University of Eastern FinlandFaculty of Social Sciences and Business StudiesBusiness School

CRYPTOASSETS: VALUE AND PRICE DRIVERS OF A NEW ASSET CLASS

Master’s Thesis, Accounting and FinanceAsko Pekka Korhonen (234545)

May 10, 2019

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UNIVERSITY OF EASTERN FINLANDFaculty of Social Sciences and Business StudiesAccounting and Finance

KORHONEN ASKO PEKKA: Cryptoassets: Value and price drivers of a new asset class. Kryptovarat: Uuden omaisuuslajin hinta- ja arvoajurit.Master’s Thesis. 73 pagesSupervisors: Jyrki Niskanen & Markus Mättö 05/2019 keywords: cryptoassets, decentralization, censorship resistance, cryptocurrency

Cryptoassets have been around for 10 years now and with a growing potential to disrupt many fields of human life it is time we start considering them as a legitimate asset class worth studying. From offering censorship-resistant value transferring capabilities in Bitcoin to powering decentralized applications through Ethereum, the impact these decentralized systems and the assets that power them will have in our lives will vastly grow in the coming years, when the world shifts from a centralized model of running internet services to a fairer, more inclusive digital world. This study aims to measure and analyze metrics in the underlying blockchains of the cryptoassets and in the cryptoasset markets, to make observations and assumptions about the drivers that affect cryptoassets’ prices and values the most. The study provides a literature review explaining the basics of blockchain technology and relevant information regarding the nature of cryptoassets as an asset class. The research in this study contains correlation and regression analyses that have been conducted for six cryptoassets in three timeframes. The timeframes include vastly different market conditions to see if the same observations stand during contrasting phases in the market cycle. The study show that not all of the cryptoassets in the study are affected by the same metrics. The prices and value of the three most valuable cryptoassets in the study, bitcoin, ether and litecoin are better explained by the variables used in this model compared to the smaller cryptoassets. Additionally, the prices of ether and litecoin seem to be driven more by actual usage of the blockchains and the price of bitcoin by speculative trading of the asset. The variables do the best job of explaining price movement during the bull market phase for all of the assets.

Kryptovarat ovat olleet olemassa yli 10 vuotta ja niiden kasvaneen vaikutuksen, sekä potentiaalin vuoksi on korkea aika, että niitä tutkitaan omana omaisuuslajinaan. Bitcoinin tarjoama sensuurinvastainen arvon siirtäminen ja Ethereumin avulla toteutettavat hajautetut applikaatiot johtavat megatrendiä, joka tulee lähivuosina vaikuttamaan elämäämme yhä enemmän ja johtamaan yhä vähemmän keskitettyihin palveluntarjoajiin nojaavaan malliin. Tutkimuksen tarkoitus on mitata ja analysoida muuttujia kryptovarojen lohkoketjuissa ja kryptovaramarkkinoilla, jotta voimme tehdä havaintoja muuttujista, jotka vaikuttavat niiden hintoihin ja arvoihin. Tutkimukseen kuuluu kirjallisuuskatsaus, jossa esitetään lohkoketjuteknologian perusteet ja tietoa liittyen kryptovaroihin. Tutkimukseen kuuluu korrelaatio ja regressioanalyysejä, joita on suoritettu kuudelle eri kryptovaralle kolmen eri aikavälin aikana. Päinvastaiset markkinasyklit antavat mahdollisuuden tutkia, vaikuttavatko samat muuttujat hyvin erilaisissa markkinatilanteissa. Tulosten mukaan varoihin ei vaikuta samat muuttujat. Verrattuna pienempiin varoihin, suurempien kryptovarojen hinnanmuutokset selittyvät paremmin tutkimuksen muuttujilla. Etherin ja litecoinin hinnan muutokset pohjautuivat eniten kryptovarojen käyttöön ja bitcoinin siihen liittyvään spekulatiiviseen kaupankäyntiin. Muuttujat selittivät hinnanvaihtelua parhaiten härkämarkkinoiden aikana.

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Table of Contents

1 Introduction.......................................................................................................................4

1.1 Research background....................................................................................................8

1.2 Goals of the study...........................................................................................................9

1.3 Study structure.............................................................................................................10

2 Literature review.............................................................................................................11

2.1 A brief history of digital assets...................................................................................11

2.2 Properties of permissionless blockchain technology.................................................13

2.2.1 The double spending problem.................................................................................18

2.2.2 The Byzantine generals problem............................................................................19

2.3 Cryptoassets..................................................................................................................21

2.3.1 Attributes of cryptoassets........................................................................................21

2.3.2 Cryptoasset markets................................................................................................31

2.3.3 Benefits of public cryptoasset powered decentralized networks.........................34

3 Research Methodology....................................................................................................42

3.1 Research material........................................................................................................42

3.2 Research methods and hypotheses.............................................................................44

3.3 Research reliability and validity.................................................................................47

4 Results and analysis.........................................................................................................48

4.1 Correlation analysis using Pearson’s correlation coefficient...................................48

4.2 Correlation analysis using Spearman’s rank correlation coefficient......................55

4.3 Regression analysis......................................................................................................60

5 Conclusions.......................................................................................................................67

Bibliography............................................................................................................................70

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1 Introduction

The beginning of the age of cryptocurrencies can be traced back to January 3, 2009 when the

first block, also known as the genesis block of the Bitcoin network was mined, thus

effectively creating the first decentralized, censorship resistant, public blockchain network in

the world. The structure of the network is originally described in a document known as the

Bitcoin whitepaper, published by a pseudonym known as Satoshi Nakamoto in 2008 (Bitcoin

2008). Fast forward ten years to the year 2019 and we can see that in recent years the total

amount of cryptoassets in the world has grown substantially, blockchain and cryptoassets are

experimented with in an evergrowing list of sectors, Bitcoin has become generally known to

the public and the whole sector of cryptoassets has grown from a radical idea to a new market

worth over 100 billion USD.

While many of the new cryptoassets created in recent years have often been copies or slightly

adjusted copies of existing cryptoassets, the amount of totally new cryptoassets with new and

unique ideas seems to be growing steadily as well, effectively increasing our knowledge of

what can be accomplished using these systems. In the years after the creation of Bitcoin, some

developers launched competing cryptocurrencies with the goal of improving upon the design

of it, essentially trying to better the electronic cash aspects of the protocol. Most of these

cryptocurrencies faded into history, but some like Litecoin and Dogecoin were able to create a

community of users and exist to this day. In recent years the majority of new innovations in

the sector have been concentrated on creating new and better smart contract platforms to

improve upon the shortcomings of the Ethereum protocol, or to create solutions of top of

existing protocols to either improve their performance or create decentralized applications.

Nick Szabo (1997) explains the idea behind smart contracts by pointing out that ”many kinds

of contractual clauses can be embedded in the hardware and software we deal with, in such a

way as to make breach of contract expensive for the breacher”. Buterin (2014) explains that

they are ”systems which automatically move digital assets according to arbitrary pre-specified

rules”.

Now in the year of 2019 it is safe to say that public blockchains have grown from the promise

of decentralizing electronic payments with Bitcoin to decentralizing all forms of digital

services that are currently run by centralized agents, such as Uber and Airbnb. The promise of

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"decentralizing everything" has resulted from the creation of turing complete smart contract

platforms, with the first one, Ethereum, being launched in 2015. These protocols enable

developers to create their own networks powered by tokens, often referred to as decentralized

applications or Dapps, on top of existing decentralized public blockchain networks such as

Ethereum. This is possible because Ethereum is "a blockchain with a built-in Turing-complete

programming language, allowing anyone to write smart contracts and decentralized

applications where they can create their own arbitrary rules for ownership, transaction formats

and state transition functions" (Buterin 2014). "A Turing machine is theoretical abstraction

that express the extent of the computational power of algorithms. Any system that is Turing

complete is sufficiently powerful to recognize all possible algorithms" (Teller 1994).

However, public blockchains have had their shortcomings in regards to being scalable and

trustworthy enough to be actually usable by the majority of the world. This is because of what

is referred to as the scalability trilemma, the problem of having to make tradeoffs to optimize

between the scalability, security and decentralization of the underlying network. (Ethereum

Wiki 2019). A common example of this is that as of 2019, the fairly recently launched and

more scalable networks such as EOS and Tron have arguably sacrificed decentralization for

greater throughput in terms of transactions per second. This generally leads to less censorship

resistance and lower security of the network.

Bitcoin core developers have tried to tackle the challenge of scalability by keeping the

protocol layer or layer 1 of Bitcoin unchanged and building off-chain layer 2 solutions such as

the Lightning Network on top of it. The goal of the Lightning network is to create and use a

network of micropayment channels to scale Bitcoin to be able to handle "billions of

transactions per day with the computational power available on a modern desktop computer

today" (Poon & Dryja 2016). Other members of the Bitcoin community have forked and

permanently diverged from the original Bitcoin protocol to change some attributes of it in

order to enable better scalability on a newly established network.

The Ethereum developers, having to try and scale a more complex and general type of

blockchain, are working on completely revamping the core layer of the network by sharding

the blockchain, which enables the blockchain to have a higher throughput by parallel

processing of transactions, switching to a new consensus mechanism that lowers the energy

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demanded to secure the network and developing a new virtual machine to execute code more

efficiently. The goal of these upgrades is to upgrade the Ethereum network to a state generally

referred to as Ethereum 2.0 or Serenity. There are also various layer 2 solutions being

developed to scale the Ethereum blockchain and provide it with better privacy. Scaling

solutions include plasma chains, "a proposed framework for incentivized and enforced

execution of smart contracts which is scalable to a significant amount of state updates per

second (potentially billions)" (Buterin & Poon 2017) and state channels, "rather than using the

blockchain as the primary processing layer for every kind of transaction, the blockchain is

instead used purely as a settlement layer, processing only the final transaction of a series of

interactions, and executing complex computations only in the event of a dispute" (Buterin

2016a). The privacy features of the network are being improved with the use of zero

knowledge proofs.

In a field with so many different approaches and visions to what public blockchains should

and could be used for, how much decentralization is necessary or optimal in a public

blockchain network and most of all how to best build these platforms, it is generally

extremely hard for anyone to come up with a universal solution to the scalability trilemma.

Considering this, it is highly unlikely that one universal public blockchain network will

become super dominant in the short term, highlighting the importance of trying to distinguish

general value and price drivers that apply to a majority of decentralized public blockchain

networks. Although problems regarding the scalability of these networks will have to be

solved first in order for the majority of the people in the world to be able to benefit from

them, considering all the effort and resources being channeled to solving these problems, it

seems to be more of a matter of when and not if, that these platforms will scale to meet

worldwide demand.

The reliability and trustworthiness of smart contracts, "systems which automatically move

digital assets according to arbitrary pre-specified rules" (Buterin 2014) and cryptoasset

exchanges, have also been questioned after some high profile hacks that have cost users

significant amounts of money. However it is fair to note that most of the hacks regarding the

industry have been directed at exchanges that have not provided strong enough security for

the assets they have been entrusted to hold. Smart contracts have also been described to be

highly reliable but very hard to perfectly code, which has lead to bugs and attack vectors that

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hackers have been able to exploit. In general, public blockchain networks like Bitcoin and

Ethereum are extremely secure due to their decentralized nature. Due to the continuous

research being done to create more efficient and secure smart contracts, bettering the auditing

processes regarding them and the continuously improving education regarding them, we can

expect the quality of smart contracts to steadily improve in the coming years, improving the

trustworthiness of the decentralized networks.

Researchers are constantly striving to improve existing protocols or create new ones that offer

better speed, scalability, security, privacy and other attributes that better the usability of these

decentralized networks. These protocols that can be described as decentralized public

blockchain networks powered by native cryptoassets, enable never before seen ways of

creating value by making new kinds of automated peer to peer value transfer and business

models possible. This is done by removing intermediaries to create networks where value can

be automatically transferred from anywhere and at any time, in a permissionless, tamper-

proof and censorship resistant way. As of writing, there are over 2000 cryptoassets in the

world, with a cumulative market capitalization of over 100 billion USD. Compared to

traditional asset classes like stocks or gold, the market cap of cryptoassets might still seem

quite insignificant, but many researchers expect the sector to grow rapidly in the coming

years, making the asset class an extremely relevant and interesting point of research.

Blockchain technology enables us to create decentralized networks and applications that

Bitcoin was the first real world example of. Instead of relying on an intermediary, the users of

these networks can trust the unchanging code of the protocol, which removes the need to trust

the user they are interacting with in the network. Honkanen (2017) explains that in networks

such as these, the mutual trust is created by using a consensus mechanism in a network of

thousands or millions of computers and servers. The system can not be shut off or changed

from one single point, since the information regarding the network has been saved to a

network of multiple computers. The information saved in the blockchain can not be altered or

deleted, because the information is available to everyone in the same form and at the same

time, anywhere in the world. Because of this it highly unlikely that established public

blockchain networks could be forced to shut down or attacked in a way that would hault their

usability, making it extremely likely that their relevance will only grow in the future,

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increasing the need to further understand these networks and the market that they have

created.

The cryptoasset market became reasonably mainstream and went through a significant growth

phase in 2017, followed by an epic meltdown in early 2018 that lead to a yearlong and still

ongoing bear market (Coinmarketcap 2019). However, even though the prices of the

cryptoassets have not performed well, the progress in developing these systems has continued

to steadily move forward. In addition to this, and despite the enormous volatility in

cryptoassets and the market overall, pretty much all of the significant assets and projects in

the market are still alive and well which strongly signals that this new asset class and its

established players are here to stay.

This study will focus on trying to identify value and price drivers that effect the market prices

of permissionless blockchain based cryptoassets. Since sufficient data is not available for a

major part of the cryptoassets in the world, the study will focus on few established

cryptoassets that can be studied.

1.1 Research background

The research will mostly concentrate on studying cryptoassets as investment vehicles.

Because of this the general term of cryptoasset will be used in most parts instead of the more

commonly known and historically used term cryptocurrency, unless the term cryptocurrency

is especially referred to. The asset class itself is also constantly morphing into a field of

varying types of tokens and coins, so the term "cryptocurrency" seems more and more

outdated by the day. Since the value and price drivers of traditional investment vehicles and

asset classes are generally well known, for cryptoassets to be taken seriously as an asset class,

further research is needed to distinguish the value and price drivers of cryptoassets.

Distinguishing the core value drivers behind cryptoassets enable more accurate price

discovery and help investors make better, more educated and more rational investment

decisions regarding this new asset class. Quite a few well known and established players in

the world of finance, such as Warren Buffett and Jamie Dimon have made claims that Bitcoin

is a "fraud" and worth nothing, so the research will also try to find variables that show why

certain cryptoassets have value. In a field as vast as the cryptoasset market, there are plenty of

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scams and absolutely worthless tokens about, so the study will concentrate on the most

established assets that have the necessary information regarding them available.

The birth of and rise in the popularity of blockchain technology has been supported by several

so called megatrends. A growing dissent towards traditional financial institutions after the

worldwide financial crisis of 2007-2008, a trend towards a cashless society and the growing

financial power of digital natives are some drivers behind the phenomenon. By enabling

payment services that have been especially designed for the internet-era, cryptoassets and

payment services provided by them complement internet commerce and the use of mobile

devices and mobile payment systems. In addition, cryptoassets enable borderless and limitless

global payments with minimal fees, encouraging users to engage in worldwide trade. (Jaag &

Bach 2015).

