# Legal NLP Meets MiCAR:
Advancing the Analysis of Crypto White Papers

Carolina Camassa
Bank of Italy
<EMAIL_ADDRESS>

###### Abstract

In the rapidly evolving field of crypto assets, white papers are essential
documents for investor guidance, and are now subject to unprecedented content
requirements under the European Union’s Markets in Crypto-Assets Regulation
(MiCAR). Natural Language Processing (NLP) can serve as a powerful tool for
both analyzing these documents and assisting in regulatory compliance. This
paper delivers two contributions to the topic. First, we survey existing
applications of textual analysis to unregulated crypto asset white papers,
uncovering a research gap that could be bridged with interdisciplinary
collaboration. We then conduct an analysis of the changes introduced by MiCAR,
highlighting the opportunities and challenges of integrating NLP within the
new regulatory framework. The findings set the stage for further research,
with the potential to benefit regulators, crypto asset issuers, and investors.

##  1 Introduction

White papers are the cornerstone of information disclosure for Initial Coin
Offerings (ICOs), a modern fundraising method that sells tokens to a diverse
group of investors (Fisch, 2019). These fundraisings represent an alternative
method for entrepreneurial finance, with similarities to IPOs, venture
capital, and presale crowdfunding Howell et al. (2020).
When a new ICO is announced, it is usually accompanied by the white paper, a
text document that contains information for the investors and bears some
similarities, at least in intent, with an IPO prospectus. However, unlike
their regulated counterparts, white papers have so far operated in a largely
unregulated landscape.
Thanks to this regulatory vacuum, the type of content varies widely, but it
usually includes Bourveau et al. (2022); Zhang et al. (2019) a description of
the service offered and its value proposition, the composition of the project
team, financial details about the offer, and explanations of the technologies
underlying the project. Due to their public accessibility and richness in
data, white papers have been a frequent subject of studies aimed at extracting
market insights through textual analysis (Fisch (2019); Thewissen et al.
(2022); Florysiak and Schandlbauer (2022) among others).

The current ICO market is characterized by a pronounced level of information
asymmetry between crypto issuers and investors Bourveau et al. (2022); Chen
(2019). These circumstances, easily explained by the lack of regulation, led
to investigation into the prevalence of fraud and scams among ICOs Liebau and
Schueffel (2019); Karimov and Wójcik (2021). It is not uncommon for white
papers to be a vehicle for misinformation, sometimes resorting to imprecise
claims and exaggerated language Momtaz (2021) to lure investors.

Figure 1: A shared research objective in existing studies that apply NLP to
crypto asset white papers is predicting the degree of success of an ICO. The
figure shows the experiments surveyed in section 2.1, grouped according to the
type of prediction model used and the variables extracted from the text.

###  1.1 Introduction to the MiCA Regulation

In this context of fragmented or absent regulation of the crypto assets
market, the European Union has been making an effort to unify regulation in
its member states on this aspect. The vehicle for this unification is the
Regulation on Markets in Crypto Assets (abbreviated as MiCAR). The text of the
regulation was published on the Official Journal of the European Union on June
9th, 2023 150 (2023) 111OJ L 150, 9.6.2023, p. 40–205, and the norms will
become fully applicable by December 30th, 2024. Its entry into force will
provide clarity and protection for European citizens, but it will also require
considerable effort on the part of issuers Florysiak (2022).

The MiCAR aims to regulate crypto asset public offerings—commonly known as
ICOs—the admission of those crypto assets to trading on platforms, and
additional related services to customers.
One key focus is the standardization of white paper content; the regulation
mandates the inclusion of specific elements, such as details about the token
offering, the associated risks, and team composition, with an emphasis on
textual clarity and readability.

These new rules are engineered not just to to enable European citizens to make
informed investment decisions, but also to assist national authorities in
monitoring crypto asset offerings.

###  1.2 Motivation and main contributions

The goal of this study is to overview current Natural Language Processing
applications to the analysis of crypto asset white papers, and identify how
new applications can assist stakeholders in adapting to the upcoming MiCA
regulation.
Given that much of the existing research comes from economics and finance,
this work aims to bridge the domain gap, enabling future Computer Science (CS)
researchers to leverage all relevant work on the topic. It also provides a
starting point for researchers and practitioners in the legal NLP domain to
adapt existing methods to this new topic.
The organization of this work is as follows:

  * •

A survey of NLP applications to crypto asset white papers and Initial Public
Offering (IPO) prospectuses is provided in Section 2.

