The Chinese ban of Initial Coin Offerings
Anne Fokkema
10735496
Economics and Business; Finance and Organization
Supervisor: Jeroen Ligterink
30
thJanuary 2018
Abstract
Despite the rapid growth and decentralized system of cryptocurrency, various governments made regulations concerning cryptocurrencies, because of several accusations of illegal business use. China, once the leader in bitcoin-trading with a market share of 90%, banned Initial Coin Offerings on the 4th of September 2017. The Chinese government saw ICOs as an illegal way of public financing. This study is the first to examine what the impact of regulation on the value of different cryptocurrencies is. The findings show that there is a significant negative effect on the value of selected cryptocurrencies.
Statement of Originality
This document is written by Anne Fokkema who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no
sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of
Table of Content
1. Introduction ... 4
2. Literature Review... 5
2.1. The Bitcoin... 5
2.2. Cryptocurrency seen as currency or as financial asset? ... 6
3. Research Question and Hypotheses ... 9
4. Methodology ... 10
5. Data ... 11
6. Results ... 14
7. Conclusion ... 20
1. Introduction
In 1982, Chaum presented the first idea of an anonymous cryptography based payment scheme, and since then numerous academic papers have been published with innovative and improving elements for cryptocurrencies (Barber et al., 2012). One of them was from Satoshi Nakamoto, who in 2008 introduced the bitcoin. Bitcoin is a decentralized cryptocurrency, which is an electronic coin based on a chain of digital transactions, called blockchains (Nakamoto, 2008). With a market capitalization of $248 billion and a price of $14,820, bitcoin is considered to be the most popular and widely known cryptocurrency (coinmarketcap.com)1.
Despite the rapid growth and decentralized system of cryptocurrency, various governments regulated cryptocurrencies (Cheung et al., 2013). The United States, China, South Korea, Russia and Japan all introduced regulations, because of several accusations of illegal business use (Cheung et al., 2013). China, once the leader in bitcoin-trading with a 90% share (coindesk.com)2, banned Initial Coin Offerings (ICOs) on the 4th of September 2017 (fortune.com)3. Startup firms can finance themselves through ICOs, which are based on blockchains, the underlying technology of cryptocurrencies. The People’s Bank of China reasoned that ICOs are an unauthorized and illegal way of public financing, which involves financial crimes such as the illegal distribution of financial tokens, illegal fundraising and illegal issuance of securities and financial fraud (fortune.com)3. The restriction from Chinese regulators that followed was that all cryptocurrency exchanges needed to be shut down before the end of September 2017. Thus, the Chinese government has not made it illegal to own bitcoins as an individual, however, both restrictions make the use of owning cryptocurrency very limited. As a consequence of the restrictions the bitcoin-trading share of China
decreased to 10% (coindesk.com)2. Additionally, after the announcement of the ban on ICOs the price of bitcoin initially decreased, but ever since it has increased dramatically
(coinmarketcap.com)1.
The goal of this research is to examine what the impact of regulation on the value of different cryptocurrencies is. I do this by comparing the effect of the ban in China on ICOs on the prices of five selected Chinese cryptocurrencies, relative to bitcoin. To determine the
1 Retrieved 4th of January 2018 from: https://coinmarketcap.com 2 Retrieved 4th of January 2018 from: https://www.coindesk.com
3 Retrieved 4th of January 2018 from: http://fortune.com/2017/09/05/china-bitcoin-blockchain-ico-ban/
effect of the Chinese ban on the selected Chinese cryptocurrencies, its effect on the exchange rate return in US dollar of the selected Chinese cryptocurrencies will be examined.
So far, there has been little research about the effect of regulation on cryptocurrencies. Barber et al (2012), Chan et al (2017) and Yermack (2013) looked mostly at the
technological aspect of bitcoin and if cryptocurrency could be classified as a currency, like most other research. Brown (2016) focused on the criminality surrounding bitcoin, and Kristoufek (2014) emphasized the main drivers of cryptocurrency. So, this research is the first to look closely at the impact of regulation on the price development of cryptocurrencies, taking the ban of ICOs in China as change in regulation.
The findings of this research suggest that there is a negative impact on the value of different Chinese cryptocurrencies relatively to bitcoin, due to the ICO ban in China.
The setup of this paper is as follows. The next section presents an overview of the literature, followed up by the research question and the hypotheses. After this, the set-up of the research, including the regression, is explained in the methodology section. Following this section, the dataset and the results are described and discussed. Finally, a conclusion will end this research.
2. Literature Review
The technology behind bitcoin is a quite new and complex phenomenon. So, in the first part of this section bitcoin’s technology is explained. The second part of this section is used to provide an overview of the discussion whether bitcoin can be seen as a currency or as a financial asset. Finally, a short section is assigned to the relation of cryptocurrency and regulation.
