The Effect of Limited Short-Sales on Returns

Author: Marconi Moscoso Cruz Supervisor: Dr. F.G.P. Frehen Second Reader: Dr. F.S. Hosseini Tash

Tilburg University Department of Finance

October 3, 2019

Abstract

This paper examines the effect of limited short-sale possibilities on cryptocurrency returns. Short-sale possibilities are identified by a unique set of short-selling announcement dates made public by cryptocurrency exchanges, introducing short-selling for specific . The effect of short-sales on returns is measured using structural break test analysis and through the use of a Fixed Effects regression, where the effect of short-sale possibilities is identified as the coefficient on a dummy variable that takes the value of one if it is possible to short and zero otherwise. Overall, I find that after short-sales have been opened, average cryptocurrency returns decrease significantly to a lower average level than before short-sales were opened. This result is in line with existing literature on the effect of short-sales on equity returns. In more detail, it appears from the structural break test that investors anticipate the opening of short-sales, and start pushing up prices and returns up to the moment cryptocurrency exchanges open short-sales. This behavior and finding seem to be in line with studies on bubbles in joint with heterogeneous beliefs and short-sale constraints. Moreover, across the entire sample, limited evidence is found in favor of the feedback loop on returns, but there is significant evidence for positive feedback in turnover. In other words, it seems that past returns are not an indication of positive future returns, while past returns appear to and indication for more trading activity.

Keywords: ; Bubbles; Crashes; Cryptocurrency; Heteregeneous Beliefs; Returns; Short-Sales; Speculation; Turnover; Volatility

1 Contents

1 Introduction 3

2 Literature Review5 2.1 Short-sales and Returns...... 5 2.2 Heterogeneous Beliefs and Short-sale Constraints...... 6

3 Cryptocurrency Bubbles7 3.1 The First Run: May 2012 - April 2013...... 7 3.2 The Second Run: July 2013 - December 2013...... 8 3.3 The Third Run: January 2016 - December 2017...... 10 3.4 A Short History of ICOs...... 12

4 Data 13 4.1 CoinMarketCap...... 13 4.2 Shorting Dates...... 13

5 Structural Changes in Returns 13 5.1 Model and Methods...... 14 5.1.1 The Model...... 14 5.1.2 The Tests...... 14 5.2 The Event Date...... 15 5.3 Structural Break Test Results...... 15 5.3.1 Single Break in Returns...... 15 5.3.2 Multiple Breaks in Returns...... 17

6 Methodology 19 6.1 The ”No Short-Sale” Assumption...... 19 6.2 Regression Models...... 19 6.2.1 Pooled OLS Regression...... 19 6.2.2 Fixed Effects Regression...... 20 6.3 Control Variables...... 20

7 Empirical Results 22 7.1 Pooled OLS Results...... 22 7.2 Fixed Effects Results...... 22

8 Robustness Tests 23 8.1 Robustness of the Shorting Dummy...... 23 8.2 Placebo Test...... 23

9 Feedback Loop Dynamics 25 9.1 Feedback Loop Results...... 25

10 Conclusion and Discussion 26

2 1 Introduction

The past decade, cryptocurrencies have attracted a lot of attention from retail investors, insti- tutional investors, banks, and regulators. Especially the media has played an important role in spreading the news across the globe. Bitcoin, developed by Nakamoto(2008), is the first of many cryptocurrencies that is a peer-to-peer electronic cash system that allows individuals to send pay- ments from one person to another. It is called a cryptocurrency due to its cryptographic nature that ensures security and privacy.1 Cryptocurrencies are decentralized, which means that they do not need a third party to play the role of an intermediary. Therefore, unlike the monetary system, a cryptocurrency does not have a higher authority that decides what the rules are and what laws should be implemented, and it does not have a physical representation. Also, the value of cryptocurrencies are not based on any tangible asset, firm or a country’s economy. Instead, its value is linked to the security of algorithms that tracts all transactions in the network, making them transparent for all users participating in the network. The extreme increase in the usage of cryptocurrencies is linked to low-cost transactions, the possibility to exchange money or informa- tion with another person without any third party facilitating the transaction, and the fact that it is not run by governments or financial institutions. Since inception, Bitcoin has remained the number one cryptocurrency in the world, based on market capitalization. On December 17, 2017, Bitcoin hit a new record high of $19.783,06. For comparison, the price of Bitcoin stood at $1.200,00 on November 29, 2013. This extreme growth presented an opportunity to obtain a total return of approximately 1550%, which cannot be generated by any other asset. Against all odds, the price of Bitcoin has been breaking records since the first official exchange rate of $0,003, established on March 17, 2010.2 Bitcoin has opened doors for thousands of other cryptocurrency projects, of which some famous ones like , , , and . According to CoinMarketCap3 there are over two thousand listed cryptocurrencies. But these are not all cryptocurrencies that exists, since they only try report on cryptocurrencies that meet their legal standards. The explosive surge and fall in prices of cryptocurrencies in the past decade, has led many academics, practitioners, and regulators describe the developments of these cryptocurrencies as a ”bubble”.4 In March 2017 the Securities and Exchange Commission (SEC) declined a proposal for a Bitcoin ETF5, giving price volatility and speculation as the two main reasons. These two features could be the cause of bubbles that have been observed in cryptocurrency markets in the past decade, which makes the detection and impact of such bubbles on prices and returns an important topic of research. According to Aliber and Kindleberger(2017), bubbles are interpreted as an explosive rise in prices, that eventually fall sharply as a reaction to some event. However, a more common interpretation of a bubble is when asset prices are persistently higher than their fundamental value. In the literature of behavioral finance, a popular explanation for the cause of bubbles is the irrationality of investor’s sentiment, such as overconfidence, in turn leading to herd behavior and speculative trading.6 Due to limits in shorting, or high cost of arbitrage, rational investors and traders cannot easily arbitrage and eliminate asset price bubbles.7 The cryptocurrency market shares the characteristic of limits in short-selling with financial markets, but since it is a new developing market, there are almost no possibilities to short-sell cryptocurrencies. To be able to short-sell, one needs to borrow a cryptocurrency from another investor to sell it immediately, and close the position in the future by buying and returning the cryptocurrency. Since the general cryptocurrency investor type is nonprofessional, lacks investment

1See Ammous(2018) 2On January 15, 2010, an individual on the Bitcointalk forum who called himself ”dwdollar”, proposed the idea of creating a real exchange where people could trade with each other. Shortly after, on March 17, 2010, the first public Bitcoin exchange Bitcoinmarket.com went live. The first trade was made for a price of $0.003. According to records, the first official fiat-Bitcoin trade was made by a Finnish computer devel- oper Martti Malmi, on October 12, 2009. He went by the name ”Sirius” and sold 5050BTC for $5.02 (or $0,00099 for 1BTC) to NewLibertyStandard. See the post by Martti Malmi himself for the first transaction: https://twitter.com/marttimalmi/status/423455561703624704. 3CoinMarketCap is website that, among other things, tracks the market capitalization, trading volume, number of coins supplied and outstanding for cryptocurrencies. 4https://cointelegraph.com/news/crypto-bubbles-why-traders-believe-altcoins-are-overpriced 5An ETF, or exchange traded fund, is an investment fund that is traded on a stock exchange, which is consists of many types assets like stocks, bonds, and commodities. 6See Shiller(1980) 7See Shleifer et al.(1990)

3 experience, and has, in general, a very optimistic view about cryptocurrencies reaching ”the moon” any time soon, the demand for short-sales is very small. This means that the lending market, therefore, is not mature enough. Also, for this to happen, exchanges need to make the market available for investors, in other words, they need to create a lending market if there is enough demand for shorting. At the moment, the demand for a lending market is very low, therefore the number of exchanges that offer margin trading is very limited. And those who do, offer a very limited amount of cryptocurrencies to short. Most of these cryptocurrencies are established, have a high market capitalization, and are known by the average investor. Besides the limited demand for shorting, the cryptocurrency market is very volatile. This means that taking short positions become very risky since you can lose your position very fast. Even though there exist margin calls, due to high volatility you may need to invest more money into your margin account, or your position will be closed. Managing margin calls in a highly volatile environment like cryptocurrency markets is very demanding and requires full attention, experience, and knowledge. The implications of overpricing may become severe for na¨ıve and new investors that start buying overpriced cryptocurrencies in the hope to resell them again at a higher price to new investors. Based on the extreme price increase in cryptocurrencies, showing no insight into fundamental values, cryptocurrency markets can be characterized as bubble markets. Since the majority of cryptocurrencies cannot be shorted, it may be the case that these limits in short-sales play an important role in the creation of bubbles. In a market where the media spreads news of extreme price surges in the cryptocurrency space and investors show signs of herd behavior, more investors will enter the market and push prices further up, enlarging the bubble. If some exogenous shock hits the cryptocurrency markets (like the opening of short-sales) or no new investors buying at a higher price, prices start to decline, to trigger a crash. These crashes can cause extreme negative returns for new investors and existing investors. The literature on bubbles, in joint with short-sales and heterogeneous beliefs regarding cryp- tocurrencies is very thin. Most papers analyze the behavior of returns in comparison to traditional asset classes, analyze the behavior of volatility using different estimation methods like the Au- toregressive Conditional Heteroscedasticity (GARCH), look into the detection of bubbles or try to come up with a fundamental value for cryptocurrencies.8 One of the first existing study related to bubbles and heterogeneous beliefs in cryptocurrencies comes from Wei(2018), where he tests the re-sale option theory of Scheinkman and Xiong(2003). To mitigate the issue of calculating the fundamental value of cryptocurrencies, he distinguishes between three types of cryptocurrencies, namely ”joke”, ”scam”, and ”mainstream”. He argues that joke and scam cryptocurrencies do not provide the holder with any claims of future cash flows nor can they be used as a medium of exchange. Using turnover as a measure of heterogeneous beliefs, he finds that joke and mainstream cryptocurrencies are being traded in the hope to re-sell them at a higher price, therefore confirm- ing overvaluation and the resale option theory. For scam cryptocurrencies he finds no significant results in relationship between turnover and mispricing as investors’ dispersion in beliefs is lower when they realize the project they invested in is a scam. This paper analyzes the effect of the limited short-sale possibilities on cryptocurrency returns using a unique data set that contains the dates of the opening of short-sale markets for cryptocur- rencies across various exchanges. Two methods will be used to assess the effect of the limited short-sales on returns. The first method is based on a structural break test where a single shift in returns tested and then a test for multiple breaks is conducted for further insights. The second method is based on a Pooled OLS and Fixed Effects regression analysis where short-sale possibil- ities are identified by a coefficient on a dummy variable that takes the value of one if shorting is possible, and zero otherwise. Also, this research extends by analyzing the feedback behavior of returns and turnover. Testing for a single break in returns using a Chow test, I find that the opening of short- sales significantly coincides with the structural break found in the return series. Average returns decrease significantly to a lower level compared to the level before shorting. Testing for multiple breaks signals a second shift in returns, but 14 days prior short-sales are opened, which suggest that investors anticipate the opening of short-sales and therefore increase prices (and returns) up to the point that short-sales are opened. Using regression analysis, where the focus lies on dummy coefficient, I find that after controlling for cryptocurrency and time fixed effects, short-sales tend to decrease by 0.6% per day, or 18.25% a month. Across the entire sample, no sign of feedback of

8See Cheah and Fry(2015); Katsiampa(2017b); Bianchi and Dickerson(2018); Liu and Tsyvinski(2018); Gerlach et al.(2019)

4 past returns on returns is found, but there is significant evidence for positive feedback in turnover. In other words, it seems that past returns are not an indication of positive future returns, while past returns appear to and indication for more trading activity The limited research on the behavior of cryptocurrencies, in joint with short-sale restrictions and heterogeneous beliefs, triggered me to analyze the impact of short-sale possibilities on returns. Also, the highly volatile environment cannot be attributed to news regarding fundamental changes in cryptocurrencies. Therefore, it also interests me to identify if the behavior of cryptocurrency markets matches the behavior of traditional financial markets. To my knowledge, this is the first paper that contributes to the literature regarding short-selling and cryptocurrencies by analyzing the impact of the limited short-sale possibilities using the unique announcement of short-selling dates, across different cryptocurrencies and exchanges. The remainder of this paper is organized as follows. Section 2 reviews literature on the relation between short-selling and returns, and bubbles in joint with short-sell restrictions and heteroge- neous beliefs. Section 3 investigates the bubble periods in the cryptocurrency markets, with the main focus on Bitcoin. Section 4 describes the data used. Section 5 analyzes the structural changes in returns. Section 6 describes the methodology. Section 7 discusses the results and findings of the Pooled and Fixed Effects regression models. Section 8 presents robustness tests of the estimated dummy coefficient. Section 9 analyzes the feedback loop behavior of returns and turnover. Section 10 concludes and discusses the research.

