Top 4 ratios to make sense of cryptoassets

Discussing the use of NVT, NVD, NVU and NVN


At Protos Asset Management we are particularly excited about advancements in valuation models of cryptocurrencies and would like to summarize a few of our learnings. In this article we discuss existing and introduce new cryptoeconomic ratios which draw on utility, innovation, adoption and publicity of a given network. Visit our cryptocurrency monitor: Protos Terminal.


Context

Valuation models have been popularized by Warren Buffet and are generally implemented with ratio analysis. Ratio analysis is commonly used to study the relationship between the market price and the intrinsic (true) price of a given asset. It is used to indicate which assets are currently cheap (buy) and which ones are expensive (sell) by showing the relative value of an asset across its peers.

The price-earnings-ratio is famous for its application on equities and compares the price of an asset to the earnings of the company (Fama and French, 1992). Given the unique properties of cryptoeconomics, most of the existing ratios for traditional assets require to be adjusted or reinvented.

In this piece we discuss a 4-factor model that attempts to answer four basic questions:

1: How much turnover (utility) does the network have in relation to how it is priced?

2: How much developer activity (innovation) does the network have in relation to how it is priced?

3: How many users (adoption) does the network have in relation to how it is priced?

4: How much news (publicity) does the network receive in relation to how it is priced?

4-Factors

The underlying networks of cryptoassets have arguably different properties as common companies. Cryptoassets don’t have cash flows that we could use for a discounted cash flow analysis. Progress is driven by a developer community not by employees and customers are yet proxied with active addresses. However, even though factors have apparently different implications on asset value than it is the case for companies, it makes sense to speak analogous language for the sake of understanding.

We identify a compressed set of measurable factors that we consider to be linked to cryptoasset returns:

  • On-chain transaction volume: measuring the network’s turnover and serves as a proxy for its utility in transferring value.
  • Github commits: measuring the activity of the network’s developer community and serves as proxy for the ability to innovate over time.
  • Active addresses: measuring the adoption of the network and serves as proxy for the network’s potential customers.
  • News: measuring the publicity that a network receives and serves as proxy for the networks ability to create hype.

We acknowledge that this is a limited selection of factors and encourage fellow researchers to challenge this choice and engage with us in discussions. We consider this a useful framework, and continue our work to statistically prove any significant relationships. Other factors that we are working on are hash rates, inflation rates, volatilities as well as wealth distributions and mining distributions.

NVT — Network Value to Transaction Volume

The discussion of on-chain transaction volume and its relation to the network value has been going on for a while now. Willi Woo, Chris Burniske, Dmitry Kalichkin and the team of Coinmetrics have all made valuable contributions.

Even though it might arguably be the best current proxy for utility, there still exist severe constraints, as for instance the speculative fraction of the on-chain volume that is used to transfer value from one exchange to the other and the negligence of any utility other than transfer of value. The basic question that the ratio seeks to answer is:

Q: How much turnover (utility) does the network have in relation to how it is priced?

We calculate today’s NVT, using previous closing network value (market capitalization) divided by the 90-day moving average of on-chain transaction volume as proposed by Dmitry Kalichkin.

NVD — Network Value to Developer Output

A company’s capacity to adapt to externalities, innovate and advance its product is generally to be considered key. Traditionally analysts would investigate amongst many other factors, management, team and cash reserves to assess these abilities. With open source projects we are offered the necessary transparency to quantify development activity on a given product. As such we can draw on Github code submissions as a valuable datapoint.

While it is rather difficult to evaluate the quality of any code submitted, it can serve as a proxy for overall activity. While relative assessments on code submissions seem arbitrary, the ratio especially adds value on the extremes. As such the ratio calls out networks that have shown no activity and those that show particular high levels of activity.

When it comes to collecting the data, there are many pitfalls that need to be considered. For instance, networks have naturally many submissions at its inception, which causes a positive bias towards those networks that have been created recently. Additionally forks inherit the commits from the parent network, which falsely credits the new comer. As investors have shown to value networks based on total commits, some networks already pursue to game this, by for instance artificially boosting numbers with readme files. Valentin Mihov nicely summarizes his findings and concludes that only the number of code pushes, issue interactions, pull request interactions, github wiki edits, comments on commits and number of repos that were open sourced should be collected.

