Abstract: We examine a question which many in the crypto-currency community frequently ask: “When is the next global financial crisis going to happen?” We attempt to answer this by first explaining how that since 2008, the epicentre of financial risk seems to have shifted from the banks to the asset management industry. We therefore argue that a repeat of 2008, where retail banking deposits and payment systems are under threat, is unlikely. In particular, we assert that corporate debt investment funds and unconventional debt investment vehicles, encouraged by the deceptively low volatility and low return environment, could be the area where the fragility in the financial system is most significant.

(Ten years since the global financial crisis, as the newspapers from the time exposed to the sunlight turn yellow and pink, at some point credit conditions could tighten significantly again, but will the asset management industry, rather than the banking sector, be the epicentre of risk?)

Overview

Due in part to the timing of its launch, Bitcoin is said to have been born out of the financial chaos and scepticism resulting from the 2008 global financial crisis. Therefore, many Bitcoin investors and members of the crypto-currency community often seem to ask:

When is the next global financial crisis going to happen?

Due to the demand, we will attempt to address the issue.

First, we will look into the question itself. In our view, there appears to be three main assumptions behind the question:

1. The next global financial crisis will arrive in the next few years and it’s inevitable one will occur every decade or so;
2. Such a crisis will have a positive impact on the price of Bitcoin;
3. The next global financial crisis will look similar to the last one, resulting in many questioning the integrity of the banking system and electronic payment systems.

Of these three assumptions, we only really agree with the first one. Although we think the latter two assumptions could possibly hold true, there is significant uncertainty about them.

As for the second assumption, we touched on this issue in March 2018, when we noted that Bitcoin was trading more like a risk-on asset than a safe-haven asset. Of course the Bitcoin price has fallen a lot since then and this could change going forwards. If Bitcoin does respond well in the next crisis (when liquidity is constrained), that will be a huge positive for Bitcoin and the store of value investment thesis. Although, there is no significant evidence for this yet. A decoupling of the Bitcoin price from many of the alternative coins, which more clearly have a risk-on type investment thesis (e.g. world computer or high capacity payment network), is necessary for this to occur, in our view.

As for the third assumption, the mechanics of the next global financial crisis, that is the focus of this report.

Bank Balance Sheets In Developed Markets Are Relatively Healthy

As the famous saying goes, “History doesn’t repeat itself but it often rhymes.” Over the last decade, bank management teams and banking regulators have operated in the shadows of 2008. As a result, bank balance sheets and capital ratios have significantly strengthened. Bank Tier 1 capital ratios in developed markets have improved from around 5% pre-crisis to around 12% today (Figure 1). The more basic ratio of equity to total assets, which is more difficult to manipulate, also illustrates a similar story: improving from c5% to c9% in the period (Figure 2).

Figure 1 – US & UK Bank’s Aggregate CET1 Capital Ratios

(Source: UK aggregate data from the Bank of England, US data from the Federal Reserve)

Figure 2 – US Bank’s Aggregate Tangible Equity to Total Assets Ratio (Banks with over $50 billion in assets)

(Source: Federal Reserve)

Perhaps even more revealing and compelling than the above ratios, is the following more simple chart (Figure 3). It illustrates that the main western banks have not expanded their balance sheets at all since the global financial crisis. Actually, the sample of the nine major banks we have reviewed experienced a significant decline in total assets in aggregate, from US$19.3 trillion in 2008 to US$15.6 trillion in 2018. One could argue that M&A activity is a driver of the below chart, but our point still stands.

Figure 3 – Total Assets Of Selected Banks In Developed Markets – US$ Trillion

(Source: BitMEX Research, Bank Earnings, Bloomberg)

(Note: The chart represents the total reported assets for JP Morgan, Bank of America, Citigroup, Wells Fargo, HSBC, RBS, Deutsche Bank, Credit Suisse and UBS.)

In our view, financial leverage is one of the the primary drivers of financial risk. The epicentre of risk in the financial system appears to have shifted since 2008. In 2008, the risk was caused by leverage in the banking system and the interrelationships between this and the securitisation of the mortgage market. Today, the equivalent risk is leverage in the asset management industry and in particular the corporate debt sector, driven by the deceptively low volatility environment.

Growth In Leverage In The Asset Management Industry

The asset management industry is far more opaque than banking and determining the degree of leverage is far more challenging. Therefore, it is difficult to conclude on either the extent of leverage in the asset management industry or the timing of any financial crisis related to this leverage.

A 2015 Bank of International Settlement (BIS) report entitled “Leverage on the buy side,”  focused on the shift in risk from the banking system to the asset management industry. The report notes that while investment fund leverage has been reasonably stable in equities, in the fixed income area, it has grown considerably since 2008, especially in emerging markets. The BIS report concludes with the following:

Leverage in the banking system was an important ingredient in the 2008 global financial crisis. Since then, asset managers (the “buy side”) have quickly increased their footprint in global financing, helped by the sharp retrenchment of banks nursing their balance sheets back to health. Balance sheet information for investment funds is much less readily available than for highly regulated banks. Using information provided by a market data vendor, we found that leverage on the buy side is not negligible, although it seems to vary considerably depending on the type of fund. Equity fund portfolios seem to be minimally leveraged, while fixed income funds tend to resort abundantly to borrowed money.

(Source: BIS)

The BIS report used data from investment fund flow specialist EPFR, and although we agree with the report’s conclusions, it is difficult to formulate a strong view of the data’s reliability. Although we have not found good sources of global data ourselves, US-domiciled investment funds over a certain size are required to submit data to the SEC about the extent of leverage used. The SEC has complied this data since Q2 2013 and we have summarised the main trends in the below charts (Figures 4, 5, and 6).

The data shows that, unlike the banking sector, the asset management industry has expanded considerably since 2008 (Figure 4). At the same time, leverage also appears to have increased, although producing a clear chart since 2008 illustrating this is difficult.

