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Exploring the
Physical Properties of
Regulatory Ecosystems
daniel martin katz
blog | ComputationalLegalStudies.com
corp | LexPredict.com
page | DanielMartinKatz.com
edu | illinois tech - chicago kent law
lab | TheLawLab.com
page | bommaritollc.com
michael j bommarito
blog | ComputationalLegalStudies.com
corp | LexPredict.com
edu | illinois tech - chicago kent law
lab | TheLawLab.com
Regulatory Dynamics Revealed by Securities Filings
There is a Significant
Ongoing Conversation
Regarding the Size and
Intrusiveness of the
Regulatory State
This is one of the grand
debates in law + politics…
Often these conversations
are not particularly
empirical in nature
Can we bring some
scientific tools to better
inform the conversation?
This links to some of
our broader interest in
Legal Complexity
Legal Uncertainty
Legal Risk
MJ Bommarito & DM Katz. A Mathematical Approach to the Study of the
United States Code. Physica A: Statistical Mechanics and its Applications,
389(19), 4195-4200 (2010).
DM Katz & MJ Bommarito. Measuring the Complexity of the Law: The
United States Code. Artificial Intelligence and Law, 22(4), 337-374. (2014)
J.B. Ruhl, Daniel Martin Katz & Michael Bommarito,
Harnessing Legal Complexity, 355 Science 1377 (2017)
We believe that
Legal Complexity
is one of the primary
underlying vectors
for our field
Technology
Process Improvement
Design Centric Methods
Law
∩
could also be called
legal complexity mitigation
strategies
http://prawfsblawg.blogs.com/prawfsblawg/2017/03/-complexity-mitigation-strategies-for-
law-law-land-and-beyond-and-some-other-thoughts-on-hadfield-su.html#more
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
How can we better
understand the sources
of legal complexity,
legal uncertainty &
legal risk ?
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
The Fundamental Concept
for the presentation …
At scale,
Securities Filings can offer
us at least some insight
into the manner in which
legal rules impact
regulatory targets
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
So today —
A Presentation in
Three Parts …
Intro + 10-K’s as a Data Source
Insights into Legal Complexity / Uncertainty via 10-K’s
Paper 1: Measuring Temperature
Temperature + Diversity of the U.S. Regulatory Ecosystem
Intro + 10-K’s as a Data Source
Insights into Legal Complexity / Uncertainty via 10-K’s
Paper 1: Measuring Temperature
Temperature + Diversity of the U.S. Regulatory Ecosystem
Dynamics + Microclimates Revealed by Securities Filings
Paper 2: Regulatory Dynamics
Intro + 10-K’s as a Data Source
Insights into Legal Complexity / Uncertainty via 10-K’s
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
10K’s AS A 

DATA SOURCE{ }
PART I
< What is a 10-K ? >
“A Form 10-K is an annual report
required by the U.S. Securities and
Exchange Commission (SEC), that gives
a comprehensive summary of a
company's financial performance.”
Publically
Traded
Companies
and those that
meet the
registration
requirements
must file
Designed to
provide the
market with
useful
information
relevant to the
valuation of the
company
Significant Literature
Exploring Market Reaction
to Securities Filings
Campbell, J. L., Chen, H., Dhaliwal, D. S., Lu, H. M., &
Steele, L. B. (2014). The information content of mandatory
risk factor disclosures in corporate filings. Review of
Accounting Studies, 19(1), 396-455.
Nelson, Karen K., and Adam C. Pritchard. "Carrot or stick?
The shift from voluntary to mandatory disclosure of risk
factors." Journal of Empirical Legal Studies 13.2 (2016):
266-297.
Bao, Yang, and Anindya Datta. "Simultaneously discovering
and quantifying risk types from textual risk disclosures."
Management Science 60.6 (2014): 1371-1391.
Recent
Examples
from
Literature
Data Collection and Pre-Processing
Among other things,
Form 10-K contains
a range of relevant
information about
regulatory exposure

(and other risks)
Data Collection and Pre-Processing
“…the Financial Executives
Research Foundation reveals a
mean and median 2015 expense
of $1.8M and $522,205,
respectively.”
