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“Fair market value of bitcoin: halving effect”
AUTH ORS Artur Meynkhard https://orcid.org/0000-0003-3995-4648
ARTICLE INFO
Artur Meynkhard (2019). Fair market value of bitcoin: halving effect. Investment
Management and Financial Innovations, 16(4), 72-85.
doi:10.21511/imfi.16(4).2019.07
DOI http://dx.doi.org/10.21511/imfi.16(4).2019.07
RELEASED ON Thursday, 28 November 2019
RECE IVED ON Thursday, 17 October 2019
ACCEPTED ON Friday, 15 November 2019
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JOURNAL "Investment Management and Financial Innovations"
ISSN PRINT 1810-4967
ISSN ONLINE 1812-9358
PUBLISHER LLC “Consulting Publishing Company “Business Perspectives”
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NUMBER OF REFERENCES
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NUMBER OF FIGURES
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NUMBER OF TABLES
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© The author(s) 2019. This publication is an open access article.
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Investment Management and Financial Innovations, Volume 16, Issue 4, 2019
http://dx.doi.org/10.21511/im.16(4).2019.07
Abstract
e purpose of this article is to analyze the eect that halving has on the fair market
value of bitcoins. e main hypothesis of the study is that the decline in the cost of min-
ers’ remuneration for mining is a signicant factor that aects the price of cryptocur-
rencies. e article examines the factors that regulate the issuing process. e signi-
cance of a limited supply of bitcoin is detailed in the article, as well as the mechanism
for the implementation of the issue of new bitcoins. e study compares the historical
ination data of the US dollar and the projected data on the ination of bitcoin. e
article analyzes the main technical element of cryptocurrency – halving – when the
miner’s reward is halved. is analysis includes the mathematical methods of statisti-
cal data processing. Research results show that reducing remuneration by half every
four years leads to an increased market value of the cryptocurrency. is relationship
is clearly illustrated by the Kendall rank correlation method.e results of the study
can have a signicant impact on the fundamental assessment of bitcoin and can also
enable investors to assess any of the existing and operating cryptocurrencies according
to this method.
Artur Meynkhard (Russia)
Fair market value
of bitcoin: halving effect
Received on: 17 of October, 2019
Accepted on: 15 of November, 2019
INTRODUCTION
Many signicant problems in the global nancial system were exposed
in the 2008 economic crisis. e recession also gave incentives to cre-
ate an alternative structure for the world economy. us, changes were
made, and new economic tools and technologies began to be used.
Bitcoin has been attracting more and more attention from economists,
politicians, and traders since its introduction in 2009. In particular,
the cryptocurrency started to dominate in the nancial press, due to
its phenomenal growth in market value and the number of transac-
tions made.
Bitcoin’s success has inspired many cryptocurrency projects based on
various Blockchain technologies. Since the beginning of 2017, lead-
ing cryptocurrencies, such as litecoin, dash, monero, have gone up in
price by several thousand percent, resulting in a substantial increase
in trading volumes. By 2018, cryptocurrency market capitalization
has increased from 18 to 830 billion US dollars. In addition, daily trad-
ing volume with various cryptocurrencies has increased from several
thousand to hundreds of thousands of dollars, and sometimes even to
millions of US dollars.
erefore, an intriguing question arises: what is the fundamental rea-
son for price changes of crypto assets in the long run? is issue is cru-
cial for two reasons. Firstly, there is no suitable model for assessing the
impact that bitcoin emission has on its price. Secondly, the majority
of cryptocurrency market participants use technical analysis as their
© Artur Meynkhard, 2019
Artur Meynkhard, Laboratory
Assistant, Department of Financial
Markets and Banks, Financial
University under the Government of
the Russian Federation, Russia.
cryptocurrency, bitcoin, emission, ination, halving
Keywords
JEL Classification E31, E42, G15
is is an Open Access article,
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and reproduction in any medium,
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BUSINESS PERSPECTIVES
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Investment Management and Financial Innovations, Volume 16, Issue 4, 2019
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trading strategy, which stresses the importance of trends, trading volumes, and volatility levels in the
process of making trading decisions. At the same time, halving – remuneration reduction – is complete-
ly ignored, despite being an important technical aspect in functional analysis of bitcoin.
e study of the interdependence between the reduction of mining expenses and the cost of a bitcoin
can provide useful information for crypto assets market participants (investors, miners, etc.). If an
investor understands the mechanism of issuing new bitcoins, he/she can use this knowledge to adjust
portfolios or create new investment and hedging strategies. In turn, miners appreciate halving and its
positive impact on the value of bitcoin, which will allow for planning the sale of bitcoins more carefully,
covering mining expenses.
When investors face macroeconomic uncertainty, information about technical aspects, that have a di-
rect impact on the market price of cryptocurrencies will help them choose the right time and amount
of necessary cryptocurrencies for their portfolio adjustments based on their preferences regarding risk.
1 e author’s calculations.
1. LITERATURE REVIEW
AND THEORETICAL BASIS
In their works, Mba, Pindza, and Koumba (2018)
and Briere, Oosterlinck, and Szafarz (2015) con-
sider the possibility of further diversifying crypto-
currency-based investment portfolios for private
and institutional investors. For example, if an in-
vestor who had 100 thousand US dollars at the be-
ginning of 2011 decided to invest 1% of his funds
in bitcoins, he would have earned an average an-
nual yield of 298.64% for 8 years, and his total cap-
ital would have increased from 100 thousand US
dollars to 7.06 million US dollars1.