Figure 4 – US Fund Industry Gross Asset Value (US$ billion)

(Source: BitMEX Research, SEC)

Although there are competing methodologies, the most basic method for establishing the degree of leverage for an investment fund is calculating the gross asset value over the net asset value, sometimes referred to as the gearing ratio. Unfortunately the time span in the below chart (Figure 5) is limited, but it appears to indicate a moderate expansion of leverage, at least in the hedge fund sector.

Figure 5 – US Private Fund Industry Gearing Ratio – Gross Asset Value/Net Asset Value

(Source: BitMEX Research, SEC)

The gearing ratio underestimates true leverage, by ignoring the impact of derivatives. Disclosure of the notional value of derivative exposure is also required by the SEC. The below chart illustrates the growth in usage of derivatives by US-based hedge funds.

Figure 6 – US Private Fund Industry – Hedge Funds – Notional Value Of Derivatives/Net Asset Value

(Source: BitMEX Research, SEC)
(Note: Adjustments were made to reflect changes in how the SEC reports the data.)

New Corporate Debt Market Vehicles

In addition to the increased use of leverage in the fixed income market by investment funds, the mechanics of the debt markets are becoming increasingly complicated and opaque. The replacement of the role of the banks in the corporate debt markets, has resulted in the rapid growth of a whole range of interrelated, non-mutually exclusive investment structures. Some of these structures are summarized in the table below.

Type of Debt	Description/Comments	Reference
Collateralized Loan Obligations (CLOs)	CLOs are when a group of loans from multiple companies, are pooled together to form a security. The product is typically split into different tranches, lower risk tranches with lower returns and higher risk tranches with higher returns. Investors in the highest risk tranche are the last to be paid in the event of any insolvencies. Typical buyers of these products are pension funds, insurance companies and hedge funds. They are particularly popular with yield hungry Asian investors.	Market growth – Figure 7
Leveraged Loans	These are typically variable rate loans provided to companies who are already highly indebted. In the majority of cases the loan is fully unsecured. The typical holders of such instruments are pension funds and other private investors. The Bank of England recently estimated the size of the leveraged loan market globally at US$2.2 trillion and compared it to the size of the US subprime mortgage market in 2006 (US$1.3 trillion).	Market growth – Figure 8 Credit quality – Figure 15
Private debt deals	This is similar to the leveraged loan market, except the debt does not normally trade on a secondary market.	Market growth – Figure 9
Bond fund ETFs and mutual funds	ETFs have expanded in popularity in all asset classes in the period, corporate bond funds are no exception.	Market growth – Figure 10
Private Equity		Credit quality – Figure 16

(Note: The fields in the above table are not meant to be mutually exclusive)

As the following charts from various sources indicate, all these non-bank mechanisms for providing corporates with financing have grown considerably since the last global financial crisis.

Figure 7 – Size of Collateralized Loan Obligation (CLO) Market – US$ billion

(Source: Citi, FT)

Figure 8 – Size of US Leveraged Loan Market – US$ billion

(Source: S&P, FT)

Figure 9 – Size of Private Debt Market – US$ billion

(Source: Bank of America, FT)

Figure 10 – Size of the Top Bond ETFs Targeting US Investors – US$ Billion

(Source: BitMEX Research Bloomberg)

(Note: The chart represents the sum of the market capitalisations of the following bond ETFs: iShares Core U.S. Aggregate Bond ETF, Vanguard Total Bond Market ETF, iShares iBoxx $ Investment Grade Corporate Bond ETF, Vanguard Short-Term Corporate Bond ETF, Vanguard Short-Term Bond ETF, Vanguard Intermediate-Term Corporate Bond ETF, iShares J.P. Morgan USD Emerging Markets Bond ETF, Vanguard Total International Bond ETF, iShares MBS Bond ETF, iShares iBoxx $ High Yield Corporate Bond ETF, PIMCO Enhanced Short Maturity Strategy Fund, Vanguard Intermediate-Term Bond ETF, iShares Short-Term Corporate Bond ETF, SPDR Barclays High Yield Bond ETF, iShares Short Maturity Bond ETF)

Corporate Debt Markets Conditions

As Figure 11 below illustrates, corporate debt levels have increased considerably since 2008, with gross debt of Russell 3000 companies now totalling US$11 trillion, compared to being just over US$8 trillion at the time of the last crisis. Corporations have taken advantage of low interest rates and the new investment products mentioned above, to borrow money at record levels.

However, as the red line on Figure 11 illustrates, Russell 3000 corporate balance sheet conditions still appear reasonably healthy, with an aggregate net debt to EBITDA of just under 2.5x.   Although this ratio has been increasing in the last few years, it is nowhere near as large as the c3.7x level which was present before the 2008 financial crisis. This perceived strength is caused by a few large tech giants hoarding cash and the strong economy boosting earnings. If the economy turns, corporate balance sheets could start to look unhealthy again, as earnings fall.

Figure 11 – Corporate Debt levels

(Source: BitMEX Research, Corporate Data, Bloomberg)
(Note: The figures consist of aggregate data for all the Russell 3000 companies.)

There is a considerable volume of corporate bonds set to mature in the coming years. This could exacerbate the impact of any liquidity crisis or stress in the fixed income sector. As our analysis shows (Figure 12), US$ 880 billion of corporate debt in the US will mature in 2019.

Figure 12 – Corporate Bond Maturity Wall – US$ billion

(Source: BitMEX Research, Bloomberg)
(Note: Figures are based on a database of around 6,400 US corporate bonds with an aggregate amount outstanding of US$5.7 trillion.)

Perhaps the most alarming indicator is the quality of the corporate debt. Figure 13 shows the credit rating distribution of the outstanding investment grade corporate debt over time.  At the end of 2018, almost 50% of the bonds had been rated at the lowest possible rating for investment grade securities, a far higher proportion than any time in the past 30 years. Figure 14 indicates that the situation from 2021 will get even worse, when the overwhelming majority of corporate debt maturing will be at the lowest investment grade rating.