2.1. The Bitcoin
The main difference between cryptocurrency and physical currency like the Euro or the US dollar, is that a cryptocurrency does not rely on a trusted third financial institution, like a bank (Nakamoto, 2008). Thus, instead of relying on trust, the bitcoin is a peer-to-peer payment system based on cryptographic proof (Nakamoto, 2008). This means that direct payment transaction between two parties are possible, without going through a financial institution (Nakamoto, 2008). In a peer-to-peer system every computer connected is
with and to every other computer member of the network (Brown, 2016). In this way, a decentralized system is created which deals with forgery and double-spending problems (Li et al., 2016). The cryptographic transactions are saved in ‘the blockchain’, containing transaction records of all the bitcoin owners. When a bitcoin transaction occurs, the current owner uses his/her unique private key, which validates the ownership, to send the transaction instruction to the receiver. Then, the transaction instruction is recorded in a new block, with the public key of the receiver (Li et al., 2016). Each block is protected by a unique hash and is only added to the chain when it is proofed to be valid, by meeting a ‘hash-rate criterion’ (Kristoufek, 2014). This is practiced by miners, using powerful computers, through trial and error (Li et al, 2016). After a transaction fee in bitcoin’s a new bitcoin is generated. This increases the bitcoin supply, and is also the award for the first miner who solves the mathematical problem behind the hash-rate criterion (Kristoufek, 2014).
The hash-rate criterion adjusts every 2016 blocks. This is to control the speed by which a block is created, and consequently the mining difficulty (Kristoufek, 2014). In this way the supply of bitcoins is balanced and there won’t be an overflow of bitcoins
(Kristoufek, 2014). Nakamoto designed the bitcoin system such that the pre-determined maximum of 21 million bitcoins will be in circulation in 2040, generating a block approximately every ten minutes (Li et al., 2016). With a market capitalization of $280 billion (coinmarketcap.com)1, bitcoin gained significant acceptance, however it is certainly not the only cryptocurrency (Barber et al., 2012). The success of bitcoin procreated a lot of other cryptocurrencies, there are 1355 different cryptocurrencies generated at this current date, all with minor modification differences (Cheung et al., 2013).
2.2. Cryptocurrency seen as currency or as financial asset?
Over time, the view on what is acceptable as payment, and how this payment is made, has changed (Ali et al., 2014). An early method of acceptable payment was the exchange of precious metal coins, which was followed by banknotes who were redeemable in those precious metals, while today fiat currency is commonly used (Ali et al., 2014). With fiat currency is meant currency that has no intrinsic value due to a physical commodity, but is valuable because of government decree (Li et al., 2016). According to Jevons (1875), money is identified to the extent how it fulfills three conditions; it needs to act as a store of value, as a medium of exchange and as a unit of account. The store of value entails people’s beliefs with which to transfer the purchasing power of today in the future. With the extent to which
money serves as a medium to exchange is meant with which to make payments. Lastly, money as a unit of account needs to measure the value of any particular item that is for sale (Ali et al., 2014).
Since the introduction of bitcoin, its price increased dramatically, experienced quick growth and had huge volatility swings (Cheung et al., 2013). So, within the existing
literature, economics debate about the question whether cryptocurrency fulfills the three critical conditions for it to be considered as currency, or that it should be considered as a financial asset (Chu et al., 2015).
Nakamoto (2008) states that cryptocurrency is an alternative to physical currency, mainly used as a direct medium of exchange between users, which is in line with the theoretical definition of money. The theoretical definition of money defines money as ‘any asset that is generally accepted for payment for goods or services, or for debt settlement’ (Kabút, 2015).
Additionally, decentralized cryptocurrencies can find support in the Austrian School of Economics. They state that fiat currency is only possible if governments monopolize the issuance of fiat currency, which establishes the ability of centralized manipulation (Clegg, 2014). When central banks generate an increase in the supply of the fiat currency, it causes inflation, which actually centralizes the value of the currency to the central bank (Clegg, 2014). Fergusson (2011) argues that a country’s budget can indeed be balanced in this way, but at the cost of the savings of citizens, their pensions, their confidence and their trust. A shortcoming of centralized fiat currency, is thus that an institution with special permissions to increase the money supply, such as a government or central bank, can redistribute value to itself (Clegg, 2014).Cryptocurrency would prevent this from happening, because of its decentralized system.
Kristoufek et al. (2015) found that in the long run bitcoin appreciates when it is used mainly for transactions in trade, which is in line with the quantity theory of money. The quantity theory of money states that for a given change in the growth rate of the quantity of money in circulation consequently has an equal change in the growth rate of nominal income and in inflation (Duck, 1993). In other words, when the amount of money in circulation doubles, the price levels will also double, which has inflation as a consequence. Thus, in light of this, cryptocurrency could be seen as valid money, because it appreciates in the long run.