Many scientists have been studying bitcoin from
multiple perspectives ever since it appeared.
Corbet, Lucey, Urquhart, and Yarovaya (2018)
and Dierksmeier and Seele (2018) addressed the
issue of classifying cryptocurrencies, determining
whether they are a medium of exchange and pay-
ment or just a speculative investment.
Yi, Xu, and Wang (2018) conducted a study related
to the correlation between cryptocurrencies and
traditional assets and assessed whether crypto-
currencies could be used as a hedging or diversi-
cation asset.
e vast majority of economic literature concerning
bitcoin and cryptocurrencies, in general, is dedicat-
ed to studying economic factors, such as the mar-
ket power of supply and demand, production cost of
cryptocurrencies, the inuence of the public inter-
est through mass media. Chaim and Laurini (2018),
Balcilar, Bouri, and Gupta (2017) emphasized price
volatility and opportunities in trading correlation
strategies. However, little research has been done
on the eects that technical elements of a separate
cryptocurrency have on its market value.
Supporters of the traditional theory believe that
investors should look for investment and hedging
opportunities on a certain market by evaluating
the eectiveness of other markets. However, such
strategies are not benecial in the current state
of the cryptocurrency market. is is due to the
dierence between cryptocurrencies’ underlying
technologies along with its market environment
and traditional nancial assets (stocks, bonds,
etc.). Doreitner and Lung (2018) give a more de-
tailed discussion on this topic in their work.
Bitcoin operates on blockchain technology, which
involves the formation of blocks containing infor-
mation about transactions users carry out within
a network. Each new block generated by a network
is built into a chain of blocks, which contains in-
formation not only about new transactions, but
also all previously conducted operations (Brühl,
2017). is technology allows for a structured da-
tabase, leaving it in the public domain. Moreover,
this technology of distributed registries excludes
spoong, identity the, data deletion, and it does
not allow interested persons to violate the proper-
ty rights of the owners.
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It is worth noting, however, that the primary
purpose of Bitcoin is to create an alternative
method of making transactions. is approach
essentially eliminates intermediaries out of the
money-ow chain (like banks and other nan-
cial institutions) between buyers and sellers, as
well as the need for government bodies to con-
trol and regulate activities of nancial organiza-
tions. As stated by Nakamoto (2008), the struc-
ture of peer-to-peer data transport is based on
the idea of equality of all participants in a net-
work. is eliminates the need to drag and syn-
chronize special servers, decreasing the odds of
performance deterioration of any given system.
e result is that every network participant is
both its client and server for storing data. is
process allows bitcoin to have full independence
in network connectivity.
Bitcoin’s key ideas include, as previously discussed,
independence from intermediaries and regula-
tors and the autonomy of the network function-
ing. However, bitcoin’s intent, according to its
creator (or creators), was to eliminate the chief, in
their opinion, the disadvantage of modern money
equivalents – ination. e idea of perfect mon-
ey, which in the long run does not lose its value,
was fully realized in this cryptocurrency and sig-
nicantly contributed to its success. erefore,
in order to prevent ination, the code of crypto-
currency originally had several basic regulating
principles:
1) limited issue;
2) increase/decrease in complexity of mining;
3) remuneration halving for the generated block.
Let us examine the three factors in further detail:
1. e Central Bank is the single regulatory body,
which carries out and supervises the entire is-
sue of monetary funds in a centralized mon-
etary system. It prints new banknotes based
on the data on goods and services produced
in the country. is controls the money sup-
ply and prices, given a prosperous and stable
monetary policy has been established, like
many researchers proved in energy forecast-
ing (Nyangarika, Mikhaylov, & Tang, 2018;
Nyangarika, Mikhaylov, & Richter, 2019a;
Meykhard, 2019; Mikhaylov, 2019).
In a decentralized monetary system, human inter-
vention in the process of currency issuance is re-
duced to zero. However, the release of new bitcoins
into circulation is totally under control of a special
cryptographic algorithm, which follows the rules
of peer-to-peer networks. is algorithm deter-
mines the frequency, time, and amount of issued
monetary units (Sauer, 2016). Any attempts to
modify the amount of issuance of new monetary
units will be cryptographically rejected (Nelson,
2018; Mikhaylov, 2018b).
e creation of bitcoin units epitomizes the is-
suance of legal-tender coins on a predetermined
algorithm. e algorithm’s imposed limit on the
maximum possible amount of bitcoins is 21 mil-
lion coins (Nakamoto, 2008).
e issuance of new bitcoins follows the comple-
tion of forming new blocks of transactions. e
frequency, with which the blocks are generated,
is constant: six blocks per hour. e amount of
mined coins by bitcoin network gets reduced in a
geometric progression: every 210 thousand mined
bitcoin blocks, the amount of mined bitcoin blocks
next cycle will be reduced by 50%, which corre-
sponds to a four-year issue cycle. As a result, the
algorithm determining bitcoin issuance develops
a clear timetable, according to which the number
of issued bitcoins will never exceed more than 21
million coins (Table 1).
2. e rate of complexity is essential to the pro-
cess of cryptocurrency production. Aside
from the fact that this indicator helps miners
determine which equipment must be used for
the extraction of cryptocurrency and what
power it needs to possess, the complexity of
mining regulates the pace at which bitcoins
are issued.