Figure 13 – S&P Credit Rating Distribution of US Corporate Bonds Over History

(Source: Bloomberg, HSBC USD IG index constituents, including financial and non financial companies)

Figure 14 – S&P Credit Rating Distribution of US Corporate Bonds Outstanding By Maturity

(Source: BitMEX Research, Bloomberg)
(Note: Figures are based on a database of around 6,400 US corporate bonds with an aggregate amount outstanding of US$5.7 trillion.)

Evaluating the credit quality of some of the less conventional debt vehicles mentioned above, is more challenging. However, a recent report from Moody’s indicated that there has been a significant deterioration in the level of protection for investors in the leveraged loans market, as Figure 15 below illustrates.

Figure 15 – Moody’s Assessment Of The Covenant Quality For Leveraged Loans (US & Canada)

(Source: Moody’s, Bloomberg)
(Note: 5.0 is the weakest score, 1.0 is the strongest.)

Figure 16 – Average Total Debt To EBITDA Multiple Of Private Equity Deals

(Source: S&P, FT)

Low Volatility Environment

In our view, the unconventional monetary policies in advanced economies have squeezed investment returns and volatility, all while reducing borrowing costs; this has created an incentive for asset managers to use more leverage and take on more risk. At the same time the same policies have encouraged corporates to take on more debt. It is the fixed income sector, more than any other, which has been impacted by this low volatility. “Risk Parity” type investment strategies have become increasingly popular, where funds manage risk by constructing portfolios according to the risk (volatility) of each asset class and then using leverage to increase returns. The leverage mitigates the impact of lower returns from the higher weighting to the lower risk assets. This typically involves a larger weighting to fixed income, rather than equities, while incorporating more leverage to offset the low returns of these supposedly lower risk assets.

In February 2018, there was a sharp increase in volatility as the VIX skyrocketed and investment strategies focused on shorting the VIX, such as the Velocity Shares Daily Inverse VIX ETN, plummeted in value to almost zero. This was discussed in the March 2018 edition of the BitMEX Crypto Trader Digest. The victims of this were a small number of opportunistic investors looking for easy returns and the impact of the “volocaust” was limited on the rest of the financial system. However, in a way, the February 2018 event was a microcosm for what is happening more generally in fixed income markets. This time the more mainstream investors, are taking advantage of artificially low volatility and cheap borrowing costs. At some point the market will correct, and the impact of this will be far greater than in February 2018, when a multi-trillion dollar asset class unwinds, rather than one only worth several hundred million dollars.

The sequence of events can be described as follows, with various different factors contributing to exacerbate risk:

1. Some catalyst occurs, causing a sharp increase in volatility.
2. Investors will need to de-risk their portfolios, concentrating first in the most liquid markets, fixed income.
3. In the most liquid markets, machines dominate trading and machines are likely to all withdraw liquidity at the same time.
4. Fixed income markets become volatile, illiquid and dysfunctional, as investors rush for the exit.
5. Securitised bond-based assets, such as CLOs and Bond ETFs, trade at a significant discount to their net asset values.
6. The contagion spreads across other liquid asset classes, such as equities.
7. Over the coming years the newly-established components of the debt machine begin to dry up; corporates struggle to refinance and the economy suffers.

Of course we do not know what will be the main catalyst leading to increased volatility. It could be a geopolitical event, excessive levels of emerging market US Dollar-based debt, high levels of leverage in the Chinese asset management industry, passive ETFs, high frequency traders, the contraction of central bank balance sheets too quickly, a large unexpected corporate bankruptcy, the Eurozone debt crisis, even a catastrophic consensus bug in Bitcoin causing volatility ect ect…  

The point is, that whatever the particular event is, it doesn’t really matter. What does matter is the inherent instability and fragility of the financial system, driven by artificially low volatility and excessive leverage. Many may point their fingers at the particular catalyst after the event and blame it for the crisis, but that could be somewhat intellectually dishonest.

Conclusion

Banks are more crucial to the financial system and society than asset managers. If asset managers come under pressure, whilst some high net worth individuals may experience a write down in their assets; retail and corporate deposits should be safe; and therefore the coming crisis could be less intense than 2008. However, critically, the potential for government intervention to mitigate the impacts of the crisis may be more limited than in 2008.

Firstly and most obviously, the toolkit available to central bankers has been greatly diminished, with interest rates already low and their balance sheets already large. Secondly, and perhaps more importantly, is the political side of things. One cannot know for sure, but the typical people behind the Trump, Brexit, or the Yellow Vest movement may not be supportive of certain kinds of government intervention in financial markets.

In today’s more “populist” political climate, it may be more challenging to justify programs such as quantitative easing or other programs designed to increase asset prices at the relative expense of those earning median type salaries, who don’t own a large pool of financial assets. Therefore, in the next crisis, managing the perceived risk of a “political uprising” could significantly reduce the scope of action central bankers may be willing to take.

Keep in mind, there was also political opposition to central bank policy in the aftermath of 2008, with the rebellion peaking in around 2011. Another key difference this time is that available tools to those leading the rebellion then, such as social media, are now more developed. Political uncertainty in the West seems to have increased since 2008. If this uncertainty begins to interact with financial volatility, risks could be exacerbated.

As for when such a crisis will occur, we obviously do not know. In our view, the charts in this report identify a problem, but they do not seem to suggest that we are necessarily right on the precipice of a major crisis; it could be several years away. As for how to profit from such events, this is perhaps even more challenging than predicting their timing. Maybe one could construct a portfolio of VIX calls, long dated corporate bond ETF puts, index-linked government bonds, hedge funds specializing in volatility, gold and maybe to a lesser extent, even Bitcoin. Again, although one cannot know when these events will occur, perhaps now is a time to adjust one’s investment portfolio.

