Abstract: In this piece we present data on a relatively new phenomenon, Initial Exchange Offerings (IEOs). The ICO market is down around 97% in Q1 2019 (YoY), based on the amount of capital raised. In this relatively challenging climate to raise funds, some projects have changed the “C” in ICO to an “E”, perhaps in an attempt to assist with raising capital. At least for now, to some extent, this appears to be working, with almost $40m having been raised so far this year. However, we remain sceptical about the prospects for long term investors.

Overview

We consider an Initial Exchange Offering (IEO) as the issuance and sale of a token based on public-private key cryptography, where participation in the issuance occurs exclusively through one trading platform or exchange. This piece provides a basic overview of the largest IEOs and tracks various IEO token metrics, including investment performance.

ICO market

First we briefly look at the ICO market. As the following chart indicates, the market has dried up following a massive boom in 2017 & 2018.

Funds raised by ICOs – US$M

Source: BitMEX Research, icodata.io
Notes: Data as at 25 April 2019

As the below chart illustrates, the investment returns of the 2018 ICOs has been poor, many of the projects are down around 80% from the ICO price, if the coin even trades at all. Peak to trough, project token prices typically declined much further than this.

Top ten ICOs by funds raised in 2018 – Investment performance data

ICO Name	Funds raised – US$m	Return based on average ICO price
EOS	4,234	(4%)
Telegram	1,700	Coin not listed
Dfinity	195	Coin not listed
Bankera	150	(87%)
t0	134	Coin not listed
Basis	125	Returned capital to investors
Orbs	118	(64%)
PumaPay	117	(93%)
Jet8	33	(99%)
Unikoin Gold	32	(88%)

Source: BitMEX Research, tokendata.io
Notes: Data as at 25 April 2019

Changing a “C” into an “E” – The IEO market

Perhaps in an attempt to address some of the concerns about the poor investment returns and the lower levels of enthusiasm for ICOs, IEOs appear to have gained in popularity. Below is a list of the major IEOs and the main exchange platforms involved.

List of IEO token sales

Coin	IEO Date	IEO issue amount vs total coin supply	Return vs first exchange trade price	Return vs IEO price	US$m raised in IEO
Binance
Gifto	21/12/2017	3.0%	(90.5%)	142.2%	0.4
Bread	26/12/2017	7.9%	(84.2%)	164.6%	0.8
Fetch.AI	02/03/2019	6.0%	(55.0%)	203.6%	4.1
BitTorrent	03/02/2019	6.0%	54.8%	433.9%	7.5
Celer	24/03/2019	6.0%	(29.4%)	82.0%	4.5
Matic	24/04/2019	19.0%	Ongoing
Binance Total					17.3
Huobi
TOP	26/03/2019	7.5%	10.2%	357.0%	3.3
Newton	16/04/2019	2.0%	(23.0%)	197.0%	4.8
Huobi Total					8.1
Bittrex
VeriBlock	02/04/2019	3.3%	(30.9%)	(33.0%)	7.0
RAID	Canceled
OKEX
Blockcloud	10/04/2019	5.0%	(43.1%)	599.7%	2.5
BitMax
Dos Network	11/04/2019	14.2%	(55.1%)	74.2%	1.7
Kucoin
MultiVAC	03/04/2019	6.0%	(30.5%)	20.0%	3.6

Source: BitMEX Research, IEO Launchpad websites, Coinmarketcap
Notes: Data as at 25 April 2019

The number of IEOs taking place has intensified in recent months, as the model is proving somewhat successful. Smaller exchange platforms are attempting to replicate the model, as the long list of IEOs below illustrates.

Other IEOs with limited data available

Coin	IEO Date	Platform
Coin Analyst	07/07/2018	Exmarkets
SID Token	15/11/2018	Exmarkets
Rebglo	01/12/2018	Coineal
Aerum	01/01/2019	Exmarkets
TerraGreen	21/01/2019	Exmarkets
Verasity	01/03/2019	Probit
Percival	05/03/2019	Coineal
Decimated	06/03/2019	Exmarkets
Menapay	15/03/2019	Exmarkets
Linix	16/03/2019	Probit
Levolution	18/03/2019	Coineal
WeGen	18/03/2019	Probit
Spin Protocol	19/03/2019	Probit
CharS	20/03/2019	Probit
Windhan Energy	21/03/2019	Exmarkets
HUNT	23/03/2019	Probit
KIZUNA GLOBAL TOKEN	25/03/2019	Coineal
PUBLISH	26/03/2019	Probit
ZeroBank	31/03/2019	Coineal
REDi	03/04/2019	Probit
VenusEnergy	04/04/2019	Exmarkets
Bit Agro	05/04/2019	Exmarkets
XCon	06/04/2019	Coineal
YellowBetter	08/04/2019	Bitker
Link by BlockMason	09/04/2019	BW
GTEX Gaming Platform	12/04/2019	Coineal
AlienCloud	16/04/2019	IDAX
Evedo	16/04/2019	Bitforex
PantheonX	18/04/2019	BW
NUVO	19/04/2019	Probit
Grabity	19/04/2019	BW
Farm2Kitchen	22/04/2019	Exmarkets
Cryptobuyer	23/04/2019	Coineal
Airsave Travel	01/05/2019	Exmarkets

Source: BitMEX Research, IEO Launchpad websites

With respect to all but one of the tokens, investors have earned strong positive returns based on the IEO price. However, after the tokens begin trading, the investment returns have typically been poor. This is illustrated by the below chart, which rebases the token price to the IEO issuance price.

IEO Investment performance since launch (IEOs in 2019)

Source: BitMEX Research, IEO Launchpad websites, Coinmarketcap
Notes: Data as at 25 April 2019

US$38.9m has been raised so far by IEOs in 2019 (up to 25th April). Binance has been the most prolific IEO platform by a considerable margin.

Top exchange platforms by IEO funds raised – US$m

Source: BitMEX Research, IEO Launchpad websites, Coinmarketcap
Notes: Data as at 25 April 2019

The proceeds from IEOs can be relatively small, however on average only 4.4% of the total token supply is made available in the sale. Therefore, there are opportunities for project teams to make considerable profits from selling coins they granted to themselves. The 2019 IEOs were priced at a level which implies a total market capitalisation of US$907.7m, based on the disclosed total token supply.

Top exchange platforms by IEO token market capitalisation at IEO price – US$m

Source: BitMEX Research, IEO Launchpad websites, Coinmarketcap
Notes: Data as at 25 April 2019

Conclusion

While exchanges, traders & subscribers may have done very well from IEOs thus far, we are less confident on the outlook for long term investors. However, this is simply a high level analysis – we have not looked into any of the individual projects in detail.

Disclaimer

Any views expressed on BitMEX Research reports are the personal views of the authors. BitMEX (or any affiliated entity) has not been involved in producing this report and the views contained in the report may differ from the views or opinions of BitMEX.

The information and data herein have been obtained from sources we believe to be reliable. Such information has not been verified and we make no representation or warranty as to its accuracy, completeness or correctness. Any opinions or estimates herein reflect the judgment of the authors of the report at the date of this communication and are subject to change at any time without notice. BitMEX will not be liable whatsoever for any direct or consequential loss arising from the use of this publication/communication or its contents.

If we have made any errors in relation to particular projects, we apologise and are happy to correct the data as soon as possible.

Abstract: We summarise and provide context for a recent Bitcoin softfork upgrade proposal, which includes a new digital signature scheme (Schnorr), as well as a complementary upgrade called Taproot, which adds new capabilities that extend Bitcoin’s smart contracting capability. The upgrades are structured to ensure that they simultaneously improve both scalability and privacy. Other than increased complexity, there are no significant downsides to the proposal, and the most controversial aspect of it is likely to be the lack of other anticipated features. We conclude that although many will be enthusiastic about the upgrade and keen to see it rolled out, patience will be important.

(Source: Pexels)

Overview

On 6th May 2019, Bitcoin protocol developer Pieter Wuille posted a softfork upgrade proposal to the Bitcoin developer mailing list, called “Taproot”. If this proposal is accepted, it is likely to complement the Schnorr signature softfork upgrade, which Pieter posted in July 2018. The benefits of these proposals are related to both scalability (efficiency) and privacy. Scalability and privacy enhancements now appear somewhat interrelated and inseparable. Removing details about transactions, ensures both that transactions are smaller (improving scalability) and that they reveal less information and are therefore potentially indistinguishable from transactions of different types, thereby improving privacy.

Schnorr Signatures

The Schnorr signature scheme was patented in 1991 by Claus Schnorr and the patent expired in 2008. Although the Schnorr scheme is said to be stronger, a variant of it, the Digital Signature Algorithm (DSA) scheme was more widely adopted, as the patent for this scheme was made available worldwide royalty free. However, Dr Schnorr himself always maintained that DSA should be covered under his patent.

