Weekly Feature

Analyzing Crypto Supply Distribution Patterns

By Nate Maddrey and the Coin Metrics Team

Those who control the wealth often control the power. But up until now, wealth distribution has been relatively hard to track. People often hide their wealth or obfuscate the true amount of assets that they hold. Cryptoassets take a big step towards making wealth distribution more transparent. 

Cryptoassets are the first asset class where it’s possible to track the full supply distribution throughout its history. Since every cryptoasset transaction is public and auditable, on-chain data can be used to calculate the balances held by every address at any given block. We can then look at the distribution of the size of the balances held by individual addresses to gain insights about the supply. 

However, supply distribution is not a perfect representation of wealth distribution. People often create multiple addresses, and it is difficult to figure out which addresses belong to a specific individual. Additionally, one address could be owned by many individuals, like an exchange cold wallet. To get an accurate cryptoasset wealth distribution you would need to know who controls each address. But transparent, auditable supply distribution gives a fascinating estimation of wealth distribution, and can also tell a lot about the usage patterns of a cryptoasset. 

For example, supply getting consistently more distributed could be a sign that the asset is getting real usage as a medium of exchange. Furthermore, analyzing sudden changes in the amount of supply held by addresses with large balances may lead to insights about selling and trading patterns. 

In this piece we explore the supply distributions of eight cryptoassets, and analyze what the changes in distribution tell us about each asset’s usage. 

Methodology

The charts throughout this piece show the percentage of supply held by addresses holding certain fractions of the total supply. 

We first looked at the balances held by each individual address. We then created groups of addresses holding different sized balances, ranging from relatively small to relatively large. To remain consistent across different cryptoassets, we grouped address balances by fractions of total supply, starting with addresses that hold at least one ten-billionth (1/10B) of total supply (0.0000000001%) and going up to addresses holding at least one one-thousandth (1/1K) of total supply (0.001%). For context, at time of writing, the total Bitcoin (BTC) supply is 18,214,117 so one ten-billionth of total BTC supply is 0.0018214117 BTC, equivalent to about $19. 

We then grouped these addresses into different discrete ranges based on balance size. We started with addresses that hold at least 1/10B but not more than 1/1B, then at least 1/1B but not more than 1/100M, etc., going up to addresses that hold 1/1K of total supply or greater (1/1K+). 

Finally, we calculated the sum of the supply held by all the addresses in each range, to get a percent of total supply held by each group of addresses. We include the cryptoasset’s price on the second y-axis axis (using log scale) to provide context about price changes during supply distribution movements.

It’s also important to note that there are some meaningful differences between the protocol design of different blockchains. For example, the supply of UTXO-based blockchains like Bitcoin becomes slightly more distributed over time as the UTXO set becomes more dispersed due to natural usage (new addresses are often created for each transaction on Bitcoin). This does not happen, however, in account-based chains like Ethereum where addresses are frequently re-used.

All of the data used in this piece is available as part of our Network Data Pro product. You can find more information about Coin Metrics Network Data Pro here. 

Supply Distributions

Bitcoin

BTC supply was initially held by a few individuals, but over time it has gradually been distributed to millions of different addresses.

The percentage of BTC supply held by large addresses (with a balance of at least 1/1K of total supply) peaked at about 33% in February 2011. As of February 2020, those addresses hold about 11% of total supply. Conversely, the percentage of supply held by smaller addresses with balances of 1/10M and lower has been steadily increasing since 2011. 

There was a relatively large decrease in percentage of supply held large addresses near the end of 2011 through early 2013, before large price increases. Additionally, there was a decrease in December 2018 that was likely caused by Coinbase redistributing its cold wallets.

Source: Coin Metrics Network Data Pro

Ethereum

Unlike BTC, Ethereum had a crowdsale to initially distribute Ether (ETH). The supply of ETH started off highly concentrated but has gradually become more distributed over time. 

The percentage of supply held by addresses with the largest balances (at least 1/1K of total supply) peaked at about 60% in July 2016. The amount held by these large addresses saw a significant decline as the ICO bubble deflated throughout the end of 2017 and into 2018. As of February 2020, these addresses hold about 40% of total ETH supply.

The percentage of supply held by relatively small addresses (with 1/100K of total supply and lower) has been steadily increasing since 2016. 

Source: Coin Metrics Network Data Pro

Litecoin

Litecoin (LTC) had several large dips in the amount held by large addresses (at least 1/1K of total supply) throughout 2013 just prior to the December 2013 price spike, and throughout 2017 before the January 2018 price peak. Interestingly, nearly 46% of supply is still held in large LTC account compared to 11% held in large Bitcoin accounts. 

Source: Coin Metrics Network Data Pro

Bitcoin Forks

Bitcoin forks inherit BTC’s supply distribution (at the time of forking), so may appear distributed simply because BTC itself is relatively distributed. But unlike BTC, Bitcoin Cash (BCH) supply held by large addresses has gotten more concentrated over time.

In August 2017, when it forked from BTC, about 14% of BCH supply was held by large addresses with balances of at least 1/1K of total supply. As of February 2020, large addresses hold about 29% of BCH, compared to about 11% for BTC.

Source: Coin Metrics Network Data Pro

Bitcoin SV (BSV) percentage of supply held by addresses with balance of at least 1/1K has remained relatively flat, outside of a significant dip in February 2019, and a sudden increase in June 2019. In August 2018, when BSV forked from BTC, these large addresses held about 26% of BSV supply. As of February 2020, they hold about 24%.