Abstract: We note that the distribution of nonce values in the Bitcoin block header does not appear to be random, with unexplained gaps emerging, where nonces occur less often. We then speculate on why this may be the case and provide charts illustrating the phenomenon. Although in our view the explanation for this is likely to be benign, it remains a mystery.

Overview and Recent Tweets

The Bitcoin nonce forms part of the block header, which is used by miners to provide entropy as part of the Proof of Work process, to try and find a hash meeting the difficulty requirement. Although it may depend on how mining software and hardware is configured, in theory the distribution of the nonce values should be random. In 2009, when Satoshi is presumed to have been a significant miner, the nonce value followed a particular pattern, as we discussed in an earlier piece.

On 4th January 2019, @100trillionUSD tweeted a graphic illustrating the nonce value distribution for Bitcoin. It seemed to show that the nonce value was random from mid 2010 to the start of 2016, after which point four mysterious regions appeared, where nonces occurred less often.

The weird #bitcoin nonce pattern is still there at block 556995 …
I would expect nonces to be totally random, like block 100000-400000. But ever since block 400000 there are 4 areas with less nonces. Of course before block 100000 it was Satoshi playing around. pic.twitter.com/XsIRhDXXAq

— planɃ (@100trillionUSD) January 4, 2019

A few days later, on 7th January 2019, @khannib noted that Monero also appeared to have an unusual nonce value distribution. The Monero hardfork, which may have prevented the usage of ASICs, appears to have made the distribution random again, perhaps indicating ASICs cause the pattern.

Interesting patterns emerge when looking at $XMR nonces. Red overlay is mining difficulty.

Can you spot the ASICs?

H/T @100trillionUSD who did the same for BTC pic.twitter.com/s23WrdTIY0

— Antoine Le Calvez (@khannib) January 7, 2019

On 23rd January 2019, TokenAnalyst dug further into the Bitcoin nonce value distribution pattern, by colouring the nonce values for the relevant mining pools.

Time of each block vs nonce. Values coloured by top-10 most active mining pools (by number of blocks mined since Jan 2016). Dashed-blue vertical lines are block rewards halving. Red line is log difficulty. As you can see – mining is now dominated by a few large pools. pic.twitter.com/wvgtdJmaHA

— TokenAnalyst (@thetokenanalyst) January 23, 2019

A further tweet from TokenAnalyst implies that Antpool is a major cause of the unexpected nonce value distribution, while Bitfury and Slushpool have nonce values which perhaps do not significantly contribute to the “white spaces”.

New Nonce Value Distribution Scatter Charts

We have replicated the above analysis, producing similar scatter charts (starting in 2018); in an attempt to shed more light onto the issue.

We have also produced an individual scatter chart for Antpool, BTC.com, F2Pool, Slushpool and Bitfury. The charts appear to agree with TokenAnalyst’s data, in that the “white spaces” are more clearly visible for Antpool, than for Slushpool and Bitfury. Although, with respect to Slushpool the white spaces are still visible, but they are more faint. Bitfury may not have found enough blocks for one to see a clear pattern. A statistical analysis may also be possible, although the human brain interpreting these scatter charts may be just as useful compared to some forms of statistical analysis.

 

Bitcoin nonce value distribution – All nonces (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – Antpool (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – BTC.com (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – F2Pool (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – Slush (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – Bitfury (Since 2018)

(Source: BitMEX Research)

Bitcoin Cash ABC

Bitcoin Cash ABC also shares the same nonce value distribution pattern as Bitcoin.

Bitcoin Cash ABC nonce value distribution – (Since 2018)

(Source: BitMEX Research)

 

AsicBoost

It is possible that covert AsicBoost may be either a contributing factor or cause of the pattern. The pattern began to emerge around the time many speculate covert AsicBoost started; and this pattern could be a quirk in the implementation of covert AsicBoost, which requires the manipulation of the nonce. However, the usage of covert AsicBoost is believed to have stopped in Bitcoin in 2018, when this pattern continued. Although it’s possible that despite covert AsicBoost itself being stopped, the quirk in the firmware remains.

In the below chart we have looked at the nonce value distribution for blocks mined using overt AsicBoost. Again, the pattern remains visible, albeit faintly. This may suggest the pattern has nothing to do with covert AsicBoost, but it’s far from conclusive.

Bitcoin nonce value distribution – Overt AsicBoost blocks (Since 2018)

(Source: BitMEX Research)

Conclusion

For now, the unusual nonce value distribution on Bitcoin remains a mystery. The community may wish to dig into this issue further and conduct more analysis, such as reviewing mining pool software and ASICs in more detail. We would guess this is nothing more than a meaningless anomaly with a benign cause; however a mystery in Bitcoin like this could be intriguing to some analysts.

Abstract: This is our third major piece on ICOs. In our first piece in September 2017 we focused on the interrelationships between ICO team members. In our second piece, in October 2018, we tracked the Ethereum balances in the ICO treasury accounts. In collaboration with TokenAnalyst, this piece focuses on the treasury balances of the ICO tokens themselves, on the Ethereum network. This report is based on tokens where the team controlled holding’s were worth an astonishing US$24.2 billion on issuance (in reality liquidity was too low for this value to be realized). Today this figure has fallen to around US$5 billion, with the difference primarily being caused by a fall in the market value of the tokens, alongside US$1.5 billion of transfers away from team address clusters (possibly disposals).

