- No Comments
How to calculate the future cost of bitcoin and other crypto assets (part 2)
Fundamental concepts, on the basis of which formalized methods for evaluating crypto acts can be created, are just beginning to emerge. Nevertheless, experiments in this field have been conducted for more than 10 years.
In the first part of the article by Kevin Liu from Coinmetrics, six basic approaches to the study of the assessment of cryptoactives were briefly considered: the equation of metabolism, the model of discounted future utility, the Metalf law, regression models of the price, models of the cost of production and identifying bubbles in the market.
In the second part, five more approaches are considered:
- ratios used in fundamental analysis;
- “Age” analysis based on the distribution of unused transactions outputs (UTXO);
- analysis based on the realized capitalization;
- factor investment;
- Analysis based on social networks content.
Foundation -based approach is one of the most widely used in the assessment of crypto assets. Drawing inspiration in the field of shares analysis, including price / profit ratio, fundamental coefficients are often used to identify periods of reassessment and underestimation of assets.
Willy Wu was the first to apply the fundamental ratios to cryptoactives by introducing the NVT (Network Value to Transactions) indicator, calculated by the division of the market capitalization of the asset into daytime volume transactions.
The logic of this approach is that the daily volume of transactions indicates the degree of use and usefulness. The high values of the NVT indicator indicate the formation of price bubbles, and low marks can serve to identify attractive points of entering the market.
Dmitry Kalichkin from Cryptolab Algorithmic in 2018 developed this idea by proposing an improved version of the indicator – NVT Signal. In particular, the researcher managed to eliminate some disadvantages of metric that prevented its use in real time.
NVT Signal indicator and bitcoin price. Source: Coinmetrics
Co -founder CoinMetrics Jacob Frank in 2018 presented a new ratio for evaluating crypto assets arising from the Metalf law – Network Value to Metcalfe Ratio. This metric is empirically tested by Dmitry Kalichkin and dismantled in his article.
Ledger Capital co -founder Vikram Arun also investigated alternative approaches to a more accurate assessment of assets based on the daily volume of transactions. Based on the Law Metalf and NVT Ratio, Arun describes the Network Value/Transactions to Growth (NVTG) indicator. This tool more accurately evaluates the cost of transactions, since the model excludes the payments associated with the mixing of repeated transfers between exchange wallets and other “non -economic” operations.
In 2018, the Bitcoin Network Momentum instrument presented in 2018 by Philipus, the fundamental ratios are not used, but the daily volume of onchain transaction is involved in native units.
The basis of some other ratios is the income of miners, which is the volume of capital in dollar terms, which ensures the security of the network and affects the internal cost of the crypto active. Miners are also a constant and significant source of sales pressure.
Taking all this into account, the researcher The Block Matteo Leibovits proposed the Fe Ratio Multiple tool, representing the ratio of miners and transaction commissions. According to the analyst, this indicator shows how safe the network remains as the block awards decrease, and can also “implicitly” indicate the effectiveness of the asset in the quality of the means of maintaining the value.
Cryptopoiesis researcher proposed in 2019 the Bitcoin Barometer – Puell Multiple indicator. It is the ratio of the daytime volume of the issue in the dollar terms to the same indicator, but already a smoothed 365-day sliding medium.
In studies where fundamental ratios are involved, a variety of aspects are touched upon. Thanks to the “age” UTXO-analysis and the use of the realized capitalization, several additional fundamental relationships were introduced:
- Indicator “mobility”
- The ratio of market capitalization to the realized (Market Cap Versus Realized Cap, MVRV);
- Profit Profit Ratio, SOPR) profitability coefficient;
- The ratio of the implemented capitalization to the cost of transactions (Realized Capitalization to Transaction Value (RVT) and T. D.
Fundamental ratios are perhaps the most developed direction of analysis of crypto acts due to the relatively simple interpretation of indicators and their applicability for the timely adoption of market decisions.
However, some of these tools are criticized for the insufficient accuracy of forecasts. In particular, the effectiveness of NVT is reduced as the popularity of offchain transactions increases. Also, forecasts distort large movements of the funds between the wallets of exchanges and castedial services.
Age UTXO analysis
In the “age” analysis, aspects of the proposal of crypto assets are studied by studying the behavior of holders. Under the [Simple_tooltip Content = ’’ unfinished outputs ’] utxo [/simple_tooltip] implies a transaction exit controlled by the user, which is designed to become the input of the future transaction. This accounting model is used in bitcoin and some other networks. Some concepts of UTXO analysis were successfully adapted to accounts based on accounts such as Ethereum.