Source: Coin Metrics Network Data Pro

Ripple and Stellar

Ripple (XRP) and Stellar (XLM) are both account-based chains, and both have official foundations that hold a large percentage of supply. About 85% of total XRP supply is held by addresses with balance of at least 1/1K. 

About  95% of total XLM supply is held by addresses with a balance of at least 1/1K of total supply. This is largely because the Stellar Development Foundation (SDF) holds over half of XLM supply. According to the SDF’s mandate, it currently holds 29.4B XLM. Additionally, the SDF recently burned 50% of total XLM, bringing the supply down to 50B. These burned XLM still appear on-chain since they were sent to a burn address, and therefore get counted as part of the supply distribution. 

Source: Coin Metrics Network Data Pro

Tether

Tether, which is the largest stablecoin by most measures, has released tokens on multiple blockchains. For this analysis, we looked at the Omni (USDT-Omni), Ethereum (USDT-ETH), and Tron (USDT-TRX) versions of Tether separately.

All three versions of Tether started out 100% concentrated. But USDT-Omni and USDT-ETH have gotten increasingly distributed over time. This could be a signal that they are being used as a medium of exchange, which would explain why supply is flowing from addresses holding large balances to addresses holding smaller balances. The Tron version of Tether (USDT-TRX), however, has stayed almost 100% concentrated, which signals that it is likely not getting much usage as a medium of exchange (however, Tether was only introduced on Tron in May of 2019, so is still relatively new).

Also of note, the USDT-Omni distribution trend reversed and started becoming more concentrated in January 2018, near the peak of the market wide price bubble.

Source: Coin Metrics Network Data Pro

Conclusion

Cryptoasset supply distribution gives a clearer window into wealth distribution than any prior asset class, and also provides some interesting insights into trading patterns. The increasing distribution of assets like BTC and Tether is a positive sign that these assets may be getting real usage, and are ending up in the hands of more individual users. We will continue to analyze supply distribution and report on this in the future.

Network Data Insights

Summary Metrics

It was another positive week for the major cryptoassets. ETH continues its strong run, leading the pack in most metrics. Notably, ETH’s realized cap, which can be thought of as the average cost basis of all holders of the asset, increased by 3.6%, while BTC’s increased by 1.3%.

IOTA has been in the news recently after the network was shut down following a hack. We analyze the price implications of this incident in this week’s Market Data Insights section.

Network Highlights

The median transaction fee for both BTC and ETH has increased at least 60% over the last 30 days, outpacing all other major cryptoassets. Median block fees typically rise due to an increased demand for block space, potentially because of increased usage.

Source: Coin Metrics Network Data Pro

Dai (DAI), Paxos (PAX), USD Coin (USDC), and True USD (TUSD)  transfer counts have all been growing faster than Omni-based Tether (USDT), Ethereum-based Tether (USDT_ETH), and Tron-based Tether (USDT_TRX) over the last 30 days. Although Tether is still by far the largest stablecoin in terms of market cap, this may be an early sign that other stablecoins could start closing the gap in 2020.

Source: Coin Metrics Network Data Pro

Market Data Insights

Many assets were relatively flat for the week with a few important exceptions: ETH (+14%) and Tezos (XTZ) (+21%). 

The BTC options market has been pricing in increased volatility over the next several months as reflected in the spread between realized and implied volatility, in part because of elevated open interest in BTC futures markets. But it has yet to materialize. In fact, BTC has traded in a narrow range over the past week compared to other assets. The spread between BTC’s realized volatility and other assets has widened, most significantly in ETH. 

Source: Coin Metrics Reference Rates

Investigating Recent IOTA Price Action

Market efficiency and maturation is a recurring theme for The State of the Network. The recent IOTA incident is another valuable data point to benchmark the industry’s progress. 

At 12:00 PM (17:00 UTC) on Tuesday, February 12, 2020, the IOTA Foundation sent a tweet stating that they were investigating suspicious behavior with the Trinity wallet.

Less than 30 minutes later, IOTA announced on their Status Page that they were shutting down the Coordinator, effectively shutting down the network.

However, it was another 24 hours before IOTA sent a second tweet about pausing the Coordinator, saying they were “working with law enforcement and cybersecurity experts to investigate a coordinated attack” and paused the Coordinator in order to protect users.

As the below chart indicates, the price was not particularly responsive to this news. In fact, the price did not even drop to levels seen on February 10th. The only noticeable change in price occurred around 08:00UTC on February 13th, roughly 15 hours after the first tweet and 8 hours before the second tweet. 

This lack of substantial price action is somewhat surprising given that the net effect was the shut down of IOTA.

Source: Coin Metrics Reference Rates

To our knowledge, none of the constituent exchanges for the CM Reference Rates halted trading in IOTA. This is notable for two reasons: 

1. There was no way for users to deposit or withdraw IOTA once the Coordinator had been shut down. This means that only IOTA already on exchanges could be traded.
2. More importantly, IOTA claims that the attacker was using exchanges to liquidate their stolen holdings, after some obfuscation, and that exchanges have flagged the applicable transactions.

One might expect these developments to contribute to reduced trade volume following an initial spike. As can be noted from the charts below, while there does appear to be a spike in trade volume, it primarily occurs only after the second IOTA tweet– the response to the first tweet was limited. Additionally, rather than dry up completely after the spike, trade volume appears to actually increase in some markets, even hours after the second tweet. 

A final interesting note regarding the trading activity: at various intervals, there appears to be large volume spikes across some markets prior to the first tweet from IOTA. The earliest social media activity we could find about the IOTA issue was on the IOTA Discord channel, starting at around 10:35AM EST on Wednesday, February 12th. This roughly co-incidents with the timing of a volume spike in the bitfinex-miota-btc-spot market, as can be seen in the chart below. 

