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June 26, 2024
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Why on-chain data analysis has become a “must-have” for crypto funds

Why on-chain data analysis has become a “must-have” for crypto funds
Summary

Until recently, crypto trading was subject to a paradox. While the investment thesis of many crypto funds rested on the benefits of decentralization, fund analysts relied heavily on market data to analyze the market. Now, the picture is radically changing, as a new wave of specialists develop on-chain data analysis models that provide novel ways to uncover market opportunities. This article explains how on-chain data analysis differs from conventional methods and explores its growing importance for investment professionals in the cryptocurrency industry.

Background — toward a fundamental analysis of crypto assets

To engage in an asset class, you need to truly understand it. This has been one of the barriers holding back many investment managers and institutions from entering the cryptocurrency sector. For investors and analysts with a background in traditional equities, the mantra that past performance is not indicative of future returns has been firmly ingrained, and they look to a stock’s fundamentals to provide more context. These key metrics, like cashflow, return on assets and profit retention among many others, are part of the basic due diligence involved in analyzing a stock.

Until recently, however, the conventional wisdom was that a fundamental analysis of cryptocurrency assets was not possible. In a market characterized by highly fragmented liquidity, analysts relied on the market data provided by actors like exchanges, brokers and OTC desks to assess market movements. For experienced crypto market participants with deep knowledge of the sector, this patchwork of indicators was enough to provide the basis for some educated guesses, but it was far from ideal.

However, this now looks set to change with the advent of new on-chain data analysis & metrics that provide insight into the basic health and security of an asset, buyer and seller behavior, and potential price inefficiencies. Portfolio managers, professional investors and analysts alike can now combine this on-chain data with market data to gain a deeper, more comprehensive and holistic understanding of cryptocurrencies.

Why on-chain analysis has become indispensable for crypto analysts

The cryptocurrency market exhibits some fundamental structural differences to that of traditional equities. For instance, there is no principal exchange in each jurisdiction for crypto assets and as a result, trading takes place over many different centralized exchanges, brokers, and OTC desks, in addition to trading now being conducted across decentralized exchanges and DeFi platforms. In light of these factors, there is no way to get a global overview of a particular asset without consulting both market and on-chain data in tandem. Coupled with the fact that on-chain data also provides insights into the fundamental health and security of an asset, network or protocol, it is easy to see why crypto funds are increasingly incorporating on-chain data into their research, selection, portfolio management and decision support processes.

Nuant has been designed to provide investment professionals, research and data analysts with a single dashboard that provides industry-wide coverage of both on-chain and market data that is driving the performance of the fund or strategy. In addition to a wide range of customizable default metrics, analytics and visualizations, it empowers analysts and data scientists to curate their own bespoke queries and analytics using a new, purpose-built scripting language called NQL (Nuant Query Language). This radically simplifies the process of creating custom queries and involves far less code than other major scripting languages like SQL or Python.

Nuant Query Language: custom query
NQL - the Nuant Query Language: this query depicts the rate of change of the price of Ethereum when the rate of change of the number of transactions is greater than a specific value and when the rate of change of the number of active addresses of Ethereum is greater than a specific value.

The on-chain metrics gaining prominence for crypto data analysts

On-chain data insights and  metrics can be very broadly classified into three groups. Firstly, on-chain data can yield information about the fundamental network health of a crypto asset. These metrics provide insight into the user adoption, viability, and security of the underlying decentralized network that underpins a particular asset. Secondly, a wide range of trends relating to buyer and seller behavior can be gleaned by examining on-chain data. Finally, on-chain data provides the basis for calculating a number of relative valuation metrics that can be used to try to identify price inefficiencies in the market.

In the sections which follow, we will examine some of the key metrics in each of these categories. For the sake of consistency and clarity, the focus will be on Bitcoin, but in many cases, identical metrics can be used for other proof-of-work networks and assets. Proof-of-stake assets such as Ethereum may use different variations, albeit with the same goals.

Network health and security metrics

One of the appeals of Bitcoin compared to conventional fiat currencies is the predictability of monetary policy, which is hard-coded into the network protocol through mechanisms such as the Bitcoin halving cycle. Bitcoin supply is easy to accurately verify by examining metrics such as total circulating supply or daily issuance of new coins.

The total daily transaction volume allows analysts to gauge how much activity there is on the network. Another useful statistic for assessing network usage is the total number of active addresses, which captures the number of addresses used by exchanges, miners and individuals on a particular day.

In terms of security, the Bitcoin protocol provides incentives for miners to deploy their hardware to secure the network in the form of the block reward and transaction fees. The miner revenue metric captures the total amount of newly minted coins and transaction fees earned by miners each day. When analyzing this metric, it is important to keep the Bitcoin halving cycle in mind, whereby the reward granted to miners for successfully mining a new block is reduced by half after every 210,000 blocks, which equates to roughly every 4 years.

A good indication of the aggregate security of the Bitcoin network is provided by the hash rate metric, which measures the total combined computational power being used to mine and process transactions. Despite the downturn in the cryptocurrency market, Bitcoin’s hash rate has proven highly resilient, showing approximately 86% year-on-year growth between August 2021 and August 2022, rising from 112 to 208 exahash per second.

