Ethereum Chronicles Part V: The Last Battle – An evaluation model for Ether



The piece can be downloaded as a PDF or read in its entirety below. The rest of the series can be found here.

  • Ethereum can be seen as a business that generates revenue through transaction fees.
  • From this perspective, we estimate the price of an Ether at $ 2,839, with a possible range between $ 273 and $ 3,943.
  • The annualized 90-day price volatility of ETH is ~ 500% higher than that of the S & P 500.
  • The new valuation methodologies and assumptions will continue to emerge as the blockchain technology matures; our model influences the uncertainty that Ethereum can overcome various challenges related to safety, volatility and scalability.
  • Our top-down quantitative modeling approach scales the total addressable market available for all cryptographic platforms before analyzing how Ethereum can capture; the model can be modified to adapt to different cryptocurrencies that compete with Ethereum.
  • The model can be downloaded here .

The uncertainty of the future of Ethereum is reflected in the price fluctuations of its native currency. The annual volatility of 90-day Ether prices exceeded 98% at the end of May 2018, around six times the volatility of the S & P 500 over the same period. Questions about whether Ethereum can scale, if regulators will allow Ethereum to thrive and if security vulnerabilities can be corrected satisfactorily, along with doubt about when the blockchain user experience will improve significantly and skepticism around the truth Incremental value of distributed ledger technology has created a wide range of views on potential applications of the Ethereum platform.

In addition, the development of open source is difficult to manage. For Ethereum, this has manifested itself in technical challenges that have become exponentially more difficult to solve, mainly because Ethereum's industry-leading adoption levels require new changes to be compatible with more users and more previously developed solutions than the competition. While Ethereum developers continue to reprocess elementary portions of the platform such as the consensus mechanism, interoperability and governance, other blockchains have iterated on the Ethereum smart contract base model in an attempt to establish a second-move advantage. Cardano, BigChainDB, EOS, Ethereum Classic, NEO, QTUM and Stellar have all designed their infrastructure to strategically eliminate the current market dominance of Ethereum and, for the time being, it seems to work.

In our view, the challenges mentioned above must be taken into consideration in any assessment that claims to be a credible cryptocurrency pricing model. In particular, we believe that where many cryptographic models fail to assume that the Total Addressable Market for blockchain solutions is unlimited and that all global trade can one day move onto a platform like Ethereum.

The reality is that blockchain solutions solve a particular type of problem related to data reliability. For companies that do not have significant data quality inefficiencies as the root cause blockchain solutions represent a cumbersome and immature alternative to centralized database solutions and therefore will not have an impact on these markets.

With this in mind, we offer a quantitative model that can be used to design a long-term price range for ether and to share our assessment of the long-term viability of Ethereum as a cryptocurrency.

Overview of the quantitative model

a high-level overview of our quantitative model for determining the price of Ether (ETH):

Step 1 – Resize the total addressable market: [19659018] The promise of Ethereum technology is focused on creating a platform that facilitates more secure, independent and censorship-resistant trade . To accurately estimate the price range of Ether, we must first identify the areas of commerce that could benefit significantly from the improvements mentioned above. Once identified, we adopt a top-down approach to size these areas in order to quantify the universe of commerce that Ethereum could theoretically interrupt, in total (known as Total Addressable Market, or TAM) . [19659010] Step 2: Sizing the available market available: Then we estimate the portion of TAM that could migrate to blockchain-enabled smart contract systems, such as Ethereum and other cryptographic platforms (known as the available Market, or SAM) . We use the historical adoption rates of equally disruptive technologies as a proxy for how the crypt can impact the trade areas that will be applicable from Phase 1 over the next 10 years, based on the impact and speed with which we believe that the crypt will change the sector. [19659010] Step 3 – Estimate the target market (ie, the portion of the available stock market that Ethereum will acquire): So, let's consider the competition: Ethereum is competing with other blockchain-enabled smart contract platforms that will also penetrate in some parts of the market. We estimate the long-term market share of Ethereum on what will become an oligopolistic cryptocurrency landscape.

Step 4 – Estimate of the percentage transaction fees: We therefore adapt to the fact that the intended use of Ether is as a means of paying transaction fees. More specifically, the purpose of Ether is to compensate the independent parties (miners) for the validation of the trade performed on the Ethereum virtual machine. This means that only a percentage of the trade performed on the Ethereum platform will be realized as a cash flow for miners who manage the Ethereum protocol.