Source: CM Market Data Feed

Note that Coin Metrics uses the ticker ‘miota’ to refer to IOTA.

CM Bletchley Indexes (CMBI) Insights

After a strong start to the week, most Indexes gave up their returns over the weekend to close the week out relatively flat. The CMBI Ethereum Index was the best performer of the week, reaching returns of 25% intra-week before finishing the week 14% up. The Bletchley 20 (mid-caps) had their first negative week for the year, finishing as this week’s worst performer, down 3%.

Source: Coin Metrics CMBI

Coin Metrics Updates

This week’s updates from the Coin Metrics team:

• Coin Metrics is hiring! We recently opened up 5 new roles, including Blockchain Data Engineer and Data Quality and Operations Lead. Please check out our Careers page to view the openings.

As always, if you have any feedback or requests, don’t hesitate to reach out at [email protected]

Subscribe and Past Issues

Coin Metrics’ State of the Network, is an unbiased, weekly view of the crypto market informed by our own network (on-chain) and market data.

If you’d like to get State of the Network in your inbox, please subscribe here. You can see previous issues of State of the Network here.

Check out the Coin Metrics Blog for more in depth research and analysis.

Weekly Feature 

Cryptoasset Valuation Research Primer, Part 1

by Kevin Lu and the Coin Metrics Team

Cryptoassets represent a significant innovation in the evolution of money and the modern financial system. Valuing cryptoassets remains very much an open question. Foundational concepts on which a formal discipline of crypto valuation can be built are only beginning to be established. Without a firm anchor to existing methods of asset valuation, we have seen intense experimentation over the past 10 years. 

We conducted a comprehensive literature review to identify all major facets of cryptoasset valuation research that has been conducted so far. All methods were considered, from theoretical valuation frameworks, to empirical valuation models, to novel indicators that have application to valuation. 

In short, we are interested in all research that can be used to understand the current value of cryptoassets, estimate the value of cryptoassets, or predict future values of cryptoassets. All publication mediums are considered, regardless of pedigree, from forum postings to academic journals. The most salient articles from both academic and industry researchers are included. 

In this piece, we explore six main categories of crypto valuation research: equation of exchange, discounted future utilities model. Metcalfe’s law, price regression models, cost of production models, and asset bubble identification. In an upcoming Weekly Feature, we will review factor investing, transfer value-based ratios, realized capitalization, and the emerging field of UTXO analysis as Part 2 of our cryptoasset valuation research primer.

Equation of Exchange 

Fisher’s equation of exchange embodies a strand in the literature that has been influential in the field of token design. Originally designed to explore the relationship between money supply and price level in the field of monetary economics, it has been applied to the field of cryptoasset valuation and currently stands as one of the most widely explored theoretical frameworks. 

The core idea is simple and intuitive: the equation of exchange is the relation MV = PQ, where M is the nominal amount of money, V is the velocity of money, P is the price level, and Q is the index of real expenditures. This is succinctly explained in Wang (2014).

The equation is adapted to take into account the unique aspects of cryptoassets such as Bitcoin (BTC). First, all quantities are defined in units of fiat currency, setting P equal to 1. M is defined as the number of cryptoasset in existence multiplied by the price of a single unit of the cryptoasset (i.e. the market capitalization). Q is the amount of value transferred across the network. And the interpretation of V remains unchanged. 

The implication of this model is that the value of a cryptoasset has an inverse relationship with velocity — that is, high levels of velocity lead to lower cryptoasset valuations. Wang (2014) finds that the price of BTC is determined solely by the likelihood that BTC will be saved. Ciaian, Rajcaniova, and Kancs (2015) empirically test the impact of supply and demand factors on BTC price, including velocity (which they proxy by using BTC days destroyed), and find that they have a significant impact. 

BTC velocity has decreased in recent years as price has increased, which fits with the equation of exchange model. The following chart shows adjusted velocity (i.e. velocity computed using adjusted transfer value which filters out self-sends and spam transactions) of the one-year active supply (i.e. supply that has been transacted at least once within the last year) of BTC.

The literature’s fixation on velocity continues in Buterin (2017) and Pfeffer (2017), two seminal articles that apply the equation of exchange to the valuation of utility tokens. Both researchers posit that utility tokens are susceptible to extremely high levels of token velocity because users only procure small amounts of tokens to use a service provided by the network and providers of the service immediately sell any tokens they receive. Buterin (2017) concludes that the value of a cryptoasset “depends crucially on the holding time” of the token and argues for token sinks — mechanisms which reduce the token supply or token velocity (for example, transaction fee burning). Pfeffer (2017) concludes that token velocity could be high at equilibrium, and the value of a utility token will converge to a low level that is a fraction of the actual cost of the computing resources needed to maintain the network. 

ETH adjusted velocity of one-year active supply increased leading up to the 2018 price peak, and has since leveled off. ETH’s 2017 rise in velocity also coincides with the ICO boom and the rise of utility tokens, which has since subsided. ETH’s velocity appears to be relatively in line with ETH price.

Samani (2017) and Samani (2018) present ideas on token economic models which address the “velocity problem” to allow a utility token to accrue value. Burning and staking models are considered. Collectively, the articles published by industry researchers under this topic in 2017 and 2018 were strongly influential in the field of token design. 