Previously blockchain based decentralized networks have mostly been used for creating

digital currencies with a goal of reaching critical mass and being able to rival national

currencies at some point. Bitcoin being the first and most widely used example of this.

However, with the emergence of smart contract platforms such as Ethereum that enable the

automated transfer of value, new kinds of projects have emerged with their own cryptoassets,

called tokens because they exist on top of existing networks, that have never before seen roles

in the ecosystem of the decentralized network in question. The value or price of these assets is

determined in a highly speculative free market with little evidence as to what the real value of

these assets is, leading to a growing movement of industry experts trying to figure out ways to

value cryptoassets. Since determining absolute ways of valuing these assets seems to be out of

reach so far, this study will only focus and concentrate on trying to determine the factors that

drive the prices and values of the assets.

1.2 Goals of the study

The study provides a literature review with necessary background information regarding

blockchain technology and cryptoassets for anyone not familiar with the subject and to give a

clear view of why we should consider cryptoassets to be an asset class of their own and an

increasingly interesting subject of study. The main goal of the study is to identify value and

price drivers of cryptoassets by comparing the variables of the underlying blockchain with the

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price development of the cryptoasset in question and comparing market and google trends

data to the cryptoassets’ prices. Two types of correlation analysis and multiple regression

analysis is performed for every assets' price and variables, with the historical data spanning a

bull- and bear market phase. The data is analyzed for the whole historical sample and

separately for the bull and bear markets for the study to be able to determine if the results

differ in vastly different market conditions.

1.3 Study structure

The study contains a literary review which provides information about the history of digital

assets, blockchain technology in general, attributes of cryptoassets and the role of cryptoassets

in decentralized networks, with these networks being compared to centralized ones to

highlight some of the key differences. The goal of the literary review is to provide a basis for

understanding the basic mechanics of cryptoassets, unique qualities that the assets possess,

how and why they exist and the basics of the markets they are traded in. Since blockchain

technology powers the decentralized networks that cryptoassets exist in and most of the data

used in the study comes from the underlying blockchains of the cryptoassets, the basics of

blockchain technology are also introduced.

The literary review is followed by the research that has been conducted to test the theory and

hypotheses relevant to the study. The research has been divided into several chapters,

beginning with the introduction of the study material and highlighting some key aspects of it.

Research methods and hypotheses are introduced and explained, followed by the results and

analysis. The study concludes with conclusions by the author.

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2 Literature review

2.1 A brief history of digital assets

Cryptographic currencies originated from David Chaum's proposal for "untraceable

payments" from the year 1983. The system involved bank-issued cash in the form of blindly

signed coins. Unblinded coins could be exchanged between users and merchants, and

redeemed after the bank verifies that they have not been previously redeemed. The blind

signatures provide unlikability akin to cash, preventing the bank from linking users to coins.

In the 1990's many variations and extensions of Chaum's scheme were proposed. Notable

contributions included removing the need for the bank to be online at the time of the

purchase, allowing coins to be divided into smaller units and improved efficiency. Several

startup companies such as DigiCash and Peppercoin attempted to bring electronic cash

protocols into practice but ended up failing in the market. None of the schemes from this first

wave of cryptocurrency research achieved significant deployment or mainstream adoption.

(Bonneau et. al 2015).

One of the key parts of Bitcoin, called moderately hard proof-of-work puzzles, was proposed

in the early 1990's as a way of combating email spam although it was never widely deployed

for this purpose. Many other potential applications followed, including proposals for a fair

lottery system, minting coins for micropayments, and preventing various forms of denial-of-

service and abuse in anonymous networks. The latter, Hashcash, was an alternative to using

digital micropayments. Proof-of-work was also used in distributed peer-to-peer networks to

detect sybil nodes, similar to its current use in Bitcoin consensus. Another essential element

of Bitcoin is the public ledger, which effectively makes detecting double-spending detectable.

In auditable e-cash, which was proposed in the late 1990s, the bank maintains a public

database to detect double-spending and ensure the validity of coins, however the idea of

publishing the entire set of valid coins was dismissed as impractical and only a Merkle root

was published instead. B-money, which was proposed in 1998, appears to be the first system

where all transactions are publicly and anonymously broadcasted. B-money received minimal

attention from the academic research community, most likely because it was proposed on the

Cypherpunks mailing list. Smart contracts, which were proposed in the early 1990s, enable

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parties to formally specify a cryptographically enforceable agreement, highlighting Bitcoin's

scripting capabilities. (Bonneau et. Al 2015).

In 2008, Bitcoin was announced and a white paper posted under the pseudonym Satoshi

Nakamoto to the Cypherpunks mailing list (Bonneau et. al 2015). A few weeks after the

Emergency Economic Stabilization Act rescued the U.S. financial system from collapse

(Nakamoto 2008b) introduced the Cypherpunks mailing list to a peer-to-peer electronic cash

system "based on cryptographic proof instead of trust, allowing any two willing parties to

transact directly with each other without the need for a trusted third party" (Catalini & Gans

2017). The post was quickly followed by the source code of the original reference client. The

first Bitcoin block, called the genesis block was mined on or around January 3, 2009 and the

first use of Bitcoin as a currency is thought to be a transaction from May 2010. The first

transaction is thought to have happened between a user ordering pizza delivery from another

in exchange for 10000 bitcoins. (Bonneau et. al 2015).

For the first time in history value could be reliably transferred globally between two distant,

untrusting parties without the need for an intermediary by using Bitcoin. By combining

cryptography and game theory the distributed and public transaction ledger on the Bitcoin

blockchain could be used by any participant in the network to cheaply verify and settle

transactions in the bitcoin cryptocurrency. Relying on rules designed to incentivize the

propagation of new, legitimate transactions, to reconcile conflicting information and to

ultimately agree at regular intervals about the true state of a shared ledger in an environment

where not all participating agents can be trusted, Bitcoin was the first platform, at scale, to

rely on decentralized, internet-level consensus for its operations. The platform was able to

settle the transfer of property rights in the underlying digital token, bitcoin, by combining a

shared ledger with a game theory based incentive system designed to securely maintain it,

without needing to rely on third party such as a central clearinghouse or market maker. From

an economics perspective this new market design solution, which has since been adopted and

extended by other types of digital platforms, removes the costs arising from the presence of a

single platform operator while still allowing the participants of the marketplace to access and

use shared infrastructure and transact with other participants. (Catalini & Gans 2017).

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Bitcoin originated from the cypherpunk community who sought to use cryptography to secede

from government control of money (Narayanan & Clark 2017) and Bitcoin's pseudonymous

inventor Satoshi Nakamoto (2008b) said Bitcoin would be very attractive to the liberitarian

viewpoint. Many in the crypto-anarchist community view cryptocurrencies as a means to free

citizens from the monetary depredations of governments (Popper 2015). Despite the

technology's revolutionary origins it has become quite apparent that not only are there many

possible use cases of distributed ledger technology for government (Walport 2016), but that

government action might be a key factor in the adoption and development of this

technological innovation through regulation, legislation and public investment. As an

example, governments can use blockchain technology to benefit from the service efficiencies

they may bring. Perhaps counter-intuitively given their revolutionary origins, private

blockchain applications will most likely need government cooperation to facilitate adoption

and the development of a blockchain powered economic system. (Berg, Davidson & Potts

2018.)

2.2 Properties of permissionless blockchain technology

A permissionless blockchain establishes a digital ledger that has free entry for updaters. The

ledger is used to record transactions in blocks that are bound together in a continuous order to

form the ledger. (Saleh 2018). A blockchain is used to ensure the distributed verification,

updating and storage of the record of transaction histories. The block in a blockchain is just a

set of transactions that have been previously conducted between the users of the blockchain.

Creating a continuous chain of these blocks that together contain the whole history of past

transactions allows one to create a shared ledger where anyone can publicly verify the amount

of balances or currency a user owns. (Chiu & Koeppl 2017). The blockchain is literally a

chain of transaction blocks that newly verified blocks are added to. A coin or cryptoasset is

constituted by its transaction history on the network and can be traced back to the block it was

mined from. "Each input into a transaction points to the output of a previous transaction".

(Harwick 2015). In a blockchain network, individual transactions are mined into blocks by

participants of the system, receiving a reward for their efforts. These blocks are subsequently

added to a chain of blocks with every block confirmed by all of the participants of the system

and time stamped for verification. Each participant of the system holds a copy of the

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decentralized ledger of transactions. (Krückeberg & Scholz 2018). Merkle trees are utilized to

minimize storage (Merkle 1990).

Figure 1. Valid transactions in a centralized ledger/bank account and in a permissionless

cryptocurrency (BIS 2018).

Authenticated Public Key Distribution enables a system in which every user or participant

holds a randomly computed public enciphering key as well as a private deciphering key. The

sender signs encrypted information with their private key and the recipient encrypts it with

their public key. This allows the transmitted information to be authenticated as sent by the

sender while being decipherable by the recipient. (Krückeberg & Scholz 2018). The entire

history of every transaction is kept track of by every computer on the network with the ledger

of transactions, the blockchain, being continuously updated. Conditions can arise in which

there are competing blockchains among different users: someone on the network can try to

forge a transaction, or two transactions are received in a different order by different users.

This is solved by the protocol defining strict rules by which only one is accepted. The

blockchain with the most computing power behind it will be preferred, so for an attacker to be

able to forge a transaction, they would have to make sure that their own blockchain was

longer than the legitimate one. This would require the attacker to have more computing power

at his disposal than the total computing power of the honest nodes in the network. Contrary to

mainstream belief the Bitcoin blockchain is not that private since all transaction records are

public and anonymity is upheld only by keeping the account owners private. (Harwick 2015).

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Ensuring network consensus requires the validators of the network to compete for the right to

update the chain with a new block. (Chiu & Koepple 2017). Due to the permissionless nature

of public blockchains, an updating mechanism that theoretically allows any participant to

update the ledger is necessary. To accomplish this, nearly all of the major blockchains use a

mechanism called Proof-of-Work (PoW). (Saleh 2018). In addition to Proof-of-Work, there

are several different kinds of consensus mechanisms that differ in the way that blocks are

validated, such as Proof of Stake (PoS), Delegated Proof of Stake (DPoS) and Proof of Burn

(PoB). PoW being the first and most widely used one. The Mechanisms are mainly relevant

for reasons of system security, but economic implications regarding token supply and

volatility arising from possible insecurity are tightly linked to the consensus mechanisms.

These factors have given rise to a new scientific field of Cryptoeconomics, which seeks to

balance considerations of cryptography and economic incentives. Consensus mechanisms

record valid transactions by implementing Time Stamping and Witnessed Digital Signatures.

Time stamps are used to provide a proof of existence for every transaction at or before a

certain point in time, while Witnessed Digital Signatures are used to serve as proof of validity

of a transaction. Combining an encryption protocol with a consensus mechanism enables the

continuous maintenance of a public ledger of transactions. (Krückeberg & Scholz 2018).

In a PoW system, validators are made to solve a cryptographic puzzle after checking a

transaction. The cryptographic puzzle can be thought of as a number-guessing exercise, where

the computer of the validator guesses random numbers until it, or another validator in the

network guesses the right one. Proof-of-work ties the mining process to real-world resources

by requiring each "guess" to cost computing power and electricity. The more and the better

resources a validator has, the more "guesses" they can generate, increasing the chance to

guess the right number. The first validator to guess the right number has the right to broadcast

their ledger update to other validators in the network, who check the correctness of the

number. If the number is correct, all other validators will update their ledgers to match the

ledger of the validator that guessed the number. As a reward, the validator is rewarded with a

pre-determined amount of the network's cryptocurrency. Every validator is free to invest as

much money on real-world resources as they like, which creates a competitive marketplace

between all the validators in the network. (Sabar, R. 2017). In PoW systems miners use their

processing power to compete in solving a computationally costly problem. A reward structure

is needed to incentivize the miners because transaction validation and mining are costly. In

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PoW systems such as Bitcoin these rewards are financed by the creation of new bitcoins with

every new block and transaction fees. (Chiu & Koeppl 2017). This means that the influence

individual miners have over the development of the blockchain is defined and limited by the

computational effort or work invested into the maintenance of the system (Krückeberg &

Scholz 2018). From the qualities of PoW stated above we can state that the underlying idea of

PoW consensus is to rely on rewards to ensure security. However, this security comes at the

price of having to use energy to maintain the consensus and with increasing hash power

directed to ensuring the security of the Bitcoin network, the energy consumption of the

network is now greater than the energy consumption of Columbia (Digiconomist 2019). Saleh

(2018) also argues that the economic design of PoW possesses negative welfare implications.

The basis for Saleh's argument is that since miners receive compensation for their work in

newly minted coins, the existing stock of the cryptocurrency is diluted and the holders of the

cryptocurrency suffer a welfare loss. Saleh (2018) further argues that "these welfare losses

represent an economy-wide welfare loss because the blockchain's permissionless nature

implies that miners face competition so that miner welfare gains do not off-set household

welfare losses". (Saleh 2018).

In recent years, these drawbacks in PoW -systems have lead to many new ideas on how to

reach network consensus, with delegated-proof-of-stake (dPoS) (EOS Go. 2019) which is

used by the EOS-blockchain and PoS -systems (Buterin 2016b), which the Ethereum

blockchain is transferring to, as the most widely developed ones. Since the technical details of

these consensus mechanisms are beyond the scope of this study, the study will not include

indepth analysis of these systems, but a general overview is given to highlight some of the

basic differences. PoS systems are based on requiring validators to stake their money in order

to validate transactions and the system relies on penalties rather than rewards to ensure

security. "Validators put money (“deposits”) at stake, are rewarded slightly to compensate

them for locking up their capital and maintaining nodes and taking extra precaution to ensure

their private key safety, but the bulk of the cost of reverting transactions comes from penalties

that are hundreds or thousands of times larger than the rewards that they got in the

meantime. The “one-sentence philosophy” of proof of stake is thus not “security comes from

burning energy”, but rather “security comes from putting up economic value-at-loss”. (Buterin

2016b). The PoS mechanism does not require computationally intensive work to be done,

meaning it is less resource intensive than PoW. Hence the mechanism being more eco-

17

friendly than PoW. The security in PoS is assured by relying on the self-interest of

participants not to implement malicious transactions, protecting the value of the participants'

own token holdings. (Krückeberg & Scholz 2018). These facts are important to note since the

underlying consensus mechanism of the blockchain may have an effect on the way the

cryptoasset of the blockchain is valued, or how the underlying variables in the blockchain

affect the price of the cryptoasset.

Combining the blockchain to its native currency in a system such as Bitcoin or Ethereum, the

blockchain makes it possible for a decentralized network of economic agents to agree about

the true state of the shared data at regular intervals. This shared data can represent exchanges

of currency, intellectual property, equity, information or other types of contracts and digital

assets, which makes blockchain a general purpose technology allowing users to trade scarce,

digital property rights and create an ever growing list of novel types of digital platforms

without having to rely on an intermediary. (Catalini & Gans 2017). Decentralized

applications, also known as dApps, are blockchain based applications that attempt to create

financial peer-to-peer architecture that would reorganize society into a set of decentralized

networks of human interactions (Cong et. al 2018).