Step 5 – Calculate the discounted gas tariffs: Miners' cash flows, in the form of transaction fees (eg gas tariffs), may be discounted over the projection period of our model to arrive at an estimate of the current value of the Ethereum network, denominated in US dollars.

Step 6 – Use binomial prices to calculate the value from "State Up" and "State Down": So we consider that Ethereum is still a nascent technology that must overcome many challenges to achieve and maintain any long-term value. We adjust the estimate of the Ethereum network value in Step 5 by factoring in a potential scenario & # 39; Down-state & # 39; which makes Ether worthless. The evaluation of a binomial tree of a period is based on an estimate of the probability of the project to achieve the "state towards the top" compared to the "low-state" to generate an expected value of the Ethereum network today.

Step 7 – Calculate by Ether value: We then divide the correct Ethereum network value for the estimated long-term supply of Ether to arrive at a price for Ether.

Step 8 – Sensitivity analysis for Ether: Given the uncertainty associated with model assumptions, we create sensitivity analyzes to estimate a range of potential long-term values ​​for Ether.

The following descriptions elaborate our reasoning concerning the quantitative model.

Phase 1 – Dimensioning the total addressable market (TAM)

To identify the markets that could be influenced by the adoption of Ethereum, we have thought about the industries that are well adapted to benefit from the unalterable, unacceptable and of the censorship salient features of the blockchain. Therefore, we have focused on sectors that have clear problems with data quality, reliability between counterparts and dispersed and / or silent value chains. Furthermore, we have considered whether industries have an existing presence in online commerce, or will eventually evolve into an industry with a significant online presence. [1]

For each selected sector, we conducted research for [1] identify the size of the market in 2017 and [2] predict future growth during our model. Using data from Statista.com and our beliefs for each sector, [2] we estimate growth as follows:

Internet of Things (IoT) The IoT market is still relatively new. Hardware and software technologies continue to be financed and built in large numbers, particularly in the automotive, home appliance and manufacturing sectors. Therefore, we expect annual growth of 17% up to 2028, after which the long-term growth rate of 4% will be achieved.
Energy Given that the global energy business is made up of traditional oil / gas and renewable energy sources, there are many changes in the sector. That said, we believe that much of the new technology in this area will change where humans generate their energy rather than the amount of energy they consume. Basically, the population, economic growth and improvements in energy efficiency will be key drivers of energy demand. Therefore, we anticipate that the industry should approach the long-term growth rate of 4% in 2018 and beyond.
Investment / Financial Services With the advent of FinTech, the business models of financial services that are over 30 years old are at risk of serious inconvenience. We project that the FinTech sub-sectors, including alternative financing, the alternative loans and personal financing will reach a growth of 18% until 2028. This huge change reflects the potential for technological change in the sector. Inevitably, the growth rate of 18% + will decrease, as the impact of technology is permeated by the market in the coming decade. After 2028, we expect the industry to achieve 4% long-term annual growth.
Digital advertising Digital advertising is a consumer downstream of large amounts of data / data intermediation (see below) and is an equally mature Internet industry. In line with the recent history of the statesman, we expect annual growth to be lower than in the large data / data brokerage sector. We estimate an annual growth rate approaching 10% up to 2028, subsequently flattening the 4% long-term rate.
Digital Media The digital media revolution began when YouTube, Netflix and other entertainment services began moving entertainment consumption to the Internet about a decade or more before 2018. Thus, the digital media industry It is a relatively mature Internet sector. The advent of live streaming and other forms of P2P entertainment leaves room for higher-than-long-term growth over the next decade. Therefore, we maintain annual growth rates of 6% up to 2028 and 4% beyond that date.
Gaming Digital Collectibles The gaming industry itself is relatively mature compared to other Internet-based businesses. However, new revenue streams related to digital collectibles and online artwork should increase annual growth rates from the relatively stagnant 5% observed in recent history. We project the adoption of digital collectibles (for example, CryptoKitties) to generate healthier annual growth rates approaching 8% up to 2028 until the long-term growth rate of 4% is achieved .
Remittance /