The high velocity thesis has garnered its fair share of criticism, however. Evans (2018) presents one of the most salient criticisms, suggesting that previous frameworks view token velocity as an exogenous variable (a value determined outside the model) that can be tuned to a desired level through token mechanism design. Instead, Evans (2018) models velocity as endogenous to the model and a function of PQ. Koralewski (2018) makes similar criticisms regarding the exogeneity of velocity. Weber (2018) challenges the entire foundation of the velocity thesis by arguing that the equation of exchange has been mathematically misapplied and presents two correct applications. Similarly, Locklin (2019) presents a devastating criticism of the equation of exchange and argues that commonly held conclusions regarding token velocity and optimal token design are false. 

Despite its criticisms, the equation of exchange remains one of the most widely adopted frameworks for token valuation. In the coming years, as winners and losers emerge in the utility token space, we expect more empirical research to be done in this area to test the token velocity thesis. 

Discounted Future Utility Models

The discounted future utility family of models takes inspiration from valuation using a total addressable market approach, discounted cash flow analysis, and the equation of exchange. It represents a significant extension of the theory presented in equation of exchange research by advancing an empirical model to estimate the value of utility tokens. Token velocity, among other assumptions, is one of the key parameters in the model. This family of models most closely resembles a discounted cash flow model found in traditional finance, but adapted to the unique characteristics of cryptoassets. 

Burniske (2017a) represents the first significant contribution to the literature by introducing a model to value a hypothetical utility token. Key determinants of the model are the token’s supply characteristics such as the number of tokens in circulation, the total size of the market that the utility token is used to purchase services from, the adoption curve of the network, token velocity, and the discount rate. Winton (2017) introduces a similar model with many of the same model parameters but also allows for different return expectations from different cohorts of investors. 

Burniske (2017b) introduces the terms “current utility value” and “discounted expected utility value”, and presents a theoretical framework for how value derived from the two sources fluctuates throughout the lifecycle of an utility token. 

The application of discounted future utility models remains an area where considerable empirical research can be conducted. Functional networks upon which utility tokens are built are still under active development, and a few significant projects are seeing meaningful amounts of activity. Modeling the value of utility tokens with novel token designs such as burning, discounting, and staking also remain an active area of experimentation and research.

Metcalfe’s Law 

Metcalfe’s law states that the value of a communications network is proportional to the square of the number of connected users of the system. The foundation of this law is in the mathematical relationship that each user of a communication network can make (n – 1) connections with other users. If each connection is considered equally valuable, the total value of the network is proportional to n (n – 1) / 2, which asymptotically approaches n2. Metcalfe’s law has been successfully applied to the valuation of social networks. As an example, Zhang, Liu, and Xu (2015) empirically test Metcalfe’s law and several other network effect laws by using data from Tencent and Facebook and find that Metcalfe’s law fits better than competing laws.

Application of Metcalfe’s law to valuing cryptoassets is straightforward and was first conducted in gbianchi (2014), where the key insight was made to define a user of the Bitcoin network as the number of addresses with zero balance, a proxy chosen after backtesting and considering other alternatives. A formula to predict the price of BTC based on the square of the number of addresses with zero balance is presented. The article also made a significant contribution to another thread in the literature by introducing the idea of tracking the number of addresses between certain balance thresholds. 

Alabi (2017) tests Metcalfe’s law on BTC, Ethereum (ETH), and Dash (DASH), and illustrates how deviations from predicted values can be used to identify asset bubbles. Peterson (2018) applies Metcalfe’s law using an alternative representation for users: the number of wallets from blockchain.info. The model is used to identify a period of suspected market manipulation in 2013. Franek (2018) tests Metcalfe’s law and competing laws on BTC and ETH, and introduces a price-to-Metcalfe ratio to identify periods of over or undervaluation. Kalichkin (2018) combines predictions from Metcalfe’s law with predictions from Odlyzko’s law and similarly introduces a price-to-Metcalfe ratio. 

The application of Metcalfe’s law to cryptoassets touches upon another emergent element in the literature: the study of the number of users of a particular cryptoasset. While the transparency afforded by blockchain ledgers allows for many candidate proxies to represent the number of users, we still lack the clarity of a concept similar to daily active users, a common metric used to track the usage of internet applications. The mapping of on-chain activity to real world entities and individuals is still unclear and an active area of research. As more work is done in this adjacent area, a more precise application of Metcalfe’s law to valuing cryptoassets is possible. 

Price Regression Models

Price regression models refer to an approach to cryptoasset valuation where price is regressed on another variable, typically time (or a variable that is a function of time). The defining characteristic of this approach is that predicted price values can be generated far into the future, with some models predicting prices that are unimaginable today. While some practitioners may dismiss this family of models because of its simple approach, we believe it is a mistake to ignore them entirely — early models have had remarkably accurate out-of-sample results, have reliably identified historical periods of over and undervaluation, and still receive considerable attention from market participants.

Trololo (2014), building on early prior research in the field of cryptoasset valuation, represents the genesis of the price regression family of models. Using a model that regresses price on the natural log of time, Trololo (2014) was able to predict the date that BTC would reach $10,000 with an error of only a few days at a time when the current price was $275. Residuals from the model are used to identify periods of over and undervaluation. 

Awe & Wonder (2018) uses a similar approach with updated data and provides a prediction for the low of the market cycle with very good out-of-sample accuracy. Burger (2019) presents various price regression models using subsets of the data to test for robustness of fit.  

PlanB (2019) was the next significant advancement in the field and represents one of the most impactful articles in cryptoasset valuation research. Taking inspiration from Ammous (2018), PlanB (2019) posits that there is a relationship between BTC’s value and its stock-to-flow ratio. Stock-to-flow ratio is defined as the inverse of annualized supply issuance, and represents BTC’s scarcity and suitability as a store of value. A test using empirical data finds co-integration between market value and stock-to-flow ratio with high goodness of fit. The model predicts a BTC price of $55,000 after the next halving in May 2020. 