Opposed to the view that cryptocurrency can be seen as a valid currency, Ali et al (2014) argue that virtual currencies fulfill the criteria for being money only to some extent.
Especially to the extent of being a medium of exchange, and only for a little amount of thousand people globally (Ali et al., 2014). They state that the prospect for future demand is uncertain for digital currencies, because digital currencies don’t have intrinsic demand and no financial institution as background (Ali et al., 2014). Therefore, their future demand prospect is largely based on belief, which is speculative and thus acts more like a financial investment. Ali et al. (2014) also argue that the acceptance of receiving bitcoin as payment is indeed increasing, however this does not necessarily mean that it is widely used. The group of users is still very small in comparison with other currency. Also, most bitcoin owners hold their bitcoins, instead of spending them daily (Ali et al., 2014). After this all, Ali et al. (2014) conclude that cryptocurrencies cannot extensively be considered as a medium of exchange, thus not as a valid currency. In the online market, it has some function as medium of
exchange, according to Yermack (2013). However, it plays almost no role in the commercial markets as a function of payment. In addition, most of these payments are transactions
between speculative investors (Yermack, 2013). Thus, according to Yermack, cryptocurrency is a financial asset.
To the extent as unit of account, the small number of cryptocurrency transactions are isolated and not connected (Ali et al., 2014). Furthermore, the fact that bitcoin is very volatile, makes it not an acceptable unit of account. As a consequence, the price of bitcoins has to be recalculated often, because of the fact that the value changes extremely per day. This would be confusing to the consumer and generates additional costs for the trader (Ali et al., 2014).
If bitcoin was a good store of value, the holders of bitcoin would spend it and expect to get the same economic value in return that it was worth when it was acquired (Yermack, 2013). An acceptable store of value also includes the protection of cryptocurrency against theft and crime (Yermack, 2013). However, the security of the ‘digital wallets’, which are computer accounts, has become problematic (Yermack, 2013). It has been attacked by organized crime, theft, hackers, and money laundering (Kristoufek, 2015). This is possible because of the decentralized regulation and the possibility to avoid the anti-money laundering principle of ‘know-your-customer’, both create additional and attractive opportunities for criminal operation (Brown, 2016). Also, Kristoufek (2015) states that managing the risk involved with the volatility of bitcoin has become challenging. Consequently, owning a bitcoin and in particular storing a bitcoin comes with great risk, which is not aligned with the condition of functioning as an acceptable store of value and unit of account (Kristoufek, 2015).
In line with the thought that cryptocurrency is a financial asset, Kristoufek (2015) states that the mining of bitcoins can be seen as an investment opportunity, because the computational power, the costs of the needed hardware and the electricity is used as an investment to generate bitcoins.
As mentioned above, the decentralization and anonymity are both elements of cryptocurrency that provide ways for untraceable money flows and criminal operation (Brown, 2016). The fact that cryptocurrency is decentralized and anonymous is in conflict with regulation by the government (Böhme et al., 2015). However, contrary to the fact that decentralization and anonymity make it hard to regulate, there are significant criminal challenges associated with cryptocurrency that might need regulation (Böhme et al., 2015). Money laundering, bitcoin-specific crime and bitcoin-facilitated crime are the three classes of criminal issues for which bitcoin can be regulated (Böhme et al., 2015). With bitcoin-specific crimes is meant attacks on bitcoin itself and its infrastructure, for example theft and attacks on mining pools (Böhme et al., 2015). Bitcoin-facilitated crimes are payments for illegal online goods and services, for example payment of funds in extortion (Böhme et al., 2015). Consumer protection is another category for which regulation is justified (Böhme et al., 2015). This came to light when the bitcoin exchange Mt. Gox failed in 2014 and lost more than $300 million worth of bitcoins (Böhme et al., 2015). Because of all the significant criminal issues associated with cryptocurrency, various governments regulated
cryptocurrencies (Cheung et al., 2013).
The goal of this research is to examine what the impact of regulation on the value of different cryptocurrencies is. I do this by comparing the effect of the ban in China on ICOs on the prices of five selected Chinese cryptocurrencies, relative to bitcoin. To determine the effect of the Chinese ban on the selected Chinese cryptocurrencies, its effect on the exchange rate return in US dollar of the selected Chinese cryptocurrencies is examined.
