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The Three Forms of (Legal) Prediction - Experts...

The Three Forms of (Legal) Prediction - Experts, Crowds + Algorithms

Professor Daniel Martin Katz presentation -- The Three Forms of (Legal) Prediction - Experts, Crowds + Algorithms as well as a discussion of SCOTUS and Associated Stock Market Returns aka "Law on the Market"

Daniel Martin Katz

February 17, 2021
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  1. Experts, Crowds + Algorithms Applied to SCOTUS daniel martin katz

    The Three Forms of (Legal) Prediction blog | ComputationalLegalStudies.com edu | illinois tech - chicago kent law lab | TheLawLab.com page | DanielMartinKatz.com
  2. TODAY I WOULD LIKE TO BEGIN WITH A PRACTICAL SET

    OF LEGAL AND LAW RELATED PROBLEMS
  3. ABSTRACTION OF A PROJECTING WEIGHTS INTO A DECISION f( )

    dimension 1 dimension 2 dimension 3 . . . . dimension n OUTPUT (Prediction, Decision, etc.) and / or INPUTS
  4. OR MORE REALISTICALLY IT SHOULD BE CALLLED THE USE OF

    ALGORITHMS TO INTERROGATE PATTERNS IN DATA
  5. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict

    Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work
  6. #Predict Relevant Documents #Predict Case Outcomes / Costs Data Driven

    Legal Underwriting Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work
  7. #Predict Relevant Documents #Predict Case Outcomes / Costs Data Driven

    Legal Underwriting Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work #Predict Regulatory Outcomes Data Driven Lobbying, etc.
  8. TODAY I WANT TO TAKE THESE AND OTHER IDEAS AND

    DISCUSS THEIR APPLICATION TO SCOTUS PREDICTION
  9. EVERY YEAR, LAW REVIEWS, MAGAZINE AND N E W S

    PA P E R A R T I C L E S , T E L E V I S I O N A N D RADIO TIME, CONFERENCE PANELS, BLOG P O S T S , A N D T W E E T S A R E D E V O T E D T O QUESTIONS SUCH AS: H O W W I L L T H E C O U R T R U L E I N T H I S PARTICULAR CASE? IN THE DIRECTION OF WHICH PARTY WILL AN INDIVIDUAL JUSTICE VOTE?
  10. YOU COULD START WITH HOLMES AND THE LEGAL REALISTS BUT

    THESE WERE *NOT* REALLY SCIENTIFIC EFFORTS
  11. FRED KORT, PREDICTING SUPREME COURT DECISIONS MATHEMATICALLY: A QUANTITATIVE ANALYSIS

    OF THE “RIGHT TO COUNSEL” CASES, 51 AMER. POL. SCI. REV. 1 (1957). 1957 S. SIDNEY ULMER, QUANTITATIVE ANALYSIS OF JUDICIAL PROCESSES: SOME PRACTICAL AND THEORETICAL APPLICATIONS, 28 LAW & CONTEMP. PROBS. 164 (1963). 1963 A CO U P L E O F E A R LY E F F O RT S
  12. COMPUTERWORLD JULY 1971 PROGRAM WRITTEN IN FORTRAN (THE 91% PREDICTION

    MARK WAS LIMITED TO CERTAIN CASES) HAROLD SPAETH
  13. JEFFREY A. SEGAL, PREDICTING S U P R E M

    E C O U R T C A S E S PROBABILISTICALLY: THE SEARCH AND SEIZURE CASES, 1962-1981, 78 AMERICAN POLITICAL SCIENCE REVIEW 891 (1984) 1984 A N I M P O RTA N T L AT E R E F F O RT
  14. Columbia Law Review (2004) Theodore W. Ruger, Pauline T. Kim,

    Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project: B U T T H I S WA S T H E PA P E R T H AT I N S P I R E D O U R E F F O RT S 2004
  15. Columbia Law Review October, 2004 Theodore W. Ruger, Pauline T.

    Kim, Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project:
  16. Case Level Prediction Justice Level Prediction 67.4% experts 58% experts

    From the 68 Included Cases for the 2002-2003 Supreme Court Term
  17. “Software developers were asked on two separate days to estimate

    the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently. Judgments made by different people are even more likely to diverge.”
  18. FA N TA SY S COT U S I S

    A S CO O L A S I T S O U N D S
  19. FA N TA SY S COT U S WA S

    F O U N D E D B Y J O S H B L AC K M A N I N 2 0 0 9
  20. P R I Z E S A N D S

    P O N S O R S H I P H AV E VA R I E D B U T T H E R E H AV E B E E N T E N S O F T H O U S A N D S O F $ $ A N D P R I Z E S D I ST R I B U T E D OV E R T H E Y E A R S
  21. U S E R S C R E AT E