Despite all constraints and difficulties, it adds another considerable perspective on the value of an asset. The basic question that the ratio seeks to answer is:

Q: How much developer activity (innovation) does the network have in relation to how it is priced?

We calculate today’s NVD, using previous closing network value (market capitalization) divided by the trailing twelve months cumulative Github commits (at our continuous best effort cleaned of noise). To normalize the ratio, we show NVD in millions.

NVU — Network Value to Users

As measurements of the current utility of networks are difficult to pinpoint, so is the definition of customers. While you may be in possession of a token, you may never make use of the designated use case. We are using unique active addresses to proxy customers although most utility token are solely held for the purpose of speculation. The proxy uses a consistent assumption with the NVT ratio, which in abstract assumes that utility can be solely described by transfer and store of value. Accordingly, for that use case unique wallet addresses seem to be a rather accurate proxy for customers.

In addition the metric captures other interesting features. The application of Metcalfe’s Law on cryptoeconomics has been fueling discussion for a while. Proponents argue that the value of a network can be described by the square of the number of connected users in the system. Ken Alabi, Tom Lee and others have concluded that Metcalfe’s Law can describe ~90% of bitcoin prices, but only on an absolute level. The explanatory power is much lower using the appropriate measure of log returns relative to squared unique active addresses.

NVU requires more research and fine trimming and we encourage others to contribute to this discussion. We have seen several definitions of potential ratios, the most simple being the following. The basic question that the ratio seeks to answer is:

Q: How many users (adoption) does the network have in relation to how it is priced?

NVN — Network Value to News

It appears that news and hype periods have considerable impact on prices and accordingly it is important to consider whether or not the current network value is backed by unusual recent news mentions. Research on the relationship between news and stock prices produced interesting insights from which we can learn (Chan, 2001; Mullanaithan and Shleifer, 2002; Dyck and Zingales, 2003). For instance Shabani and Paper (2011) find that there is a significant immediate increase in prices unrelated to whether it is a positive or negative news event.

In particular this measure makes it very simple to spot newcomers, that were elevated by a recent hype wave. While we currently only track the absolute amount of news, we are looking to include more sophisticated sentiment analysis in the future. To normalize the ratio, we consider NVN in millions. The basic question that the ratio seeks to answer is:

Q: How much news (publicity) does the network receive in relation to how it is priced?

Case Example

The image below shows a screenshot of our terminal including the respective values. Measures have been percent ranked across the top 100 assets, such that the color-scale signals whether the cryptoasset ranks high or low in comparison. NVU is yet excluded, as we continue to collect necessary data.

Screenshot: http://protosterminal.com/; as of 11.04.2018

I) Bitcoin — Relatively to its current market value, our ratios indicate:

High transaction utility, very low activity of the developer community and moderate publicity.

II) Cardano — Relatively to its current market value, our ratios indicate:

Very high transaction utility, high activity of the developer community and low publicity.

III) Dogecoin — Relatively to its current market value, our ratios indicate:

Very high transaction utility, very low activity of the developer community and very high publicity.

Using Ratios

According to the valuation theory by Fama and French (1992), equities with the lowest price-to-earnings ratio should perform best in the future. In other words, a low price-to-earnings ratio indicates stocks that are cheap.

The 4 ratios from above have the same idea in mind and might assist in selecting cheap cryptoassets. Cheap cryptoassets should have low ratios across all metrics and perform well in the future. However, while any of these models may assist in forming educated investment decisions, it is important to note that cryptomarkets appear to be still driven by a range of inefficiencies and irrational market dynamics.

Similarly, any ratio on its own is likely to lack explanatory power on returns — instead, it’s the combination of methods that may reveal an embracing view. We use the Protos Terminal internally to track movements.


All of these models require more work to refine. Please reach out.

This piece is a product of Protos Research and endless discussions with Philipp Kallerhoff.

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