  * •

Section 3 goes into detail on how the new MiCA regulation will impact the
structure and content of white papers.

  * •

Finally, Section 4 highlights the challenges and opportunities of harnessing
NLP techniques to aid all stakeholders involved in the MiCAR compliance
process.

Study |  Dataset size |  Sentiment analysis |  Topic
modeling |  Document
similarity |  NER
---|---|---|---|---|---
Florysiak and Schandlbauer (2022) |  2,665 |  |  |  ✓ |  ✓
Liu et al. (2021) |  1,629 |  ✓ |  ✓ |  |
Meoli and Vismara (2022) |  451 |  ✓ |  ✓ |  |  ✓
Momtaz (2021) |  495 |  ✓ |  |  |
Morin et al. (2021) |  3,299 |  |  |  ✓ |
Thewissen et al. (2022) |  5,210 |  ✓ |  ✓ |  |  ✓
Yen et al. (2021) |  1,064 |  |  |  ✓ |
Table 1: Overview of existing literature applying NLP techniques to crypto
asset white papers, along with the dataset size and the techniques used. Most
works come from the economics and finance domains.

##  2 Related work: NLP in ICO White Papers and IPO Prospectuses

###  2.1 NLP and ICO white papers

A survey of the field shows that most existing studies on crypto asset white
papers are published in economics and finance venues, with little overlap with
CS research.

A recurring research goal is using information extracted from the content of
white papers to make predictions about the likelihood of success of an ICO. In
this context, the type of signal extracted can vary widely, as do the
predictive models used. Some studies on the topic relies exclusively on manual
text analysis Bourveau et al. (2022); Thewissen et al. (2023); Fisch (2019);
this review focuses instead on those studies that incorporate at least one NLP
method, setting dictionary-based approaches as the minimum threshold for
inclusion222Excluded work typically relies on page length Samieifar and Baur
(2021); Bourveau et al. (2018) or readability metrics such as the Gunning-Fog
index or the Flesch Reading Ease score Zhang et al. (2019)..
Figure 1 shows a taxonomy of the studies grouped according to the variables of
interest and the type of model, and Table 1 lists the dataset size and NLP
tasks for each.

Momtaz (2021) uses a dictionary-based feature extraction method first proposed
by Hu and Liu (2004) to capture the exaggeration bias in white papers. Momtaz
argues that, in the absence of regulation, issuers of crypto asset tokens may
have an incentive to positively exaggerate the quality of their venture to
attract investors. The analysis confirms that there is a pervasive positive
bias in the content of white papers, and that ICOs with this characteristic
raise more funds in less time.

A common strategy is to use variations of the Latent Dirichlet Allocation
(LDA) algorithm Blei et al. (2003) to extract topic information from the white
papers. The output of the model can be used, for example, to quantify the
amount of technical information included in the text (Liu et al., 2021). This
variable shows that crypto assets with high technology indexes are more likely
to have positive long-run performances of the associated ICOs. Thewissen et
al. (2022) use sentLDA (Bao and Datta, 2014), a sentence-level version of LDA,
to extract 30 topics from an extensive collection of white papers. The topics
generated by the algorithm are evaluated and manually labeled by the authors,
and the topic assignment is then used as a regression variable. The results of
the regression show that the topics contained in a white paper can partially
explain the success of ICOs.

Given the large amount of new ICOs published each year, some research has
focused on looking for similarities among published white papers. Using a
term-frequency approach initially developed for IPO prospectuses by Hanley and
Hoberg (2010), Florysiak and Schandlbauer (2022) and Yen et al. (2021) assess
the informational value of these documents. Here, a white paper is considered
more informative if it introduces new or additional information not found in
similar papers. The outcomes indicate a correlation between high informational
content, or uniqueness, and success metrics such as fundraising and post-ICO
market values. Separately, Morin et al. (2021) investigate text similarity
across white papers using three metrics: TF-IDF, cosine similarity, and
pairwise similarity. Their findings reveal that 19% of ICO white papers
exhibit high similarity to previously published ones.
Finally, Meoli and Vismara (2022) combine sentiment analysis, NER, topic
modeling, sentiment analysis and document structure analysis to extract useful
features from the text. The features are then used to train a set of binary
classifiers, with different machine learning approaches, to predict whether an
ICO was successful or not. The best machine learning model outperforms the
benchmark, a traditional econometric forecasting model. A feature importance
analysis confirms the relevance of the information extracted from the text,
especially the document structure and the sentiment score.