One of the most innovative aspects of a blockchain protocol is the state-contingency of

transactions, which makes cryptocurrencies fundamentally different from fiat money.

Executing transactions by means of cash gives the user no way of eliminating the asymmetric

information prior to the transaction, making possibilities of adverse selection and market

break down omnipresent. A typical economy reduces this problem by relying on the

intermediation by a credible third-party, such as banks, or agents are forced to purchase

insurance to unload the risk. If these entities did not exist, the cash market would suffer from

asymmetric information problems. Due to its secure nature of blockchain, cryptocurrency is

possibly immune from the adverse selection problem. Transaction information stored in a

blockchain is strongly protected from tampering, meaning that it is nearly impossible to

rewrite past information or write fraudulent information. Blockchain also allows transactions

that are based on complex scripts, meaning that users can write code on the blockchain

detailing the specific conditions that they wish the transactions to fulfill. These features imply

that a transaction can be state-contingent, meaning that it is executed if and only if a specific

condition is satisfied, and the validity of state information is highly credible. (Aoyagi &

18

Adachi 2018). According to Chiu & Koeppl (2017) "a blockchain is like a book containing

the ledger of all past transactions with a block being a new page recording all the current

transactions".

2.2.1 The double spending problem

One of the biggest problems with digital currency and cryptoassets is the double spending

problem. This is a situation in which a user, after having conducted a transaction, tries to

convince the validators to accept an alternative history in which some payment was not

conducted. If an attack like this succeeded, the user would keep both the balances and the

product or service he/she obtained while the counterparty of the transaction would be left

without payment. (Chiu & Koeppl 2017).

The Bitcoin protocol enables users to conduct transactions using bitcoins in a way that all

participants of the network can agree on the ownership of the units of the currency and the

order of the transactions. Public key cryptography is used to determine ownership and ”the

entire network needs to unanimously agree on association between units of the currency and

public keys”. (Rijnbout 2017). The units of the currency are transferred between users by

digitally signing transactions from one public key to another (Miller & Laviola 2014). The

problem in this context is that the network needs to be able to confirm that no earlier

transactions were signed for the same units of the currency by the previous owner (Nakamoto

2008a). Since there is no central authority to verify transactions, the network needs to solve

the problem in a decentralized manner (Rijnbout 2017).

The problem is solved by publicly announcing every transaction to the network and agreeing

that the transaction that arrived first is valid (Rijnbout 2017). Every node in the network

checks to see if the output of a transaction has been previously spent and the transactions are

timestamped by including them in a block (Nakamoto 2008a). According to Nakamoto

(2008a) the order of transactions can be agreed upon by using a solution in which the hash of

a block needs to include the hash of the previous block, the new transactions, and a time

stamp to prove the data existed at the given time or it would not have been in the hash.

(Nakamoto 2008a).

19

So a proof-of-work based blockchain is secured against this type of attack by dealing with

transaction history backwards. Current transactions have to be linked to transactions in all

previous blocks, making the blockchain dynamically consistent. This means that if a person or

group of people attempt to attack the blockchain and revoke a transaction in the past, they

have to propose an alternative blockchain and perform the proof-of-work for every newly

proposed block. This makes it extremely costly to rewrite the history of transactions if the part

of the chain that needs to be replaced is long. Double spending attacks can also be

discouraged by introducing a confirmation lag into the transactions, meaning that users have

to wait for some blocks before the transaction is conducted, making it harder to alter

transactions in a sequence of new blocks. (Chiu & Koeppl 2017).

2.2.2 The Byzantine generals problem

One key feature of reliable computer systems is that they must be able to handle

malfunctioning components that give conflicting information to different parts of the system

(Lamport, Shostak & Pease 1982). The main concern in the Byzantine generals problem is

”whether unanimity can be achieved in an unreliable distributed system” (Rijnbout 2017).

Lamport et al. (1982) describe the situation by presenting a setting in which a group of

generals are camped with their troops around an enemy city. The generals must agree upon a

common battle plan by only communicating via a messenger. However, it is possible that one

or more of the messengers are traitors with the goal of confusing the others. ”The problem is

to find an algorithm to ensure that the loyal generals will reach agreement”. By only using

oral messages, this problem is only solvable if more than two-thirds of the generals are loyal,

so a single traitor can confound two loyal generals. However, according to Lamport et al.

(1982) the problem is solvable for any number of generals and possible traitors using

unforgeable written messages. (Lamport et al. 1982).

The problem was further described and updated to the modern age with Nakamoto (2008b)

suggesting a setting where the generals are able to communicate via a network. Instead of

attacking a city, the generals want to attack the king’s Wi-Fi by brute forcing the password.

The password must be cracked and all logs deleted before the attack is detected for the attack

to stay hidden. However, each general does not have enough computing power to brute force

the password on their own within a short enough amount of time. Hence the generals need to

20

coordinate and attack at the same time to accumulate enough computing power to brute force

the Wi-Fi before the attack is detected. The time of the attack itself is not important, just that

the attackers attack at the same time. So the generals agree to attack at the first proposed time,

but the problem is that the network has lag and communication is not instantaneous. A result

of this is that each general might receive the messages in different order. (Nakamoto 2008b).

Bitcoin, which is based on a Byzantine consensus protocol, solves the problem by utilizing a

public and decentralized proof-of-work blockchain in order to reach consensus on the

ownership of the units of the currency, bitcoins. (Miller & Laviola 2014). According to

Rijnbout (2017), since the Bitcoin network achieves consensus on the order of transactions

without needing to rely on a central authority, the generals should be able to as well. The

problem in this case is solved by using a proof-of-work system that only needs to reach

consensus between the generals and does not keep track of the order of transactions. Each of

the generals start working on solving a moderately difficult hash-based proof-of-work and

each of the problems is difficult enough that a solution will be found, on average, every ten

minutes. However, this is if and only if all of the generals are working at once. When a

solution is found by any of the generals, the solution and the included plan of attack are

broadcasted to the shared network. When each of the generals receive the solution, they adjust

their version of the problem to include the plan that was broadcasted in the solution. (Rijnbout

2017).

The process continues with the generals working on the next proof-of-work problem, so each

subsequent solution is tied to a chain after the first one. If any of the generals still happen to

be working on a different plan they will switch to this chain, because it is the longest available

chain, or the chain where the majority of the computing power is. Since a solution is found on

average every ten minutes, after an hour the chain will consist of six proof-of-work solutions,

on average. Now each general is able to verify the amount of computing power that was used

in order to build a chain this long in an hour. Each of them can conclude whether enough of

the generals are working on the same chain that includes the same version of the plan, in order

to initiate a successful attack. In order for the chain to reach six proof-of-work solutions in an

hour, the majority of the generals must have been working on the same chain and plan of

attack. Relying on this information the generals can safely attack on the time included on this

chain. (Rijnbout 2017).

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2.3 Cryptoassets

2.3.1 Attributes of cryptoassets

Cryptoassets are cryptographically secured digital or virtual assets (Cong et. al 2018). EBA

(2019) adds that they "are a type of private asset that depend primarily on cryptography and

distributed ledger technology as part of their perceived or inherent value". Cryptoassets, such

as Bitcoin, exhibit innovative features either as a currency, payment system, or more

generally, as a technology (Bach & Jaag 2015). Trust in a cryptoasset system consists of three

elements: the security of the blockchain, the health of the mining or staking ecosystem and the

value of the cryptoasset. (Chiu & Koeppl 2017). Cryptoassets use cryptography to control

transactions, alter the supply and prevent fraud. Transactions are validated by network nodes

and the supply of most cryptoassets increases at a predetermined rate and cannot be changed

by any central authority. (Gandal & Halaburda 2014). In digital currency systems, the means

of payment is a string of bits. This is problematic since these strings of bits can be easily

copied and re-used for payment, leading to a a situation where the digital token can be

counterfeited by using it twice. This is known as the double-spending problem. In traditional

payment systems this problem has been overcome by relying on a trusted third-party. Users

have to trust and pay fees to this third party to manage a centralized ledger and transfer

balances by crediting and debiting user accounts. Cryptoassets remove the need for a third-

party. (Chiu & Koeppl 2017).

22

Figure 2. Digital tokens in centralized systems and decentralized systems (Chiu & Koeppl

2017).

Cryptoassets are digital tokens that exist within a specific cryptoasset system. A cryptoasset

system generally consists of a peer to peer network, a consensus mechanism and a public key

infrastructure. In the absence of a central authority, the rules governing the system are

enforced by all network participants, also known as nodes. Generally the rules of the system

consist of properties such as what constitutes a valid transaction, the total supply of the digital

tokens and how these digital tokens are issued. (Hileman & Rauchs 2017). The correct record

of transactions is maintained by consensus between the validators, so that users can be sure to

receive and keep ownership of balances. Having this consensus requires the network to

prevent double-spending of the currency and to make sure that users can trust the validators to

accurately update the ledger. (Chiu & Koeppl 2017). Each node can independently verify the

entire transaction history as everyone has a copy of the shared ledger. The shared ledger is

known as a blockchain, since it consists of a chain of blocks comprised of transactions.

(Hileman & Rauchs 2017). Each block is a set of transactions that have been conducted and

confirmed between the users of the cryptocurrency (Chiu & Koeppl 2017). The ledger is

23

constantly updated by a process called mining, through which new units of the native token

are created. The system has very low friction in entering and exiting it, since anybody is free

to join and leave the system at any time. The system is also pseudonymous or anonymous

since there are no identities attached to users. (Hileman & Rauchs 2017). According to

Hileman & Rauchs (2017) "The main property of a reasonably decentralized cryptocurrency

or cryptoasset is that the native token constitutes a censorship-resistant digital bearer asset. It

is a bearer asset in the sense that the person who controls the respective private key controls

the particular amount of cryptocurrency associated with the corresponding public key, and

censorship-resistant in the sense that, in theory, nobody can freeze or confiscate cryptoasset

funds nor censor transactions performed on the integrated payment network".

Blockchain-based decentralized networks often introduce their own native assets that the

users of the network need to hold to be able to transact in the network. This is called

"monetary embedding" by Cong et. al 2008. Since potential users of these blockchain-based

decentralized networks are based around the globe, using traditional fiat-money is not a

convenient solution because of the money being subject to countries' legal and economic

influences and transactions costs of currency exchange. Having a standard unit of account in

the network also mitigates risks of asset-liability mismatches when they are denominated in

different units of account. Arguably the most important aspect of having a native asset is

being able to align the incentives of miners, validators and users to contribute to the stability,

functionality and prosperity of the ecosystem. When the native asset is launched, developers

can create rules of coin supply and distribution that cannot be changed later on, leading to a

certain degree of scarcity. If a decentralized application is developed and launched without a

native currency, instead being based on another cryptoasset, the incentive of users is not

directly linked to the decentralized application in question and the price is linked to another

cryptoasset. (Cong et. al 2018).

In order for a cryptoasset to have positive price, there needs to be a net demand for the

cryptoasset. In theory, or in the future when decentralized exchanges gain enough volume, a

user transacting on a decentralized blockchain based network can exchange US dollars for the

native cryptoasset and make a transfer on the blockchain, and immediately afterwards

exchange the native asset back into US dollars. In this case the velocity of the native asset is

24

infinity, there is no net demand for the asset and there exists an equilibrium of zero US dollar

price. (Cong et. al 2018).

However in practice, a positive demand for cryptoassets can be determined because of an

aspect of monetary embedding: users of the decentralized blockchain based network need to

hold the assets in order to profit from on-chain activities. First, a demand for the asset can

arise because decentralized service providers, like stakers in a proof of stake -system, need to

hold the asset to be able to serve the network. These assets need to be held so stakers who fail

to perform their job to a pre-specified standard, or act maliciously towards the network can be

penalized through a built-in mechanism, such as slashing in Ethereum's Casper FFG. Second,

some decentralized blockchain based networks also allow for the use of smart contracts,

which can automate transfers of assets once predetermined contingencies are met or triggered.

These smart contracts require that a corresponding amount of cryptoassets are escrowed prior

to the contingencies being triggered. Third, generation of decentralized consensus cannot be

achieved instantaneously and there is a technical limit on how quickly transactions can be

validated and recorded. This limit varies greatly with the decentralized blockchain based

network in question, but the decentralized nature of the validation process means it always

takes time to ensure robustness and synchronization of the network consensus. During this

time the cryptoassets are "locked" since they cannot be liquidated by the sender or receiver of

the transaction. Users of the network are thus exposed to the fluctuation of the cryptoassets'

price, even if the holding period of the asset is for an instant. The holding period is important

because it exposes the owner of the cryptoasset to price fluctuations, meaning that in addition

to users caring about the surplus from conducting trade on the network, the users of the

network also care about the future coin price, which depends on the future user base. (Cong

et. al 2018).

Hileman & Rauchs (2017) divide cryptocurrency and blockchain innovations into two groups:

1. new public blockchain systems that feature their own blockchain such as Ethereum,

Peercoin and Zcash 2. Decentrali<ed applications, also known as dApps and other additional

layers such as sidechains that are built on top of existing blockchain systems. These include

projects such as Augur, Golem and the Raiden network. The cryptoassets of native blockchain

systems such as Bitcoin are generally referred to as "coins" while the cryptoassets of projects

built on top of platforms are generally referred to as "tokens". (Hileman & Rauchs 2017).

25

Harwick (2015) argues that it is easy to see how cryptoassets might be used as money by

analyzing how they fit the textbook qualities of a useful commodity for indirect exchange.

The first quality is portability. Cryptoassets excel in portability due to the fact that they have

no physical form. Users can send and receive cryptoassets from any device that supports

cryptowallets and there are no geographical limits or restrictions on amounts sent. The second

quality is durability. Cryptoassets are superior to cash since even though they can be lost, the

assets don't get worn out or depreciate due to physical wear. The third quality is divisibility.

Cryptoassets are extremely flexible in the sense that, for example, bitcoins are divisible to

eight decimal places and in principle there are no technical limits to the divisibility a protocol

might allow. The fourth quality is security. Protocol level theft and counterfeiting of assets is

extremely difficult, but third-party applications such as closed-source wallets and exchanges

are suspectible to hacks. Social engineering and phishing attacks may also set the user at risk

if they are not competent in handling assets. (Harwick 2015).

Figure 3. The money flower: a taxonomy of money (Bech & Garratt. 2017).

26

Figure 4. Private electronic money and cryptocurrencies (Gradstein, Krause & Natarajan

2017).

The larger the community of users utilizing the decentralized network is, the more surplus can

be realized through trades of the native cryptoasset among the users of the network. The

utility of using cryptoassets increases when more and more people use them. Requiring

participating agents to hold cryptoassets to conduct transactions on the network is consistent

with many current applications. Cryptoassets are the required medium of exchange for

transactions and business operations on the decentralized networks that utilize blockchain

technology. This is either because of protocol design or the fact that using the native

cryptoasset provides higher convenience yield relative to other alternative currencies. (Cong,

Li, & Wang 2018).

