Exploring A Bright-Line Framework for Evaluating Cryptocurrencies as Securities. Abstract. In May of 2019, The Blockchain Association and Flipside Crypto have partnered to explore a bright-lines data model for evaluating when token transactions are securities, using 3 simple tests.
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1. Presented by:
Angela Minster, Flipside Crypto
Kristin Smith, Blockchain Association
The SEC’s token guidance: not quantitative enough
Exploring a potential bright-lines data model for
evaluating when token transactions are securities
(Off) the Chain - May 8, 2019 - Boston MA
3. WHAT THE SEC SAYS
Nature of token transactions can change over time
Sales of Bitcoin and Ethereum are not securities transactions
Director Hinman’s “Digital Asset Transactions: When Howey Met Gary (Plastic)” speech (June 2018)
Framework for “Investment Contact” Analysis of Digital Assets (April 2019)
60+ (mostly qualitative) factors that make a token transaction more
or less likely to be an investment contract
Characteristics of “reliance on the efforts of others” and “reasonable
expectation of profits” more likely indicate that a token is a security
Characteristics of “use or consumption of the token by purchasers”
make it less likely that a token is a security
4. The reality: No easy way to apply the
SEC’s factors.
Many of the factors are qualitative and, therefore, can’t be
objectively measured. For the quantitative factors there is
no methodology for weighing factors.
When do factors that make it less likely to be a security
outweigh those that make it more likely?
5. Open public blockchains
have an enormous amount
of publicly available data...
Can we use this
data to develop a
bright line test?
6. 3 POSSIBLE DATA-BASED TESTS FOR EVALUATING TOKENS
FACTOR SEC’S QUALITATIVE STATEMENT PROPOSED METRIC
Primary
Purchase
Motivation
Indicates if tokens are “held or transferred only in
amounts that correspond to a purchaser’s expected
use”
Indicates if token was “offered and purchased in
quantities significantly greater than any likely user
would reasonably need”
Off vs. On Exchange
Transfer Size
Primary
Purchase
Motivation
Economic
Throughput
High concentration indicates presence of “Active
Participants (AP)” “The AP has the ability to realize
capital appreciation from the value of the digital
asset”
Economic
Decentralization
Ownership
Concentration
1.
2.
3.
7. FLIPSIDE CRYPTO DATA
Blockchain
Parsing
Address
Categorization
Metrics developed
from the SEC
guidelines
Transaction-level data for ETH,
BTC and 593 Tokens (+more)
Filtering out exchange trading,
junk accounts, etc. to find true
utility transactions by potential
users
Translating qualitative
statements into comparable
quantitative metrics for ETH,
BTC and 593 tokens.
8. Each point on the graph shows the
the average on-exchange transfer
size vs. the average off-exchange
transfer size of an ERC-20 token.
Buying tokens on an exchange and
then USING them.
Lower Off-Exchange Transfer
Amounts is associated with
Consumptive / Utility Usage.
Primary Purchase Motivation
TEST 1 OF 3: ON VS. OFF EXCHANGE TRANSFER SIZE
9. Line shows equal transfer sizes.
Notice where BTC and ETH are.
ETH’s position above the line
indicates less speculative / more
utility purchasing behavior.
POSSIBLE DECISION RULE:
On or above the line (i.e. On
exchange ≥ Off exchange)
Primary Purchase Motivation
TEST 1 OF 3: ON VS. OFF EXCHANGE TRANSFER SIZE
10. POSSIBLE DECISION RULE:
On or above the line (i.e. On
exchange ≥ Off exchange)
Primary Purchase Motivation
TEST 1 OF 3: ON VS. OFF EXCHANGE TRANSFER SIZE
449 TOKENS PASS
144 FAIL
11. This graph shows the average
transfer size vs. the number of
transfers (frequency) as of
March 2019, excluding
centralized exchange transfers.
By limiting the analysis to non-
exchange transfers, it becomes
easier to understand the nature
of those transactions that are
more likely to be consumptive.
TEST 2 OF 3: ECONOMIC THROUGHPUT
Primary Purchase Motivation
Fewer Large Transactions
(think: Gold)....
or Lots of small transactions
(think: paypal)?
12. What’s the decision point? Lower
than average? How about a
generous: lower than or equal to
BTC?
POSSIBLE DECISION RULE:
Points below the (somewhat
arbitrary) BTC line
TEST 2 OF 3: ECONOMIC THROUGHPUT
Primary Purchase Motivation
13. POSSIBLE DECISION RULE:
Points below the (somewhat
arbitrary) BTC line
TEST 2 OF 3: ECONOMIC THROUGHPUT
508 TOKENS PASS
85 FAIL
Primary Purchase Motivation
14. This chart represents the
distribution of tokens at different
percentages of tokens held by the
100 largest holders as of March 31,
2019. Most tokens have high
ownership concentration.
TEST 3 OF 3: OWNERSHIP CONCENTRATION
449 TOKENS PASS
144 FAIL
Economic Decentralization
15. 73 percent of ETH is held by the 100
largest holders. Given that the SEC
does not find ETH to be a security.
A case can be made that other
tokens should not be deemed
securities if less than 75 percent of
the tokens are held by the largest
100 holders.
POSSIBLE DECISION RULE:
75% or less of supply held by
the top 100 addresses
TEST 3 OF 3: OWNERSHIP CONCENTRATION
449 TOKENS PASS
144 FAIL
Economic Decentralization
16. DECISION RULE: 75% or less
of supply held by the top 100
addresses
TEST 3 OF 3: OWNERSHIP CONCENTRATION
449 TOKENS PASS
144 FAIL
209 TOKENS PASS 384 FAIL
17. A QUANTITATIVE APPROACH WOULD RESULT IN THE
FOLLOWING:
Passed
ZERO
Metrics
Passed
ONE
Metric
Passed
TWO
Metrics
Passed
ALL THREE
Metrics
299 15410634
✓ ✓ ✓✓✓Ø
18. ● SEC Guidance is difficult to evaluate because of qualitative factors and lack
of weighting methodology
● Crypto networks are incredibly transparent, and it IS possible to use data to
evaluate these networks in an objective manner that are in line with
qualitative policy goals
● Any data-driven test should include guidelines that address:
○ How would pass vs. fail be determined?
○ Do some tokens need separate categorizations when their construction itself leads to failing a
test?
○ How are the factors considered in aggregate? (E.g. A weighting system? Pass on at least 40
of 60?)
CONCLUSIONS