Every 2016 blocks found in the bitcoin network,
the diculty of mining is recalculated. If miners
found one block of transactions every 10 minutes,
as the developer (developers) of the network orig-
inally intended, in order to maintain the planned
issuance of 21 million coins, locating this quota of
blocks would take two weeks. e work of Böhme,
Christin, Edelman, and Moore (2015) indicates
that when 2016 blocks are found in a timeframe
shorter than the intended, the complexity of the
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mining algorithm increases. And vice versa, if
nding 2016 blocks takes signicantly more time,
the complexity of the mining algorithm decreas-
es. e network supports the uniform generation
of bitcoins by using this algorithm and does not
allow the creation of more coins than originally
planned (Lopatin, 2019a; Denisova, 2019).
e increase and decrease of the complexity of
mining depend on the hash rate of the network
and the amount of time spent on nding the previ-
ous 2016 blocks. Hash rate is a unit that measures
the eective capacity of equipment that is used for
cryptocurrency mining. Hash rate starts to grow
when new members join the process of extracting
bitcoins (Lopatin, 2019b; Denisova, Mikhaylov, &
Lopatin, 2019).
When new members connect their equipment to
the network, they increase the computing power,
which leads to a reduction of the amount of time
it takes to nd a transaction block. us, we can
make the following conclusion: the higher the
hash rate of the network, the greater the number
of miners involved in the extraction of cryptocur-
rency, and hence, the less time it takes to nd a
transaction block. All this leads to an increase in
the complexity of mining. On the contrary, the re-
duction of hash rate indicates that fewer miners are
involved in the process of mining, which means
that the time to nd a transaction block increas-
es, and the complexity of the network decreases
(Nyangarika, Mikhaylov, & Richter, 2019b).
3. As was mentioned previously, the total out-
put of bitcoins is limited to 21 million coins.
When the last block out of the 210 thousand
limit set by the system is found, the reward for
the next found block, according to the plan,
is halved. e reward for nding a block of
transactions has decreased two times in the
ten years that bitcoin has existed.
So, on November 28, 2012, the number of new bit-
coins that the network generates had reached its limit,
and the reward was reduced from 50 BTC to 25 BTC
and in early July of 2016 – from 25 BTC to 12,5 BTC.
e following reduction of remuneration (halving) is
estimated to take place in May of the year 2020.
In addition to inuencing the overall earnings of
miners, halving that occurs once every four years
signicantly aects the process of issuing new bit-
coins (Table 1). is directly aects the market val-
Table 1. Bitcoin emission
Source: www.coinmarketcap.com, Thomson Reuters, the author’s calculations.
Year
Bitcoin blocks
by cumulave
totals
Block remuneraon,
bitcoin
The amount of
mined bitcoins
The amount of mined
bitcoins of the maximum
emission, %
The amount of
mined bitcoins by
cumulave totals
2009 210,000 50 10,500,000.00 50% 10,500,000.00
2012 420,000 25 5,250,000.00 25% 15,750,000.00
2016 630,000 12.5 2,625,000.00 12.5% 18,375,000.00
2020 840,000 6.25 1,312,500.00 6.25% 19,687,500.00
2024 1,050,0 00 3.12 5 656,250.00 3 .125% 20,343,750.00
2028 1,260,00 0 1.5625 328,1 25.0 0 1.5625% 20,671,875.00
2032 1,470,00 0 0.78125 164,062.50 0.78125% 20 , 8 35 , 93 7.5 0
2036 1,680,0 00 0.390 625 82,0 31.25 0.390625% 20,917,968.75
2040 1,890,000 0.19531 25 41, 015 .63 0 .19531 25% 20,958,98 4.38
2044 2,100,000 0.09765625 20,507.81 0.0976 5624% 20, 979,492.1 9
2048 2,310,000 0.048828125 10,253.91 0.048828143% 20,989,746.09
2052 2,520,000 0.0244140625 5,1 26.95 0.0 244140 476% 20,994, 873.05
2056 2,730,000 0.01220703125 2,563.48 0.01220704762% 20 , 9 97, 4 36 . 52
2060 2,940,000 0.0 061 0351 562 5 1,281 .74 0.0 06103 52381% 20,998.718.26
2064 3,150,000 0.0030517578125 640.87 0. 00 305176191% 20,999,359.13
2068 3,360,000 0.0015258789 063 320.43 0.00152585714% 20,999,679.57
2072 3,570,000 0.0007629394531 160.22 0.00076295238% 20,999,839.78
2076 3,780,000 0.00 03814697 266 80.11 0.00038147619% 20,999,919.89
2080 3,990,000 0.00019 07348633 40.0 6 0.00019076191% 20,999,959.95
… … … … … …
2140 6,930,000 0.00 ≈ 0.001222534 100% 21,000,000.00
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ue of cryptocurrency. So, participants of the bitcoin
network who mined 100 BTC per month, and then
sold them to oset their production costs, begin to
produce two times fewer coins aer remuneration
halving, which leads to a decrease in the supply of
the “new” bitcoins in the market. With the same lev-
el of demand and twice-decreased supply side, the
market starts to react by increasing the market val-
ue of cryptocurrency. e same opinion is shared
by Kroll, Davey, and Felten (2013) and Nair and
Cachanosky (2017). ey write that at the same lev-
el of demand and twice-decreased supply side, the
market starts to react by increasing the market value
of cryptocu rrency.
