Modeling bitcoin’s fair value with on-chain data

When I came across bitcoin in late-2014 I viewed it as a speculative asset whose price was driven by fear and greed. During the previous year it had gone from $12 to $1,200, an increase of 9,900%, before subsequently losing 85% the following year. The concept of a decentralized internet money was fascinating, but bitcoin appeared little more than an experiment.

Fast forward to 2017 and bitcoin had made a resounding comeback. Payment processors that facilitate bitcoin as a means of exchange were on the rise. Bitpesa, an FCA regulated company that enables cross border payments over the Bitcoin Network were on track for over $200MM of volume in 2017. In the US, Bitpay was closing in on $1Bn worth of bitcoin transactions for its second consecutive year. Bitcoin was no longer an experiment; it was the world’s first digitally native currency.

Armed with a curious but critical approach, I began to search for data in order to quantify a valuation for the Bitcoin Network. I was not disappointed.

Blockchains, unlike traditional payment networks like Visa and Mastercard, provide a vast array of relevant data about network usage to anyone who knows where to look.

This data includes the value and velocity of transfers, the number of daily active users, the estimated profitability of the network guardians (or miners), the Profit/Loss position of individual bitcoin holders, total network demand, miners’ net inventory positions and much more. All of which can be downloaded directly from the blockchain.

Once the data is downloaded, cleaned and processed it is possible to compute a range of metrics to extrapolate a value for bitcoin (and other similar crypto-asset networks). There are a number of different approaches to determine bitcoin’s market value, including both price and non-price indicators.

One of the most interesting non-price models is the network effect. The model describes the positive relationship between the growth in active users and the network’s value. The approach was first used by Robert Metcalfe to model the growth of social media platforms such as Facebook and Tencent. Similar to tech stocks, the Bitcoin Network facilitates interaction between users over a digital medium, gaining value as more people use it. Modelling the network effect over the past two years we can identify three points where the market price of bitcoin was within 5% of that derived through modelling it with this approach.

For those looking for a shorter-term signal, the Network Value to Transaction Ratio (NVT) is the kingpin for establishing bitcoin’s fair market value.

The NVT measures the value of bitcoin based on its utility as a payment network. Put another way, the NVT measures the market capitalisation of bitcoin relative to the total value transferred over the network in a given period. The NVT is comparable to a price to sales ratio and has proved highly effective at signalling when bitcoin is trading at a premium. Moving into a cash position when bitcoin reaches a premium to this fair value between 2018 to the present day would have seen an outperformance of +40% with significantly lower volatility then a buy and hold strategy.

Bitcoin’s meteoric rise and subsequent fall in 2018 left many crypto-asset investors reeling. The problem wasn’t that the technology was broken. It was that investors failed to quantify a reasonable valuation. As the technology has matured, so too have the analytics. Data companies such as ByteTree now provide a live window into the blockchain to provide network statistics and financial metrics in real-time. For the first time in the crypto-asset market investors are able to make informed, data-driven decisions.

Find out more about modelling bitcoin’s value using on-chain data at

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