Most "utility tokens" issued through ICOs have a built in purpose because they are required

means of payment for products and services from other users of the network. The benefits of

using utility tokens increases with the growing size of the networks user base, resulting in a

situation where the coin price reflects expected future growth of the networks user base and

community, becoming higher if the expected user-base growth is stronger. The same case

happens when the technology of the platform is expected to improve, resulting in better

scalability or lower entry barriers, which induces more agents to join the community and

leads to the expectation of coin price appreciation, affecting agents' decisions to join the

community and hold on to their tokens. A feedback loop exists where token price affects user

27

base size through agents' expectations of the future, and at the same time the user base size

affects token price because of higher utility from trade surplus and through stronger token

demand when a growing number of agents participate. In addition to the feedback loop's

effect on user base growth and coin price, it also affects the volatility of both variables and a

native currency can lower user-base volatility. Cong & et al (2018) conclude that the

existence of tokens as a native currency serves for technological purposes as practitioners

argue and more importantly advances the growth of user base through agents' expectation of

future technological progress, larger user base, and higher coin price. (Cong et al. 2018).

Cryptocurrencies differ from notes issued by financial institutions in the times of free banking

for three primary reasons. First, most cryptocurrencies are fully fiduciary, while notes in the

free banking era mostly represented claims against deposits in gold or other assets. Second,

cryptocurrencies are issued by computer networks according to a predetermined criteria and

are not directly related to credit like traditional monies. Third, cryptoassets such as Ether can,

among other things, work as an automatic escrow account. (Fernandez-Villaverde & Sanches

2018.)

Cryptoassets are tough to define and understand, because they are novel technologies,

tradeable instruments and political phenomena (Sabar 2017). Hileman & Rauchs (2017) group

the use cases for cryptoassets into four major categories: speculative digital asset/investment,

medium of exchange, payment rail and non-monetary use cases. The main use case from these

four seems to be speculation. Coinbase and ARK Invest conducted a joint report in 2016 that

estimated that 54% of Coinbase users use bitcoin strictly as an investment. Globally the

trading volumes for bitcoin have been significantly higher than network transaction volumes,

with this figure being even higher for most other cryptoassets. However, a rising amount of

cryptoasset transactions do not appear on a public ledger since they are conducted off-chain

via internal account systems that are operated by centralized entities such as exchanges,

wallets and payment companies. Hileman & Rauchs (2017).

On a micro-level, cryptoassets differ in terms of the features offered by each asset. Each

cryptocurrency has its own rules for transacting on the network and its own ledger to record

these transactions. The most valuable cryptocurrencies tend to differ from each other in

aspects such as transaction speed and degree of privacy. Every one of these cryptocurrencies

28

is a separate system or network. (Sabar 2017). However some cryptoassets, usually referred

to as tokens, are smart contracts running on top of a decentralized network, such as Ethereum

or EOS, making them part of the same system with the underlying protocol.

On a macro-level, all cryptocurrencies have their own pre-programmed monetary policy

embedded into the code of the network and they tend to differ in most aspects of monetary

policy, such as the long-term total amount of coins issued and the speed of their issuance. The

most valuable cryptocurrencies, such as Bitcoin, which has a long-term fixed supply of 21

million bitcoins, are deflationary. (Sabar, R. 2017). However the second most valuable

cryptoasset Ethereum, which is designed to act as a turing complete decentralized computer,

has an inflationary issuance model better suited to the goals that the network is trying to

achieve. Another macro-level difference between decentralized networks with cryptoassets is

their way of achieving tamper-resistance. In Bitcoin this is achieved using a mechanism called

proof of work, which forces validators participating in the network to solve cryptographic

puzzles. There are several other tamper-resistance mechanics being developed, with goals

such as boosting transactions speeds or limiting the amount of real world resources consumed

by the decentralized network. (Sabar, R. 2017).

On a meta-level, Sabar (2017) describes cryptocurrencies as a "profound social experiment in

how people agree on rules to govern themselves in the absence of central authority". Adding

that "their governance structures are fascinating". The governance structures between

different networks vary widely, with the optimal form of governance structure being a

constant point of debate in the community. In Bitcoin, there are a handful of people called

core developers that can make changes to the code of the network. Despite being able to make

changes to the code, the core developers can not freely make changes to the protocol as they

wish, since the code changes do not become real unless the miners of the network accept the

code changes. (Sabar, R. 2017). If the core developers were to try and force a change of the

code without the support of the miners, the chain would split resulting in what is known as a

hard fork. In addition to this, if the miners accepted the changes but the users of the network

did not support them, they could easily stop holding bitcoins and hurt miners that are being

paid in bitcoins, by putting sell-side pressure on the asset (Sabar, R. 2017). In these

decentralized networks there are many game-theory based cryptoeconomic factors that by

design try to balance the incentives of all participants of the network. Some other forms of

governance include adding stakeholders, often called masternodes to the network. In Dash

29

these masternodes have to buy 1000 dash to become a masternode and gain the right to vote

on changes to the network's rules. The idea behind this approach is that those that have the

most invested in the success of the network are the most interested in seeing it succeed.

(Sabar, R. 2017). A third model of governance identified by Sabar (2017) is that of Ethereum.

Sabar describes this as being close to "de-facto benevolent dictatorship" because of founder

Vitalik Buterin "wielding significant influence on community decisions in the face of

conflicts". Because of the micro, macro and meta-level differences in the nature of

cryptoassets and their underlying decentralized networks, cryptoassets provide different kinds

of utility and serve a wide variety of purposes. Every user has the possibility to "vote with

their feet" and choose the cryptoasset with a monetary policy and governance structure mostly

suited to their preference. (Sabar, R. 2017).

BiS (2018) states that "cryptocurrencies such as Bitcoin promise to deliver not only a

convenient payment means based on digital technology, but also a novel model of trust",

adding that "delivering on this promise, hinges on a set of assumptions: that honest miners

control the vast majority of computing power, that users verify the history of all transactions

and that the supply of the currency is predetermined by the protocol". According to BiS

(2018) achieving decentralized consensus has become an environmental disaster, referring to

the amount of electricity consumed by the Bitcoin network. That however is not the biggest

issue with cryptocurrencies, as the shortcomings of cryptocurrencies relate to the "signature

property of money: to promote "network externalities" among users and thereby serve as a

coordination device for economic activity". Relating to this BiS (2018) identifies the three

biggest shortcomings of cryptocurrencies as scalability, stability of value and trust in the

finality of payments.

BiS (2018) argues that the problems with scalability relate to every user having to download

and verify the history of all transactions ever made, with the ledger growing ever larger

because of every single transaction being added to the same ledger. This leads to the

conclusion that "to keep the ledger's size and the time needed to verify all transactions

manageable, cryptocurrencies have hard limits on the throughput of transactions". Handling

the number of digital retail transactions currently handled by selected national retail payment

systems would lead to the ledger size growing "beyond that of servers in a matter of months".

Another issue is that the needed processing capacity to handle all the transactions would be so

30

great that "only supercomputers could keep up with the verification of the incoming

transactions". BiS (2018) argues that these issues would lead to communication volumes that

"could bring the internet to a halt, as millions of users exchanged files on the order magnitude

of a terabyte". Additionally, blockchain-based cryptocurrencies limit the number of

transactions added to the ledger with new blocks being able to be added only at pre-specified

intervals. This can lead to situations where the number of incoming transactions is already so

big that new added blocks are already at the maximum permitted size, leading to system

congestion that results in incoming transactions going to a queue (known as a mempool in

Bitcoin) and soaring fees as users compete (are willing to pay more fees) to have their

transactions included in the upcoming blocks. There have been times in Bitcoin's history that

the transactions have been in queue for several hours, "limiting usefulness for day-to-day

transactions". BiS (2018).

The second shortcoming or key issue with cryptocurrencies, according to BIS (2018) is their

unstable value. BIS (2018) points this as the result of the absence of a central issuer that

would have the mandate to guarantee the currency's stability, or in other words, control the

supply of the issued currency. To guarantee the stability of a currency "the authority needs to

be willing at times to trade against the market, even if this means taking risk onto its balance

sheet and absorbing a loss" according to BIS (2018). Since there are no central agents with

obligations or incentives to stabilize the value of a currency in a decentralized cryptocurrency

network, decreasing demand for the cryptocurrency will lead to a decrease in the price of the

currency. Unstable valuations are also driven by the speedy issuance of new cryptocurrencies

that are "very closely substitutable with one another". BIS (2018).

BIS (2018) also uses the Dai stablecoin as an example of a protocol that tries to peg its value

to that of the US dollar, but can't fully emulate it because of the "inherent instability" caused

by the lack of a central issuer. According to BIS (2018) this "inherent instability" can be seen

in the Dai currency because a few weeks after it's launch, the currency's value had a brief low

of 0.72 USD. BIS (2018). Relating to this, it is fair to point out that after the end of January

2018, the price of Dai has steadily been within 3 cents of 1 USD despite a bear market in the

ether cryptoasset, which is used as collateral for Dai tokens (Coinmarketcap 2019).

31

The third major issue with cryptocurrencies "concerns the fragile foundation of the trust in

cryptocurrencies". According to BIS (2018) "permissionless cryptocurrencies cannot

guarantee the finality of individual payments". This is because of a situation, where although

users can verify that a specific transaction is included in a ledger, there can be rival versions

of the ledger without the user knowing about the rival versions of the ledger (BIS 2018). The

problems with payment finality are furthered by the threat of miner manipulation. If a group

of miners control more than 50% of the networks computing power, the underlying

cryptocurrency can be manipulated by this group of miners. According to BIS (2018) "One

cannot tell if a strategic attack is under way because an attacker would reveal the forged

ledger only once they were sure of success. This implies that finality will always remain

uncertain". BIS (2018).

BIS (2018) states that another reason for the fragile trust in cryptocurrencies can be attributed

to forking. A process where some amount of the cryptocurrency's users decide to launch a

new version of the ledger and protocol, while the rest of the community continues to use the

original one. Forks are usually the results of community disagreements regarding the

governance or technical updates of a cryptocurrency and lead to a network split, with a new

network being launched in addition to the existing one. BIS (2018) goes on to to state that

"forking may only be symptomatic of a fundamental shortcoming: the fragility of

decentralized consensus involved in updating the ledger and, with it, of the underlying trust in

the cryptocurrency". BIS (2018) also claims that "theoretical analysis suggests that

coordination on how the ledger is updated could break down at any time, resulting in a

complete loss of value". BIS (2018).

2.3.2 Cryptoasset markets

When created, a cryptocurrency initially exists in a vacuum. The system has no connections to

other systems such as other cryptocurrency systems, traditional finance or the real economy.

In order to participate in the network and earn the cryptoasset, users need to start mining it. In

this phase the asset can only be used for transacting with users of the same system, since there

is no way to spend or sell the asset outside of the native system. To address this problem,

cryptoasset exchanges are established to let users trade cryptoassets for other cryptoassets

and/or national currencies. As a result of these marketplaces, a price that is denominated in a

32

major cryptoasset such as bitcoin or ether, or a price denominated in national currencies can

be established for these tokens and they become digital assets that have a certain value.

Cryptoasset exchanges open up the initially closed system by connecting it to traditional

finance by providing on-off ramps for new users to join the system. As transaction volumes

increase and the cryptoasset gains trust, merchants begin accepting the cryptoasset as a

payment method, making the token a medium of exchange. Payment companies emerge to

help merchants facilitate cryptoasset payments, reducing exposure to price volatility and

acting as gateways between cryptoassets and the whole global economy. (Hileman & Rauchs

2017).

As this is happening, a large variety of actors with their own motives emerge to provide

supporting services such as data services, media and consulting. There is a very low barrier to

innovating on top of these existing cryptoasset systems and projects emerge to build complex

overlay networks on them, expanding the utility of these systems by enabling non-monetary

use cases. Cryptoasset platforms with more robust features are launched in order to remove

the inherent complexities of using cryptoassets, making it easier for mainstream users to use

cryptoassets. Due to the very wide range of projects, activities, products, services and

applications in the cryptocurrency industry its difficult to comprehensively catalogue

everything taking place in this new economy. (Hileman & Rauchs 2017).

A number of projects and companies have emerged to facilitate the use of cryptoassets for

mainstream users by providing products and services for them and by building the necessary

infrastructure for dApps that run on top of public blockchains. A cryptocurrency ecosystem

needs a diverse set of actors to build interfaces between public blockchains, traditional

finance and various economic sectors. These kinds of services add significant value to the

cryptoassets by enabling the usage of public blockchains and their native cryptoassets in the

broader economy. (Hileman & Rauchs 2017).

Hileman & Rauchs (2017) identify four key cryptoasset industry sectors. These are

exchanges, wallets, payments and mining. All of these serve primary functions that can be

described as follows: for exchanges they are the purchase, sale and trading of cryptoassets,

offering liquidity and setting a reference price, for wallets the storage of cryptoassets by

handling key management, for payments the facilitating of payments using cryptoassets, and

33

for mining the securing of the global ledger generally by computing large amounts of hashes

in order to find a block that gets added to the blockchain.

Estimating the number of cryptoasset holders and users is extremely challenging, since any

individual can use as many wallets as they like from a variety of service providers. Coinbase

and ARK Research have done calculations on their own user data and estimated that in 2016

globally around 10 million people have owned bitcoin. The Global Cryptocurrency 2017

Benchmarking study esimates that there are between 2.9 million and 5.8 million unique users

actively using cryptoasset wallets. However, this number does not include users who store

their assets in online exchanges and payment service providers. This suggests that the total

number of cryptocurrency users is a lot higher than the 2.9 million to 5.8 million estimate.

(Hileman & Rauchs 2017). As of 16.1.2019 there are 2108 listed cryptoassets with a total

marketcap of over 122 000 000 000 USD (Coinmarketcap 2019).

Figure 5. Factors affecting cryptoassets price (Sovbetov 2018).

34

2.3.3 Benefits of public cryptoasset powered decentralized networks

Markets exist to facilitate the voluntary exchange of goods and services between buyers and

sellers. In order for a transaction to be executed, the involved parties need to verify key

attributes of a transaction. When an exchange is executed in person the buyer can directly

assess the quality of the goods he or she is receiving, while the seller can verify the authencity

of the cash used for the purchase. In this scenario the only intermediary involved is the central

bank that has issued and is backing the fiat-currency used in the exchange. This is important

to note when comparing a cash-based transaction to a transaction that is performed online.

When a transaction is performed online, there are one or more financial intermediaries

brokering it by, for example, verifying that the buyer has sufficient funds. The role of the

intermediaries is to add value to marketplaces by reducing information asymmetry and the

risk of moral hazard through third-party verification. Oftentimes, this involves procesesses

such as imposing additional disclosures, monitoring participants, maintaining trustworthy

reputation systems, and enforcing contract clauses. As markets develop they scale in size and

geographic reach, which makes verification services more valuable as most parties do not

have pre-existing relationships. These parties rely on intermediaries to ensure the safety of

transactions and to enforce contracts. In extreme cases verification costs can become

prohibitively high, leading to unraveling markets and a situation where beneficial trades do

not take place. (Catalini & Gans 2017).

