Analyzing the Primary Value Drivers of Leading Cryptocurrencies - An Empirical Study
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Analyzing the Primary Value Drivers
of Leading Cryptocurrencies
An Empirical Study
PREPARED BY
Robert Richter, CFA
Philipp Rosenbach
Commissioned by Iconic Funds & Cryptology Asset Group
1
DISCLAIMER
Iconic Funds GmbH is the holding company of a series of
subsidiaries that manage and issue crypto asset investment
products. Collectively, Iconic Funds GmbH and its subsidiaries
are branded as “Iconic Funds.” Iconic Funds is a joint venture be-
tween Iconic Holding GmbH and Cryptology Asset Group p.l.c.
Cryptology Asset Group p.l.c. (ISIN: MT0001770107;
Ticker: CAP) (“Cryptology”) is a leading European crypto
asset and blockchain-related business model investment
company. Founded by Christian Angermayer’s family
office, Apeiron Investment Group and crypto-legend Mike
Novogratz, Cryptology is the largest publicly traded holding
company for blockchain- and crypto-based business models in
Europe. Noteworthy portfolio companies include crypto-giant
and EOSIO software publisher Block.one, leading HPC pro-
vider Northern Data, commission-free online neobroker next-
markets, and crypto asset management group Iconic Holding.
Collectively, Iconic Funds and Cryptology are referred here-
into as “THE PARTIES.”
In no event will you hold THE PARTIES, their subsidiaries, or
any affiliated party liable for any direct or indirect investment
losses caused by any information in this report. This report is
not investment advice or a recommendation or solicitation to
buy any securities.
THE PARTIES are not registered as investment advisors in any
jurisdiction. You agree to do your own research and due
diligence before making any investment decision with respect
to securities or investment opportunities discussed herein.
Articles and reports include forward- looking statements, esti-
mates, projections, and opinions which may prove to be sub-
stantially inaccurate and are inherently subject to significant
risks and uncertainties beyond THE PARTIES’ control. These
articles and reports express opinions, which have been based
upon generally available information, field research, inferenc-
es and deductions through our due diligence and analytical
process.
THE PARTIES believes all information contained herein
is accurate and reliable and has been obtained from
public sources. THE PARTIES believe it to be accurate
and reliable. However, such information is presented
“as is,” without warranty of any kind.
2
Executive Summary
This report analyzes the relationship between Payment coins’ values are largely driven by
a selection of 25 leading cryptocurrencies financial cluster data. Bitcoin’s value was
such as Bitcoin, Ethereum and XRP, and their found to be primarily driven by its Stock-to-
underlying value drivers. It conclusively shows flow ratio. For other coins it was found that
that the value drivers of the many different Bitcoin movements heavily influence their
cryptocurrencies differ based on their under- own price movements. In most cases this is
lying structure and use of a blockchain. expected, especially for forks of Bitcoin, but
in the case of Litecoin and Monero it was
Until now, the majority of relevant research pa- observed that GitHub and social media ac-
pers focussed on a limited selection of crypto- tivity are strong and medium value drivers,
currencies. This report, however, analyzes a respectively.
wide range of cryptocurrencies and their po-
tential value drivers. It is hypothesized that the IOT was found to be driven by development
price of each category of cryptocurrencies cluster data. Network usage is a strong
is driven by a different set of attributes. Data driver of Chainlink, for instance. Due to
was sourced from Santiment, Quandl.com and data limitations, it was not possible to infer
Etherscan.io and grouped into the following the same conclusion for IOTA, however it is
clusters: a reasonable assumption that IOTA is driven
by the same cluster. Furthermore, the price
• Financial: contains financial information of Bitcoin and Ether were found to be value
such as prices, transaction volumes or drivers as well.
exchange inflows and outflows
• Development: measures the develop- Lending: Lending coins appear to be driv-
ment activity on each blockchain protocol en by the financial cluster, i.e. the price of
• Social: reflects the presence of each Ether and Bitcoin, showing overall respon-
protocol on social media, such as Twitter siveness to the market. Additionally, the
• Usage: reflects how widely a protocol MVRV ratio closely tracks price. dApps are
is in use driven by the financials and the development
• Network: measures the size or sophisti- clusters. Here, the price of Ether is a sig-
cation of the protocols’ network nificant driver since it is the leading block-
chain offering smart contracts for dApps
Rather than attempting to build the ultimate pric- (in terms of market capitalisation). GitHub
ing model for cryptocurrencies, the analysis activity was also found to be a value driver,
relied on a visual analysis of the trends in whereas developer activity appears to be
cryptocurrencies and their underlying value less impactful on asset price.
drivers. The research definitively showed that
different categories of cryptocurrencies are
driven by separate fundamentals.
3
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Research Partners
4
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
5
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Introduction
Recently, Bitcoin, Ethereum and other cryptocurrencies For example, Bitcoin is considered to be a new
have significantly risen in value. The blockchain eco- ‘digital gold’ and should therefore be driven by
system is rapidly growing while institutional investors its stock to flow ratio. Ethereum’s key feature is the
are starting to enter the market and regulation is ability to build dApps on top of its infrastructure,
providing a legal framework to operate within. The so it would be expected that ETH is driven by
expansion of the blockchain industry, its expanding the number of applications and smart contracts
recognition and the rising prices of cryptocurrencies developed within the network and their associated
raise the question which fundamental drivers are in- transaction fees. These relationships are investigated
fluencing the market value and prices of cryptocurrencies. further in this report.
In our previous research reports, the value of including As outlined in more detail in the following chapter,
cryptocurrencies in a portfolio allocation and whether this topic has been partially analyzed in a number
traditional investment strategies are applicable to crypto- of articles and papers which focussed on either a
currencies were investigated. Up until now, however, certain coin or driver. However, a wholistic empirical
it has not been analysed what the ultimate driver of prices analysis of the major coins and a large base of
for this particular asset class and the cryptocurrencies potential drivers has not been conducted yet. The
comprising it are. This report aims to answer this question remainder of this report is organized as follows: The
by analysing the statistical relationship between coins second section is intended to provide an overview
such as Bitcoin, Ethereum and XRP and underlying drivers. of the most relevant literature from the academic
and industrial sector. Section three introduces the
The hypothesis is that there is not one unique set of data and section four is intended to provide an
drivers that is applicable for all cryptocurrencies. overview of the methodology used to obtain the
Instead, drivers are expected to depend on the results. Section five shows the results of the statis-
unique characteristics of the underlying protocols. tical analysis and Section 6 concludes this report.