Real Resources are used to
produced these reports:
Source: F. E. R. Foundation, 2016 audit fee report, 2016.
Data Collection and Pre-Processing
Countervailing Incentives:
Report to get a form of
‘securities fraud insurance’
Data Collection and Pre-Processing
Countervailing Incentives:
Does not enumerate all risks
under the sun because it
may scare investors
Report to get a form of
‘securities fraud insurance’
Data Collection and Pre-Processing
data is *not* perfect
but it is large scale
characterization across
many organization and
sectors of the manner in
which regulations impact
(large) companies
< Data Acquisition >
https://www.sec.gov/edgar/searchedgar/companysearch.html
Beginning of
an
Open
Source
Quarterly
Index
for Research
and Products Coming Soon!
Registered Companies34,000+
Data*(through Q3 2016)
Registered Companies
Total Number of 10-K’s
34,000+
160,000+
Data*(through Q3 2016)
Registered Companies
Total Number of 10-K’s
34,000+
160,000+
Data*(through Q3 2016)
Years in Question1994 - 2016*
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
Measuring Temperature
and Diversity of the U.S.
Regulatory Ecosystem{ }
PART II
We thought about how we
might develop a window
into the impact of
regulations on companies
Although it is partial and
in some ways limited …
We offer a
mean-field
characterization
Across all 10-K’s, we
simply identify + track the
number of act / agency
references
Consider
a simple
example
2009 10-K filing
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
Lets look at just
one page of this 10-K
2009 10-K filing
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
Text of the
10-k for
Company i
in year y
Synonym +
Fuzzy String
Matching to Act,
Agency, U.S. Code
and CFR Masterlist
Gramm Leach
Bliley
Financial
Services
Modernization
Act
GLBA
Graham Leach Bliley
Financial Services
Modernization” Act
Gramm Leach
Bliley Financial
Services
Modernization
Act of 1999
Data Collection and Pre-Processing
GLB
Registered Companies
Total Number of 10-K’s
34,000+
160,000+
Data*(through Q3 2016)
Years in Question1994 - 2016*
Registered Companies
Total Number of 10-K’s
34,000+
160,000+
Data*(through Q3 2016)
Years in Question1994 - 2016*
Total Number of
Act / Agency References
4.5 million
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
mean-field characterization
mean-field characterization
Energy =
Total
References
Across all
Filings
Temperature =Energy =
Total
References
Across all
Filings
mean-field characterization
References
Per Filing as
a Function of
Time
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
Paper 1
(Currently Under Review)
M. Bommarito., D. Katz,
Measuring the Temperature
and Diversity of the U.S.
Regulatory Ecosystem (2016)
V available at
https://arxiv.org/pdf/1612.09244.pdf
https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2891494
2
Summary
Available
Here
https://www.law.ox.ac.uk/business-law-
blog/blog/2017/02/measuring-temperature-
and-diversity-us-regulatory-ecosystem
What is
the global
average
temperature?
Has this changed
over time?
Paper 1
(Currently Under Review)
Paper 1
(Currently Under Review)
Note:
Some Norms,
Incentives,
Requirements
have changed
over 20+ years…
(for example
Sarbanes–Oxley
Act of 2002)
Paper 1
(Currently Under Review)
We observe
significant
growth both in
the raw count
and the per
filing number of
references
A Mean - Field Characterization
Diversity
Using BitString Encoding
as an Information Theoretic
Representation for the Self-
Identified Regulatory Exposure
0
1
1
0
0
0
0
0
1
…
Encoding
Regulatory
Bitstring for
Company i
in year y
For each
{company, year} pair
Encode as 1 for the
presence of an act / agency
Pseudocode for BitString Encoding
Encode as 0 otherwise
0
1
1
0
0
0
0
0
1
…
Encoding
Regulatory
Bitstring for
Company i
in year y
Text of the
10-k for
Company i
in year y
Synonym +
Fuzzy String
Matching to Act,
Agency, U.S. Code
and CFR Masterlist
Gramm Leach
Bliley
Financial
Services
Modernization
Act
GLBA
Graham Leach Bliley
Financial Services
Modernization” Act
Gramm Leach
Bliley Financial
Services
Modernization
Act of 1999
A Mean - Field Characterization
We have 160,000+ of these
{year, company} bitstrings
Is the Diversity of the System
Growing or Contracting?