When Bitcoin was launched, in 2009, it therefore used a variant of DSA, Elliptic Curve Digital Signature Algorithm (ECDSA) for its digital signature scheme, due to its widespread adoption. However, the original Schnorr signature scheme was always more simple and efficient than DSA, with less burdensome security assumptions. After 10 years of experience of Bitcoin usage, it is becoming more apparent that these efficiency advantages could be important. Therefore it seems sensible that Bitcoin should migrate over to the Schnorr signature scheme.

The main benefit with Schnorr signatures, is that multi-signature transactions appear onchain as a normal single signature transaction. Using Schnorr signatures, multiple signers can produce a joint public key and then jointly sign with one signature, rather than publishing each public key and each signature separately on the blockchain. This is a significant scalability and privacy enhancement. This implies that Schnorr signatures result in significant space savings and savings to verification times, with the comparative benefits getting larger as the number of signatories on a traditional multi-signature transaction increase.

Schnorr signature space saving estimates

We have tried to calculate the potential Bitcoin network capacity increase this aggregation feature of Schnorr multisig can provide. However, due to the large number of assumptions involved, our 13.1% capacity increase figure below should be considered as a very approximate estimate.

Savings estimates based on UTXO count

Estimated current multi-signature usage by UTXO count	5.9%
Effective network capacity increase assuming 100% Schnorr adoption	13.1%

(Source: BitMEX Research calculations and estimates, p2sh.info)

(Notes: The estimates ignore the impact of Schnorr’s smaller signature size and only include the benefits of joining the public keys and signatures. The capacity increase was estimated by using p2sh.info related to multi-signature usage and applying a savings multiple to each multi-signature type (ranging from 50% to 85%). A network wide capacity increase was estimated by assuming the UTXO usage proportion was typical of blockchain usage and applying a higher weight to larger multi-signature transactions. Unspent P2SH outputs were allocated to multi-signature types in proportion to the spent outputs. This figure should only be considered as a very approximate estimate. Data as at 07 May 2019 )

The above estimated capacity increase can be considered as small, however one should consider the following:

• Economic usage of multi-signature technology is far more prevalent than by merely looking at the UTXO count. Around 21.5% of all Bitcoin is stored in multi-signature wallets, a far higher figure than the 5.9% adoption by UTXO count
• Multi-signature adoption is growing rapidly, as the below chart indicates. While at the same time new systems like the lightning network require multi-signature adoption and with Schnorr signature making multi-signature systems more powerful, adoption is likely to increase

Bitcoin stored by P2SH address type – chart shows strong growth of multi-signature technology

(Source: p2sh.info)

Therefore, although based on the current usage of the network, according to our basic calculation, even 100% Schnorr adoption only results in a 13.1% network capacity increase, in the long term the potential space savings and network capacity gains are likely to be far higher than this.

Merkelized Abstract Syntax Tree (MAST)

MAST was an idea worked on by Bitcoin protocol developer Dr Johnson Lau in 2016. Dr Lau has written for BitMEX Research in the past, in his February 2018 piece entitled The art of making softforks: Protection by policy rule. The MAST idea is that transactions can contain multiple spending conditions, for example a 2 of 2 multi-signature condition, in addition to a time lock condition. In order to avoid putting all these conditions and scripts into the blockchain, the spending scripts can be structured inside a Merkle tree, such that they only need to be revealed if they are used, along with the necessary Merkle branch hashes.

Graphical illustration of MAST spending conditions

(Source: BitMEX Research)
(Notes: The diagram is trying to illustrate a transaction structure assuming MAST was used in conjunction with Schnorr. In the above construction funds can be redeemed the cooperative way if both Bob and Alice sign, or in an uncooperative way after a timelock. The above is supposed to illustrate the type of structure which could be required when opening and closing lightning network channels)

Based on the above design, it can be assumed that only one spending condition will need to be revealed. For example, to spend the output, all the signers need to do is provide one Schnorr multi-signature and the hash at the top of the right hand side of the Merkle tree (Hash (1 & 2)). Therefore despite the existence of a Merkle tree, in the majority of cases, where everything goes as planned, only a single signature and 32-byte hash is required. More concisely, in order to verify a script, you need to prove that it is part of the Merkle tree by revealing other branch hashes.

However, the disadvantage of this structure is that even in normal optimal circumstances, when the single key and script on the top left of the Merkle tree is provided, one still needs to publish another hash to the blockchain (Hash (1 & 2) in the above diagram), using up 32 bytes of data. This weakness also reduces privacy, since third parties can always determine if more complex spending conditions exist, as the top branch of the Merkle tree is always visible.

Taproot

As far as we can tell, the origins of the Taproot idea are from an email from Bitcoin developer Gregory Maxwell in January 2018. Taproot is similar in construction to MAST, except at the top of the Merkle tree. In the case of Taproot, in the cooperative or normal scenario, there is an option for only a single public key and single signature to be published, without the need to publish evidence of the existence of a Merkle tree. An illustration of the Taproot transaction structure is provided below.

Graphical illustration of Taproot spending conditions

(Source: BitMEX Research)

(Notes: The diagram attempts to illustrate the same spending criteria as the MAST diagram above)

The tweaked public key on the left (or address) can be calculated from the original public key and the Merkel root hash. In the event of a normal or cooperative payment, on redemption, the original public key is not required to be onchain and the existence of the Merkle tree is not revealed, all that needs to be published is a single signature. In the event of a lack of cooperation or abnormal redemption, the original public key is revealed along with information about the Merkle tree.

The benefits of Taproot compared to the original MAST structure are clear, in the cooperative case, one is no longer required to include an extra 32-byte hash in the blockchain or the script itself, improving efficiency. In addition to this, the transactions looks “normal”, just a payment with a public key and signature, the existence of the other spending conditions do not need to be revealed. This is a large privacy benefit, for example when opening a lightning channel or even doing a cooperative lightning channel closure, to an external third party observer, the transaction would look exactly like a regular spend of Bitcoin. The transaction could be structured such that only in an uncooperative lightning channel closure would the existence of the Merkle tree need to be revealed. The more different types of transactions look the same, the better it is for privacy, as third parties may be less able to determine which types of transactions are occurring and establish the flow of funds. A long term objective from some of the Bitcoin developers may be to ensure that, no matter what type of transaction is occurring, at least in the so-called cooperative cases, all transactions look the same.

The confusion over Signature aggregation

The potential scalability benefits of reducing the number of signatures needed on the blockchain are large and therefore the concept tends to generate a lot of excitement. Schnorr signatures do provide the capability to aggregate signatures in multi-signature transactions, which should be a significant benefit to Bitcoin. However, the inclusion of this and the existence of other signature aggregation related ideas, has lead to some unrealistic expectations about the potential benefits, at least with respect to this upgrade proposal. As far as we can tell, for this particular upgrade proposal, the only aggregation benefits are in the form of joining signatures in multi-signature schemes, not for multiple inputs or multiple transactions.

Summary table of signature aggregation ideas

	Included in softfork proposal
Combined public key and signatures in multi-signature transactions – Included as part of Schnorr	Yes
Joint signature for multiple inputs in a transaction	No
Joint signature for multiple inputs in multiple transactions (Grin coin has some capabilities in this area, using Mimblewimble)	No

(Source: BitMEX Research)

Conclusion

In our view, the benefits associated with this softfork are not likely to be controversial. This softfork appears to be a win-win-win for capability, scalability and privacy. The largest area of contention is likely to be the absence of the inclusion of other ideas or arguments over why to do it this particular way.

That being said, many are likely to be excited about the potential benefits of these upgrades and keen to see these activated on the network as fast as possible. However, when it comes to Bitcoin, and in particular changes to consensus rules, the need for patience cannot be overstated.

Abstract: On 18th April 2019, the BitMEX Research Bitcoin Cash SV node experienced 2 block re-organisations. First a 3 block re-organisation, followed by a 6 block re-organisation. In this brief piece, we provide data and graphics related to the temporary chainsplit. The chainsplit appears to be caused by large blocks which took too long to propagate, rather than consensus related issues. Our analysis shows there were no double spends related to the split.

Chainsplit diagram – 18 April 2019

Source: BitMEX Research
Notes: The above image indicates there were two valid competing chains and a non-consensus split occurred at block 578,639. Our node followed the chain on the left until block 578,642, then it jumped over to the right. About an hour later, it jumped back over to the left hand side. The chain on the left continued, while the chain on the right was eventually abandoned.

Chainsplit transaction data

	Number of transactions
Main chain (within 6 blocks)	754,008
Fork chain	1,050,743
Overlap (within 6 blocks)	753,945
Eventual double spends	0

Source: BitMEX Research

Based on our analysis of the transactions, all the TXIDs from the forked chain (on the right), eventually made it back into the main chain, with the obvious exception of the coinbase transactions. Therefore, it is our belief that no double spends occurred in relation to this incident.