The earliest contribution to this area was made in 2011 by BYTECOIN, proposing the concept of Bitcoin Days Destroyed. It is an alternative way to measure the cost of transactions. The metric is calculated by multiplying the transaction amount to BTC by the number of days from the moment of the last movement of the coins participating in it. At the same time, BTC is given more weight, which lay motionless for a long time, and the smaller one – coins that moved relatively recently.
For example, the 1 BTC 1 BTC has been motionless to motionless than one bitcoin, the transaction of which occurred a day ago. The approach based on the analysis of time from the moment of the last use of coins laid the basis for subsequent studies.
The schedule below illustrates the dynamics of cost movement in BTC in accordance with Bitcoin Days Destroyed, smoothed out seven -day sliding medium.
A significant contribution to this field of research was made by JratCliff63367 in 2014. He distributed a general BTC proposal for various groups, depending on the prescription of the latest use of coins.
In subsequent years, the term “active proposal”, as well as more informal, “Hodl Waves”, was fixed in industry terminology.
A few years ago, the co -founder of Unchained Capital Dhruv Bansal raised this topic, contributing to the popularization of this concept. The researcher was the first to conclude on the basis of the dynamics of active monetary mass, proposing the application of the model in predicting market cycles and the field of trading.
Using the above terminology, independent Bitcoin developer Tamas Bloommer in 2018 proposed an indicator mobility, designed to ensure a better understanding of the behavior of long -term and short -term investors.
Researcher Ikigai Asset Management Hans Haughee in 2019 somewhat expanded the model Bitcoin Days Destroyed, Having offered categories such as Binary AdJusted BDD, “Value of Coins Destroyed) and Reserv Risk.
The essence of the “age” approach to evaluating crypto assets is that various groups of holders have their own motivation, risk tendency, information advantages and moods, depending on the preferred time to keep the coins. For example, long -term investors hold huge amounts of various crypto assets, while their behavior significantly affects prices.
This field of research is one of the most promising, promising the emergence of new fundamental concepts.
This is a relatively new, specific for cryptocurrencies, the approach to the assessment, which has made a significant contribution to the sphere of oncha-analysis. Unlike market capitalization, according to which each coin is estimated at the current price, the realized capitalization takes into account the price of a cryptoactive during the last of its onache moves. For example, if 10 BTC lasted the last time when bitcoin cost $ 1000, then these ten coins will be estimated at $ 10,000 (10 x $ 1000). If, say, 5 BTC was the last time they were in motion at a price of $ 10,000, their implemented capitalization will be $ 50,000 (5 x $ 10,000).
If UTXO-analysis is associated with the “age” of the coin from the moment of onchain-transfer, the analysis based on the realized capitalization takes into account the price of the asset at the time of its movement on the network.
The article from Coinmetrics on the application of the method based on the realized capitalization also presents the Market Capitalization to Realized Capitalization (MVRV) metric, which Murad Mahmudov and David Pewell tried in 2018 to identify the overvaluation and underestimation of assets. Cryptoanalyst AWE & Wonder has somewhat improved MVRV, adapting an indicator for more reliable trade solutions. The modified version of the tool is called MVRV Z-Score.
MVRV indicator and bitcoin price. Source: Coinmetrics
In 2019, the Checkmate-analyst in 2019 presented the metric “Realized capitalization to the volume of transactions” (Realized Capitalization to Transaction Value. This tool works according to the same principles as NVT, except that it uses the implemented capitalization instead of a market.
Nick Carter from Coinmetrics in 2018 presented a concept closely related to the capitalization implemented “Thermal capitalization”, in accordance with which each coin is taken into account at a price at the time of its extraction. Based on the assumption that in the long run the profitability of miners is slightly above the break -in, “thermal capitalization” can be interpreted as total costs to maintain network safety.
Bitcoin analyst David Pewell in 2019 presented another metric related to the capitalization-“delta-capitalization”, calculated by the formula:
Implemented capitalization – smoothed in a sliding average market capitalization
The founder of Adamant Capital Tour Demestr, researchers Tamas Bloommer and Mihil Lekrauvit developed a metric to determine unrealized profit and loss of investors by subtracting the realized capitalization from the market. Using the indicator of “mobility”, the investigation was illustrated in the behavior of long -term investors.