We use Hamming Distance
as a Mean-Field Characterization
Diversity of the Overall
Regulatory Ecosystem
We believe that
this is linked to an increase
in specialization in the
overall economy …
(i.e. there are lots of
Economic Microclimates)
Also, this implies a lawyer
specialization story as well …
(i.e. there are lots of
Regulatory Microclimates)
In other words, economic
and regulatory specialization
implies that the knowledge
necessary to be the General
Counsel of one company is
local not global knowledge
(i.e. it is less portable outside of
the respective microclimate )
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
Regulatory Dynamics
Revealed by the
Securities Filings of
Registered Companies
{ }
PART III
Regulatory Dynamics Revealed
by the Securities Filings of
Registered Companies
Paper 2
(Currently This is A Work In Progress)
In Bommarito & Katz 2016,
we explored the mean-field
temperature and diversity of
the regulatory ecosystem
and mentioned that there
were likely to be differential
regulatory dynamics and
microclimates therein
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
Data Collection
Pre-Processing
0
1
1
0
0
0
0
0
1
…
Encoding
Regulatory
Bitstring for
Company i
in year y
Text of the
10-k for
Company i
in year y
Synonym +
Fuzzy String
Matching to Act,
Agency, U.S. Code
and CFR Masterlist
Gramm Leach
Bliley Financial
Services
Modernization
Act of 1999
Bit String EncodingFigure 1:
Gramm Leach
Bliley
Financial
Services
Modernization
Act
GLBA
Graham Leach Bliley
Financial Services
Modernization” Act
GLB
We store a value for each
{Company, Act, Year}
We store a value for each
{Company, Act, Year}
So we can consider
either companies or
acts/agencies as the
unit of analysis
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
(I)
Act/
Agency
Centric
View
The Company /
Act Matrix
(a)
Sequencing of Regulatory Bitstrings for a Given YearFigure 2:
Sequencing of Regulatory Bitstrings for a Given Year
Securities Act
is part of the
universal
profile
Actually, it is
axiomatic
to this dataset
Figure 2:
Each Company
is a very
narrow column
of this graph
Notice
that this overall
plot is
pretty sparse
Notice
that this overall
plot is
pretty sparse
This implies that
most regulations
do *not* impact
most companies
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
Dynamics Revealed
by Exploring Time
Series Signatures
(b)
Characterizing the
Impact of a Rule
on the Broader
Regulatory Risk
Environment
For most scientific inquries -
there is a tradeoff of
granularity versus generality
In the spirit of
complex systems /
physics, we would
like to offer a
generalization …
Track the frequency of
act references
as a function of time
And analyze
that time series
signature …
Some
Motivating
Examples
Clean Water Act
Sarbanes Oxley
Y2K
Figure 3
We are
undertaking
a version of
this approach
Riley Crane and Didier Sornette. "Robust dynamic
classes revealed by measuring the response function of
a social system." Proceedings of the National Academy
of Sciences 105, no. 41 (2008): 15649-15653.
