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In January 3, 2009, Satoshi Nakamoto gave rise to the "Bitcoin Block Chain" creating the first block of the chain hashing on his computers central processing unit (CPU). Since then, the hash calculations to mine Bitcoin have been getting more and more complex, and consequently the mining hardware evolved to adapt to this increasing difficulty. Three generations of mining hardware have followed the CPU's generation. They are GPU's, FPGA's and ASIC's generations. This work presents an agent based artificial market model of the Bitcoin mining process and of the Bitcoin transactions. The goal of this work is to model the economy of the mining process, starting from GPU's generation, the first with economic significance. The model reproduces some "stylized facts" found in real time price series and some core aspects of the mining business. In particular, the computational experiments performed are able to reproduce the unit root property, the fat tail phenomenon and the volatility clustering of Bitcoin price series. In addition, under proper assumptions, they are able to reproduce the price peak at the end of November 2013, its next fall in April 2014, the generation of Bitcoins, the hashing capability, the power consumption, and the mining hardware and electrical energy expenses of the Bitcoin network.
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Modeling and Simulation of the Economics of Mining
in the Bitcoin Market
Luisanna Cocco,
1
Michele Marchesi,
1
1
Department of electrical and electronic engineering, University of Cagliari,
Piazza D’Armi, 09123 Cagliari, Italy.
E-mail: {luisanna.cocco, michele}@diee.unica.it
Abstract
In January 3, 2009, Satoshi Nakamoto gave rise to the ”Bitcoin Block Chain” creating
the first block of the chain hashing on his computers central processing unit (CPU). Since
then, the hash calculations to mine Bitcoin have been getting more and more complex,
and consequently the mining hardware evolved to adapt to this increasing difficulty. Three
generations of mining hardware have followed the CPU’s generation. They are GPU’s,
FPGAs and ASIC’s generations.
This work presents an agent based artificial market model of the Bitcoin mining process
and of the Bitcoin transactions. The goal of this work is to model the economy of the mining
process, starting from GPU’s generation, the first with economic significance.
The model reproduces some ”stylized facts” found in real time price series and some
core aspects of the mining business. In particular, the computational experiments performed
are able to reproduce the unit root property, the fat tail phenomenon and the volatility
clustering of Bitcoin price series. In addition, under proper assumptions, they are able to
reproduce the price peak at the end of November 2013, its next fall in April 2014, the
generation of Bitcoins, the hashing capability, the power consumption, and the mining
hardware and electrical energy expenses of the Bitcoin network.
keywords: Artificial Financial Market, Bitcoin, Heterogeneous Agents, Market Simu-
lation
1 Introduction
Bitcoin is a digital currency alternative to the legal ones, as any other crypto currency. Nowa-
days, Bitcoin is the most popular cryptocurrency. It was created by a cryptologist known as
”Satoshi Nakamoto”, whose real identity is still unknown [40]. Like other cryptocurrencies,
1
arXiv:1605.01354v1 [q-fin.TR] 4 May 2016
Bitcoin uses cryptographic techniques and, thanks to an open source system, anyone is allowed
to inspect and even modify the source code of the Bitcoin software.
The Bitcoin network is a peer-to-peer network that monitors and manages both the genera-
tion of new Bitcoins and the consistency verification of transactions in Bitcoins. This network
is composed of a high number of computers connected to each other through the Internet. They
perform complex cryptographic procedures which generate new Bitcoins (mining) and manage
the Bitcoin transactions register, verifying their correctness and truthfulness.
Mining is the process which allows to find the so called ”proof of work” that validates a
set of transactions and adds them to the massive and transparent ledger of every past Bitcoin
transaction known as the ”Blockchain”. The generation of Bitcoins is the reward for the val-
idation process of the transactions. The Blockchain was generated starting since January 3,
2009 by the inventor of the Bitcoin system himself, Satoshi Nakamoto. The first block is called
”genesis block” and contains a single transaction, which generates 50-Bitcoin for the benefit of
the creator of the block. The whole system is set up to yield just 21 million Bitcoins by 2040,
and over time the process of mining will become less and less profitable. The main source of
remuneration for the miners in the future will be the fees on transactions, and not the mining
process itself.
In this work, we propose an agent-based model with the aim to study and analyse the mining
process and the Bitcoin market starting from September 1, 2010, the approximate date when
miners started to buy mining hardware to mine Bitcoins. The proposed model simulates the
mining process and the Bitcoin transactions, by implementing a mechanism for the formation
of the Bitcoin price, and specific behaviors for each typology of trader. We try to reproduce
the generation of Bitcoins, the main stylized facts present in the real Bitcoin market and the
economy of the mining process. The model described is built on a previous work of the authors
[15], which modeled the Bitcoin market under a purely financial perspective. In this work, we
fully consider also the economics of mining.
The paper is organized as follows. In Section Related Work we discuss other works related
to this paper, in Section Mining Process we describe briefly the mining process and we give
an overview on the mining hardware and on its evolution over time. In Section The Model we
present the proposed model in detail. Section Simulation Results presents the values given to
several parameters of the model and reports the results of the simulations, including an analysis
of Bitcoin real prices, and a robustness analysis. The conclusions of the paper are reported in
Section Conclusions. Finally, appendix deals with the calibration to some parameters of the
model.
2
2 Related Work
The study and analysis of the cryptocurrency market is a relatively new field. In the last years,
several papers appeared on this topic given its potential interest and the many issues related to
it (see for instance the works [3, 5, 7, 24, 28, 29, 39, 53]).
However, very few works were made to model the cryptocurrencies market. We can cite
the works by Luther [37], who studied why some cryptocurrencies failed to gain widespread
acceptance using a simple agent model; by Bornholdt et al. [6], who proposed a model based
on Moran process to study the cryptocurrencies able to emerge; by Garcia et al. [27], who
studied the role of social interactions in the creation of price bubbles; by Kristoufek [31] who
analysed the main drivers of the Bitcoin price; and by Kaminsky et al. [30] who related the
Bitcoin market with its sentiment analysis on social networks.
In this paper we propose a complex agent-based model in order to reproduce the economy of
the mining process and the main stylized facts of the Bitcoin price series. Our model is inspired
by business, economic and financial agent-based models that depict how organizations, or in
general the economy of a country, create, deliver, and capture value.
As regards the business models, Amini et al. [2] presented a agent-based model with the
aim to analyze the impact of alternative production and sales policies on the diffusion of a new
product; Cocco et al. [12, 13, 14] proposed agent-based models to simulate the software market
and analyze the business processes and policies adopted by proprietary software firms and Open
Source software firms; Li et al. [35] researched the dominant players behavior in supply chains
and the relationship between the selling prices and purchasing prices in supply chains by using a
multi-agent simulation model; Rohitratana et al. [51] studied the pricing schemes of the market
of the Software as-a-Service and on the market of the proprietary or traditional software; finally,
Xiaoming et al. [56] studied how a firm maximizes its profit by determining the production and
sales policies for a new product during the lifetime of the product.
Concerning economic models, in [11] the authors presented one of the most significant
agent-based model developed to date in order to study the European economy. In particular,
they show how monetary policies, i.e, credit money supplied by commercial banks as loans
to firms, influence the economy of a country. In [21, 23] agent based keynesian models are
presented in order to investigate the properties of macro economic dynamics and the impact
of public polices on supply, demand and the fundamentals of the economy, and to study the
interactions between income distribution and monetary and fiscal policies.
As regards artificial financial market models, they reproduce the real functioning of markets,
trying to explain the main stylised facts observed in financial markets, such as the fat-tailed dis-
tribution of returns, the volatility clustering, and the unit-root property. For a review, see works
[8] and [9]. Raberto et al. [47] and Cincotti et al. [10] proposed the Genoa Artificial Stock Mar-
3
ket (GASM) an agent-based artificial financial market characterized by actual tracking of status
and wealth of each agent, and by a realistic trading and price clearing mechanisms. GASM is
able to reproduce some of the main stylised facts observed in real financial markets.
This paper is built on GASM, adding specific features and a mix of zero-intelligence and
trend-following traders with the aim to model the Bitcoin exchange market and the economics
of mining.
3 Mining Process
Today, every few minutes thousands of people send and receive Bitcoins through the peer-to-
peer electronic cash system created by Satoshi Nakamoto. All transactions are public and stored
in a distributed database called Blockchain which is used to confirm transactions and prevent
the double-spending problem.
People who confirm transactions of Bitcoins and store them in the Blockchain are called
”miners”. As soon as new transactions are notified to the network, miners check their validity
and authenticity and collect them in a block. Then, they take the information contained in the
block of the transactions, which include a variable number called ”nonce” and run the SHA-
256 hashing algorithm on this block, turning the initial information into a sequence of 256 bits,
known as Hash [18].
There is no way of knowing how this sequence will look before calculating it, and the
introduction of a minor change in the initial data causes a drastic change in the resulting Hash.
The miners cannot change the data containing the information of transactions, but can
change the ”nonce” number used to create a different hash. The goal is to find a Hash hav-
ing a given number of leading zero bits. This number can be varied to change the difficulty
of the problem. The first miner who creates a proper Hash with success (he finds the ”proof-
of-work”), gets a reward in Bitcoins, and the successful Hash is stored with the block of the
validated transactions in the Blockchain.
In a nutshell,
”Bitcoin miners make money when they find a 32-bit value which, when hashed together
with the data from other transactions with a standard hash function gives a hash with a
certain number of 60 or more zeros. This is an extremely rare event”, [18].
The steps to run the network are the followings:
New transactions are broadcast to all nodes; each node collects new transactions into a
block; each node works on finding a difficult proof-of-work for its block; when a node
finds a proof-of-work, it broadcasts the block to all nodes; nodes accept the block only if
all transactions in it are valid and not already spent; nodes express their acceptance of the
4
block by working on creating the next block in the chain, using the hash of the accepted
block as the previous hash”, [40].
Producing a single hash is computationally very easy, consequently in order to regulate the
generation of Bitcoins, over time the Bitcoin protocol makes this task more and more difficult.
The proof-of-work is implemented by incrementing the nonce in the block until a value
is found that gives the block’s hash with the required leading zero bits. If the hash does not
match the required format, a new nonce is generated and the Hash calculation starts again [40].
Countless attempts may be necessary before finding a nonce able to generate a correct Hash.
The computational complexity of the process necessary to find the proof-of-work is adjusted
over time in such a way that the number of blocks found each day is more or less constant (ap-
proximately 2016 blocks in two weeks, one every 10 minutes). In the beginning, each generated
block corresponded to the creation of 50 Bitcoins, this number being halved each four years,
after 210,000 blocks additions. So, the miners have a reward equal to 50 Bitcoins if the created
blocks belong to the first 210,000 blocks of the Blockchain, 25 Bitcoins if the created blocks
range from the 210,001th to the 420,000th block in the Blockchain, 12.5 Bitcoins if the created
blocks range from the 420,001th to the 630,000th block in the Blockchain, and so on.