2. METHODS
e period from November 1, 2010 to December
31, 2018 (98 months) was taken in order to analyze
the impact of halving on the value of bitcoin. is
time frame was divided into weekly intervals in-
dicating the historical opening and closing prices.
In order to reduce the impact of price volatility and
improve the quality of the obtained results, the fol-
lowing method of estimation did not include the
weekly maximum and minimum price values for
the selected period. is kind of search data helped
reduce the impact of high price volatility that the
cryptocurrency market is prone to. A characteris-
tic feature of bitcoin that distinguishes it from gov-
ernment-issued at currencies is its limited issuance.
e maximum possible amount of bitcoins that may
exist will never exceed the mark of 21 million cryp-
tocurrency units (Nakamoto, 2008).
A miner gets rewarded for each found transaction
unit/block (blocks are generated every 10 min-
utes). e amount of remuneration is xed and
occurs aer 210 thousand transaction blocks are
found. e reduction of payment is always twofold
– from 50 BTC to 25 BTC (November 2012), from
25 BTC to 12.5 BTC (July 2016), and so on.
e award received by miners is called emission.
When the bitcoin system was launched, when it
was not so popular, the award for the found trans-
action block was 50 BTC. For one hour, the system
produced a turnover of 300 BTC or 7200 BTC per
day. For the entirety of 2009, 2,625,000 BTC were re-
leased into circulation, which is 12.5% of the sum of
the maximum issuance of bitcoin. By the end of 2012,
210 thousand blocks of transactions were found. is
marked a decline in emissions by half to 25 BTC for
each new-found block. In this regard, the system be-
gan to generate 150 BTC per hour or 3600 BTC per
day, and 1,312,500 BTC per year (Mikhaylov, 2018a;
Mikhaylov, Sokolinskaya, & Nyangarika, 2018;
Mikhaylov, Sokolinskaya, & Lopatin, 2019).
e process of remuneration reduction will con-
tinue forever. However, by the year 2140, bitcoin
supply would peak, and the overall supply will
total 21 million cryptocurrency units. Aer that,
miners, whose computer power will be used to
locate blocks of transactions, will be receiving
remuneration only from the commission or fees
paid by the members of the system when making
payments in cryptocurrency. e process of issu-
ing new bitcoins units will stop (Figure 1).
With limited issuing of cryptocurrency, the ina-
tion level of a bitcoin invariably falls with every
passing year. is is because the algorithm of re-
muneration halving (twofold payment reduction)
is embedded in the foundation of the system. e
presence of this algorithm within the system of
bitcoins gives way for the progressive decrease in
the level of remuneration, which results in a small-
er supply of coins (Table 2).
Bitcoin issuance can be easily predicted since the
system is algorithmic, and nobody will ever be
able to inuence the results of emission.
Constant reduction of bitcoin issuance by 50 per-
cent every four years leads to the reduction of bit-
coin ination. erefore, by 2025, the level of ina-
tion will be less than 1% and will amount to 0.83%
per year. And by 2037, it will be less than 0.1%. By
the year 2053, the ination rate would drop to a
level that will be completely invisible.
If we make a comparative analysis with at cur-
rencies that are prone to ination, the advantage of
bitcoins becomes so evident, that it is undeniable.
e US dollar was taken for the comparative anal-
ysis of ination levels during the period from 1914
to 2014 (100 years). Bitcoin ination data are taken
from Table 2.
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Figure 1. Bitcoin emission for the years 2009–2140
Source: www.coinmarketcap.com, Thomson Reuters, the author’s calculations.
Table 2. Bitcoin inaon
Source: www.coinmarketcap.com, Thomson Reuters, the author’s calculations.
Year Bitcoin
emission
Total supply of
bitcoins
Bitcoin
inaon, % Year Bitcoin
emission
Total supply of
bitcoins
Bitcoin
inaon, %
2009 2625000 2 625 000,00 –2075 40,05432 20 999 799,73 0.00019
2010 2625000 5 250 00 0,00 100.00 2076 40,0 5432 20 999 8 39,78 0.00019
2011 2625000 7 875 000,0 0 50.00 2077 20,027161 20 9 99 859,81 0.00010
2012 2625000 10 500 00 0,00 33.33 2078 2 0,027161 20 999 879,84 0.00010
2013 1312 500 11 812 500,00 12.50 2079 20, 027161 20 999 899,86 0.00010
2014 131250 0 13 125 00 0,00 11.11 2080 20,027161 20 999 919,89 0.00010
