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Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito

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Exploring the Physical Properties of Regulatory Ecosystems: Regulatory Dynamics Revealed by Securities Filings — Professors Daniel Martin Katz + Michael J Bommarito

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Exploring the Physical Properties of Regulatory Ecosystems - Professors Daniel Martin Katz + Michael J Bommarito

  1. 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. 2. There is a Significant Ongoing Conversation Regarding the Size and Intrusiveness of the Regulatory State
  3. 3. This is one of the grand debates in law + politics…
  4. 4. Often these conversations are not particularly empirical in nature
  5. 5. Can we bring some scientific tools to better inform the conversation?
  6. 6. This links to some of our broader interest in Legal Complexity Legal Uncertainty Legal Risk
  7. 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. 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. 9. J.B. Ruhl, Daniel Martin Katz & Michael Bommarito, Harnessing Legal Complexity, 355 Science 1377 (2017)
  10. 10. We believe that Legal Complexity is one of the primary underlying vectors for our field
  11. 11. Technology Process Improvement Design Centric Methods Law ∩
  12. 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
  13. 13. How can we better understand the sources of legal complexity, legal uncertainty & legal risk ?
  14. 14. The Fundamental Concept for the presentation …
  15. 15. At scale, Securities Filings can offer us at least some insight into the manner in which legal rules impact regulatory targets
  16. 16. So today — A Presentation in Three Parts …
  17. 17. Intro + 10-K’s as a Data Source Insights into Legal Complexity / Uncertainty via 10-K’s
  18. 18. 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
  19. 19. 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
  20. 20. 10K’s AS A 
 DATA SOURCE{ } PART I
  21. 21. < What is a 10-K ? >
  22. 22. “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.”
  23. 23. Publically Traded Companies and those that meet the registration requirements must file
  24. 24. Designed to provide the market with useful information relevant to the valuation of the company
  25. 25. 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
  26. 26. Data Collection and Pre-Processing Among other things, Form 10-K contains a range of relevant information about regulatory exposure
 (and other risks)
  27. 27. 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.
  28. 28. Data Collection and Pre-Processing Countervailing Incentives: Report to get a form of ‘securities fraud insurance’
  29. 29. 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’
  30. 30. 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
  31. 31. < Data Acquisition >
  32. 32. https://www.sec.gov/edgar/searchedgar/companysearch.html
  33. 33. Beginning of an Open Source Quarterly Index for Research and Products Coming Soon!
  34. 34. Registered Companies34,000+ Data*(through Q3 2016)
  35. 35. Registered Companies Total Number of 10-K’s 34,000+ 160,000+ Data*(through Q3 2016)
  36. 36. Registered Companies Total Number of 10-K’s 34,000+ 160,000+ Data*(through Q3 2016) Years in Question1994 - 2016*
  37. 37. Measuring Temperature and Diversity of the U.S. Regulatory Ecosystem{ } PART II
  38. 38. We thought about how we might develop a window into the impact of regulations on companies
  39. 39. Although it is partial and in some ways limited …
  40. 40. We offer a mean-field characterization
  41. 41. Across all 10-K’s, we simply identify + track the number of act / agency references
  42. 42. Consider a simple example 2009 10-K filing
  43. 43. Lets look at just one page of this 10-K 2009 10-K filing
  44. 44. 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
  45. 45. Registered Companies Total Number of 10-K’s 34,000+ 160,000+ Data*(through Q3 2016) Years in Question1994 - 2016*
  46. 46. 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
  47. 47. mean-field characterization
  48. 48. mean-field characterization Energy = Total References Across all Filings
  49. 49. Temperature =Energy = Total References Across all Filings mean-field characterization References Per Filing as a Function of Time
  50. 50. 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
  51. 51. Summary Available Here https://www.law.ox.ac.uk/business-law- blog/blog/2017/02/measuring-temperature- and-diversity-us-regulatory-ecosystem
  52. 52. What is the global average temperature? Has this changed over time? Paper 1 (Currently Under Review)
  53. 53. Paper 1 (Currently Under Review) Note: Some Norms, Incentives, Requirements have changed over 20+ years… (for example Sarbanes–Oxley Act of 2002)
  54. 54. Paper 1 (Currently Under Review) We observe significant growth both in the raw count and the per filing number of references
  55. 55. A Mean - Field Characterization Diversity
  56. 56. Using BitString Encoding as an Information Theoretic Representation for the Self- Identified Regulatory Exposure
  57. 57. 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
  58. 58. 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