2 Literature Review

This paper examines two subjects that are different, but at the same time often related to each other. First, this research contributes to the limited existing literature on short-sales in cryptocur- rency markets. Second, this paper is related to the research on bubbles in joint with heterogeneous beliefs and short-sale restrictions. This chapter presents on one hand an overview of the literature on the relation between short-sales and returns, and on the other hand provides an overview on the relation between heterogeneous beliefs and short-sale constraints.

2.1 Short-sales and Returns There is extensive research related to the impact of short-sales on the level of returns in financial markets. One of the first insights on the relation between short-sales and equity returns comes from Seneca(1967), who finds that the level of short-interest is negatively related to future prices. In line with Seneca’s finding, Asquith et al.(2005) use institutional ownership as a measure of short-sale restrictions and find that the negative relation between short-interest and future returns is more pronounced when firms are likely to experience short-sale restrictions. Using data from institutional investors, Cohen et al.(2007) look at the supply and shorting demand and find that an increase in shorting demand hurts future returns. Other numerous papers on short-sales and returns are consistent with the negative relation between shorting and returns. Aitken et al. (1998) analyze the price reaction to short-sales using data on equities listed on the Austrian Stock Exchange. They find that abnormal returns immediately decrease after short-sale transactions have been publicized. Desai et al.(2002) analyses the level of short positions of equities listed on NASDAQ, and find that stocks with a high level of short-interest experience negative returns. Also Diether et al.(2005) study shorting data of NASDAQ listed companies. Their focus is on the relation between short positions and past returns, and if this relation predicts future negative abnormal returns. According to their findings, short-sellers are contrarian traders, they increase their shorting positions after positive abnormal returns, and reduce shorting positions after negative abnormal returns. In line with Diether et al., Blau et al.(2010) study the contrarian behavior of short-sellers during high market volatility. They study short-selling behavior of S&P500 equities during moments the index moves two standard deviations away from its mean. They confirm contrarian and unsophisticated trading behavior of short-sellers by finding extreme high levels of shorting during extreme down days, and extreme low levels of shorting during up days. This indicates that although short-sellers are contrarian, there are still some investors who trade in the direction the market moves in times of high volatility. Those who follow the market, are less able to predict negative returns. Overall, they argue the existence of a negative relationship between shorting activity and future returns on down days.

5 Besides the negative relation between shorting activity and returns, others argue that short- sellers are more informed than the average investor, and therefore are better able to predict which stocks will underperform and experience negative returns. According to Boehmer et al.(2008) examine NYSE short positions between 2000-2004 and find that short-sellers can predict which stocks will experience lower or negative returns over the next twenty days. Like Boehmer et al. Engelberg et al.(2012) relates news announcements and short positions, and find that the negative relation between future returns and short-sales is twice as more pronounced in the presence of news. Although various papers find evidence on the negative relation between short-sales and returns, to our knowledge, limited to no work has been presented on the relation between short-sales and cryptocurrency returns.

2.2 Heterogeneous Beliefs and Short-sale Constraints A fundamental question within the asset pricing theory regarding bubbles is whether short-selling has predictable changes in prices and returns. Aliber and Kindleberger(2017) elaborate on the arguments of many economists that bubbles are caused by the purchase of securities for re-sale, instead of buying them as investment income. Miller(1977) argues that if investors have hetero- geneous beliefs (differences in opinions) about an asset’s fundamental value, and short-sales are restricted, prices will reflect the opinion of the most optimistic investors. In other words, short-sale constraints do not allow pessimistic investors’ negative information influence price, allowing prices to be higher than they would be if short-sale was allowed. Harrison and Kreps(1978) study Miller’s static insight of heterogeneous beliefs in a dynamic setting. Assuming short-sales are not possible, they argue that since an investor knows there are other investors in the market who are willing to pay more for an asset than he does, he, therefore, is willing to purchase an asset at a higher price than the price he would pay if he is obliged to hold an asset forever. This speculative component of an investor is thus driven by the investor’s right to re-sell an asset in the future as opposed to the situation where the investor, after purchasing an asset, is forced to hold it forever. Harrison and Kreps do not explain what the source of investors’ heterogeneous beliefs is. Al- though there is a big literature explaining different ways of generating heterogeneous beliefs, the majority indicates that overconfidence is a common aspect of human behavior. Scheinkman and Xiong(2003) use overconfidence as the source of generating heterogeneous beliefs, and acts in joint with short-sale constraints as the main driver of bubbles. They find that when many investors exhibit heterogeneous beliefs caused by overconfidence, the volatility of differences in beliefs in- creases, resulting in more trading activity and therefore a higher trading volume and a bigger bubble. Scheinkman and Xiong name the value embedded in prices when shot-sales are restricted and investors have heterogeneous beliefs, the re-sale option. A numerous of other papers have expanded the work on bubbles in joint with heterogeneous beliefs and short-sale restrictions. Chen et al.(2002) use the breadth of ownership as a measure of heterogeneous beliefs and use this to find the effect of short-sales restrictions on returns. When the breadth is low, there are few investors with a long position and vice versa. They find that stocks whose breadth decrease, in turn, generate lower average returns, compared to stocks whose breadth increases. Diether et al.(2002) use financial analysts forecasts to measure differences in opinions among investors. They find that stocks who have higher dispersion in analysts forecast earn average lower returns. According to Hong and Stein(2003), due to limits in short-sales, pessimistic investors initially do not invest in the markets, therefore not revealing negative information in prices. However, if some previously optimistic investors leave the market early, pessimistic investors may become the marginal buyers, in turn prevailing the less optimistic prices, and therefore starting a decrease in prices and the start of a crash. Hong et al.(2006) show the importance of asset float dynamics concerning bubbles. They argue that the size of a bubble is negatively related to asset float and the time left to trade. Looking at the Internet bubble, Hong and Stein(2007) provide insight into the increasing turnover ratio of 1000 glamour stocks in the period 1986-2005. Also, they showed that Internet stocks were correlated with their cumulative returns. In the Chinese market, Xiong and Yu(2011) found that warrants were trading above fundamental values, and showed that the market was driven by frenzied speculation. They use turnover as a measure of heterogeneous beliefs and find a positive relationship between past returns, turnover and warrants returns, in line with the Feedback theory of Shiller(2000). Cryptocurrency markets show enough characteristics in line with the theory on bubbles in joint

6 with heterogeneous beliefs and limits in short-sales. Therefore I base the analysis on the impact of short-sales on returns, on the same theoretical thoughts.

3 Cryptocurrency Bubbles

“I think of Bitcoin as a remarkable social phenomenon. It’s an epidemic of enthusiasm [. . . ] it is a speculative bubble. That doesn’t mean that it will go to zero. Speculative bubbles recur. We had a bubble in Bitcoin in 2013, and it looked like it was done —it fell from $1,000 to $200— but now look, it comes back.” – Robert Shiller, June 26, 2018.

Before I go into the analysis of the effect of limits in short-sales on the returns of crypto assets during bubbles and crashes, it is useful to put into context what events may drive the behavior of the three main bubbles of Bitcoin. Previous research on bubbles by Aliber and Kindleberger (2017); Sornette(2017); Brunnermeier and Oehmke(2013), has shown that bubbles normally occur as a rational response to new developments or technological innovations. Building on the same view, and following the identified bubble periods of Bitcoin by Gerlach et al.(2019), the main idea of this chapter is to focus on the events or information that may have triggered the bubbles and the use of Bitcoin. It is known that correlation is not causation, therefore the discussion of the three bubble periods is more qualitative, having as goal giving context of the socio-economic behavior in which the bubbles were identified.

3.1 The First Run: May 2012 - April 2013 The financial crisis of 2007-2008, revealed how vulnerable and fragile the financial state of some small- and medium-sized economies are. It revealed how bad the debt levels were for some countries within the European Union (EU), like Greece, Ireland, Portugal, Spain, and Cyprus. From the above-mentioned countries, Cyprus and Greece stood out by their unsustainable debt levels. As shown in Figure1 (b), both countries reached their lowest economic state in 2012. During that time, Greece and Cyprus requested a bail out program, where they asked for capital injection to bailout their economies and financial sector9. The behavior of governments and financial institutions across economies lead to the increase of distrust in the financial system, which triggered a wave of bank runs and search for safe havens. Bitcoin, created by Nakamoto(2008), came into play as an alternative store of value. It was primarily designed to be uncontrollable by governments and central banks, and being independent of monetary policies. Therefore, it is interesting to observe that the lift-off of the first Bitcoin bubble occurred during the same period that Greece and Cyprus reached a local minimum. Another factor possibly having an additional impact on the rise of Bitcoin, could be investments made by Silicon- Valley entrepreneurs10. Research by Gandal et al.(2018); Griffin and Shams(2018), revealed market manipulations in this bubble period, as well as in future bubble periods. Fast forward to 2013, ”The Year of Bitcoin”. During this year, where Bitcoin exchange Mt. Gox was dominating, several events occured that boosted Bitcoin’s price. To start with, on March 16, 2013, the Cypriot government announced that they would accept a AC10 billion bailout after reaching an agreement with the Eurogroup, European Commission, European Central Bank (ECB), and International Monetary Fund (IMF). The Cyprus economy reached a critical level, which lead banks to declare a bail-in tax, which affected the deposits of account holders. In turn, the announcement lead to massive bank runs, as people wanted to protect their personal savings11. The Cypriot bank run occurred at the same time Bitcoin’s explosive growth started 201312. According to Luther(2015), after the announcement, the use in Bitcoin-related apps in the Eurozone rose significantly by 10%, which may be an additional factor that influenced Bitcoin’s run. The situation in Cyprus was also observed across other European countries like Spain. People feared for their savings, in response to a similar intervention as taken by the Cypriot government13. Since people were seeking an alternative investment that could not be touched by any government

9https://www.ft.com/content/80320e0e-bed0-11e1-b24b-00144feabdc0 10https://www.cnbc.com/id/100635418 11https://money.cnn.com/2013/03/16/news/economy/europe-cyprus-bailout/index.html 12https://money.cnn.com/2013/03/28/investing/bitcoin-cyprus/index.html 13https://www.bloomberg.com/news/articles/2013-03-20/jittery-spaniards-seek-safety-in-bitcoins

7 or financial institution, Bitcoin was the new kid on the block, serving as a new alternative hedge. According to Gerlach et al.(2019), the number of transactions increased during the first bubble, indicating a move a potential move towards Bitcoin. At the time Cyprus announced the bailout operation, Bitcoin was trading approximately $46, and peaked to a value of approximately $266 on Mt. Gox, on April 1014. In two days, Bitcoin’s value dropped more than 70%, reaching a value of $50, in a few days 15. In other words, the price dropped by 4,32 times from its all-time-high (ATH). The crash was a response to the instability announcement of Mt. Gox, which suffered from high trading volume, and later had to deal with DoS attacks16. What caused Bitcoin price’s surge during the period between May 2012 and April 2013, is not the same as the reason what made it crash. According to Sornette and Cauwels(2014), the origin of the source that causes a bubble to crash is in general unrelated to the source of a bubble. That this case of Bitcoin’s crash, where price reached very high levels in a short period, associated with sensitivity to biased or unfavorable news reports.

Evolution Bitcoin price, Greece and Cyprus Indices (a) 1000

100

10

2012−01 2012−07 2013−01 2013−07 2014−01 Date (b) 3000

1000

300

100 2012−01 2012−07 2013−01 2013−07 2014−01 Date

Figure 1: The comparison between Bitcoin, Greece, and the Cypriot financial market index, in the period 2012-2014. Graph (a), shows the logarithmic price of Bitcoin, including the start and end dates of the bubbles. The start dates are represented by the green vertical lines, with dates 2012-05-12 and 2013-07-03, respectively. The ending dates are represented by the red vertical lines, with dates 2013-04-9 and 2013-12-04, respectively. Graph (b), shows the price of both the Greek FTSE ATHEX 20 Index (green line) and Cypriot General Price Index (red line), for the same period as Bitcoin. The time series data are gathered from Thomson Reuters Datastream.

3.2 The Second Run: July 2013 - December 2013 Shortly after the crash in April, the second bubble of Bitcoin started to get traction, reaching a new ATH at the end of 2013. Although the EU crisis was still underway, Bitcoin’s hype contributed to its rice. The use of the economic instability as the main reason for Bitcoin’s rise is not enough. There were several other factors that gave Bitcoin new boosts towards a bubble: the birth of new cryptocurrency exchanges in Asia, the shutdown of dark web marketplace Silk Road by the FBI, the increasing demand for mining, and the first ICO took place. Famous Asian cryptocurrency exchanges like Bithumb, HitBTC, , Okcoin were founded during the second phase of the bubble in 2013. By founding these exchanges, investors were able to enter the Bitcoin market. In addition, these exchanges they showed the public that they were willing to invest in the future of cryptocurrencies, promoting adoption to make cryptocurrency investment and usage mainstream. The new investments drove the price Bitcoin to new highs, reaching a new ATH of $1.242, on November 29.