(Source: BitMEX Research)

(Note: A reminder of the various interconnections between ICO team members, from our September 2017 interactive graphic)

Team controlled token holdings (Own tokens) – summary data

US$ billion
Value of ICO coins allocated to token teams	21.5
Issuance to team post ICO	2.7
Total issuance to team controlled wallets	24.2
Coins leaving the team address cluster (Perhaps sales)	(1.5)
Profits/(losses) due to token price changes	(12.0)
Net impact of Noah (token burn)	(4.4)
Net Impact of EOS	(1.2)
Current team holdings	5.0

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018, based on data for 108 tokens)

Of the US$24 billion worth of tokens ICO project teams issued to themselves, 54% of the value has been lost due to coin price reductions. The peak valuation of the team holdings of their own tokens, using the individual price peak for each coin, is over US$80 billion. This larger figure implies US$70 billion of “losses” from the peak. Although peak valuation highly is dubious due to a lack of liquidity and most of the tokens were granted to the teams essentially for nothing, therefore classifying these price movements as losses may not be appropriate. Unlike ICO investors, the teams did not have an offering price or initial investment. However, some trading activity occurred at these ridiculously high valuations, therefore we believe it’s still interesting to consider these figures, while bearing these caveats in mind.

Based on current illiquid spot prices, the ICO teams still appear to own around US$5 billion of their own tokens, money they essentially got from nothing, depending on ones view. At the same time the teams may have realized gains of US$1.5 billion by selling tokens, based on coins leaving team address clusters. Although this figure may also be an overestimate, as coins could have left the team address cluster for a variety of reasons.

Data Caveats & weaknesses in calculation methodology

• The liquidity of many of these tokens is low and therefore the US Dollar values may be gross overestimates, this applies to both the initial allocation, current value and the value of any losses. In some cases, the value of tokens given to the team, for instance with projects such as Veritaseum or Noah, were almost comically large relative to the real trading volume in the coins. Therefore it can be considered unrealistic to value the team holdings based on the exchange price of the tokens.
• The challenge and uncertainty involved in producing this dataset surrounds the allocation of the tokens to the team address cluster. TokenAnalyst conducted this allocation. The methodology used was imperfect and we have not dug into individual projects. The data was obtained by analysing the token smart contracts and transaction patterns on the Ethereum blockchain and applying machine learning type techniques to establish a team controlled address cluster for the team of each project. The data is therefore a probabilistic estimate and is likely to be inaccurate at the individual project level. However, the primary motivation for this report was to produce macro data about the team holdings of ICO tokens on Ethereum. Although this analysis has produced results which are far from perfect, we believe one can draw reasonable macro conclusions from the analysis.
• As mentioned above, our analysis is based on reviewing smart contract data and transaction patterns, not documents and policies of individual projects. Therefore, it’s possible we included tokens as part of a team balance, although in reality they are held as part of another form of reserves, escrow or some other category, where it’s inaccurate to attribute the coins to the team’s own funds.
• The data assumes the issuance date is the same date as when the first price data appeared on Coinmarketcap, this may not be a reliable assumption.

Summary data

Value of coins issued to team controlled address clusters (own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Token data up to Dec 2018, data based on prices at the time(s) of issuance)

Loss in value of team controlled holding (own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Proportional loss in value of coins in team controlled address clusters (own tokens) – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Value of coins transferred out of team controlled address clusters (Own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018. Huobi and Qash are exchanges and the tokens appear to have been sent to their respective platforms. It is possible the above figures represents sales/”cashing out”, although there could be other reasons for the transfers)

Current value of coins in team controlled address clusters (Own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

The raw data – Team holdings of own tokens – US$ million

Token	Value at ICO	Post ICO issuance	Transfers away from team cluster	Loss in value	Current value
VERI	4,762	0	(15)	(3,196)	1,552
NOAH	4,478	0	(4,423)		55
KIN	980	0	(0)	(703)	277
AGI	863	0	(27)	(814)	22
POLY	842	0	(17)	(727)	99
HT	643	0	(366)	(29)	248
GNO	636	0	0	(533)	103
QASH	617	0	(177)	(300)	140
MKR	596	0	(46)	(445)	105
TEL	452	0	(8)	(408)	36
ITC	334	0	(7)	(323)	4
ZRX	333	0	(9)	(155)	169
ZIP	266	0	0	(226)	41
BLZ	256	0	(32)	(207)	17
GTO	241	0	(67)	(157)	17
BNB	219	110	0	118	447
BTO	198	0	(28)	(165)	5
ICX	160	0	(79)	(67)	14
ETHOS	153	0	(15)	(123)	16
TNT	152	0	(10)	(133)	9
CENNZ	143	0	(6)	(121)	15
AST	141	0	(24)	(104)	13
KEY	132	0	(2)	(124)	6
BIX	118	2	0	(85)	35
CVC	117	0	(1)	(75)	41
FSN	100	0	(6)	(75)	19
OCN	100	0	(31)	(64)	5
DEW	95	0	(1)	(87)	7
SRN	89	0	(15)	(69)	4
MDS	88	0	(8)	(75)	5
EDO	83	0	(11)	(58)	15
ABT	76	0	0	(71)	5
WTC	69	0	(50)	17	37
INS	68	0	0	(66)	2
PPT	65	0	(55)	(5)	5
IHT	65	0	(2)	(58)	5
CPT	65	0	(0)	(43)	21
SPHTX	64	0	0	(60)	4
DRGN	58	0	(47)	(2)	8
MCO	54	0	(89)	72	37
XYO	54	0	(6)	(23)	25
RCN	54	0	0	(48)	6
DPY	47	0	(23)	(22)	2
THETA	45	0	0	(30)	16
MANA	41	0	(95)	127	73
R	40	0	0	35	75
APPC	35	0	(24)	(9)	2
CMT	33	0	(1)	(25)	8
FUEL	32	2	0	(29)	5
CREDO	31	0	(0)	(6)	25
DMT	31	0	(17)	(12)	2
POWR	30	166	0	(154)	42
LRC	30	8	0	(21)	17
WPR	26	0	0	(24)	2
AMB	24	0	0	(17)	7
RNT	22	0	(1)	(15)	7
ENG	22	0	0	(12)	10
COB	22	0	(10)	(5)	7
GTC	20	126	0	(141)	6
REN	19	0	(3)	(13)	3
DENT	19	635	0	(564)	90
UTT	19	0	(0)	(11)	8
AE	13	0	(19)	6	0
DATA	11	0	(3)	(6)	3
BRD	10	17	0	(21)	7
SNGLS	8	0	0	(3)	6
LEND	6	0	(7)	3	2
RLC	6	0	(5)	2	3
PLR	6	3	0	(4)	5
HVN	5	0	(5)	0	1
CVT	5	11	0	(8)	9
LYM	5	0	(4)	0	2
SAN	5	0	(7)	5	4
GNT	4	0	(12)	31	23
KICK	3	2	0	(4)	1
DGD	2	0	(5)	5	3
EDG	2	0	(29)	28	1
ENJ	2	0	(0)	1	2
RHOC	1	14	0	(13)	1
ARN	0	6	0	(6)	1
ELF	0	45	0	(40)	6
PAY	0	142	0	(132)	11
DAI	0	1	0	0	1
HPB	0	134	0	(119)	15
CRPT	0	3	0	(2)	1
HOT	0	7	0	0	7
SALT	0	95	0	(92)	3
NAS	0	71	0	(50)	21
NGC	0	12	0	(11)	1
CPC	0	12	0	(9)	3
GVT	0	3	0	(2)	2
SNM	0	14	0	(11)	2
BTM	0	9	0	(1)	8
QRL	0	7	0	(6)	2
NULS	0	71	0	(52)	19
POE	0	58	0	(54)	4
TEN	0	29	0	(15)	13
MTL	0	188	0	(177)	11
WINGS	0	18	0	(15)	3
SPANK	0	106	0	(93)	13
OMG	0	195	0	(154)	41
STORJ	0	133	0	(85)	48
BAT	0	38	0	14	52
VIBE	0	10	0	(8)	2
IOST	0	218	0	(185)	34
Total	21,513	2,723	(14,805)	(4,396)	5,035