6
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Literature review
The value drivers of cryptocurrencies are a topic that has
received significant attention in academia and industry.
Whilst all blockchain protocols have common features emphasizes that the long-term appreciation potential
such as their immutability, automation of trust and the fact coupled with the hedge against inflation makes crypto-
that they are virtually impossible to shut down, cryptocur- currencies an attractive asset for Treasury departments.
rencies are also expected to have different value drivers
due to differences in their protocol set-up. The opinions and With respect to economic variables, Walther et al. (2019)
findings are as diverse as the blockchain ecosystem itself find that the Global Real Economic Activity Index is the best
and each research tends to hone in on one specific value predictor for volatility. Their conclusion is that cryptocurren-
driver rather than looking at the system holistically. cies are driven by global trends rather than localized ones
The remainder of this section summarizes the analysis because of the global, decentralized nature of crypto-
of cryptocurrency price behavior showing that a large suite currencies.
of potential value drivers has been analysed from financials
to network infrastructure and social media. Considerable production costs such as the hardware
needed, the infrastructure as well as the energy re-
The Financial Times (2021) recently found that Bitcoin quired to mine coins also ensure a certain price level,
appears to be primarily driven by its Stock to Flow ratio because the value of a cryptocurrency is unlikely to drop
and by whether derivatives for it can be traded. Tradimo below its production costs. Regulation is a significant
(2021) argues that node count is a significant driver be- driver of prices as well. Markets lacking a sufficient de-
cause it measures the size of the network and proxies the gree of regulation are less likely to be considered suitable
number of active wallets. The price-to-node ratio is viewed investments for large financial companies such as pen-
as an indicator for strength of the community and resistance sion or hedge funds and will therefore hamper the price of
to crisis. The article lists additional attributes as value drivers those assets (Tradimo, 2021).
for cryptocurrencies, such as popularity which measures
whether exchanges offer the coin and the number of use Bhambhwani, et al. (2019) argues that cryptocurrencies
cases and applications. For example, PayPal’s announce- are fundamentally driven by two factors, namely the trust-
ment to offer services in BTC, ETH, Litecoin and Bitcoin worthiness of the network and their respective adoption.
cash have signifi-cantly increased their retail exposure, and They stipulate that both factors are long term value drivers.
hence, their potential to increase in popularity (Das, 2020). The authors find that computing power and the number of
In contrast, Balcilar et al. (2017) find that trading volume is active users are the best proxies for trustworthiness and
not a useful predictor of Bitcoin volatility. adop-tion, respectively. Rathan et al (2019), on the other
hand, find social media activity is a significant driver of the
From an economic view these articles list inflation, price of cryptocurrencies. In particular, they find Twitter
production costs and regulation as price drivers. In- and Google trends are meaningful drivers of prices.
flation is assumed to be a driver because crypto-
currencies are not subject to monetary policy set by central The following sections build upon this research but aim to
banks. Consequently, they are a good store of value com- combine the findings into a wholistic model to determine
pared to fiat currencies in inflationary periods. Das (2020) what factor ultimately drives the value of cryptocurrencies.
7
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Data
The data for this report is sourced from a variety was reduced based on economic understanding
of sources, which are all described in the following: as well as best practice. The first course of action
was to categorize the data into different buckets,
Santiment is an online platform providing as shown below:
large amounts of cryptocurrency related • Financial: This group contains financial infor-
data.¹ The database offers a wide range of mation such as prices, transaction volumes or
data sets, including financial data as well as exchange inflows and outflows.
development activity, social media trends and • Development: This group measures the de-
overall usage of the cryptocurrency universe. velopment activity of each blockchain protocol.
The main drivers used for this report have been
Quandl.com is a provider of financial data the activity on GitHub and overall developer
including alternative investments. Quandl.com activity.
has several libraries related to Bitcoin, • Social: This category reflects the presence of
including the stock to flow of Bitcoin and each protocol on social media, such as Twitter
the difficulty of mining a block. and Google trends.
• Usage: Whilst not an official category on
Etherscan.io is a database containing infor- Santiment, this bucket was created to reflect
mation on the Ethereum network including the how widely a protocol is used.
number of smart contracts deployed and the • Network: Measures the size or sophistica-
difficulty of the network. tion of the network
Depending on the coin, the number of attributes Table 1 provides a list of drivers that were considered
that could be sourced is large. In order to reduce relevant in this report including their description. The
the number of attributes to a manageable and table also explains which of the five categories the
meaningful size, the number of potential drivers cryptocurrency was allocated to .
¹ www.Santiment.net
² The data was initially sourced at a daily frequency. Daily data
would maximize the data points available but is rather noisy.
Monthly data is less noisy in comparison but reduces the
number of available data points drastically. In order to strike
the right balance between data availability and noise reduction,
the analysis was conducted based on weekly data.
8
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Table 1:
Classification Attribute Description
Development DEV activity This attribute measures the development activity of a project done in its public GitHub repositories.
A developer's time is a relatively expensive resource (especially in crypto), so high development
activity implies that:
Development Github activity The attribute measures the total GitHub activity i.e. it counts all events. It is always equal
or bigger than the dev activity.
Development Number of smart Measures the number of smart contracts deployed and verified on the Ethereum mainnet.
contracts
Development Number of dApps Measures the number of decentralized Apps deployed on the Ethereum mainnet.
Sourced from https://www.stateofthedapps.com/stats/platform/ethereum#new
Financial Prices Measures the price of the cryptocurrency in USD at close of day.
Financial Stock-to-Flow Measures the scarcity of Bitcoin and is often used in the analysis of raw materials.
Financial Exchange inflow The exchange inflow measures how many coins/tokens are moved from non-exchange
to exchange wallets.
Financial Exchange outflow The exchange outflow measures how many coins/tokens are moved from exchange
to non-exchange wallets.
Financial Exchange balance The exchange balance measures the difference in inflow and outflow of coins into exchanges.