3. Research Question and Hypotheses
The transaction usage of bitcoins is in connection with the fundamental aspects of its value (Kristoufek, 2014). However, there are two effects between the usage of bitcoin and the price of bitcoin that possibly contradict themselves (Kristoufek, 2014). The first effect comes from the fact that the usage of bitcoin has a positive effect on its price, namely that the more frequently bitcoin is used, the higher its demand, and thus its price will become higher
(Kristoufek, 2014). However, if the price of bitcoin is driven by speculation, volatility and uncertainty, it can result in a negative effect, namely a decrease of the bitcoin price
(Kristoufek, 2014).
As mentioned in section two of this paper, various governments regulated cryptocurrencies, as cryptocurrencies create an additional and attractive opportunity for criminal operation because of the decentralized regulation and anonymity of cryptocurrency. So, the Chinese regulations of banning ICOs followed by the prohibition of all
cryptocurrency exchanges could have an impact on the usage of cryptocurrencies, but also on the speculation, volatility and uncertainty of cryptocurrencies, which all can affect the value of cryptocurrencies.
In regard of finding out what the impact of regulation on the value of different cryptocurrencies is, the following research question is to be answered in this paper:
What is the impact of regulation on the value of selected Chinese cryptocurrencies, relative to Bitcoin?
To answer this question, the following hypothesis is tested. Given the fact that on the 4th of September 2017 ICOs where banned by the Chinese authorities and thereafter all cryptocurrency exchanges were prohibited, it is expected that there is a negative effect on the exchange rate return in US dollar of the selected Chinese cryptocurrencies, because these restrictions limit the usefulness of owning cryptocurrency.
4. Methodology
In this paper, the following regression is performed to examine if the Chinese ban of Initial Coin Offerings had an effect on the return of the exchange rate of five selected Chinese cryptocurrencies. The five selected Chinese cryptocurrencies are: Binance, NEO, OmiseGo, Loopring and Qtum. These cryptocurrencies are chosen, because they are all from Chinese origin, so they represent the Chinese cryptocurrency market. Using a difference-in-difference analysis, which is a way to estimate causal relationships, comparing the difference-in-difference in outcomes before and after the changing event for groups affected by the intervention to the same difference for unaffected groups (Bertrand, 2004).
are regressed on a constant, the log exchange rate return of the bitcoin, a dummy who has the value one when the date of the return appears to be after the ban and zero when the date of the return appears to be before the ban, and the interaction between de dummy variable and log return of bitcoin.
Rit = ß0 + ß1RBitcoin+ ß2Dateafter +ß3Dateafter*RBitcoin + εt
The exchange rate data is obtained from coinmarketcap.com, which provides the daily exchange rate in US dollar of all cryptocurrencies. For the regression, the log of the daily exchange rate return of the cryptocurrencies is used.
Rit is the dependent variable, which is the daily log exchange rate return of the five
selected Chinese cryptocurrencies on the date t. Binance, NEO, OmiseGo, Loopring and Qtum will represent the Chinese cryptocurrencies. Thus, there will be five different
regressions, each of them representing another Chinese cryptocurrency. RBitcoin is the daily log
exchange rate return of the bitcoin. Dateafter is a control dummy, which has the value one
when the date of the return appears to be after the ban. On September 4th 2017, the
announcement of the Chinese ban on Initial Coin Offerings was made. So, from this date on the dummy variable will be one, and before this date the value will be zero. ß3Dateafter*RBitcoin
is a cross variable, representing the interaction between Dateafter and Rbitcoin, and
ε
t is the errorterm for the observation at t.
However, when the data was collected for the five Chinese cryptocurrencies, the data of Loopring turned out to be only available after August 30th, which causes a negative impact on the number of observation of approximately 26.3%4. So, the decision has been made to leave out Loopring in the regressions.
The Ordinary Least Squares regression results will be compared with the results using the robust regression.
5. Data
The data for the Bitcoin, Binance, NEO, OmiseGo, and Qtum is collected for the period
4 101 = old number of observations, 137 = new number of observations 1-(101/137) = 0.26277*100% ≈ 26.3%
of 5 months, from the 12th of July 2017 un till the 9th of December 2017. In the table below, the summary statistics are shown.
Table 1: Summary statistic log returns
Variable Obs Mean Std. Dev. Min Max
Bitcoin 137 0,0129448 0,0523750 -0,2075298 0,2251190 Binance 137 0,0233934 0,1370769 -0,4086348 0,6751739 NEO 137 0,0115382 0,1099324 -0,3285224 0,5088927 OmiseGo 137 0,0141782 0,1108685 -0,2724051 0,5416499 Qtum 137 0,0049515 0,0893675 -0,4517458 0,2569515
One can separate the data set into the period before the Chinese ban of Initial Coin Offerings, which is 27th of July 2017 until the 3rd of September 2017, and the period from and including the day of the announcement of the Chinese ban of Initial Coin Offerings, which is the 4th of September 2017 until the 9th of December 2017. There are 137 observations per cryptocurrency.