    A LO G I N A N D ACC E S S T H E S I T E
  22. O N A CA S E B Y CA S

    E B A S I S , U S E R S CA N E N T E R T H E I R R E S P E C T I V E P R E D I C T I O N S
  23. U S E R S A R E F R

    E E TO C H A N G E T H E I R P R E D I C T I O N S U N T I L T H E DAT E O F F I N A L D E C I S I O N
  24. https://fivethirtyeight.com/features/ obamacares-chances-of-survival-are-looking- better-and-better/ ( S O M E T I

    M E S I N I N T E R E ST I N G WAY S A S S H O W N A B OV E ) U S E R S A R E F R E E TO C H A N G E T H E I R P R E D I C T I O N S U N T I L T H E DAT E O F F I N A L D E C I S I O N
  25. F O R E AC H CA S E ,

    W E A R E A B L E TO T R AC K TO P E R F O R M A N C E O F P L AY E R S A N D CO M PA R E I T TO T H E O U TCO M E O F T H E CA S E S
  26. 4 2 5 L I ST E D CA S

    E S 6 S COT U S T E R M S
  27. 7 2 8 4 U N I Q U E

    PA RT I C I PA N T S 4 2 5 L I ST E D CA S E S 6 S COT U S T E R M S
  28. 7 2 8 4 U N I Q U E

    PA RT I C I PA N T S 4 2 5 L I ST E D CA S E S 6 3 6 8 5 9 P R E D I C T I O N S 6 S COT U S T E R M S
  29. W E H AV E A S I G N

    I F I CA N T A M O U N T O F P L AY E R T U R N OV E R WO R K I N G W I T H R E A L DATA ( ~ 3 % O F M A X PA RT I C I PAT I O N )
  30. S O M E T I M E S F

    O L K S C H A N G E T H E I R VOT E S 6 3 6 8 5 9 5 4 5 8 4 5 F I N A L P R E D I C T I O N S OV E R A L L P R E D I C T I O N S WO R K I N G W I T H R E A L DATA
  31. T H E N U M B E R O

    F P L AY E R S H A S D E C L I N E D B U T T H E E N G AG E M E N T R AT E H A S I N C R E A S E D WO R K I N G W I T H R E A L DATA
  32. W E B E L I E V E T

    H I S I S * N OT * R E A L LY S U RV I VO R S H I P B I A S B U T R AT H E R A R E V E L AT I O N M E C H A N I S M ( I . E . YO U ST I C K A R O U N D I F T H I N K YO U A R E G O O D AT T H E U N D E R LY I N G TA S K )
  33. C R O W D S O U R C

    I N G C R O W D S O U R C I N G D O E S * N OT * R E F E R TO A S P E C I F I C T E C H N I Q U E O R A LG O R I T H M
  34. C R O W D S O U R C

    I N G G E N E R A L LY R E F E R S TO A P R O C E S S O F AG G R E G AT I O N A N D / O R S E G M E N TAT I O N O F I N F O R M AT I O N S I G N A L S
  35. VA R I O U S S I G N

    A L TY P E S T H E I N P U T S I G N A L S CA N A S S U M E M A N Y D I F F E R E N T F O R M S I N C LU D I N G F R O M M O D E L S O R I N D I V I D UA L S O R S E N S O R S ( O R S O M E CA S E S E V E N OT H E R C R O W D S )
  36. CROWD OF INDIVIDUALS The most well know approach involves extracting

    ‘wisdom’ from crowds where crowds are built from individual people
  37. CROWD OF SENSORS Note crowds need not be composed of

    humans but could be networked IT systems Decentralized Distributed Ledgers -or- Oracles -or- IOT sensors with Crowdsourcing Validation #Blockchain #InternetofThings #Crypto
  38. Random Forest Model Breiman, L.(2001). Random forests. Machine learning, 45(1),

    5-32. Grow a set of differentiated trees through bagging and random substrates (predict using a consensus mechanism) C R O W D O F M O D E L S
  39. A S W E R E V I E W

    E D T H E C R O W D S O U R C I N G L I T E R AT U R E …
  40. W E O B S E RV E D T

    H AT I T WA S D I F F I C U LT TO A P P LY T H E P R I N C I P L E S TO C R O W D S S U C H A S O U R S
  41. C R O W D S O U R C

    I N G I S ‘ U N D I S C I P L I N E D ZO O O F M O D E L S ’ J E S S I CA F L AC K P R O F E S S O R S A N TA F E I N ST I T U T E D E C . 2 7 , 2 0 1 7 ( V I A T W I T T E R )
  42. W E AG R E E … A N D

    T H U S I N T H E PA P E R W E J U ST STA RT E D OV E R …
  43. A N D AT T E M P T E

    D TO B U I L D C R O W D S F R O M F I R ST P R I N C I P L E S …
  44. W E O U T L I N E A

    G E N E R A L F R A M E WO R K F O R CO N ST R U C T I N G C R O W D S F R O M F I R ST P R I N C I P L E S
  45. I N T H E C L A S S