14https://money.cnn.com/2013/04/12/investing/bitcoin-bubble/index.html 15https://www.theguardian.com/technology/2013/oct/21/bitcoin-price-surges-to-post-crash-high 16https://bitcoinmagazine.com/articles/the-bitcoin-crash-an-examination-1365911041

8 Evolution number of transactions on (a) 10000

1000

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2012 2014 2016 2018 Date (b) 3e+05

1e+05

3e+04

1e+04

2012 2014 2016 2018 Date

Figure 2: The Bitcoin price in comparison to the number of transactions made on the blockchain network. It can be seen that in 2012, the number of transactions jumped to a higher level, in response to a higher demand for Bitcoin as a safe haven. 17

Since the beginning of July, the bubble started to develop at a fast pace, and on October 2, 2013, the dark web marketplace Silk Road was shut down by the FBI18. The takedown signalled the cryptocurrency community that the authorities had their eyes on the developments taking place in the cryptocurrency space, and were willing to prevent any illegal activities related to drugs and Bitcoin. Silk Road was by far the biggest dark web market place: according to a survey from the site self suggested that 70% of all listed products were related to drugs related 19. Therefore, the shutdown of the platform received attention from all over the world, hitting news headlines on almost every major news outlet. The seizure helped ”clean” Bitcoin’s image in the eye of the public, which in turn let the US senate announced a statement regarding the future prospects of Bitcoin20. The shutdown of Silk Road and announcement made by the US senate are seen as important factors contributing to extreme price surge during the second Bubble21. Other factors potentially contributing to the second bubble is the increase in mining hash rate (computing power), and the number of registered wallets. During the second phase of 2013, the hash rate grew at a faster pace than the number of new created wallets. The hash rate is a measure of processing power of the .22 The number of new created Bitcoin wallets is a sign of the increasing size of network of the Bitcoin blockchain. Therefore, a faster growth rate of hashing power compared to the growth rate in new created wallets, shows that miners, on average, increased their individual computing power to validate transactions on the blockchain. The most likely reason for the increase in hashing power is the rapid increase in innovation, and therefore the creation of more efficient hardware. The crash of December 2013, and the long-lasting effect thereafter, was triggered by two major events. The first one is the Bitcoin-ban induced by the Chinese government, that stated that financial institutions were not allowed to use Bitcoin as a and provide related services to their customer base23. This caused the first significant drop in price, destabilizing the . Second, in February 2014, a further decline in price was triggered by DoS attacks on two major Bitcoin exchanges and BTC-e, and the exit-scam by Mt.Gox. Mt. Gox was at the time the biggest Bitcoin exchange, handling approximately 70% of the world wide Bitcoin volume. Mt. Gox started in February suspending Bitcoin trading and halting deposits and withdrawals, as they were having system issues after an allegedly DoS attack. On February 24, 2014, the exchange

18https://money.cnn.com/2013/10/02/technology/silk-road-shut-down/index.html 19https://blockonomi.com/history-of-silk-road/ 20https://www.govinfo.gov/content/pkg/CHRG-113shrg86636/pdf/CHRG-113shrg86636.pdf 21https://www.businessinsider.com/senate-bitcoin-hearing-2013-11?international=truer=USIR=T 22https://thenextweb.com/hardfork/2019/08/05/ugh-this-is-what-bitcoins-hash-rate-means-and-why-it-matters/ 23https://www.nytimes.com/2013/12/06/business/international/china-bars-banks-from-using-bitcoin.html

9 declared itself bankrupt after a major robbery worth more than $400 million USD24. According to a leaked document ”Crisis Strategy Draft”, Mt. Gox had just 2000BTC on its account, while customer deposits were estimated to be 600.000BTC. In two years time, the exchange was robbed from 744.000BTC25. The collapse of the biggest exchange, Mt.Gox, was seen as major setback for the cryptocurrency world. A lot of customers lost their money, and could not recover it because the transactions were difficult to trace. Bitcoin’s status suffered again and the cryptocurrency went into a long-term decline, starting to recover at the beginning of 2016. According to Fry and Cheah(2016), from 2014 on, they find evidence for a negative bubble effect in the cryptocurrency space, which indicates that long ”recession” was an overestimated reaction of the public in response to the news events occurring before the crash.

3.3 The Third Run: January 2016 - December 2017 During the long crash between 2014 and 2016, many Asian cryptocurrency exchanges were lead- ing the cryptocurrency hype and adoption, with 50% to 90% of Bitcoin trading volume.26 The increase in the number of exchanges originating from Asia, in combination with the increase in trading volume, can be seen as a major factor triggering the third bull run of Bitcoin and other major cryptocurrencies, identified at the beginning of 2016. The reason behind the increased de- mand during that period is difficult to pinpoint, but Gerlach et al.(2019) start by identifying the devaluation of the Chinese Yuan. On August 11, 2015, the People’s Bank of China (PBoC) devalued the Yuan against the USD by 2%, with the goal to prevent further drops in export and give the economy a boost27. The Yuan kept losing its value until January 2017. In response to this event, Chinese investors began to move their capital into safer options, causing a leakage in China’s capital28. In response to this, the Chinese government began to stem the outflow of capital. The strict capital control made it for the average Chinese investor very difficult to move their assets outside the country and invest in other markets, therefore, Bitcoin was (again) a very good exit option29. At the beginning of 2017, the PBoC demanded exchanges to comply with financial regulations (before that there were no explicit laws regarding cryptocurrencies), because they suspected ex- changes of illegal activities, such as money laundering, and faking exchange volume . This was made possible after the Chinese authorities demanded exchanges maintain a zero-fee structure 30. The largest exchanges in China, BTCC, Huobi and OKCoin, were now obliged to maintain a non-zero fee structure and stop margin trading31. This measure led to heavy decline in trading volume across major exchanges like Bitfinex, BTCC, Huobi, OKCoin, and Okcoin. Cryptocurrency exchanges started with the zero-fee structure at the end of 2013, as demanded by policy. According to research by Willy Woo, exchanges were reporting fake volumes, in excess of 40 times their normal volume 32. Therefore, the huge drop observed in January 2017, as observed in Figure3, partly represents the normal exchange volume after the drop, as result of the change in fee structure, from zero tot non-zero 33. Later in February, 2017, the PBoC demanded exchanges to halt withdrawals, while at the same time allowing withdrawals denominated in Yuan34. The reason for this was to decrease the capital outflow from China. In June 2017, the PBoC lifted the withdrawal restriction, after exchanges began to adapt to the regulatory framework 35. The lift on withdrawals had a positive impact on the price of Bitcoin,

24https://www.wired.com/2014/03/bitcoin-exchange/ 25https://stanfordreview.org/what-happened-at-mtgox-the-collapse-of-the-worlds-largest-bitcoin-exchange/ 26 https://www.jbs.cam.ac.uk/fileadmin/userupload/research/centres/alternative − finance/downloads/2017 − global − cryptocurrency − benchmarking − study.pdf 27https://www.theguardian.com/business/2015/aug/11/china-devalues-yuan-by-2-to-boost-flagging-economy 28https://www.wsj.com/articles/china-capital-outflows-bubble-below-the-surface-1474357050 29https://cointelegraph.com/news/chinas-bitcoin-capital-flight-hits-mainstream-as-analysts-fear-crackdown 30https://bravenewcoin.com/insights/bitcoin-price-analysis-pboc-crushes-volume 31https://www.coindesk.com/chinas-central-bank-continue-bitcoin-exchange-inspections; https://www.coindesk.com/volume-traders-china-bitcoin-exchanges 32https://www.coindesk.com/estimating-data-china-real-bitcoin-trading-volumes 33https://qz.com/915316/chinas-bitcoin-traders-are-finding-new-ways-to-trade-after-an-official-clampdown/ 34https://www.bloomberg.com/news/articles/2017-06-01/china-s-largest-bitcoin-exchanges-to-again-allow- withdrawals 35https://www.bloomberg.com/news/articles/2017-06-01/china-s-largest-bitcoin-exchanges-to-again-allow- withdrawals

10 Bitcoin price, Chinese Bitcoin Trading Volume, and CNY/USD (a) 1000

100

10 2012−01 2012−07 2013−01 2013−07 2014−01 Date

(b) 1e+07 1e+06 1e+05

Ex. Volume 1e+04 2012 2014 2016 2018 Date () 7.00 6.75 6.50 6.25 CNY/USD 6.00 2012 2014 2016 2018 Date

Figure 3: The Bitcoin price in comparison to the Chinese Yuan, and trading volume of Bitcoin on Asian exchanges. This graph shows a simultaneous change in regime of the Chinese currency and Bitcoin trading volume across Asian exchanges, at the start of 2017. but then, on September 4, 2017, the PBoC declared Initial Public Offerings illegal. In addition, in the same month, the Chinese Cenral Bank ordered exchanges to stop all trading activities in the Chinese market36. Again, the amount of negative news regarding cryptocurrency regulations resulted in a drop in trading volume across 2017. The measures undertaken by the Chinese authorities had some consequences. One of them was that people began to trade over the counter (OTC) through peer-to-peer (P2P) exchanges, and one of the exchanges that made personal trade possible was LocalBitcoins37. LocalBitcoins showed an increase of more than 250% in trading volume in the month September as effect of the measures undertaken by the Chinese Central Bank38. In addition, as a response to these measures, exchanges like Huobi and OkCoin started to expand operations outside of the country to compete in other markets and not lose trading volume. Therefore they started to introduce P2P trading, supporting different fiat 39. What can be concluded from this is that the restrictions put implace by the PBoC did not have permanent effect on cryptocurrency exchanges and Bitcoin, because trading activity started to move from one country to another, recovering from regulatory constraints. Although the Chinese market had a big influence on the start of the third bubble, other factors had an impact on the price formation as well. These factors were the new mining pools that became active, like viabtc and btc.com. The creation of these mining pools made sure that Bitcoin and other cryptocurrencies got more world wide attention, at the same time pushing for better mining efficiency. For the smaller miners (like people at home using less powerful mining hardware), these pool were also a perfect way to increase their profits, as this was more profitable than competing against other big miners with more computing power. Another factor of influence was the growth of the cryptocurrency market as whole. According to Gerlach et al.(2019), the search queries Bitcoin and Blockchain on Google were increasing since the end of 2015, increasing from 2016 on wards. From January 2017, the increasing interest in alternative investment in cryptocurrencies began to grow, this could be noticed from increased search of the word Cryptocurrency. During the course of 2017, Bitcoin began to lose its dominance as other cryptocurrencies began to attract more attention from investors and speculators. Specially the increase in initial coin offerings attracted many more people to invest for a quick profit. Then, since November 2017, the

36https://qz.com/1079908/huobi-and-okcoin-chinas-two-biggest-bitcoin-exchanges-will-halt-all-trading-services- for-local-customers/ 37https://www.scmp.com/tech/innovation/article/2125282/chinas-bitcoin-crackdown-raises-cryptocurrency- demand-they-move-peer 38https://medium.com/@mattahlborg/nuanced-analysis-of-localbitcoins-data-suggests-bitcoin-is-working-as- satoshi-intended-d8b04d3ac7b2 39https://www.coindesk.com/chinas-bitcoin-exchanges-shift-p2p-model-domestic-crackdown

11 total market capitalization increased by 400%, dropping on November 18, which eventually started to crash in January 2018. Bitcoin’s market capitalization dropped to an all time low by February 2018, after having lost 60% of its value. The year 2017 is being recognized as the year that Bitcoin went mainstream, other cryptocur- rencies were developed, and initial coin offerings became the normal way for blockchain startups to fund their projects. Many of these blockchain startups began to promote their ideas through so-called ”whitepapers”, where they explained the utility of the tokens within their network. Af- ter investors put money in these ideas, in return they were given ”tokens” that could be traded on exchanges, with the idea of making a quick buck, or tons of money. Since the hype of cryp- tocurrencies in 2017, many potential replacements for Bitcoin are being developed, increasing the competition and maybe the existence of Bitcoin. Those who belive in the potential of Bitcoin argue that Bitcoin will become the next store of value asset, to become the ”social” norm for Gold, and help portfolio managers to diversify their investments. Time will tell.

