The logic bomb
If you could only choose between gold or bitcoin, what would it be?
The vast majority of us are hardwired to choose gold. We lean into what we know, the old-but-good ‘comfort blanket’ option always seems favourable when it is sitting next to a fluid yet, unquantifiable digital spectre. The majority of real-world investment managers are not “long” on crypto and with cryptos’ current mainstream public record – it is no surprise. Why choose complex algorithms when you can choose solid gold?
When determining whether to invest in traditional financial (TradFi) assets, retail and institutional investors typically seek to understand the associated risk; and the possibility of losing capital on an investment or a business venture. Risk and returns are balanced – unless there is a short to medium-term arbitrage.
This applies to cryptocurrencies too, and despite the preference for gold, both retail and institutional investors are increasingly exploring cryptocurrency to diversify their portfolios. The crypto market is indeed growing: the global market cap stands around $2.45 Trillion with JP Morgan reporting $16 billion in capital inflows into spot Bitcoin ETFs alone so far in 2024. There is a large amount of capital at stake, highlighting the urgent need for risk valuations.
Yet, financial experts collectively agree that the cryptocurrency landscape is fundamentally different to the TradFi landscape, so why apply old metrics to new frontiers? If the average investor understands crypto to be different, surely it needs a newer, more relevant model to understand. Applying TradFi risk assessment methods to cryptocurrencies would be like using a hammer as a screwdriver - the wrong tool for the job. We need a new tool - a new way of approaching risk, which is native to cryptocurrency architecture.
The existing risk models and methods have been developed to assess risk in TradFi. This includes commonly implemented methods like Price over Earnings (P/E), Value at Risk (VaR), manual assessments and background checks. A quick analysis shows that they are tailored to the specific qualities of TradFi.
Price over Earnings
P/E ratio is used to assess risk associated with stocks. The ratio measures a company’s share price relative to its earnings per share. This makes it easy to compare two stocks as it shows you which stock you pay relative more or less to gain the right for earnings. Importantly, this measurement depends on the fact that stocks generate yield and earnings are easily quantifiable. Traditional companies and shares are geared to maximise profits: they are centralised entities where, ultimately, the goal is to increase shareholder value, the central governing body of the asset.
With some exceptions, the majority of cryptocurrencies are not designed to optimise earnings. They operate in a decentralised way meaning that capital is not designed to be channelled to a central body. Where it is easy to measure the profits a CEO and the board of a company achieve for shareholders (via stock prices and so on), there is no such central body working for the same goal in cryptocurrencies. Instead, value is distributed, sporadically amongst the ecosystems.
Value at Risk
Value at Risk is a statistical measure used to assess the probability of financial loss on a specific portfolio of assets. It estimates the maximum potential loss that a portfolio could incur over a specified time period, within a stated confidence level. It essentially specifies the maximum monetary value at risk through looking at either historical or statistical data.
There are significant limitations in applying VaR calculations to the cryptocurrency market. The cryptocurrency market is relatively immature, with thousands of currencies being created daily. This means there is a short trading history and as such there is simply no historical data. VaR relies on normal distribution, yet Cryptocurrencies are also characterised by high volatility, rapid price changes and non-normal return distribution, meaning significant and sudden losses are quite common. This is amplified by the relative immaturity of the crypto market which means that it is vulnerable to events like market manipulation fraud, and new regulations which further impact trading leading to fat-tailed or skewed distributions. Since VaR methods rely on either historical data and/or conventional cash flows and normally distributed returns they are not applicable to cryptocurrencies.
Manual Assessments and Background Checks
The vast number of cryptocurrencies created every day make manual assessments of assets, whilst theoretically possible, incredibly inefficient in practice. The anonymity of cryptocurrencies makes background checks impossible. For example, the creator/s of Bitcoin, today’s most high-profile cryptocurrency, has never been publicly identified.
We can see that TradFi metrics are incompatible with cryptocurrency. The key qualities of TradFi which are used to measure risk do not apply to cryptocurrencies, namely, stable distributions, quantifiable profits, and transparent and centralised earnings. Meaning the TradFi risk assessment methods are either inefficient or entirely impossible to apply to cryptocurrencies. Cryptocurrencies need to be understood as separate asset classes, with a tailored method of calculating risk. Among the complexity and fast-evolving landscape, there exist some key features that can be drawn upon to determine risk, we need a model, or tool, that can process these features into quantifiable data points.
The answer is right in front of us
The blockchain technology on which cryptocurrencies operate creates two key interlinked features, transparent data and decentralisation. Blockchains act as ledgers, publicly storing every single transaction relating to a token, in real time. Unlike TradFi counterparts, which often operate in line with quarterly reporting cycles, this presents unprecedented data transparency. Yet the vast complex on-chain data can be difficult for retail investors and institutions to process effectively and ultimately assess risk.
If analysed in the right way, this data can accurately inform investors of the risks associated with digital assets. This requires presenting the data in a user-friendly format, but, more significantly, understanding which factors should be taken into consideration when determining the risk.
The Network
Cryptocurrencies are decentralised ecosystems meaning they need to be valued as a network, not as a business or based on profit. The vast majority of cryptocurrencies are culture coins: assets whose value is entirely rooted in the psychology of its holders. Culture coins include Bitcoin, Dogecoin and Rai. This is mirrored by the fact that cryptocurrencies are incredibly volatile, where social events, impact the value and pricing of the coin. Open-source projects are successful as long as they build and maintain healthy decentralised networks.
As such, when mapping the transactions and assessing risk, we need to look at factors that indicate the strength of the network. The key factors include decentralisation, distribution, and diversity. In a decentralised network, it is important that tokens and transactions are not concentrated in the hands of a few token holders, but that there is a balanced and equitable network, where tokens are spread more evenly amongst participants. This helps demonstrate greater participation and greater distribution. Since a culture is only as strong as the social network underlying it, it is desirable to have the token distributed as widely as possible. To sustain this kind of decentralised network, it is important that the actors within the network are diverse: this refers to the diversity of wallet sizes and wallet behaviour. A token owned by many different classes of owners, who deploy different strategies and have different goals and motivations, will react differently to the same event, which reduces price reactivity. These factors ultimately develop a detailed picture of the activity and strength of the network.
Given the distinct differences, the TradFi risk models are doomed to fail in cryptocurrencies. We need a native model for understanding risk in cryptocurrencies that takes into account blockchain-specific architecture, and the key data points: the network. We need a tool which uses the vast amounts of available data to assess the strength of the network.