Part |  Description
---|---
A |  Information about the offeror or the person seeking admission to trading
B |  Information about the issuer, if different from the offeror or person seeking admission to trading
C |  Information about the operator of the trading platform
D |  Information about the crypto asset project
E |  Information about the offer to the public of crypto assets or their admission to trading
F |  Information about the crypto assets
G |  Information on the rights and obligations attached to the crypto assets
H |  Information on the underlying technology
I |  Information on the risks
Table 2: Content requirement categories for crypto assets “other than”—one of
the three types of assets regulated by MiCAR.

###  2.2 NLP and IPO prospectuses

Textual analysis methods have also been used to extract information from the
contents of IPO prospectuses. A prospectus is a document required by national
financial regulatory authorities to present an investment offering—such as
stocks, bonds, and mutual funds—to the public, or to obtain admission to
trading on a regulated market. It contains all the details of the financial
offer and must inform investors of the risks involved with investing. These
features make the documents partially similar, at least in intent, to crypto
asset white papers.

Tao et al. (2018) train a custom embedding model using the word2vec algorithm
Mikolov et al. (2013), to find and analyze sentences in the text that
represent “forward looking statements” (FLS). These statements are meant to
provide useful information about the company’s future performance. Li et al.
(2018) apply a keyword-based approach to the same task.
Sharpe and Decker (2022) perform sentiment analysis to look for the effect of
text sentiment on the probability of an IPO being withdrawn.
Finally, Hanley and Hoberg (2010) devise a method—previously mentioned in
2.1—to separate the information content of a prospectus into standard and
informative components. The components are derived from term frequency data
and they are used to quantify how much of the content is also found in
documents published in the same time period—or in the same industry—and how
much of it is unique to a specific document.

###  2.3 Conclusions from the survey

While there seems to be a healthy amount of research interest in the topic,
most of the existing literature covers a very limited array of NLP techniques
and tasks. As an hypothesis, this might be a result of the lack of
intersection between this particular domain of economics and finance research
and CS research.
More advanced NLP methodologies have succesfully been applied in various
regulatory and legal settings. The following section contains examples of such
works and argues for the transferability of these computational approaches to
the analysis and regulation of future crypto asset white papers.

##  3 How MiCA changes the regulatory landscape

The MiCA regulation is broad and covers three categories of crypto assets:
asset-referenced tokens, e-money tokens, and crypto assets other than333For
brevity; the full definition in the text is “crypto assets other than asset-
referenced tokens or e-money tokens”.. The latter category also includes
utility tokens, a specific type of crypto asset that has no financial purposes
and is only intended to provide access to a good or a service supplied by its
issuer. Some types of crypto assets, such as Non-Fungible Tokens (NFTs), are
not covered by the regulation.
For each crypto asset category , the regulation contains norms regarding the
content and issuance of the white paper, the conduct of business, in addition
to organizational and financial requirements.

In alignment with the objective of the study, the rest of the work will focus
only on the norms for white paper content, and in particular those valid for
the crypto “other than” category. While the requirements vary across the three
asset categories, the differences are marginal in the scope of the analysis.
Another reason supporting this choice is the fact that the “other than”
category has the largest set of requirements for white papers—see Appendix A.

#### Minimum content requirements.

Each whitepaper must satisfy a comprehensive list of minimum content
requirements444As outlined in Annexes I, II, and III of the regulation. that
are organized around macro-areas, listed in Table 2 in the case of “other
than” crypto assets. A comparison with the other two categories is provided in
Appendix A.

Part |  Disclosure item |  White paper sample
---|---|---
E |  E5: the total number of crypto assets to be offered to the public or admitted to trading. |  A total of 200 million tokens is put into circulation through a private sale and a public ICO.
E14: Information about technical requirements that the purchaser is required to fulfil to hold the crypto assets. |  To engage with the XXX protocol, individuals must first download XXX App.
H |  H1: The consensus mechanism, where applicable. |  The IBFT PoA is the default consensus mechanism on Galo Blockchain.
H5: Information on the audit outcome of the technology used, if such an audit was conducted. |  Two separate security assessments were conducted over a period of several months and conducted by the audit firms audit1 and audit21.
I |  I4: A description of the risks associated with project implementation. |  (…), no security measures can provide absolute protection against unauthorized access and data breaches.