The usefulness of this metric comes from the fact that transactions from missing /unknown exchange
wallets to missing /unknown exchange wallets cancel each other - for example the way Coinbase
works makes it impossible to detect exchange wallets.
Financial Exchange funds flow Returns the difference between the tokens that were deposited minus the tokens that were
withdrawn from an exchange for a given coin per day.
Financial Exchange percent Exchange wallet holding percent of total supply.
supply
Financial Transaction volume This metric shows the aggregate amount of coins /tokens across all transactions that happened
on the network for a given asset in an interval.
Financial Velocity The metric shows the average number of times that a coin /token changes wallets each day. Simply put,
a higher velocity means that a coin /token is used in transactions more often within a set time frame.
Social Social Dominance Social Dominance for an asset compares the Social Volume of that asset to the combined social volume
of all available assets. Social Dominance of 50 for an asset means that half of all messages/posts
regarding assets are discussing exactly this asset.
Social Social volume The attribute measures the total number of text documents that contain the given search term at least
once. Examples of documents are telegram messages and reddit posts. If a single short telegram message
includes the word crypto more than once, this message will increase the social volume of the word crypto
by 1. If a long reddit post contains the word crypto 10 times, this again will increase the social volume
of the word crypto by 1.
Social Twitter followers Returns the historical count of twitter followers.
Usage Active Addresses Active Addresses refers to the number of unique addresses that participated in transactions on a blockchain.
Usage Gas used This attribute measures the 'Gas Fees' by a blockchain. When you send tokens, interact with a contract or
do anything else on the blockchain, you must pay for that computation. That payment is calculated in Gas.
Network Difficulty Measures the difficulty of mining the next block.
Network Network growth Measures the number of addresses that have joined the network.
9
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Methodology
The ultimate goal of the analysis is is to establish – on a univariate basis
to determine which drivers explain – which attributes are economically
the price movements in cryptocur- intuitive drivers that are supported
rencies best. Various options exist by underlying data as value drivers.
to analyse the data, and the author
opted for a univariate approach. Note that “soft” indicators such as
Rather than attempting to build multi- regulation were deliberately left out
variate models that may or may of this analysis because regulation
not work depending on the type is applied at the national level
of model, the focus is placed on whereas cryptocurrencies operate
first identifying which characteristic globally. Since there is no unified
seems to be driving a coin’s price global cryptocurrency regulation, it
movement. would not have been possible to
accurately predict the impact natio-
In other words, instead of using a nal regulation has on the market.
brute force approach to build a sta- The following section shows the results
tistical model, the aim of this report of the analysis.
10ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
11ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Results
This section outlines the results of the univariate analysis. For the
interest of the reader, charts are provided which show the direct
relationship between the price of the cryptocurrency and the potential
value driver. This section focusses on Bitcoin, Ethereum, Compound
(ERC-20 token), XRP and the Binance coin to outline the approach.
Towards the end of the section the analysis is summarized for all the
coins analyzed in scope of this report.
Bitcoin
Bitcoin is the largest and most well- price level started to increase in the Difficulty: The difficulty to mine Bit-
known of all cryptocurrencies. Many following months. After the halving coin has significantly increased since
articles have been written about its event in May 2020, the price level 2017. As the chart shows, the price
potential value drivers and many also rose in the following months. of Bitcoin has not reflected the in-
experts consider Bitcoin as a digital Since market liquidity was low in creased difficulty.
gold, which is why the stock-to-flow the first few years of Bitcoin, only the
model is hypothesized to be a key past two halvings provide meaningful Developer Activity: Developer
driver. The results of the analysis of information. Since the last halving activity was rather volatile in the
potential Bitcoin value drivers are occurred in 2020, the time-horizon past. Particularly, the price increase
shown in Figure 1. was not sufficiently long and therefore of December 2017 was not met with
will require further research to con- increased developer activity. How-
Stock-to-Flow: Bitcoin is regularly cretely prove the stock-to-flow model ever, prior to the most recent increase,
compared to gold, due to its inherent as the main driver of the Bitcoin price. developer activity has risen.
scarcity. Modelling the value of Bit-
coin akin to gold was first suggested Active Addresses: The plot of active Social Dominance: The dominance
by Twitter personality Plan B (2019), Bitcoin addresses against the Bitcoin of Bitcoin on social media appears to
who used the stock-to-flow approach price is shown in the top right panel have decreased over the past years.
to analyse the value of Bitcoin. The of Figure 1. It shows a steady increase This is unsurprising given that other
top left panel of Figure 1 compares in active addresses over the years. The coins, such as Ether and XRP, have
the price evolution of Bitcoin and its number of active addresses closely entered the market. The trend in social
stock-to-flow ratio.³ The step-chang- tracks the price until mid-2020, but it dominance does not appear to be in
es in the stock-to-flow ratio are driven does not explain the recent spike in the line with price movements.
by Bitcoin halvings. The theory sug- Bitcoin price.
gests that the price of Bitcoin should
increase as the stock-to-flow ratio in-
creases, making Bitcoin more scarce.