  59. 59. We have 160,000+ of these {year, company} bitstrings
  60. 60. Is the Diversity of the System Growing or Contracting?
  61. 61. We use Hamming Distance as a Mean-Field Characterization
  62. 62. Diversity of the Overall Regulatory Ecosystem
  63. 63. We believe that this is linked to an increase in specialization in the overall economy … (i.e. there are lots of Economic Microclimates)
  64. 64. Also, this implies a lawyer specialization story as well … (i.e. there are lots of Regulatory Microclimates)
  65. 65. 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 )
  66. 66. Regulatory Dynamics Revealed by the Securities Filings of Registered Companies { } PART III
  67. 67. Regulatory Dynamics Revealed by the Securities Filings of Registered Companies Paper 2 (Currently This is A Work In Progress)
  68. 68. In Bommarito & Katz 2016, we explored the mean-field temperature and diversity of the regulatory ecosystem
  69. 69. and mentioned that there were likely to be differential regulatory dynamics and microclimates therein
  70. 70. Data Collection Pre-Processing
  71. 71. 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
  72. 72. We store a value for each {Company, Act, Year}
  73. 73. We store a value for each {Company, Act, Year} So we can consider either companies or acts/agencies as the unit of analysis
  74. 74. (I) Act/ Agency Centric View
  75. 75. The Company / Act Matrix (a)
  76. 76. Sequencing of Regulatory Bitstrings for a Given YearFigure 2:
  77. 77. 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:
  78. 78. Each Company is a very narrow column of this graph
  79. 79. Notice that this overall plot is pretty sparse
  80. 80. Notice that this overall plot is pretty sparse This implies that most regulations do *not* impact most companies
  81. 81. Dynamics Revealed by Exploring Time Series Signatures (b)
  82. 82. Characterizing the Impact of a Rule on the Broader Regulatory Risk Environment
  83. 83. For most scientific inquries - there is a tradeoff of granularity versus generality
  84. 84. In the spirit of complex systems / physics, we would like to offer a generalization …
  85. 85. Track the frequency of act references as a function of time
  86. 86. And analyze that time series signature …
  87. 87. Some Motivating Examples Clean Water Act Sarbanes Oxley Y2K Figure 3
  88. 88. 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.
  89. 89. 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)
  90. 90. Equation (1)
  91. 91. Taxonomy of Behavioral Signatures Figure 4
  92. 92. (‘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
  93. 93. 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
  94. 94. 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
  95. 95. In sum, we are able to use this framework to *classify* any regulation by its behavioral signature
  96. 96. (II) Company Centric View
  97. 97. In Bommarito & Katz 2016, we undertook a simple encoding and selected a well known distance metric (i.e. hamming distance)
  98. 98. Once we have a distance metric or some manner to encode edges…
  99. 99. we can generate a network projection of the overall company landscape …
  100. 100. 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
  101. 101. Figure 5
  102. 102. Figure 5
  103. 103. Figure 5
  104. 104. Figure 5
  105. 105. Figure 5
  106. 106. Figure 5
  107. 107. Figure 5
  108. 108. Regulatory Mircoclimates ?
  109. 109. This is a currently ongoing step in the research …
  110. 110. 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.
  111. 111. 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.
  112. 112. 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.
  113. 113. We have the Act Centric Dendrogram Figure 6 (which is still a bit of a ridiculogram)
  114. 114. Next Step is to Generate a Company Centric Dendrogram Figure 7
  115. 115. Some Potential Questions to Be Evaluated
  116. 116. Does the Fortune 100, 500 have a categorically different experience than their sector counterparts ? SIZE > SECTOR?
  117. 117. From a regulatory risk perspective — which companies bridge clusters ?
  118. 118. Does this tell us something about where the economy (or a sector thereof) is heading ?
  119. 119. This part is still a work in progress …
  120. 120. 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
  121. 121. Fellow Stanford CodeX Center for Legal Informatics Director of Research The Law Lab IllinoisTech - Chicago Kent Law
  122. 122. Associate Professor of Law The Law Lab @ Illinois-Tech Affiliated Faculty Stanford CodeX Center for Legal Informatics Founder + Director
  123. 123. Chief Strategy Officer LexPredict Chief Executive Officer LexPredict
  124. 124. LexPredict.com
  125. 125. thelawlab.com
  126. 126. http://www.legalanalyticscourse.com/Professor Daniel Martin Katz Professor Michael J. Bommarito II Advanced Class
  127. 127. ComputationalLegalStudies.com BLOG
  128. 128. Michael J. Bommarito II @ mjbommar computationallegalstudies.com lexpredict.com bommaritollc.com illinois tech - chicago kent college of law@ thelawlab.com
  129. 129. Daniel Martin Katz @ computational computationallegalstudies.com lexpredict.com danielmartinkatz.com illinois tech - chicago kent college of law@ thelawlab.com

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