2015 1312 500 14 437 500,00 10 .00 2081 10,01358 20 999 929,90 0.00004768
2016 1312 50 0 15 750 00 0,00 9.091 2082 10,01358 20 999 939,92 0.00004768
2017 65625 0 16 406 250,00 4.166 20 83 10,01358 20 999 949,93 0.00004768
2018 656250 17 062 500,0 0 4.000 2084 10,01358 20 999 959,95 0.00004768
2019 656250 17 718 750,00 3.846 2085 5,0067902 20 999 964,95 0.00002384
2020 656250 18 375 000,00 3.703 7 2086 5,00 67908 20 999 969,96 0.00002384
2021 328125 18 703 125,00 1.7857 2087 5,0067908 20 999 974,97 0.00002384
2022 328125 19 031 250,00 1.754 4 2088 5,0 067908 20 999 979,97 0.00002384
2023 328125 19 359 375,00 1.72 41 2089 2,5033954 20 999 982,48 0.00001192
2024 328125 19 687 500,00 1.6949 2090 2,5033954 20 999 984,98 0.00001192
2025 164 062,5 19 8 51 562,50 0.8333 20 91 2,5033954 20 999 987,48 0.00001192
2026 164 062,5 20 015 625,00 0.8264 2092 2,5033954 20 999 989,99 0.00001192
2027 164 062,5 20 179 687,50 0.8197 20 93 1,2 516977 20 999 991,24 0.00000596
2028 1640 62,5 20 343 750,00 0.8130 2094 1, 2516 977 20 999 992,49 0.00000596
2029 82031 ,25 20 425 781,25 0.4032 2095 1,2 51697 7 20 999 993,74 0.00000596
2030 82031,25 20 507 812,50 0.4 016 2096 1, 2516 977 20 999 994,99 0.00000596
2031 82031,25 20 589 843,75 0.4000 2097 0,6258 489 20 999 995,62 0.00000298
2032 820 31,25 20 671 875,00 0.3 98 41 2098 0,6258 489 20 999 996,24 0.00000298
2033 4101 5,6 3 20 712 890,63 0.19841 2099 0,6258 489 20 999 996,87 0.00000298
2034 4101 5,63 20 753 90 6,25 0.19802 2100 0,6258489 20 999 997,50 0.00000298
2035 4101 5,6 3 20 794 921,88 0.19763 2101 0,312 9244 20 9 99 997,81 0.00000149
2036 4101 5,6 3 20 835 937,50 0.19724 2102 0,31 29244 20 999 998,12 0.00000149
2037 2 05 0 7, 82 20 856 445,31 0.09843 2103 0, 312924 4 20 999 9 98,44 0.00000149
2038 205 0 7, 82 20 876 953,13 0.09833 2104 0,31 2924 4 20 999 998,75 0.00000149
2039 20 5 0 7,8 2 20 897 460,94 0.09823 2105 0,1564622 20 999 998,90 0.000000745
2040 2 0 50 7, 8 2 20 917 968,75 0.09814 2106 0,1564622 20 999 999,06 0.000000745
20 41 10253,91 20 928 222,66 0.04902 2107 0,1564622 20 999 999,22 0.000000745
-
5 000 000
10 000 000
15 000 000
20 000 000
25 000 000
-
500 000
1 000 000
1 500 000
2 000 000
2 500 000
3 000 000
2009
2015
2021
2027
2033
2039
2045
2051
2057
2063
2069
2075
2081
2087
2093
2099
2105
2111
2117
2123
2129
2135
Bitcoin emission Bitcoin general offer
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The fundamental differences between these in-
flationary indicators are noticeable when con-
ducting comparative analysis (Figures 2, 3). The
US dollar, representing government-issued fiat
currencies, shows moderate inflation for over
100 years. However, the inflation rate exceed-
ed 10% three times in the period of 1968–1983
(Brown, 2017). Deflation can also be observed,
but it occurred at a time when the gold standard
was used. Def lation had not occurred after the
gold standard was canceled by President Nixon
in 1971 (Fratianni & Hauskrecht, 1998).
Bitcoin data are more predictable. Inf lation
shows a steady downward trend with every
passing year.
As mentioned earlier, the rate of remuneration
within the bitcoin network is directly inuenced
by halving (twofold reduction of remuneration).
It is hard to imagine that halving is the technical
element that exerts a signicant inuence on the
market value of bitcoins.
The first halving in the bitcoin system occurred
in the middle of November in 2012. The num-
ber of new units generated by the network when
finding a transaction block was reduced from
50 BTC to 25 BTC, which greatly affected the
supply of bitcoins in the market. When halv-
ing happened, the market price was $12.5 for
a bitcoin. A year later, a new price maximum
was set at $1150 for a bitcoin. Since halving oc-
Table 2 (cont.). Bitcoin inaon
Year Bitcoin
emission
Total supply of
bitcoins
Bitcoin
inaon, % Year Bitcoin
emission
Total supply of
bitcoins
Bitcoin
inaon, %
2042 102 53,91 20 938 476,56 0.0490 0 2108 0,1564622 20 999 999,37 0.000000745
2043 10253,91 20 948 730,47 0.04 897 2109 0,0782311 20 999 999,45 0.000000372
2044 102 53,91 20 958 984,38 0.04895 2110 0,0782311 20 999 999,53 0.000000372
2045 512 6,95 3 20 964 111,33 0.02446 2111 0,0782311 20 999 999,61 0.000000372
2046 5126,9 53 20 969 238,28 0.0244 6 2112 0,0782311 20 999 999,69 0.000000372
2047 51 26,9 53 20 974 365,23 0.02445 2113 0,0391156 20 999 999,73 0.000000186