Over time, mining Bitcoin is getting more and more complex, due to the increasing number
of miners, and the increasing power of their hardware. We have witnessed the succession of
four generations of hardware, i.e. CPU’s, GPU’s, FPGAs and ASIC’s generation, each of them
characterized by a specific hash rate (measured in H/sec) and power consumption. With time,
the power and the price of the mining hardware has been steadly increasing, though the price
of H/sec has been decreasing. To face the increasing costs, miners are pooling together to share
resources.
3.1 The evolution of the mining hardware
In January 3, 2009, Satoshi Nakamoto created the first block of the Blockchain, called ”Genesis
Block”, hashing on the central processing unit (CPU) of his computer. Like him, the early min-
ers mined Bitcoin running the software on their personal computers. The CPU’s era represents
the first phase of the mining process, the other eras being GPU’s, FPGAs and ASIC’s eras (see
web site https://tradeblock.com/blog/
the-evolution-of-mining/).
Each era announces the use of a specific typology of mining hardware. In the second era,
started about on September 2010, boards based on graphics processing unit (GPU) running in
parallel entered the market, giving rise to the GPU era.
About in December 2011, the FPGAs era started and hardware based on field programmable
gate array cards (FPGA) specifically designed to mine Bitcoins was available in the market.
5
Finally, in 2013 fully customized application specific integrated circuit (ASIC) appeared, sub-
stantially increasing the hashing capability of the Bitcoin network and marking the beginning
of the fourth era.
Over time, the different mining hardware available was characterized by an increasing hash
rate, a decreasing power consumption per hash, and increasing costs. For example, NVIDIA
Quadro NVS 3100M, 16 cores, belonging to the GPU generation, has a hash rate equal to 3.6
MH/s and a power consumption equal to 14 W [16]; ModMiner Quad, belonging to the FPGA
generation, has a hash rate equal to 800 MH/s and power consumption equal to 40 W [16];
Monarch(300), belonging to the ASIC generation, has a hash rate equal to 300 GH/s and power
consumption equal to 175 W (see web site https://tradeblock.com/mining/.
3.2 Modeling the Mining Hardware Performances
The goal of our work is to model the economy of the mining process, so we neglected the first
era, when Bitcoins had no monetary value, and miners used the power available on their PCs,
at almost no cost. We simulated only the remaining three generations of mining hardware.
We gathered information about the products that entered the market in each era to model
these three generations of hardware, in particular with the aim to compute:
the average hash rate per US$ spent on hardware, R(t), expressed in
H
sec$
;
the average power consumption per H/sec, P (t), expressed in
W
H/sec
.
The average hash rate and the average power consumption were computed averaging the
real market data at specific times and constructing two fitting curves.
To calculate the hash rate and the power consumption of the mining hardware of the GPU
era, that we estimate ranging from September 1st, 2010 to September 29th, 2011, we computed
an average for R and P taking into account some representative products in the market during
that period, neglecting the costs of the motherboard.
In that era, motherboards with more than one Peripheral Component Interconnect express
(PCIe), started to enter the market allowing to install, by using adapters, multiple video cards in
only one system and to mine criptocurrency, thanks to the power of the GPUs. In Table 1, we
describe the features of some GPUs in the market in that period. The data reported are taken
from the web site http://coinpolice.com/gpu/.
As regards the FPGA and ASIC eras, starting about on September 2011 and on December
2013, respectively, we tracked the history of the mining hardware by following the introduction
into the market of Butterfly Labs company’s products. We extracted the data illustrated in
Table 2 from the history of the web site http://www.butterflylabs.com/ through the web site
web.archive.org.. For hardware in the market in 2014 and 2015 we referred to the Bitmain
6
Table 1: GPU Mining Hardware.
Date Product Hash Rate GH/$ Consumption W/GH
23/09/2009
Radeon 5830 0.001475 593.22
Radeon 5850 0.0015 398.94
Radeon 5870 0.0015 467.66
Radeon 5970 0.0023 392
22/10/2010
Radeon 6870 0.0015 503.33
Radeon 6950 0.002 500
Radeon 6990 0.0018 328.95
Technologies Ltd company, and in particular, to the mining hardware called AntMiner (see web
site https://bitmaintech.com and Table 2).
Starting from the mining products in each period (see Tables 1 and 2), we fitted a ”best hash
rate per $” and a ”best power consumption function” (see Table 3). We call the fitting curves
R(t) and P (t), respectively.
We used a general exponential model to fit the curve of the hash rate, R(t) obtained by using
eq. 1:
R(t) = a e
(bt)
(1)
where a = 8.635 10
4
and b = 0.006318.
The fitting curve of the power consumption P(t) is also a general exponential model:
P (t) = a e
(bt)
(2)
where a = 4.649 10
7
and b = 0.004055.
Fig. 1 (a) and (b) show in logaritmic scale the fitting curves and how the hash rate increases
over time, whereas power consumtpion decreases.
4 The Model
We used the Blockchain.info, a web site which displays detailed information about all trans-
actions and Bitcoin blocks, providing graphs and statistics on different data, for extracting the
empirical data used in this work. This web site provides several graphs and statistical analysis
of data about Bitcoins. In particular, we can observe the time trend of the Bitcoin price in the
market, the total number of Bitcoins, the total hash rate of the Bitcoin network and the total
number of Bitcoin transactions.
7
Table 2: Butterfly Labs Mining Hardware: FPGA Hardware from 09/29/2011 to 12/17/2012, ASIC
Hardware from 12/17/2012 to December 2013 and AntMiner Hardware for 2014 and 2015.
Date Product Price $ Hash Rate GH/s Hash Rate
GH
sec$
Power Consumption
W
GH/sec
09/29/2011- 12/2/2011 The Single 699 1 0.0014 19.8
12/2/2011- 12/28/2011
The Single 699 1 0.0014 19.8
Rig Box 24980 50.4 0.0021 49
12/28/2011- 05/1/2012
The Single 599 0.832 0.0014 96.15
Rig Box 24980 50.4 0.0021 49
05/1/2012- 12/17/2012
The Single 599 0.832 0.0014 96.15
Mini Rig 15295 25.2 0.0016 49
12/17/2012- 04/10/2013
BitForce Jalapeno 149 4.5 0.0302 1
BitForce Little Single SC 649 30 0.0462 1
BitForce Single SC 1299 60 0.0462 1
BitForce Mini Rig SC 29899 1500 0.0502 1
04/10/2013- 05/31/2013
Bitcoin Miner 274 5 0.0182 6
Bitcoin Miner 1249 25 0.02 6
Bitcoin Miner 2499 50 0.02 6
05/31/2013- 10/15/2013
Bitcoin Miner 274 5 0.0182 6
Bitcoin Miner 1249 25 0.02 6
Bitcoin Miner 2499 50 0.02 6
Bitcoin Miner 22484 500 0.0222 6
10/15/2013- 12/10/2013
Bitcoin Miner 274 5 0.0182 6
Bitcoin Miner 2499 50 0.02 6
Bitcoin Miner 22484 500 0.0222 6
Bitcoin Minin Card 2800 300 0.1071 0.6
Bitcoin Minin Card 4680 600 0.1282 0.6
12/10/2013- 01/22/2014 AntminerS1 734.18 180 0.245 2
01/22/2014- 07/4/2014 AntminerS2 1715 1000 0.583 1.1
07/4/2014- 10/23/2014 AntminerS4-B2 1250 2000 1.6 0.69
10/23/2014- 03/25/2015 AntminerS5-B5 419 1155 2.756 0.51
03/25/2015-30/09/2015 AntminerS7-B8 454 4730 10.42 0.27
Table 3: Average of Hash Rate and of Power Consumption over time.
Date Simulation Step Average of Hash Rate
GH
sec$
Average of power Consumption
W
GH/sec
September 1, 2010 1 0.0017 454.87
September 29, 2011 394 0.0014 19.8
December 2,2011 458 0.00175 34.4
December 28,2011 484 0.0017 72.575
May 1, 2012 608 0.0029 72.575
December 17, 2012 835 0.03565 1
April 10, 2013 953 0.0194 6
May 31, 2013 1004 0.0201 6
October 15, 2013 1141 0.1351 3.84
December 10, 2013 1197 0.0595 3.84
January 22, 2014 1240 0.245 2
July 4, 2014 1403 0.583 1.1
October 23, 2014 1484 1.6 0.69
March 25, 2015 1667 2.756 0.51
September 30, 2015 1856 10.42 0.27
8
(a) (b)
Figure 1: (a) Fitting curve of R(t) and (b) fitting curve of P(t).
The proposed model presents an agent-based artificial cryptocurrency market in which
agents mine, buy or sell Bitcoins.
We modeled the Bitcoin market starting from September 1st, 2010, because one of our goals
is to study the economy of the mining process. It was only around this date that miners started
to buy mining hardware to mine Bitcoins, denoting a business interest in mining. Previously,
they typically just used the power available on their personal computers.
The features of the model are:
there are various kinds of agents active on the BTC market: Miners, Random traders and
Chartists;
the trading mechanism is based on a realistic order book that keeps sorted lists of buy and
sell orders, and matches them allowing to fulfill compatible orders and to set the price;
agents have typically limited financial resources, initially distributed following a power
law;
the number of agents engaged in trading at each moment is a fraction of the total number
of agents;
a number of new traders, endowed only with cash, enter the market; they represent people
who decided to start trading or mining Bitcoins;
miners belong to mining pools. This means that at each time t they always have a pos-
itive probability to mine at least a fraction of Bitcoin. Indeed, since 2010 miners have
been pooling together to share resources in order to be able avoiding effort duplication to
9
optimally mine Bitcoins. A consequence of this fact is that gains are smoothly distributed
among miners.
On July 18th, 2010,
”ArtForz establishes an OpenGL GPU hash farm and generates his first Bitcoin block”
and on September 18th, 2010,
”Bitcoin Pooled Mining (operated by slush), a method by which several users work
collectively to mine Bitcoins and share in the benefits, mines its first block”,
(news from the web site http://historyofBitcoin.org/).
Since then, the difficulty of the problem of mining increased exponentially, and nowaday it
would be almost unthinkable to mine without participating to a pool.
In the next subsections we describe in detail the model simulating the mining, the Bitcoin
market and the related mechanism of Bitcoin price formation.
4.1 The Agents
Agents, or traders are divided into three populations: Miners, Random traders and Chartists.
Every i-th trader enters the market at a given time step, t
E
i
. Such a trader can be either
a Miner, a Random trader or a Chartist. All traders present in the market at the initial time
t
E
i
= 0 holds an amount c
i
(0) of fiat currency (cash, in US dollars) and an amount b
i
(0) of
crypto currency (Bitcoins), where i is the trader’s index. They represent the persons present in
the market, mining and trading Bitcoins, before the period considered in the simulation. Each
i-th trader entering the market at t
E
i
> 0 holds only an amount c
i
(t
E
i
) of fiat currency (cash, in
dollars). These traders represent people interested in entering the market, investing their money
in it.