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Conclusion & summary data

This analysis highlights the lack of standards and transparency in the ICO market, especially when it comes to the allocation of tokens to the founding team’s wallet. Teams were often able to mint, burn, buy, and sell (their own) tokens at will, without analysts being able to easily track what is occurring. We would often see tokens in exchange clusters, and it was hard to tell whether the token project “paid” the exchange to list tokens or the token project just transferred their treasury to the exchange to cash out.

To be fair, perhaps we could improve the analysis by spending more time reading the specific documentation of the individual projects and by speaking to the teams involved. This would have resulted in a more robust dataset.

But one thing about ICOs that many people often overlook, is that ICO teams often make profits in two ways from the issuance:

1. Selling the newly issued tokens (often for Ethereum), and,
2. Issuing themselves their own tokens.

Our October 2018 report focused on the former, while this report focuses on the latter. The summary table below combines the figures from both of our reports.

ICO team profits	US$ billion
ICO process
Ethereum Raised	5.4
Own tokens issued to founding teams	24.2
Total raised	29.6
Changes in coin price
Ethereum profits/(losses) – Mostly realised	0.8
Own token profits/(losses) – Mostly unrealised	(17.6)
Total profits/(losses) post issuance	(16.8)
Total ICO team profits	12.8

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Ethereum price data to October 2018, Own token price data to January 2019)

Although, as we have repeatedly explained, there are many inaccuracies and assumptions involved in producing the data. Based on our methodology, it appears as if ICO teams have profited by almost US$13 billion from this ICO process. In our view, this money was made incredibly easily, with very little work, accountability or transparency. Therefore, ICOs have proven to be an extremely attractive way for project founders to raise funds. The results for investors of course, have not been as attractive.

The ICO cycle now appears to be dying down to some extent and it’s much harder to raise funds than it was in late 2017. But with so much money made and lost, the events of 2017 and early 2018 are not likely to be quickly forgotten. Entrepreneurs will remember the success (and keep trying to raise money) while investors will remember the pain. A repeat of this cycle within a few years is therefore less likely than many may think.

Abstract: In this piece, we look at a common problem facing both distributed exchanges and cross-chain atomic swaps: what we call the “inadvertent call option.” Non-custodial fully-distributed trading systems often inadvertently create an American-style call option, rather than the more simple desired operation of exchanging one asset for another. We review how this same issue applies to some specific distributed trading platforms like Bisq and particular cross-chain atomic swaps constructions. We then look at how IDEX solves this problem, but then requires users to trust the platform operator, to some extent, by removing some benefits of distributed exchanges. We conclude that despite the added complexity, in some circumstances it may be better to embrace the call option feature as a viable product, rather than ignoring or fighting it.

Overview

Alongside distributed stablecoins, distributed exchanges (DEXs) are often seen as one of the two holy grails within the cryptocurrency ecosystem. However, similar to distributed stablecoins, the challenges involved in DEXs are often underestimated. In this piece, we focus on one specific challenge with distributed exchange systems: instead of allowing simple exchange, these systems often inadvertently produce American-style call options.

The Theory Of The Inadvertent Call Option

When trading one cryptocurrency asset for another in any fully non-custodial system, one party must act first and the second party must follow. In theory, at some point, this second party then has optionality: – he or she can either follow through and complete the trade, or take no action and stop the trade. In the time interval between the first party taking the necessary action and the second party being required to act, if the price of the token the second actor is attempting to buy falls in value, or the price of the token he is selling increases in value, he could refuse to complete the trade. This means the following:

1. The trader who acts first has written an American call option on the spread between the two assets.
2. The trader who acts second has purchased an American call option on the spread between the two assets.

These exchanges can either happen atomically or as two separate transfers. Let’s consider Alice buying Bitcoin from Bob, using Litecoin.