    I C CO N D O R C E T J U R Y S E T T I N G , M O D E L S TY P I CA L LY U S E P R E D I C T I O N S F R O M A L L PA RT I C I PA N T S
  46. H O W E V E R , M O

    D E L S CA N A L S O TA K E I N TO ACCO U N T I N F O R M AT I O N ( S I G N A L S ) F R O M S O M E S U B S E T O F PA RT I C I PA N T S ( D E F I N E D U S I N G E I T H E R I N C LU S I O N R U L E S O R E XC LU S I O N R U L E S )
  47. E X P E R I E N C E

    P E R F O R M A N C E R A N K STAT I ST I CA L T H R E S H O L D I N G W E I G H T I N G C R O W D CO N ST R U C T I O N R U L E S
  48. C R O W D CO N ST R U

    C T I O N R U L E S T H E I N T E R AC T I O N O F T H E S E R U L E S Y I E L D S * N OT * A N I N D I V I D UA L M O D E L B U T R AT H E R A M O D E L S PAC E
  49. T H E M O D E L S PAC

    E M O D E L S PAC E F E AT U R E S 2 7 7 , 2 0 1 P OT E N T I A L M O D E L S
  50. T H E M O D E L S PAC

    E 1 + ( 2 8 · 9 9 · 1 0 0 ) = 2 7 7 2 0 1 F I R ST T E R M I S T H E S I M P L E ST C R O W D S O U R C I N G M O D E L W I T H N O S U B S E T O R W E I G H T I N G R U L E S S E CO N D T E R M CO R R E S P O N D S TO 2 8 M O D E L S ( CO M B I N AT I O N O F P E R F O R M A N C E T H R E S H O L D / W E I G H T I N G R U L E S ) F O R E AC H CO M B I N AT I O N O F 9 9 R A N K A N D 1 0 0 E X P E R I E N C E T H R E S H O L D S
  51. W E S I M U L AT E T

    H E P E R F O R M A N C E O F * E AC H * O F T H E 2 7 7 , 2 0 1 P OT E N T I A L C R O W D M O D E L S M O D E L ( S ) ACC U R ACY
  52. A LT H O U G H I T I

    S A L A R G E M O D E L S PAC E W E D O H I G H L I G H T T H E P E R F O R M A N C E O F F O U R E X A M P L E M O D E L S ( A N D T H E N U L L M O D E L )
  53. B A S E L I N E A LWAY

    S G U E S S R E V E R S E N U L L M O D E L
  54. M O D E L 1 A L L C

    R O W D S I M P L E M A J O R I TY
  55. M O D E L 2 F O L LO

    W T H E L E A D E R W I T H N O T H R E S H O L D I N G
  56. M O D E L 3 F O L LO

    W T H E L E A D E R W I T H E X P E R I E N C E T H R E S H O L D I N G ( X P = 5 )
  57. M O D E L 4 M A X I

    M U M ACC U R ACY ( T H E R E A R E AC T UA L LY S E V E R A L M O D E L CO N F I G U R AT I O N S W H I C H O F F E R R O U G H LY E Q U I VA L E N T P E R F O R M A N C E ) E X P E R I E N C E T H R E S H O L D O F 5 C R O W D S I Z E I S CA P P E D AT 2 2 E X P O N E N T I A L W E I G H T W I T H A L P H A O F 0 . 1
  58. W E S I M U L AT E T

    H E P E R F O R M A N C E O F * E AC H * O F T H E 2 7 7 , 2 0 1 P OT E N T I A L C R O W D M O D E L S R O B U ST N E S S O F P E R F O R M A N C E
  59. ROBUSTNESS VISUALIZED T H I S I S A L

    L R E L AT I V E TO T H E N U L L M O D E L ( O F A LWAY S G U E S S R E V E R S E )
  60. ROBUSTNESS VISUALIZED T H E CO N TO U R

    P LOT F L AT T E N S T H E D I M E N S I O N A L I TY O F T H E S PAC E ( E AC H C E L L I S T H E AV E R AG E M O D E L P E R F O R M A N C E OV E R A L L OT H E R M O D E L PA R A M E T E R AT E AC H E X P E R I E N C E , R A N K CO M B O )
  61. N OT A L L M E M B E

    R S O F C R O W D A R E M A D E E Q UA L
  62. W E M A I N TA I N A

    ‘ S U P E R C R O W D ’ W H I C H I S T H E TO P N O F P R E D I C TO R S U P TO T I M E T- 1
  63. H T T P S : / / A R

    X I V. O RG / A B S / 1712 . 0 3 84 6 H T T P S : / / PA P E R S . S S R N . C O M / S O L 3 / PA P E R S . C F M ? A B S T R AC T _ I D = 3 0 8 5710
  64. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174698 Katz DM, Bommarito MJ II, Blackman J (2017), A