Price regression models have had remarkable success in prediction and adoption. But their legitimacy rests on the assumption that BTC, in particular, will continue on a path to a long-term equilibrium where it becomes a global store-of-value asset, comparable to gold. The next few years will generate additional data which can be used to test the primary assumptions of these models, such as the co-integration between BTC’s value and its stock-to-flow ratio. 

Cost of Production Models 

Cost of production models touch on a strand in the literature which quantify the costs of mining in order to value cryptoassets. Such an approach is intuitively straightforward and is rooted in classical economics where, for example, Adam Smith introduced the concept of a natural price and market price for commodities. The natural price is the price level that is equal to the cost of the various factors of production necessary in producing a commodity. The market price is the actual price that the commodity is sold. Smith argues that the natural price is the central price to which all commodities are continually gravitating. 

Satoshi Nakamoto succinctly explains the foundational logic behind this approach: “The price of any commodity tends to gravitate toward the production cost.  If the price is below cost, then production slows down. If the price is above cost, profit can be made by generating and selling more.  At the same time, the increased production would increase the difficulty, pushing the cost of generating towards the price.”

The application of a cost of production model for valuing BTC occurred out of necessity as early as 2009, the first year of BTC’s existence. New Liberty Standard, the first website to offer a BTC exchange, was also the first to establish an exchange rate for BTC — the first published exchange rate in October 2009 was 1,309.03 BTC to one U.S. Dollar. In the absence of an established market, the administrator of the website calculated the exchange rate using a simple model that approximated the cost of electricity needed to mine BTC. 

Hayes (2015) represents the first serious treatment of the subject and offers a model based on the cost of electricity, the efficiency of miner technology, the market price of BTC, and the difficulty of mining. Hayes (2016) conducts a cross-sectional analysis on 66 cryptocurrencies and finds that a cryptoassets value can be explained by mining difficulty, the rate of supply issuance, and the type of mining algorithm used. 

The Cambridge Bitcoin Electricity Consumption Index, using miner hardware performance in Bevand (2017) provides a lower bound, upper bound, and best guess estimate for the electricity consumption of BTC. Edwards (2019) presents a model where BTC’s value (referred to as its energy value) is a function of energy input, supply issuance, and the fiat cost of energy input. Predicted values from the model using empirical data find good fit. 

Understanding BTC’s cost of production has an important impact on miners who have a role in the formation of asset bubbles-and-crashes due to their procyclical behavior. The economics of mining and conceptual model for estimating the cost of production of cryptoassets is now well understood. Further advancements in this field are likely to be in gathering more accurate empirical data upon which these models are based on. 

Asset Bubble Identification 

The tendency for speculators to create bubbles in financial assets is deeply rooted in human psychology. Cryptoassets, without a firm anchor to traditional methods of asset valuation, are particularly susceptible to bubbles and bubble-and-crash cycles have happened several times in BTC’s short history. The size and frequency of bubbles in cryptoassets invites the application of bubble detection techniques, first developed in traditional financial assets. 

Cheah and Fry (2015) is among the earliest articles to apply established bubble detection techniques to BTC. Using a variety of bubble detection models, it finds empirical evidence that BTC is prone to substantial speculative bubbles. Using a recently developed detection technique, Cheung and Su (2015) finds many short-lived bubbles and three large bubbles in the period between 2011 to 2013. Wheatley, Sornette, Huber, Reppen, and Gantner (2018) presents a generalized version of Metcalfe’s law which does not require network value to grow proportionally to the square of the number of users to model the fundamental value of BTC. Deviations from predicted values from this model are considered bubbles and they are formally tested using a textbook bubble detection technique. Four bubbles are detected and an ex ante prediction is provided that performs well out-of-sample. 

Conclusion

The Dutch East India Company, founded in 1602, was the first corporate entity to issue bonds and shares to the public, and in doing so became the world’s first formally listed public company. It then took a period of over 300 years for the necessary foundational concepts to be developed until the formal discipline of equity valuation was established in the 1930s. With cryptoassets, we stand on the shoulders of giants, and substantial progress has been made over the past 10 years in the emergent field of cryptoasset valuation research. Existing valuation methods across many disciplines are being adapted to suit cryptoassets. At the same time, unique cryptoasset-specific methods are being actively developed. Foundational concepts are only beginning to be established and many concepts likely remain undiscovered. 

In an upcoming issue of State of the Network, we continue with Part 2 of our comprehensive literature review and cover other significant advances in cryptoasset valuation research. Factor investing, transfer value-based ratios, realized capitalization, and the emerging field of UTXO analysis are among the topics covered. We also share our outlook on the most promising future directions of valuation research. 

Network Data Insights

Summary Metrics

The major cryptoassets had another strong week, continuing a hot start to 2020. BTC was up across most metrics, but ETH was up even more in almost every category. Although BTC led the way for much of 2019, we have now seen ETH and many smaller-cap assets outperform BTC at the start of 2020. ETH posted five days of consecutive positive returns from February 5th through February 9th, which has only happened 21 times in ETH’s history.

ETH also outpaced BTC this past week in usage growth. ETH active addresses grew by 21.5% and transaction count grew by 13.2% week-over-week, while BTC active addresses grew by 4.2% and transaction count grew by 3.3%.