During the data collection period of all analyzed cryptocurrencies, Qtum has the smallest mean, with a value of 0,0049515, and Binance the largest mean, with a value of 0,0233934. Bitcoin has a standard deviation of 0,0523750, which is the smallest of all analyzed cryptocurrencies. Binance has the largest standard deviation of all analyzed cryptocurrencies with a value of 0,1370769.
The lowest daily log return of the four analyzed cryptocurrencies belongs to Qtum, being -45,17%. The largest daily log return, 67,52%, belongs to Binance, which also has the largest standard deviation of all analyzed cryptocurrencies. Bitcoin, with the smallest
standard deviation, has also the lowest minimum daily log return of -20,75%. Bitcoin, thus has the lowest decreasing one-day shock.
In figure 1, one can see the relative appreciation and depreciation of the value of the cryptocurrencies compared to the starting date of 25/7/2017, where the prices of each
cryptocurrency are scaled back by the starting price at the starting date to 100. Also, the date of the ban, being 04/09/2017, is marked.
Figure 1: Relative price appreciation or depreciation of all cryptocurrencies
During the time from 30/08/2017 until the date of the announcement of the ban of ICOs, 4/09/2017, the exchange price of bitcoin depreciated from $4565.3 to $4236.31. Binance depreciated from $2.35 to $1.05, NEO from $33.1 to $21.83, OmiseGo from $11.07 to $8.78 and Qtum from $16.84 to $10.98. So, all experienced a depreciation.
The depreciation continued from 04/09/2017 on until 14/09/2017 for bitcoin, with its low point of $3154.95. From then, bitcoin experienced an appreciation until 8/11//17, with a high point of $7459.59, followed by a rapid decrease until 12/11/17, where it had a value of $5950.07. Since this date it again experienced a dramatic appreciation to $17899.7 on 7/12/17.
From 14/09/2017 onwards Binance appreciated to $1.82 on 3/10/2017. Thereafter, it looks quite stable, with a high point of $2.03 on 8/11/2017. Then, it depreciates to $1.51 on 17/11/2017, and ever since it experienced an appreciation to $2.61 on 9/12/2017.
From 04/09/2017 until 14/09/2017 NEO depreciated, like bitcoin, further to $15.69. Then it appreciated rapidly to $36.74 on 02/10/2017. With short but powerful appreciations and depreciations, it has a low point on 02/11/2017 with $24.5, a high point on 18/11/2017 with $42.74 and finally a value of $35.71 on 09/12/2017.
0 500 1000 1500 2000 2500 3000 7/2 5/1 7 8/1 /1 7 8/8 /1 7 8/1 5/1 7 8/2 2/1 7 8/2 9/1 7 9/5 /1 7 9/1 2/1 7 9/1 9/1 7 9/2 6/1 7 10 /3 /1 7 10 /1 0/1 7 10 /1 7/1 7 10 /2 4/1 7 10 /3 1/1 7 11 /7 /1 7 11 /1 4/1 7 11 /2 1/1 7 11 /2 8/1 7 12 /5 /1 7 Rel at ive pri ce cha ng es of a ll crypt ocurre nci es Date
Relative prices
OmiseGo experienced an appreciation until 13/09/2017 $10.61, and from then depreciated again until 01/11/2017 to $6.05, as low point. Then, until 05/12/2017, it appreciated to $10.08, and decreased to $8.58 on 09/12/2017.
After 14/09/2017, Qtum experienced an increase in price to $13.51 until 12/09/2017. Afterwards, it depreciated until 02/11/2017 to $9.8, and appreciated again with as high point $15.49 on 28/11/2017. Then, it depreciated to $12.08 on 09/12/2017.
From Figure 2, one can see that around the date of the ban of ICOs, all
cryptocurrencies are impacted in a negative way, and after two weeks they appear to recover.
Figure 2: Exchange rates of all cryptocurrencies
6. Results
The regression results for the regressions of the four different Chinese cryptocurrencies are summarized in Table 2.
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 0 5 10 15 20 25 30 35 40 45 50 7/25/17 8/25/17 9/25/17 10/25/17 11/25/17 Bi tcoi n Ex change R at e Chi nes e C rypt o Ex change R at e Date
Prices
Table 2: Regression results
The dependent variable in the regression is the log exchange rate return of the Chinese cryptocurrencies. Bitcoin is the log exchange rate return of bitcoin. Date dummy is a dummy equal to one when the date is after 4/09/2017. Bitcoin*Date dummy is the interaction between the log exchange rate return of bitcoin and the dummy. The standard errors are reported in parentheses.
