25% could be 10%, 5% or even 2%. So long as the community gets a piece of the project’s success, the project itself WILL be successful.

Importance of DAOs

A DAO therefore needs to be deployed from the get go and shouldn’t be tied to some secondary attempt at getting a bag at your expense. A DAO whose aims should be promoting the project, ensuring it is successful long term, and aids local causes (please!!).

This can be done very simply: have ticket holders lock their ticket NFT and they receive governance tokens in exchange. The tokens can then be used to participate in DAO proposals; “What should we, as a community, do with all this revenue“? Followed by a community vote on how these funds are used.

If they want their rare ticket back (to accrue perks and attend relevant event(s)) they need to exchange their tokens for their ticket. VIPs would also get more governance tokens (and thus voting rights) since they paid more for their tickets. Simple, and not an obvious cash grab.

What’s next then?

I can’t say that this model can work in Saint Lucia. I remain very pessimistic about the prospect of this becoming successful. If some big event planner utilises a similar solution, my lack of marketing skills wouldn’t be able to guarantee that it would be successful given the market its working in.

Still, the next step to the problem at hand would be to build the solution of course! I’ve already started the initiative, though I can’t say it’s THE solution (and there is still much work to be done on the Governance structure). If you’re interested in taking a look, you can check it out in my repo

https://github.com/AzorAhai-re/ticketing

It uses the Azuki ERC721A standard to create a very gas efficient solution. Much of what I talk about in this article is reflected here, with the aim of making NFTs suck-less (and used in some capacity in Saint Lucia).