The data does indeed show evidence ³ The stock to flow ratio was calculated based on mined bitcoins. The bitcoins mined
on a daily basis represent the flow, whereas the existing stock of bitcoin represent the
of this behavior and the stock-to-flow stock. In order to align with Plan B (2019), the daily mined volume was multiplied by
ratio appears to be a leading driver a factor of 365 to represent the annual volume. The graph shows a 3-day moving
average to reduce volatility. The data was sourced from Quandl.com
of the price of Bitcoin. After the price
jumped in August 2016, the average
12ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Figure 1: Bitcoin Drivers₄
Bitcoin Price vs. Stock-to-Flow Bitcoin Price vs. Active Addresses
45 1.4
45 45
40
40 40 1.2
35
35 35
Bitcoin Closing Price in Thousands
Bitcoin Closing Price in Thousands
1.0
Active Addresses in Millions
30
30 30
0.8
Stock-to-Flow
25
25 25
20 0.6
20 20
15
15 15
0.4
10
10 10
0.2
5
5 5
0 0
0 0
Mar 13
Aug 13
Nov 13
Mar 14
Aug 14
Nov 14
Mar 15
Aug 15
Nov 15
Mar 16
Aug 16
Nov 16
Mar 17
Aug 17
Nov 17
Mar 18
Aug 18
Nov 18
Mar 19
Aug 19
Nov 19
Mar 20
Aug 20
Nov 20
Mar 13
Aug 13
Nov 13
Mar 14
Aug 14
Nov 14
Mar 15
Aug 15
Nov 15
Mar 16
Aug 16
Nov 16
Mar 17
Aug 17
Nov 17
Mar 18
Aug 18
Nov 18
Mar 19
Aug 19
Nov 19
Mar 20
Aug 20
Nov 20
Closing Price Stock-to-Flow Closing Price Active Addresses
Bitcoin Price vs. Difficulty Bitcoin Price vs. Developer Activity
2,5E+13 45
45 45
40
40 40
2,0E+13 35
35 35
Bitcoin Closing Price in Thousands
Bitcoin Closing Price in Thousands
30
30 30
Developer Activity
1,5E+13
25
25 25
Difficulty
20
20 20
1,0E+13
15
15 15
10
10 0,5E+12 10
5
5 5
0 0
0 0
Mar 13
Aug 13
Nov 13
Mar 14
Aug 14
Nov 14
Mar 15
Aug 15
Nov 15
Mar 16
Aug 16
Nov 16
Mar 17
Aug 17
Nov 17
Mar 18
Aug 18
Nov 18
Mar 19
Aug 19
Nov 19
Mar 20
Aug 20
Nov 20
Mar 13
Aug 13
Nov 13
Mar 14
Aug 14
Nov 14
Mar 15
Aug 15
Nov 15
Mar 16
Aug 16
Nov 16
Mar 17
Aug 17
Nov 17
Mar 18
Aug 18
Nov 18
Mar 19
Aug 19
Nov 19
Mar 20
Aug 20
Closing Price Difficulty Closing Price DEV Activity Nov 20
Bitcoin Price vs. Social Dominance
120
45
4 Note: This report was written as part of an analysis of fundamental value drivers
40
100 of cryptocurrencies. Accordingly, value drivers that are clearly not fundamental in
35 nature have been excluded. An example would be the current price fluctuation of the
Bitcoin Closing Price in Thousands
30
80 Dogecoin. This currency, introduced to the world in 2018, has seen temporary price
Social Dominance
gains of over 100% per day due to a pump and dump scheme by an internet commu-
25
60 nity. Since these fluctuations are not attributable to any fundamental driver,
20 the have not been included in this report.
40
15
10
20
5
0
0
Mar 13
Aug 13
Nov 13
Mar 14
Aug 14
Nov 14
Mar 15
Aug 15
Nov 15
Mar 16
Aug 16
Nov 16
Mar 17
Aug 17
Nov 17
Mar 18
Aug 18
Nov 18
Mar 19
Aug 19
Nov 19
Mar 20
Aug 20
Nov 20
Closing Price Social Dominance
13ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Ethereum
The key selling point of the Ethereum Number of Smart Contracts: The Transaction volume and social
blockchain is that decentralized apps number of verified smart contracts ex- dominance do not appear to have
(dApps) can be built on top of it to hibits interesting behavior as shown a meaningful impact on the price
facilitate new functionality and new in the middle left panel of Figure 2. movements of Ethereum.
business models. Unlike Bitcoin the During the so-called “crypto winter”
strategy is not to create value through it appeared to be a lagging indica-
scarcity, but rather through an ever- tor whereas during the most recent
expanding network. Consequently, price increase, towards the end of
the potential drivers differ to those 2020, it appears to be a leading in-
of Bitcoin and are outlined in Figure dicator. In any case, the time series
2. Note that this analysis also serves exhibits a similar pattern as the price
as a proxy for other blockchains of Ethereum and thus appears to be
with similar characteristics, such as a reasonably good price predictor.
Polkadot, Cardano or Tron.
Active Addresses: The number of active
Number of dApps: The top left users of the Ethereum chain also ap-
panel shows the number of dApps pear to drive prices. As shown over time,
developed on the Ethereum block- the evolution of the number of active
chain over time. Comparing the users closely mirrors price movements.
number of contracts developed to
the price behavior of Ethereum Difficulty: The difficulty is shown in
shows that the number of contracts the top right panel of Figure 2. The
is a lagging indicator rather than a time series moves in line with the price
leading one. The number of dApps movements to some extent, but less
created only increased significantly so compared to the number of smart
after the price spike of 2017. contracts and active addresses.
14Ethereum Closing Price Ethereum Closing Price Bitcoin Closing Price
0
200
400
600
800
1000
1200
0
200
400
600
800
1000
1200
1400
0
200
400
600
800
1000
1200
1400
Jul 15 Jul 15 Apr 15
Sep 15 Sep 15 Jun 15
Nov 15 Nov 15 Aug 15