Table 3: Examples of the minimum content requirements from MiCAR, accompanied
by redacted excerpts taken from existing, unregulated, white papers. The first
column shows the requirement category. All categories and their descriptions
are listed in Table 2.

Table 3 shows some examples of these requirements, accompanied by related
excerpts taken from existing (pre-regulation) white papers.

#### Other requirements

In addition to minimum content requirements, other MiCAR norms impact white
paper content and could have implications for future NLP applications on the
topic.

  * •

Future value of the assets: issuers of crypto assets are forbidden555Articles
6.4, 6.5a, 19.3, 19.4a. from making claims about the future value of the asset
in the white paper, aside from clarifying that the token might lose its value
partially or in full 666Does not apply to e-money tokens.. These kinds of
forward-looking statements have previously been analysed in IPO
prospectuses—see Section 2.2.

  * •

Document language: according to the regulation777Articles 6.9, 19.8, and
51.8., “the white paper shall be drawn up in an official language of the home
Member State, or in a language customary in the sphere of international
finance”. Using a language other than English could pose some challenges that
are examined in Section 4.

  * •

Document template: the MiCA regulation states888Articles 6.10-6.12 that the
white paper must adhere to the template and form established by the European
Securities and Markets Authority (ESMA) and the European Banking Authority
(EBA). As of October 2023, no template has been published yet, although
consultations on technical requirements are in progress.

##  4 NLP in the MiCAR Landscape: Opportunities and Challenges

###  4.1 What NLP can do: exploring possibilities.

Even if there is currently a gap in NLP research applied to the topic of
crypto white papers, other legal domains have benefited from collaboration
with CS researchers Zhong et al. (2020).

Tasks involving the interpretation and analysis of legal texts have been a
popular subject for the legal NLP community. Current research mostly relies on
pre-trained Transformer-based Vaswani et al. (2017) models, with rising
interest in the use of Large Language Models (LLMs) Guha et al. (2023); Yu et
al. (2023).

The rest of the section explores some avenues for applying existing NLP
techniques to the MiCA regulatory framework.

#### Predicting the outcome of compliance checks.

Given a text passage from a white paper and a MiCAR requirement, it would be
beneficial to have a model capable of correctly and automatically check if the
passage complies with the regulation.
There have been attempts to solve this problem for different regulatory
domains. For example, Zufall et al. (2020) attempt to automate the decision of
whether an online post is subject to the European Union’s Legal Framework
against the Expression of Hatred. As part of their approach to classify hate
speech from a legal standpoint, they trained GBERT Chan et al. (2020) as a
multi-class classifier on a manually annotated hate speech dataset. The model
showed poor performance, suggesting that the task might be too complex for the
model’s capabilities. The task could be similarly challenging in the case of
MiCAR, making it a good candidate for further research.

#### Matching passages with requirements.

If automating compliance checks proves too difficult—or even
undesirable—another useful application could be simply to identify which
regulation norms are relevant to each text passage. In a similar task,
Ravichander et al. (2019) collects a set of annotated questions about the
contents of privacy policies, in which the model must determine if the text
passage contains an answer to the provided question, and classify the
question-passage pair as Relevant or Irrelevant. Abualhaija et al. (2022) use
language models from the BERT family Devlin et al. (2019), finetuned on the
task of Question Answering, to assist requirements engineers in finding text
passages relevant to their analysis of compliance requirements.

#### Question answering and information retrieval.

Letting users ask questions about the content of a white paper—be it a
potential investor trying to understand the project, or a regulator looking
for a specific paragraph—is one of the most obvious and potentially useful
applications of NLP to this topic. Given the length and complexity of the
regulation, stakeholders could also benefit from an efficient way to retrieve
norms from the MiCAR text. QA and IR systems in the legal and regulatory
domain pose some specific challenges compared to traditional question
answering Abdallah et al. (2023). Among others, they usually require highly
specialized datasets curated by legal experts, and there is less room for
error since an imprecise or factually wrong answer could negatively impact a
legal decision.

To mitigate the data problem, the authors of LegalBench Guha et al. (2023)
collect over 150 annotated datasets in the legal domain, including QA tasks.
Since regulated white papers will be publicly available, the research
community could contribute to this effort in the future with a QA dataset
inspired by the MiCA regulation.