P2P Payments

The peer-to-peer payment industry (P2P) is one that could be radically changed by the advent and the adoption of cryptocurrencies, since people begin to use and understand the advantage of sending instant cross-border payments for de minimis commissions (a service that banks are not currently able to match). We project an annual growth rate of 20% up to 2028, after which the long-term growth rate of 4% is achieved.
Big Data / Data Brokerage The big data / mediation data as a sector has been around for over a decade, and is closer to the maturity of many other Internet industries. Particularly in the context of new regulations and control over how user data is shared and protected (for example, GDPR, the Facebook and Cambridge Analytica incident), we believe that growth in this sector will slow down in future by its recent rates of 20% +. We project annual growth of 12% up to 2028, after which the sector will approach the long-term growth rate of 4%.
Electronic Retail Commerce Electronic Retail Commerce is a rather mature Internet industry compared to other emerging internet industries here. Despite its age, we believe that brick and mortar retailing will continue to move online in the next decade, resulting in 15% annual growth in 2028, until we reach our long-term estimate of 4% perpetual growth. Furthermore, the recent dominance of B2C, e-commerce platforms such as Amazon, suggests that the reliability of centralized data is not a significant obstacle to consumer adoption in the retail sector. Therefore, we envisage blockchain as a use case only valid for localized P2P business, which included about 1% of all eCommerce in 2017. We apply our assumptions of growth of 15% to 1% of the TAM of the e-commerce 2017 to estimate the realistic portion of Ecommerce interruption available for Ethereum.
Automated Insurance While the insurance industry is ancient and archaic like other financial services business models, automated insurance options provide an unproven but promising use of blockchain technology. Decentralized insurance systems could be created to enable small communities to self-assure using smart-contract functionality and resources as reserves. Although there is little data surrounding this emerging sector, we expect growth of 10% until 2028, after which we will achieve the long-term growth rate of 4%.
Awards Award programs or loyalty programs, when they exist today are silent in nature. Although there is some overlap that would allow, for example, to use airline miles to redeem hotel rooms, most of the award programs do not overlap with one another. Cryptocurrencies and blockchain technology could allow a liquid market to reward points, creating a new, more efficient sector from the inefficiencies that exist today. We project a 25% growth up to 2028, after which we will reach the 4% rate.

Reference model: Refer to the "Model" section "Step 1" section in our model for details on the Total Addressable Market projections ( TAM).

Step 2 – Resize the available Market (SAM)

Next, we estimate the percentage of TAM that will migrate (d & # 39; now referred to as available serviced market or SAM) to intelligent encrypted contract solutions such as Ethereum and others.

To do this, we consider the historical precedent determined by the emergence of radically disruptive technologies. For five historical scenarios, we collected data on market penetration rates in the first 10 years after market introduction. We have therefore categorized each of the scenarios in terms of speed and impact of technology able to acquire market shares, assigning the scenario to a category of low, medium or high penetration. [3] We calculated the data average for the scenarios grouped in the same category to achieve a market penetration percentage expected for low, medium and high penetration scenarios in each of the first 10 years after the introduction. In practice, we assume 0% cryptographic adoption today:

With these data, we assign to each sector from Step 1 a category of low, medium or high, in order to estimate the market penetration of crypto into that industry the next 10 years, as described below. Note that although some of the reasons in the table below overlap with our market growth logic derived from Phase 1, this is due to the fact that in some sectors we believe that blockchain will be able to facilitate additional growth in the overall market (i.e. , the projections of Step 1) and attract market shares from existing incumbents (eg, Step 2 projections).