Timestamps of the blocks related to the split – 18 April 2019

Local Clock	Block Timestamp	Height	Hash	Size (MB)	Log2 Work
11:39:47	11:39:19	578,638	000000000000000001ccdb82b9fa923323a8d605e615047ac6c7040584eb2419	3.1	87.803278
12:04:51	12:04:37	578,639	0000000000000000090a43754c9c3ffb3627a929a97f3a7c37f3dee94e1fc98f	8.6	87.803280
12:28:01	12:20:36	578,640	00000000000000000211d3b3414c5cb3e795e3784da599bcbb17e6929f58cc09	52.2	87.803282
12:43:42	12:29:39	578,641	0000000000000000050c01ee216586175d15b683f26adcfdd9dd0be4b1742e9e	42.1	87.803285
12:59:27	12:51:40	578,642	00000000000000000a7a25cea40cb57f5fce3b492030273b6f8a52f99f4bf2a8	76.2	87.803287
13:05:18	12:32:39	578,640	000000000000000007ad01e93696a2f93a31c35ab014d6c43597fd4fd6ba9590	35.5	87.803282
13:05:18	12:33:16	578,641	0000000000000000033ed7d3b1a818d82483ade2ee8c31304888932b7729f692	0.1	87.803285
13:05:18	12:41:38	578,642	00000000000000000ae4a0d81d4c219139c22ba1a8a42d72b960d63a9e157914	1.0	87.803287
13:05:19	12:56:37	578,643	00000000000000000590821ac2eb1d3c0e4e7edab586c16d5072ec0c77a980dc	0.8	87.803289
13:19:36	13:14:22	578,644	0000000000000000001ae8668e9ab473f8862dc081f7ac65e6df9ded635d338e	128.0	87.803291
13:21:56	13:18:07	578,645	0000000000000000049efe9a6e674370461c78845b98c4d045fe9cd5cb9ea634	107.2	87.803293
14:12:54	13:15:36	578,643	0000000000000000016b62ec5523a1afe25672abd91fe67602ea69ee2a2b871f	23.8	87.803289
14:12:55	13:43:35	578,644	000000000000000003e9d9be8a7b9fc64ef1d3494d1b0f4c11845882643a6439	1.3	87.803291
14:12:55	14:01:34	578,645	0000000000000000052be8613e79b33a9959535551217d7fdacc2d0c1db1e672	0.0	87.803293
14:12:55	14:06:35	578,646	00000000000000000475ab103a92eb6cb1c3c666cd9af7b070e09b3a35a15d66	0.0	87.803296
14:27:09	14:24:37	578,647	0000000000000000062bade37849ade3e3c4dfa9289d7f5f6d203ae188e94e4f	77.0	87.803298

Source: BitMEX Research

If one is interested, we have provided the above table which discloses all the relevant details of the blocks related to the chainsplit, including:

• The block timestamps
• The local clock timestamps
• The block hashes
• The block sizes
• The total accumulated PoW up to each block

With the above details one can follow what occurred in relation to the chainsplit and create a timeline.

Conclusion
Our primary motivation for providing this information and analysis is not driven by an interest in Bitcoin Cash SV, but instead a desire to develop systems to analyse and detect these type of events on the Bitcoin network. Systems are being developed on our website, https://forkmonitor.info, to help detect chainsplits, caused either by poor block propagation or consensus related issues. This event on Bitcoin Cash SV is good practice for us.

As for Bitcoin Cash SV, the block sizes were particularly large during the period of the re-organisations. On the forked chain, the last two blocks were 128MB and 107MB respectively. On the main chain many of the blocks were over 50MB. Therefore, in our view, it is likely the large sizes of the blocks were the root cause of the re-organisations, as miners couldn’t propagate and verify these large blocks fast enough, before other blocks on different chains were found.

As for the implications this has on Bitcoin Cash SV, we have no comment. We will leave that to others.

Abstract: BitMEX Research examines the market dynamics of Lightning network routing fees and the financial incentives for Lightning node operators to provide liquidity. We identify the interrelationship and balance between Lightning routing fees and investment returns for channel liquidity providers, as a major challenge for the network, rather than the computer science aspects of the routing problem. We conclude that if the Lightning network scales, at least in theory, conditions in wider financial markets, such as changing interest rates and investor sentiment may impact the market for Lightning network fees. However, regardless of the prevailing economic conditions, we are of the view that in the long term, competition will be the key driver of prices. Low barriers to entry into the market could mean the balance favours users and low fees, rather than investment returns for liquidity providers.

Please click here to download a PDF version of this report

Overview

We first wrote about the Lightning network back in January 2018, when it was mostly theoretical. Today, as the Lightning network transitions from abstract to experimental, we felt it was time to take another look. The primary focus of this report is to analyse the Lightning network from a financial and investment perspective, notably with respect to fees and the incentives for Lightning network providers.  We will not examine other aspects of the technology.

The routing problem

Critics of the Lightning network often point to routing as a major problem, typically making claims like its “an unsolved problem in computer science”. In general, we do not really agree with this characterization of the routing problem and do not see the computer science of routing to be a major challenge, finding paths between channels to make payments may be relatively straightforward and similar to other P2P networks, such as Bitcoin.

However, what we do think its a major challenge is the interaction or balance between the financial and economic aspects of liquidity provision and payment routing. Lightning network node operators need to be incentivised by routing fees to provide sufficient liquidity, such that payments can be made smoothly. Liquidity needs to be allocated specifically to the channels where there is demand and identifying these channels may be challenging, especially when new merchants enter the network. This balance between ensuring the network has low fees for users, while also ensuring fees are high enough to incentivise liquidity providers, is likely to be a significant issue. As we explain further in this article, the magnitude of this problem and the fee rates at which the market clears, may depend on economic conditions.

Lightning fee market dynamics

For onchain Bitcoin transactions, users (or their wallets) specify the fee for each transaction when making a payment and then miners attempt to produce blocks by selecting higher fee transactions per unit block weight, in order to maximise fee revenue. In contrast, Lightning currently appears to work the other way around, routing node operators set the fee and then users select a path for their payment, selecting channels in order to minimise fees. With Lightning, suppliers initially set fees rather than users. Lightning may therefore offer a superior fee architecture, as suppliers are providing a specialised service and it is more suitable that suppliers compete with each other over fee rates, rather than ordinary users, where the priority should be on simplicity.

In Lightning there are two types of routing fees node operators must specify, a base fee and a fee rate.

Two types of Lightning network fees

Fee type	Description	Convention
Base fee	A fixed fee charged each time a payment is routed through the channel	This is expressed in thousandths of a Satoshi. For example a base fee of 1,000 is 1 satoshi per transaction.
Fee rate	A percentage fee charged on the value of the payment	This is expressed in millionths of a Satoshi transferred. For example a fee rate of 1,000 is, 1,000/1,000,000, which is 0.1% of the value transferred through the channel. Equivalent to 10bps.

Investment capital

In order to provide liquidity for routing payments and to earn fee income, Lightning node operators need to lock up capital (Bitcoin) inside payment channels.

Two types of channel capacity

	Description	Creation
Inbound capacity	Inbound liquidity, are funds inside the node’s payment channels which can be used to receive incoming payments. These funds are owned by other participants in the Lightning network. If the payment channels are closed, these funds will not return to the node operator.	An inbound balance is created in one of two ways: * When another network participant opens a payment channel with the node * When the node operator makes a payment via an existing channel
Outbound capacity	Outbound liquidity, are funds inside the node’s payment channels which can be used to make outbound payments. These funds are owned by the node operator and part of their investment capital. The node operator may consider the opportunity costs of other investments, while considering the total outbound balance. If the payment channels are closed, these funds will return to the node operator.	An outbound balance is created in one of three ways: * When the node operator opens a payment channel with another network node * When the node operator receives a payment via an existing channel * When payments are routed through the node and fees are received

Graphical illustration of a channel’s inbound and outbound capacity

(Source: Bitcoin Lightning Wallet)
(Note: The orange balance is the inbound capacity, while the blue balance is the outbound capacity)

The operation of the Lightning fee market

Becoming a successful routing node is harder than one may think. At the time of writing, according to 1ml.com, there are 7,615 public Lightning nodes. However, it is likely that only a few hundred of these nodes are doing a good job providing liquidity, by managing the node, rebalancing channels and setting fees in an appropriate manner.

Node operators may need to:

• Adjust both fee rates and the base fee, monitor the impact of the adjustments and calibrate for the optimal income maximising settings
• Analyse the network and look for poorly connected Lightning nodes with high payment demand, such as a new merchant
• Analyse the fee market, not just for the network as a whole, but the high demand low capacity routes you are targeting
• Constantly monitor and rebalance ones’ channels, to ensure there is sufficient two way liquidity
• Implement a custom backup solution for the latest channel states, to protect funds in the event that the node machine crashes

Currently, there are no automated systems capable of doing the above functions. If this does not change, specialist businesses may need to be setup to provide liquidity for the Lightning network. However, just as with liquidity, the challenges in overcoming these technical issues do not necessarily mean payments will become difficult or expensive. These technical challenges may simply adjust the equilibrium market fee rate. The more difficult these problems are to overcome, the higher the potential investment returns will be to channel operators and the greater the incentive will be to fix the problems. It will be demand that drives Lightning’s success, not the challenges for node operators.