2048 51 26,9 53 20 979 492,19 0.02444 2114 0,0391156 20 999 999,77 0.000000186
2049 25 63,477 20 982 055,66 0.01222 2115 0,0391156 20 999 999,80 0.000000186
2050 256 3,477 20 984 619,14 0.01222 2116 0,0391156 20 999 999,84 0.000000186
2051 2 563,47 7 20 987 182,62 0.01222 2117 0,0195578 20 999 999,86 0.000000093
2052 2563,477 20 989 746,09 0.01221 2118 0,0195578 20 999 9 99,88 0.000000093
2053 1281 ,738 20 991 027,83 0.0 0611 2119 0,0195578 20 999 999,90 0.000000093
2054 1281,738 20 992 309,57 0.0 0611 2120 0,0195578 20 999 999,92 0.000000093
2055 1281 ,738 20 993 591,31 0.00 611 2121 0,0 097789 20 999 999,93 0.000000047
2056 1281,738 20 9 94 873,05 0.0 0611 2122 0,0097 789 20 999 9 99,94 0.000000047
2057 64 0,8691 20 995 513,92 0.00305 2123 0,0097 789 20 999 999,95 0.000000047
2058 64 0,8691 20 99 6 154,79 0.00305 2124 0,0 097789 20 999 999,96 0.000000047
2059 64 0,8691 20 996 795,65 0.00305 2125 0,0 048894 20 999 999,97 0.000000023
2060 640,8691 20 997 436,52 0.00305 2126 0,0 048894 20 999 999,97 0.000000023
2061 320,4346 20 997 756,96 0.00153 2127 0,0 048 894 20 999 999,98 0.000000023
2062 320,4346 20 998 077,39 0.00153 212 8 0,004 8894 20 999 999,98 0.000000023
2063 320,4346 20 998 397,83 0.00153 2129 0,00244 47 20 999 999,98 0.000000012
2064 320,4346 20 998 718,26 0.00153 2130 0, 0024 447 20 999 999,99 0.000000012
2065 16 0,217 3 20 998 878,48 0.00076 2131 0,0 0244 47 20 999 999,99 0.000000012
2066 16 0,217 3 20 999 038,70 0.00076 2132 0,00 244 47 20 999 999,99 0.000000012
2067 16 0,217 3 20 999 198,91 0.00076 2133 0,0012224 20 999 999,99 0.000000006
2068 16 0,217 3 20 999 359,13 0.00076 2134 0,0012224 20 999 999,99 0.000000006
2069 80,10864 20 999 439,24 0.00038 2135 0,0012224 20 999 999,99 0.000000006
2070 80,10864 20 999 519,35 0.00038 2136 0,0012224 21 000 000,00 0.000000006
2071 80,10864 20 999 599,46 0.00038 2137 0,0006112 21 000 00 0,00 0.000000003
2072 80,10864 20 999 679,57 0.00038 2138 0,0006112 21 000 00 0,00 0.000000003
2073 40,0 5432 20 999 719,62 0.00019 2139 0,0006112 21 000 000,00 0.000000003
2074 4 0,054 32 20 999 759,67 0.00019 2140 0,0006112 21 000 000,0 0 0.000000003
79
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Figure 2. US dollar inaon 1914–2014, %
Source: www.coinmarketcap.com, Thomson Reuters.
Figure 3. Bitcoin inaon 2009–2109, %
Source: www.coinmarketcap.com, Thomson Reuters, the author’s calculations.
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
1914
1918
1922
1926
1930
1934
1938
1942
1946
1950
1954
1958
1962
1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
2014
0,00%
20,00%
40,00%
60,00%
80,00%
100,00%
120,00%
2009
2013
2017
2021
2025
2029
2033
2037
2041
2045
2049
2053
2057
2061
2065
2069
2073
2077
2081
2085
2089
2093
2097
2101
2105
2109
Figure 4. Logarithmic graph of the value of the bitcoin 2011–2019, US dollars
Source: www.tradingview.com
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Table 3. Average price change and bitcoin block remuneraon
Source: www.coinmarketcap.com, Thomson Reuters, the author’s calculations.
Time period
Average price
change per month,
US dollars
Block
remuneraon,
bitcoin
Time period
Average price
change per month,
US dollars
Block
remuneraon,
bitcoin
November 2010 –0.002 50.0 December 2014 –23.1 3 25.0
December 2010 0.02 50.0 January 2015 –9.20 25.0
January 2011 0.12 50.0 February 2015 8.64 25.0
February 2011 0.01 50.0 March 2015 0.4 8 25.0
March 2011 –0.03 50.0 April 2015 –5.66 25.0
April 2011 0.55 50.0 May 2015 –2.38 25.0
May 2011 2.69 50.0 June 2015 8.58 25.0
June 2011 –0.28 50.0 July 2015 2.98 25.0
July 2011 –0.55 50.0 August 2015 –8.34 25.0
August 2011 –0.96 50.0 September 2015 –0.59 25.0
September 2011 –0.78 50.0 October 2015 23.24 25.0
October 2011 –0.4 0 50.0 November 2015 11.6 8 25.0
November 2011 –0.03 50.0 December 2015 9. 23 25.0
December 2011 0.62 50.0 January 2016 –14. 89 25.0
January 2012 0.12 50.0 February 2016 7.1 9 25.0
curred, the rise in the cost of one unit of a bit-
coin amounted to 9200%.
Aer that, halving took place in early July of 2016.
Compensation for the found transaction block
declined from 25 BTC to 12.5 BTC. Once again,
halving led to a drop in the supply of bitcoins and
aected the price of cryptocurrency. At the time
of halving, the market price was $670 for a unit
of bitcoin. Within 520 days a bitcoin cost $19,500,
which was a new high in its price. is time, halv-
ing resulted in 2910% rise in the cost of bitcoin.