#### LLMs for reasoning and summarization.

As demonstrated in LegalBench Guha et al. (2023), Large Language Models can
now achieve good performance on some challenging legal reasoning tasks in a
zero-shot setting. While supervised approaches may still outperform these
models, there is a clear advantage in using LLMs as they don’t require labeled
datasets. Selecting the appropriate instruction and few-shot examples for the
prompt can further improve performance Yu et al. (2023); Trautmann et al.
(2022).

Aside from reasoning, another promising application of LLMs is summarization.
The emergence of training methods that use human preference data Stiennon et
al. (2020) has led to models that can produce high-quality summaries of text,
including those in the legal domain Pont et al. (2023), without domain-
specific training. In the MiCAR context, both the regulation text and the
white papers are long and often dense, making summarization a useful tool for
regulators and investors interested in quickly analyzing their contents.

#### Named Entity Recognition.

Some of the MiCAR requirements entail entity extraction, from the traditional
kind (e.g., the list of persons involved in managing the project) to more
domain-specific types of entities such as Legal Norm and Organization. Named
entities are also useful for document organization and search. However, NER
algorithms are particularly sensible to domain shifts: When general-purpose
NER algorithms are used in texts from a narrow domain, there is often a
performance degradation. To work around this limitation, Au et al. (2022) and
Smădu et al. (2022) train entity recognition algorithms on annotated legal
datasets in different languages. Given that crypto asset white papers are also
financial documents, NER models trained on data from the financial domain
might also be an option Salinas Alvarado et al. (2015); Francis et al. (2019).

###  4.2 Challenges

NLP can simplify the understanding of crypto asset white papers, aid
regulators in MiCAR compliance, and reduce the effort required for issuers to
generate compliant documents. It can also benefit investors by enabling them
to make more informed decisions, if they are given the tools to better
assimilate the contents of white papers.
However, there are also potential obstacles due to the narrow scope of
application and the complexity of the subject matter.

#### Adapting to the legal and regulatory domain.

As previously mentioned, performance degradation can occur when using general-
purpose language models on narrow domains. This is not limited to entity
recognition, but also impacts other tasks. One way of diminishing this effect
could be making use of the models released by the legal NLP research
community, such as LEGAL-BERT Chalkidis et al. (2020) and PolBERT, a
subsequent model trained on the Pile of Law Henderson* et al. (2022) dataset.
Since white papers contain terms and language specific to the financial
domain, models like FinBERT Araci (2019) and FinGPT Yang et al. (2023) could
also be appropriate. However, the world of crypto assets brings even more
lexical issues with its acronyms and neologisms999The Cryptopedia glossary
from Gemini contains over 1000 terms unique to the crypto community..

#### Domain shift between white papers and regulation.

In the past, the issuers of crypto asset white papers have used very different
language compared to the legal jargon found in regulatory texts. This
difference might make it harder to find semantic similarities between white
paper passages and the relevant regulation articles—one of the possible
applications mentioned in Section 4.1. Keymanesh et al. (2021) encounter a
similar issue in developing a QA system that answers citizen queries about
privacy policies, and they partially overcome it by using paraphrasing
techniques for query expansion.

#### Language.

The regulation allows using either the local language of the state the crypto
asset will be issued in, or a language commonly used in finance. Although we
can expect most papers to be in English, the usage of other languages might
introduce difficulties, especially in combination with the aforementioned
domain shift. There is ongoing research Niklaus et al. (2023); Chalkidis et
al. (2023) that aims to make available language models and NLP datasets that
support multiple languages in the legal domain.

#### Document structure and length.

In the largest study encountered Thewissen et al. (2022), with a sample of
5210 white papers, the median document length is 30 pages, and the maximum is
167. As the number of required disclosure items rises with the introduction of
MiCAR, we might see these numbers increasing.

Handling long documents in NLP presents unique challenges. Both traditional
sequence models and transformer-based models struggle, with most BERT-based
models having a maximum sequence length of 512 tokens. Even among recent LLMs,
few exceed the limit of 4,096 tokens101010A limit which includes both input
and output tokens.. To get around this problem, Mamakas et al. (2022) modify
Longformer Beltagy et al. (2020) and Legal-BERT Chalkidis et al. (2020) to
handle texts up to 8,192 tokens.