Internet-of-Things (IoT) Medium – In the next decade, the explosion of available data that will result from increased adoption of IoT devices will make data quality a challenge fundamental to many hardware vendors. At the same time, many IoT devices will be owned by centralized technology giants (for example, Amazon, Google, Apple), automotive manufacturers (for example: Ford, Honda, Toyota) and other companies, preventing complete domination of decentralized public blockchains. As such, we imagine that the penetration of the blockchain technology market will be average.
Energy Medium – Data quality from utilities, power plants and other renewable energy production mechanisms is difficult to guarantee due to silos measuring systems, time delay between production of energy and reporting and inefficient energy storage technology. While blockchain is able to solve these data-related problems, the lack of simplified technology systems and the industrial nature of this sector suggest that change may not be rapid. Imagine that the penetration of the blockchain technology market will be average
Investment / Financial Services Media – The scale and resources of the existing financial services conglomerates make the penetration of the market for startups difficult. However, consumers' increasing demand for transparency, ease of use and accessibility, driven primarily by the millennials that plan their long-term financial futures, offers ample opportunities for decentralized disruption by the blockchain. Imagine that penetration into the market of blockchain technologies is average.
Digital Advertising Medium – Digital advertising has recently been dominated by companies such as Facebook and Google, which represent significant entry barriers for activated blockchain-startups. On the contrary, we believe there is a real need to improve data quality for online advertisers – a problem that blockchain is uniquely designed to address. As a result, we imagine that penetration into the market of blockchain technologies is average.
Digital Media Low – The media is becoming more decentralized with the advent of micro-influencers and social media platforms like Instagram, making it easy for anyone to be a content maker. However, digital media networks such as Comcast / NBC, Universal, FOX, Netflix and other centralized institutions still present significant barriers to entry for large-scale market penetration. Therefore, we imagine that the penetration of the blockchain technology market is low.
Gaming / Digital Collectibles High – The ownership of digital collector's items is a new trend that we believe will become even more popular with the improved transparency and validity of property and provenance data provided by blockchain technology. Therefore, we imagine that the penetration of the blockchain technology market is high.
Remittance / P2P Payments High – Slow processing times and high transaction fees associated with bank transfers make the P2P payment remittance / blockchain a particularly powerful use. As such, we imagine that the penetration of the blockchain technology market will be high.
Big Data / Data Brokerage High – Given the digital nature of the industry, combined with a stronger focus on cybersecurity and data security, we imagine that market penetration of blockchain technologies will be high.
Retail Ecommerce Low – Retail e-commerce has been a fairly successful centralized business model, and therefore we imagine the market penetration of
Automated insurance Alto – We believe that blockchain solutions would be the main component of the infrastructure on which an automated decentralized insurance market could be created. Because it is so fundamental to this thriving InsurTech field, we estimate that market penetration will be high.
Awards High – Similar to automated insurance, we believe that blockchain solutions are the main component of the infrastructure on which a decentralized multi-platform loyalty market could be created. Therefore, we estimate that market penetration will be high.

Reference model: Refer to the "Step 2" tab of our model for more information on our SAM assumptions.

Step 3 – Estimate the target market (ie, the part of the SAM Ethereum will capture)

Next, in order to predict how much the trade of Ethereum alone will facilitate in the long run, we project the percentage of SAM that Ethereum can realistically capture while competing with all the other encrypted available. We believe that the crypto-landscape will become an oligopoly in the next decade, as the number of coins is consolidated by the more than 1,600 cryptocurrencies existing today to a handful of important actors who hit critical mass and created sufficient network effects in 2028. [19659010] To estimate the market share of Ethereum as part of this potential oligopoly, we first made an analysis of pre-existing, technology-oriented oligopolistic industries. For the first six revenue-generating competitors, we have identified the percentage of market share over a five-year historical period. From these data, we were able to calculate the average market share attributable to each of the first six income generating companies in an oligopoly, through technology-oriented industries. These market share averages serve as a proxy for the long-term market share that will be acquired by Ethereum, based on the position that Ethereum obtains within the oligopoly by 2028. For example, based on the Our results, if Ethereum holds a place in the top six cryptocurrencies by 2028, we expect it to capture the underlying market share:

As we imagine that the follow-up of the Ethereum developers and global notoriety can suffice to keep it a cryptocurrency widely adopted in 2028, we do not foresee Ethereum surpasses Bitcoin in terms of adoption. Although Bitcoin currently has limited intelligent contract functionality, we believe that by 2028 its additional intelligent contract solutions can compete with those of Ethereum. Therefore, we expect Ethereum to remain the number two in the market and will reach a long-term market share associated with this position.

We believe it will take about five years for Ethereum to fully realize the market share associated with its oligopolistic number two position. Therefore, beyond the first half of the time period of our model (ie, 2018-2023), we project a constant and incremental change from the current share of Ethereum of the encrypted market to its long-term market share expected from 2023, after of what we hold the Ethereum market share the long-term percentage share. As an initial starting point for the current cryptographic market share of Ethereum, we use the percentage of ICO tokens launched on Ethereum compared to all available platforms, including Ethereum (ie around 91.2%).

Model Reference: Please refer to the "Step 3" form for further information on our hypotheses concerning the reference market of Ethereum.

Step 4 – Estimate of Percent Transaction Fees

The Ether was intended to be a means by which to pay transaction fees (known as gas taxes) to miners to validate transactions on the blockchain. Although many people speculate on Ether's price movements today, Ether's fundamental value is based on its use as a tax payment on gas. Therefore, in order to accurately assess future cash flows of the Ethereum network, we need to approximate what percentage of trading on the Ethereum platform will be captured as gas tariffs. We take this step because the value of Aether is not derived directly from the trade (that is, the value of the network transaction) that Ethereum facilitates, but rather indirectly, from the value of the transaction fees generated as a result of the facilitating trade. In this sense, we are approaching Ethereum as if it were a profitable business, with gas payments as net revenue.