Renato Shirakashi researcher in 2019 presented the profitability coefficient of exit (SOPR, Spent Output Profit Ratio). It is calculated by the division of the realized value (in USD) by the cost when creating an exit and can be used to determine local minimums and maximums.
The approach based on the implemented capitalization is characteristic exclusively for the cryptocurrency market, where it is supposed to extract information directly from the blockchain.
The ability to assess the balance value of the assets of each of the investors can find serious application in the analysis of market moods, as well as in the context of the behavioral economy.
This approach involves the identification of specific characteristics of crypto assets to explain the dynamics of their profitability. This area is based on research in the field of traditional financial assets and, in particular, on the three-factor model of Fama Frendy, where the following parameters appear:
- market risk;
- Prize for size – additional profitability received for owning shares of small companies;
- additional profitability obtained as a result of owning shares of value.
Researchers significantly expanded this model by adding many factors, applying it to various classes of assets, including cryptocurrency.
The first serious approach to evaluating crypto assets using the factor investment methodology was proposed by Stefan Hubrich in 2017. It contains an innovative interpretation of [Simple_Tooltip Content = ’in the traditional market – an underestimation factor. For example, when, first of all, promotions underestimated by the P / E or EV / EBITDA ’] Value factor are selected [ / simple_tooltip] as a market capitalization relationship to transactions. The use of [Simple_tooltip Content = ’’ is also meant a high priority of assets that grow faster than the rest for a fairly long period of time ’] the momentum [/simple_tooltip] in the context of crypto actors. Such an aspect as the pace of emission of coins is also taken into account. According to the expert, the factor approach to investing in crypto assets allows you to achieve a high profit rate.
Researchers of Yale University Yukun Liu and Alekh Tsivinski in 2018 made a significant contribution to this field of research. They tested a wide range of traditional, macroeconomic and specific factors for crypto actors. Scientists came to the conclusion that the momentum and the “investor’s attention effect” largely explain the dynamics of the profitability of assets. However, many other factors are not so effective in forecasting.
CEO Quantigic Solutions and professor of Free University Tbilisi Zura Kakushadze in 2018 confirmed the significance of the moment-effect, however, identifying the relative inefficiency of the liquidity factor. Liu, Tsivinski and SI Wu from the School of Business Leonard n. Stern in 2019 tried to apply previously identified factors in the field of portfolio management.
The further development of this area of research depends on the evolution of the cryptocurrency market. Factor investment involves an assessment of a large number of assets, identifying their various characteristics, as well as building portfolios based on various parameters. Also, the prerequisite for further research is the possibility of researchers and data suppliers to identify conceptually consistent information on various assets.
The relationship between the prices of crypto assets and data from social networks has been studied for a long time. The methodology for assessing the cost of crypto acts is still in the early stages of development, however, based on the existing historical data, the conclusion already follows that prices can significantly deviate from fundamentally reasonable values for a long time. Thus, the quantitative assessment of investors’ attention is also an active field of research.
The Czech researcher Ladislav Cristocoufect in 2013 was the first to use the volume of search queries in Google and Wikipedia as an indicator of investor interest, and also studied the correlation of this indicator with the price of BTC.
David Garcia, Claudio Tessone, Pavlin Mavrodiev and Nicholas Peroni in 2014 involved a wider set of data for this purpose, including information from the social networks Twitter and Facebook. Researchers emphasized the significance of the influence of “word of mouth” and new BTC users on market dynamics.
Using a very similar methodology supplemented by several onchain metrics, researchers at the University of Economics and Business in 2015 came to the conclusion that in the short term, the mood of users on Twitter affect the BTC price.
Scientists from the University of Nikolai Copernicus found that mention in newspapers and magazines may also affect the price.
Conclusions about the significant influence of social media on prices are also contained in later studies.
It is common that crypto acts are difficult to evaluate due to an insufficiently developed methodology. However, a more detailed study of the current state of this area of research shows that this statement may not be entirely true.
Over the past 10 years, the concepts of classical economics, monetarism, elements of analysis of discounted cash flows and other areas of financial analysis have been successfully applied in the field of assessment of crypto assets.
Some researchers have reached noticeable progress in onchain analysis, which is specific for cryptocurrencies. Data from open blockchains containing the history of all transactions allow you to study investors’ behavior with unprecedented transparency, inaccessible to the world of traditional finance.
Subscribe to FORKLOG https://gagarin.news/news/blockchain-hard-fork-why-split-the-network-into-two-chains/ news in Telegram: Forklog Live – the whole news feed, FORKLOG – the most important news and polls.