Until the regulation templates for white papers are released, it cannot be
determined whether they will be required to adhere to a provided document
structure. For documents that are not pre-segmented, Aumiller et al. (2021)
propose a segmentation system for legal documents that uses topic modeling to
split a given text into semantically coherent spans for downstream
applications.

###  4.3 Implications for stakeholders

Regulators. NLP could help regulators by partially automating compliance
checks, reducing the administrative burden on competent authorities. It can
also improve the speed and accuracy of oversight with ad-hoc tools for domain
experts, enabling them to respond more rapidly to market changes and potential
infringements. Issuers of crypto assets. For issuers, NLP-based tools can
assist in drafting white papers that are compliant with MiCAR guidelines from
the start. This can reduce the time and costs associated with legal
consultations and revisions, making it easier to enter and operate within the
European market. Potential investors. From an investor’s perspective, NLP-
generated analyses of white papers can provide a more transparent and easily
interpretable data source, democratizing access to investment information
about the crypto asset market.

##  5 Conclusions

Crypto asset white papers are valuable data sources for NLP practitioners due
to their public availability and rich informational content. The entry into
force of the MiCAR opens new avenues for NLP applications to assist various
stakeholders, including regulators, issuers, and potential investors, in
navigating compliance and regulatory oversight.

Our survey of existing literature on the textual analysis of white papers
revealed an active but siloed field that could benefit significantly from
interdisciplinary collaboration between computer science, finance, and legal
experts. Existing work in the legal NLP field could serve as a foundation for
developing algorithms and models tailored to this new regulatory landscape.

Researchers have a clear opportunity to contribute to this emerging area, with
the potential not only to streamline regulatory processes but also to create a
more transparent and accountable crypto asset ecosystem.

## Limitations

The work inevitably contains some speculative elements, given that, while the
MiCAR text is public, no regulated white papers are available yet, and no
regulatory workflows are yet finalized or known. The new white papers will be
expected to follow a template designed by the ESMA, which has not been
released yet. Therefore, the analysis of possible NLP applications,
opportunities, and challenges is partially grounded in the structures and
contents of existing, unregulated white papers, and it may not be entirely
applicable to regulated ones.

## Acknowledgments

The author would like to thank Marco Benedetti and Andrea Gentili for their
valuable feedback. Special thanks are also extended to Claudia Biancotti,
Daniele Natalizi, and Mattia Suardi for their insights into clarifying various
aspects of the MiCAR regulation.

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##  Appendix A MiCAR Content Requirements Categories

###  A.1 Categories for Crypto Assets “other than”

Found in Annex I of the regulation.

  * •

Part A: Information about the offeror or the person seeking admission to
trading

  * •

Part B: Information about the issuer, if different from the offeror or person
seeking admission to trading

  * •

Part C: Information about the operator of the trading platform in cases where
it draws up the crypto asset white paper

  * •

Part D: Information about the crypto asset project

  * •

Part E: Information about the offer to the public of crypto assets or their
admission to trading

  * •

Part F: Information about the crypto assets

  * •

Part G: Information on the rights and obligations attached to the crypto
assets

  * •

Part H: Information on the underlying technology

  * •

Part I: Information on the risks

| Part A | Part B | Part C | Part D | Part E | Part F | Part G | Part H | Part I
---|---|---|---|---|---|---|---|---|---
Crypto “other than” | 10 | 8 | 9 | 6 | 19 | 2 | 10 | 5 | 5
Table 4: Category count for each Part in the content requirements, for crypto
assets “other than”.

###  A.2 Categories for Asset-Referenced Tokens and E-money Tokens

Found in Annexes II and III of the regulation.

  * •

Part A: Information about the issuer of the token

  * •

Part B: Information about the token

  * •

Part C: Information about the offer to the public of the token or its
admission to trading

  * •

Part D: Information on the rights and obligations attached to the token

  * •

Part E: Information on the underlying technology

  * •

Part F: Information on the risks

  * •

Part G: Information on the reserve of assets

| Part A | Part B | Part C | Part D | Part E | Part F | Part G
---|---|---|---|---|---|---|---
Asset-referenced Tokens | 12 | 5 | 15 | 17 | 5 | 6 | 5
E-money tokens | 13 | 3 | 4 | 8 | 6 | 3 | -
Table 5: Category count for each Part in the content requirements, for asset-
referenced and e-money tokens.