To design the portion of trade that Ethereum miners can expect to achieve in gas tariffs, we performed an analysis of transaction fees within each of the industries identified in Step 1. We believe that in any industry, the existing transaction fee structure today can serve as an appropriate measure of commissions 'expectations on miners' transactions on the Ethereum platform. To arrive at this specific measure of the sector, we [1] calculated the ratio "Revenue in network transaction value" for three companies within each sector, which we then calculated an average to arrive at a commission estimate. Industry-specific transaction transaction o [2] Searched and identified percentage transaction costs associated with each sector:

Reference model: Refer to the "Step 4" tab for further details information on our assumptions regarding expected revenue to calculate the transaction value ratio.

Step 5 – Calculate Discounted Gas Rates

With the information from steps 1 to 4, we can now estimate the dollar value of the gas commissions generated by decentralized applications (dApps) Ethereum over the next 10 years. The following formula is performed for each sector, in each year from 2018 to 2028, as follows:

We calculate the present value of these cash flows by discounting a rate of 10% [4] which is commensurate to the long-term historical returns of emerging markets. To calculate the current value for the terminal year, we use Gordon's growth model for an increasing dividend. We then add all the cash flows of the sector in all periods to obtain a current value for the aggregated Ethereum network.

Reference model: Refer to the "Model" tab, section "Step 5" for further information on the current value calculations.

Step 6 – Use binomial prices to calculate the value from "Advanced Status" and "State Down"

As previously noted, we have evaluated the Ethereum network assuming it has been successful. Using binomial tree evaluation methods, we can now consider the possibility that the project does not get this success. We assume that if the project does not work, no trade will go to Ethereum and the value of Ether will approach $ 0 in the long run. Nel nostro calcolo di valutazione, assumiamo un periodo di detenzione di 10 anni (t) per riflettere la durata del nostro modello:

Il nostro approccio valutativo deriva dalla teoria che un investitore razionale sarebbe disposto a pagare il valore atteso di Pagamenti di etere secondo la seguente formula:

Il calcolo dell'albero binomiale si basa su una stima della probabilità di Ethereum di realizzare lo "stato alto" rispetto allo "stato basso" per generare un valore atteso del Rete Ethereum. Ethereum ha stabilito una posizione come la più grande, la smart criptazione completa abilitata da Turing fino ad oggi. Allo stesso tempo, le incertezze della regolamentazione, della sicurezza e dell'infrastruttura suggeriscono che esiste un potenziale serio che il progetto potrebbe essere ostacolato.

Per stimare la probabilità che Ethereum raggiunga il suo stato "alto", abbiamo sfruttato uno studio che ha esaminato il i tassi di mortalità delle imprese in diverse fasi dopo il lancio [5] Ethereum sta attualmente concludendo il suo quarto anno di attività. Deducendo dallo studio, il 50% delle imprese rimane operativo durante gli anni 5-14 (vale a dire, nel caso di Ethereum, per i 10 anni coperti dal nostro modello). Se i risultati dello studio sono veri per Ethereum, ciò implica una probabilità del 50% di realizzare lo "stato-basso" e una possibilità del 50% di realizzare lo "stato-alto". Abbiamo ritenuto che una probabilità del 50% di successo fosse appropriata dato che l'intero spazio blockchain deve ancora dimostrare la sua longevità.

Eseguiamo quindi il calcolo del valore atteso moltiplicando le valutazioni "Up-state" e "Down-state" delle rispettive probabilità del 50%, sommando i valori per arrivare a un valore corretto per il rischio della rete Ethereum.

Modello di riferimento: Fare riferimento alla scheda 'Modello', sezione 'Passaggio 6' per ulteriori informazioni sulla metodologia di valutazione dell'albero binomiale.

Step 7 – Calcolare un valore per Ether

Ora dividiamo il valore regolato della rete Ethereum per il numero stimato di Ether in circolazione, calcolando un prezzo per Ether.

Dato che la Prova di Puntata ( POS) l'implementazione per Ethereum è imminente g, dobbiamo proiettare il tasso di inflazione associato nell'offerta di Ether nel corso del periodo di tempo del modello (vale a dire, nei prossimi 10 anni). Vitalik Buterin stima che dopo l'implementazione del POS, ci sarà un tasso iniziale di inflazione attorno al 5%. A lungo termine, immaginiamo che questo tasso si avvicinerà a quello di un'economia stabile, ovvero del 2% circa. Pertanto, abbiamo stimato l'inflazione utilizzando un tasso misto (vale a dire la media del 3,5%) di questi due numeri. Estrapoliamo l'offerta di oggi utilizzando questo tasso di inflazione combinata per arrivare alla fornitura 2028 di Ether.