Network Highlights

Velocity of one-year active supply (i.e. the supply that has been transacted at least once within the last year) of stablecoins is near all-time highs. The following chart shows the average velocity of one-year active supply for the following stablecoins: Tether (Omni, Ethereum, and Tron), Paxos, USD COIN, DAI, TrueUSD, and Gemini Dollar. Increasing velocity suggests that stablecoins are changing hands more often, which suggests they are potentially increasingly being used as a medium of exchange.

Tezos (XTZ) realized cap has been growing faster than most other assets over the start of 2020. Realized cap can serve as a proxy for investor cost basis. Although BTC realized cap growth outpaced XTZ over most of 2019, XTZ has surged past BTC in 2020.

Market Data Insights

A large move widely expected by market participants has not yet materialized. Open interest on BitMEX’s XBTUSD contract remains high and the difference between realized and implied volatility in the options market remains elevated. A sharp drop in BTC’s price from $10,100 to $9,800 over the weekend caused some liquidations in the XBTUSD contract but failed to trigger a cascade of further liquidations. 

Market breadth is quite positive with nearly all assets in our coverage universe holding onto strong weekly gains. The recent pattern of other assets outperforming BTC has continued with BTC only gaining +9%. ETH (+21%) is a notable performer, along with XTZ (+41%) reaching an all-time high, and Binance Coin (+31%). 

Ethereum Classic (+3%), Dash (+12%), and ZCash (+7%) have been significant outperformers over the past several weeks but only saw relatively modest gains this week. NEM (+35%) is now a project to watch — it had one of the strongest weekly gains and is up +98% over the past month. 

CM Bletchley Indexes (CMBI) Insights

This week, cryptoassets and indexes continued their impressive start to the year. Even-weight indexes performed strongest again, demonstrating the market wide nature of this week’s performance and the strength of some of the lower value assets in each index. 

Of the market cap weighted indexes, the Bletchley 20 (mid-cap) and Bletchley 40 (small-cap) continued to perform best, providing weekly returns of 19% and 18% respectively. Both these indexes are yet to have a negative performing week in 2020. 

All indexes outperformed the CMBI Bitcoin Index for the week, with the Bletchley 20 Even and the CMBI Ethereum Index performing the strongest, returning 24% and 21% respectively.

Coin Metrics Updates

This week’s updates from the Coin Metrics team:

• Coin Metrics is hiring! We recently opened up 4 new roles, including Blockchain Data Engineer and Data Quality and Operations Lead. Please check out our Careers page to view the openings.

As always, if you have any feedback or requests, don’t hesitate to reach out at [email protected]

Subscribe and Past Issues

Coin Metrics’ State of the Network, is an unbiased, weekly view of the crypto market informed by our own network (on-chain) and market data.

If you’d like to get State of the Network in your inbox, please subscribe here. You can see previous issues of State of the Network here.

Check out the Coin Metrics Blog for more in depth research and analysis.

Setting the Stage for A Total Return Crypto Index 

by Huyette Spring, Ben Celermajer, and the Coin Metrics Team

A Background on Corporate Actions

In traditional capital markets, corporate actions are events that could bring a change to the securities (equity or debt) of a public company. Such events can include mergers and acquisitions, stock splits, dividends and rights issues. Effective handling of such events is standardized and routine, and most investors experience them seamlessly and automatically (the dividend just “shows up” in your brokerage account).

While unglamorous, the significance of accounting for corporate actions is hard to overstate. Imagine that you bought the S&P 500 on the first day of 1988. If you only received the return generated from changes in the prices of the index, you would have earned 1,183%. But if you accounted for the corporate actions of all the companies within the S&P 500, in a so-called Total Return Index, your return was more than double, at over 2,500%.

The key insight here is that financial product creation cannot simply abstract away the economic reality of holding the underlying assets — asset holders are entitled to those full returns.  

Cryptoassets aren’t just an incremental asset class innovation — they are a first principles re-evaluation of ‘money’. As such, there has yet to be a defined or transparently articulated set of industry standards that dictate how to treat crypto corporate actions or define what an investor will receive under a set of circumstances. The result is that even if cryptoasset holders are credited with the results of any crypto corporate actions there is inconsistency in application and no clarity or certainty around the process. 

Such inconsistencies are a gating item for broader adoption. The crypto industry’s nascency has masked the criticality of effective corporate actions handling (along with other market infrastructure components), but this is not a situation that can persist. As the industry grows, both retail and institutional holders of cryptoassets will expect to receive the economic returns of the assets that they hold. 

This feature will discuss the methodology and objective eligibility criteria that Coin Metrics has designed to manage corporate actions in crypto, specifically hard forks.  

CMBI Corporate Actions Policy

As part of the recent launch of the CMBI Single Asset Index Series, Coin Metrics has developed the CMBI Fork Legitimacy Policy in an attempt to promote standardization, improve transparency and apply institutional rigor to such crypto corporate actions. 

Generally speaking, there are currently three main types of crypto corporate actions:

1. Forks
2. Airdrops
3. Staking Yields

The CMBI Fork Legitimacy Policy initially addresses forks, which we consider to be the most significant of the three crypto corporate actions, while the other two (airdrops and staking yields) will be the focus of later iterations.

As noted above, crypto financial products must provide holders with the economic return of underlying assets. With the circumstances under which asset holders should experience a return defined, Coin Metrics can develop two series for every index product: 

• A Price index, which simply tracks the price
• A Total Return index, which tracks to the return investors would experience by holding the underlying assets (i.e. a cryptoasset and all its associated ‘legitimate’ forks)

Deep Dive on CMBI Hard Fork Legitimacy Policy

Unknown to many, there are over 73 forks of Bitcoin alone. But only a handful of the new cryptoassets created by these forks are sufficiently large enough or adopted enough to have an economic impact. 