The wealth distribution of traders follows a Zipf law [34]. The set of all traders entering
the market at time t
E
i
> 0 are generated before the beginning of the simulation with a Pareto
distribution of fiat cash, and then are randomly extracted from the set, when a given number
of them must enter the market at a given time step. Also, the wealth distribution in crypto
cash of the traders in the market at initial time follows a Zipf law. Indeed, the wealth share in
the world of Bitcoin is even more unevenly distributed than in the world at large (see web
site http://www.cryptocoinsnews.com/owns-Bitcoins-infographic-wealth-distribution/). More
details on the trader wealth endowment are illustrated in Appendix.
10
Miners Miners are in the Bitcoin market aiming to generate wealth by gaining Bitcoins. At
the initial time, the simulated Bitcoin network is calibrated respecting the Satoshi original idea
of Bitcoin network where each node participates equally to the process of check and validation
of the transactions and mining. We assumed that miners in the market at initial time (t
E
i
= 0)
own a personal PC such as Core i5 2600K, and hence they are initially endowed with a hashing
capability r
i
(0) equal to 0.0173GH/sec, that implies a power consumption equal to 75W [16].
Core i5 is a brand name of a series of fourth-generation x64 microprocessor developed by Intel
and brought to market in October 2009.
Miners entering the market at time t
E
i
> 0 acquire mining hardware, and hence a hashing
capability r
i
(t), which implies a specific electricity cost e
i
(t), investing a fraction γ
1,i
(t) of their
fiat cash c
i
(t).
In addition, over time all miners can improve their hashing capability by buying new mining
hardware investing both their fiat and crypto cash. Consequently, the total hashing capability of
i th trader at time t, r
i
(t) expressed in [H/sec], and the total electricity cost e
i
(t) expressed
in $ per day, associated to her mining hardware units, are defined respectively as:
r
i
(t) =
t
X
s=t
E
i
r
i,u
(t) (3)
and
e
i
(t) =
t
X
s=t
E
i
P (s) r
i,u
(s) 24 (4)
where:
r
i,u
(t = t
E
i
> 0) = γ
1,i
(t)c
i
(t)R(t) (5)
r
i,u
(t > t
E
i
) = [γ
1,i
(t)c
i
(t) + γ
i
(t)b
i
(t)p(t)]R(t) (6)
R(t) and P (t) are, respectively, the hash rate which can be bought with one US$, ex-
pressed in
H
sec$
, and the power consumption, expressed in
W
H/sec
. At each time t, their
values are given by using the fitting curves described in subsection Modeling the Mining
Hardware Performances;
r
i,u
(t) is the hashing capability of the hardware units u bought at time t by i th miner;
γ
i
= 0 and γ
1,i
= 0 if no hardware is bought by i th trader at time t. When a trader
decides to buy new hardware, γ
1,i
represents the percentage of miner’s cash devoted to
buy it. It is equal to a random variable characterized by a lognormal distribution with
11
average 0.15 and standard deviation 0.15. γ
i
represents the percentage of miner’s Bitcoins
to be sold for buying the new hardware. It is equal to a random variable characterized
by a lognormal distribution with average 0.175 and standard deviation 0.075. The term
γ
1,i
(t)c
i
(t)+γ
i
(t)b
i
(t)p(t) expresses the amount of personal wealth that the miner wishes
to devote to buy new mining hardware, meaning that on average the miner will devote
35% of her cash and 17.5% of her bitcoins to this purpose. If γ
i
> 1 or γ
1,i
> 1, they are
set equal to one;
is the fiat price per Watt and per hour. It is assumed equal to 1.4 10
4
$, consid-
ering the cost of 1 KWh equal to 0.14$, that we assumed to be constant throughout
the simulation. This electricity price is computed making an average of the electricity
prices in the countries in which the Bitcoin nodes distribution is higher; see web sites
https://getaddr.bitnodes.io and http : //en.wikipedia.org/wiki/Electricity pricing.
The decision to buy or not new hardware is taken by every miner from time to time, on
average every two months (60 days). If i th miner decides whether to buy new hardware
and/or to divest the old hardware units at time t, the next time, t
ID
i
(t), she will decide again is
given by eq. 7:
t
ID
i
(t) = t + int(60 + N(µ
id
, σ
id
)) (7)
where int rounds to the nearest integer and N(µ
id
, σ
id
) is a normal distribution with average
µ
id
= 0 and standard deviation σ
id
= 6. t
ID
i
(t) is updated each time the miner takes her
decision.
Miners active in the simulation since the beginning will take their first decision within 60
days, at random times uniformly distributed. Miners entering the simulation at time t > 1 will
immediately take this decision.
In deeper detail, at time t = t
ID
i
(t), every miner can decide to buy new hardware units, if
her fiat cash is positive, and/or to divest the old hardware units. If trader’s cash is zero, she issues
a sell market order to get the cash to support her electricity expenses, c
i,a
(t) = γ
i
(t)b
i
(t)p(t).
Each i th miner belongs to a pool, and consequently at each time t she always has a
probability higher than 0 to mine at least some sub-units of Bitcoin. This probability is inversely
proportional to the hashing capability of the whole network. Knowing the number of blocks
discovered per day, and consequently knowing the number of new Bitcoins B to be mined per
day, the number of Bitcoins b
i
mined by i th miner per day can be defined as follows:
b
i
(t) =
r
i
(t)
r
T ot
(t)
B(t) (8)
where:
12
r
T ot
(t) is the hashing capability of the whole population of miners N
m
at time t defined
as the sum of the hashing capabilities of all miners at time t,
P
N
m
i
r
i
(t);
the ratio
r
i
(t)
r
T ot
(t)
defines the relative hash rate of i th miner at time t.
Note that, as already described in section Mining Process, the parameter B decreases over
time. At first, each generated block corresponds to the creation of 50 Bitcoins, but after four
years, such number is halved. So, until November 27, 2012, 100,800 Bitcoins were mined in
14 days (7200 Bitcoins per day), and then 50,400 Bitcoins in 14 days (3600 per day).
The decision of a miner to buy and/or divests a hardware unit u depends on the Bitcoins
potentially obtained mining with the new hardware. A miner buys new hardware units if the
daily cost, given by the expense in electricity, e
i,u
(t), associated to these units is smaller than
the gain expected in Bitcoin. Hence, before buying new hardware units the following constraint
has to be evaluated:
e
i,u
(t) < b
i,u
(t)p(t) (9)
where:
b
i,u
are the Bitcoins potentially mined by unit u at time t: b
i,u
(t) =
r
i,u
(t)
r
T ot
(t)
B(t)
p(t) is the Bitcoin price at time t.
Only if this constraint is respected the miner can buy new hardware. In this case, she issues
a market order acquiring an amount of fiat cash c
i,a
(t) = γ
i
(t)b
i
(t)p(t) in the next time steps.
She invests 50% of this amount to buy new hardware and keeps the remaining 50% as cash, to
pay the electricity bill for her hardware.
If the constraint in eq. 9 is not respected, the miner anyway issues a market order equal to
c
i,a
(t) =
γ
i
(t)b
i
(t)p(t)
2
to support her electricity expenses.
A miner divests her old hardware units if the expense in electricity associated to that units
is 20% higher than the gain expected in Bitcoins using that hardware, at the current price.
Therefore, the following constraint has to be respected for each value of k, with k going from 0
to current time t:
e
i,u
(k) 1.2
r
i,u
(k)
r
T ot
(t)
B(t)p(t) (10)
The model also includes a mechanism that enables 10% of miners to invest and/or divest
their hardware also at a time t 6= t
ID
i
(t). This mechanism is triggered when the price relative
variation, in a time window τ
M
equal to 15 days, is positive and is higher than a threshold
T h
M
equal to 0.016. This because, in the real market, the investments of miners grow when the
13
profitability of mining activity increases. So, increasing the interest of miners in buying new
hardware in these periods is a plausible assumption.
Random Traders Random Traders represent persons who enter the crypto-currency market
for various reasons, but not for speculative purposes. They issue orders for reasons linked to
their needs, for instance they invest in Bitcoins to diversify their portfolio, or they disinvest to
satisfy a need for cash. They issue orders in a random way, compatibly with their available
resources. In particular, buy and sell orders are always issued with the same probability. The
specifics of their behavior is described in section Buy and Sell Orders.
Chartists Chartists represent speculators, aimed to gain by placing orders in the Bitcoin mar-
ket. They speculates that, if prices are rising, they will keep rising, and if prices are falling,
they will keep falling. In particular, i th chartist issues a buy order when the price relative
variation in a time window τ
C
i
, is higher than a threshold T h
C
= 0.01, and issues a sell order
if this variation is lower than T h
C
. τ
C
i
is specific for each chartist, and is characterized by a
normal distribution with average equal to 20 and standard deviation equal to 1. Chartists usu-
ally issue buy orders when the price is increasing and sell orders when the price is decreasing.
However, 10% of Chartists decide, instead, to adopt a contrary strategy, and place a sell order
instead of a buy order, or vice-versa. This contrarian behavior is common in financial markets,
and is typically modeled also in market models [49]. Note that a Chartist will issue an order
only when the price variation is above a given threshold. So, in practice, the extent of Chartist
activity varies over time. In general the modelled Chartists’ behavior is key to produce large
price variations, and to the reproduction of the basic statistical proprieties of the real returns.
All Random traders and Chartists entering the market at t = t
E
> 0, issue a buy order to
acquire their initial Bitcoins. Over time, at time t > t
E
only a fraction of Random traders and
Chartists is active, and hence enabled to issue orders. Active traders can issue only one order
per time step, which can be a sell order or a buy order.
Orders already placed but not yet satisfied or withdrawn are accounted for when determining
the amount of Bitcoins a trader can buy or sell. Details on the percentage of active traders, the
number of the traders in the market and on the probability of each trader to belong to a specific
traders’ population are described in Appendix.
4.2 Buy and Sell Orders
The Bitcoin market is modelled as a steady inflow of buy and sell orders, placed by the traders
as described in [15]. Both buy and sell orders are expressed in Bitcoins, that is, they refer to a
given amount of Bitcoins to buy or sell. In deeper detail, all orders have the following features:
14
amount, expressed in $ for buy order and in Bitcoins for sell order: the latter amount is a
real number, because Bitcoins can be bought and sold in fractions as small as a ”Satoshi”;
residual amount (Bitcoins or $): used when an order is only partially satisfied by previous
transactions;
limit price (see below), which in turn can be a real number;
time when the order was issued;
expiration time: if the order is not (fully) satisfied, it is removed from the book at this
time.