In order for Lightning fee markets to work, node operators may need to adjust fees based on the competitive landscape, this could be based on algorithms or be a manual process, aimed at maximising fee income. In an attempt to emulate what may eventually become standard practise, BitMEX Research experimented with modifying the fee rate on one of our nodes over a three month period, as the below section reveals.

Fee rate experimentation

BitMEX Research decided to conduct a basic experiment to try and evaluate the state of the fee market, even in the Lightning network’s current nascent state. We set up a Lightning node and regularly changed the fee rate to attempt to determine which rates would maximise fee revenue, just as node operators may eventually be expected to do as the network scales.

Our basic non-scientific analysis from one node is illustrated in the scatter chart below. It appears to indicate that fee rates do currently have an impact on a lighting node’s fee income. The daily fee income appears to quickly accelerate as one increases the fee rate from 0 till around 0.1 bps. Once the fee is increased above this rate, average daily fee income appears to gradually decline. Therefore, based on this experiment, it appears as if the revenue maximising fee rate is around 0.1 bps, which is certainly very low when compared to other payment systems. However, of course, this is only the fee for one hop, a payment may have multiple hops. At the same time, the current Lightning fee market is barely exists, indeed BitMEX Research may be one in only a handful of Lightning nodes that has significantly experimented with economic revenue maximising behaviour by changing fees. Once the network scales and other parties try to maximise revenue, fee market conditions are likely to be very different. This exercise should therefore only be considered as an illustrative experiment, rather than anything particularly revealing about lighting fee markets.

Lightning node daily fee income versus the feerate

(Source: BitMEX Research)
(Lightning fee income data charts – notes and caveats:
* Daily data from 31st December 2018 to 24th March 2019
* Data from one Lightning node
* The base fee was 0 across the period
* The investment return data excludes onchain Bitcoin transaction fees, when including the impact of fees all but the most optimal fee rate buckets would show a negative investment return
* The data includes both weekdays and weekends, in general Lightning network traffic is significantly lower at weekends
* The fee rate was changed every day at around 21:00 UTC. The fee rate was reduced each day and then jumped up to the top of the fee rate range after several days of declines, to begin the next fee rate downwards cycle. The reason for this was that some wallets (e.g. mobile wallets) did not always query the fee rate each time it attempted to route a payment through the node, therefore when increasing the fee rate, many payments would fail. For example, when opening a channel from a mobile wallet to the Lightning node, then increasing the fee rate and immediately attempting to make a payment, the payment often failed as the wallet attempted to pay with a fee which was too low. In our view, in order to Lightning network fee markets to work, node operators may need to regularly change fees and therefore wallets may need to query fee rates more often
* Channel rebalancing occurred manually, once every two weeks. Approximately 30 minutes was spent on each occasion
* The Lightning node was running LND and the software was updated to the master every two weeks
* Approximately 30% of the channels (by value) were opened using the autopilot, the other 70% were opened manually
* The investment return was calculated by taking the outbound channel capacity of the network each day, annualising the investment return based on the daily fee income and then calculating a simple average based on all the days with a fee rate inside the particular range
* The data is based on one node only and its particular set of channels, the experience for other node operators may be very different
* We tried to use our public node for this experiment, however the fee income was too sporadic, with some network participants regularly paying well above the advertised fee rates by considerable amounts, making the data unreliable
* Unfortunately we needed to use a log scale for both axis. With respect to the fee rate we were unsure of which rates to charge, even which order of magnitude to set, therefore we tried a wide range of fees, from 0.0001% to 0.5% and a log scale was appropriate. At the same time, the daily fee income was highly volatile, ranging from 0 satoshis to over 3,000 satoshis. Therefore a log scale was deemed most appropriate. As the network develops and fee market intelligence improves, a linear scale may be more appropriate)

Fee incomes and investment returns

In addition to daily fee income, one can also consider the annualized investment return associated with running a lighting node and the various fee rates. This is calculated by annualising the daily fee income and dividing this number by the daily outbound liquidity.

The highest annualised investment investment return achieved in the experiment was 2.75%, whilst the highest fee bucket investment return was almost 1%. This seems like a reasonably attractive return for what should in theory be a relatively low risk investment, at least once the ability to backup lighting channels in real time becomes implemented. Existing Bitcoin investors could be tempted by these returns and provide liquidity to the Lightning network, or alternatively US dollar based investors could buy Bitcoin, hedge the Bitcoin price exposure using leverage and then attempt to earn Lightning network fee income.

Lightning node annualised investment return by fee bucket

(Source: BitMEX Research)

Of course, liquidity providers in the current Lightning network are not likely to be motivated by investment returns.  Current node operators are likely hobbyists, with the overwhelming majority of node operators making losses when considering the onchain fees required to open and rebalance Lightning channels. Although this hobbyist based liquidity probably can sustain the network for a while, in order to meet the ambitious scale many have for the Lightning network, investors will need to be attracted by the potential investment returns.

Lightning network fees and economic conditions

A 1% investment yield may seem attractive in the current low yield environment, however the Lightning network may initially have difficulty attracting the right commercial liquidity providers. Investors in this space are typically looking for a high risk high return investment, which appears to be the opposite end of the spectrum for the relatively low risk low return investment on offer for Lightning liquidity providers. Therefore a new type of investor, one that fits this profile, may be needed.

If the Lightning network reaches a large scale, it is possible that the highly liquid investment product, with stable low risk returns, is sensitive to economic conditions.

Consider the following scenario:

1. The federal reserve base rate is 1.0%
2. Lightning node operators are typically earning an annualised investment yield of 1.5% on their outbound balance
3. Due to robust economic conditions and inflationary pressure, the federal reserve open market committee increase interest rates from 1% to 3%.
4. Due to the more attractive investment returns, Lightning network node operators withdraw capital from the Lightning network and purchase government bonds
5. Due to the lower levels of liquidity in the Lightning network, users are required to pay higher fees to route payments and the Lightning network becomes more expensive

However, if Lightning network liquidity is large enough for the above logic to apply, Lightning would have already been a tremendous success anyway.

The risk free rate of return

In some ways, if the Lightning network matures, one can even think of the investment returns from running a Lightning node as Bitcoin’s risk free rate of return, or at least a rate of return free from credit risk. In traditional finance this is often the rate investors earn by holding government bonds, where the government has a legal obligation to pay the principal and coupon and a means to create new money to pay the holders of the bonds, such that the risks are near zero. In theory, all other investment projects or loans in the economy should have a higher return than this risk free rate. The same could apply to Bitcoin, with Lightning node liquidity providers return rates being considered as the base rate within the Bitcoin ecosystem.

In the future, if most of the technical challenges involved in running nodes have been overcome and there are competitive fee setting algorithms, this Lightning network risk free rate could ultimately be determined by:

• Conditions in wider financial markets – higher interest rates could mean a higher Lightning network risk free rate
• The demand for Lighting network transactions – more demand or a higher velocity of money, should increase the Lightning network risk free rate

Conclusion

Whether specialist hedge funds and venture capital investors will have the same enthusiasm about becoming Lightning network liquidity providers, as they did for the “staking as a service” business model for proof of stake based systems in mid 2018 remains to be seen. While the investment returns for Lightning network liquidity providers do not yet look compelling, with the network in its formative stages, we do see potential merit in this business model.

In our view, the Lightning network can easily scale to many multiples of Bitcoin’s current onchain transaction volume without encountering any economic fee market cycles or issues, all based purely on hobbyist liquidity providers. However, if the network is to reach the scale many Lightning advocates hope, it will need to attract liquidity from yield hungry investors seeking to maximise risk adjusted investment returns. Should that occur, unfortunately the network may experience significant changes in fee market conditions as the investment climate changes over time.

However, it is relatively easy to set up a node, provide liquidity and try to earn fee income by undercutting your peers. Where the balance is ultimately struck between the operational channels of running nodes, the extent of liquidity provision and the investment returns, we obviously do not know. However, if we are forced to guess, based on the architecture and design of the Lightning network, we would say the system is somewhat rigged towards users and low fees, rather than liquidity providers.

Abstract: BitMEX Research is delighted to announce the launch of a new website to monitor the Ethereum network, Nodestats.org. The website connects to five different Ethereum nodes and collects data every five seconds. The main focus of the website is providing metrics related to the computational resources each Ethereum node requires. While analysing some of the metrics, we may have identified issues with respect to the integrity of the data reported by the nodes, which may be of concern to some Ethereum users. Nodestats.org was produced in collaboration with TokenAnalyst, who are BitMEX Research’s Ethereum network data and analysis partner.

(Screenshot of website as at 12 March 2019)

Overview

Nodestats.org compares the statistics of the two largest Ethereum node client implementations by overall adoption – Geth and Parity. Within these client implementations, Nodestats.org compares the performance of different node configurations – fast, full, and archive nodes.