3.4 A Short History of ICOs Bitcoin is the first of many cryptoassets developed since its inception. In the cryptocurrency space, we can broadly distinguish between two different types of assets, namely ”coins” and ”tokens”. Coins (like Bitcoin) are a form of digital money that stores value through an encryption process40. Within the ”coin terminology” there are also ”altcoins”, or alternative coins, like Litecoin, and have the the same properties and use case as Bitcoin. The other type of cryptocurrencies are tokens. Tokens are digital assets that are issued by blockchain company or project. These tokens can be used within the ecosystem to pay, or represent a utility in the network. The big difference between coins and tokens is that a token gives you the right to participate in the network itself, while coins are only used as medium of exchange between two parties. The idea of a ”token” comes from J.R. Willet, a computer scientist, that came up with a new project proposal called Mastercoin, that works on top of the Bitcoin Protocol41. His idea was to use the base protocol layer of Bitcoin to build other applications on top of it, since building and testing an own blockchain is costly and time consuming. Coinciding with his new project, he introduced a new way of funding projects in the crypto community, that would be used to fund his own project. After launching the project on July 31, 2013, people were able to invest in Mastercoin using Bitcoin42. In return investors received tokens that represented a piece of the protocol. Willet mentioned that by building new applications on top of the Bitcoin Protocol would increase utility, in turn increasing the projects value in Bitcoin. In total, the project raised $600.000 USD. The new way of funding projects received the name Initial Coin Offering (ICO), because it was similar to an Initial Public Offering (IPO). In comparison to IPOs, ICOs had the advantage of lower transaction costs, and the power of attracting every investor in the world. Since the success of Mastercoin, ICOs received attention from other blokchain projects. On September 28, 2013, the next ICO named NextCoin was made public, raising $17.000 USD after the investment period ended. Other successful projects who conducted an ICO between 2013 and 2014 are Token, Maidsafecoin, Swarm, Ethereum, where the project of Ethereum became one of the leading protocols used to build applications and start an ICO. The year 2017 became the year of ICOs. Through the new way of funding projects, startups in the blockchain space began to grow at a fast pace. According to a study by Satis Group Crypto Research, an amount $11,9 billion USD has been used to fund over 900 projects 43. Although it is a very attractive way of investing and generating future returns, about $1,4 billion USD (11%) went to scam projects. According Chod and Lyandres(2018), ICO projects in the pre-R&D phase show significant amount of information asymmetry. Since the first ICO back in 2013, in 2017 there were still no regulations, therefore they point out that in the absence of a regulatory framework, the ICO market could operate as a market for lemons. Catalini and Gans(2018) argue that for many tokens, the intrinsic value cannot be determined since the tokens that are given to investors do not have a claim on the company’s equity.

40https://medium.com/coinbundle/whats-the-difference-between-tokens-coins-9942fe42f275 41https://medium.com/hackernoon/ico-101-history-of-initial-coin-offerings-icos-part-1-from-mastercoin-to- ethereum-4689b7c2326b 42https://bitcoinmagazine.com/articles/mastercoin-a-second-generation-protocol-on-the-bitcoin-blockchain- 1383603310 43https://cointelegraph.com/news/new-study-says-80-percent-of-icos-conducted-in-2017-were-scams

12 4 Data 4.1 CoinMarketCap The main data gathered for this study are daily (closing) prices, market capitalization, and ex- change volume. The data ranges from 28/04/2013 till 08/08/2019, and is sourced from CoinMar- ketCap 44. The prices on CoinmarketCap are calculated using the volume weighted averages of all cryptocurrency pairs listed on multiple exchanges, converted to USD. Since shorting cryptocurrencies is very limited, it is difficult to find a big sample of exchanges that offer margin trading for multiple cryptocurrencies. Therefore, our research is limited to cryptocurrencies on five famous exchanges that offer margin trading: Bitfinex, Binance, , Poloniex, and Huobi.45 In total I found 40 unique cryptocurrencies that can shorted across these five exchanges. Across these five exchanges, there are a total of 75 cryptocurrencies that can be shorted. Each of the above mentioned exchange offers margin trading, respectively on 21, 13, 8, 16 17, cryptocurrencies. Table2 shows the list of cryptocurrencies to be analyzed, including their full name, symbol (ticker), begin and end date of the sample, the shorting date (event date), and the exchange that corresponds to the shorting announcement date. Table3 presents the summary statistics of each cryptocurrency seperately, Table4 presents the descriptive statistics of the entire sample to be analyzed.

4.2 Shorting Dates To be able to analyze the effect of short-sales on returns, the announcement post of exchanges are needed, where they state that short-sales have been opened for a specific cryptocurrency. We use the date of this announcement as the event date or shorting date, and can be found on the exchanges’ blog, news repository, or twitter feed. Table1 lists all links to the announcements, and are presented as a confirmation of the used shorting dates in this analysis. Note that each cryptocurrency listed on one of the five exchanges has its own shorting date. In other words, the shorting date of Ethereum, for example listed on Bitfinex, is different from Ethereum listed on Binance, since these exchanges do not open margin trading at the same time. The event dates that will be used per cryptocurrency are exchange dependent. For example, Bitfinex is one of the oldest exchange of the five, offering margin trading since 2012. The majority of the cryptocurrencies that are available for margin trading on the other four exchanges can be margin traded on Bitfinex. Therefore I decided to use the shorting dates of Bitfinex for 19 (out of the 40) cryptocurrencies that are being traded on the other four exchanges. Then, for the other shorting dates, I do not have to choose between exchanges because they all list different cryptocurrencies for shorting, which means that if a cryptocurrency is only listed on Binance, I chose Binance’s shorting date announcement for that specific cryptocurrency. Note that for Bitcoin the Chicago Mercantile Exchange (CME) futures date is used.46

5 Structural Changes in Returns

Before analyzing the impact of limits in short-sales on cryptocurrency returns, I start by carrying out a structural break test in returns and defining the dates using the methodology of Bai and Perron(1998, 2003). A structural break occurs when there occurs a sudden change in a time series. Testing for these sudden changes can give more insight in the problem trying to be studied. The insight in this case would be if the introduction of short-sales as indicated by the exchanges themselves, indeed coincides with structural break found in the series. In other words, the goal of this analysis is to investigate whether change in returns, based on the market event date (shorting

44https://coinmarketcap.com 45There are other exchanges that offer margin trading, but they offer a limited amount of cryptocurrencies that can be shorted, are not different from the others. 46The Chicago Board Options Exchange (CBOE) launched Bitcoin futures on 10/12/2017. A week later, com- petitor CME launched futures on Bitcoin as well, enjoying trading volume of approximately six times larger CBOE. A possible reason for the big difference is that CME made its product available to all traders by listing the futures on their exchange, while CBOE listed their futures on CBOE Futures Exchange (CFE), where people mostly trade CBOE Volatility Index (VIX) futures. See: https://www.cryptoglobe.com/latest/2019/03/latest-research-shows- why-cboe-had-to-concede-the-bitcoin-futures-game-to-cme/

13 date), is in line with the estimated break date found in the time series. Therefore, using a structural break test it will become clear if there is a significant decline in average returns after short-sales have been introduced.

5.1 Model and Methods There is an extensive literature focused on testing against changes in coefficients in linear regression models. Classical tests such as the Chow test assume that there is just a single break under the alternative or that the moment when the change happens is known upfront. As time have passed, there has been development regarding methods that try to extract the date of a shift if one has occurred or methods that try to find multiple shifts at once, like [cite]. This section summarizes the model used to find structural changes, the test statistics, and the idea behind the tests.

5.1.1 The Model To start with, consider a standard linear model that will be fitted to the data and is defined as follows:

0 yi = xiβi + ui (i, ..., n) (1)

where yi is the observation of the dependent variable at time i, xi is a k × 1 vector of control variables, with the first component usually equal to unity, and βi is the k × 1 vector of regression coefficients, which may vary over time. In this case the dependent variable used is returns and the control variables used are turnover, volatility, exchange volume, and illiquidity. For more information on the control variables, see section 6.3. This analysis is focused on testing the hypothesis of regression coefficients remaining stable over time, therefore the hypothesis to test is defined as:

H0 : βi = β0 (i, ..., n) (2) against the alternative hypothesis that the coefficients do not vary over time. In a wide range of applications it is reasonable that there are multiple m break points in a time series, where the coefficient changes from one stable regression relationship to a different one. These differences are called segments. In other words, there are m + 1 segments, where the regression coefficients are stable or roughly constant. Then, the model can be written in the following way:

0 yi = xtβi + uj (i = ij−1 + 1, ..., ij)(j = 1, ..., m + 1) (3)

where j is the segment index. Im,n = {i1, ..., im}, presents the set of breakpoints and is also called the m-partition, and by convention i0 = 0 and im+1 = n.

5.1.2 The Tests There are two important classes of structural break tests, namely the generalized fluctuation tests47, and F statistics (or Chow statistic) test48. Generalized Fluctuation Tests, test against multiple shifts in the time series, whereas F statistics tests against a single-shift known alternative in the time series. In our case, the alternative is the event date at t = 0. Tests based on F statistics are designed to have good power of estimation. F statistics test against a single-break alternative that is known upfront. The idea in this analysis is to compute an F statistic (or Chow statistic) for the moment that exchanges announced short-sales, i.e., at time t = 0. The alternative can be defined as:  βA (1 ≤ i ≤ i0) βi = (4) βB (i0 < i ≤ in)

47Kuan and Hornik(1995) 48Hansen(1992); Andrews(1993); Andrews and Ploberger(1994)

14 where i0 is a certain changepoint in the interval (k, n − k). Chow(1960) was the first one who proposed such a test for a structural change at time i0, known upfront. Chow proposed to fit two separate regression models, for the two sub samples defined by i0 and reject whenever: uˆ0uˆ − eˆ0eˆ F = (5) i0 eˆ0e/nˆ − 2k wheree ˆ are the residuals from the full model, where the coefficients in the sub samples are separately estimated, andu ˆ are the residuals from the restricted model, where the parameters are just fitted once for all observations. Then the null hypothesis of structural stability is rejected, if the above mentioned statistic exceeds a certain critical value. Generalized fluctuation tests try to analyze the stability in the time series by analysing fluctua- tions in cumulative or moving sums (CUSUMs or MOSUMs) of residuals, or parameter estimates. The idea is that under the null hypothesis of parameter stability, the fluctuations in the parameters move within a limited range, governed by central limit theorem, whereas under the the alternative hypothesis fluctuations generally increase, and cross a certain limit. Thus, in other words, there is evidence for structural break if a certain fluctuation process, or time series, crosses a certain limit with probability α. In this analysis the OLS-based CUSUM process is used, and this process is a 0 ˆ scaled cumulative sum process of the OLS residualsε ˆ = yi − xiβ, defined as:

[ns] 1 X efp(s) = εˆ (0 ≤ s ≤ 1) (6) √ˆ i σ n i=1

The mathematics idea and derivations behind the theory and formulas are not the focus of this research, therefore a more concise explanation can be found in Kleiber and Zeileis(2008).

5.2 The Event Date As each cryptocurrency has its own event date, it is complicated to analyze the aggregate impact of short-sales on returns at one point in time. Therefore, returns are aggregated across cryptocur- rencies to get one time series of average of returns at each point in time. The data is analyzed within an event window of 101 days, in other words, 50 days before and after the event date at t = 0. Note that you could use any event window, it depends on how far your event is being priced in the series.

5.3 Structural Break Test Results In this subsection, the structural break analysis conducted on the returns time series, is discussed. A structural break is when a time series abruptly changes its trend at a certain point in time, and follows a new trend after. The purpose of this analysis is to investigate whether a change in returns at the time shorting is introduced, is in line with the estimated break date found in the time series. Based on this, it is possible to assess whether the average change in returns after shorting is significantly lower.

5.3.1 Single Break in Returns In the first analysis, a test for one significant shift in returns is conducted. To have a less noisy time series of returns, the simple moving average of returns with three-period time lag is calculated. Therefore, the window analyzed has changes from 101 observations, to 99 observations, ranging from t = −49 to t = 49. Remember, exchanges announced the introduction of short-selling at t = 0, so it is expected that shortly after, a significant negative change in average returns occurs. Testing for one break point using a Chow test, a significant structural change corresponding to break date t = 0 is found. Figure4 shows the time series of F statistics across the sample. As can be observed, the F statistics peaks t = 0 and has value of F = 7.9, and corresponds to a p − value = 0.0000034. The red line corresponds to the boundaries of asup F test at the 5% level. From this structural analysis it can be concluded that the estimated break in average returns is linked to the short-sale announcements made by cryptocurrency exchanges. In line with the stated hypothesis, average returns seem to be significantly lower than before, as the estimated model (blue) drops below the expected average line (green).

15 F statistics − Returns 40 30 20 F statistics 10 0

−30 −20 −10 0 10 20 30

Time

Figure 4: F Statistics for the Average Returns

Figure5 shows the graphical analysis of a single-shift in returns. This graph shows returns (in grey), two fitted models, and the break point (dashed vertical line) at t = 1. To visualize the findings two models are fitted. One of them, that is called m = 0, is a linear model with constant parameters. It is a model under the null-hypothesis of ”no structural” breaks. As can be observed, the fit is quite poor as the data is mostly under the fitted mean, up to t = −20, from where the data starts to move above the fitted mean model to the point t = 0. The second model is a two-segment model (m = 1) that captures one break point, and therefore changes its level after a change in trend. As mentioned earlier, a simple explanation for this change is the introduction of short-selling at t = 0.