The amount of each buy order depends on the amount of cash, c
i
(t), owned by i-th trader at
time t, less the cash already committed to other pending buy orders still in the book. Let us call
c
b
i
the available cash. The number of Bitcoins to buy, b
a
is given by eq. 11
b
a
=
c
b
i
β
p(t)
(11)
where p(t) is the current price and β is a random variable drawn from a lognormal distribu-
tion with average and standard deviation equal to 0.25 and 0.2, respectively for Random traders
and equal to 0.4 and 0.2, respectively for Chartists. In the unlikely case that β > 1, β is set
equal to 1.
Similarly, the amount of each sell order depends on the number of Bitcoins, b
i
(t) owned by
i-th trader at time t, less the Bitcoins already committed to other pending sell orders still in the
book, overall called b
s
i
. The number of Bitcoins to sell, s
a
is given by
s
a
= b
s
i
β (12)
where β is a lognormal random variable as above. Short selling is not allowed.
The limit price models the price to which a trader desire to conclude his/her transaction.
An order can also be issued with no limit (market order), meaning that its originator wishes
to perform the trade at the best price she can find. In this case, the limit price is set to zero.
The probability of placing a market order, P
lim
, is set at the beginning of the simulation and is
equal to 1 for Miners, to 0.2 for Random Traders and to 0.7 for Chartists. This because, unlike
Random Traders, if Miners and Chartists issue orders, then they wish to perform the trade at
the best available price, the formers because they need cash, the latters to be able to gain by
following the price trend.
Let us suppose that i-th trader issues a limit order to buy a
b
i
(t) Bitcoins at time t. Each buy
order can be executed if the trading price is lower than, or equal to, its buy limit price b
i
. In the
15
case of a sell order of a
s
i
(t) Bitcoins, it can be executed if the trading price is higher than, or
equal to, its sell limit price s
i
. As said above, if the limit prices b
i
= 0 or s
i
= 0, then the orders
can be always executed, provided there is a pending complementary order.
The buy and sell limit prices, b
i
and s
i
, are given respectively by the following equations:
b
i
(t) = p(t) N
i
(µ, σ
i
) (13)
s
i
(t) =
p(t)
N
i
(µ, σ
i
)
(14)
where
p(t) is the current Bitcoin price;
N
i
(µ, σ
c
i
) is a random draw from a Gaussian distribution with average µ ' 1 and standard
deviation σ
i
1.
The limit prices have a random component, modelling the different perception of Bitcoin
value, that is the fact that what traders ”feel” is the right price to buy or to sell is not constant,
and may vary for each single order. In the case of buy orders, we stipulate that a trader wishing
to buy must offer a price that is, on average, slightly higher than the market price.
The value of σ
i
is proportional to the ”volatility” σ(T
i
) of the price p(t) through the equation
σ
i
= Kσ(T
i
), where K is a constant and σ(T
i
) is the standard deviation of price absolute
returns, calculated in the time window T
i
. σ
i
is constrained between a minimum value σ
min
and
a maximum value σ
max
(this is an approach similar to that of [47]). For buy orders µ = 1.05,
K = 2.5, σ
min
= 0.01 and σ
max
= 0.003.
In the case of sell orders, the reasoning is dual. For symmetry, the limit price is divided by
a random draw from the same Gaussian distribution N
i
(µ, σ
c
i
).
An expiration time is associated to each order. For Random Traders, the value of the expira-
tion time is equal to the current time plus a number of days (time steps) drawn from a lognormal
distribution with average and standard deviation equal to 3 and 1 days, respectively. In this way,
most orders will expire within 4 days since they were posted. Chartists, who act in a more
dynamic way to follow the market trend, post orders whose expiration time is at the end of
the same trading day. Miners issue market orders, so the value of the expiration time is set to
infinite.
4.3 Price Clearing Mechanism
We implement the price clearing mechanism by using an Order Book similar to that presented
in [50].
16
At every time step, the order book holds the list of all the orders received and still to be
executed. Buy orders are sorted in descending order with respect to the limit price b
i
. Orders
with the same limit price are sorted in ascending order with respect to the order issue time. Sell
orders are sorted in ascending order with respect to the limit price s
j
. Orders with the same
limit price are sorted in ascending order with respect to the order issue time.
At each simulation step, various new orders are inserted into the respective lists. As soon as
a new order enters the book, the first buy order and the first sell order of the lists are inspected
to verify if they match. If they match, a transaction occurs. The order with the smaller residual
amount is fully executed, whereas the order with larger amount is only partially executed, and
remains in the head of the list, with its residual amount reduced by the amount of the matching
order. Clearly, if both orders have the same residual amount, they are both fully executed.
After the transaction, the next pair of orders at the head of the lists are checked for matching.
If they match, they are executed, and so on until they do not match anymore. Hence, before the
book can accept new orders, all the matching orders are satisfied.
A sell order of index j matches a buy order of index i, and vice versa, only if s
j
b
i
, or if
one of the two limit prices, or both, are equal to zero.
As regards the price, p
T
, to which the transaction is performed, the price formation mecha-
nism follows the rules described below. Here, p(t) denotes the current price:
when one of the two orders has limit price equal to zero:
if b
i
> 0, then p
T
= min(b
i
, p(t)),
if s
j
> 0, then p
T
= max(s
j
, p(t)),
when both orders have limit price equal to zero, p
T
= p(t);
when both orders have limit price higher than zero, p
T
=
b
i
+s
j
2
.
5 Simulation Results
The model described in the previous section was implemented in Smalltalk language. Before the
simulation, it had to be calibrated in order to reproduce the real stylized facts and the mining
process in the Bitcoin market in the period between September 1st, 2010 and September 30,
2015. The simulation period was thus set to 1856 steps, a simulation step corresponding to one
day. We included also weekends and holidays, because the Bitcoin market is, by its very nature,
accessible and working everyday.
We set the initial value of several key parameters of the model by using data recovered from
the Blockchain Web site. The main assumption we made is to size the artificial market at about
17
Figure 2: Price of Bitcoins in US$.
1/100 of the real market, to be able to manage the computational load of the simulation. Table
4 shows the parameter values and their computation assumptions in detail.
In Appendix other details about the calibration of the model are shown. Specifically, the
calibration of the trader wealth endowment, the number of active traders, the total number of
traders in the market and the probability of a trader to belong to a specific traders’ population
are described in detail.
The model was run to study the main features which characterize the Bitcoin market and the
traders who operate in it. In order to assess the robustness of our model and the validity of our
statistical analysis, we repeated 100 simulations with the same initial conditions, but different
seeds of the random number generator. The results of all simulations were consistent, as shown
in the followings.
5.1 Bitcoin prices in the real and simulated market
We started studying the real Bitcoin price series between September 1st, 2010 and September
30, 2015, shown in Fig. 2. The figure shows an initial period in which the price trend is
relatively constant, until about 950
th
day. Then, a period of volatility follows between 950
th
and
1150
th
day, followed by a period of strong volatility, until the end of the considered interval.
18
Table 4: Values of simulation parameters and the assumptions behind them.
Param. Initial Value Description and discussion
N
t
(0) 160 Number of initial traders. Obtained dividing by
100 the number of traders on September 1st,
2010 estimated through the fitting curve shown
in eq. 15 (see Appendix).
N
t
(T ) 39,649 Total number of traders at the end of the sim-
ulation. Obtained dividing by 100 the num-
ber of traders on September 30, 2015 estimated
through the fitting curve shown in eq. 15.
B 72 or 36 Bitcoins mined per day. Obtained dividing
by 100 the Bitcoins which are mined every
day. They are 72 until 853th simulation step
(November 27th, 2012), and 36 from 853th
simulation step onwards.
p(0) 0.0649 $ Initial price. The average price as of September
2010.
B
T
(0) 23,274 $ Total initial crypto cash. Obtained dividing by
100 the number of Bitcoins on September 1st,
2010 and keeping just 60% of this value, be-
cause we assume that 40% of Bitcoins are not
available for the trade.
q 200,000 $ Constant used in Zipfs law (
q
i
0.6
), used to assign
the initial cash for traders entering at t > 1.
c
s
1
20,587 $ Initial cash of the richest trader entering the
simulation at t = 1.
b
s
1
4,117 $ Initial Bitcoin cash of the richest trader entering
the simulation at t = 1.
19
Figure 3: Bitcoin simulated Price in one simulation run.
The Bitcoin price started to fall at the beginning fo 2014, and is continuing on its downward
slope until September 2015.
It is well known that the price series encountered in financial markets typically exhibit some
statistical features, also known as ”stylized facts” [42, 38]. Among these, the three uni-variate
properties which appear to be the most important and pervasive of price series, are (i) the unit-
root property, (ii) the fat tail phenomenon, and (iii) the Volatility Clustering. We examined daily
Bitcoin prices and found that also these prices exhibit these properties as discussed in detail in
[15].
As regards the prices in the simulated market, we report in Fig. 15 the Bitcoin price in one
typical simulation run. It is possible to observe that, as in the case of the real price, at first
the price keeps its value constant, but then, after about 1000 simulation steps, contrary to what
happens in the reality, it grows and continues on its upward slope until the end of the simulation
period.
Figs. 16 (a) and (b) report the average and the standard deviation of the simulated price,
taken on all 100 simulations. Note that the average value of prices steadily increases with time,
in contrast with what happens in reality. Fig. 16 (b) shows that the price variations in different
simulation runs increase with time, as the number of traders, transactions and the total wealth
in the market are increasing.
In the proposed model, the upward trend of the price depends on an intrinsic mechanism
in fact, the average price tends to the ratio of total available cash to total available Bitcoins.
Since new traders bring in more cash than new mined Bitcoins, the price tends to increase.
In reality, Bitcoin price is also heavily affected by exogenous factors. For instance, in the
past the price strongly reacted to reports such as those regarding Bitcoin ban in China, or the
MtGox exchange going bust. Moreover, the total capitalization of the Bitcoin market is of the
20
(a) (b)
Figure 4: (a) Average Price and (b) standard deviation computed on the 100 Monte Carlo simulations
performed.
order of just some billions of US$, so if a large hedge fund decided to invest in Bitcoins, or if
large amounts of Bitcoins disappeared because of theft, fraud or mismanagement, the effect on
price would be potentially quite large. All these exogenous events, that can trigger strong and
unexpected price variations, obviously cannot be part of our base model.
In section Other Results, we shall describe the results obtained when some random traders
adopt speculative behaviors, in addition to the speculative behaviour that characterizes Chartists.
Simulating this behavior allows to reproduce the Bitcoin price peak in December 2013 and its
subsequent fall.
Despite inability to reproduce the decreasing trend of the price, the model presented in
Section The Model, is able to reproduce quite well all statistical properties of real Bitcoin prices
and returns. The stylized facts, robustly replicated by the proposed model, are the same of a
previous work of Cocco et al. [15], and do not depend on the addition of the miners to the
model.