Intermediaries typically charge a fee in exchange for their services. This is one of the costs

buyers and sellers incur when they are not able to efficiently verify all relevant transaction

attributes without a third party. In addition, costs may stem from the intermediary having

access to transaction data, exposing the participants to privacy risk. Censorship risk is also

present with the intermediary being able to select which transaction to execute. (Catalini &

Gans 2017). These costs worsen as the intermediaries gain market power. This often results

from the informational advantage they develop over transacting parties through their

intermediation services. (Stiglitz 2002). Transacting through an intermediary always involves

disclosure to a third-party, which increases the chance that the information will be later reused

for purposes not included in the original contractual arrangement. As an increased share of

economic and social activity is conducted online and digitized, keeping data secure is

becoming increasingly problematic and information leakage more prevalent. Some classic

35

examples of this are the theft of social security numbers and credit card data, or the resale of

customer data to entities such as advertisers. Blockchain based decentralized networks can

prevent information leakage by allowing market participants to verify transaction attributes

and enforce contracts without needing to expose the underlying information to an

intermediary or third-party. The technology allows for the verification of attributes in a

privacy-preserving way by allowing an agent to verify that some piece of the provided

information is true, for example that the user is credit-worthy, without accessing all the

background information of the user, such as past transaction records. (Catalini & Gans 2017).

Blockchain technology completes the lowering of verification costs, which digitization has

brought for many types of transactions to nearly zero, by allowing for costless verification. A

blockchain such as Bitcoin, can be used for cheap verification of ownership and exchanges in

the cryptocurrency. A drawback to this is that the Bitcoin network consumes computing

power to secure transactions and extend its distributed ledger, although the energy

requirement is small compared to the costs of labor and capital involved in upkeeping and

securing transactions on traditional financial infrastructure. A system such as Bitcoin also has

low barriers to entry and innovation. This quality combined with the ability to fork the

underlying code ensures a higher level of competition for different types of services. Whereas

in existing payment platforms intermediaries have access to all transaction data and

accumulate market power, effectively centralizing the market. Some transactions also require

additional verification. Problems with transactions may emerge, requiring a thorough audit of

the transaction attributes. Often such audit processes are costly, and require both labor and

capital. Blockchain technology fundamentally changes this by allowing costless verification

when a problem emerges. Any transaction attribute or information on the agents and goods

involved that has been stored on a distributed ledger can be cheaply verified in realtime by

any of the market participants. In its most simplistic form, shared data in the distributed

ledger can represent outstanding balances in an underlying cryptographic token and past

transactions. However in more complex platforms, the shared data can also cover the code

and data required in order to perform specific operations. Taking the Ethereum blockchain as

an example the shared data can be used to run an application or to verify that a contract clause

is enforced. Operations such as these are often referred to as smart contracts and they can be

automated in response to future events, which adds substantial flexibility to the process of

verification. (Catalini & Gans 2017).

36

So far the applications that have resulted from the reduction in the cost of verification have

mostly been complementary to incumbents, as they improve existing value-chains by

lowering the cost of settlement and reconciliation of transactions. Despite the fact that many

verification steps can now be commoditized, intermediaries still have a role in providing a

user-friendly experience, handling edge cases and for certifying information that requires

offline forms of verification. (Catalini & Gans 2017). According to Catalini and Gans (2017)

"this also explains why implementations of the technology targeted at identity and provenance

have been slower to diffuse: While the verification of digital attributes can be cheaply

implemented on a blockchain, the initial mapping between offline entities and their digital

representations is still costly to bootstrap and maintain. Therefore, as verification costs fall,

this key complement to digital verification becomes more valuable". (Catalini & Gans 2017).

With verification becoming cheaper the scale at which it can be efficiently implemented in

drops. Data integrity can be built from the ground up, from the most basic transaction

attributes to the most complex ones, on a distributed ledger. A robust reputation and identity

system could be built from all the transactions an economic agent has throughout the whole

economy, making it substantially harder to alter or fake transactions attributes. Compared to

what previously involved a time consuming and costly audit, is now a process that can run

continuously in the background to ensure market safety and compliance, lowering the risk of

moral hazard. A consequence of this shift is the ability for easier defining of property rights at

a smaller scale than before, as digital assets or fractions of them can be easily traded,

exchanged and verified at a low cost. The ability to implement costless verification at the

level of a single bit of information will lead to fundamental changes in the designs of

information markets, contracts and digital property rights. (Catalini & Gans 2017).

Blockchain technology is also able to lower the cost of networking. The effects of reduction

in the cost of networking can be seen in two phases: first in the phase of bootstrapping a new

platform and in the second phase of operating it. The first phase is of referred to as a token

sale Initial Coin Offering (ICO). In this phase a native token of the new platform is used to

crowdfund the development of the platform. (Catalini & Gans 2017). The crowdfunding is

conducted by entrepreneurs or decentralized autonomous organizations that sell tokens to

investors around the globe. Usually the tokens are representations of claims on issuers'

cashflow, rights to redeem issuers' products and services on the new platform, medium of

37

exchange among the users of the new platform or a combination of all three. Tokens operate

on top of an existing decentralized cryptoasset utilizing network that allows for the use of

smart contracts to create decentralized applications. Most of the ICOs that raised funds in

2016 and 2017 did so by issuing "utility tokens" that are required means of payment to pay

other users of the network for products or services, or represent opportunities to provide

services in the decentralized network for profit. (Cong, Li & Wang 2018). These for profit

generating services include tasks such as acting as a dispute resolutioner or staking your

tokens to confirm transactions in the network. A key innovation of blockchain technology is

highlighted here: it allows for peer-to-peer communication of value and information in

decentralized networks (Cong et. al 2018).

In the second phase, a built-in incentive system is used to determine the specific conditions

under which contributors can earn tokens for providing the necessary resources needed for the

platform's operations. Some examples of these are computing power for Bitcoin, applications

for Ethereum and storage for Filecoin. (Catalini & Gans 2017). An interesting observation is

that Initial Coin Offerings that seemingly came out of nowhere are now more celebrated and

debated than conventional IPOs, having raised 3.5 billion USD in more than 200 ICOs in

2017, according to CoinSchedule (Cong, Li & Wang 2018).

In the bootstrapping phase the actual utility that the platform can deliver to users is limited by

its small scale and network effects created by incumbents work against users switching from

existing solutions. This leads to the first phase relying heavily on contributions from early

adopters and expectations about the future value of the ecosystem by investors. The early

adopters may be willing to dedicate time and effort to support a new platform because they

want to take part in creating an alternative to established products and derive utility from

further development of the technology. Early investors serve a different role. As in traditional

early-stage capital markets the investors come in early because they expect the token to

appreciate in value leading to significant gains on their initial investment. Many individuals

are simultaneously early adopters and investors, meaning that they contribute both effort and

capital to these projects. For individuals that serve both of these roles a native token serves a

similar purpose to founder and early-employee equity in startups, allowing these projects to

attract top global talent without the need to raise investment from traditional angel investors

and venture capitalists. (Catalini & Gans 2017).

38

The second phase of growth requires the platform to have been able to attract mainstream

users, because it creates value for the users or their business. This early majority mainly cares

about the potential of the technology to make existing processes cheaper or new ones

possible, and is indifferent to the technological details of the adopted solutions. The

bootstrapping phase is associated with extremely high volatility but as uncertainty around a

platform's potential is resolved the tokens tend to enter a more stable growth trajectory. This

is quite similar to the to the process of early-stage startup funding and growth. In the

cryptoasset space, the time it takes for a developer team to raise capital and for a new token to

reach a critical mass of early adopters and investors, has been substantially shortened by token

sales and Initial Coin Offerings. (Catalini & Gans 2017).

The reduction in the cost of networking constitutes an architectural change to value creation

and capture because of its effects on market power, privacy risk and censorship risk.

Architectural innovations open opportunities for entrants to reshape market structure by

destroying the usefulness of the knowledge and assets incumbents have accumulated.

Blockchain reduces the market power of intermediaries, which allows blockchain based

platforms to operate with lower barriers to entry and innovation. Even though we have had the

ability to crowdsource ideas, talent and capital online for multiple years, existing solutions

rely on a central clearinghouse to match demand and supply, maintain reputation systems and

trust, and ensure the safety of transactions, the open innovation protocols that can be built

using a crypto token differ from the existing solutions in several ways. The open innovation

protocols enable the creation of platforms where rents are more equally distributed among

contributors, consumers do not have to expose their private data to third-parties, and a broader

segment of both developers and users can benefit from the returns to direct and indirect

network effects the use of a shared standard and infrastructure creates. (Catalini & Gans

2017).

In the current model, most consumers and businesses do not own or control the digital and

financial assets they rely on everyday. They are renting these resources on the internet. This is

a direct result of our current system's inability to generate and trade scarce, digital goods and

establish digital property rights without an intermediary. Prior to Bitcoin, all forms of digital

cash were worthless in the absence of a central clearinghouse. These forms of digital cash and

other digital goods could be easily copied and double spent without an intermediary. This

39

problem is solved by crypto tokens by allowing for the creation and exchange of scarce,

digital assets without the negative effects that result from assigning market power to a third-

party. Market power in these digital markets leads to higher prices, higher switching costs,

and higher privacy and censorship risk. (Catalini & Gans 2017).

The privacy risk is especially present in markets where consumers pay for services by

allowing intermediaries to mine their personal data (Catalini & Gans 2017). Machine

intelligence has led to a situation where access to data can reinforce market power because of

incumbents that increasingly compete on developing and training the most effective

prediction algorithms (Agrawal, Gans, & Goldfarb 2016). According to Athey, Catalini &

Tucker (2017) "The trend of consumers relinquishing private information in exchange for free

or subsidized digital services is unlikely to change, as small incentives, frictions in navigation

and irrelevant information can all be used by intermediaries to persuade even privacy

sensitive individuals to give up sensitive information". Startups such as the Basic Attention

Token and Blockstack have launched crypto tokens to combat this phenomenon by having

their crypto tokens targeted at increasing consumers' ability to control how, when and why

their private data is accessed and monetized. These platforms are in the early adopter phase

and if successful, they would shift us from a context where intermediaries pledge to "not be

evil", to one where they "can't be evil" in the first place. (Catalini & Gans 2017).

According to Catalini & Gans (2017) censorship risk occurs when an intermediary revokes a

participant's access to the marketplace and digital assets through fiat as in online censorship;

when the intermediary degrades access to some participants in terms such as speed or

features, reducing their ability to effectively compete, and when the intermediary loses control

over the marketplace because of a technical failure or an attack. Online platforms such as

Amazon Cloud Services, Google's Search, AdSense and Facebook have been observed to

exhibit all three cases of censorship risk and are heavily concentrated markets because of

network effects and economies of scale in data collection, storage and processing. This gives

power to a small number of established incumbents and makes the underlying services less

resilient to targeted attacks and errors. In the blockchain space solutions to these problems are

being deployed by startups such as Filecoin, which among many other projects is attempting

to turn data storage and transfer into a commodity, and through distributed computing

platforms such as Ethereum. (Catalini & Gans 2017).

40

Decentralized cryptoasset powered payment networks bear the potential to substantially

influence the existing financial system. There are a myriad of opportunities for individuals,

businesses and the whole economy. For companies a main advantage of crypto-payment-

systems is the resulting independence from traditional financial intermediaries. It is possible

to decrease transaction fees and therefore reduce the costs from non-cash payments by

substituting traditional payment methods with cryptocurrencies. For naturally digital

companies, such as online businesses, this is especially relevant. Crypto-payment-systems

also provide a quick and low-cost way for sending money directly from person to person

around the world. (Bach & Jaag 2015). There are several different kinds of crypto-payment-

systems with differing attributes. Some facilitate the direct exchange of a cryptoasset, such as

a system using bitcoin, and others such as Stellar rely on so-called anchors to facilitate faster

and cheaper foreign currency payments that convert one fiat currency to another.

Cryptoasset systems are not bound to any particular geographical location or jurisdiction,

meaning that the integrated payment network is born global with a global reach and can be

used to transfer funds all over the world within a short time. Transaction fees with

cryptoassets are substantially lower than fees charged by traditional payment network

operators, and the fees are generally based on the transaction size measured in bytes, instead

of being based on the amount transferred. These qualities make cryptoassets useful for cost-

effective micropayments. (Hileman & Rauchs 2017). According to Bach & Jaag (2015) The

crypto enabled combination of low transaction costs and fast, easy usage can provide new

methods of revenue schemes based on microtransactions. Adding tipping systems to blogs

and easier crowd-funding of projects can be made possible with cryptoassets. Previously

small transactions have often been economically non-viable because of the transaction costs

outweighing the benefits or even the value of the transactions. Cryptoasset transactions are

also irreversible, which can be advantageous for individuals and companies. Bach & Jaag

(2015) refer to the case of online merchants: "Payments by credit cards can be reversed after

the purchase, thus online merchants are exposed to the risk that customers reverse their

payments after the respective order has already been shipped. In fact, payment irreversibility

may strengthen e-commerce by reducing its overall risk, if merchants have more reputation to

lose than customers". (Bach & Jaag 2015).

41

The open source nature of cryptoasset protocols can be seen as an another advantage. Every

individual is free to contribute to its structure and it is easy to add improvements and

extensions to the system, leading to basically unlimited innovation potential. Cryptoassets can

also, in the case of countries with unstable currencies, provide an alternative store of value. In

high-inflation or politically risky countries it may be beneficial to hold cryptocurrencies as

assets in addition to national currency. Cryptoassets do not fall under the authority of the

government, meaning they can not be devaluated or held back for fiscal or other purposes.

(Bach & Jaag 2015).

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3 Research Methodology

3.1 Research material

According to Creswell (2017) ”Research seeks to develop relevant true statements, ones that

can serve to explain the situation that is of concern or that describes the causal relationships of

interest. In quantitative studies, researchers advance the relationship among variables and

pose this in terms of questions of hypotheses”. A quantitative approach to research means that

in practice knowledge is being developed by primarily using postpositivist claims. These can

be expressed as cause and effect thinking, use of measurement and observation and testing

theories. In the heart of most studies that use quantitative research is the testing of a theory.

(Creswell 2017).

Objectivity is a very essential quality of a competent inquiry, which highlights the importance

of examining research methods and conclusions for bias (Creswell 2017). Another key aspect

to consider when analyzing or judging quantitative research is its generalizability. Generally

more confidence is expressed towards findings that may hold true in a number of situations or

contexts and therefore can be thought of as representing some standard of universal truth. If

the findings of the conducted quantitative research are not generalizable, the researcher should

state the limitations of study and further explain how the study is affected by these limitations.

(Carter & Hurtado 2007).

The main theory of this study is that the key variables in the underlying public blockchains of

cryptoassets affect and drive the value and prices of these assets, therefore the research

concentrates on trying to identify if and which metrics drive the value and prices of these

cryptoassets. One variable used in the study, exchange volume in USD, does not derive from

the underlying blockchains of the cryptoassets, but from the exchanges that facilitate the

trading of these cryptoassets. Google trends data is also used to measure public interest and its

effects towards the projects.

The findings are further analyzed by studying how big of an influence each variable has. The

research seeks to make postpositivist claims about the price action of the cryptoassets

43

analyzed for the study by taking advantage of Creswell’s (2017) ideas of cause and effect

thinking, use of measurement and observation and testing a general theory. The research tries

to be as objective as possible by using the same variables and timeframe for analyzing all of

the cryptoassets used in the study. However one variable, median transaction value in USD,

was not available for one of the assets, Lumens, in the study. It is therefore only used in

analyzing five of the possible six cryptoassets used in the study.