Jan 16 Jan 16 Oct 15
Mrz 16 Mrz 16 Dec 15
Mai 16 Mai 16 Feb 16
Closing Price
Closing Price
Closing Price
Jul 16 Jul 16
Sep 16 Apr 16
Sep 16
Nov 16 Nov 16 Jun 16
Jan 17 Jan 17 Aug 16
Mrz 17 Mrz 17 Oct 16
Mai 17 Mai 17 Dec 16
Jul 17 Jul 17 Feb 17
New dApps
Sep 17 Sep 17 Apr 17
Figure 2: Ethereum Drivers
Nov 17 Nov 17 Jun 17
an 18
Transaction Volume
an 18 Aug 17
Mrz 18 Mrz 18 Oct 17
Verified Smart Contracts
Mai 18 Mai 18
Jul 18 Dec 17
Jul 18
Ethereum Price vs. New dApps
Sep 18 Feb 18
Sep 18
Nov 18 Nov 18 Apr 18
Jan 18 Jan 18 Jun 18
Mrz 18 Mrz 18 Aug 18
Mai 18 Mai 18 Oct 18
Jul 18 Jul 18 Dec 18
Ethereum Price vs. Transaction Volume
Sep 18 Sep 18 Feb 19
Nov 18 Nov 18 Apr 19
an 19 an 19 Jun 19
Mrz 19
Ethereum Price vs. Verified Smart Contracts
Mrz 19 Aug 19
Mai 19 Mai 19
Jul 19 Oct 19
Jul 19
Sep 19 Dec 19
Sep 19
Nov 19 Feb 20
Nov 19
Jan 20 Jan 20 Apr 20
Mrz 20 Mrz 20 Jun 20
Mai 20 Mai 20 Aug 20
Jul 20 Oct 20
Jul 20
Sep 20 Dec 20
Sep 20
Nov 20
Nov 20 Feb 21
0
20
40
60
80
100
120
140
160
0
5
10
15
20
25
30
35
40
45
50
0
0.5
1.0
1.5
2.0
2.5
3-0
3.5
4.0
Transaction Volume in Millions Verified Smart Contracts New dApps
Ethereum Closing Price Ethereum Closing Price Ethereum Closing Price
0
200
400
600
800
1000
1200
1400
0
200
400
600
800
1000
1200
1400
0
200
400
600
800
1000
1200
1400
Jul 15 Jul 15 Jul 15
Sep 15 Sep 15 Sep 15
Nov 15 Nov 15 Nov 15
Jan 16 Jan 16 Jan 16
Mrz 16 Mrz 16 Mrz 16
Mai 16 Mai 16 Mai 16
Closing Price
Closing Price
Closing Price
Jul 16 Jul 16 Jul 16
Sep 16 Sep 16 Sep 16
Nov 16 Nov 16 Nov 16
Jan 17 Jan 17 Jan 17
Mrz 17 Mrz 17 Mrz 17
Mai 17 Mai 17 Mai 17
Difficulty
Jul 17 Jul 17 Jul 17
Sep 17 Sep 17 Sep 17
Nov 17 Nov 17 Nov 17
Active Addresses
Social Dominance
an 18 an 18 an 18
Mrz 18 Mrz 18 Mrz 18
Ethereum Price vs. Difficulty
Mai 18 Mai 18 Mai 18
Jul 18 Jul 18 Jul 18
Sep 18 Sep 18 Sep 18
Nov 18 Nov 18 Nov 18
Jan 18 Jan 18 Jan 18
Mrz 18 Mrz 18 Mrz 18
Ethereum Price vs. Active Addresses
Mai 18 Mai 18
Ethereum Price vs. Social Dominance
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Mai 18
Jul 18 Jul 18 Jul 18
Sep 18 Sep 18 Sep 18
Nov 18 Nov 18 Nov 18
an 19 an 19 an 19
Mrz 19 Mrz 19 Mrz 19
Mai 19 Mai 19 Mai 19
Jul 19 Jul 19 Jul 19
Sep 19 Sep 19 Sep 19
Nov 19 Nov 19 Nov 19
Jan 20 Jan 20 Jan 20
Mrz 20 Mrz 20 Mrz 20
Mai 20 Mai 20 Mai 20
Jul 20 Jul 20
Jul 20
Sep 20 Sep 20
Sep 20
Nov 20 Nov 20
Nov 20
0
10
20
30
40
5
60
70
0
100
200
300
400
500
600
700
800
0
0.5
1.0
1.5
2.0
2.5
3-0
3.5
4.0
4.5
5.0
Social Dominance Active Addresses in Hundred Thousands Difficulty
15ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Figure 3: ERC20 Compound
Compound Price vs. Active Addresses
ERC20 Token – Compound
300 6 Since Ethereum offers the opportunity
to create ERC20 tokens on its mainnet,
250 5
the question arises what drives the val-
ue of these tokens? This example dis-
Active Addresses in Thousands
4
Compound Closing Price
200
cusses the token Compound (COMP)
150 3
but is likely applicable to a range of
100 2 ERC20 tokens. The results are present-
ed in Figure 3 below.
50 1
0 0 Active Addresses: Whilst it must
be borne in mind that the number of
19 Jun 20
26 Jun 20
03 Jul 20
10 Jul 20
17 Jul 20
24 Jul 20
07 Aug 20
14 Aug 20
21 Aug 20
28 Aug 20
04 Sep 20
11 Sep 20
18 Sep 20
25 Sep 20
02 Oct 20
09 Oct 20
16 Oct 20
23 Oct 20
30 Oct 20
06 Nov 20
13 Nov 20
20 Nov 20
27 Nov 20
04 Dec 20
11 Dec 20
18 Dec 20
25 Dec 20
01 Jan 21
08 Jan 21
15 Jan 21
22 Jan 21
data points is limited – due to the fact
Closing Price Active Addresses
that Compound was only launched
in 2020 – there is only limited co-
Compound Price vs. Ethereum Closing Price movement between its closing price
and the number of active addresses.
300 1.400
1.200
Ethereum: Akin to Stober & Prof. Dr.
250
Sandner (2020) it was hypothesized
1.000
that the ERC20 token moves in line
Compound Closing Price
200
Ethereum Closing Price
800
with the Ethereum price, where the
150
600 Ethereum price reflects the market. As
100
400
shown price movements are in line,
particularly the sharp increase in Q4
50 200
of 2020.
0
0
Transaction volume does not appear
19 Jun 20
26 Jun 20
03 Jul 20
10 Jul 20
17 Jul 20
24 Jul 20
07 Aug 20
14 Aug 20
21 Aug 20
28 Aug 20
04 Sep 20
11 Sep 20
18 Sep 20
25 Sep 20
02 Oct 20
09 Oct 20
16 Oct 20
23 Oct 20
30 Oct 20
06 Nov 20
13 Nov 20
20 Nov 20
27 Nov 20
04 Dec 20
11 Dec 20
18 Dec 20
25 Dec 20
01 Jan 21
08 Jan 21
15 Jan 21
22 Jan 21
to significantly influence Compound’s
Closing Price Ethereum Closing Price
closing price.
Compound Price vs. Transaction Volume
Ripple XRP
300 2.5 As Trading Education (2021) pointed out,
XRP differentiates itself from Bitcoin,
250 2.0
Ethereum and other cryptocurrencies
through its centralisation. Whilst most cur-
Transaction Volume in Millions
Compound Closing Price
200
1.5
rencies are based on decentralisation,
150
1.0
Ripple is centralized amongst key players
100 of the financial industry. Rather than trying
0.5 to disintermediate banks entirely, XRP aims
50
to reduce transaction costs within the finan-
0
0 cial services industry. Therefore the price of
XRP is likely driven by its uptake in industry.