Taken in sum, network and security metrics provide insight into the underlying factors that may influence an asset’s value over the long term.

Buyer and seller behavior metrics

One of the advantages of a publicly accessible blockchain ledger like Bitcoin is that it allows us to analyze the positions and behavior of holders globally. This can provide information about the balance of wealth on the network and what type of buyers are entering or leaving the market.

The coin days destroyed (CDD) metric allows crypto analysts to gain a sense for long-term holder behavior. Every time 1 BTC is held in a wallet for a day, this counts as a coin day. For example, if an investor held 0.5 BTC for 30 days, this would equate to 15 coin days. CDD is calculated by taking the number of coins transacted over a particular timeframe and multiplying it by the time they had been held prior to the transaction. A variant of CDD is binary CDD, which can be either one, if CDD is higher than average, or 0 if CDD is less than average. When CDD is above average or rising, this generally indicates that longer term holders are taking coins out of storage and liquidating them, possibly to take profits.

Another metric that analyzes buyer and seller behavior according to the length of their positions is HODL waves. The HODL wave chart categorizes all Bitcoin in circulation according to how long they have been held by their current owner, whether that be one day, one month, one year etc. The length of time since a coin last moved is also commonly referred to as the “lifespan” of that coin. Analysts have observed that an influx of young coins with shorter lifespans tends to coincide with bull runs, as newer investors buy in and longer-term holders take profits. Conversely, older coins tend to be more prevalent in bear markets.

Relative valuation metrics

While network health metrics can play an important role in developing an investment thesis and buyer behavior, relative valuation metrics provide important context for market movements. These are among the most relevant to active portfolio managers on a day-to-day basis. In particular, these indicators can help crypto funds to identify short- to medium-term price inefficiencies that might be exploited to generate alpha.

For instance, the market-value-to-realized-value ratio (MVRV) is calculated by taking the total market cap of Bitcoin and dividing it by its realized capitalization. Realized capitalization is a variation of market capitalization that values each unspent transaction output based on the price when it was last transacted rather than its current market value. When MVRV is above 1, the market is selling at a profit. Conversely, when it is below 1, sellers are suffering losses. In the past, Bitcoin price tended to peak at an MVRV value of around 10, but more recently, it achieved highs at values of between 7 and 9.

The market-value-to-thermo-value (MVTV) ratio examines Bitcoin’s current price relative to the implied value received by Bitcoin miners from the network. The ratio is considered by some prominent crypto analysts such as Geert Jancap to be analogous to the EV-to-EBITDA (enterprise value to earnings before interest, taxes, depreciation, and amortization) metric used in traditional equity valuations.

Conclusion and wrap up

When professional crypto funds initially entered the crypto sector, off-chain market data was easier to source and could be more simply applied to some conventional data analysis techniques. While on-chain data clearly held potential, it was not at the point of maturity to clearly ascertain how it could be harnessed to drive better and more timely investment decisions. As a result, many crypto portfolio managers, traders and analysts made a pragmatic decision to exclusively rely on market data in the short term.

This market is now beginning to mature and crypto portfolio managers and their analysts are expanding their horizons. New on-chain data analysis techniques make it possible to unlock the full potential of on-chain data, providing valuable insights into the general health, buyer and seller behavior, and relative valuation of a particular asset. By combining it with off-chain data, managers and analysts alike can now gain a clearer understanding of the factors driving and affecting the market and consequently their fund and strategy performance.

In a highly complex and fragmented market, data analysts, portfolio managers and professional traders alike need a comprehensive understanding of the market - which can only be achieved by combining on-chain and market data. Nuant provides that capability with an integrated and highly customizable portfolio monitoring, analytics and decision support  platform, specifically designed from the ground-up for the crypto markets, providing all relevant on-chain and market data at your fingertips and enabling you to make timely and informed decisions from them.

References and Further Reading

Cointree (2021) How Are Crypto Investors and Traders Using On-Chain Analysis? Available at: https://www.cointree.com/learn/on-chain-analysis/.

Dodd, L. (2021) On-Chain Data: A New Framework to Evaluate Bitcoin, ARK Invest. Available at: https://ark-invest.com/articles/analyst-research/on-chain-data-bitcoin/.

Foy, P. (2021) What Is On-Chain Analysis? Available at: https://itrustcapital.com/cms/what-is-on-chain-analysis.

GeertjanCap [@Geertjancap] (2020) ‘market-value-to-thermo-value (MVTV) ratio’, Twitter. Available at: https://twitter.com/Geertjancap/status/1309223044267769856.

Sandner, P. (2022) ‘Using On-Chain and Market Metrics to Analyze the Value of Crypto Assets’, Medium, 29 May. Available at: https://philippsandner.medium.com/using-on-chain-and-market-metrics-to-analyze-the-value-of-crypto-assets-80141ad714d6.

What is Crypto On-Chain Analysis? (2021) MLQ.ai. Available at: https://www.mlq.ai/what-is-crypto-on-chain-analysis/.

Author
Nuant
Updated on
June 26, 2024