From Table 2, one can see that all the Bitcoin coefficients of the four Chinese
cryptocurrencies are significant. This means that all the four Chinese cryptocurrencies follow the bitcoin before the Chinese ban, although with different slopes. The coefficient for
Binance is the largest, with a value of 2,03. This means that changes in the daily log return of bitcoin translate to amplified changes in daily log return of Binance with a factor of 2,03. The coefficient of Binance is followed by the coefficient of OmiseGo, with a value of 1,62, NEO with a value of 1,19 and finally Qtum with a value of 1,14. Thus, changes in the daily log return of bitcoin translate to amplified changes with a factor of 1,62, 1,19 and 1,14, for OmiseGo, NEO and Qtum, respectively.
After the Chinese ban on ICOs, all the bitcoin coefficients of the four Chinese cryptocurrencies are adjusted downwards, but still follow the bitcoin. However, now only three out of the four Chinese cryptocurrencies are significant. The significant coefficient for OmiseGo changes the most, with the value of -1,24. Thus, the ban had a negative effect of 1,24 on the bitcoin coefficient of OmiseGo. This means that the bitcoin coefficient changes to 0,38, which is 1,24 subtracted from 1,62. So, after the ban, changes in the daily log return of bitcoin translate to amplified changes in daily log return of OmiseGo with a factor of 0,38. The second largest change in coefficient belongs to Binance, with a value of -1,19. Thus, the
Binance NEO OmiseGo Qtum
Bitcoin 2,03235*** 1,192303*** 1,624672*** 1,135491*** (0,4850537) (0,4162292) (0,4030136) (0,3192076) Date dummy -0,0329549 -0,0127186 -0,0336459* -0,007981 (0,0238679) (0,0204813) (0,019831) (0,0157072) Bitcoin*Date dummy -1,195103** -0,7593765* -1,235563*** -0,4937637 (0,5331617) (0,4575111) (0,4429847) (0,3508668) Constant 0,03131 0,0127186 0,0282333* 0,0004448 (0,0200869) (0,0172367) (0,0166894) (0,0132189) Observations 137 137 137 137 R-squared 0,2129 0,0988 0,1694 0,198 Adjusted R-squared 0,1951 0,0785 0,1506 0,1799
*** = 1% significance level, ** = 5% significance level, * = 10% significance level Coefficient and (Standard Deviation)
ban had a negative effect of 1,19 on the significant bitcoin coefficient of Binance. This means that the bitcoin coefficient changes to 0,84, which is 1,19 subtracted from 2,03. So, after the ban, changes in the daily log return of bitcoin translate to amplified changes in daily log return of Binance with a factor of 0,84. For NEO, the effect is with the value -0,76, but not very significant. The negative effect is much less obvious for Qtum, because the significance confidence is too high.
A robust version of the regression, which is an alternative to the Ordinary Least Squares regression, is also performed. The observations are now weighted differently based on how they behave. The idea of this kind of regression is that it is less affected by outliers or influential observations (stata.com)5. This could be the case as prices of cryptocurrencies may behave erratic. Table 3 shows the results of the robust regression.
Table 3: Robust regression results
The dependent variable in the regression is the log exchange rate return of the Chinese cryptocurrencies. Bitcoin is the log exchange rate return of bitcoin. Date dummy is a dummy equal to one when the date is after 4/09/2017. Bitcoin*Date dummy is the interaction between the log exchange rate return of bitcoin and the dummy. The standard errors are reported in parentheses.
Table 3 shows that the coefficients for bitcoin of Binance, OmiseGo and Qtum are significant, so they still follow the bitcoin before the ban. The coefficient for bitcoin of NEO is not significant, so this may indicate that one should be careful to draw a too strong
conclusion on the earlier regression results. The coefficient for OmiseGo is the largest, with a
5 Retrieved 29th of January 2017 from: https://stats.idre.ucla.edu/stata/dae/robust-regression/
Binance robust NEO robust OmiseGo robust Qtum robust Bitcoin 1,307769*** 0,0840537 1,754452*** 0,7067441*** (0,3739664) (0,3192134) (0,3264529) (0,2669433) Date dummy 0,004107 -0,0081398 -0,0190551 -0,0062792 (0,0184017) (0,0157075) (0,0160637) (0,0131354) Bitcoin*Date dummy -0,4660691 0,3063063 -1,19432*** -0,3159538 (0,4110566) (0,3508732) (0,3588307) (0,2934189) Constant -0,0071519 -0,0020313 0,0139609 0,0049615 (0,0154866) (0,0132191) (0,0135189) (0,0110546) Observations 137 137 137 137
*** = 1% significance level, ** = 5% significance level, * = 10% significance level
value of 1,75 followed by the coefficient of Binance, with a value of 1,31, NEO with a value of 0,84 and finally Qtum with a value of 0,71. So, in other words, changes in the daily log return of bitcoin translate to amplified changes in daily log return of OmiseGo, Binance, NEO and Qtum with a factor of 1,75 1,31, 0,84, 0,71, respectively.