The timeframe used in the study furthers the objectivity of the research by including two very

contrasting market cycles. This also enhances the generalizability of the study by making the

results of the study applicable to varying market conditions. The timeframe is split in a way

that the bull market phase of the research, 10.10.2016 – 7.1.2018 includes 455 daily

observations. The latter phase that includes a significant bear market spans the dates 8.1.2018

– 10.10.2018 and includes 276 daily observations. The two distinctive market phases do not

have an equal number of sample dates because the data was gathered on 11.10.2018 and

worked on from there on.

Figure. 6 Total market capitalizations of cryptoassets 10.10.2016 – 10.10.2018. 1.7.2018

close marks the end of the bull market and beginning of bear market (Coinmarketcap 2019).

Since most of the approximately 2000 cryptoassets in the world have not been around for a

long enough time to make the study’s timeframe, the research concentrates on a few

established cryptoassets that there is sufficient data available of. In addition to being in

existence for a long enough time to have their data available for the whole timeframe that is

used in the study, the cryptoassets are also traded in several marketplaces and have significant

cumulative trading volume on these exchanges. Although all of the cryptoassets used in this

study have varying amounts decentralization, it is important to note that they are all public

44

and open source projects. The cryptoassets chosen for this study are Bitcoin, Ether, Litecoin,

Dogecoin, Dash and Lumens. All of these cryptoassets possess their own underlying

blockchain, from which accurate and highly reliable data can be extracted from.

Bitcoin, Litecoin, Dogecoin and Dash can be considered more traditional cryptocurrencies,

with differences in areas such as the amount of total coins to be created, speed of payments,

inflation and privacy features. The native currency of the Ethereum network, Ether, can be

thought of as something resembling digital oil. While ether can be used to make payments

like a traditional cryptocurrency, it is also needed to execute computations on the ethereum

virtual machine, meaning that you need to have ether if you want to run programs on the

Ethereum network (Ethereum 2019). Lumens are the native cryptoasset of the Stellar Lumens

network. Lumens can be used to make payments just like any other cryptocurrency , but they

are also needed to pay transactions fees and make accounts on the Stellar Network and can be

used as a bridge currency when making fiat currency to fiat currency exchanges on the

network (Stellar 2019). The data set used in the research was downloaded from

www.coinmetrics.io (Coinmetrics 2019), a professional provider of crypto asset market and

network data.

3.2 Research methods and hypotheses

A quantitative research method is used in the study because it provides the best and most

suitable way of analyzing the available data in order to provide answers for the research

question at hand, ”if and which of the studiable variables drive the value and prices of

cryptoassets”. According to Lowhorn (2007) quantitative research is often one of two types,

experimental or descriptive. This study uses an experimental research approach which tests

the accuracy of a theory by determining the independent variables, that are controlled by the

researcher, to see if they cause an effect on the dependent variable, which is the variable being

measured for change (Lowhorn 2007). In the case of this research the independent variables

are metrics from the underlying blockchains of the cryptoassets, exchange volume from the

crypto exchanges and google trends data, with the dependent variable being the price of a

cryptoasset. Creswell (2017) cites Morse (1991) in regards to quantitative research: A

quantitative approach is the best approach to use when testing a theory of explanation and

45

when the problem is ”identifying factors that influence an outcome, the utility of an

intervention or understanding the best predictors of outcomes”.

The research uses two different types of correlation analysis to reveal if and how the

independent variables affect the dependent variable: the Pearson product moment correlation

coefficient or Pearson correlation coefficient and Spearman’s rank correlation. The Pearson

correlation coefficient ”indicates the strength and direction of a linear relationship between

two variables” (Sriram 2006). It is also one of the most classic statistical tools in finance

(Albanese, Li, Lobackeskiy & Meissner 2011). Pearson’s correlation coefficient gives a value

between +1 and -1, with +1 and -1 meaning that there is a perfect positive/negative linear

relationship between the variables (KvantiMOTV 2019). Generally a value of 0 means that

there is no relationship, or the variables are independent of each other (Choudhury 2009). The

function of the Pearson correlation coefficient is:

P1 (X,Y) =

”X and Y are sets of variate pairs with rages Rx and Ry, respectively; cov(Y,Y) is the

covariance of X and Y and σ(X) and σ(Y) are the finite standard deviations of X and Y

respectively” (Albanese et al. 2011).

The second correlation coefficient used in the study is Spearman’s correlation coefficient,

measuring the dependence of two variables via a monotonic function. It measures the strength

and direction of association between two ranked variables. (Laerd Statistics. 2019). It is

sometimes referred to as the Pearson correlation coefficient for variables. The function of

Spearman’s correlation coefficient is defined as:

P2 = 1-

The research intends to measure the correlation between independent variables and the

dependent variable to find out if the independent variables have an effect and how significant

this effect is towards the dependent variable. Correlation can be thought of as a notion that

involves more than one process and unfolds over long time horizons (Albanese et al. 2011).

The independent variables used in the study are total daily transaction volume on the

46

blockchain measured in US dollars, daily transaction count on the blockchain, total

transaction volume in US dollars on all crypto exchanges, daily active addresses on the

blockchain, median daily transaction value in US dollars and weekly worldwide google trends

data. The dependent variable is the daily price of each asset.

In addition to the correlation analysis, a multiple regression analysis is conducted to see how

the aforementioned independent variables affect the dependent variable. The independent

variables and dependent variable of price are the same as in the correlation analyses, except

for google trends data and the multiple regression analyses are conducted separately for each

asset and separately for each timeframe. Regression analysis is a statistical technique that is

used to examine and model the relationship between variables (Montgomery, Peck & Vining

2012). There are numerous ways to apply regression and the technique is used across many

fields. According to Montgomery et al. (2012) regression analysis might be the most widely

used statistical technique.

The study also uses multiple linear regression analysis, which is a regression model with

multiple independent variables, or regressor variables. Multiple regression analysis is used for

its two distinct purposes: prediction and drawing conclusions about individual predictor

variables. According to Mason & Perreault (1991) when multiple regression is used for

prediction, ”the researcher is interested in finding the linear combination of a set of predictors

that provides the best point estimates of the dependent variable across a set of observations.

Predictive accuracy is calibrated by the magnitude of the R2 and the statistical significance of

the overall model”. The second purpose of multiple regression analysis is to draw conclusions

about the individual independent variables. When conducting the analyses with this in mind,

”the focus is on the size of the regression coefficients, their estimated standard errors, and the

associated t-test probabilities”. (Mason & Perreault 1991).

The timeframe that the data is collected from includes two very significant and contrasting

market cycles. The timeframe is split in a way that the bull market phase of the research,

10.10.2016 – 7.1.2018 includes 455 daily observations. The latter phase that includes a

significant bear market spans the dates 8.1.2018 – 10.10.2018 that includes 276 daily

observations. Pearson’s correlation analysis, Spearman correlation analysis and multiple

regression analysis are conducted for the bull and bear market phases, as well as the total

47

timeframe that includes both phases. This is done to see if results vary in contrasting market

phases and timeframes.

3.3 Research reliability and validity

The quality of a research can be evaluated by using the two most important and fundamental

features, reliability and validity, in the evaluation of any measurement instrument or tool used

in research (Mohajan 2017). Lowhorn (2007) describes reliability as the ”ability of

researchers to come to similar conclusions using the same experimental design or participants

in a study to consistently produce the same measurement”. In the case of this study all the

data used is publicly available for anyone to analyze and the research method that is used,

correlation analysis, is generally well accepted as being reliable. Hence the research is easy to

test for and encouraged by the author to test for its reliability. ”Validity refers to the ability of

an instrument to measure what it is supposed to measure” Lowhorn (2007). Mohajan (2017)

cites Altheide & Johnson (1994) by characterizing the two features as reliability representing

the stability of findings and validity representing the truthfulness of findings. Since all the

data used in the study is gathered from publicly available and reliable sources and the research

method used is one that is generally accepted, as long as it is properly conducted, the study

can be thought to produce reliable and valid results.

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4 Results and analysis

4.1 Correlation analysis using Pearson’s correlation coefficient

In order to make observations about the value and price drivers of the cryptoassets used in the

study, Pearson’s correlation coefficient is used to measure the linear relationship between two

variables. In this case the correlation of the independent variables of total daily transaction

volume on the blockchain measured in US dollars, daily transaction count on the blockchain,

total transaction volume in US dollars on all crypto exchanges, daily active addresses on the

blockchain, median daily transaction value in US dollars and weekly worldwide google trends

data with the dependent variable, the daily price of each asset. Pearson’s correlation is

measured from three distinctive timeframes: the total sample of 10.10.2016 – 10.10.2018, a

bull market phase of 7.1.2018-10.10.2018 and a bear market phase of 8.1.2018 – 10.10.2018.

Pearson’s correlation gives a value between +1 and -1, with +1 meaning perfectly positive

correlation and -1 perfectly negative correlation. Choudhury (2009) gives the following,

admittedly contestable, guidelines on the strength of the linear relationship: 1 to 0,5 or -1 to -

0,5 = strong, 0,5 to 0,3 or -0,5 to -0,3 = moderate, 0,3 to 0,1 or -0,3 to -0,1 = weak, 0,1 to -0,1

very weak or none.

Table 1. Total sample timeframe. Pearson’s correlation coefficient 10.10.2016 – 10.10.2018

Daily price of:

Transaction volume in

USD

Transaction count

Exchange Volume

USD

Active addresses

Median Transaction

value

Google Trends

BTC 0,75 -0,05 0,93 0,51 0,01 0,72

ETH 0,62 0,91 0,86 0,91 0,13 0,46

LTC 0,58 0,84 0,68 0,89 -0,07 0,47

Doge 0,54 0,72 0,68 0,72 0,05 0,58

DASH 0,37 0,11 0,60 0,57 0,08 0,27

XLM 0,11 0,17 0,63 0,08 0,27

49

Observing table 1, for the total timeframe of observed days, the variable of transaction

volume conducted on the blockchain in USD value, shows the greatest correlation value with

the price of bitcoin (BTC). Of the other five cryptoassets four fall into the 0,6-0,4 range,

exhibiting moderate correlation. XLM price shows very weak correlation. Transaction count

shows interestingly varying correlation, ranging from 0,9 for ETH to -0,05 for BTC.

Exchange volume in USD is the only variable to show a moderately strong correlation of +0,6

for all the assets. Especially strong values are measured for two of the cryptoassets, 0,93 for

BTC and 0,86 for ETH. Active addresses show a moderate to strong correlation with five of

the six assets, with a strong 0,9 measure for BTC and ETH. Contrary to the other assets, XLM

shows very weak correlation. Median transaction value seems to have no, or at the most very

weak correlation. Google Trends data shows moderate correlation for all of the assets, with

BTC having the highest value of 0,7.

Ethereum is the only cryptoasset to show very strong correlation measures for the transaction

count, exchange volume in USD and active addresses variables. LTC and Doge display

moderately strong measures for all of these variables as well. Transaction count and daily

active addresses are direct measures of actual daily usage of the blockchains, so strong

measures in these variables are indicative of actual usage correlating with the prices. The

exchange volume in USD variable displays the amount of trading volume that each

cryptoasset is subjected to and can be thought of as a measure of speculation for the

cryptoasset. Interestingly only three assets show bigger correlation measures for the ”usage”

variables than for ”speculation” variable of exchange volume in USD: ETH, LTC and Doge.

However, the correlation measures are noticeably bigger only in the case of LTC.

An interesting observation can be made when studying these two variables and BTC, which is

by market share and market cap the biggest cryptoasset by far. In the case of BTC the

transaction count variable shows a very weak negative correlation and active addresses a

moderate correlation measure. The difference between the correlation of transaction count and

exchange volume in USD is the biggest for BTC and the difference between the measures of

active addresses and exchange volume in USD the second biggest. These observations suggest

that the price of BTC correlates more with the volume of trading on exchanges than with the

actual usage of the blockchain during the total timeframe.

50

Table 2. Bull market phase. Pearson’s correlation coefficient 10.10.2016 – 7.1.2018.

Daily price of:

Transaction volume in

USD

Transaction count

Exchange Volume

USD

Active addresse

s

Median Transaction

value

Google Trends

BTC 0,92 0,48 0,94 0,83 0,05 0,90

ETH 0,88 0,96 0,86 0,97 0,23 0,84

LTC 0,82 0,94 0,76 0,94 -0,05 0,65

Doge 0,70 0,82 0,82 0,76 0,08 0,80

DASH 0,44 0,87 0,82 0,76 0,83 0,41

XLM 0,19 0,35 0,83 0,32 0,64

In table 2 we can see that transaction volume on the blockchain in USD shows a slightly

greater measure of correlation for the prices of all of the assets during the bull market phase.

Strong 0,9 values are measured for BTC and ETH, with 0,8 for LTC. Again XLM exhibits the

lowest measure, a very weak correlation of 0,2. Transaction count on the blockchain also

shows greater measures of correlation for all of the assets during the bull market phase.

Compared to the total timeframe, BTC and DASH exhibit the greatest change in correlation

for transaction count. During the bull market phase BTC shows a correlation measure that is

0,43 higher and DASH has a correlation measure that is 0,76 higher. Strong or very strong

values are measured for ETH (0,96), LTC, (0,94), DASH (0,87) and Doge (0,82).

Exchange volume in USD shows nearly identical or slightly higher correlation for all assets

during the bull market phase. The correlation measures are quite strong with all of the

cryptoassets exhibiting a correlation of 0,76 or greater. BTC has a noticeably high correlation

of 0,94. Active addresses also exhibit nearly identical or higher correlation measures during

the bull market phase. Five of the six cryptoassets have moderately strong measures, with

ETH (0,97) and LTC (0,94) showing the strongest measures. Interestingly XLM has a

correlation of 0,32 which is noticeably lower than the second lowest measure of 0,76. Median

transaction values exhibit very low correlation except for DASH, which has a distinctively

strong correlation of 0,83. Google trends correlation measures are stronger during the bull

market phase as well, with the strongest measures for BTC (0,9), ETH (0,84) and LTC (0,8).

51

In the bull market phase the actual usage measures of transaction count and active addresses

again show very strong correlation measures for ETH and LTC, with strong measures for

Doge and DASH as well. This is not surprising since rising prices in bull markets tend to

bring in new speculators and users. The transaction counts can go up from the new users

sending their newly acquired cryptoassets from exchanges to private wallets and the newly

acquired wallets contribute to the rise of active addresses. The rising prices can also activate

dormant users to trade cryptoassets and make purchases, positively contributing to rising

transaction counts and active addresses. Google trends data also shows higher correlation

measures during the bull market, indicating that rising public interest correlates with rising

prices during the bull market.

Comparing the correlation of actual usage measures, transaction count and active addresses,

with the exchange volume in USD variable, there are only two cryptoassets that display

higher correlation with the actual usage variables during the bull market phase: ETH and

LTC. The gap between the usage variables and exchange volume in USD is also noticeably

smaller for BTC during the bull market phase.