The main purpose of Nodestats.org is as follows:

1. To provide metrics comparing the computational efficiency of the different Ethereum implementations. For instance by comparing requirements related to:
   • CPU usage
   • Memory (RAM)
   • Bandwidth
   • Storage space
2. To compare the resource requirements between running Ethereum node software and that of other coins, such as Bitcoin
3. To evaluate the strength of the Ethereum P2P network and transaction processing speed, by looking at metrics related to whether the nodes have processed blocks fast enough to be at the chain tip or whether poor block propagation results in nodes being out of sync for a significant proportion of the time

Nodestats.org began collecting data at the start of March 2019 and it is too early to draw any firm conclusions. However, we are saving the data and hope to analyse the long term trends at a later point. The Nodestats.org data is produced by querying our five Ethereum nodes or machines running the nodes, every five seconds (720 times per hour) and then storing the results in a database. Various rolling averages and other metrics produced from this data, are displayed on the Nodestats.org website.

Description of the Nodestats metrics

Name	Description	Initial findings
% of time in sync	This represents the percentage of time the node has verified and downloaded all the block data, up what the P2P network is informing the node is the chain tip. The hourly metric is calculated by determining if the node is at the tip every 5 seconds, which should be 720 queries per hour. The proportion of these queries where the node says it is at the tip is the reported metric. This field is based on the web3 “isSyncing” field, which we believe uses the highest block the node has seen, the “highestBlock” field, to determine if the node is behind what its peers regard as the highest block ever seen.	Nodes typically report they are at the tip around 99.8% of the time, which means that in only around 1 of the 720 hourly queries are the nodes not at the chain tip. The only exception here is the Ethereum Parity full node, which we talk about later in this report. We believe the data integrity of this metric is poor, for instance in the case of the Parity full node the integrity of the information provided is weak, as we explain later in this report. Going forwards we aim to establish a more effective way of calculating this metric.
% of time on conflicting chain	This represents the percentage of time the node is following a different or conflicting chain to the node opposite it on the website. This is determined by storing all the block hashes in our database, if the nodes have a different block hash at the same height, they are considered to be on different chains.	Typically Nodestats.org is not able to identify times when the clients are following different chains. As such this metric is normally 0%. (i.e. 0 times out of 720 in a one hour period)
CPU Usage	This represents the average percentage utilization of the machine’s CPU resources. All the machines Nodestats.org are using have the “Xeon(R) CPU E5-2686 @ 2.30GHz” processing unit with two cores. The exception to this is the archive node, which has 16 cores. All the nodes are using the AWS “i3.large” machines, with the exception of the archive node, which is running “i3.4xlarge”.	Generally speaking, CPU usage tends to be between 0.01% and 1.0%. Parity tends to be towards the 1% level, while Geth appears to use less CPU power. Geth’s CPU usage appears less stable than Parity’s, with Geth’s CPU demand occasionally spiking to around the 1% level.
Memory Usage	Nodestats.org takes a reading from the machines every 5 seconds, related to how much memory is being utilized by the Ethereum client. All the machines Nodestats.org are using have 14GB of Ram, with the exception of the archive node, which is a 120GB of Ram machine.	Generally speaking, however much RAM is available, the nodes use up the overwhelming majority of it (e.g. over 95%). The memory demands of the clients appear to be reasonably stable.
Peer count	The node provides Nodestats.org with the number of network peers, every 5 seconds.	Parity tends to have around 450 peers, while Geth only has around 8. Geth’s peer count is more volatile than Parity, as it appears to occasionally fall to around 6.
Upstream bandwidth	Nodestats.org takes a reading from the machine every 5 seconds, related to the total network upstream bandwidth of the server.	Parity, which has more peers, tends to use over 100KB/s of bandwidth (in each direction). In contrast Geth tends to only use around 4KB/s of bandwidth. Geth’s bandwidth demand tends to be more volatile than Parity, with occasional spikes to around 60KB/s.
Downstream bandwidth	Nodestats.org takes a reading from the machine every 5 seconds, related to the total network downstream bandwidth of the server.
Chain data size	This metric represents the total data utilized by all the directories dedicated to the client. Unlike the other metrics, the disclosed figure is the absolute value, not a rolling 1 hour average.	Currently, Parity requires around 180GB, Geth uses just under 200GB, and the full archive node uses up 2.36TB of data.

The Parity full node is still syncing

The Parity full node was started on 1 March 2019, at the time of writing (12 March 2019) it has still not fully synced with the Ethereum chain. The client is around 450,000 blocks behind, and based on its current trajectory, it should catch up with the main chain tip in a few days. Due to the slow initial sync, the “% of time in sync” metric is shown as near 0%, as the client is never in sync.

The Ethereum Parity Full node machine has the following specifications:

• Dual Core 2.3GHz
• 14GB of RAM
• SSD storage
• 10 Gb/s internet connection

The fact that a machine with the above specification takes over 12 days to sync may indicate that it is the initial sync issues could be a greater concern for the Ethereum network than post sync issues, such as block propagation. While the slow initial sync is a potential problem, at least for this system setup, Ethereum has not yet reached a point where the node cannot catch up, as the sync is faster than the rate of blockchain growth.

Data integrity issues

The Parity full node also sometimes reports that it is in sync, despite being several hundred thousand blocks behind the chain tip. For instance in the screenshot at the start of this piece, the website reports that the node is fully synced 0.02% of the time, indicating the node falsely thought it was at the tip for some periods of time.

As the chart generated from the Parity full node logs below illustrates, the highest block seen on the network figure, in blue, appears potentially incorrect. The highest block number seen on the network figure, sometimes falls in value as time progresses and has remained consistently well behind the actual chain tip (shown in green). On occasion this potentially buggy figure fell towards the height of the verified chain (orange) and our website incorrectly reports the node as in sync. This may be of concern to some Ethereum users, since the Parity full node has many connections to the network, therefore this may be a bug.

Ethereum Parity Full Node Block Height Data – 11 and 12 March 2019 (UTC)

• 

(Source: Ethereum Parity full node logs)

This potential bug could undermine this whole metric for our website, even for the other nodes, as the highest tip seen field may not function appropriately and our figures may be inaccurate. However, we continue to include this metric, since the Nodestats.org website displays the data reported by the nodes, regardless of our view on the integrity of the data. We may look to implement our own improved metric in the future.

One could argue the impact of this potential bug could be severe in some limited circumstances, if exploited by an attacker in the right way. For example a user could accept an incoming payment or smart contract execution as verified, while their node claims to be at the network chain tip. However, the client may not really be at the chain tip and an attacker could exploit this to trick the recipient into delivering a good or service. The attacker would need to double spend at a height the vulnerable node wrongly thought was the chain tip, which could have a lower proof of work requirement than the main chain tip. Although successful execution of this attack is highly unlikely and users are not likely to be using the highest seen block feature anyway.

Conclusion

Like its sister website, Forkmonitor.info, Nodestats.org is very much a work in progress. Along with TokenAnalyst, over the coming months and years, we plan to add more features, such as:

• Improving the integrity of the data, by being less reliant on what the nodes report and developing our own calculation methodologies
• Charts & tools for analysing longer term trends
• Improved granularity of the data
• Fork detection systems
• Data related to other peers

For now, Nodestats.org provides a useful tool to assess the approximate system requirements for running Ethereum nodes. At at a very basic level, it also provides mechanisms to assess the reliability of the Ethereum network and its various software implementations. However, we accept that the “% of time in sync” metric may not be reliable, but it does highlight a potential issue.

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?)

Please click here to download a PDF version of this report

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

— planB (@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.

 

Abstract: We evaluate Bitcoin Cash ABC’s new rolling 10 block checkpoint system. The new system does defend against “deep” hostile reorgs; however, it increases the risk of consensus chain splits and provides new opportunities for a would-be attacking miner. Another tradeoff is that the change increases the damage hostile miners can do to the network, but it reduces the potential reward for such behaviour. It is not clear at this point if this change is a net benefit, although it is a fundamental change to the system and it may therefore be better to spend more time assessing the dynamics involved before the network adopts this technology.

Overview

Bitcoin Cash ABC added a new rolling checkpoint system in software version ABC 0.18.5, which was released on 21st November 2018. Essentially, the new mechanism finalizes a block once it has received 10 confirmations, which prevents large blockchain reorgs. Therefore even if an alternative chain has more proof of work, if it conflicts with a checkpoint, the node will not switch over to the most work chain.

This feature may have been added as a defence against potential attackers including from supporters of the rival Bitcoin Cash SV chain, who have indicated they may wish to attack Bitcoin Cash ABC.

Security Analysis of the New Checkpointing Mechanism

The new rolling checkpoint mechanism includes a trade-off:

• The risk of a deep reorg is reduced.
• The risk of a consensus chainsplit is increased.

