This is yet another valuation method put forward by Chris Burniske. The idea behind this valuation theory is to visualize the willingness of the market to pay for transactional value. To calculate this, simply divide the network value by the daily transactional volume.
The reason I choseto explore this theory is because it uses metrics that are inherent to cryptoassets. The theory is based on the PE ratio used to value companies. Using the same logic for cryptoassets there should be some metrics to analyse cryptoasset valuations. Furthermore, the metrics used in this theory are available on many different sites. Market capitalisation and daily transaction volume can be consulted on Coinmarketcap.com. This makes it an easy and trackable metric to follow.
The inspiration of this theory comes from the equity markets. In the equity market the company value is divided by the earnings. Cryptoassets do not have earnings so the second best option is to use the daily transaction volume.
When doing this over a long period of time there should occur some stable equilibrium points. When the network value increases are not met by increasing transaction volume it should be an indication of an overpriced cryptoasset.
Cryptoassets are still very young which means for many of them this equilibrium will not yet be visible. That being said many analysts are already using this theory. Chris Burniske has found that the sweet spot for Bitcoin is when the network value is 50 times its daily transaction volume. In figure 36 are two lines visible. The red one is the NVT ration for Bitcoin and the yellow one is the logarithmic price of Bitcoin. Remarkable in this chart is the fact that peaks in prices are often followed by peaks in NVT ratio. The NVT ratio is at its lowest when the price of Bitcoin has been low for a long time.
This ratio will be different for every cryptoasset because transaction volumes and market capitalisations may differ greatly among them. Furthermore, most cryptoassets are younger than two years old. This means that the equilibrium spot will be difficult to spot, as there are not enough data available yet. Let us analyse if there are any equilibrium spot visible on some of the most well-known cryptoassets. To produce these NVT ratio charts, I used coinmetrics.io. Coinmetrics offers the possibility to produce charts using all kinds of technical data regarding cryptoassets.
The first cryptoasset I will tackle is Litecoin. Litecoin is one of the older cryptoassets created by Charlie Lee in 2011. This is a very good cryptoasset to begin with because of the data available. Figure 37 shows the NVT ratio for Litecoin. There are sure some big fluctuations in this graph but it appears the NVT ratio is always drawn to the green rectangle. Whenever the NVT ratio goes under 10 it quickly goes back above. Above 20, the NVT ratio rises very quickly but it always comes falling back down to the rectangle. I will continue to monitor this graphic in the future.
The next cryptoasset I will tackle is Dogecoin. Dogecoin was initially meant as a joke but it succeeded in developing a loyal community around itself. Next thing you know Dogecoin has grown to a multibillion-dollar cryptoasset. Dogecoin was created in 2013 meaning that there should be just enough data available to interpret the NVT ratio chart. Hereunder in figure 38, the NVT ratio is shown for Dogecoin. During a long period, the NVT ratio stayed between 4 and 10. This is why I have taken this area as its equilibrium spot.
The next cryptoasset I will analyse is Neo. Neo is a smart contract platform just like Ethereum. Before the rebranding Neo was called Antshares. Unfortunately, there is no data available from the time Neo was still called Antshares. The rebranding occurred in June 2017. Therefore, the conclusions drawn from this chart need to be taken with caution. Figure 39 shows the NVT ratio for Neo.
An equilibrium spot appears to form between 30 and 75. The fluctuations above 75 can vary greatly but appear to come rather quickly back down to the equilibrium spot. This chart clearly needs more years of data to be able to conclude anything with certainty.
These equilibrium spots are not always so visible. Maybe there are some cryptoassets with no equilibrium. On the other hand, maybe we need to alter this metric in order to see patterns more quickly. The charts in figure 40 and 41 are to show that not every cryptoasset has these equilibriums. Ripple and Zcash are two cryptoassets that have been in the top 15 for quite some time. Nonetheless, there are no equilibrium spots to be seen in these charts. The NVT ratio for Ripple started with huge fluctuations and has then be near zero for quite some time. Zcash on the other hand seemed to had found an equilibrium but the NVT then rose steeply and stayed there. Again, no equilibrium can be drawn in this chart.
Before the real institutional money can come to crypto-land they will require more and better valuation models than the ones we currently have. In traditional markets they use all sorts of metrics and valuation methods before deciding to invest or not. The current crash in the crypto-market proves just that. During this crash our knowledge of cryptoassets will have to improve in order to attract these institutional investors.