19 Jun 20
26 Jun 20
03 Jul 20
10 Jul 20
17 Jul 20
24 Jul 20
07 Aug 20
14 Aug 20
21 Aug 20
28 Aug 20
04 Sep 20
11 Sep 20
18 Sep 20
25 Sep 20
02 Oct 20
09 Oct 20
16 Oct 20
23 Oct 20
30 Oct 20
06 Nov 20
13 Nov 20
20 Nov 20
27 Nov 20
04 Dec 20
11 Dec 20
18 Dec 20
25 Dec 20
01 Jan 21
08 Jan 21
15 Jan 21
22 Jan 21
Which driver is most aligned with the XRP
Closing Price Transaction Volume
price movements is shown in Figure 4.
16XRP Closing Price XRP Closing Price XRP Closing Price
0
0,5
1
1,5
2
2,5
3
3,5
0
0,5
1
1,5
2
2,5
3
3,5
0
0,5
1
1,5
2
2,5
3
3,5
Aug 13 Aug 13 Aug 13
Nov 13 Nov 13 Nov 13
Feb 14 Feb 14 Feb 14
May 14 May 14 May 14
Aug 14 Aug 14 Aug 14
Closing Price
Closing Price
Closing Price
Nov 14 Nov 14 Nov 14
Feb 15 Feb 15 Feb 15
May 15 May 15 May 15
Figure 4: XRP Drivers
Aug 15 Aug 15 Aug 15
Bitcoin
XPR Price vs. Bitcoin
Nov 15 Nov 15 Nov 15
DEV Activity
Feb 16 Feb 16 Feb 16
Active Addresses
May 16 May 16 May 16
Aug 16 Aug 16 Aug 16
Nov 16 Nov 16 Nov 16
Feb 17 Feb 17 Feb 17
XRP Price vs. Active Addresses
May 17 May 17 May 17
Aug 17 Aug 17 Aug 17
XPR Price vs. Development Activity
Nov 17 Nov 17 Nov 17
Feb 18 Feb 18 Feb 18
May 18 May 18 May 18
Aug 18 Aug 18 Aug 18
Nov 18 Nov 18 Nov 18
Feb 19 Feb 19 Feb 19
May 19 May 19 May 19
Aug 19 Aug 19 Aug 19
Nov 19 Nov 19 Nov 19
Feb 20 Feb 20 Feb 20
May 20 May 20 May 20
Aug 20 Aug 20 Aug 20
Nov 20 Nov 20 Nov 20
0
20
40
60
80
100
120
140
160
180
200
0
20
40
60
80
100
120
140
160
180
0
5
10
15
20
25
30
40
45
Development Activity Active Addresses in Thousands Bitcoin Closing Price in Thousands
XRP Closing Price XRP Closing Price
0
0,5
1
1,5
2
2,5
3
3,5
0
0,5
1
1,5
2
2,5
3
3,5
Aug 13 Aug 13
Nov 13 Nov 13
Feb 14 Feb 14
May 14 May 14
Aug 14 Aug 14
Closing Price
Closing Price
Nov 14 Nov 14
Feb 15 Feb 15
May 15 May 15
Aug 15 Aug 15
Network
Nov 15 Nov 15
Feb 16 Feb 16
May 16 May 16
Transaction Volume
Aug 16 Aug 16
Nov 16 Nov 16
Feb 17 Feb 17
XRP Price vs. Network Growth
May 17 May 17
XPR Price vs. Transaction Volume
Aug 17 Aug 17
Nov 17 Nov 17
ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Feb 18 Feb 18
ing is observed from early 2019 on.
May 18 May 18
growth is defined as the number of new
centralized, its value lies in the number of
to Bitcoin, XRP did not experience a
institutions using the technology. Network
Network growth: Since XRP is not de-
significant price increase towards the
previously peaked in 2017, but contrary
of Bitcoin and XRP. Both currencies
end of 2020. In fact a price de-coupl-
Figure 4 shows the price movements
Bitcoin Price: The top left panel of
Aug 18 Aug 18
Nov 18 Nov 18
Feb 19 Feb 19
May 19 May 19
Aug 19 Aug 19
Nov 19 Nov 19
Feb 20 Feb 20
May 20 May 20
Aug 20 Aug 20
Nov 20 Nov 20
0
5
10
15
20
25
30
40
45
0
5
10
15
20
25
Transaction Volume in Billions Network Growth in Thousands
17ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
addresses being created on the of crypto exchange tokens using Active Addresses: Active addres-
network and is therefore a suitable the example of Binance. Crypto ses behave similarly to transaction
measurement for uptake in the in- exchanges provide users the op- volume. This would be expected
dustry. It is presented in the top right portunity to trade on the crypto because active addresses are like-
panel. As shown, network growth market, thus enabling easier market ly to trade, and thus, the higher the
and the XRP closing price closely access for investors. From a theo- number of active users, the higher
move together. The 2017 spike is ac- retical perspective, the price of ex- the trading volume. By visual in-
curately picked up and the marginal change coins is likely to be driven spection, transaction volume ap-
price increase in December 2020 is by trading volume of exchanges pears to be a better fit than the
well mirrored by network growth. as their business model is built on number of active addresses.
fees. The results of the analysis are
Active Addresses: Compared shown in Figure 5. Social volume does not appear to re-
to the network growth, the active late to the price of the Binance coin.
addresses are an inferior price Transaction Volume: The transaction
predictor as shown in the panel. It volume is shown in the top left panel of Based on the principles outlined
appears that it is not the number Figure 5. It closely mirrors Binance’s above, many cryptocurrencies
of active users but rather the size peak in June 2019 and February and value drivers were investi-
of the total network that determine 2020 and appears to be a meaning- gated to determine which value
the price of XRP. ful driver. However, transaction vol- drivers have an impact on the re-
ume fails to predict the sharp price spective cryptocurrencies. In the
Transaction volume and develop- increase at the end of 2020. interest of brevity, the results are
ment activity do not appear to be summarized in Table 2. As shown,
driving the price of XRP. Bitcoin: In line with other coins the coins are grouped into dif-
investigated, the price of Bitcoin ferent categories depending on
appears to be a driver of the their characteristics. Each of these
Crypto exchanges - Binance coin closing price. In par- groups is expected to be driven
Binance ticular the Bitcoin price explains by different factors, each of which
Over the years, multiple crypto ex- the movements in Binance that are analysed in turn.
changes have opened, such as Bi- are not explained by transaction
nance, Coinbase and Bitfinex. This volume, namely the peaks in 2018
section analyses potential drivers and December 2020.