Network Risk Analysis of the New Checkpoint System

	Latency issues	Attack scenario
Reorg risk	No change It it unlikely that latency problems will cause nodes to be out of sync with each other by 10 blocks, therefore, this is largely a non-issue, in our view. The new checkpointing system is therefore not likely to cause problems here. Although with a block size of up to 32MB, there could be some latency issues in a small number of circumstances and it is possible nodes could be out of sync by 10 blocks. The checkpoint doesn’t seem to solve any issues to do with latency. If latency issues cause a 10 block reorg, the user may want to follow the most work chain. Therefore we do not think there is any benefit here.	Risk reduced The risk of a deep hostile reorg is now reduced or limited to 10 blocks.
Consensus split	New small risk introduced In the unlikely scenario that poor network connectivity causes nodes to be out of sync with each other by 10 blocks or more, the conflicting checkpoints could cause a consensus split resulting in two or more coins.	New risk introduced Although the reorg risk is now reduced, the hostile miner now has a new attack vector. The attacker can attempt to mine a 10 block long (or longer) chain in secret and then publish the chain at a time designed to cause conflicting checkpoints on the network, causing a chain split.

Attacking Miner: An Alternative Option to a Reorg

As indicated above, if a hostile miner is producing a shadow chain, once this diverges from the “honest” chain by more than 10 blocks, it is essentially useless as it cannot reorg the honest chain, even if it has more work. Therefore the attacker might as well give up and stop extending the shadow chain.

However, this also means that as soon as the 10th block since the split has been produced on the “honest chain,” the attacker might as well publish the shadow chain at this point, depending on the attacker’s objectives. (i.e. release the shadow chain as soon as the attacker receives the block in red indicated in the below diagram.) This could then cause a consensus chain split, with some nodes having received the red block first and some receiving the shadow chain first, resulting in conflicting checkpoints.

(Source: BitMEX Research)

This attack may cause a consensus chain split, which could be just as damaging to the network as continuing on to do a hostile reorg. It is also cheaper than continuing on to do a deep reorg, since the hostile miner can stop earlier. Therefore it is not clear to us why this new checkpointing defence is a material improvement. Although the risks in this section are unlikely to materialise (and could require the attacker to have a majority of the hashrate), they seem at least as likely to occur as the problem the new checkpointing system is trying to mitigate against.

Advantages of the Checkpointing System

• Although the new checkpointing mechanism may have a limited impact on security within a 10 block window, when looking back more deeply from the current chain tip, security may be increased over longer timeframes. This may be very useful to some exchanges or merchants who can now wait for more than 10 blocks before crediting a user account and achieve a higher level of assurance. However, a key focus of Bitcoin Cash is to increase transaction speeds, so this benefit may not be desirable for the Bitcoin Cash community.
• Although a new attack vector is opened up by this mechanism, providing a new way for hostile miners to instigate a consensus split as we explained above, the incentive to do this is less clear than for a “normal” deep reorg attack. A normal reorg attack can be used to initiate a double spend against an exchange, whereby the attacker could profit. While it is possible to also attempt a double spend attack using this new chain split-related attack vector, the outcome is less clear, as it is not obvious which side (if any) will be the winner or which chain an individual exchange may follow. Therefore, although this attack is potentially more devastating on the network, the incentives for it are less obvious. We view this as a significant positive.

Other issues

Centralisation and More Developer Power

Another common criticism of checkpoints is that it gives developers more power and increases centralisation since developers normally manually insert the checkpoints when they release new versions of the software (like Bitcoin used to have). However in our view, this does not apply in this case as the checkpoints are automatically generated by the node software and not manually generated by the development team. Therefore this a non-issue.

Long Range Attack and the Initial Sync

As Eric Wall explained on Twitter, the new checkpoint mechanism opens up the ability to sybil attack nodes not on the latest chaintip. For example, nodes still in the initial sync or nodes related to users who temporarily shut down their nodes for several days. An attacker needs to launch his own relay nodes and generate a new 10 block long chain at any point in the past.

This lower work chain can then be broadcast to nodes (including the specific targeting of nodes not at the current tip), potentially causing these nodes to conduct the checkpoint prematurely, on an alternative chain. Not only does this leave these nodes on a different chain, but this chain is under the control of the attacker. This seems to be a significant flaw of the checkpointing system.

Satoshi’s “original vision” appears to imply that the ability of nodes to be switched off and then verify what happened when it was gone is potentially important:

Nodes can leave and rejoin the network at will, accepting the proof-of-work chain as proof of what happened while they were gone.
(Source: Bitcoin Whitepaper)

To some extent this Bitcoin Cash ABC upgrade abandons that philosophy, and requires nodes to be online 24×7.

Conclusion

The new Bitcoin Cash ABC checkpointing system is a fundamental change to the core network and consensus dynamics, resulting in a number of trade-offs. These changes may not have been adequately explored before the upgrade. Although we do not think it is likely such a change will result in an immediate crisis, it’s not likely to prevent one either.

Overall Summary of the Checkpointing System’s Impact

Positives:

• Reduces the incentive for a miner to attack the chain
• Provides more assurances for merchants and exchanges for transactions with over 10 confirmations

Negatives:

• Increases the ability of a miner to instigate a devastating attack on the network
• Introduces new attack vectors on nodes which are still syncing to the main chain

 

Abstract: We present an in depth report into the cryptocurrency exchange ecosystem. The market is broken down by almost all the possible characteristics (Exchange type, exchange region and trading pairs). The robustness and authenticity of exchanges are evaluated  using metrics such as web traffic, average trade sizes, order book depth, security polices and price reliability. The report was produced by CryptoCompare and uses the CryptoCompare’s Aggregate Pricing Index (the CCCAGG), for much of the analysis.

 

(Note: Current CCCAGG Constituent Exchanges, Sized by 24H Volume)

 

Please click here to download a PDF version of CryptoCompare’s report

 

Executive Summary

Major Exchange News in October

• Bitstamp was acquired by Belgium-based Investment Firm NXMH for ~400 million USD according to reports.
• Cryptoassets on Gemini are now fully insured with Aon.
• Coinbase adds 0x to its trading platform as well as USDC after announcing its collaboration with Circle on the CENTRE Consortium.
• Korean exchange Bithumb starts a new DEX, while Huobi and OKEX list stablecoins GUSD, TUSD, PAX and USDC.
• Chainalysis will help Binance comply with anti-money laundering (AML) regulations around the globe, and
• Coinfloor becomes the first exchange to obtain a Gibraltar license.

Exchange Market Segmentation

Spot volumes constitute less than three quarters of total market volumes on average (less than 7 billion USD) compared to futures volumes (3.2 billion USD). BitMEX and BitflyerFX average more than one quarter of total volumes while traditional exchanges such as CME and CBOE constitute just under 1%.

Within total spot volumes, exchanges with taker fees represent approximately 90% of the exchange spot market volumes, while transaction-fee based and no-fee exchanges represent the remaining 10%.

Exchanges that offer fiat to crypto pairs constitute just under a quarter of spot market volumes on average (~2 billion USD) while exchanges that offer only crypto to crypto pairs constitute approximately three quarters (~4.7 billion USD). In terms of exchange count however, approximately half of all exchanges offer fiat to crypto pairs.

Transaction-Fee Mining Volumes

The top trans-fee mining exchange by average 24h volume was EXX (160 million USD), followed by Coinex (114 million USD) and Coinbene (113 million USD). The total average 24h-volume produced by trans-fee mining associated exchanges on CryptoCompare totals just over 550 million USD. This constitutes approximately 10% of total exchange volume over the last 30 days.

Decentralized Exchanges

The total average 24h-volume produced by the top 5 decentralized exchanges on CryptoCompare totals just under 2.4 million USD. This constitutes just 0.4% of total exchange volume. The top 3 on CryptoCompare by 24h volume include Waves Dex, IDEX and Dex.

Volume, Pairs and Coins

Binance remains the top exchange in terms of 24h volume with an average of 977 million USD. This is followed by OKEX (405 million USD) and Bitfinex (368 million USD). Yobit offers the highest number of pairs at 7,032, followed by Cryptopia (4,321) and CCEX (2,140).

Bitcoin to Fiat Volumes

The US Dollar represented half of BTC fiat trading on average over the past 30 days, followed by JPY (21%) and KRW (16%). Bitcoin trading to Korean Won (KRW) increased sharply after the 7th of October. The pair previously represented a tenth of bitcoin trading among the top 5 fiats on average. Between the 7th and 15th of October it represented a third on average, a 230% increase stemming from Korean exchange Bithumb’s spike in trading volumes.

Country Analysis

Maltese-registered exchanges produce the highest total daily volume at just under 1.4 billion USD, followed by those based legally in South-Korea (~840 million USD) and Hong Kong (~560 million USD). Among the top 10 volume-producing countries, the highest number of large exchanges (with significant volume) are based legally in the USA, the UK and Hong Kong. Binance and OKEX represent the vast majority of Malta’s volumes, while Bithumb and Upbit dominate in South Korea.

Trade Data Analysis

CoinEx, a well-known trans-fee mining exchange, has a significantly higher trade frequency and lower trade size than other exchanges in the top 25. This may point to algorithmic trading, given its almost 176 thousand trades a day at an average trade size of 125 USD. In contrast, Bithumb and HuobiPro had an average trade size of just under 3,000 and 1,500 USD respectively and significantly lower trades per day (12-18 thousand).