Fitted Models for Returns (BP = 1)

0.04 m = 0 m = 1 0.02 0.00 Average Returns Average −0.02

−40 −20 0 20 40

Time

Figure 5: Fitted Models for Average Returns with One Break Point

To reinforce these findings and give a more specific explanation of the effect op the opening of short-sales on returns, I make use of causal inference analysis by Brodersen et al.(2015). This analysis is different from the one before in the sense that it makes use of a state-space model. Since this is not the focus here, the goal is of this part is only show what a different model would have estimated and confirm that prior results are robust. The causal impact is measured as the difference between the observed response value (in this case returns) and the (unobserved) predicted value that would have been obtained under the alternative treatment, i.e. the situation when no short-sales would have taken place. The same regression model is used as before. See Figure6 for the plots of the analysis. During the post-intervention period, the response variable had an average value of approx. - 0.0034. By contrast, in the absence of an intervention, we would have expected an average response of 0.012. The 95% interval of this counterfactual prediction is [0.0036, 0.020]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had

16 on the response variable. This effect is -0.015 with a 95% interval of [-0.023, -0.0070]. Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of -0.17. By contrast, had the intervention not taken place, we would have expected a sum of 0.58. The 95% interval of this prediction is [0.18, 0.98]. The above results are given in terms of absolute numbers. In relative terms, the response variable showed a decrease of -129%. The 95% interval of this percentage is [-198%, -60%]. This means that the negative effect observed during the intervention period is statistically significant.

0.10 original 0.05 0.00 −0.05

0.05 pointwise 0.00 −0.05 −0.10

0.0 cumulative −0.3 −0.6 −0.9 −1.2 0 25 50 75 100

Figure 6: Plot of the estimated model and the fitted model, based on counterfactual predictions. The black line presents the estimated model and the blue line presents the predicted model.

5.3.2 Multiple Breaks in Returns Now that a structural break is found in average returns at the time of the introduction of short- selling, the next points of interest are the multiple break points (m+1) that could give more insight in the behavior of the average returns. Testing for multiple breaks is conducted by studying the cumulative sums (CUSUMs) the recursive residuals, and reject the null hypothesis of parameter stability if their fluctuations are extreme. Figure7 plots the CUSUM process along with 5% boundaries corresponding to the t-statistic. It can be seen that CUSUM process exceeds slightly its upper boundary; hence indicating a structural break. Analyzing for multiple breakpoints (m + 1), can be done using the Bayesian Information Criteria (BIC) and Residual Sum of Squares (RSS). The BIC and RSS are model selection criteria, and are used to choose the optimal m breakpoints. According to Bai and Perron (2003), the Akaike Information Criteria (AIC) usually overestimates the number of break points, but the BIC performs better as a selection model. As shown in Figure8, the BIC criteria selects a model with two break points (m = 2) as the optimal model, i.e. a 3-segment partition model. These to break points correspond to break date t = −14 and aganin t = 1, with a confidence interval of 5%. Figure9 shows the graphical analysis of a multiple-shift model in returns. The graph shows average the returns, two fitted models, and the break point (dashed vertical line) at t = 1. Unlike a model with one break point as found by the Chow test (or F test), it becomes clear that a model with two breaks suits the series better. As can be seen, the average returns show as significant positive change 14 days before shorting is introduced. This behavior may indicate the anticipation of the opening of a short market. As consequence, investors (or traders) drive up prices to generate returns before the exchanges officially announce short-sales. After short-sales has been opened, investors dump their positions, as they know that other participants will go short. These results are in line with the insights of Aitken et al.(1998); Desai et al.(2002), who argue that abnormal returns immediately decrease after short-sales have been opened and the transactions have been publicised. This analysis seems to be in line with the re-sale option theory by Harrison and Kreps(1978); Scheinkman and Xiong(2003), which argues that investors who exhibit heterogeneous beliefs, and are restricted from short-selling, will increase prices of assets in hope to re-sell them to someone else at a higher price, increasing their returns. Since no one is able to short, the opposite position

17 OLS−based CUSUM test − Returns 1.0 0.0 −1.0 Empirical fluctuation process

−40 −20 0 20 40

Time

Figure 7: OLS-CUSUM test for Returns

BIC and Residual Sum of Squares

BIC 0.012 −550 RSS 0.008 −570 −590 0.004

0 1 2 3 4 5 6

Number of breakpoints

Figure 8: BIC and RSS for Models with m Breakpoints.

Fitted Models for Returns (BP = 2)

0.04 m = 0 m = 1 0.02 0.00 Average Returns Average −0.02

−40 −20 0 20 40

Time

Figure 9: Fitted Models for Average Returns with Two Break Points. cannot be taken, in the mean time causing explosive price increases. At some point a maximum price is reached, and the ones who cannot re-sell their position at a higher price will have to sell it at lower price, in turn causing prices decline, generating negative returns.

18 6 Methodology

This chapter outlines the testing procedure to capture the effect of short-sale possibilities on cryptocurrency returns during bubbles and crashes. Section 1 starts by clarifying why the ”no short-sale” assumption is of importance in our analysis. Section 2 describes the two main regression models to best estimated. Section 3 describes the variables used in the Pooled OLS and Fixed Effects regression.

6.1 The ”No Short-Sale” Assumption According to Harrison and Kreps(1978); Scheinkman and Xiong(2003), overconfident investors, who exhibit heterogeneous beliefs and face short-sale constraints, reflect the price of the most optimists, driving up prices. Although optimists can express their opinions in prices, pessimists cannot, in turn causing them to stay out of the markets, or only being able to purchase assets at a higher price. This behavior causes prices to increase significantly, creating a bubble, that will explode suddenly at a certain point in time. This theory captures the several important features of the cryptocurrency markets. Since the market for crypcurrencies was started by a small community, the main investors are people who have some to no experience investing or trading. This causes the differences in opinions to be very different between investors, causing extreme speculative trading behavior. Specially in a market that is still not regulated, and decisions are made based on forum news, group chats, or Twitter analysis from other investors. As the cryptocurrency market is still not fully regulated, institutional investors are not able to invest in cryptocurrencies easily and safe enough, without having to protect their investments from exchange hacks. This is one of the reasons why shorting cryptocurrencies is very limited. Shorting requires a margin account, which is considered as a short-term loan. It may not be very risky to sell short, since margin calls will be triggered that will limit your risk if the value of your cryptocurrencies fall below the exchange’s minimum requirement. However, in a market like cryptocurrencies, prices are mainly driven by investors who like to chase speculative positions to trade, making it a highly volatile environment to invest in. These dynamics changes the riskiness of shorting, increasing the number of margin calls even faster. And since investors seek to make fast profits, shorting can become very risky. To be able to short one needs other investors or institutions who are willing to lend and bear the risk, and at the moment, in this type of market there are not enough players who would like to take this risk. Based on the extreme price increase of cryptocurrencies since 2012, the behavioral biases among investors and the limited short-sale possibilities, I spell out the importance of ”no short-sale” assumption in this analysis. Therefore, in this research I take the viewpoint that after short-sales have been opened, cryptocurrency returns tend to decrease significantly to a lower average level prior short-selling.

6.2 Regression Models In this section the models to be estimated are discussed. The data of the cryptocurrencies has longitudinal form, therefore two panel data models are used to estimate the effect of short-selling on bubbles. The first one is a Pooled Ordinary Least Squares (POLS) model, the second model is a Fixed Effects (FE) model. An advantage of a longitudinal data set is that you have a large sample and therefore it allows us to look at dynamic relationships. In addition, it allows us to control for unobserved heterogeneity. In this analysis, I use returns as the dependent variable, or more specific, natural logarithm of returns:   Pit Rit = log (7) Pit−1

6.2.1 Pooled OLS Regression I start the analysis by estimating a POLS regression. This estimation method lumps all observa- tions together, and estimates the model as if it is one big broad cross-section. Using POLS can be useful to investigate the impact of a certain event. In this case the impact of the introduction of short-sales on cryptocurrency returns is evaluated. The main advantage of using POLS is that

19 parameters can be estimated more precisely, as the method uses more data. A POLS regression does not allow for differences in parameters, but it does allow for differences in variables over time. To be more specific, a POLS does not allow for cross-sectional differences in intercept or slope. The model is defined as follows:

Rit = α + Xitβ + δDit + eit i, ..., N, t, ..., T (8) where Xit is a vector with control variables turnover, volatility, exchange volume, and illiquidity ratio, discussed further under the section Main Control Variables. β is a (n × 1) column vector containing the coefficients of the control variables. We identify the effect of short-sale possibility of cryptocurrency i as the coefficient, δ, on the dummy variable, Dit, that has a value of one when shorting is possible, and zero otherwise. As indicated earlier, the POLS estimator is obtained by lumping all observations together. This is done by stacking the data over i and t into one long regression with NT observations. If the Cov[eit, Xit] = 0, then if N −→ ∞ or T −→ ∞, this is sufficient for consistent parameter estimates. In short, if this model is correctly specified and control variables are uncorrelated with the error term, then it can be estimated consistently. However, for a given cryptocurrency, the error term is likely to be correlated with the regressors over time. This may cause downward biased estimated standard errors. As a potential consequence of this, I additionally estimate a second model, a FE regression model explained in the next subsection.

6.2.2 Fixed Effects Regression Unlike POLS, a FE model takes into account differences across cryptocurrencies, and therefore it allows for different intercepts across cryptocurrencies. In addition, a very important implication of a Fixed Effects model is that it controls for, or partials out, the effect of time-invariant cryptocurrency characteristics. Controlling for these time-invariant effects is being done by demeaning variables. In other words, individual cryptocurrency’s period average is subtracted from the sample observation, or observed values, both for the explanatory variables and dependent variable. Hence, within each group, the demeaned variables all have a mean of zero. For time-invariant variables, the demeaned variables will have a value of 0 for every case, and since they are constants, they will drop out of the analysis. This basically gets rid of all between-subject variability (which may be contaminated by omitted variable bias). This leaves only the within-subject variability to analyze. This means that the estimation model does not have an intercept (only cryptocurrency specific). The model for this analysis is defined as follows:

Rit = Xitβ + δDit + Ci + Tt + εit (9) where Xit is a vector with control variables turnover, volatility, exchange volume, and illiquidity ratio, discussed further under the section Main Control Variables. β is a (n × 1) column vector containing the coefficients of the control variables. The effect of short-sale possibility of cryp- tocurrency i is identified as the coefficient, δ, on the dummy variable, Dit, that has a value of one when shorting is possible, and zero otherwise. Ci is a cryptocurrency specific fixed effect, and Tt is a time fixed effect to capture the influence of time-invariant variables. Note that Cit is an unobserved random variable and is potentially correlated with control variables in Xit. If there are fixed effects, and they are correlated with the control variables in Xit, then the POLS estimators are inconsistent. The main difference between a FE and POLS regression is that in FE method it is possible to account for individual heterogeneity, while in POLS not. Comparing both models in this analysis teaches us if the effect of short-sales on cryptocurrency returns is found significantly across indi- vidual cryptocurrencies (taking into account the specifics of cryptocurrencies) or just across the entire sample. If the effect is found to be significant across the sample, it would mean that the general effect of the opening of short-sales on returns is only cryptocurrency specific. Therefore the limit in short-sale possibilities would no have a significant influence on individual cryptocurrency returns.

6.3 Control Variables In this part, a short and concise motivation is given for the selection of the control variables to be used in the estimation models. The main control variables that will be used are: turnover, volatility, exchange volume, and illiquidity.

20 Turnover The turnover ratio is a measure for how many times (in this case a cryptocurrency) has changed hands over a period of time. In this research I use the same approach as Chen et al.(2009) and Xiong and Yu(2011), and use turnover as a measure of heterogeneous beliefs. The theory, according to Scheinkman and Xiong(2003), is that greater heterogeneity or differences in opinions (driven by overconfidence) leads to more trading activity and thus a higher turnover rate. The turnover measure is also consistent with other literature on overconfidence (see Shefrin and Statman, 1998). The turnover ratio for cryptocurrency i at time t is:

Volumei,t T urnoveri,t = (10) Market Capitalizationi,t where V olumei,t is the aggregate trading volume across all cryptocurrency pairs in US dollars, that takes place on exchanges. Market Capitazalitioni,t is the total market capitalization of cryp- tocurrency i at time t in USD. The natural log turnover, log(TURNi,t) will be used.

Volatility According to Cochrane(2002), during the Internet bubble, the high prices of internet stocks were correlated with both high turnover and high volatility. Scheinkman and Xiong(2003) show that the size of the bubble is positively related to return volatility. One of the first persons that inves- tigated volatility of cryptocurrencies is Katsiampa(2017a). He argues that a type of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model describes best Bitcoin’s volatility. The claim that GARCH models are the best suitable to describe the heteroskedasticity in times of high price increases in cryptocurrency markets, is also inline with findings by Bouoiyour and Selmi (2015). Therefore, the the return volatility is estimated using the GARCH model of Bollerslev (1986)). For simplicity we estimate a GARCH(1,1) model, defined as follows:

2 2 2 σt = ω + α1Zt−1 + β1σt−1 (11)

where α1 > 1, β1 and ω > 0. The main feature of this model is that it captures volatility clustering in the series. The persistence parameter in this model is captured by the summation of α1 and β1. Weak stationarity holds when α1 + β1 < 1. The persistence parameter defines the amount of volatility clustering captured by the model. Volatility is identified by VOLi,t.