Table 2: Payments IOT Lending
Bitcoin Bitcoin Cash Bitcoin SV Ripple Stellar Monero Litecoin Dash Zcash IOTA Chainlink Compound Nexo
Bitcoin
Stock to Flow
Misc.
Ethereum
Gas used
Developer activity
Net. Use DEV
Github activity
Active Addresses
Network growth
Difficulty
Exchange balance
Exchange funds flow
Exchange inflow
Exchange outflow
Exchange percent supply
Financial
Miners balance
MV RV Ratio
NVT Ratio
Price vol Diff
Transaction volume
Velocity
Twitter followers
Social
Social Dominance
Social volume
18 Legend Good value driver Medium value driver Poor value driver No data availableICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Figure 5: Binance Drivers
Binance Price vs. Transaction Volume Binance Price vs. Transaction Volume
4,5 8 45 45
4,0 7 40 40
3,5 35 35
6
Bitcoin Closing Price in Thousands
Transaction Volume in Millions
3 30 30
Binance Closing Price
Binance Closing Price
5
2,5 25 25
4
2 20 20
3
1,5 15 15
2
1 10 10
0,5 1 5 5
0 0 0 0
Jul 17
Sep 17
Nov 17
Jan 18
Mar 18
May 18
Jul 18
Sep 18
Nov 18
Jan 19
Mar 19
May 19
Jul 19
Sep 19
Nov 19
Jan 20
Mar 20
May 20
Jul 20
Sep 20
Nov 20
Jul 17
Sep 17
Nov 17
Jan 18
Mar 18
May 18
Jul 18
Sep 18
Nov 18
Jan 19
Mar 19
May 19
Jul 19
Sep 19
Nov 19
Jan 20
Mar 20
May 20
Jul 20
Sep 20
Nov 20
Closing Price Transaction Volume Closing Price Bitcoin
Binance Price vs. Active Addresses Binance Price vs. Social Volume
45 45 45 140
40 40 40
120
35 35 35
Active Addresses in Thousands
100
30 30 30
Binance Closing Price
Binance Closing Price
Social Volume
25 25 25 80
20 20 20 60
15 15 15
40
10 10 10
20
5 5 5
0 0 0 0
Jul 17
Sep 17
Nov 17
Jan 18
Mar 18
May 18
Jul 18
Sep 18
Nov 18
Jan 19
Mar 19
May 19
Jul 19
Sep 19
Nov 19
Jan 20
Mar 20
May 20
Jul 20
Sep 20
Nov 20
Jul 17
Sep 17
Nov 17
Jan 18
Mar 18
May 18
Jul 18
Sep 18
Nov 18
Jan 19
Mar 19
May 19
Jul 19
Sep 19
Nov 19
Jan 20
Mar 20
May 20
Jul 20
Sep 20
Closing Price Active Addresses Closing Price Social Volume Nov 20
dApps Exchanges
Ethereum EOS TRON Cardano Polkadot Nem Neo Binance Coin UNUS SED LEO Uniswap Huobi Crypto.com
Bitcoin
Stock to Flow
Misc.
Ethereum
Gas used
Developer activity
Net. Use DEV
Github activity
Active Addresses
Network growth Yes
Difficulty
Exchange balance
Exchange funds flow
Exchange inflow
Exchange outflow
Exchange percent supply
Financial
Miners balance
MV RV Ratio
NVT Ratio
Price vol Diff
Transaction volume
Velocity
Twitter followers
Social
Social Dominance
Social volume
Legend Good value driver Medium value driver Poor value driver No data available 19ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Payments: Bitcoin is included in growing blockchain industry as the most prominent of which is
this category because it was the well as general digitalisation. This Ethereum. The category includes
first major cryptocurrency aiming is best proxied by the price of other blockchains that allow the
to revolutionize the payments sec- Bitcoin, so it would be expected development of smart contracts
tor. As shown, the payment cate- to find Bitcoin is a value driver. and dApps, such as EOS and
gory includes other cryptocur- The analysis showed that network Cardano. It would be expected
rencies such as Monero, Stellar usage, measured by the number that these coins are driven by de-
and Ripple. Bitcoin differs from of active addresses and network velopment activity, such as GitHub
these coins, as it is driven by the growth, is indeed a value driver of activity or developer activity. The
Stock-to-flow model. It would be Chainlink. Due to data limitations, reason is that the added value of
expected that the other coins are it was not possible to infer the these chains is their ability to host
particularly driven by the usage of same conclusion for IOTA, how- dApps. Increased development
the network, such as with XRP. The ever it is a reasonable assumption activities signal a chains’ pop-
reasoning is that the more peo- that IOTA is driven by the same ularity and, theoretically, drive
ple/companies use the network, driver. Furthermore, the price of price. The analysis showed that the
the higher its value is compared Bitcoin and Ether were found to dApps category is heavily influ-
to other payment networks. Fur- be value drivers as well. enced by the price of Ether, since
thermore, financial attributes such this is the main blockchain offering
as the transaction volume would Lending: Compound and Nexo smart contracts and dApps (mea-
be expected to impact price. The were slotted to the Lending cate- sured by market capitalisation).
analysis showed that financial gory. Since they are ERC20 tokens, it GitHub activity was also found to
drivers do not appear to move in would be expected that the price be a value driver, whereas develo-
line with the prices of the coins. of Ether is a value driver. The price per activity appears to have less
However, it was found that Bitcoin of Ether is a proxy for the overall of an impact on the price.
movements is a strong indicator of popularity of the Ethereum network,
other price movements. In many which both of these coins are part Exchange: The exchange coins such
cases, this should be expected, of. The analysis showed that lend- as Binance Coin, Leo, Uniswap
since Bitcoin Cash and Bitcoin SV ing coins are indeed driven by and Huobi would be expected
are hard forks from Bitcoin. In the Ether movements as well as Bitcoin, to be driven by the usage of ex-
case of Litecoin and Monero, showing overall responsiveness changes i.e. transaction volumes.