Transaction volume on the blockchain measured in USD tends to get higher correlation

measures during the bull market phase. This suggests that the value exchanged on the

blockchain tends to correlate more with price and thus be higher during the bull market phase,

at least when measured in US dollars. This is not surprising since the USD value exchanged

rises with soaring prices even if the blockchain is not actually used more, because of

transactions having a higher USD value.

52

Table 3. Bear market phase. Pearson’s correlation coefficient 8.1.2018 – 10.10.2018.

Daily price of:

Transaction volume in

USD

Transaction count

Exchange Volume

USD

Active addresses

Median Transaction

value

Google Trends

BTC 0,82 0,33 0,84 0,71 0,82 0,77

ETH 0,76 0,82 0,71 0,75 0,15 0,75

LTC 0,72 0,67 0,66 0,76 -0,09 0,69

Doge 0,38 0,18 0,66 0,22 -0,04 0,67

DASH 0,31 -0,07 -0,26 0,05 0,09 0,22

XLM 0,08 0,04 0,60 -0,05 0,62

Table 3 shows us that the transaction volume on the blockchain in USD variable for the bear

market phase shows moderately strong correlation for LTC (0,72), ETH (0,76), and BTC

(0,82). Compared to the other two measured timeframes, these three cryptoassets exhibit

stronger correlation during the bear market compared to the total timeframe, but weaker

correlation compared to the bull market phase. However the values only vary by

approximately 0,2. The other cryptoassets, Doge, DASH and XLM show weaker correlation

measures compared to both of the other timeframes. The correlation values for these

cryptoassets do not vary greatly, with Doge having the greatest variance (around 0,3) in

values.

Transaction count correlation measures during the bear market phase vary greatly, from a

moderately strong correlation measure of 0,82 for ETH to a -0,07 for DASH. In general the

measures are lower compared to the other two phases, with Doge (0,18), DASH (-0,07) and

XLM (0,04) showing very weak measures. Interestingly DASH shows a strong correlation

measure during the bull market phase (0,87) but very weak measures during the other

timeframes. Contrary to this, Doge shows moderately strong measures during the other two

timeframes, but a very weak -0,07 during the bear market phase. ETH has the steadiest

correlation measures when looking at all three timeframes, with only a 0,14 gap between

highest (0,96) and lowest (0,82) measured value. BTC and DASH are the only cryptoassets to

measure negative, albeit very weak, measures of correlation. BTC for the whole timeframe

and DASH during the bear market phase.

53

The exchange volume in USD variable for the bear market phase shows moderately strong

correlation measures, 0,6 – 0,84 for five of the six assets, with DASH being the only one with

a weak and negative -0,26 measure. Compared to the other two timeframes, correlation values

tend to be nearly identical or slightly lower across the board. BTC and LTC have noticeably

steady measures of correlation that are within 0,1 across all timeframes. The correlation

measures are quite steady in all timeframes for three other cryptoassets as well, them being

within 0,15 for ETH, 0,16 for Doge and 0,23 for XLM. DASH seems to be the only outlier

having measures ranging from -0,26 during the bear market phase to 0,83 during the bull

market phase.

The active addresses variable for the bear market phase shows moderately strong measures for

BTC (0,71), ETH (0,75) and LTC (0,76). Contrary to these, the other three cryptoassets show

weak or very weak correlation measures: 0,22 for Doge, 0,05 for DASH and -0,05 for XLM.

Compared to the bull market phase, correlation values are lower during the bear market phase.

ETH and LTC have the least variance in measured values, with values within approximately

0,2 across all timeframes. Noticeably BTC is the only cryptoasset that has the lowest

correlation value, when measuring for the whole timeframe. XLM is the only cryptoasset to

have a negative correlation measure in any of the timeframes, with a very weak measure of -

0,05 during the bear market phase.

The median transaction value variable shows very weak correlation measures during the bear

market phase for all of the cryptoassets, except for a strong 0,82 for BTC. This is quite

contradicting when looking at the BTC measures for the other two timeframes, 0,01 for the

total timeframe and 0,05 during the bull market. Google trends correlation measures during

the bear market phase are moderately strong for BTC (0,77), ETH (0,75), LTC (0,69), Doge

(0,67) and XLM (0,62). DASH is the only one with a moderately weak correlation measure of

(0,22). The correlation values seem to be generally lower or nearly equal during the bear

market phase compared to the bull market phase. BTC seems to have the strongest and least

varying correlation measures across all timeframes, with a lowest correlation value of 0,72

during the whole timeframe and the highest 0,9 during the bull market phase.

Although the overall correlation measures are lower during the bear market phase compared

to the bull market, again the only two cryptoassets with higher correlation measures for the

54

usage variables compared to the speculative variable of exchange volume in USD are ETH

and LTC. These are the only two cryptoassets to have strong to very strong correlation

measures for the usage variables across all timeframes and higher measures for the usage

variables compared to the speculative variable across all timeframes, suggesting that their

prices and value are driven more by the actual usage of the blockchains rather than the

volume of speculative trading of the cryptoassets. On the other hand XLM seems to have the

biggest measures for the speculative variable compared to the usage variables, suggesting that

the cryptoasset’s price is driven more by speculation than actual usage. In the case of XLM

the transaction volume in USD variable also shows very weak correlation across all

timeframes, suggesting that the amount of value traded on the blockchain does not correlate

with the price.

55

4.2 Correlation analysis using Spearman’s rank correlation coefficient

Spearman’s rank correlation coefficient in used to measure the strength of a monotone

relationship between two variables. ”It does not require the assumption that the relationship

between the variables is linear, nor does it require the variables to be measured on interval

scales”. (Hauke & Kossowski 2011). As in Pearson’s correlation, when Spearman’s rank

correlation coefficient gives a value of 1 there is perfect positive correlation between data

pairs and perfect negative correlation when the value is -1 (Gauthier 2001). The same

variables that were used for Pearson’s correlation coefficient are used for Spearman’s rank

correlation as well.

Table 4. Total sample timeframe. Spearman’s correlation coefficient 10.10.2016 – 10.10.2018

Daily price of:

Transaction volume in

USD

Transaction count

Exchange Volume

USD

Active addresses

Median Transaction

value

Google Trends

BTC 0,79 -0,28 0,97 0,29 0,83 0,75

ETH 0,69 0,94 0,88 0,93 -0,04 0,55

LTC 0,68 0,93 0,85 0,97 0,33 0,54

Doge 0,89 0,82 0,88 0,80 0,68 0,31

DASH 0,78 0,82 0,74 0,79 0,36 0,12

XLM 0,78 0,57 0,90 0,53 0,25

Observing table 4 we can see that the transaction volume on the blockchain measured in USD

variable for the total timeframe shows moderately strong Spearman’s rank correlation

measures for five of the six cryptoassets, with Doge having the strongest (0,89) correlation

value. The transaction count variable shows strong correlation for ETH (0,94) and LTC

(0,93), moderately strong correlation for Doge (0,82), Dash (0,82) and XLM (0,57).

Interestingly BTC shows a moderately weak negative correlation of -0,28, contrary to the

other cryptoassets.

56

The variable of exchange volume in USD for the total timeframe shows strong correlation

values for all of the cryptoassets, with the lowest value being 0,74 for DASH and the highest

0,97 for BTC. The active addresses variable shows very strong correlation values for ETH

(0,93) and LTC (0,97) and moderately strong values for Doge (0,8) and DASH (0,79). BTC

has a moderately weak value of 0,29 and XLM a value of 0,53.

The median transaction variable gives varying values with BTC having the highest correlation

of 0,83 and ETH the only negative and very weak correlation value of -0,04. For the Google

trends variable BTC has the highest measure again (0,75) with Doge (0,31), DASH (0,12) and

XLM (0,25) having weak to very weak values. The values seem to greatly vary between the

cryptoassets, since ETH (0,55) and LTC (0,54) have values falling well in between the higher

and lower values.

Table 5. Bull market phase. Spearman’s correlation coefficient. 10.10.2016 – 7.1.2018.

Daily price of:

Transaction volume in

USD

Transaction count

Exchange Volume

USD

Active addresses

Median Transaction

value

Google Trends

BTC 0,89 0,31 0,96 0,69 0,89 0,89

ETH 0,90 0,95 0,87 0,95 0,41 0,86

LTC 0,86 0,95 0,83 0,95 0,65 0,77

Doge 0,83 0,74 0,89 0,69 0,72 0,67

DASH 0,84 0,88 0,88 0,83 0,86 0,30

XLM 0,76 0,51 0,88 0,39 0,59

Table 5 shows that during the bull market timeframe, the variable of transaction volume in

USD shows strong correlation values for all of the cryptoassets. Nearly all of the values are

slightly stronger compared to the total timeframe, with the only exception being Doge.

However the value of Doge is only slightly (0,06) lower during the bull market phase. The

transaction count variable during the bull market phase shows strong correlation values for

ETH (0,95), LTC (0,95), Doge (0,74) and DASH (0,88). BTC has the lowest correlation of

57

0,31. Compared to the total timeframe the values are nearly identical for nearly all of the

assets, only differing by 0,08 at the most, but not including BTC. BTC had a negative -0,28

correlation value for the total timeframe, but during the bull market phase is is reversed to a

moderately weak 0,31.

The Exchange volume in USD variable during the bull market phase shows strong correlation

values for all of the assets, with the lowest being 0,83 for LTC and the highest a 0,96 for

BTC. Compared to the total timeframe the values for all of the cryptoassets are nearly

identical, with the biggest change in DASH (0,14). The active addresses variable shows

moderate variance between the correlation values for the cryptoassets, since the highest value

is 0,95 for ETH and LTC both and the lowest 0,39 for XLM. The values are nearly the same

during the bull market phase compared to the total timeframe for ETH, LTC and and DASH.

XLM and Doge have values of that are a little bit lower during the bull market phase. BTC

has the biggest change in correlation value compared to the total timeframe (0,4 higher).

The values for the variable of median transaction value during the bull market phase have

lesser variance between cryptoassets than they do for the whole timeframe. During the bull

market phase nearly all of the assets show moderately strong to strong correlation values, with

BTC having the highest of 0,89. ETH is the only exception to the strong correlation values,

having a measure of 0,41 during the bull market phase. The correlation values are higher

compared to the total timeframe during the bull market phase for all for all of the

cryptoassets.

The google trends variable during the bull market phase shows moderately strong to strong

values for nearly all of the cryptoassets as well, with BTC having the highest value of 0,89.

Dash is the only cryptoasset that has a moderately weak value (0,3). Compared to the total

timeframe the correlation measures are higher during the bull market phase for all

cryptoassets.

58

Table 6. Bear market phase. Spearman’s correlation coefficient. 8.1.2018 – 10.10.2018

Daily price of:

Transaction volume in

USD

Transaction count

Exchange Volume

USD

Active addresses

Median Transaction

value

Google Trends

BTC 0,78 0,09 0,83 0,55 0,82 0,71

ETH 0,80 0,78 0,57 0,70 0,50 0,55

LTC 0,86 0,60 0,69 0,85 0,79 0,58

Doge 0,74 0,00 0,77 0,09 0,43 0,16

DASH 0,73 0,29 -0,46 0,29 0,51 0,22

XLM 0,44 0,16 0,52 0,09 0,33

From table 6 we can observe that the transaction volume in USD variable shows mostly

strong correlation values during the bear market timeframe. Five of the six assets have values

between 0,73 (DASH) and 0,86 (LTC). The main exception is XLM that has a value of 0,44.

Compared to the total timeframe (0,78) and bull market timeframe (0,76) values, XLM has a

noticeably lower correlation value that is 0,34 lower than the former and 0,32 lower than the

latter during the bear market phase. When comparing all of the correlation measures during

the bear market phase to the measures of the bull market phase, the correlation values are

slightly lower or the same for all other cryptoassets.

The transaction count variable shows great variance of values across the board, with ETH

having the highest measure of 0,78 and Doge the lowest measure of 0,00. ETH (0,78) and

LTC (0,6) are the only cryptoassets with moderately strong measures of correlation, with all

the other cryptoassets showing moderately weak (DASH 0,29) to weak measures. Compared

to the total timeframe, the measures are noticeably weaker for all assets except BTC, with

ETH having the lowest drop of 0,16. Compared to the bull market phase the correlation

values are noticeably lower for all cryptoassets.

The exchange volume in USD variable shows moderately strong to strong measures for BTC

(0,83), ETH (0,57), LTC (0,69), XLM (0,52) and Doge (0,77). DASH is the only cryptoasset

59

with a negative correlation measure during the bear market phase (-0,46). The correlation

measures for all cryptoassets are lower during the bear market phase compared to the other

two timeframes. The active addresses variable shows a wide range of values for the

cryptoassets during the bear market phase. Moderately strong to strong values are measured

for BTC (0,55), ETH (0,7) and LTC (0,85) and weak measures for Doge (0,09), DASH (0,29)

and XLM (0,09). Correlation values are weaker during the bear market phase comparing to

the other two timeframes, with the exception of BTC having a stronger correlation of 0,55

during the bear market compared to the total timeframe measure of (0,29).

Median transaction value shows positive moderately strong correlation values for BTC (0,82)

and LTC (0,79). Doge has the lowest measure of 0,43. Compared to the other two timeframes

there are no clear trends. The measures for BTC are the most steady with measures varying by

only 0,07 across all timeframes. The measures for ETH and DASH vary the most, with the

ETH measures varying by 0,54 and DASH measures by 0,5 across all timeframes.

The Google trends data shows great variance in measures during the bear market phase,

similar to the other two timeframes. BTC has a moderately strong measure of 0,71 similar to

the other two timeframes, with values varying by 0,18. The three biggest cryptoassets by

market cap BTC, ETH and LTC have nearly identical measures for the whole timeframe

compared to the bear market phase. Similar to the total timeframe, the other three cryptoassets

show very weak correlation during the bear market phase.

60

4.3 Regression analysis

According to Watsham & Parramore (1997) A regression model indicates that variation in the

dependent variable of Y can be explained by variation in the independent variable of X and by

the error term e. The goal is to find out how much of the variation in the dependent variable Y

is caused by the independent variable of X and how much by the error e. Since this study aims

to explain the changes in Y with several independent variables, a multiple regression model is

applied. Watsham & Parramore (1997) explain that the formula for the true relationship

between the dependent variable of Y and the various independent variables, the Xis is given

by:

A coefficient in the regression model explains the change in the dependent variable when an

independent variable changes by 1 unit and all other independent variables stay constant. To

evaluate the goodness of fit of the model, adjusted R2 is used. (Watson & Parramore 1997).

The multiple regression analysis conducted for the study measures the effects of independent

variables that are the total daily transaction volume on the blockchain measured in US dollars,

daily transaction count on the blockchain, total transaction volume in US dollars on all crypto

exchanges, daily active addresses on the blockchain, median daily transaction value in US

dollars, with the dependent variable of the price of each cryptoasset. The tests are conducted

separately for each asset and timeframe.

61

Table 7. BTC regression models during different timeframes.