5.2 Traders’ Statistics
Figs. 5 - 7 show the average and the standard deviation of the crypto and fiat cash, and of the
total wealth, A(t), of trader populations, across all 100 simulations. These simulations were
carried with miners buying new hardware using an average percentage of 15% of their wealth,
that demonstrated to be optimal.
Figure 7(a) highlights how Miners represent the richest population of traders in the market
in the beginning of the simulation. However, from about 1400th step onwards, Random traders
become the richest population in the market. This is mainly due to the higher number of Random
21
traders with respect to Miners. Note also that the standard deviation of the total wealth is much
more variable than the former two figures. This is due to the fact that the wealth is obtained
by multiplying the number of Bitcoins by their price, which is very volatile among the various
simulations, as shown in Fig. 16(b).
(a) (b)
Figure 5: (a) Average and (b) standard deviation of the Bitcoin amount for all trader populations during
the simulation period across all Monte Carlo simulations.
(a) (b)
Figure 6: (a) Average and (b) standard deviation of the cash amount for all trader populations during the
simulation period across all Monte Carlo simulations.
Fig. 8, shows the average of the total wealth per capita for all trader populations, across all
100 Monte Carlo simulations. Miners are clearly the winners about from the 380th simulation
step onwards, thanks to their ability to mine new Bitcoins. Specifically, thanks to the percentage
22
(a) (b)
Figure 7: (a) Average and (b) standard deviation of the total wealth for all trader populations during the
simulation period across all Monte Carlo simulations.
of cash that Miners devot to buy new mining hardware, Miners are able to acquire a wealth per-
capite that ranges about between $1,000 at the beginning of the simulation and $14,000 at the
end. This is due to the optimal percentage of cash devoted to buy new hardware, that is drawn
from a lognormal distribution γ with both average and standard deviation set to 0.15, as already
mentioned in The Agents.
Figure 8: Average across all Monte Carlo simulations of the total wealth per capita, for all trader popu-
lations.
We varied the average percentage of their wealth that Miners devote for buying new hard-
ware, γ, to verify how this variation can impact on Miners’ success. Remember that the actual
percentage for a given Miner is drawn from a log-normal distribution, so these percentages are
fairly different among Miners.
23
Figures 9 (a) and (b) show the total wealth per capita for Miners, for increasing values
of the average of γ. It is apparent that Miners’ gains are inversely proportional to γ, so the
general strategy of devoting more money to buy hardware is not successful for Miners. This
is because if all Miners devote an increasing amount of money to buy new mining hardware,
the overall hashing power of the network increases, and each single Miner does not obtain the
expected advantage of having more hash power, whereas the money spent on hardware and
energy increases. The wealth per-capite ranges between about $1,000 at the beginning of the
simulation and $8,000 at the end, for γ = 0.25 (see fig. 9 (a)) and about between $1,000 and
$6,000 for γ = 0.35 (see fig. 9 (b)).
(a) (b)
Figure 9: Average across all Monte Carlo simulations of the total wealth average per capita for all trader
populations (a) for γ = 0.25 and (b) for γ = 0.35.
Having found that Miners’ wealth decreases when too much of it is used to buy new hard-
ware, we studied if increasing money spent in mining hardware is a successful strategy for
single Miners, when most other Miners do not follow it. Fig. 10 (a) shows the ratio of initial
Miners’ total wealth computed at the end and at the beginning of a single simulation,
A
f
m
i
(T )
A
f
m
i
(0)
,
versus their actual value of γ
i
, that is their propension to spend money to buy mining hardware.
The average < γ >= 0.15 in this simulation. The correlation coefficients is equal to -0.14, so
it looks that there is no meaningful correlation between mining success and the propension to
invest in hardware. In Fig. 10 we can see that two of the three most successful Miners, able
to increase their wealth of about 100 and 45 times, have a very low value of γ
i
, (less than 0.1),
whereas the third one, who was able to increase his wealth forty times, has a high propension
to invest (γ
i
' 0.62).
On the contrary, we found that the total wealth, A
f
m
i
(T ), of the miners at the end of the
simulation is correlated with their hashing capability r
f
m
i
(T ), being the correlation coefficient
24
equal to 0.788, as shown in Fig. 10 (b). This result is not unexpected because wealthy Miner
can buy more hardware, that in turn helps them to increase their mined Bitcoins.
(a) (b)
Figure 10: Scatterplots of (a) the increase in wealth of single Miners versus their average wealth percent-
age used to buy mining hardware, and (b) the total wealth of Miners versus their hashing power at the
end of the simulation.
Figures 11 - 14 show some significant quantities related to the Miner’s population.
(a) (b)
Figure 11: (a) Comparison between real hashing capability and average of the simulated hashing capa-
bility across all Monte Carlo simulations in log scale, and (b) average and standard deviation of the total
expenses in electricity across all Monte Carlo simulations in log scale.
Fig. 11(a) shows the average hashing capability of the whole network in the simulated
market across all Monte Carlo simulations and the hashing capability in the real market, being
25
both these quantities expressed in log scale. Note that the simulated hashing capability should
be about 100 times lower than the real one, due to the reduced dimension of the simulated
market with respect to the real one. The simulated hash rate does not follow the upward trend
of the Bitcoin price at about 1200th time step that is due to an exogenous cause (the step price
increase at the end of 2013), that is obviously not present in our simulations. However, in Fig.
11(a) the simulated hashing capability is actually about two orders of magnitude lower than the
real one, as it should be.
In general, Bitcoin mining hardware become obsolete from a few months to one year after
you purchase them. ”Serious” miners usually buy new equipment every month, re-investing
their profits into new mining equipment, if they want that their Bitcoin mining operation to
run long term (see web site http://coinbrief.net/profitable-bitcoin-mining-farm/. In our model,
miners divest their mining equipment about every ten months.
Figure 12 (a) shows the average and standard deviation of the power consumption across
all Monte Carlo simulations. Figure 12 (b) shows an estimated minimum and maximum power
consumption of the Bitcoin mining network, together with the average of the power consump-
tion of Fig. 12 (a), in logarithmic scale. The estimated theoretical minimum power consumption
is obtained by multiplying the actual hash rate of the network at time t (as shown in Fig. 11(a))
with the power consumption P (t) given in eq. 2. This would mean that the entire hashing
capability of miners is obtained with the most recent hardware. The estimated theoretical maxi-
mum power consumption is obtained by multiplying the actual hash rate of the network with the
power consumption P (t 360), referring to one year before. This would mean that the entire
hashing capability of miners is obtained with hardware one year old, and thus less efficient. The
estimated obsolescence of mining hardware is between six months and one year, so the period
of one year should give a reliable maximum value for power consumption.
The simulation results, averaged on 100 simulations, show a much more regular trend,
steadily increasing with time which is natural due to the absence of external perturbations
on the model. However, the power consumption value is of the same order of magnitude of the
”real” case. Note that the simulated consumption shown in Fig. 12 (b) is multiplied by 100, that
is the scaling factor of our simulations, that have 1/100
th
of the real number of Bitcoin traders
and miners.
Fig. 12 (b) also shows a white circle, at time step corresponding to April 2013, with a value
of 38.8 MW. This value has been taken by Courtois et al, who in work [18] write:
In April 2013 it was estimated that Bitcoin miners already used about 982 Megawatt hours
every day. At that time the hash rate was about 60 Tera Hash/s. (Refer to article by Gimein
Mark ”Virtual Bitcoin Mining Is a Real-World Environmental Disaster”, 13 April 2013
published on web site www.Bloomberg.com.).
In fact, the hash rate quoted is correct, but the consumption value looks overestimated of
26
(a) (b)
Figure 12: (a) Average and standard deviation of the power consumption across all Monte Carlo simula-
tions, and (b) Estimated minimum and maximum power consumption of the real Bitcoin Mining Network
(solid lines), and average of the power consumption across all Monte Carlo simulations, multiplied by
100, the scaling factor of our simulations (dashed line). For the meaning of the circles, see text.
one order of magnitude, even with respect to our maximum power consumption limit. We
believe this is due to the fact that the authors still referred to FPGA consumption rates, not fully
appreciating how quickly the ASIC adoption had spread among the miners.
As of 2015, the combined electricity consumption was estimated equal to 1.46 Tera Wh
per year, that corresponds to about 167 MW (see article ”The magic of mining”, 13 January
2015 published on web site www.economist.com.). This value is reported in Fig. 12 (b) as a
black circle. This time, the value is slightly underestimated, being at the lower bound of power
consumption estimate, and is practically coincident with the average value of our simulations.
Figures 13 (a) and (b) show an estimate of the expenses incurred every six days in electricity
(a) and in hardware (b) for the new hardware bought each day in the real and simulated market.
Note that also the values of the simulated expenses are average values across all Monte
Carlo simulations.
These expenses were computed assuming that the new hardware bought each day in the real
(simulated) market, and hence the additional hashing capability acquired each day, is equal to
the difference between the real (simulated) hash rate in t and the real (simulated) hash rate in
t 1.
For both these expenses, contrary to what happens to the respective real quantities, the
simulated quantities do not follow the upward trend of the price due to the constant investment
rate in mining hardware.
Figure 14 (a) and (b) show the average and standard deviation, across all Monte simulations,
27
(a) (b)
Figure 13: Real expenses and expenses average in electricity across all Monte Carlo simulations (a) and
real expenses and expenses average in hardware across all Monte Carlo simulations every six days (b).
of the expenses incurred every six days in electricity and in new hardware respectively, showing
the level of the variation across the simulations.
(a) (b)
Figure 14: Average and standard deviation of the expenses in electricity (a) and of the expenses in new
hardware across all Monte simulations.
Remembering that our model sizes the artificial market at about 1/100 of the real market
and that the number of traders, their cash and their trading probabilities are rough estimates of
the real ones, the simulated market outputs can be considered reasonably close to the real one.
28
5.3 Other Results
It is known that Bitcoin price is driven by speculation, government regulation and investors
behavior, and its volatility depends also on Bitcoin acceptance and usage. In 2012 and 2013
prices had a wild ride, until they reached a peak of $1,150 in December 2013. In 2014 Bitcoin
price fell following the shutdown of historical Mt. Gox exchange site and reports regarding
Bitcoin ban in China.
Trying to reproduce this market trend, we introduced in the model a particular speculative
behaviour by some traders. The speculative mechanism implemented stems from a report, called
the ”Willy Report”, published by an anonymous researcher, which alleges suspicious trading
activity at Mt. Gox. ”The Willy Report: proof of massive fraudulent trading activity at Mt.
Gox, and how it has affected the price of Bitcoin”, was posted on May 25, 2014 in web site
https://willyreport.wordpress.com/.