Exchange Volume Miller(1977) argues that high volume does not always imply that prices will increase, the increase in volume could also have been caused by increased selling pressure. Therefore volume should not be the only measure to be taken into account when investigating buying activity. However, volume does attract investor attention, and in the presence of short-sale restrictions, this will cause an increase in buying activity, in turn increasing prices. Scheinkman and Xiong(2003); Xiong and Yu (2011) show in their study of bubbles, in joint with heterogeneous beliefs and short-sale restrictions, that the size of a bubble is positively related to trading volume and return volatility. Bianchi and Dickerson(2018) study the information that trading volume contains and their impact on future returns of cryptocurrencies. They find that the interaction between past volume and passed returns is positively correlated with future returns. Trading volume seems an important factor explaining returns in joint with short-sales, therefore volume is included in the regression. Exchange volume will be included as the natural log, log(XV OLMi,t).

Illiquidity Amihud and Mendelson(1986) argue that an asset is liquid when it can be sold or bought at the same market price quickly, and at low trading cost. According to Brennan and Subrahmanyam (1996), there is a premium linked to transaction costs, and they find that illiquidity is positively related to required rates of return, after adjusting for the Fama French factors. Also, Amihud (2002) studies the effect of illiquidity on the cross-section of returns, and find that illiquidity increases the rate of returns of equities. Other studies like P´astorand Stambaugh(2003) find a significant positive relation between liquidity and returns. Due to lack of credability in the bid-ask

21 spread measure, I control for liquidity by using the proposed illiquidity measure of Amihud(2002). The main reasons for the use of this measure are simplicity of calculation using daily data, and because it is widely used in literature. The equation is defined as follows:

Di,t 1 X |Ri,t| Illiquidity = ∗ (12) i,t D V ol i,t t=1 i,t where Di,t is the number of days for which the data is available for cryptocurrency i at day t. |Ri,t| is the absolute return of cryptocurrency i on day t, and V oli,t is the respective trading volume on that day. Amihud’s illiquidity measure captures the price’s response with respect to trading volume. We expect a less liquid stock price to respond more to changes in volume than more liquid stocks. So, if we measure a high illiquidity ratio, this would imply that stock prices are more sensitive to trading volume. Illiquidity will be used in the regression as log(ILLIQi,t).

7 Empirical Results

This section discusses the main empirical results of the effect of the limited short-sale possibilities on returns across 40 cryptocurrencies. The effect of short-sales on returns is captured by the coefficient on a dummy variable that takes the value one if shorting is possible and zero otherwise. The output of both Pooled OLS and Fixed Effects regression models are discussed in Table5. The main takeaway from the regression results is that the coefficient on the dummy variable is negative and significant, in both POLS and FE.

7.1 Pooled OLS Results The main variable of interest is the dummy variable D, that is one after short-selling has been opened, and zero before. As expected, the coefficient on the dummy variable is negative (−0.005) and significant at the 1% level. In other words, when short-sales are introduced by exchanges, cryptocurrency returns tend to be on average 0.5% lower per day. On monthly basis we are talking about average returns being 15.21% lower. Turnover is positive (0,002) and significant at the 1% as well. This positive relationship means that a 1% increase in turnover tends to increase returns by 0.2% a day. Volatility has a negative relation with returns, but not significant. On the other hand, volume is positive (0.003) and significant at the 1% level, indicating a 0.3% daily increase in returns if volume increases by 1%. The coefficient of Illiquidity is positive (0.003) and significant at the 1% level. Which means that over time, when illiquidity increases by 1%, returns tend increase by 0.3% daily. Although the signs and results seem promising, the model’s R2 is very low, indicating a poor fit. Note that R2 is always low when returns are used as dependent variable. In the next section the FE results will be discussed and compared to the POLS output.

7.2 Fixed Effects Results First of all, it is clear that both FE estimations have a higher R2 than the POLS, indicating a better data fit. This means that there are time-invariant characteristics that we need to control for in order to estimate the net effect of the control variables on returns. Likewise, the model including a time fixed effect has a higher R2 than the one not taking into account time fixed effects. Hence, it can be argued that returns are being influenced by time specific characteristics. Therefore the focus lies on the output including both coin and time fixed effects. As expected, it can be observed that the coefficient on the dummy variable D is significant neg- ative, after taking into account coin and time fixed effects. After short-sales have been introduced, average returns tend to be 0.8% lower on a daily frequency, or 24.3% monthly. Looking at turnover, a positive relation with returns over time is found, with a coefficient of 0.003 significant at the 1% level. This means that returns tend to increase by 0.3% when turnover increases by 1%. These findings are consistent with the resale option hypothesis as indicated by Scheinkman and Xiong(2003); Xiong and Yu(2011), who argue that turnover tends to increase with mispricing. In this case, as the bubble increases, people will generate positive returns till the moment they know when to get out without having to generate negative returns. The relation between volatility and returns is significant and negative. While volatility does not

22 play a significant role in the POLS, taking into account coin and time specific effects seems to indicate the importance of controlling for time-invariant characteristics. The results indicate that a 1 unit increase in volatility, decreases returns by 20.9%, significant at the 1% level. Volume shows a significant positive (0.011) relationship with returns at the 1% level. A 1% increase in volume, increases returns by 1.1%, indicating an important role in the volume behavior of cryptocurrencies towards returns. The illiquidity ratio is positive (0.008) and significant at the 1% level. An increase in illiquidity ratio by 1% increases returns by 0.8%. A higher illiquidity ratio means that cryptocurrency prices are very sensitive to more trading volume. In the next section two different robustness tests are discussed, at first focusing on the last column of Table5, and then performing a placebo test.

8 Robustness Tests

In this section two different robustness checks are performed to confirm if prior results are not biased. First, I change the regression model described in equation (13), by removing all explanatory variables, and keep only the dummy variable D to assess if the results are not influenced by other covariates. Second, I create a placebo group consisting of 40 randomly chosen cryptocurrencies ranked in the top 200 based on market capitalization, that are not eligible for short-selling. Then, using fictive shorting announcement dates, the same model as equation (13) will be tested.

8.1 Robustness of the Shorting Dummy Prior analysis has shown that after short-sales have been introduced, returns tend to decrease significantly. However, to show that this behavior is robust and not influenced by other controls, I analyze the robustness of the shorting dummy, controlling for cryptocurrency and time fixed effects. The model is simple and is described as follows:

Ri,t = δDi,t + Ci + Tt + εit (13) In the last of column of Table5, the result is presented. Even after removing all explanatory variables, the coefficient of the dummy variable is significant negative, with a value of -0.003 at the 1% level. In other words, this can be interpreted as an average drop in returns of 0.3% a day, or a 9.13% average drop a month, after short-sales have been introduced.

8.2 Placebo Test An important question when analyzing robustness is whether similar outcomes are found on cryp- tocurrencies that are not eligible for short-selling. A different method to test if prior findings are only found in the treatment group, is to use a placebo group and assign fictive or random short- sales announcement dates. To be able to test the effect of short-sales on returns of the placebo group, it is necessary that these cryptocurrencies are available for trading during the period that random dates are generated. Therefore, to make sure all data points are available during all dates, we generate random dates between date of 01/12/2017 and 08/08/2019 (end of sample). The procedure is as follows. After creating a placebo group, random shorting dates are gen- erated within the range stated above. Then, to test the effect of the fictive short-sale dates on returns, the same fixed effects regression, defined by equation (13), will be used. Afterwards, the 95% confidence interval of the estimated dummy coefficient will be tested by simulating 10.000 intervals. Where the interval is defined as follows:

δ ˆ ˆ ˆ ˆ CI95% = [δ − 1.96 ∗ SE(δ), δ + 1.96 ∗ SE(δ)] (14)

The hypothesis is that after short-sales have been introduced, average returns do not change and therefore the estimated coefficient of the dummy variable should be zero. This can be tested using a two-tailed test of the hypothesis H0 : δ = 0 versus the alternative H1 : δ 6= 0, at the 5% level. Note, δ is the coefficient of the dummy variable. Again, the focus is on the dummy coefficient, δ, which represents the differences in group averages. Turning to the regression output, in the first column, we see that the coefficient of on the dummy variable is insignificant negative (-0.001) with a standard error and p-value of 0.001853 and 0.57875, respectively. As mentioned before,

23 the significance of this value can be assessed by th two-tailed test. Based on these statistics, the Null hypothesis cannot be rejected, and therefore we state that the coefficient is not significantly different from zero. The same can be concluded from the estimate in the second column, which presents the output of a robustness test similar to the robustness test for the treatment group.

Treatment (grey) and Placebo (colours)

0.04

0.02 Returns 0.00

−0.02 −20 0 20 Relative days

Figure 10: Returns Plot of The Treatment Group and Placebo Group: This graph presents the comparison between the cryptocurrencies that are eligible for shorting (treatment group, consisting of 40 cryptocurrencies), and a placebo group consisting of 40 cryptocurrencies with three different, randomly generated dates. This graph only shows three different generated dates for example purposes.

As can be seen from Table6, the average return across the entire sample is 0.0004 or 0.04%, which is very small, or almost zero. For information purposes, Table7 shows the summary statistics of returns, turnover, volatility, and illiquidity of the placebo group. The regression output can be found in Table8. Based on the 10.000 simulated intervals, the lower and upper value of the confidence interval can be calculated, which is [-0.00186, 0.00017]. According to equation 14, it is expected that the fraction of 10.000 simulations that contains δˆ = −0.001 is approximately 95%. By computing the average of the simulated intervals it can be confirmed that the coefficient δˆ = −0.001 lies within the interval stated above, with a confidence level of 95%. It can be concluded that those intervals for which H0 : δ = 0 cannot be rejected at the 5% level. In other words, we cannot reject the Null hypothesis as the estimated coefficient is not significantly different from zero. To visualize the intervals that do and do not contain the ”true” value, a plot is presented in Figure 11. In this plot, 10.000 confidence intervals have been simulated and the first 100 of them plotted. The red lines represent intervals that do not cover the ”true” value of the delta coefficient.

Confidence Intervals 80 40 Sample 0

−0.002 −0.001 0.000 0.001 0.002 µ

Figure 11: Plot of 100 simulated confidence intervals of the sample average returns, µ. The grey bars represent the confidence intervals for which the null hypothesis is not rejected. The red bars represent the confidence intervals for which the null hypothesis is rejected. As can be seen, for the first 100 simulations, the true null hypothesis is rejected 2 times.

24 9 Feedback Loop Dynamics

In behavioral finance, research suggests that various behavioral biases can lead individual investors to have a positive feedback on past returns. According to Shiller(2000), bubbles are driven by three factors. One, a sudden event causes which starts to increase prices. Second, in response to this price increase, investors are triggered to buy more, in turn increasing prices further, creating positive feedback on prices. Third, through social contagion, like personal connections or news outlets, more investors are noticing the price increase and join the buying, triggering an even more rapidly price increase. Since these characteristics are met by the cryptocurrency markets, it is of importance to test if there is a positive feedback loop in returns and turnover. The feedback effect is tested by following Xiong and Yu(2011), using lagged returns and turnover in the following models: + RETi,t = h0 + h1RETi,t−1 + h2RETi,t−1 + h3∆TURNi,t−1 + Ci + Ti + Ξi,t (15)

+ ∆TURNi,t = h0 + h1RETi,t−1 + h2RETi,t−1 + h3∆TURNi,t−1 + Ci + Ti + Ξi,t (16) where RETi,t is the natural logarithm of returns, and TURNi,t is the turnover rate for of a certain cryptocurrency i at time t. The main parameter of interest is the lagged returns, RETi,t−1. + RETi,t−1 = MAX[ri,t, 0] is added to the regression to take into account the asymmetries in the feedback loop. ∆TURNi,t−1 is the lagged change in turnover, Ci and Ti represent a cryptocurrency, and time fixed effect, respectively. Note that we are dealing with changes, and not levels. This means that all variables are stationary.