Now after the Chinese ban on ICOs, the bitcoin coefficients of Binance, OmiseGo and Qtum are adjusted downwards, but still follow the bitcoin. However, only the coefficient for OmiseGo is significant, and changes the Bitcoin coefficient with a value of -1,19. Because of the fact that Binance and Qtum are not significant anymore, the earlier conclusion for these cryptocurrencies are less apparent. NEO’s shift in coefficient is positive, however not significant.
Table 4: Regression result of Chinese cryptocurrency average The dependent variable in the regression is the average log exchange rate return of the Chinese
cryptocurrencies. Bitcoin is the log exchange rate return of bitcoin. Date dummy is a dummy equal to one when the date is after 4/09/2017. Bitcoin*Date dummy is the interaction between the log exchange rate return of bitcoin and the dummy. The standard errors are reported in parentheses.
To get a better overall idea of the effect of the ban, table 4 shows the regression on bitcoin of the average of the four Chinese cryptocurrencies. One can see that here the bitcoin coefficient also is significant, meaning that the average of the four Chinese cryptocurrencies follows the bitcoin before the Chinese ban, with a coefficient of 13,26. So, changes in the daily log return of bitcoin translate to amplified changes in daily log return of the average of the four cryptocurrencies with a factor of 13,26.
Chinese cryptocurrency average Bitcoin 13,25801*** (2,809921) Date -0,1872217 (0,1382671) Bitcoin*Date -8,073911* (3,088611) Constant 0,034779 (0,1163634) Observations 137 R-squared 0.2444 Adjusted R-squared 0.2274
*** = 1% significance level, ** = 5% significance level, * = 10% significance level Coefficient and (Standard Deviation)
After the Chinese ban on ICOs, the bitcoin coefficient of the average of the four Chinese cryptocurrencies is adjusted downwards, but still follows the bitcoin. The significant coefficient changes with the value -8,07, so the average bitcoin coefficient becomes 5,19. This means that after the ban, changes in the daily log return of bitcoin translate to amplified changes in the daily log return of the average of the four cryptocurrencies with a factor of 5,19.
Table 5 shows the robust regression result on bitcoin of the average of the four Chinese cryptocurrencies. The bitcoin coefficient is significant, meaning that the average of the four Chinese cryptocurrencies follows the bitcoin before the Chinese ban, with a
coefficient of 9,45. So, changes in the daily log return of bitcoin translate to amplified changes in daily log return of the average of the four cryptocurrencies with a factor 9,45.
After the ban on ICOs in China, the bitcoin coefficient of the average of the four Chinese cryptocurrencies is also adjusted downwards, but still follows the bitcoin. The significant coefficient changes with the value -4,51, so the average bitcoin coefficient becomes 4,94. This means that after the ban, changes in the daily log return of bitcoin translate to amplified changes in the daily log return of the average of the four
cryptocurrencies with a factor of 4,94.
The results of the individual regressions, robust individual regressions, the regression with the average of the four cryptocurrencies and the robust regression with the average of the four cryptocurrencies all indicate that the Chinese ban has had a negative impact on the exchange rate return of the selected Chinese cryptocurrencies. After the ban, in the first regression, the coefficient of bitcoin of Binance, OmiseGo and NEO are adjusted downwards with statistical significance. In the robust regression, only the coefficient of bitcoin for OmiseGo is adjusted downwards in a statistical significant way after the ban. So, one should be careful to draw a too strong conclusion from the regression results of the first regression of Binance and NEO. In the third regression, using the average of the Chinese cryptocurrencies, the coefficient of bitcoin is adjusted downwards in a statistical significant way. In the final robust regression, again using the average of the Chinese cryptocurrencies, the coefficient of bitcoin is also adjusted downwards in a statistical negative way.
So, there are differences in significance of the bitcoin coefficient between the individual regression and the robust individual regression. Not all the four Chinese cryptocurrencies adjust downwards in a statistical significant way in both the regressions.
Only OmiseGo is adjusted downwards in a statistical significant way in both the regressions. This makes that conclusions about the impact of the Chinese ban on ICOs on the individual regression results should be carefully drawn. In contrast, the downward adjustment and statistical significance stays observable for both the regression and the robust regression for the average of the four cryptocurrencies. So, overall based on the regressions of the average Chinese cryptocurrencies there is indeed evidence that supports the conclusion that the ban has had a negative impact on the development of Chinese cryptocurrencies.