Dependent variable:

BTC Price per dayTotal Timeframe

Bull Market

Bear Market

Coefficients t pCoefficients

t p Coefficients t p

Intercept 4599,591 15,9880,00

0 -629,065 -1,8850,06

0 6668,802 16,8300,00

0

TxVolumeUSD 0,000 13,0040,00

0 0,000 13,2690,00

0 0,000 2,9850,00

3

txCount -0,022-

15,6930,00

0 -0,020-

11,6020,00

0 -0,015 -7,9040,00

0exchangeVolume(USD) 0,000 33,093

0,000 0,000 22,088

0,000 0,000 4,772

0,000

activeAddresses 0,004 6,0630,00

0 0,011 12,1150,00

0 0,003 4,6880,00

0

medianTxValue(USD) 0,002 0,9270,35

4 0,001 0,6580,51

1 1,638 5,5430,00

0

Adj. R sq. 0,91   0,95 0,79

The multiple regression analyses for BTC (Table 7) show that the variables in the regression

model explain over 90% of the change in the dependent variable of price for the whole

timeframe. Statistically significant variables, ones with p < 0,05, are transaction value in

USD, transaction count, exchange volume in USD, and active addresses. Transaction count

has the most significant coefficient with a measure of -0,022. This means that somewhat

interestingly, the addition of transactions has a negative effect on the price development. For

the bull market phase the multiple regression analysis gives an adjusted r square of 0,95,

explaining 95% of the change in the dependent variable of price. Statistically significant

variables are the same as for the whole timeframe: transaction value in USD, transaction

count, exchange volume in USD, and active addresses. The coefficient for transaction count

stays nearly the same as for the whole timeframe (0,2) and the coefficient for active addresses

increases to 0,0106 during the bull market. Based on this the increase in transaction count

does not have as big of a negative effect on the price of bitcoin during the bull market as it

does during the whole timeframe and active addresses have a significantly bigger positive

impact on the price during the bull market than during the whole timeframe. The multiple

regression model has an adjusted r square of 0,79 during the bear market phase, meaning that

it does a significantly worse job of explaining the change in the dependent variable during the

bear market. Statistically significant measures are recorded for all of the independent

variables during the bear market, with median transaction value in USD having a statistically

significant measure in only this timeframe and a high coefficient of 1,63 as well. Other

62

variables with noticeable coefficients are transaction count and addresses, both of which have

smaller coefficients during the bear market than the bull market and the total timeframe.

Table 8. ETH regression models during different timeframes.

Dependent variable:

ETH Price per dayTotal Timeframe

Bull Market

Bear Market

Coefficients

     t pCoefficients

  t pCoefficients

  t p

Intercept -19,009 -2,6760,00

8 7,206 2,1560,03

2 -299,098 -5,3710,00

0

TxVolumeUSD 0,000 7,7190,00

0 0,000 8,3090,00

0 0,000 6,5160,00

0

txCount 0,001 8,7880,00

0 0,000 3,9760,00

0 0,00210,98

70,00

0exchangeVolume(USD) 0,000 5,365

0,000 0,000

-0,433

0,665 0,000 -1,071

0,285

activeAddresses 0,000 -1,6670,09

6 0,001 4,2440,00

0 -0,002 -6,1760,00

0medianTxValue(USD) 0,001 1,999

0,046 0,001 3,905

0,000 0,003 2,357

0,019

Adj. R sq. 0,87   0,94 0,75

The regressions for Ethereum are shown in table 8. The regression model for the whole

timeframe has a moderately strong adjusted r square of 0,87. All other variables, except for

active addresses score statistically significant measures, however, the coefficients of the

independent variables are quite small. It is worthwhile to note that all of the variables have a

positive coefficient. The multiple regression model does a better job of explaining the change

in the independent variable during the bull market phase, having an adjusted r square of 0,94.

During the bull market all other variables except for exchange volume in USD are statistically

significant and have positive coefficients. As was the case with Bitcoin, the regression model

has the lowest adjusted r square for Ethereum during the bear market as well (0,75). The

model again does a significantly worse job of explaining the changes in the dependent

variable during the bear market. Statistically significant variables are all the variables except

for the exchange volume in USD variable, as was the case during the bull market. Transaction

count and median transaction value in USD have by far the biggest coefficients during the

bear market. The bear market phase is also the first phase when a statistically significant

variable, active addresses, has a negative coefficient.

63

Table 9. LTC regression models during different timeframes.

Dependent variable:

LTC Price per dayTotal Timeframe

Bull Market

Bear Market

Coefficients

     t pCoefficients

     t pCoefficients

     t p

Intercept 2,473 1,4260,15

4 3,070 2,3350,02

0 29,135 3,561 0,000

TxVolumeUSD 0,000 -5,2450,00

0 0,000 0,6210,53

5 0,000 3,002 0,003

txCount -0,001 -6,6260,00

0 0,001 3,6130,00

0 -0,001 -4,726 0,000exchangeVolume(USD) 0,000 3,306

0,001 0,000 -1,267

0,206 0,000 5,238 0,000

activeAddresses 0,001 18,7660,00

0 0,000 2,7050,00

7 0,001 7,110 0,000medianTxValue(USD) 0,000 -0,938

0,349 0,000 -0,774

0,439 -0,001 -1,671 0,096

Adj. R sq. 0,82   0,88 0,65

The regression analyses for Litecoin are shown in table 9. A moderately strong adjusted r

square of 0,82 is recorded for the total timeframe. Only one variable is not statistically

significant during this timeframe, the median transaction in USD variable. Transaction

volume in USD and transaction count have negative coefficients and the active addresses and

exchange volume in USD variables have positive coefficients during the timeframe.

The model does a better job of explaining the price movement during the bull market, having

an adjusted r square of 0,88. During the bull market statistically significant variables are

active addresses and transaction count. Both of these variables also record positive

coefficients. In the bear market timeframe the adjusted r square of the model drops to a 0,65,

mirroring the movements of the regressions of the other cryptoassets. Only one variable is not

statistically significant, the median transaction value in USD. Only active addresses and

transaction count show consistent statistically significant measures throughout all timeframes.

64

Table 10. Doge regression models during different timeframes.

Dependent variable:

Doge Price per dayTotal Timeframe

Bull Market

Bear Market

Coefficients

    t pCoefficients

     t pCoefficients

    t p

Intercept 0,000 -3,234 0,001 -0,001 -5,9610,00

0 0,004 10,3960,00

0

TxVolumeUSD 0,000 2,162 0,031 0,000 0,3980,69

1 0,000 -0,8490,39

7

txCount 0,000 3,380 0,001 0,000 8,1210,00

0 0,000 -1,6280,10

5exchangeVolume(USD) 0,000 12,457 0,000 0,000 10,427

0,000 0,000 11,781

0,000

activeAddresses 0,000 5,706 0,000 0,000 -1,7200,08

6 0,000 -0,2710,78

7

medianTxValue(USD) 0,000 -0,219 0,827 0,000 0,9230,35

7 0,000 -1,1570,24

8

Adj. R sq. 0,65   0,77 0,43

The regression analyses for Doge are found in table 10. Compared to the previous three

cryptoassets of BTC, ETH and LTC, Doge has a significantly smaller market capitulation

during the timeframe that the research is conducted in. The adjusted r square measures are

also smaller for each timeframe. During the total timeframe the model does an average job of

explaining the changes in price, having and adjusted r square of 0,65, with statistically

significant variables being transaction volume in USD, transaction count, exchange volume in

USD and active addresses. All of these have positive coefficients as well. During the bull

market phase the model does a little better of explaining price movement, with an adjusted r

square of 0,77 and significant variables in transaction count, exchange volume in USD and

active addresses. In the bear market phase the model does the worst job of explaining price

movement, having an adjusted r square of 0,43 and only one statistically significant variable

in exchange volume in USD.

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Table 11. Dash regression models during different timeframes

Dependent variable:

DASH Price per dayTotal Timeframe

Bull Market

Bear Market

Coefficients t p Coefficients t p Coefficients t p

Intercept -80,322 -4,438 0,000 -78,061-

5,064 0,000 387,294 5,822 0,000

TxVolumeUSD 0,000 5,820 0,000 0,000-

0,306 0,760 0,000 5,030 0,000

txCount -0,003 -8,562 0,000 0,063 7,335 0,000 -0,001-

1,602 0,110

exchangeVolume(USD) 0,000 9,473 0,000 0,000 2,516 0,012 0,000-

3,638 0,000

activeAddresses 0,008 12,526 0,000 -0,004-

3,276 0,001 0,001 1,125 0,261

medianTxValue(USD) 0,003 1,896 0,058 0,281 4,302 0,000 0,002 1,182 0,238

Adj. R sq.  

The regressions for DASH are listed in table 11. For the total timeframe the model recorded

just a measure of 0,51 for adjusted r square, but all other variables except median transaction

value in USD recorded statistically significant p-values. The regression had an adjusted r

square of 0,78 for the bull market, with statistically significant values for all other variables

except transaction volume in USD. As was the case with other assets the model performed

the worst during the bear market, with an adjusted r square of 0,15 and statistically significant

variables in transaction volume in USD and exchange volume in USD.

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Table 12. Stellar Lumens regressions during different timeframes.

Dependent variable:

XLM Price per dayTotal Timeframe

Bull Market

Bear Market

Coefficients      t p Coefficients       t p Coefficients      t p

Intercept 0,085 15,250 0,000 0,012 4,201 0,000 0,243 30,554 0,000

TxVolumeUSD 0,000 1,133 0,258 0,000 1,926 0,055 0,000 0,904 0,367

txCount 0,000 -0,440 0,660 0,000 0,839 0,402 0,000 0,589 0,556

exchangeVolume(USD) 0,000 20,934 0,000 0,000 27,976 0,000 0,000 12,125 0,000

activeAddresses 0,000 1,646 0,100 0,000 0,184 0,854 0,000 -0,933 0,352

Adj. R sq. 0,40   0,70 0,35

The regression for Stellar Lumens can be found in table 12. The same trend of strongest

adjusted r square during the bull market continued with XLM, with the model having a

measure of 0,7 during the bull market, 0,4 for the total timeframe and just a 0,35 during the

bear market. There were also the least statistically significant variables with XLM, with only

exchange volume in USD having statistically significant values. However this variable was

constantly statistically significant across all timeframes.

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5 Conclusions

In order for legislators, investors and hobbyists to better understand and consider the

ramifications of this new technology and asset class, the study aims to argue that cryptoasset

prices and values are also driven by actual usage in addition to pure speculation by market

participants, helping to further legitimize them as a new asset class. Studying the metrics of

the cryptoassets’ underlying blockchains and market data also gives existing and potential

investors potentially valuable data on which to base their investment decisions on. Although

conclusive evidence and absolute truths are hard to establish in the case of such an emerging

market, we can make solid assumptions about the price and value drivers based on this study.

Based on the results of the two correlation analyses and multiple regression, the effects of the

independent variables vary greatly by cryptoasset and measured timeframe. The variables that

measure the actual usage rate of the blockchain, transaction count and active addresses and in

a way transaction volume in USD, seemed to affect certain cryptoassets more than others.

Especially Ethereum and Litecoin seemed to be driven more by these usage measures than the

other cryptoassets. On the other hand the price of bitcoin greatly correlated with the exchange

volume in USD variable that measures the cumulative amount of trading done with the

cryptoasset on exchanges, which would lead us to the interesting assumption that the price is

driven more by speculative behavior than actual usage. In addition to this, the amount of

transactions conducted on the Bitcoin blockchain has a negative correlation and coefficient in

the study. This suggests that as the amount of usage increases the price is driven down, or as

the transactions go down the price is driven up.

Overall, the studied variable that seemed to affect prices the least was the median transaction

value, which did not show any consistent proof that it affects the prices of any of the

cryptoassets in a significant way. All in all, the correlation measures were higher and more

steady for the three biggest cryptoassets in the study, bitcoin, ether and litecoin. However, it is

important to note that correlation does not necessarily imply causation. The multiple

regression models also did the best job of explaining the changes in price for these three

cryptoassets. In addition to this the measured correlations were highest during the bull market

phase of the study and the multiple regression models worked best during the same

68

timeframe. The findings lead us to question if the prices are actually driven more by the

increased usage, or does the increased price increase usage by bringing new users to the

ecosystem and activating older passive users. In any case, it seems that the increase in price

leads to a positive feedback loop that brings in more users and activates the whole network.

We can also analyze the nature of the bear market. Specifically why the metrics correlated

less with the cryptoassets’ prices during that timeframe and the regression models did a worse

job of explaining the changes in price. It could be that the prices plunged more than ”they

deserved to” or that they went down less than they should have. Anyway, some cryptoassets

like Stellar Lumens showed inconsistent correlations throughout all timeframes and very little

correlation during the bear market timeframe, so it can be suggested that the price movements

for the smaller cryptoassets are somewhat irrational, driven by speculation and not based on

any verifiable metrics.

In addition to this, since Bitcoin is by far the biggest and most influential cryptoasset we can

question if the speculative behavior that is concentrated on its trading affects the overall

market more than it should, driving up the prices of cryptoassets that do not have legitimate

usage during the bull runs and bring down the prices of legitimately used cryptoassets during

the bear market? Based on the research in this study it can be suggested that it does and until

the other cryptoassets grow mature enough to warrant better pricing and valuations models,

this will probably be the case in the near term.

Since we live in a time where cryptoassets as an asset class are still maturing, future studies

can further improve upon the ideas and research conducted in this study to further analyze

which metrics play significant roles in price and value formation in the future. Several

different valuation models have already been suggested as well, for example see Wei & Yiu

(2018), but the variety of different kinds of tokens, cryptocurrencies and cryptoassets makes it

extremely difficult to form a general model of valuation. This study also features only four

cryptocurrencies, one cryptoasset for a smart contract platform and a cryptocurrency that

improves fiat-currency payments. Once other cryptoassets mature enough to be studied it

would be extremely relevant to redo tests similar to ones conducted in this study to see how

the prices and value are driven for a bigger portion of the cryptoasset class. It is also highly

likely that as the asset class matures, the prices and values of the assets will be based more on

69

the actual usage of the underlying blockchains than the speculative trading and future growth

expectations placed on these networks.

Since the inherent nature of these decentralized systems is to create and shift trust in human

communications towards trusting protocols instead of third parties, some aspects of the value

of these systems will continue to be difficult to measure and distinguish. The more

participants a network like Bitcoin or Ethereum has, the more human trust is being directed at

the protocol, with the game theoretically designed rules of the network encouraging

participants to make decisions that align the interests of the participants and the whole

network. When more and more participants place their trust on the network and are able to

communicate and exchange value through it, the more valuable the network will become as a

whole and for every single user. As an example, a mobile phone is not very useful if one or

even a few people are using them, but when the mobile phone network grows to include

whole countries and most the world, the communication benefits provided by it grow

immense. However, in this case it can be argued that the value provided by one new user to

the existing users of a decentralized network does not grow linearly, which makes trying to

evaluate the future values of the networks with for example 100 000 or 100 000 000

participants extremely complex. The author would argue that because of these questions,

“how to value the trust placed on the network?” and “how to measure the value of a new

network participant or participants?”, we have quite a challenge in figuring out what these

networks should be and will be worth.

As for potential and existing investors, it is relevant to note that new technologies have

always shown extremely volatile growth periods when they have been introduced into society.

The cryptoasset markets will most likely follow the same path and experience strong boom

and bust cycles in the future as well. It would seem that investing in cryptoassets that have

their value driven more by actual usage and have the potential to grow that usage in the

future, provide the most rational choices of investment.

70

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