Coin Metrics deems a hard fork to have occurred if:

1. Two or more distinct blockchains with their own clients are in existence post-fork.
2. Each blockchain shares the same pre-fork blockchain history.
3. Native tokens on each chain are distinct assets and are not interchangeable.

Once a hard fork has been identified, the CMBI Fork Legitimacy Policy provides a framework utilizing both market data and network data to answer key questions as to the legitimacy of forked assets. 

For CMBI Indexes, the criteria outlined below are observed for up to 12 months after the fork event to determine legitimacy. 

It is worth noting that individually, each of the criteria defined below has limitations. To mitigate this, Coin Metrics have taken the following approach:

1. Criteria are evaluated over a 30 day time period. Price, volume, and on-chain transaction activity can be manipulated, but often at a cost. By requiring criteria to be met over 30 consecutive days, it becomes increasingly unlikely that the economic benefits of this manipulation exceed the costs.
2. Creating a set of market and network data criteria, which together represent a comprehensive and manipulation-resistant test. As such, a fork is deemed legitimate only once it meets all criteria outlined below.

In the section below, we will demonstrate how two forks were analyzed against the the legitimacy criteria by reviewing one fork which passed, Bitcoin Cash, and one fork which failed, Bitcoin Gold. The results are summarized in the table below. 

Market Data Criteria

Exchange Support

The adoption of a fork by exchanges plays a critical role in its investability. Only with support from multiple large exchanges can investors have liquidity to buy and sell the forked token and process large transactions. Liquidity and presence on multiple exchanges also indicates that there is enough trading taking place to determine a fair price for the forked asset. 

As such, newly forked tokens will pass the Coin Metrics exchange support criteria if there is support from:

1. At least one market with a quote currency in U.S. dollars, Bitcoin, or Ethereum on three different exchanges in Coin Metrics’ exchange coverage universe.
2. At least one exchange that is headquartered and incorporated in the United States and is registered as a Money Services Business with FinCEN or a New York BitLicense. 

Bitcoin Cash and Bitcoin Gold both passed this test.

Price

Along with the actions of exchanges, Coin Metrics deems it important to gauge the perception of a forked token by investors and trading market participants. Under the assumption that markets are at least semi-efficient, the price and market capitalization of forked tokens are proxies for investor/trader acceptance. Since forks can happen during various market regimes and the size of cryptocurrency assets continues to grow, the price of the forked asset as a percent of the price of the parent chain asset is examined. 

Considering all of this, newly forked tokens will pass the Coin Metrics price criteria if the token trades on whitelisted exchanges with the following characteristics:

1. A new native token will only be considered eligible once its 7-day price volatility (where the price is quoted in units of the parent chain) remains less than 7.5% for 30 consecutive days. Low volatility might indicate that price discovery has occurred and the manipulative price practices that sometimes occur around fork time have subdued. Since cryptoassets are inherently volatile, the test measures volatility against the parent asset (and not against USD) so that volatile movements that are in line with the market are acceptable.
2. The price of the new native token must remain at least 10% of the price of the native token on the parent chain for 30 consecutive days. This assumes that the fork resulted in a 1:1 issuance. If another ratio is observed, the criteria is adjusted accordingly (e.g. 1:2 would require a 5% ratio between new token price and previous chain token price).

Bitcoin Cash passed both of these tests. Bitcoin Gold passed the Volatility test but failed the Price test, thus failing the test overall. 

Volume

In order for financial institutions and large asset managers to liquidate forked tokens, there must be the presence of significant volume in the market. Volume acts as a further measure to ensure that the exchange, investor and trading community adopt the newly forked token. As such, newly forked tokens will pass the Coin Metrics volume criteria if the native token trades on an exchange in Coin Metrics’ exchange coverage universe, with the following characteristics:

1. The volume of the new native token must remain at least 10% of the volume of the native token from the parent chain for 30 consecutive days.

Bitcoin Cash passed this test while Bitcoin Gold failed.

Network Data Criteria

Fork Uptake

Fork uptake is a measure of the number of native units that appear active on the newly forked blockchain from the time of the fork. Here, activated is defined as being sent to an address post a fork event. 

This measure provides an indication of how many owners of the parent chain are choosing to “activate” their newly forked native units to either unlock their utility or to sell them (i.e. how much of the supply gets transacted at least once, as opposed to staying indefinitely dormant). This measure is relatively resistant to manipulation as long as the parent asset’s supply is reasonably distributed. If the parent asset is decentralized, it would take coordination amongst many different holders to fake fork uptake.  

Newly forked tokens will pass the Coin Metrics fork update criteria if it meets the following definition:

1. The fork uptake of the new native token must exceed 10% of the supply of the native token from the parent chain at the time of the fork.

Bitcoin Cash and Bitcoin Gold both passed this test.

Hash Rate

Miners are another important stakeholder in the cryptoasset ecosystem. As such, hash rate is an important metric to examine because it reflects the consensus of miners. Hash rate is also relatively resistant to manipulation because mining equipment is a scarce asset that incurs high variable costs in the form of electricity.

Newly forked tokens will pass the Coin Metrics hash rate criteria if it meets the following definition: 

1. If the forked asset shares the same consensus algorithm as the parent chain, the hash rate of the forked chain must exceed 10% of the hash rate of the parent chain for 30 consecutive days. If the forked asset uses a different consensus algorithm, this criterion cannot be applied and is ignored.

Bitcoin Cash passed this test. Bitcoin Gold has a different consensus algorithm than Bitcoin, thus this criteria is ignored.