	Description	Call option problem
Atomic trading	Either both the Litecoin transaction and Bitcoin transaction occur, or both transactions fail (e.g. Cross-chain atomic swaps).	One party must act first and then the second party can decide to execute both trades or not. This decision can be influenced by price changes in any of the two assets in the intervening period. This provides the second party to act with optionality.
Non-atomic trading	It is possible for one of the transactions in the trade to succeed and the other to fail. In this case, typically a quasi-custodial mechanism such as multi-signature escrow, is required to prevent cheating for at least one side of the trade (e.g. Bisq-type platforms).	One party must act first and then the second party can decide to execute his part of the transfer or not. Failing to execute the second transfer could result in either: The second party successfully stealing funds from the first party The third party escrow agent reversing the first transaction Either way, the second party has optionality.

As the table above illustrates, whether the trading is atomic or not, the same optionality principle applies.

One may think this is an insignificant issue, as the time periods can be short or the option value may be low; however, this is typically not the case: the option period is often around 24 hours and cryptocurrency prices can be very volatile. This high volatility is typically the reason traders wish to exchange the tokens in the first place. Therefore, the option value can be significant and impact trading.

It may be possible to mitigate or solve this problem by using more steps involving more deposits, but we have not yet observed a system achieving this. The other way to mitigate the problem is via the reputation of individual traders and a distributed web of trust-based systems, with traders revealing a form of identity. Traders can then lose reputation if they cancel trades based on price volatility. However, this may greatly increase the complexity of the systems, as functioning sybil attack resistant distributed reputation systems are also challenging to construct.

Below we examine three differently constructed distributed exchange type systems (or quasi DEXs) and explain how the call option problem arises.

Case Studies

Bisq

Summary table

Type	Non-atomic
Optionality period	24 hours (up to 8 days)
Escrow	Multisignature escrow for the trader selling Bitcoin only

Bisq (previously known as Bitsquare) is a peer-to-peer application, which enables one to buy and sell cryptocurrency with fiat money, as well as trade between crypto-tokens. Bisq is essentially a DEX, as traders connect to each other over a peer-to-peer network and transact with each other directly.

Bisq Daily Trading Volume (USD)

(Source: Coinmarketcap)

Screenshot from the Bisq platform

(Source: BitMEX Research)

Below we explain some examples of potential trading activity on Bisq and describe the resulting options.

Example 1: Acquiring Bitcoin with USD on Bisq

Alice wishes to purchase 1 BTC from Bob, using U.S. dollars:

• Step 1: Bob places 1 BTC in a 2 of 3 multisignature account. The three signatures belong to Bob, Alice, and a third-party arbitrator. This is Bob’s offer, which includes a price (e.g. US$3,800 per BTC).
• Step 2 – Alice can accept Bob’s offer by paying a small refundable deposit into another multisignature account. The fee is set by Bob (e.g. 0.01 BTC).
• Step 3 – Alice has 24 hours to conduct a bank wire transfer, paying US$3,800 into Bob’s account. If there is no dispute and the wire transfer occurs, Alice receives the 1 BTC and her deposit back. If no wire transfer occurs, Alice loses the deposit and the 1 BTC is returned back to Bob. Any dispute is mediated by the third-party arbitrator.

The above represents Alice buying Bitcoin; however, when considering the economic incentives involved, since Alice can back out of the trade with limited consequences, one could consider that, after step 2, she has acquired the following American-style call option:

Call option details

Underlying: Bitcoin Quantity: 1 Strike: $3,800 Time to expiry: 24 hours Premium: 0.01 BTC

Therefore, when Bob determines the value of the deposit Alice is required to pay, in theory he should consider Bitcoin’s volatility and use options pricing systems to ensure Alice is unable to acquire the option for a cheap price. Based on the prices currently available on Bisq, it appears many of these options are undervalued.

Example 2: Acquiring Bitcoin with Monero on Bisq

Alice wishes to purchase 1 BTC from Bob, using Monero (XMR):

• Step 1 – Bob places 1 BTC in a 2 of 3 multisignature account. The three signatures belong to Bob, Alice, and an arbitrator. This is Bob’s offer, which includes a price (e.g. 80 XMR per BTC).
• Step 2 – Alice  can accept Bob’s offer by paying a small refundable deposit and a fee set by Bob (e.g. 0.01 BTC).
• Step 3 – Alice has 24 hours to do an on-chain Monero transfer. If there is no dispute and the transfer occurs, Alice receives the 1 BTC and her deposit back. If no wire transfer occurs, Alice loses the deposit and the 1 BTC is returned back to Bob. Any dispute is mediated by the arbitrator.

Again, the above example may be considered as Alice buying Bitcoin; however, when considering the incentives, since Alice can back out of the trade with limited consequences, one could consider that she has acquired the following American-style call option:

Call option details

Underlying: Bitcoin Quantity: 1 Strike: 80 XMR Time to expiry: 24 hours Premium: 0.01 BTC

If one is trying to capture the benefits of buying a call option with a low premium, this Monero trade may be more beneficial than the U.S. dollar version, since the Monero price is more volatile, and therefore the value of the option is higher. Since the Monero price is more volatile than Bitcoin, it may be more economically correct to conclude that Alice has acquired the following put option, rather than a call option.

Put option details

Underlying: Monero Quantity: 80 Strike: 0.013 BTC Time to expiry: 24 hours Premium: 0.01 BTC

As a trader, if one wants to take advantage of this structure, one could purchase these Monero puts for a low premium and then hedge the exposure by going long Monero on a centralised platform. However, Bisq has small position limits and therefore the size of the profit-making opportunity is limited.

Although it may make marketing the platform more challenging, it might make Bisq more robust to rebrand these trades as options and encourage sellers of Bitcoin to set the deposit price such that it’s consistent with the premium payment for the equivalent option based on the price volatility of the assets involved, for example by using the Black–Scholes model.

