Cryptocurrency is enjoying the attention of both the media and the general public. As demand creates supply, a lot of companies offer a vast array of materials to cover everything about crypto. Price predictions, guides, podcasts, interviews, etc. If you look hard enough, you can find anything you want. However, while there is a lot of coverage behind the details of the cryptocurrency market, especially cryptocurrency prices, not that many works are looking at the methodology of such publications.
We’ve decided to publish a comprehensive article about how such predictions are made, what data market researchers use and what methods are used. Now, let’s take a look behind the curtains of economic advice and forecasts.
Crypto Valuation Research
While cryptocurrencies research is a young discipline, given that crypto itself is a relatively new economic trend, it has a great advantage of having all the previously accumulated economic research available. This makes it easier to both adapt the already existing methods and to develop new ones to the crypto market’s reality. There you can see the most commonly used methods to make a cryptocurrency price forecast.
Equation of Exchange
Fisher’s equation of exchange is one of the main economic laws used to explore the relationship between the price level and monetary supply. With its popularity, it is no surprise that this law was adopted to the cryptocurrency framework, to better understand the value of crypto assets, both tokens, and coins.
According to the method, the cryptocurrency price depends on its velocity, the rate at which the currency is used to buy/sell something. The higher is velocity, the lower is the price. With Bitcoin, it also compounds with the belief that BTC will be saved, which positively affects its price. This theory is supported by data showing that recently BTC velocity has decreased while the price has increased, which was further enforced by empirical tests by Ciaian, Rajcaniova, and Kancs in 2015.
The equation of exchange also attracted its share of criticism, with Evans and Locklin publishing their articles in 2018 and 2019, respectively. Evans criticizes the fact that the velocity is treated as the factor outside of the model, proposing instead to treat it as one inside one. Locklin goes further, stating in his criticism that the entire equation of the exchange price evaluation model is false.
The equation of exchange is still one of the most popular theoretical frameworks used for token and cryptocurrency price evaluation. As economists continue to amass more data to get better results, the validity of this method will also grow, making forecasts more precise.
Metcalfe’s law regulates the value of communication networks, which usually wouldn’t apply to financial entities like currencies. However, with the unique position of cryptocurrencies, their price could be evaluated using this method. According to Metcalfe’s law, the value of the network is proportional to the number of users squared, with more users providing higher value.
Applied to cryptocurrencies, this law works pretty similarly by evaluating crypto price by the number of zero balance addresses squared. Using this method, the price of cryptos can be predicted, as tests are done by Alabi in 2017 on Bitcoin, Ethereum and Dash have shown.
This application of Metcalfe’s law, as well as subsequent tests, are parts of the emerging trend in the cryptocurrency price evaluation: active usage of the user numbers. The user number can help to ascertain other aspects of the crypto assets, like identifying asset bubbles and market manipulations.
All in all, Metcalfe’s law allows us to get a wide range of useful information about the crypto market, its status, and its perspectives. Right now, its main problem is the same as one that plagues every method and theory about cryptocurrency: the lack of data that would allow for long-term forecasting. However, this is the one problem that will solve itself in time.
Price Regression Models
Price regression models is a way to ascertain the value of a crypto asset by regressing it on another variable. Usually, time is used for that. The main advantage of that method is that by using it, predictions can be cast farther into the future than those done by previous methods. It is also a simple method to use, as it doesn’t require complex calculations or models to be used.
However, this simplicity is why many researchers tend to ignore this method.
Its useful approach doesn’t always allow for the analysis of the more complex situations. It is a working model that has a lot to offer, so it is always useful to keep it in mind should the need arise. The predictions and adoptions obtained through this method are remarkably successful.
Cost of Production Models
The cost of production models is a part of economic research that aims to evaluate the costs of mining to quantify the price of crypto assets. It is a rather straightforward approach based on Adam Smith’s natural price theory. It states that there are two prices: natural price, which is equal to the cost of the production, and the market price, which is the price that the asset is sold for. Satoshi Nakamoto further expanded on this, with BTC’s exchange rate using the model of how much electricity would cost to mine.
Since then, this model was further refined to reflect the existing status of BTC mining. The Cambridge Bitcoin Electricity Consumption Index, published in 2017, allows users to see the upper, lower, and best guess estimates of the cryptocurrency BTC price based on the amount of electricity mining consumes.
In 2019, Edwards proposed an even more accurate model that you can use to predict the price of the cryptocurrency. Using those models, we can now understand the economics of mining and through them, detect crypto bubbles as well.
The creation of financial bubbles is deeply rooted in speculators’ psychology. With cryptocurrencies, it becomes even easier to create new bubbles, as they are not anchored to any traditional method of valuation. That’s why several cycles of crypto bubble creation and crashing have already happened to BTC, and that’s taking into account that Bitcoin’s history is relatively short.
As such, the identification of such bubbles becomes one of the main priorities of crypto price predictions and analysis. That’s why methods were created that detect, track and analyze the creation and progression of crypto bubbles.
Most of those models work on the generalization of Metcalfe’s law, using deviations from it to detect the existence of crypto bubbles, and so far four of them were found and tested, proving the efficiency of this method. As other methods are being developed and refined, and more data being collected, the efficiency will also rise, allowing to detect and evade bubbles early on.
Сrypto-economic research is in a strange position: while it has a lot of data available from the earlier economic models, it also brings new challenges and opportunities that never were encountered before. Along with the digital nature of a crypto market, it requires new methods to be developed.
Thankfully, economists and crypto enthusiasts both rose to the challenge, creating a new economic system covering a before unseen phenomenon that is a crypto market. Describing its rules, crypto price formation and change, along with predicting how that price is going to change in the future, is a hard work that continues even now.
What you’ve read in the article is not the definitive list of such methods, as crypto analytics keep refining them and developing new ones continuously. Even some fundamental principles of the crypto ecosystem are likely still undiscovered since it is such a new discipline. So, we will keep an eye on any new developments to help you with your cryptocurrency strategies.
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