Web User Analysis

IDAX and CoinBene appear to have lower average daily visitors compared to similarly sized exchanges by daily volume. Binance has the highest average daily visitor count, in line with its high trading volumes. Meanwhile, exchanges such as Coinbase, Cex.io and Bittrex have significantly greater numbers of daily visitors than other exchanges with similar daily volumes. ZB and EXX attract significantly lower daily visitors than similarly-sized exchanges.

Order Book Analysis

ItBit, Kraken and Bitstamp have relatively more stable markets compared to exchanges such as CoinEx, ZB and Coinbene. These exchanges appear significantly less stable given their relatively low average order book depth values over the specified period of analysis.

Exchange Security

Out of the top 100 exchanges by 24h volume, only 86% have both a public privacy policy and a terms & conditions page. A third of top exchanges store the vast majority of users’ funds in cold wallets. Exchanges itBit, Coinfloor, Bitfinex and Coinbase are among those that store the highest proportion of users’ funds offline. As a proportion of the top 100 exchanges, 11% have been hacked in the past.

KYC

Just under half of top exchanges impose strict KYC requirements, while more than a quarter do not require KYC.

Total Exchange Volumes and Market Segmentation

This section aims to provide a macro view of the cryptocurrency exchange market as a whole. An area of interest is the proportion of spot trading vs futures trading historically. We will also assess the relative proportion of exchange volumes that represent exchanges that charge fees, as well as those that implement models with no-fees or trans-fee mining. Finally, we will take a look at exchange volumes that represent crypto-crypto exchanges versus those that represent fiat-crypto exchanges.

Historical Spot vs Futures Volumes

Spot volumes constitute three quarters of total market volumes on average.

Total spot volume averaged less than 7 billion USD, while futures volume averaged over 3.2 billion USD over the period of analysis.

Futures exchanges such as BitMEX (XBT to USD perpetual futures) and BitflyerFX (BTC to JPY futures) average just under a quarter of total cryptocurrency market volumes. Traditional exchanges such as CME and CBOE trading bitcoin futures, only constitute a very small proportion of the total market at just under 1% on average.

Historical BTC to USD Futures Volumes

BitMEX’s Perpetual Bitcoin to USD Futures volumes continue to dominate the Bitcoin to USD futures market

When compared to CME’s and CBOE’s futures volumes, BitMEX has represented an average of just over 90% of the market over the last month.

Historical Spot Volumes Segmented by Predominant Fee Type

Exchanges with taker fees represent approximately 90% of the exchange spot market volumes.

On the other hand, exchanges that implement transaction-fee mining represent just over 9% of the total spot market, while those that offer no-fee spot trading represent just under 1% of the market.

Historical Crypto to Crypto versus Fiat to Crypto Exchange Spot Volumes

Exchanges that offer fiat to crypto pairs constitute just under a quarter of spot market volumes on average.

Adjusted Historical Spot Volumes

The cryptocurrency exchange market trades an average of 5.26 billion USD in adjusted volumes over the period of analysis.

Adjusted spot volumes exclude all exchanges that operate trans-fee mining or no-fee trading models.

Historical BTC to Fiat Spot Volumes – Top 5 Fiat Currencies

Bitcoin trading to Korean Wan (KRW) increased sharply from the 7th of October.

BTC to KRW previously represented a tenth of bitcoin trading among the top 5 fiats on average. Between the 7th and 15th of October it represented a third on average, a 230% increase. This increase stems from Korean exchange Bithumb’s spike in volumes.

Proportion BTC Trading to Various Fiat Currencies

The US Dollar represented half of BTC fiat trading on average over the past 30 days, followed by JPY (21%) and KRW (16%).

Summary of Volumes, Coins and Pairs

Top Exchanges by Average 24H Volume in USD

Exchange	24H volume (USD million)	Coins	Pairs
Binance	977.5	160	408
OKEX	405.0	171	511
Bitfinex	368.5	96	281
Bithumb	323.2	13	13
HuobiPro	310.2	128	293
HitBTC	295.2	427	889
ZB	247.6	58	167
Upbit	211.0	132	261
Bibox	208.9	87	210

Top Exchanges by Number of Pairs

Exchange	24H volume (USD million)	Coins	Pairs
Yobit	27.7	1,180	7,032
Cryptopia	3.5	785	4,321
CCEX	0.1	628	2,140
EtherDelta	0.2	2,058	2,059
HitBTC	295.2	427	889
TradeSatoshi	0.1	200	840
Bittrex	49.1	514	637
Livecoin	12.5	249	595
WavesDEX	0.9	163	592
IDEX	0.7	563	563
OKEX	405.0	171	511
Kucoin	10.1	189	450
Binance	977.5	160	408
Gateio	48.8	172	358
Zecoex	1.4	119	342

Historical 24h Volume – Top 8 Exchanges

The top exchange by 24h spot trading volume was Binance with an average of just under 980 million USD.

By average 24h volumes, Binance was followed by OKEX and Bitfinex with volumes of 405 million and 368 million respectively.

Bithumb saw a 356% spike in trading volumes from an average of 140 million USD to an average of 640 million USD after the 7th of October. This follows after Singapore-based BK Global Consortium bought a controlling share in the exchange.

Bitfinex saw a spike in volumes towards the 15th of October as the Bitcoin premium on Bitfinex vs Coinbase reached an all-time high of 11.28% according to CrypoGlobe.

Month on Month Average 24H Trading Volume – Top Exchanges

Average Bithumb volumes increased 187%, while those for Binance and OKEX dropped by 8% and 35% respectively

Korean exchange Bithumb saw a significant increase in average trading volumes from 96 million USD between August/September to 276 million between September/October. Meanwhile, Binance’s volumes over the same time period dropped from 974 million USD to 893 million USD. Finally, the 2nd largest exchange by 24h volumes, OKEX, saw trading volumes drop 655 million USD to 423 million USD.

Country Analysis

Exchanges maintain operations in a variety of countries, in order to serve the wider global community of cryptocurrency traders. They often change legal jurisdiction to avoid regulation in countries that might restrict their abilities to conduct business as they wish. The following country analysis aims to highlight the top 10 legal jurisdictions by the total 24h volume produced by the top exchanges legally based in each jurisdiction.

Top 10 Exchange Legal Jurisdictions – 24h Volume vs Exchange Count

Maltese-based exchanges produced the highest total daily volumes, while the highest quantity of top exchanges are based in the USA and the UK.

Maltese exchanges produce the highest total daily volume at just under 1.4 billion USD, followed by those based legally in South-Korea (~840 million USD) and Hong Kong (~560 million USD). Among the top 10 volume-producing countries, the highest number of exchanges (with significant volume) are based legally in the USA, the UK and Hong Kong.

Top 10 Exchange Legal Jurisdictions – Constituent Exchanges by Impact on Volume

Binance and OKEX represent the vast majority of Malta’s volumes, while Bithumb and Upbit dominate in South Korea.

Top 10 Exchange Legal Jurisdictions – Constituent Exchanges and Count

 

Well-known USA-based exchanges include Coinbase, Poloniex, and itBit, while those in South Korea include Upbit, Bithumb and Coinone.

Hong Kong exchanges include HitBTC, CoinEx and Bit-Z, while those in more remote jurisdictions include HuobiPro in the Seychelles, ZB in Samoa and Coinbene in Vanuatu.

Pair Offering Analysis

The following analysis aims to highlight both the total volumes produced by crypto-crypto vs fiat-crypto exchanges as well as the total number of exchanges that fall within each category.

Crypto to Crypto vs Fiat to Crypto – Average 24H Volume and Exchange Count

On average, exchanges that offer only crypto-crypto pairs constitute approximately three quarters of the total spot trading market (~4.7 billion USD)

Those that that offer fiat-crypto pairs constitute only a quarter of the total exchange market (~2 billion USD) on average. In terms of exchange count, approximately half of all exchanges offer crypto-crypto.

Trade Data Analysis

This analysis aims to shed light on the trading characteristics of given exchange. It helps to answer whether an exchange’s volumes might be the product of consistently large trades, or the product of many small trades which may suggest the use of algorithmic trading or bots.

Average 24H Trade Frequency vs Average Trade Size – Top 25 Exchanges

CoinEx, a well-known trans-fee mining exchange, has a significantly higher trade frequency and lower trade size than other exchanges in the top 25.

This may point to algorithmic trading, given its almost 176 thousand daily trades at an average trade size of 125 USD. In contrast, Bithumb and HuobiPro had an average trade size of just under 3,000 and 1,500 USD respectively.