μ
σ
τ auto-correlation
(can be thought of as memory)
set each parameter to base zero at
time = and indext 0
variance2
mean (H, M, L)
(+, -)
(H, M, L)
Equation (1)
Taxonomy
of
Behavioral
Signatures
Figure 4
(‘M’ ‘H’, ‘+’)
Anti Kickback
Fairness In Asbestos Injury Resolution
American Clean Energy And Security
Pension Funding Equity
Medicare, Medicaid, And SChip Benefits Improvement
Clusters that have a Similar
Behavioral Signature
(Not necessariy topically similar)
(‘H’ ‘H’, ‘+’)
Patient Protection And Affordable Care
Secure And Fair Enforcement For Mortgage Licensing
Dodd Frank Wall Street Reform And Consumer Protection
Energy Independence And Security
Tax Relief, Unemployment Insurance Reauthorization
Table 1
What is interesting
is that these otherwise
unrelated acts share a
physical similarity
(‘H’ ‘H’, ‘+’)
Patient Protection And Affordable Care
Secure And Fair Enforcement For Mortgage Licensing
Dodd Frank Wall Street Reform And Consumer Protection
Energy Independence And Security
Tax Relief, Unemployment Insurance Reauthorization
They are similar in the manner in
which they impact the broader
corporate risk environment
(‘H’ ‘H’, ‘+’)
Patient Protection And Affordable Care
Secure And Fair Enforcement For Mortgage Licensing
Dodd Frank Wall Street Reform And Consumer Protection
Energy Independence And Security
Tax Relief, Unemployment Insurance Reauthorization
In sum, we are able to
use this framework to
*classify* any
regulation by its
behavioral signature
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
(II)
Company
Centric
View
In Bommarito & Katz
2016, we undertook a
simple encoding and
selected a well known
distance metric
(i.e. hamming distance)
Once we have a distance metric
or some manner to encode edges…
we can generate a network
projection of the overall
company landscape …
Network Generation Procedure
a) Calculate the Hamming distance matrix as described
in Paper #1 over Acts
b) Threshold the matrix by removing all elements whose
distances are greater than D (D=3 in this figure)
c) Generate graph from resulting edge-weighted
adjacency matrix, where edge weight = 1/(1 + d)
d) Layout is Fruchterman-Reingold weighted showing only
giant component
Figure 5
Figure 5
Figure 5
Figure 5
Figure 5
Figure 5
Figure 5
Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito
Regulatory
Mircoclimates ?
This is a currently
ongoing step in
the research …
Want to
undertake
community
detection on
the network
Yang, Z., Algesheimer, R., & Tessone, C. J. (2016). A
Comparative Analysis of Community Detection
Algorithms on Artificial Networks. Scientific Reports, 6.
Goal is to
be able to
characterize
the
mesoscopic
layer Yang, Z., Algesheimer, R., & Tessone, C. J. (2016). A
Comparative Analysis of Community Detection
Algorithms on Artificial Networks. Scientific Reports, 6.
However,
there are a
range of
community
detection
methods … Yang, Z., Algesheimer, R., & Tessone, C. J. (2016). A
Comparative Analysis of Community Detection
Algorithms on Artificial Networks. Scientific Reports, 6.
We have
the Act
Centric
Dendrogram
Figure 6
(which is still a bit
of a ridiculogram)
Next Step is
to Generate a
Company
Centric
Dendrogram
Figure 7
Some Potential
Questions
to Be Evaluated
Does the Fortune 100, 500
have a categorically
different experience than
their sector counterparts ?
SIZE > SECTOR?
From a regulatory risk
perspective — which
companies bridge clusters ?
Does this tell us
something about where
the economy (or a sector
thereof) is heading ?
This part is still
a work in progress …
Paper 1: Measuring Temperature
Temperature + Diversity of the U.S. Regulatory Ecosystem
Dynamics + Microclimates Revealed by Securities Filings
Paper 2: Regulatory Dynamics
Intro + 10-K’s as a Data Source
Insights into Legal Complexity / Uncertainty via 10-K’s
Fellow
Stanford CodeX
Center for Legal Informatics
Director of Research
The Law Lab
IllinoisTech - Chicago Kent Law
Associate Professor of Law
The Law Lab @ Illinois-Tech
Affiliated Faculty
Stanford CodeX
Center for Legal Informatics
Founder + Director
Chief Strategy Officer
LexPredict
Chief Executive Officer
LexPredict
LexPredict.com
thelawlab.com
http://www.legalanalyticscourse.com/Professor Daniel Martin Katz
Professor Michael J. Bommarito II Advanced Class
ComputationalLegalStudies.com
BLOG
Michael J. Bommarito II
@ mjbommar
computationallegalstudies.com
lexpredict.com
bommaritollc.com
illinois tech - chicago kent college of law@
thelawlab.com
Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@
thelawlab.com

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