The anonymous researcher claims to have noted a suspicious bot behavior on Mt. Gox, that
spread its trading activity over many accounts, and how this fraudulent massive trading activity
impacted on the price, causing bubble and crash.
In the report the researcher writes:
”Somewhere in December 2013, a number of traders including myself began noticing sus-
picious bot behavior on Mt. Gox. Basically, a random number between 10 and 20 bitcoin
would be bought every 510 minutes, nonstop, for at least a month on end until the end of
January. The bot was dubbed Willy . . . its trading activity was spread over many accounts.
. . . Their trading activity went back all the way to September 27th.. . .
In total, a staggering about $112 million was spent to buy close to 270,000 BTC - the
bulk of which was bought in November. So if you were wondering how Bitcoin suddenly
appreciated in value by a factor of 10 within the span of one month, well, this may be why.
Not Chinese investors, not the Silkroad bust - these events may have contributed, but they
may not have been main reason. . . .
. . . there was another timetraveller account with an ID of 698630 - and this account, after
being active for close to 8 months, became completely inactive just 7 hours before the first
Willy account became active! So it is a reasonable assumption that these accounts were
controlled by the same entity. . . . There were several peculiar things about Markus. First,
its fees paid were always 0 (unlike Willy, who paid fees as usual). Second, its fiat spent
when buying coins was all over the place, with seemingly completely random prices paid
per bitcoin.. . .
. . . Since there are no logs past November 2013, the following arguments are largely based
on personal speculation, and that of other traders. . .
on January 26th, Willy suddenly became inactive and with it, the price retraced back to
a more reasonable spread with the other exchanges. Shortly after – on February 3rd to be
29
Figure 15: Price of the Bitcoin in the simulated market.
precise – it seemed as if Willy had begun to run in reverse, although with a slightly altered
pattern: it seemed to sell around 100 BTC every two hours. . . . There’s some additional
evidence on the chart that a dump bot may have been at play. At several points in time,
starting from Feb. 18th, it seemed that some bot was programmed to sell down to various
fixed price levels.. . .
At this point, I guess the straightforward conclusion would be that this is how the coins
were stolen: a hacker gained access to the system or database, was able to assign him-
self accounts with any amount of USD at will, and just started buying and withdrawing
away.. . . ” .
According to what just mentioned, we modeled a similar behaviour. We assumed that, until
the end of January 2014, 40% of Random traders entering the market were Mt. Gox accounts.
The Mt. Gox accounts have a behaviour equal to that of Random traders described in Paragraph
Random Traders until July 2012. Then, from August 2012 and until the end of January 2014,
they issue only buy orders. Next, from February 2014 they issue only sell orders. Their trading
probability is set equal to 0.1 in every period.
Fig. 15 shows the Bitcoin price in one typical simulation run, under these conditions. At
first, the price keeps its value constant, then at about 700 simulation steps, it grows as happens
in reality. The price maintains its value high for about 500 simulation steps, then its value falls
down, but after a short delay it continues on its upward slope until the end of the simulation,
due to the intrinsec mechanism of our model already previous described.
The MtGox accounts’ behaviour has a key rule in the reproduction of the price that has a
trend more similar to the real one (shown in Fig. 2) than that described in section Bitcoin prices
in the real and simulated market.
Figs. 16 (a) and (b) report the average and the standard deviation of the simulated price
30
across all Monte Carlo simulations showing the consistency of the results.
(a) (b)
Figure 16: (a) Average Price and (b) standard deviation computed on the 100 Monte Carlo simulations
performed.
All the analysis described in the previous sections was performed also for the model includ-
ing ”Mt.Gox” accounts, producing results consistent with those studied in the previous sections.
6 Conclusions
In this work, we propose an heterogenous agent model of the Bitcoin market with the aim
to study and analyse the mining process and the Bitcoin market starting from September 1st,
2010, the approximate date when miners started to buy mining hardware to mine Bitcoins, for
ve years.
The proposed model simulates the mining process and the Bitcoin transactions, by imple-
menting a mechanism for the formation of the Bitcoin price, and specific behaviors for each
typology of trader. It includes different trading strategies, an initial distribution of wealth fol-
lowing Pareto law, a realistic trading and price clearing mechanism based on an order book, the
increase with time of the total number of Bitcoins due to mining, and the arrival of new traders
interested in Bitcoins.
The model was simulated and its main outputs were analysed and compared to respective
real quantities with the aim to demonstrate that an artificial financial market model can repro-
duce the stylized facts of the Bitcoin financial market.
The main result of the model is the fact that some key stylized facts of Bitcoin real price
series and of Bitcoin market are very well reproduced. Specifically, the model reproduces quite
well the unit root property of the price series, the fat tail phenomenon, the volatility clustering
31
of the price returns, the price peak in November 2013, its next fall in April 2014, the generation
of Bitcoins, the hashing capability, the power consumption, and the hardware and electricity
expenses incurred by Miners.
The proposed model is fairly complex. It is intrinsically stochastic and of course it in-
cludes endogenous mechanisms affecting the market dynamics. The Zipf distribution of traders’
wealth, that impacts to the size of the orders and the ”herding” effect of Chartists, when a price
trend is established, play a key role in the distribution of the price returns, and hence in the
reproduction of the fat tail phenomenon. The Chartist behavior and also the variability of the
spread of limit prices as a function of past price volatility contribute to the volatility cluster-
ing. The threshold of activation of Chartists based on price relative variation, and the past price
volatility used to determine the spread of limit prices impact on the unit-root property of the
price series. The percentage of each trader’s population, the choice of a trader that trades at
a given time step, and the type of trading (buy or sell), as well as the setting of the quantity
to trade, impact on the price trend. The setting of the amount of cash to devote to buy new
hardware impacts on the wealth and hashing capability of Miners, and consequently on their
hardware and electricity expenses.
Future research will be devoted to study in deeper detail the mechanisms impacting on the
model dynamics. In particular, we will perform a comprehensive analysis of the sensitivity
of the model to the various parameters, and will add traders with more sophisticated trading
strategies, to assess their profitability in the simulated market. In addition, since the calibration
of our model is based on very few specific real data, and on many assumptions aiming to derive
the needed data from indirect real data, we plan to perform a deeper analysis of the Block Chain,
and to gather financial data from the existing exchanges, in order to extract specific information
needed for a better calibration of our model.
A Appendix
B Trader Wealth Endowment
The distributions of cash and Bitcoins, for traders in the market at initial time, follow a power-
law with exponent α set equal to 1, a value yielding the distribution known as Zipfs law [41].
This is the same assumption made in other papers [47, 49]. We assumed that 40% of the total ini-
tial crypto cash is not involved in the trading activity, given the initial Miners hoarded a fraction
of their Bitcoins (see web site http://www.coindesk.com/mit-report-bitcoin-more-likely-spent-
hoarded/.).
To create the Zipfs distribution we used the ranking property of the power-law [54]. If
the total number of traders is N
t
and the number of Bitcoins owned by them, b
i
, follows a
32
Pareto law with exponent α = 1, it is well-known from Harmonic-series theory that the total
number of Bitcoins B
T
= b
1
ln(N
t
) + γ, where γ is the Euler-Mascheroni constant and b
1
is the
number of Bitcoins owned by the richest trader. The number of Bitcoins owned by i-th trader
is consequently:
b
1
i
. We set the cash of each initial trader equal to five times the value of their
crypto cash.
A similar approach was followed to set the wealth of traders who enter the simulation at
t > 0, but in this case the traders are only endowed with cash. In this case, we had no specific
data to calibrate the wealth of these traders. We stipulated that the cash, c
s
1
, of the richest trader
is about ve times the cash owned by the richest initial trader, and that the exponent of the
Pareto law is in this case α = 0.6. Overall, we performed various simulations varying these
parameters, with no significant impact on the results.
The set of ”new” traders are generated before the simulation starts. When new traders are
needed to enter the simulation, they are chosen randomly in this set.
C Active Traders
As mentioned in subsection The agents, only a given percentage of traders is active in the mar-
ket, and hence enabled to issue orders. To compute this percentage we made some assumptions
starting from the work [52] from which we extracted Table 5, which shows the distribution of the
transactions number per entity (Entity means the common owner of multiple Bitcoin addresses)
and per address on a period between January 3rd, 2009 and May 13th 2012.
Table 5: The distribution of the number of transactions per entity and per address.
Number of Number of Number of
Transactions (n) entities addresses
1 n < 2 557,783 495,773
2 n < 4 1,615,899 2,197,836
4 n < 10 222,433 780,433
10 n < 100 55,875 228,275
100 n < 1, 000 8,464 26,789
1, 000 n < 5, 000 287 1,032
5, 000 n < 10, 000 35 51
10, 000 n < 100, 000 32 24
100, 000 n < 500, 000 7 3
n 500, 000 1 2
By analyzing Table 5, we can observed that 97.37% of all entities had fewer than 10 trans-
actions each, 2.27% of all entities had a number of transactions ranging from 10 to 100, 0.34 %
33
of all entities had a number of transactions ranging from 100 to 1,000, and the remaining 0.02
% had a number of transactions higher than 1,000.
According to the insights coming from this work, we neglected the entities having fewer than
10 transactions each and the entities having more than 1,000 transactions each. This was done
hypothesizing that the formers, typically involving a small number of Bitcoins, refer to entities
who made transactions by chance ”only to use” this new coin, whereas the latters, involving
a very high of transactions, are probably not linked to single traders, but are the addresses of
exchange sites, or of retailers accepting Bitcoins.
We considered the remaining entities, that is the entities with a number of transactions in
the range from 10 to 1,000. They issue orders with a period ranging from about one day to
about 122 days, because these data are computed on 1227 days. As a result, the daily trading
probability of an entity ranges from 0.008 to 1.
We set the values of trading probabilities for Random traders and Chartists in this range.
Specifically, we assumed that, Random Traders are active with a probability p
t
R
= 0.1, whereas
the Chartists are active with a probability p
t
C
= 0 .5. This because the interest of Chartists in
purchasing or selling Bitcoins is higher than that of Random traders. Random traders issue
orders to satisfy their needs, whereas Chartists issue orders for speculative reasons, study care-
fully the price variation over time and are readier to place orders. Note that active Chartists
actually place orders only if the price variation is above a given threshold.
D Number of Traders
One of the most attractive property of Bitcoin is to provide quasi-anonymous transactions, so
knowing the number of traders in the real market is very difficult. The Bitcoin addresses used
for the transactions are known, but a user can have, and typically has, more than one address.
At the moment of writing (last quarter of 2015) we had three figures:
1. the massive and transparent ledger of every Bitcoin transaction was generated starting
since January 3, 2009 presumably by the inventor of the Bitcoin system itself, Nakamoto
and so in January 2009 there was only one person owning Bitcoins;
2. ”according to rough estimates, 280,000 people owned Bitcoins at the end of 2013”
(http://www.whoishostingthis.com/blog/2014/03/03/who-owns-all-the-bitcoins/);
3. on April 22nd, 2014 the total number of holders was estimated equal to 1.0 million
(https://Bitcointalk.org/index.php?topic=316297.0).