Average 24H Trade Frequency vs Average Trade Size – Top Exchanges

	Exchange	AVG 24H Volume (Millions)	Average Trade Size (USD)	Trades in 24H (Thousands)
1	Binance	977.5	950	95.7
2	OKEX	405	701	48.5
3	Bitfinex	368.5	1,438	38
4	Bithumb	323.2	2,788	12.4
5	HuobiPro	310.2	1,483	18.7
6	HitBTC	295.2	2,873	12.1
7	ZB	247.6	702	29
8	UPbit	211	732	22.5
9	Bibox	208.9	1,253	16.4
10	EXX	159.9	1,134	24.1
11	BitZ	143.9	2,333	8
12	IDAX	131.5	520	37.4
13	CoinEx	113.6	125	175.6
14	CoinBene	113.2	298	35.2

Web Traffic Analysis

This analysis examines the web traffic stats of the top exchanges within CryptoCompare’s total pool of exchanges. It is based on similar studies that have attempted to make a connection between the number of unique web users per domain and the subsequent 24h trading volume for that specific domain. This analysis assumes that the more unique visitors an exchange attracts, the higher its trading volume.

Average Daily Visitors versus 24H Volume – Alexa Rankings Above 100,000

IDAX and CoinBene appear to have lower average daily visitors compared to similarly sized exchanges by daily volume.

The figure above represents the top exchanges by volume that have an Alexa ranking above 100,000. The reason for this is that according to Alexa, any ranking below this may not be statistically significant.

What we can see that exchanges such as IDAX and CoinBene have lower Average Daily Unique Visitor numbers than other exchanges with similar volumes such as Kraken, Bitstamp, and CoinEx.

Binance has the highest average daily visitor count, in line with its high trading volumes. Meanwhile, exchanges such as Coinbase, Cex.io and Bittrex have significantly greater numbers of daily visitors than other exchanges with similar daily volumes. In Coinbase’s case, this can be attributed to the exchange’s reputation and age.

Average Daily Visitors versus 24H Volume – All Alexa Rankings

ZB and EXX attract significantly lower daily visitors than similarly-sized exchanges.

The above figure represents the top 20 exchanges by 24h volume regardless of whether their Alexa rankings are below 100,000. Noticeably, unique visitor counts for exchanges ZB and EXX are significantly lower than other exchanges within a similar 24h volume band.

These exchanges maintain average daily trading volumes of 248 million and 160 million USD
respectively. Despite this, their daily unique visitor counts amount to no more than 700 visitors per day.

Although there is a chance that these web statistics may present errors given Alexa rankings below 100,000, in the interests of mitigating any potential risks, these exchanges will be flagged until clarification is provided.

Order Book Analysis

The following order book analysis investigates the relative stability of various cryptocurrency exchanges based on snapshots of the average order book depth for the top markets on each exchange in 10-minute intervals over a period of 10 days. In the context of this analysis, average depth down is defined as the cumulative volume required (in USD) to reduce the price of a given market by 10%. This is compared to the average daily volume for the top 5 pairs. The result of this analysis is that we are able estimate the relative stability of a given exchange based on the ratio of depth down to average daily pair volume.

Average Order Book Depth Down vs Average Daily Exchange Pair Volume

In relative terms, CoinBene, ZB and CoinEx have the thinnest markets.

Despite relatively large average volumes per top pair (~12 million USD), CoinBene’s average order book cumulative depth down (order book buy side) totals only 33 thousand USD. In other words, to move the price 10% downwards, a trader would need to sell 33 thousand USD worth of currency.

In contrast, Kraken which has similar average daily pair volumes (~13.5 million USD), has an average order book cumulative depth of 4.2 million USD. This is almost 130 times larger than that of CoinBene’s and therefore suggests a much more stable exchange.

Average Depth Down to Average 24H Pair Volume Ratio

ItBit, Kraken and Bitstamp have relatively more stable markets compared to exchanges such as CoinEx, ZB and Coinbene.

In the case of ZB for instance, its depth to volume ratio was just 0.4%. I.e. in order to move the price down 10%, a trader would only need to sell 0.4% of average daily pair volume. These ratios are similarly low in the case of CoinEx (0.7%) and CoinBene (0.3%).

Meanwhile other exchanges such as Bitstamp and ItBit, had ratios of 30% and 40% respectively. This is a factor of 100 times greater than those of CoinBene’s for instance.

Transaction-Fee Mining Exchanges

Average 24H Trans-Fee Mining Volumes

The total average 24h-volume produced by trans-fee mining associated exchanges on CryptoCompare totals more than 550 million USD. This constitutes approximately 10% of total exchange volume over the last 30 days.

Decentralized Exchanges

Average 24H DEX Volumes

The total average 24h-volume produced by the top 5 decentralized exchanges on CryptoCompare totals just less than 2.4 million USD. This constitutes just 0.4% of total exchange volume.

Security Analysis – Top 100 Exchanges by 24H Volume

This security analysis aims to evaluate a pool of the top 100 exchanges by 24h volume considering the proportion of exchanges with both a public privacy and a terms & conditions page. In addition, we analyse the proportion of exchanges that have been hacked in the past as well as the publicly stated proportion of cold wallet vs hot wallet storage for users’ funds. In theory, the higher the amount of funds stored in “cold storage” (i.e. offline), the less exposed the funds held by a centralized exchange will be to hackers.

Proportion of Exchanges with both a Public T&C and Privacy Policy Page

Out of the top 100 exchanges by 24h volume, only 86% have both a public privacy policy and terms & conditions page.

Proportion of Users’ Funds Held by Exchanges in Cold Storage

A third of top exchanges store the vast majority of users’ funds in cold wallets.

Proportion of Users’ Funds in Cold Storage by Exchange

Exchanges itBit, Coinfloor, Bitfinex and Coinbase are among those that store the highest proportion of users’ funds offline.

Proportion of Exchanges Hacked in the Past

11% of top exchanges have been hacked in the past.

KYC Requirements Among the Top 100 Exchanges

Just under half of top exchanges impose strict KYC requirements, while more than a quarter do not require KYC.

Those that impose partial requirements (25%) require KYC verification in order to conduct certain activities such as to withdraw fiat, to trade fiat pairs, or to increase maximum trading amounts.

Trade Data Assessment of New Exchanges

A visual inspection of the trades on the new exchanges is now carried out. Snapshot data cannot capture volatility, so these trade graphs allow the characteristic trading to be assessed in light of its effect on the CCCAGG. Graphs were produced of all trades vs the CCCAGG for the top 5 trading pairs for each new exchange over the last month.

BCEX

BCEX displays high volatility on both of the pairs that it trades. Buying of large amounts of the order book is visible, suggesting a very thin market. The price on this exchange will accordingly not reflect the price of the cryptocurrency well, so it will not be included.

CoinTiger

Top trading pairs on CoinTiger display agreement with the CCCAGG, but due to anomalous volumes further monitoring will be carried out before considering inclusion into the CCCAGG.

iCoinBay

Pairs on ICoinBay show agreement with the CCCAGG. This exchange is a possible inclusion to the CCCAGG.

Iqfinex

A flash crash on the largest trading pair elicits a longer period of assessment before consideration for inclusion into the CCCAGG.

Liqnet

Pairs on Liqnet show agreement with the CCCAGG. However, large amounts of API downtime can be observed. The quality of the exchange API will be monitored and the exchange will be considered for inclusion in the event of an improvement in API provision.

P2PB2B

Poor agreement with the CCCAGG gives grounds to exclude P2PB2B.

StocksExchange

StocksExchange displays some unusual trading activity and a flash crash. The exchange will not be included due to trading behaviour.

Example Assessment of BTC to USD and Future Exchange Methodology Additions

This section provides a quantitative analysis of trade data received from exchanges. The purpose is to provide an understanding of what the exchange trading ecosystem looks like, and to allow for selection of exchanges that best represent the price of a cryptocurrency.

In order to make comparisons across exchanges, an estimate of the trading price of the cryptocurrency needs to be ascertained. For the BTC-USD pair, all trades over a 30-day period were collated and plotted. In this time period, there were around 6.5 million unique trades. The trades are plotted such that colour indicates the density of points in the area.

All BTC to USD trades over 30 days

This graph represents the entire ecosystem of the price of BTC-USD trading over a 30-day period. This is now used to generate a representative price for BTC. The median was selected to calculate a trading price for the cryptocurrency. The motivating factor behind this measure being used was the large number of outliers in the trade data set. To keep the computation tractable, trades were grouped into 1-hour long time bins, and the median for each of these bins was computed.

For the purposes of this investigation, volume weighting was not used. This was due to high volume buying up of order books being observed when looking at individual exchange trade data. It was hypothesised that the arithmetic median would better reflect the mid-price of the order books of the exchanges, as the majority of trades take place at the mid-price. The median should therefore reflect the price that the average trade was carried out at.

The 1-hour median line was then plotted on the trade data, and a visual inspection of a section of the above graph shows that the line follows the highest trade density, which is indicative that it is a good estimate of the trading price of the cryptocurrency.

BTC to USD trades over 30 days with hourly median price line

CryptoCompare’s CCCAGG is an aggregation of trade prices, and aims to reflect the current trading price of an asset. It is possible to validate the CCCAGG price by comparing it to the median trade price. It can be seen that there is agreement between the two measures, suggesting that the CCCAGG is accurately capturing the trading price.

CCCAGG Price vs Median Trade Price for BTC to USD