Cross-Chain Atomic Swaps

Summary table

Type	Atomic
Optionality period	24 hours (or whatever the parties set as the lock time period)
Escrow	None

We believe cross-chain atomic swaps were first described by TierNolan on the Bitcointalk forum in May 2013. Cross-chain atomic swaps allow users to exchange one asset for another atomically, such that the entire process either succeeds or fails. This allows for no risk for either party losing out by only one of the two transfers completing.

The following illustration describes the on-chain atomic swap process.  It is based on a swap between Alice and Bob, with Alice exchanging 1 Bitcoin for 100 Litecoin belonging to Bob.

Cross-chain Atomic Swap Construction

#	Actor	Description
1	Alice	Alice picks a random number X.
2	Alice	Alice creates a transaction sending 1 BTC to Bob. Transaction 1 The transaction can be redeemed when either: Bob signs it and X is known, such that the hash of X is a necessary value. Both Alice and Bob sign it.
3	Alice	Alice creates and signs a transaction sending the 1 BTC output of transaction 1, back to herself. Transaction 2 The transaction is time locked for 24 hours.
4	Alice	Alice sends transaction 2 to Bob.
5	Bob	Bob signs transaction 2 and returns it to Alice.
6	Alice	Transaction 1 is broadcast to the Bitcoin network.
7	Bob	Bob creates a Litecoin transaction sending 100 LTC to Alice. Transaction 3 The transaction can be redeemed when either: Alice signs it and X is revealed, such that the hash of X is the necessary value. Both Alice and Bob sign it.
8	Bob	Bob creates and signs a transaction, sending the 100 LTC output of transaction 3 back to himself. Transaction 4 The transaction is time locked for 24 hours.
9	Bob	Bob sends transaction 4 to Alice.
10	Alice	Alice signs transaction 4 and returns it to Bob.
11	Bob	Transaction 3 is broadcast to the Litecoin network.
At this point, Alice has the optionality.If the LTC/BTC price ratio increases, she could continue the swap process. Or, if the LTC/BTC price ratio falls, Alice can end the process here. Call option details Underlying: Litecoin Quantity: 100 Strike: 0.01 BTC Time to expiry: 24 hours Premium: 0 Type: American
12	Alice	Alice spends the output of transaction 3 to herself, revealing X. Alice now has 100 LTC.
13	Bob	Bob spends the output of transaction 1 to himself, using the X Alice provided above. Bob now has 1 BTC.

(Source: BitMEX Research)

As the table illustrates, although one is attempting to structure an atomic swap, similar to Bisq, it has inadvertently resulted in an American-style call option. The same issue appears to apply to either multi-currency routing via the lightning network or off-chain lightning-based cross-chain atomic swaps, during the construction of the channels. It could be possible to solve these issues with more steps and a longer series of deposits, although this added complexity may make implementation more challenging. Just as for Bisq above, it may be more appropriate for cross-chain atomic swap developers to embrace the call options and make it the product, rather than to try to brush this problem under the carpet or solve it with added complexity.

IDEX

Summary table

Type	Atomic
Optionality period	n/a
Escrow	Partial escrow for both sides of the trade with IDEX, with a sunset clause

IDEX is an exchange platform using the Ethereum network. Traders deposit funds into an Ethereum smart contract, where the signature of both the traders and the IDEX platform is required to submit orders, execute trades, or make payments.

After a certain time horizon, users can withdraw funds from the smart contract without a signature from IDEX, which protects user deposits in the event that IDEX disappears. Order submission, order cancellation, and order matching is conducted off-chain on the IDEX servers, to allow for a fast and seamless user experience. The events are then submitted in sequence to the Ethereum blockchain and are only valid with a valid signature from the users. Therefore, IDEX is unable to steal user funds or conduct trades without user authorisation.

According to Dex.Watch, IDEX is the global number one Ethereum-based DEX, with an approximate market share of 50%. IDEX-type platforms are in many ways more advanced than the exchanges above as they can solve the call option problem by having both party’s funds partially held in escrow during the trading period.

IDEX Daily Trading Volume ( USD)

(Source: Coinmarketcap.com)

Although IDEX cannot steal user funds or conduct trades without authorisation, the order of events is determined centrally by IDEX. IDEX could fail to execute an order in a timely manner as well as front run orders or fail to execute an order cancellation in a timely manner. Therefore, while users are protected from some risks common in centralised exchanges, in practise they are still exposed to many of the risks most often talked about with respect to typical centralised exchanges. However, we still consider IDEX type platforms to potentially be a significant improvement compared to the fully centralised alternatives.  IDEX also has other limitations such as one can only trade Ethereum-based assets and the platform is eventually constrained by Ethereum network capacity.

Conclusion

In some ways Bisq’s model is more ambitious than IDEX and cross-chain atomic swaps. IDEX limits itself to tokens that exist on the Ethereum network while atomic swaps only deal with some cryptocurrencies. In contrast, Bisq attempts to handle fiat currencies such as the U.S. dollar. While solving the call option problem may be possible using Ethereum smart contracts or more complex lightning network constructions when, fiat currency is involved, it may be impossible to solve.

Of course atomic swaps and an IDEX type platform could work with US dollars if there is a working distributed U.S. dollar stablecoin. This illustrates how the two holy grails, distributed stablecoins and distributed exchanges, are interrelated. In a catch-22 type situation, each can only function robustly if the other exists.

Without a distributed stablecoin, in our view, when trading fiat currency for cryptocurrency through distributed systems, the use of call options could be inevitable. Bisq is potentially a useful distributed onramp into the cryptocurrency ecosystem; however, rather than trying to solve the call option problem, perhaps Bisq should embrace it. Maybe an effective onramp into the cryptocurrency ecosystem could be via American-style call options. While this may not be easy, it may be the only way to structure a robust highly censorship-resistant way in.