Derks, Gordijn, and Siegmann (2018) wrote that
market participants engaged in cryptocurren-
cy mining (validation of payments) are forced to
sell cryptocurrency in the open market in order
to cover the costs (depreciation of equipment, the
payment of electricity bills, sta salaries, rent,
etc.). Since the level of remuneration for the found
transaction block drops twofold every four years,
the amount of bitcoins extracted by miners also
decreases twofold from cycle to cycle. is results
in a lower supply of bitcoins in the open market
(Lischke & Fabian, 2016).
e next step of the analysis is to nd the net trad-
ing result. e net trading result (NTR) refers to
the nal price value of each interval of time (week-
ly). In order to nd the
,NTR
we need to detract
the weekly closing price from the weekly opening
price.
,
pp
NTR C O= −
(1)
where
p
C
– closing price,
p
O
– opening price,
NTR
– net trade result.
is indicator illustrates the historical results of
bitcoin price changes within a weekly interval, its
positive and negative price data, and price scale.
It is vital to weekly group intervals into periods,
which are equal or approximately equal to one
month to conduct a successful analysis. When tak-
ing historical data into account, we have 425 time in-
tervals (weeks), which equals to 98 periods (months).
In order to achieve results, the next step is to
nd the average price values
x
for each period
grouped earlier, i.e., the sum of all the values for
each selected period, divided by the number of
values in the selected period.
12
1
... ,
n
ni
i
xx x x
xnn
=
+ ++
= = ∑
(2)
where
i
x
∑
– the sum of all the values for the se-
lected period,
n
– the number of intervals in the
selected period,
x
– the average price value.
Finding data of average price values helps maxi-
mize reverse pungent speculative growth and de-
cline of the value of bitcoins in the market. us,
the eects that halving has on the market value of
bitcoins is evident (Table 3).
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3. RESULTS
Having obtained data for average price values, it is
possible to build graphs for the visual assessment
of halving’s inuence on the value of bitcoin. For
an accurate estimation, two graphs have been pre-
sented, because market prices of bitcoin for the
period of 2015–2018 are considerably higher than
the prices over the period of 2011–2015.
roughout 2011–2015 (Figure 5) the price peak
was reached aer 12 periods (12 months) and
amounted to $1,150 per BTC. Aer that, a price
correction began.
roughout 2015–2018 (Figure 6), the price maxi-
mum was reached aer 16 periods (16 months)
and amounted to $19,500 for a BTC. en price
correction started again.
Table 3 (cont.). Average price change and bitcoin block remuneraon
Time period
Average price
change per month,
US dollars
Block
remuneraon,
bitcoin
Time period
Average price
change per month,
US dollars
Block
remuneraon,
bitcoin
February 2012 –0.20 50.0 March 2016 1.79 25.0
March 2012 0.01 50.0 April 2016 8.64 25.0
April 2012 0.05 50.0 May 2016 24.07 25.0
May 2012 0.10 50.0 June 2016 19. 36 25.0
June 2012 0.38 50.0 July 2016 – 8.94 12.5
July 2012 0.92 50.0 August 2016 –2.38 12.5
August 2012 –0.12 50.0 September 2016 0.30 12.5
September 2012 0.54 5 0.0 October 2016 1 9.78 12.5
October 2012 –0.28 2 5.0 November 2016 15.49 12.5
November 2012 0.40 25.0 December 2016 58 .01 12.5
December 2012 0.22 25.0 January 2017 1.43 12.5
January 2013 1.58 25.0 Februar y 2017 6 4.71 12.5
February 2013 3.34 25.0 March 2017 –44.38 12.5
March 2013 14. 31 25.0 April 2017 68.52 1 2.5
April 2013 4.18 25.0 May 2017 232.69 12.5
May 2013 1.41 25.0 June 2017 –8.50 12.5
June 2013 –7.1 5 25.0 July 2017 1 45.76 12.5
July 2013 1.00 25.0 August 2017 3 47. 5 5 12.5
August 2013 8.75 25.0 September 2017 –56.61 12.5
September 2013 – 0.93 25.0 October 2017 59 7.7 0 12.5
October 2013 21 .61 25.0 November 2017 955.93 12.5
November 2013 186.46 25.0 D ecember 2017 6 73.93 12.5
December 2013 –11 .02 2 5.0 January 2018 –1135.98 12.5
January 2014 –23.96 25.0 Februar y 2018 818. 57 12.5
February 2014 – 60.24 25.0 March 2018 –1170 .3 0 12.5
March 2014 –27.7 8 25.0 April 2018 296 .73 12.5
April 2014 –5.21 25.0 May 2018 – 479.63 12. 5
May 2014 49.04 2 5.0 June 2018 –345.34 12.5
June 2014 1.44 25.0 July 2018 138.13 12.5
July 2014 –12.62 25.0 August 2018 63.95 12.5
August 2014 –26.91 25.0 September 2018 –172.67 12.5
September 2014 –31.10 25.0 October 2018 –32.6 4 12.5
October 2014 0.33 25.0 November 2018 –576.89 12.5
November 2014 13.63 25.0 December 2018 –62.08 12. 5
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Figure 5. The eect of emission reducon of bitcoins on the price, July 2011 – March 2015
Source: www.coinmarketcap.com, Thomson Reuters, the author’s calculations.