In addition to these figures, we have other data related to the period between January 2009
and September 2010. These data were extracted from an analysis on the daily number of down-
loads of the official Bitcoin software client from the SourceForge platform (http://sourceforge.net/projects/bitcoin).
34
In the period just mentioned, the Bitcoin network began to spread and Bitcoin had no mon-
etary value. So, for this period we assumed the number of downloads of the official Bitcoin
software client equal to the number of traders in the market. We made the assumption that a
person who downloads the Bitcoin software is mainly interested to use it to mine Bitcoins. So,
we extracted two figures from this data: the total number of downloads on May 1st, 2010 equal
to 2,769, and the total number of downloads on September 1st, 2010, equal to 30,589, and we
set the number of downloads equal to the number of traders.
So, we computed the number of traders in the market fitting the curve N
T
through the five
figures available:
1. 1 people owned Bitcoins on January 2009. He was Satoshi Nakamoto;
2. 2769 people had downloaded Bitcoin mining software on May 2010;
3. 30589 people had downloaded Bitcoin mining software on September 2010;
4. 280,000 people owned Bitcoins at the end of 2013;
5. 1000000 people owned Bitcoins on April 2014.
The fitting curve of the number of traders N
T
is defined by using a general exponential
model:
N
T
(t) = a e
(b(608+t))
(15)
where a = 2624, b = 0.002971.
Fig. 17 (a) show the fitting curve and how the number of traders increases over time.
E Probability of a trader to belong to a specific traders’ pop-
ulation
At first, users in the Bitcoin network were mainly miners. Subsequently, when the Bitcoin
network started to grow and Bitcoins started to acquire a monetary value, new users entered the
network. Most of these users did not mine, but simply traded Bitcoins. They are represented in
the model as Random traders and Chartists.
Since the percentages of the different trader populations is not known, to compute the prob-
ability of a trader to be a Miner we performed an analysis of the Bitcoin Blockchain. We
analysed the Blockchain until May 1st, 2010 and then until September 1st, 2010. Each block
in the Blockchain contains a list of validated transactions. Each transaction has in input all
35
(a) (b)
(c)
Figure 17: Fitting curve (a) of N
T
(x), (b) of the probability of a trader to be a Miner, and (c) number of
real Bitcoin transactions.
the addresses containing the amount of Bitcoins to transfer, and in output all the addresses that
receive the Bitcoins. Users can use multiple addresses.
We assumed, as in [27], that the input addresses that send Bitcoins to the same output
address must belong to an unique owner, since to proceed with the transaction it is necessary to
know the private keys of all input addresses.
In addition, we considered the input addresses that transfer more than 20 Bitcoins owned
by the same owner of the corresponding outputs. In fact, in the period from May 1st, 2010,
to September 1st, 2010 Bitcoins had no monetary value, and so it is acceptable to consider
that Miners exchanged only small amounts of Bitcoins to test the operation of the network. Of
course, this last assumption can be valid only for the period under study.
With these assumptions, we found 46,005 unique addresses on May 1st, 2010 and 82,294
36
unique addresses on September 1st, 2010. With further analysis, we found that on May 1st,
2010, out of 46,005 addresses, 43,389 were addresses that mined, and on September 1st, 2010,
out of 82,294 addresses, 55,974 were addresses that mined, so we computed the probability of
an user to be a miner. This probability is equal to 0.94% on May 1st, 2010 and equal to 0.68%
on September 1st, 2010.
Using these two figures we computed a fitting curve of the probability of an user to be a
miner p
M
. Again, it is defined by using a general exponential model:
p
M
(t) = a e
(bt)
(16)
where a = 0.9425, b = 0.002654.
Fig. 17 (b) shows the fitting curve and how this probability decreases over time. We see that
the probability of a trader to be a Miner decreases over time, going about from 0.38 to less than
0.01. Of course, defining this probability using a fitting curve computed from only two points
is questionable. In the followings, we made some considerations to validate the adoption of this
curve.
At first, the number of Bitcoin transactions was low because in the market there were mainly
miners. Over time, as Bitcoin was acquiring monetary value, the number of users interested in
exchanging Bitcoins increased. So, while the percentage of Miners in the market was decreas-
ing, also due to the increasing difficult to mine Bitcoins, the percentage of Random traders p
R
and Chartists p
C
greatly increased, according to the growth of the number of transactions, which
slowly rose until a peak on May 2012 and then at the end of the 2013 (see figure 17 (c)). .
We assumed that the random traders to chartist ratio is 7/3, meaning that 30% of traders
who are not miners are speculators, whereas the remaining 70% are non-speculative investors.
These figures are consistent with recent data obtained for the foreign exchange market [19].
The probabilities of non-miners to be a random trader or a chartist, p
R
and p
C
respectively, are
defined as a function of p
M
, respectively as:
p
R
= 0.7(1 p
M
) (17)
and
p
C
= 0.3(1 p
M
) (18)
With these probabilities, we have at the end of the simulations a number of Miners equal to
about 1000, corresponding to 100,000 miners in the real world. This is in agreement with what
an Australian bitcoin miner, Andrew Geyl, estimated (see web site http://bravenewcoin.com/news/number-
of-bitcoin-miners-far-higher-than-popular-estimates/).
37
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Supplementary resources (9)

... An implicit assumption of demand was made in Cocco et al. (2017), enhanced by speculative traders and restricted by finite resources for each agent, and is the earliest example of a limit order book-based model of the Bitcoin market attempting to explain the price increase from the start of 2012 to April 2014. This model was later extended by mining (Cocco and Marchesi 2016) and evolutionary computation (Cocco et al. 2019). Other order book models are presented in Pyromallis and Szabo (2019) and Zhou et al. (2017), where the focus is mainly on the adaptive behavior of traders. ...
... Defined as the premium a trader has to pay to liquidate a given amount of assets.4 For example inRaberto et al. (2005) a Gaussian assumption is employed, which is also used in cryptocurrency setting(Cocco and Marchesi 2016;Cocco et al. 2017Cocco et al. , 2019. Several studies relaxed the Gaussian assumption with either a lognormal assumption(Bartolozzi 2010), or a power-law assumption(Cui and Brabazon 2012;McGroarty et al. 2019). ...
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Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market, both directly influencing the price of an asset or indirectly by misinforming other market participants. Such behavior can be a source of systemic risk and increasing distrust for the market participants, consequences that call for viable countermeasures. Building on the foundations provided by the extant literature, this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018. The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies, including a fraudulent agent, initialized from empirical data and who performs market manipulation. The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume. Simulation results provide a satisfactory fit to historical data. Several price dips and volume anomalies are explained by the actions of the fraudulent trader, completing the known body of evidence extracted from blockchain activity. The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period; without this agent, it would have been very unlikely that the price had reached the heights as it did in late 2017. The insights gained from the model, especially the connection between liquidity and manipulation efficiency, unfold a discussion on how to prevent illicit behavior.
... Leading-zero(es) counting is the procedure to count the number of consecutive zeroes that appear before the first nonzero number at the beginning of a word [30]. For example, the leading-zero(es) counting unit assumes n-bits data given to the counter is: ...
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Medical data record from the patient is private data that is very confidential and secure. Securing medical data records using blockchain can prevent unauthorized parties from seeing or changing the data. However, it has a drawback in creating a block known as a mining process that takes a long time and uses enormous resources. We proposed a lightweight blockchain framework for medical record data integrity in using resources that reduce the computational. Based on the work, to build a lightweight blockchain application, the Python programming language is used. The Flask micro web server is applied in illustrating the blockchain data, while the MIT App Inventor creates an Android application to read data from IoT devices. The IoT Implementation of this work has successfully been tested to retrieve data and store it in a blockchain framework. The lightweight blockchain discussed in this paper is a mining technique using leading-zero as a difficulty level factor while ensuring data integrity and security when creating a block. We compared the block-time generation required to make a block on this system with the block-time generation on the existing network such as Bitcoin, Ethereum, Dogelite, and Litecoin. From the difficulty level of one to five, the experiment results, the block-time obtained from 0.0012677 seconds to 34.5919193 seconds. Overall for a low level of difficulty has a faster duration than the existing network. Only at the fifth difficulty level appears to have a longer processing time than Ethereum, but still quicker.
... Ancak literatür incelendiğinde, günümüzde blok zincirin pazarlamadaki rolünü ve uygulamalarını araştıran çok az çalışmanın olduğu görülmektedir. Blok zincir bankalardan internet işletmelerine kadar çeşitli alanlarda uygulanmıştır (Chen vd., 2018;Mačiulienė ve Skaržauskienė, 2021); yapay zeka (Ekramifard vd., 2020;Gupta vd., 2020;Pillai, Sivathanu ve Dwivedi, 2020); sağlık (Dimitrov, 2019;Przhedetskiy vd., 2019;Tandon vd., 2020), Bitcoin (Abramova ve Böhme, 2016;Cocco ve Marchesi, 2016;Vyshnavi, 2016), nesnelerin interneti (Krafft, Sajtos ve Haenlein, 2020;Liu vd., 2019), sosyal medya (Rathnakar, 2019;Van Osch vd., 2019); döngüsel ekonomi (Kouhizadeh, Zhu ve Sarkis, 2020); dijital analitik (Gupta vd., 2020); finansal teknoloji (Ali vd., 2020); işletmeler (Grover, Kar ve Ilavarasan, 2018); Sigorta (Kar ve Navin, 2021); siber güvenlik (Mittal vd., 2021) Pazarlama, bir ülke ekonomisinin önemli bir unsurudur. Blok zincir, dünyayı sosyal, finansal ve teknolojik yenilikler açısından değiştirmiştir. ...
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Çalışmada, öncelikle Blockchain teknolojisi hakkında bilgi verilerek, bu teknolojinin çalışma mantığı, sektörlere sunduğu fırsatlar, avantaj ve dezavantajları, uygulanması durumunda yaşanabilecek iş modeli senaryoları medya sektörü bazında incelenmiştir. Bölümün sonunda özellikle işletmelerin gelecek işlemlerini ve iş yapma yöntemlerini ilgilendiren ve Blockchain teknolojisine dayanan akıllı sözleşmeler hakkında ayrıntılı bilgi yer almaktadır.
