1. 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

2. There is a Significant
Ongoing Conversation
Regarding the Size and
Intrusiveness of the
Regulatory State

3. This is one of the grand
debates in law + politics…

4. Often these conversations
are not particularly
empirical in nature

5. Can we bring some
scientific tools to better
inform the conversation?

6. This links to some of
our broader interest in
Legal Complexity
Legal Uncertainty
Legal Risk

7. 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).

8. DM Katz & MJ Bommarito. Measuring the Complexity of the Law: The
United States Code. Artificial Intelligence and Law, 22(4), 337-374. (2014)

9. J.B. Ruhl, Daniel Martin Katz & Michael Bommarito,
Harnessing Legal Complexity, 355 Science 1377 (2017)

10. We believe that
Legal Complexity
is one of the primary
underlying vectors
for our field

11. Technology
Process Improvement
Design Centric Methods
Law
∩

12. 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

14. How can we better
understand the sources
of legal complexity,
legal uncertainty &
legal risk ?

16. The Fundamental Concept
for the presentation …

17. At scale,
Securities Filings can offer
us at least some insight
into the manner in which
legal rules impact
regulatory targets

19. So today —
A Presentation in
Three Parts …

20. Intro + 10-K’s as a Data Source
Insights into Legal Complexity / Uncertainty via 10-K’s

21. 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

22. 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

24. 10K’s AS A
DATA SOURCE{ }
PART I

25. < What is a 10-K ? >

26. “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.”

27. Publically
Traded
Companies
and those that
meet the
registration
requirements
must file

28. Designed to
provide the
market with
useful
information
relevant to the
valuation of the
company

29. 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

30. Data Collection and Pre-Processing
Among other things,
Form 10-K contains
a range of relevant
information about
regulatory exposure
(and other risks)

31. 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.

32. Data Collection and Pre-Processing
Countervailing Incentives:
Report to get a form of
‘securities fraud insurance’

33. 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’

34. 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

35. < Data Acquisition >

36. https://www.sec.gov/edgar/searchedgar/companysearch.html

37. Beginning of
an
Open
Source
Quarterly
Index
for Research
and Products Coming Soon!

38. Registered Companies34,000+
Data*(through Q3 2016)

39. Registered Companies
Total Number of 10-K’s
34,000+
160,000+
Data*(through Q3 2016)

40. Registered Companies
Total Number of 10-K’s
34,000+
160,000+
Data*(through Q3 2016)
Years in Question1994 - 2016*

42. Measuring Temperature
and Diversity of the U.S.
Regulatory Ecosystem{ }
PART II

43. We thought about how we
might develop a window
into the impact of
regulations on companies

44. Although it is partial and
in some ways limited …

45. We offer a
mean-field
characterization

46. Across all 10-K’s, we
simply identify + track the
number of act / agency
references

47. Consider
a simple
example
2009 10-K filing

49. Lets look at just
one page of this 10-K
2009 10-K filing

51. 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

52. Registered Companies
Total Number of 10-K’s
34,000+
160,000+
Data*(through Q3 2016)
Years in Question1994 - 2016*

53. 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

55. mean-field characterization

56. mean-field characterization
Energy =
Total
References
Across all
Filings

57. Temperature =Energy =
Total
References
Across all
Filings
mean-field characterization
References
Per Filing as
a Function of
Time

59. 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

60. Summary
Available
Here
https://www.law.ox.ac.uk/business-law-
blog/blog/2017/02/measuring-temperature-
and-diversity-us-regulatory-ecosystem

61. What is
the global
average
temperature?
Has this changed
over time?
Paper 1
(Currently Under Review)

62. Paper 1
(Currently Under Review)
Note:
Some Norms,
Incentives,
Requirements
have changed
over 20+ years…
(for example
Sarbanes–Oxley
Act of 2002)

63. Paper 1
(Currently Under Review)
We observe
significant
growth both in
the raw count
and the per
filing number of
references

64. A Mean - Field Characterization
Diversity

65. Using BitString Encoding
as an Information Theoretic
Representation for the Self-
Identified Regulatory Exposure

66. 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

67. 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

68. We have 160,000+ of these
{year, company} bitstrings

69. Is the Diversity of the System
Growing or Contracting?

70. We use Hamming Distance
as a Mean-Field Characterization

71. Diversity of the Overall
Regulatory Ecosystem

72. We believe that
this is linked to an increase
in specialization in the
overall economy …
(i.e. there are lots of
Economic Microclimates)