Figure 6. The eect of emission reducon of bitcoins on the price, April 2015 – November 2018
Source: www.coinmarketcap.com, Thomson Reuters, the author’s calculations.
0
10
20
30
40
50
60
-100,00
-50,00
0,00
50,00
100,00
150,00
200,00
810 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52
Average price per period, US dollar Block reward, BTC
Halving
Price peak
0,0
5,0
10,0
15,0
20,0
25,0
30,0
-1500,00
-1000,00
-500,00
0,00
500,00
1000,00
1500,00
53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97
Average price per period, US dollar Block reward, BTC
Halving
Price peak
According to the data obtained, it is easy to see
that aer halving, the price over the next four pe-
riods was still at the previous levels in both cases.
With the onset of the h period, price volatility
started to increase. Either way, the market needed
an interim period of ve months for the onset of
reactions to halving.
To validate the impact of halving on the value
of bitcoins, the rank correlation coecient by
Kendall was analyzed.
Kendall rank correlation coecient is calculated
by the following principles:
1) comparing each characteristic feature ac-
cording to their sequence number (ascending,
descending);
2) dening dierences between ranks;
3) calculating the coecient correlation accord-
ing to the formula:
( )
,
11
2
PQ
NN
τ
−
=
−
(3)
where
P
– the amount of coincidences,
Q
– the
amount of inversions,
N
– the number of periods;
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Investment Management and Financial Innovations, Volume 16, Issue 4, 2019
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4) calculating the critical point according to the
following formula:
( )
( )
22 5,
91
kp kp
n
Tz nn
+
=−
(4)
where
n
– the sample size,
kp
z
– the critical point
of the bilateral area, which can be calculated using
the following Laplace function table:
( )
1,
2
kp
z
α
−
Φ=
(5)
where
α
– the signicance level.
If
kp
T
τ
<
– the rank correlation between the
quality characteristics is insignicant. If
kp
T
τ
>
– there is a signicant rank correlation between the
quality of characteristics.
Let us compare every feature and dene the dif-
ference between ranks (X – is the average price
change per month, USD, Y – is the remuneration
for the block).
Let us calculate the correlation coecient:
( )
3028 1725 0.27.
198 98 1
2
τ
−
= =
−
(6)
Let us nd the critical point
:
kp
z
( )
1 0.05 0.475.
2
kp
z−
Φ= =
(7)
It is possible to execute the following calcula-
tions using the Laplace function table:
1.96.
kp
z=
Consequently, the critical point is determined by
completing these operations:
( )
( )
2 2 98 5
1.96 0.13.
9 98 98 1
kp
T⋅+
= =
⋅−
(8)
Let us compare the results: 0.27 > 0.13.
e rank correlation between estimates of two
types of data is important because the correlation
coecient
( )
0.27
τ
=
is bigger than the critical
point
( )
0.13 .
kp kp
TT
τ
= −>
Kendall rank cor-
relation coecient mathematically conrms the
relationship between the changes in remunera-
tion and average price values of bitcoins for the
selected 98 periods, which conrms the author’s
hypothesis.
4. DISCUSSION
Judging from the presented data, bitcoin is funda-
mentally a more attractive asset than at curren-
cy. Being an asset, which is less prone to ination,
and assuming that the current price volatility de-
creases, bitcoin has a chance to become an asset,
which the population will use as a storage of val-
ue, without the fear of money devaluation through
ination.
Today, or tomorrow, or in 10 years’ time, the
number of bitcoins in circulation cannot exceed
21 million units. erefore, over time, the cost of
bitcoin, ceteris paribus, should increase by at least
the amount of the depreciation of the traditional
currencies.
Having an algorithm of remuneration halving in
its programme code, the bitcoin system creates
a limited supply of new coins, which allows the
production of cryptocurrency up to the year 2140,
without exceeding the maximum supply of 21 mil-
lion coins (Nakamoto, 2008).
CONCLUSION
Halving is a technical element, which has a direct impact on the market supply of new coins. Reducing
remuneration every four years for each found transaction block, halving simultaneously reduces the
overall issuance of new bitcoins twofold, which leads to an increase in the market value of cryptocur-
rency. Analysis of the eect that halving Bitcoin issuance has for the periods of 2011-2015 and 2015-2018
clearly shows that in both cases, it took the cryptocurrency ve months to properly react to the halving
84
Investment Management and Financial Innovations, Volume 16, Issue 4, 2019
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that had occurred. e correlation between the level of remuneration from mining and the market price
is conrmed by the Kendall rank correlation method. e correlation coecient (τ = 0.27) is greater
than the critical point (T_kp = 0.13), which suggests that the rank correlation between the level of remu-
neration and the market price is signicant. e Kendall method can be used to conduct a comparative
analysis of remuneration and market value of other cryptocurrencies.
Although bitcoin was established as a payment system, eliminating intermediaries from the money re-
lationship chain, it is unlikely to become popular among the population as an instrument of payment
(Hong, 2016; Ciaian, Rajcaniova, & Kancs, 2016). However, bitcoin is not subject to depreciation as the
national currency is, it also does not depend on the control and regulating public bodies. All this makes
it a good tool for savings.
e program code of the bitcoin system features an algorithm that cuts the reward from mining in half
(halving). Moreover, it creates such a limited supply of new coins that cryptocurrency mining will be
possible up until the year 2140, whilst still not exceeding the maximum supply of 21 million coins.
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