... Ancak literatür incelendiğinde, günümüzde blok zincirin pazarlamadaki rolünü ve uygulamalarını araştıran çok az çalışmanın olduğu görülmektedir. Blok zincir bankalardan internet işletmelerine kadar çeşitli alanlarda uygulanmıştır (Chen vd., 2018;Mačiulienė ve Skaržauskienė, 2021); yapay zeka (Ekramifard vd., 2020;Gupta vd., 2020;Pillai, Sivathanu ve Dwivedi, 2020); sağlık (Dimitrov, 2019;Przhedetskiy vd., 2019;Tandon vd., 2020), Bitcoin (Abramova ve Böhme, 2016;Cocco ve Marchesi, 2016;Vyshnavi, 2016), nesnelerin interneti (Krafft, Sajtos ve Haenlein, 2020;Liu vd., 2019), sosyal medya (Rathnakar, 2019;Van Osch vd., 2019); döngüsel ekonomi (Kouhizadeh, Zhu ve Sarkis, 2020); dijital analitik (Gupta vd., 2020); finansal teknoloji (Ali vd., 2020); işletmeler (Grover, Kar ve Ilavarasan, 2018); Sigorta (Kar ve Navin, 2021); siber güvenlik (Mittal vd., 2021) Pazarlama, bir ülke ekonomisinin önemli bir unsurudur. Blok zincir, dünyayı sosyal, finansal ve teknolojik yenilikler açısından değiştirmiştir. ...
Chapter
Full-text available
Çalışmada, öncelikle Blockchain teknolojisi hakkında bilgi verilerek, bu teknolojinin çalışma mantığı, sektörlere sunduğu fırsatlar, avantaj ve dezavantajları, uygulanması durumunda yaşanabilecek iş modeli senaryoları medya sektörü bazında incelenmiştir. Bölümün sonunda özellikle işletmelerin gelecek işlemlerini ve iş yapma yöntemlerini ilgilendiren ve Blockchain teknolojisine dayanan akıllı sözleşmeler hakkında ayrıntılı bilgi yer almaktadır.
... Although a proper ML-based forecasting exercise is beyond the scope of the current contribution, for a comparison with the most recent reported emission forecasts (Jiang et al. 2021), Figure A1 (lower panel) illustrates how our ML approach can also deliver reasonable forecasts of Bitcoin mining CO 2 emission levels based on the evolution of the overall Bitcoin network hashrate (Cocco and Marchesi 2016) (instead of, e.g., gold-like market capitalization growth rates (Quin et al. 2021) or older technologies' rates of adoption (Mora et al. 2018). The energy required to mine cryptocurrencies in a proof-of-work scheme is measurable in the hashrates of the network, which increase with the larger participation of miners and with the increasing difficulty of the calculations (Krause and Tolaymat 2018). ...
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Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on a novel bottom-up approach, which (i) conform with recent estimates, (ii) lie within the economic model bounds while (iii) delivering much narrower prediction intervals and yet (iv) raise alarming concerns, given recent evidence (e.g., from climate–weather integrated models). We demonstrate how machine learning methods can contribute to not-for-profit pressing societal issues, such as global warming, where data complexity and availability can be overcome.
... This means that, as time goes by, the difficulty in finding a solution will become greater, and its cost increases (Eyal and Sirer 2013;Giungato et al. 2017). Thus, the process has been affected because the investment cost does not exceed the profits offered (Kristoufek 2015;Cocco and Marchesi 2016). ...
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With the development of new technologies, some concepts become relevant in the economic area, as is the case with cryptocurrencies, in general, or Bitcoin and Ethereum, in particular. Due to the impact of these tools, a detailed bibliometric study that allows us to obtain all information about cryptocurrencies must be conducted. This study will help scientific production by specifying the development and lines of related research that have been followed and are currently being followed. We have used Tableau, R (Bibliometrix R Package), and VOSviewer software to analyze the information. These have been combined to create and review unified metadata from the Web of Science (WoS) and Scopus databases. The bibliometric analysis shows 771 articles on the WoS database and 648 articles on Scopus published between 2010 and early 2019. They present the most relevant articles, research areas, countries, institutions, authors, journals, and trends during the last few years. In conclusion, the number of publications has grown in the last 3 years. The analysis shows the evolution of blockchain technology used in this type of cryptocurrency. The review of this period marks a possible end to the historical part of cryptocurrencies, thereby opening the current topic to its multiple applications.
... where n t is the total number of miners and nt j =i m jt is the total blockchain computing capacity of other miners devoted to the block t (e.g. Cocco and Marchesi 2016;Thum 2018). However, Parra-Moyano, Reich and Schmedders (2019) show that the probability of relatively bigger miners winning the mining contest is higher than that of relatively smaller miners because there is a "learning" effect when mining a particular block with larger mining computers learning faster than smaller mining computers. ...
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This paper studies to what extent the cost of operating a proof-of-work blockchain is intrinsically linked to the cost of preventing attacks, and to what extent the underlying digital ledger’s security budgets are correlated with the cryptocurrency market outcomes. We theoretically derive an equilibrium relationship between the cryptocurrency price, mining rewards and mining costs, and blockchain security outcomes. Using daily crypto market data for 2014- 2021 and employing the autoregressive distributed lag approach — that allows treating all the relevant moments of the blockchain series as potentially endogenous — we provide empirical evidence of cryptocurrency price and mining rewards indeed being intrinsically linked to blockchain security outcomes.
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The current study aims to identify cryptocurrencies, their types and characteristics and the extent of fiannciala literacy on cryptocurrencies by the traders in exchanging currencies in the Kurdistan region through an exploratory study of views of a sample of managers, company’s owners’ / Forex offices and currency exchange in the Kurdistan Region of Iraq for cryptocurrencies by designing a questionnaire form that included a number of essential phrases for study and all of them were answered. An initial survey was conducted (Pilot Test) for (30 companies / offices) currency exchange in the city of Duhok only in order to find out the suitability of the questionnaire form and its paragraphs to the study subject. Then the distribution was carried out on a more comprehensive sample that included (160) companies / offices, in three governorates (Duhok, Erbil and Sulaymaniyah). One of the most important conclusions reached by the study is that (83.1%) of individuals surveyed from the region does not have any financial knowledge about cryptocurrencies, its working mechanism, its types, how to use it and how to trade it. In other side, about (16.9%) of the respondents indicate that they have financial knowledge on cryptocurrencies. The results of the analysis revealed a (100%) that cryptocurrencies were not traded by companies and offices by the sample examined, whether it was a direct trading method or indirect trading. In light of these conclusions, the study suggests a number of recommendations, the most important of which are: recommending business organizations, especially companies specialzed in exchange currencies to follow up trading cryptocurrencies and to issue awareness leaflets continuously for their users to help them use them correctly, safely and away from risks in order to protect them from fraud and encourage the application of cryptocurrency trading to reduce the negative impacts facing the monetary policy and be an alternative to some currencies.
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In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading ( e . g ., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.
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Blockchain technology is the need of the hour today, due to its strong pillars of distribution, decentralisation, encryption, immutability and tokenization. It has a growing scope in various sectors of the economy. With vast data availability, there are challenges of big data, privacy, ransomware attacks, resulting into marketing fraud and spam. The present study reviews the use of blockchain in marketing area and seeks to identify influential aspects, research streams and research questions to propose the future research agenda of emerging market perspectives for blockchain marketing integration. The study analysed 75 articles from the international database of Scopus using bibliometric and network-based analysis. Present study firstly, identified influential aspects of literature in terms of highly cited articles, keywords, authors and publications; Secondly, identified five future research streams: (i) Blockchain and Electronic Commerce, (ii) Blockchain and Marketing; (iii) Blockchain and Data; (iv) Blockchain and Data Analytics and (v) Blockchain-Privacy and Security, Finally, suggested 18 future research questions. The study paved way for future researchers by providing future research agenda in terms of the proposed framework, which needs to be explored further to identify the relationships between five identified streams using proposed research questions. The study is unique in terms of its contribution to the literature publishing literature with an exhaustive focus on ‘identifying the blockchain-marketing integration.’ The present study fills this literature gap and proposed a framework and research questions for future researchers.
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We present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets. We first discuss some general issues common to all statistical studies of financial time series. Various statistical properties of asset returns are then described: distributional properties, tail properties and extreme fluctuations, pathwise regularity, linear and nonlinear dependence of returns in time and across stocks. Our description emphasizes properties common to a wide variety of markets and instruments. We then show how these statistical properties invalidate many of the common statistical approaches used to study financial data sets and examine some of the statistical problems encountered in each case.
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In this explorative study, we examine the economy and transaction network of the decentralized digital currency Bitcoin during the first four years of its existence. The objective is to develop insights into the evolution of the Bitcoin economy during this period. For this, we establish and analyze a novel integrated dataset that enriches data from the Bitcoin blockchain with off-network data such as business categories and geo-locations. Our analyses reveal the major Bitcoin businesses and markets. Our results also give insights on the business distribution by countries and how businesses evolve over time. We also show that there is a gambling network that features many very small transactions. Furthermore, regional differences in the adoption and business distribution could be found. In the network analysis, the small world phenomenon is investigated and confirmed for several subgraphs of the Bitcoin network.
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Crashes have fascinated and baffled many canny observers of financial markets. In the strict orthodoxy of the efficient market theory, crashes must be due to sudden changes of the fundamental valuation of assets. However, detailed empirical studies suggest that large price jumps cannot be explained by news and are the result of endogenous feedback loops. Although plausible, a clear-cut empirical evidence for such a scenario is still lacking. Here we show how crashes are conditioned by the market liquidity, for which we propose a new measure inspired by recent theories of market impact and based on readily available, public information. Our results open the possibility of a dynamical evaluation of liquidity risk and early warning signs of market instabilities, and could lead to a quantitative description of the mechanisms leading to market crashes.
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Bitcoin is a digital cryptocurrency that has generated considerable public interest, including both booms in value and busts of exchanges dealing in Bitcoins. One of the fundamental concepts of Bitcoin is that work, called mining, must be done in checking all monetary transactions, which in turn creates Bitcoins as a reward. In this paper we look at the energy consumption of Bitcoin mining. We consider if and when Bitcoin mining has been profitable compared to the energy cost of performing the mining, and conclude that specialist hardware is usually required to make Bitcoin mining profitable. We also show that the power currently used for Bitcoin mining is comparable to Ireland's electricity consumption.
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In recent years, the software market has undergone profound changes. The power balance between software firms and customers is heavily changing. The contractual power of customers is increasing and the firms must take it into account. The software appears overpriced, customers' willingness to pay is reducing, and consequently vendors are providing different pricing models for software, including terms licensing, software-as-a-service and commercial open source software. The aim of this paper is to show that a simulation-based approach to analyze the software market is viable. Therefore, in this work a business model is proposed to analyze and study how the purchase preferences of customers can modify future sce- narios in the software market. We analyzed the competition among proprietary and open source software firms, but the model can be customized to study other kind of markets, and several other assumptions can be made for studying their implications to the software business. The better way to obtain operational savings seems to be that to shift to the use of open source software, obtaining in this way all the benefits of this software typology: lower costs, greater adherence to open standards, more choice of vendor and service supplier, no vendor lock-in and flexible incremental architectures.
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