Table 5: Robust regression result of Chinese cryptocurrency average The dependent variable in the regression is the average log exchange rate return of the Chinese
cryptocurrencies. Bitcoin is the log exchange rate return of bitcoin. Date dummy is a dummy equal to one when the date is after 4/09/2017. Bitcoin*Date dummy is the interaction between the log exchange rate return of bitcoin and the dummy. The standard errors are reported in parentheses.
Chinese cryptocurrency average
Bitcoin 9,45313*** (2,19024) Date -0,1155552 (-0,1077746) Bitcoin*Date -4,512885** (2,407469) Constant -0,0091584 -0,0907014 Observations 137
*** = 1% significance level, ** = 5% significance level, * = 10% significance level Coefficient and (Standard Deviation)
7. Conclusion
The view on what is an acceptable payment method has changed. So, the debate whether cryptocurrency could be seen as a new currency or as a financial asset has started. Nakamoto clearly intended bitcoin to be a medium of exchange with its decentralized
technology, which is supported by the Austrian School of Economics. On the other hand, due to the high volatility swings of bitcoin, the small amount of cryptocurrency users, and the vulnerability for criminal activity, suggest that it is not clear whether bitcoin and
cryptocurrency in general fulfill the three conditions to be considered as currency. Due to several accusations of the illegal business use of cryptocurrencies and the approval of the use of it as a currency, restrictions are being made on the use of
cryptocurrencies. Therefore, it is interesting to research the impact of regulation on the value of cryptocurrency.
The results of the various regressions made in this research, suggest that the ban in China on ICOs had a measurable negative impact on the value of the selected Chinese cryptocurrencies, relative to bitcoin.
I note that more research may be necessary to really confirm this conclusion, as the amount of data used is limited. Using longer time series and more cryptocurrencies may result in a different conclusion.
Bibliography
Ali et al. (2014). Retrieved from;
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2499418
Barber, S., Boyen, X., Shi, E., & Uzun, E. (2012). Bitter to Better – how to make bitcoin a better currency. International Conference on Financial Cryptography and Data
Security, pp. 399-414.
Bertrand, M., Duflo E. & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics. 119(1), pp. 249-275
Blockchain.info. Retrieved November 18th, 2017, from https://blockchain.info
Breusch, T.S., & Pagan, A.R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica. 47(5), pp. 1287-1294
Brown, S.D. Cryptocurrency and criminality: The Bitcoin opportunity. (2016). The Police Journal: Theory, Practice and Principles. 89(4), pp 327-339.
Chaum, D. (1983). Blind signatures for untraceable payments. Proceedings on Advances in
cryptology, pp 199-203
Chan, S., Chu, J., Nadarajah, S., & Osterrieder, J. (2017). A Statistical Analysis of Cryptocurrencies. Journal of Risk and Financial Management. 10(12).
Cheun, A., Roca, E., & Su, JJ. (2015). Crypto-currency bubbles: an application of the
Phillips-Shi-You (2013) methodology on Mt. Gox bitcoin prices. Applied Economics. 47(23), pp. 2348-2358
Chu, J., Nadarajah, S., & Chan, S. (2015). Statistical Analysis of the Exchange Rate of Bitcoin.
CoinMarketCap.com. Retrieved November 18th, 2017, from https://coinmarketcap.com
Duck, N. (1993). Some international evidence on the quantity theory of money. Journal
of Money, Credit and Banking. 25(1), pp. 1-12.
The Economist. (2017). Once a leader in virtual currencies, China turns against them. Retrieved from
https://www.economist.com/news/finance-and-economics/21729795-after-bans-exchanges-and-initial-coin-offerings-bitcoin-miners-fear-they-are
Fergusson, A. (2011). When money dies: the nightmare of the Weimar hyper-inflation. Scribe Publications, p. viii.
Glegg, A, G., (2014). Could bitcoin be a financial solution for developing economics?
Jevons, William Stanley. Money and the Mechanism of Exchange. 2nd issu, Indiapoli: Liberty Fund, 2011. ISBN: not assigned.
Ju, L., Lu, T., & Tu, Z. (2016). Capital flight and bitcoin regulation. International Review of
Finance. pp. 445-455.
Kristoufek, L. (2014). What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. pp. 1-19.
Li, X., Wang, C. (2016). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. pp. 1-9.
Stata.com. Retrieved January 29th, 2018 from
https://stats.idre.ucla.edu/stata/dae/robust-regression/
Yermack, D. (2013). Is bitcoin a real currency? An economic appraisal. The Handbook of
Zettelmeyer, J. (2004). The impact of monetary policy on the exchange rate: evidence from three small open economies. Journal of Monetary Economics. pp. 635-652.