9.1 Feedback Loop Results Analyzing the time-series dynamics of the 40 cryptocurrencies that can be shorted, I cannot use tick or high-frequency data to analyze the dynamics at a smaller time frame, like Xiong and Yu (2011). Instead I analyse the feedback loop dynamics only with daily data, therefore feedback loops might be missed at shorter time frames. In Table9, it can be observe that lagged returns have a negative coefficient (-0.073) and are statistically significant at the 1% level, indicating that there is no positive feedback in cryptocur- rency returns. In other words, past returns decrease returns by approximately 10%. On the other hand a significant (at the 1% level) positive (0.132) feedback on truncated lagged returns is found. This means that in contrast with past returns, past positive returns are of big importance in de- termining returns. Past positive returns impact current returns by 13.2%. This indicates that there is evidence of asymmetric feedback of cryptocurrency returns to past returns, this means that increase in returns in response to positive returns is more pronounced than a decrease in re- turns in response to negative returns. The coefficient on the lagged change in turnover is negative (-0.003) and significant. This suggests that the change in past turnover has a negative impact on current returns, but its not pronounced enough. In other words there is an insignificant negative autocorrelation, that could mean that trading activity is clustered, and that decreases in trading is likely to generate further decreases in trading. Also the dynamics of the change in turnover are presented. The coefficient on past returns is significant positive (0.070), at the 1% level. This indicates a positive feedback effect of past returns on the change in turnover. The truncated lagged return measure is significant negative (-0.059) at the 1% level. This allows us to conclude that there is evidence of asymmetric feedback on change in turnover, i.e. a drop in turnover in response to negative returns, is more pronounced than an increase in turnover in response to positive returns. The coefficient on the lagged change in turnover is significant negative (-0.289), at the 1% level. This suggests that the change in past turnover has a negative impact on the change in turnover. In other words there is a negative autocorrelation, and that could mean that trading activity is clustered, and that a decrease in trading is likely to generate further decreases in trading. So, there is evidence of day-to-day feedback effect in change in turnover to past change in turnover. Additionally, I also tested the feedback loop dynamics by including past volatility. I find that past volatility changes have no impact on the effect of return or change in turnover. Also, running + the regressions without RETi,t−1 does not affect the coefficients of other variables. Overall, it can be argued that there are no sign of feedback in returns, only in changes in turnover. This result is line with Wei(2018), who finds negative serial-correlation of returns among low liquid cryptocurrencies.

25 10 Conclusion and Discussion

This paper provides empirical evidence on the effect of limited short-sale possibilities in the cryp- tocurrency markets on cryptocurrency returns. I examined the impact of unique announcements dates of the opening of short-sales by 5 different exchanges, across 40 cryptocurrencies. This effect has been tested using two different methods. The first method is based on a structural break test analysis, where a significant change in the time series of returns is analyzed. The second method is based on a Pooled OLS and Fixed Effects regression, where a dummy variable is used to identify the effect of short-sale possibilities. The dummy variable takes the value of one if it is possible to short a cryptocurrency and zero otherwise. Also, the feedback loop behavior the cryptocurrencies have been analyzed. This study provides various implications. First, this analysis examines how well theories related to short-sales and overpricing, and the impact on returns, work in the cryptocurrency space. Unlike financial markets, the cryptocurrency space has limited research on the impact of short-sales on returns. Existing literature is rather mixed about the impact regarding the effect of short-sale restrictions on returns. Due to high volatility, and low liquidity, short-sale restrictions can increase expected returns. On the other hand, short-sale restrictions can cause overpricing, due to the inability of pessimistic investors to place short positions. This limitation causes market prices to be only positively influenced by optimistic investors, who are driven by overconfidence, in turn generating differences in beliefs across markets. This implies that, after short-sales have been introduced, stock prices decline, and returns tend to decrease. In line with literature on short-sales and returns, this research confirms the significant negative impact on average returns, after the introduction of short-sales of cryptocurrencies by exchanges. More specifically, using a structural break test, I find that within days before short-sales are opened, returns show a positive structural change up to the point that short-sales are opened. Then, returns show a dramatic negative change after short-sales become available. The negative change in returns after shorting becomes available, is reinforced by using a Fixed Effects method. I find that after short-sales becomes available, average cryptocurrency returns tend to decrease by 0.6% a day, or by approximately 18.25% a month. Second, I expand the analysis by examining the feedback loop behavior of returns, and turnover across the entire sample. According to literature on bubbles, the extreme price increase is reinforced by past price increase, information flows, and heard behavior. In line with literature on re-sale option value in cryptocurrency markets, we find no evidence of the feedback of past returns on returns. On the other hand, we do find evidence of positive feedback of past returns on changes in turnover. In other words, positive returns have a positive impact on trading activity. There are various limitations to this study. To start with, this study does not analyze the behavior of turnover, volatility, and liquidity, before and after short-sales have been introduced. These measures are known to be of importance in markets where short-sales are limited or pro- hibited. Next, to test the impact of short-sales on returns, no short volume or interest has been used due to limited data availability, or accessibility, on several exchanges. This type of shorting data can be used to analyze the dynamics of short-sale restrictions, and to be able to clarify what drives the impact on returns in more detail. At last, in this research, I use the definition of extreme price increase as a sign of a bubble. A more common interpretation is the overpricing of an asset in relation to its fundamental value. To be able to test the implications of short-sales on bubbles, or overpricing, and in turn on returns, one needs to be able to define a price-to-fundamental value ratio of cryptocurrencies. Due to the limited research on the calculation of the fundamental value of cryptocurrencies, this analysis is limited to the use of returns. For future research, I have several recommendations. First, investigate the dynamics of turnover, volatility, and liquidity, before and after short-sales. In more detail, similar to traditional financial markets, it is of importance to investigate what drives the creation of crytocurrency bubbles, and which factors are significant in identifying this creation. Second, the use of shorting related data like shorting volume would be an additional factor explaining the effect of short-sales on returns, or other factors like turnover. Third, an optional method to analyze the impact of short-sales on returns would be a difference-in-differences method, using average price differential as a dependent variable. At last, more and more cryptocurrency metrics are being developed to try and value cryptocurrencies more easily. Metrics like realized market capitalization (Realized Cap), Network- Value-to-Transaction (NVT) ratio, and Market-Value-to-Realized-Value (MVRV) ratio can become

26 useful in the signaling overvaluation.49 In summary, it appears that cryptocurrency markets show similar signs as traditional financial markets, in the sense that short-sale restrictions can cause increases in prices and returns. Ad- ditionally, cryptocurrency markets are very immature and appear to exhibit extreme speculative behavior due to overconfident investors who think to know more than other market participants. This behavior will always exist but can be limited by regulation. Since the creation of Bitcoin in 2008, governments around the world have not yet put clear and strong regulations regarding trading and funding of cryptocurrency projects. Therefore, extreme speculative behavior and the creation of bubbles in the cryptocurrency markets will still be an interesting phenomenon to analyze.

49For more information on cryptocurrency metrics, visit https://charts.woobull.com

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30 Table 1: Short-sales Announcement Links Name Announcement Link 0x https://twitter.com/bitfinex/status/1052555398409048064 https://tinyurl.com/y4h8z2kw Binance Coin https://tinyurl.com/yxhn82f4 Bitcoin https://tinyurl.com/y2zvsfjx https://twitter.com/bitfinex/status/893053309686349824 https://tinyurl.com/y3yjsh2v Bitcoin SV https://twitter.com/bitfinex/status/1076120745858818049 BitShares https://twitter.com/poloniex/status/602580111209148416 BitTorrent https://tinyurl.com/y582qpdm Cardano https://tinyurl.com/y5shmxbz Chainlink https://tinyurl.com/y6xbeaee Clams https://twitter.com/poloniex/status/605919921042620416 Cosmos https://twitter.com/poloniex/status/1121144110579101696 Dash https://twitter.com/bitfinex/status/841303511941959680 https://twitter.com/Poloniex/status/603256139812347905 Eidoo https://twitter.com/bitfinex/status/923566416284078081?lang=en EOS https://tinyurl.com/y4mpmps7 Ethereum https://www.bitfinex.com/posts/100 https://www.bitfinex.com/posts/122 Factom https://twitter.com/poloniex/status/680584266548404228?lang=en HyperCash https://tinyurl.com/y4aap7gt IOST https://twitter.com/huobiglobal/status/957901473672675328?lang=en IOTA https://tinyurl.com/y4mpmps7 Litecoin https://tinyurl.com/yxuvfmfk MaidSafeCoin https://twitter.com/poloniex/status/618821581331165184 Metaverse ETP https://twitter.com/bitfinex/status/921454619909292032 Monero https://twitter.com/bitfinex/status/841303511941959680 NEO https://tinyurl.com/y4qhfoxf OmiseGo https://twitter.com/bitfinex/status/893485616708542464 Ontology https://tinyurl.com/y5sgd26v Qtum https://tinyurl.com/y2s32ygh Ripple https://tinyurl.com/y6tbvewa Santiment Network Token https://twitter.com/bitfinex/status/909867773387526144?lang=en Stellar https://twitter.com/poloniex/status/604136610687504384 https://www.bitfinex.com/posts/325 TRON https://tinyurl.com/y5rbp5l7 UNUS SED LEO https://www.bitfinex.com/posts/377 USD Coin https://tinyurl.com/yxuvfmfk https://twitter.com/bitfinex/status/841303511941959680 Zilliqa https://tinyurl.com/yyu7l8ke

Note: This table presents the links of the 40 cryptocurrencies used to gather the short-sales announcement dates. Numerous links are too long, and therefore I used a tiny url version of these links. You can copy and paste this link in your browser, and you will be redirected to the original page announcing the introduction of short-sales (or margin trading).

31 Table 2: List of Cryptocurrencies Analyzed Name Symbol Start Date End Date Event Date Exchange Binance Coin BNB 2017-07-25 2019-08-08 2019-07-11 Binance Chainlink LINK 2017-09-20 2019-08-08 2019-07-18 Binance Litecoin LTC 2013-04-28 2019-08-08 2019-08-08 Binance Ontology ONT 2018-03-08 2019-08-08 2019-07-26 Binance TRON TRX 2017-09-13 2019-08-08 2019-07-11 Binance USD Coin USDC 2018-10-08 2019-08-08 2019-08-08 Binance 0x ZRX 2017-08-16 2019-08-08 2018-10-17 Bitfinex Bitcoin Cash BCH 2017-07-23 2019-08-08 2018-12-21 Bitfinex Bitcoin Gold BTG 2017-10-23 2019-08-08 2017-12-27 Bitfinex Bitcoin SV BSV 2018-11-09 2019-08-08 2018-12-21 Bitfinex Dash DASH 2014-02-14 2019-08-08 2017-03-13 Bitfinex EOS EOS 2017-07-01 2019-08-08 2017-07-03 Bitfinex Eidoo EDO 2017-10-17 2019-08-08 2017-10-26 Bitfinex Ethereum ETH 2015-08-07 2019-08-08 2016-07-28 Bitfinex Ethereum Classic ETC 2016-07-24 2019-08-08 2016-04-22 Bitfinex IOTA MIOTA 2017-06-13 2019-08-08 2017-07-03 Bitfinex Metaverse ETP ETP 2017-06-05 2019-08-08 2017-10-20 Bitfinex Monero XMR 2014-05-21 2019-08-08 2017-03-13 Bitfinex NEO NEO 2016-09-09 2019-08-08 2017-09-18 Bitfinex OmiseGO OMG 2017-07-14 2019-08-08 2017-08-04 Bitfinex Santiment Network Token SAN 2017-07-12 2019-08-08 2017-09-18 Bitfinex Tether USDT 2015-02-25 2019-08-08 2018-12-21 Bitfinex UNUS SED LEO UNUS 2019-05-21 2019-08-08 2019-05-29 Bitfinex XRP XRP 2013-08-04 2019-08-08 2017-07-03 Bitfinex Zcash ZEC 2016-10-29 2019-08-08 2016-12-20 Bitfinex Cardano ADA 2017-10-01 2019-08-08 2018-01-08 Bitmex Bitcoin BTC 2013-04-28 2019-08-08 2017-12-17 CME* BitTorrent BTT 2019-01-31 2019-08-08 2019-02-20 Huobi HyperCash HC 2017-08-20 2019-08-08 2017-12-21 Huobi IOST IOST 2018-01-16 2019-08-08 2018-01-29 Huobi Qtum QTUM 2017-05-24 2019-08-08 2017-12-12 Huobi Zilliqa ZIL 2018-01-25 2019-08-08 2019-01-02 Huobi Augur REP 2015-10-27 2019-08-08 2016-10-16 Kraken BitShares BTS 2014-07-21 2019-08-08 2015-05-24 Poloniex Clams CLAM 2014-08-26 2019-08-08 2015-06-03 Poloniex Cosmos ATOM 2019-03-14 2019-08-08 2019-04-24 Poloniex Dogecoin DOGE 2013-12-15 2019-08-08 2015-05-26 Poloniex Factom FCT 2015-10-06 2019-08-08 2015-12-26 Poloniex MaidSafeCoin MAID 2014-04-28 2019-08-08 2015-07-08 Poloniex Stellar XLM 2014-08-05 2019-08-08 2015-05-29 Poloniex

Note: This table presents the list of the 40 cryptocurrencies analyzed between 28/04/2013 and 08/08/2019. The event date represents the day that an exchange announced the opening for margin trading for cryptocurrency. * For Bitcoin, the Chicago Mercantile Exchange (CME) futures listing date of 17/12/2017 is used. I decided to use this date because the trading volume at CME was six times higher than the CBOE’s when they launched Bitcoin futures.