Nel foglio di calcolo del nostro modello, abbiamo valutato il prezzo di Ether utilizzando due stime separate della fornitura al fine di offrire metodi alternativi al nostro approccio scelto. Le due stime di offerta separate includono: [1] i 120 milioni di cap etere suggeriti da Buterin nell'aprile 2018 e [2] la stima dell'offerta ipotizzando che la nostra inflazione del 3,5% associata al POS sia valida a lungo termine. Riteniamo che quest'ultima opzione sia la più accurata e quindi la usiamo nella nostra stima

Modello di riferimento: Fare riferimento alla scheda "Modello", sezione "Passaggio 7" per ulteriori informazioni sul nostro Ether

Fase 8 – Analisi della sensibilità per l'etere

Anche se tutti gli input del modello sono incerti in una certa misura, riteniamo che i due elementi più indeterminati dell'analisi qui riportati siano [1] la percentuale di probabilità di successo di Ethereum nel nostro valutazione dell'albero binomiale dal punto 6, e [2] il tasso di inflazione a lungo termine nella fornitura di Ether dal passaggio 7. Pertanto, abbiamo creato un'analisi di sensitività per aiutare a mostrare gli effetti dei cambiamenti in ciascuna di queste ipotesi, che ci aiuta a stabilire un gamma di prezzi di Ether che potrebbero rappresentare il suo valore.

Riteniamo che la stima iniziale del 5% di Vitalik sull'inflazione risulterà temporanea una volta integrato il sistema POS. Over the long-term, we estimate a feasible range between 2% and 6%.

With such an elaborate set of challenges facing Ethereum, we estimate that its chances of success (i.e., chance of reaching the ‘Up-state’) fall between 5% and 60%.

Model Reference: Please refer to tab ‘Model,’ section ‘Step 8’ for additional background on our sensitivity analyses.

Our model suggests that Ethereum’s value could be $2,839, with a range that falls between $224 and $3,943. If our inputs hold true, our estimate suggests that Ethereum is undervalued compared to its $273 price at the time of writing.

We recognize that many assumptions and inputs were made to arrive at these conclusions. The factors that drive Ether’s price movements are numerous, and in many cases still not well-understood. As blockchain technology evolves, business-people and investors will continue to become more familiar with the true underlying potential of cryptocurrencies and the projects they make possible. A better-informed market will eventually reduce price volatility and lead to new valuation assumptions and inputs that are more tailored to the nuances of specific blockchain projects and industry developments.

We view our model as one framework for thinking about cryptocurrency valuation, and we seek to add to the growing body of research regarding the pricing of cryptocurrency assets. As new information is released, adjustments to our model and its inputs will inevitably be necessary. We invite our readers to modify our model[6] by downloading our template here. Despite having focused on Ethereum as the subject of our valuation, the model framework we have laid out can be used for other smart contract-enabled cryptocurrencies aside from Ethereum.

[1] Supply chain logistics remain one use case where cryptocurrency and blockchain data records can add tremendous value to the overall industry. That said, due to the nature of supply chains as business-to-business (B2B) channels, we feel that many blockchain solutions emerging in the space will be private or hybrid in nature, meaning that growth applicable to Ethereum’s public network will be somewhat limited when compared to business-to-consumer (B2C) oriented businesses. Therefore, we have excluded this sector from the model.

[2] Please see the ‘References’ tab in the model worksheet for sources of our information.

[3] Low category = market share captured was less than 20% in 10 years; medium category = market share captured was between 20% and 80% captured in 10 years; high category = market share captured was greater than 80% in 10 years

[4] While a 10% discount rate may appear low for an asset as volatile and uncertain as Ether, we address this observation in the following step of our model when we factor in likelihood of failure for the entire Ethereum project. As such, the present value of the Ethereum network calculated in this step assumes that Ethereum has successfully established itself as a platform for future commerce with the critical mass adoption necessary for long-term viability.

[5] Bhattacharya, Utpal; Borisov, Alexander; Yu, Xiaoyun. “Firm Mortality and Natal Financial Care.” Feb/April 2015. Michael G. Foster School of Business, University of Washington. Journal of Financial and Quantative Analysis.

[6] Please see the ‘References’ tab in the model worksheet for sources of our information.


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