73. Also, this implies a lawyer
specialization story as well …
(i.e. there are lots of
Regulatory Microclimates)

74. 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 )

76. Regulatory Dynamics
Revealed by the
Securities Filings of
Registered Companies
{ }
PART III

77. Regulatory Dynamics Revealed
by the Securities Filings of
Registered Companies
Paper 2
(Currently This is A Work In Progress)

78. In Bommarito & Katz 2016,
we explored the mean-field
temperature and diversity of
the regulatory ecosystem

79. and mentioned that there
were likely to be differential
regulatory dynamics and
microclimates therein

81. Data Collection
Pre-Processing

82. 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

83. We store a value for each
{Company, Act, Year}

84. We store a value for each
{Company, Act, Year}
So we can consider
either companies or
acts/agencies as the
unit of analysis

86. (I)
Act/
Agency
Centric
View

87. The Company /
Act Matrix
(a)

88. Sequencing of Regulatory Bitstrings for a Given YearFigure 2:

89. 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:

90. Each Company
is a very
narrow column
of this graph

91. Notice
that this overall
plot is
pretty sparse

92. Notice
that this overall
plot is
pretty sparse
This implies that
most regulations
do *not* impact
most companies

94. Dynamics Revealed
by Exploring Time
Series Signatures
(b)

95. Characterizing the
Impact of a Rule
on the Broader
Regulatory Risk
Environment

96. For most scientific inquries -
there is a tradeoff of
granularity versus generality

97. In the spirit of
complex systems /
physics, we would
like to offer a
generalization …

98. Track the frequency of
act references
as a function of time

99. And analyze
that time series
signature …

100. Some
Motivating
Examples
Clean Water Act
Sarbanes Oxley
Y2K
Figure 3

101. 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.

102. 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)

103. Equation (1)

104. Taxonomy
of
Behavioral
Signatures
Figure 4

105. (‘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

106. 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

107. 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

108. In sum, we are able to
use this framework to
*classify* any
regulation by its
behavioral signature

110. (II)
Company
Centric
View

111. In Bommarito & Katz
2016, we undertook a
simple encoding and
selected a well known
distance metric
(i.e. hamming distance)

112. Once we have a distance metric
or some manner to encode edges…

113. we can generate a network
projection of the overall
company landscape …

114. 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

115. Figure 5

116. Figure 5

117. Figure 5

118. Figure 5

119. Figure 5

120. Figure 5

121. Figure 5

123. Regulatory
Mircoclimates ?

124. This is a currently
ongoing step in
the research …

125. 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.

126. 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.

127. 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.

128. We have
the Act
Centric
Dendrogram
Figure 6
(which is still a bit
of a ridiculogram)

129. Next Step is
to Generate a
Company
Centric
Dendrogram
Figure 7

130. Some Potential
Questions
to Be Evaluated

131. Does the Fortune 100, 500
have a categorically
different experience than
their sector counterparts ?
SIZE > SECTOR?

132. From a regulatory risk
perspective — which
companies bridge clusters ?

133. Does this tell us
something about where
the economy (or a sector
thereof) is heading ?

134. This part is still
a work in progress …

135. 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

136. Fellow
Stanford CodeX
Center for Legal Informatics
Director of Research
The Law Lab
IllinoisTech - Chicago Kent Law

137. Associate Professor of Law
The Law Lab @ Illinois-Tech
Affiliated Faculty
Stanford CodeX
Center for Legal Informatics
Founder + Director

138. Chief Strategy Officer
LexPredict
Chief Executive Officer
LexPredict

139. LexPredict.com

140. thelawlab.com

141. http://www.legalanalyticscourse.com/Professor Daniel Martin Katz
Professor Michael J. Bommarito II Advanced Class

142. ComputationalLegalStudies.com
BLOG

143. Michael J. Bommarito II
@ mjbommar
computationallegalstudies.com
lexpredict.com
bommaritollc.com
illinois tech - chicago kent college of law@
thelawlab.com

144. Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@
thelawlab.com