32 Table 3: Summary Statistics Cryptocurrency Market Capitalization (in million USD) Daily Returns Turnover Symbol Count Lowest Average Highest Last Mean Std. Dev Mean Std. Dev BTC 2294 778.411 45616.441 326502.486 213788.089 0.002 0.043 0.028 0.040 ETH 1463 32.214 20450.600 135400.736 23694.203 0.003 0.074 0.079 0.112 XRP 2196 21.970 7219.261 130853.471 13190.251 0.002 0.075 0.018 0.031 BCH 747 0.000 11792.213 66171.059 5986.043 0.000 0.085 0.106 0.115 LTC 2294 37.892 1934.892 19482.624 5671.749 0.001 0.066 0.095 0.164 BNB 745 9.987 1508.143 5479.856 4795.639 0.008 0.084 0.066 0.048 USDT 1621 0.153 949.260 4080.179 4032.631 0.000 0.006 1.187 1.847 EOS 769 0.000 4496.262 17769.451 3851.789 0.002 0.088 0.224 0.189 BSV 273 0.000 1750.802 4254.524 2573.199 0.003 0.106 0.116 0.071 XMR 1905 1.280 881.123 7274.154 1631.343 0.002 0.072 0.031 0.036 XLM 1830 0.768 1401.550 16022.043 1508.625 0.002 0.077 0.033 0.057 TRX 695 0.000 2073.487 14501.093 1459.452 0.003 0.104 0.146 0.131 ADA 677 0.000 3917.107 28885.868 1347.438 0.001 0.085 0.032 0.024 UNUS 80 0.000 968.902 1914.802 1243.866 0.002 0.041 0.007 0.002 DASH 2002 1.047 963.129 12042.753 944.887 0.003 0.078 0.060 0.098 LINK 688 45.895 213.222 1296.103 801.179 0.004 0.087 0.052 0.059 NEO 1064 0.000 1583.882 12181.325 766.932 0.003 0.095 0.148 0.190 MIOTA 787 441.078 2566.733 14915.877 764.214 -0.001 0.080 0.018 0.015 ETC 1111 49.576 1071.935 4339.511 674.133 0.002 0.083 0.215 0.259 ATOM 148 0.000 641.297 1338.109 622.080 -0.005 0.085 0.114 0.068 ONT 519 0.000 482.502 1299.029 484.107 -0.002 0.079 0.154 0.102 ZEC 1014 0.597 557.721 2649.258 445.070 -0.003 0.089 0.238 0.286 USDC 305 0.000 266.816 437.549 430.328 0.000 0.006 0.205 0.200 DOGE 2063 1.509 165.448 1925.383 353.408 0.001 0.078 0.035 0.046 BTG 655 0.000 951.214 7580.781 281.851 -0.005 0.095 0.035 0.040 QTUM 807 0.000 807.732 6985.720 268.532 -0.001 0.087 0.437 0.498 OMG 756 0.000 720.328 2624.228 200.617 0.001 0.083 0.132 0.127 BTT 190 0.000 157.663 377.441 155.946 0.002 0.067 0.392 0.288 REP 1282 0.000 194.127 1193.192 123.387 0.001 0.086 0.024 0.030 BTS 1845 7.411 178.823 2324.788 121.624 0.001 0.075 0.033 0.049 IOST 570 0.000 170.041 861.221 120.928 -0.002 0.095 0.157 0.121 ZRX 723 85.403 315.995 1166.002 113.382 0.000 0.079 0.059 0.047 HC 719 0.000 262.197 1568.712 101.877 -0.003 0.103 0.145 0.431 MAID 1929 4.926 78.135 527.454 83.676 0.001 0.069 0.008 0.010 ZIL 561 0.000 316.202 1459.797 73.975 -0.005 0.070 0.082 0.081 ETP 795 0.000 71.458 202.471 56.150 -0.003 0.134 0.138 0.198 SAN 758 8.884 64.087 459.577 39.258 0.001 0.092 0.031 0.045 FCT 1403 0.654 88.775 665.240 37.110 0.002 0.087 0.026 0.038 EDO 661 13.635 36.102 150.039 27.183 -0.002 0.072 0.072 0.076 CLAM 1809 0.007 7.244 69.076 13.247 0.001 0.111 0.023 0.062

Note: This table presents descriptive statistics of the 40 cryptocurrencies analyzed. I document the lowest, highest, average and last recorded market capitalization in millions USD. In addition, the mean and standard deviation of both returns and turnover, are presented.

Table 4: Descriptive Statistics: Treatment Group (40 cryptocurrencies)

Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max return 42,708 0.001 0.080 −1.302 −0.029 0.028 1.812 Turnover 42,128 0.121 0.449 0.000 0.009 0.085 12.745 Volatility 42,708 0.006 0.038 0.000 0.0001 0.004 3.282 Volume 42,753 424.330 1,988.700 0.000 0.455 101.470 45,106.000 Illiquidity 42,065 0.00004 0.002 0.000 0.000 0.00000 0.359

33 Table 5: Results: Pooled OLS and Fixed Effects - Core Sample

Dependent variable: Log(Return) Pooled OLS Fixed Effects Fixed Effects Fixed Effects (1) (2) (3) (4) Log(Turnover) 0.002∗∗∗ 0.004∗∗∗ 0.003∗∗∗ − (0.0004) (0.001) (0.001)

Cond.Volatility −0.045 −0.076∗∗ −0.209∗∗∗ − (0.030) (0.031) (0.027)

Log(Volume) 0.003∗∗∗ 0.003∗∗∗ 0.011∗∗∗ − (0.0004) (0.0004) (0.001)

Log(Illiquidity) 0.003∗∗∗ 0.002∗∗∗ 0.008∗∗∗ − (0.0003) (0.0003) (0.0003)

D −0.004∗∗∗ −0.009∗∗∗ −0.006∗∗∗ −0.003∗∗∗ (0.001) (0.001) (0.001) (0.001)

Constant 0.021∗∗∗ (0.004)

Coin FE − Y es Y es Y es Time FE − No Y es Y es Observations 39,838 39,838 39,838 42,708 R2 0.004 0.007 0.400 0.367 Adjusted R2 0.004 0.006 0.367 0.330 F Statistic 35.780∗∗∗ (df = 5; 39832) 55.378∗∗∗ (df = 5; 39793) 12.246∗∗∗ (df = 2055; 37743) 10.194∗∗∗ (df = 2293; 40375) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Table 6: Descriptive Statistics: Placebo Group

Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max Return 35,551 0.0004 0.098 −1.662 −0.040 0.035 1.917 Turnover 34,742 0.057 0.218 0.00000 0.006 0.046 19.860 Volatility 35,551 0.010 0.054 0.000 0.0002 0.006 3.675 Volume (in millions) 35,604 8.966 32.212 0.000 0.239 6.794 1,693.200 Illiquidity 35,544 0.00003 0.001 0.000 0.000 0.00000 0.081

34 Table 7: Summary Statistics: Placebo Group Cryptocurrency Market Capitalization (in millions) Daily Returns Turnover Symbol Count Lowest Average Highest Last Mean Std. Dev Mean Std. Dev XTZ 676 0.000 404.616 1481.052 1023.902 0.000 0.080 0.007 0.005 XEM 1591 0.771 1071.580 16584.480 546.257 0.003 0.084 0.012 0.016 VSYS 157 0.000 105.580 477.750 415.712 0.013 0.076 0.038 0.007 DCR 1276 0.000 209.444 834.587 281.089 0.003 0.078 0.011 0.015 VET 371 186.332 426.082 1079.748 276.039 -0.003 0.063 0.047 0.045 BAT 799 71.781 271.755 891.962 267.692 0.000 0.080 0.046 0.044 PAX 316 0.000 134.066 207.236 196.001 0.000 0.005 0.680 0.349 LSK 1220 0.000 458.114 3983.201 171.603 -0.001 0.121 0.026 0.025 870 0.305 387.990 4490.774 140.798 0.005 0.108 0.026 0.025 WAVES 1163 0.000 285.403 1602.660 133.464 0.000 0.079 0.040 0.045 EKT 562 0.000 23.237 128.268 128.268 0.000 0.103 0.118 0.182 BCN 1878 1.090 197.782 5541.012 126.510 0.002 0.111 0.004 0.008 MONA 1939 0.165 59.948 927.631 124.114 0.001 0.093 0.014 0.057 DGB 2010 0.089 95.006 1226.722 120.785 0.001 0.101 0.028 0.056 THETA 569 0.000 84.005 185.440 110.088 -0.001 0.084 0.101 0.148 ICX 651 0.000 590.069 4613.858 106.381 -0.001 0.089 0.066 0.041 XIN 562 0.000 102.329 612.567 102.214 -0.003 0.077 0.004 0.005 REN 534 0.000 25.655 112.860 99.631 0.001 0.078 0.055 0.098 KMD 915 0.938 196.098 1187.747 98.655 0.002 0.118 0.014 0.021 XVG 1749 0.010 162.752 3694.968 84.829 0.004 0.167 0.029 0.060 590 0.000 56.331 95.977 77.186 0.000 0.015 0.205 0.258 SNT 772 47.014 238.795 2307.952 69.837 -0.001 0.086 0.120 0.153 STEEM 1208 3.874 265.911 1976.264 68.252 -0.001 0.102 0.014 0.021 XZC 1037 0.000 72.984 533.482 66.137 0.002 0.110 0.042 0.056 ARDR 1044 0.000 188.561 2098.878 63.719 0.000 0.080 0.016 0.025 WAX 596 0.000 110.528 1263.211 59.581 -0.007 0.112 0.025 0.049 GNT 994 0.000 191.993 908.027 55.363 0.001 0.085 0.041 0.104 ENJ 646 0.000 75.656 336.972 53.050 0.002 0.097 0.088 0.135 NEXO 464 0.000 55.362 229.092 50.671 -0.002 0.076 0.082 0.060 ELF 596 0.000 139.667 609.987 47.703 -0.004 0.081 0.160 0.105 STRAT 1092 1.095 290.208 2146.407 46.323 0.003 0.088 0.024 0.025 ARK 870 3.213 166.896 1007.835 30.367 0.002 0.091 0.018 0.037 AION 660 0.000 141.801 645.071 26.786 -0.003 0.106 0.040 0.027 ANT 813 0.000 54.787 248.487 25.408 -0.001 0.077 0.008 0.010 ABT 529 0.000 35.209 153.898 23.052 -0.003 0.078 0.704 1.376 STORJ 768 14.828 67.156 348.335 21.730 -0.002 0.095 0.077 0.098 GNO 830 9.715 94.155 448.638 20.292 -0.002 0.077 0.013 0.016 FUN 773 0.000 110.416 818.589 17.029 -0.002 0.115 0.029 0.032 CVC 753 15.095 80.365 461.922 15.095 -0.002 0.079 0.082 0.088 MTL 761 7.024 62.302 261.108 14.622 -0.002 0.088 0.148 0.282

Note: This table presents descriptive statistics of the 40 placebo cryptocurrencies used in the placebo test. In this table, the lowest, highest, average and last recorded market capitalization in millions USD, are presented. In addition, the mean and standard deviation of both returns and turnover are presented.

35 Table 8: Test Results Placebo Group: Fixed Effects Model

Dependent variable: Log(Return) Fixed Effects Fixed Effects (1) (2) Log(Turnover) 0.025∗∗∗ (0.001)

Cond.Volatility −0.332∗∗∗ (0.031)

Log(Volume) −0.016∗∗∗ (0.001)

Log(Illiquidity) 0.025∗∗∗ (0.001)

D −0.001 0.001 (0.002) (0.002)

Coin FE Y es Y es Time FE Y es Y es Observations 23,985 35,551 R2 0.465 0.341 Adjusted R2 0.423 0.301 F Statistic 11.107∗∗∗ (df = 1740; 22206) 8.632∗∗∗ (df = 2009; 33502) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Table 9: Test Results: Feedback Loop Dynamics

Dependent variable: Return ∆ Turnover Return ∆ Turnover (1) (2) (3) (4) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Returni,t−1 −0.097 0.070 −0.045 0.077 (0.009) (0.015) (0.010) (0.022)

+ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Returni,t−1 0.132 −0.059 0.066 −0.107 (0.013) (0.022) (0.014) (0.030)

∗∗∗ ∗∗∗ ∆ Turnoveri,t−1 −0.003 −0.281 0.001 −0.289 (0.003) (0.005) (0.002) (0.005)

∗∗∗ ∗∗∗ ∆ Volatilityi,t−1 − − −0.049 0.092 (0.008) (0.018)

Coin FE Y es Y es Y es Y es Time FE Y es Y es Y es Y es Observations 42,041 42,043 42,019 42,019 R2 0.003 0.080 0.396 0.154 Adjusted R2 0.002 0.079 0.360 0.104 F Statistic 44.174∗∗∗ (df = 3; 41998) 1,214.157∗∗∗ (df = 3; 42000) 11.331∗∗∗ (df = 2294; 39685) 3.154∗∗∗ (df = 2294; 39685) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

36