Active Addresses

Active addresses are the number of unique addresses that were either the recipient or originator of a ledger change and can reflect the estimated amount of activity on a blockchain. 

Newly forked tokens will pass the Coin Metrics active addresses criteria if it meets the following definition:

1. The active addresses of the forked asset must exceed 3% of the active addresses of the parent chain for 30 consecutive days. 

Bitcoin Cash passed this test while Bitcoin Gold failed.

Conclusion

Broader adoption of cryptoassets requires clarity, transparency and consistency. Nowhere is this more important than in determining the economic returns entitled to cryptoasset holders. 

While it will undoubtedly change over time, the CMBI Fork Legitimacy Policy aims to bring standardization, transparency and institutional rigor to crypto corporate actions.  Hopefully the handling of corporate actions for cryptoassets will become as routine and automatic as traditional assets. Standardization around events like forks sets the stage for a crypto Total Return index, which is an important step for the continuing maturation of the crypto industry.  

Network Data Insights

Summary Metrics

Crypto markets rallied this past week, as Bitcoin (BTC) passed $9,000. There is growing evidence that BTC is beginning to predictably react to geopolitical events, and this past week’s cryptoasset rally may have (at least partially) been a reaction to the recent drop in the Chinese stock market. We explore this more in today’s Market Data Insights section.

Adjusted transfer value increased by at least 20% for all five cryptoassets in our sample, outpacing the increases in market cap. Bitcoin Cash’s (BCH) adjusted transfer value is relatively even with Ethereum’s (ETH) — over the past week, BCH had a daily average of $217M adjusted transfer value while ETH had $234M. BTC still dwarfs them both, with a daily average of $1.9B. 

Network Highlights

Tether continues to gain market share versus all other non-Tether stablecoins. 

Coin Metrics now tracks the amount of Tether that has been issued on Tron, in addition to Ethereum and Omni. The below chart shows how the combined market cap of Tether issued on Tron, Ethereum, and Omni compares to the combined market cap for all of the other stablecoins we track (DAI, USDC, GUSD, PAX, and TUSD). Tether currently accounts for about 85% of the total stablecoin market cap. Comparatively, Tether made up about 77% of the market cap on January 1st, 2019.

Bitcoin SV (BSV)  OP_RETURN transaction count has been increasing over the past week, after falling over the past two months. OP_RETURN transactions are often used to write arbitrary data onto a blockchain and are therefore often used for on-chain data storage. 

BSV OP_RETURN transaction count passed BTC and Bitcoin Cash (BCH) OP_RETURN transaction count in mid-2019, and has been mostly trending upwards since. Check out State of the Network Issue 8 for more of our coverage on how Bitcoin SV is being used for data storage.

Market Data Insights

Recent events such as the U.S.-Iran military conflict demonstrate that under certain circumstances, BTC reacts to geopolitical events. If this cause-and-effect relationship continues to strengthen, the narrative that BTC is uncorrelated to financial assets may need to be re-examined. BTC’s reaction to China’s equity market reopening after the Lunar New Year holiday adds to the growing body of evidence that BTC reacts to global events. As China’s equity markets re-opened, most shares fell by the daily limit within minutes, and a small but significant increase in BTC price was observed. 

Volatility exhibits mean-reverting behavior partially because low levels of volatility encourage higher levels of leverage and risk taking by market participants. BTC realized volatility, measured on a three month rolling basis, is now at 52% and approaching levels that it has historically bounced off of. 

In addition to this, BitMEX’s XBTUSD perpetual swap contract’s open interest has recently exceeded $1 billion dollars, a level that also has historical significance. During the summer of last year, market sell-offs were closely associated with open interest breaching this level. 

Nearly all major assets saw strong gains this week with high dispersion in returns. While BTC outperformed most assets for the majority of 2019, a trend of certain assets significantly outperforming BTC has been recently established. Strong performers this week include Litecoin (+26%), Tezos (+24%), and Cardano (+26%). 

CM Bletchley Indexes (CMBI) Insights

All Bletchley Indexes performed strongly throughout the week, finishing the week between 9% and 19% up. Despite a strong week for the CMBI Bitcoin Index, returning 9%, it was the weakest performer of all indexes. It was the small-cap assets that had the best week, with the Bletchley 40 increasing a staggering 16% for the week, after constituents MonaCoin, ZCoin and BitShares all returned over 50% and Siacoin, Ziliqa and Nano all returned over 20%.

The weekly returns above added what was already a very positive month for cryptoassets.  Over the month it was the Bletchley 20 (mid-cap assets) that performed the best, returning 70% in just 31 days. Large-cap and small-cap assets both performed in line with each other, returning ~35% for the month. 

A trend that has been discussed a bit through the January is the performance of the even indexes. This has been no truer than for the Bletchley 10, where the performance of the even index was almost double the performance of the market cap weighted index. Index design and construction in traditional capital markets can result in very interesting return profiles during different market conditions, something that Coin Metrics hopes to emulate within the cryptoasset market.

Coin Metrics Updates

This week’s updates from the Coin Metrics team:

• Coin Metrics is hiring! We recently opened up 5 new roles, including Blockchain Data Engineer and Data Quality and Operations Lead. Please check out our Careers page to view the openings.

As always, if you have any feedback or requests, don’t hesitate to reach out at [email protected]

Subscribe and Past Issues

Coin Metrics’ State of the Network, is an unbiased, weekly view of the crypto market informed by our own network (on-chain) and market data.

If you’d like to get State of the Network in your inbox, please subscribe here. You can see previous issues of State of the Network here.

Check out the Coin Metrics Blog for more in depth research and analysis.