GitHub activity is strongly linked to the market. Additionally, the As shown in Table 2, the value
to price performance and, in some MVRV ratio closely tracks the price. drivers for the exchanges are not
cases, social media volume was Whilst this is to be expected, it is not as clear-cut. However, the major-
found to be a reasonablevalue driver. the norm across the coins investi- ity appear to respond to market
gated in this report. sentiment, proxied through Bitcoin
IOT: IOTA and Chainlink were and Ethereum, as well as social
categorized as IOT related coins. dApps: The dApps category in- media usage, in particular Twitter
Both coins are dependent on a cludes a range of blockchains, followers.
20ICONIC FUNDS & CRYPTOLOGY ASSET GROUP: Analyzing the Primary Value Drivers of Leading Cryptocurrencies
Conclusion
This report analysed the drivers of a range of crypto- Similar to ERC20 tokens, crypto exchange tokens
currencies. The main contribution to existing literature were found to move in line with overall market con-
is threefold. Firstly, this analysis compared different ditions. It was found that the Binance coin is driven
types of coins rather than looking at several ERC20 by a combination of the Bitcoin price as well as
tokens or just Bitcoin. Secondly this report looked at transaction volume on Binance. This supports the
exogenous price drivers only. Most pricing models findings of Stober and Prof. Dr. Sandner (2020).
investigate structures with an autoregressive compo-
nent, which ultimately assumes that the price of a XRP differs from the other coins investigated in this
cryptocurrency contains information that cannot be report because XRP is not built on decentralization.
observed in other fundamental drivers. Thirdly most Due to its differing structure, the price drivers were
papers focus on Bitcoin, Ethereum or ERC20 coins found to be unique. Whilst some of the other cryp-
to analyse fundamental drivers. This report went tocurrencies were driven by the number of active
beyond this scope and analysed coins from a range users, XRP is seemingly driven by network growth.
of categories, namely payment, dApps, IOT, lend- This is once more in line with expectations since
ing and exchanges. XRP is built for large financial players where the
value depends on the number of companies using
The analysis showed that different categories of the network.
coins are indeed driven by separate fundamentals.
Bitcoin appears to be driven by its stock-to-flow This report has conclusively shown that the value
model. Whilst this finding is only indicative, given drivers of cryptocurrencies differ based on their
the limited number of halvings carried out, the aver- underlying structure and use of a blockchain.
age price level does seem to correlate to the stock Future research will attempt to roll out this frame-
to flow’s increase over time. In contrast, Ethereum is work to other coins to test this hypothesis further and
mostly driven by the number of verified smart con- conduct back testing once more time-series data
tracts on its blockchain. This finding is in line with ex- has become available.
pectations because Ethereum’s unique selling point
is not scarcity, but fostering a global, open network.
The number of dApps deployed also showed simi-
lar correlations but failed to pick up the most recent
Ethereum price increase. The price of ERC20 tokens
was found to move in line with that of Ethereum itself.
21
21“We deliver excellence, providing the quality assurances
investors deserve from a world-class asset manager,
as we champion our mission of driving crypto asset adoption.”
Patrick Lowry, CPA
MANAGING PARTNER, ICONIC FUNDS
CEO, CRYPTOLOGY ASSET GROUP
22
22References
Balcilar, M., Bouri, E., Gupta, R., & Roubaud, Trading Education. (2021). What Will Drive The
D. (2017). Can volume predict Bitcoin returns and Ripple XRP Price in 2021? Retrieved from https://
volatility? A quantiles-based approach. Economic trading-education.com/what-will-drive-the-ripple-
Modelling (Volume 64), pp. 74-81. doi: https://doi. xrp-price-in-2021
org/10.1016/j.econmod.2017.03.019.
Walther, T., Klein, T., & Bouri, E. (2019).
Bhambhwani, S., Delikouras, S., & Korniotis, Exogenous drivers of Bitcoin and Cryptocurrency
G. (2019). The fundamental drivers of cryptocurrency volatility – A mixed data sampling approach
prices. Retrieved from VOX: https://voxeu.org/ to forecasting. Journal of International Financial
article/fundamental-drivers-cryptocurrency-prices Markets, Institutions & Money. doi: https://doi.
org/10.1016/j.intfin.2019.101133
Das, A. (2020). What’s driving the Bitcoin price?
Retrieved from Brave New Coin: https://bravenew-
coin.com/insights/whats-driving-the-bitcoin-price
Financial Times. (2021). FT.com. Retrieved 01
25, 2021, from https://www.ft.com/content/
8db6ce04-d458-11e7-8c9a-d9c0a5c8d5c9
Plan B. (2019). Modeling Bitcoin Value
with Scarcity. Retrieved from Medium.com:
https://medium.com/@100trillionUSD/modeling-
bitcoins-value-with-scarcity-91fa0fc03e25
Rathan, K., Sai, S., & Manikanta, T. (2019).
Crypto-Currency price prediction using Decision
Tree and Regression. Proceedings of the Third Inter-
national Conference on Trends in Electronics and In-
formatics (ICOEI 2019).
Stober, A., & Prof. Dr. Sandner, P. (2020). Using
On-Chain and Market Metrics to Analyze the Value
of Crypto Assets. FSBC Working Paper, pp. 1-22.
Tradimo. (2021). What determines the value of
a cryptocurrency? Retrieved from https://learn.
tradimo.com/cryptocurrencies/crypto-value/
23
23Thank you for your time
Iconic offers an array of investment strategies from venture
to indices with unique solutions for crypto asset managers.
The marriage of state-of-the-art technology, innovative investment
products and uncompromising professionalism places Iconic at the
vanguard of crypto asset management.
Iconic Funds GmbH
Große Gallusstraße 18
60312 Frankfurt contact@iconicholding.com
Germany funds.iconicholding.com
24Thank you for your time
Cryptology Asset Group is a leading European asset manager
for crypto assets and blockchain-based businesses.
With key investments in established industry leaders like block.one,
Northern Data, nextmarkets and Iconic Funds, Cryptology drives crypto
adoption by financing the most disruptive crypto companies and assets.
Cryptology Asset Group p.l.c.
66/67, Beatrice, Amery Street,
Sliema SLM 1707 info@cryptology-ag.com
Malta cryptology-ag.com
25You can also read