    General Approach for Predicting the Behavior of the Supreme Court of the United States. PLoS ONE 12(4): e0174698.
  65. Our algorithm is a special version of random forest (time

    evolving) http://journals.plos.org/ plosone/article?id=10.1371/ journal.pone.0174698 available at RESEARCH ARTICLE A general approach for predicting the behavior of the Supreme Court of the United States Daniel Martin Katz1,2*, Michael J. Bommarito II1,2, Josh Blackman3 1 Illinois Tech - Chicago-Kent College of Law, Chicago, IL, United States of America, 2 CodeX - The Stanford Center for Legal Informatics, Stanford, CA, United States of America, 3 South Texas College of Law Houston, Houston, TX, United States of America * [email protected] Abstract Building on developments in machine learning and prior work in the science of judicial pre- diction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the jus- tice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications. Introduction As the leaves begin to fall each October, the first Monday marks the beginning of another term for the Supreme Court of the United States. Each term brings with it a series of challenging, important cases that cover legal questions as diverse as tax law, freedom of speech, patent law, administrative law, equal protection, and environmental law. In many instances, the Court’s decisions are meaningful not just for the litigants per se, but for society as a whole. Unsurprisingly, predicting the behavior of the Court is one of the great pastimes for legal and political observers. Every year, newspapers, television and radio pundits, academic jour- nals, law reviews, magazines, blogs, and tweets predict how the Court will rule in a particular case. Will the Justices vote based on the political preferences of the President who appointed them or form a coalition along other dimensions? Will the Court counter expectations with an unexpected ruling? PLOS ONE | https://doi.org/10.1371/journal.pone.0174698 April 12, 2017 1 / 18 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Katz DM, Bommarito MJ, II, Blackman J (2017) A general approach for predicting the behavior of the Supreme Court of the United States. PLoS ONE 12(4): e0174698. https://doi. org/10.1371/journal.pone.0174698 Editor: Luı ´s A. Nunes Amaral, Northwestern University, UNITED STATES Received: January 17, 2017 Accepted: March 13, 2017 Published: April 12, 2017 Copyright: © 2017 Katz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data and replication code are available on Github at the following URL: https://github.com/mjbommar/scotus-predict-v2/. Funding: The author(s) received no specific funding for this work. Competing interests: All Authors are Members of a LexPredict, LLC which provides consulting services to various legal industry stakeholders. We received no financial contributions from LexPredict or anyone else for this paper. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
  66. T H E S O U R C E CO

    D E F O R O U R A LG O PA P E R I S AVA I L A B L E O N
  67. W E CA L L O U R A LG

    O PA P E R A ‘ G E N E R A L’ A P P R OAC H
  68. B E CAU S E W E A R E

    N OT I N T E R E ST E D I N A LO CA L LY T U N E D M O D E L B U T R AT H E R A M O D E L T H AT CA N ‘ STA N D T H E T E ST O F T I M E ’
  69. G E N E R A L S COT U

    S P R E D I C T I O N 243,882 28,009 Case Outcomes Justice Votes 1816-2015
  70. G E N E R A L S COT U

    S P R E D I C T I O N 70.2% accuracy at the case outcome level 71.9% at the justice vote level 1816-2015
  71. W E P R E D I C T *

    N OT * A S I N G L E Y E A R B U T R AT H E R ~ 2 0 0 Y E A R S ( 1 8 1 6 - 2 0 1 5 ) O U T O F S A M P L E
  72. N O W I T I S WO RT H

    N OT I N G T H AT P R E D I C T I O N O R I E N T E D PA P E R S A R E C U R R E N T LY S W I M M I N G AG A I N ST M A I N ST R E A M S O C I A L S C I E N C E ( A N D L AW )
  73. CAU S A L I N F E R E

    N C E I S T H E H A L L M A R K O F M O ST Q UA N T O R I E N T E D L AW + S O C I A L S C I E N C E S C H O L A R S H I P
  74. I T I S B E ST S U I

    T E D TO P O L I CY E VA LUAT I O N ( S U C H A S D O E S T H I S PA RT I C U L A R P O L I CY I N T E RV E N T I O N AC H I E V E I T S STAT E D O B J E C T I V E S )
  75. O R I N STA N C E S W

    H E R E E STA B L I S H I N G L I N K S B E T W E E N CAU S E A N D E F F E C T A R E C R I T I CA L
  76. B U T T H E R E I S

    A N A LT E R N AT I V E PA R A D I G M # P R E D I C T I O N
  77. M AC H I N E L E A R

    N I N G P R E D I C T I V E A N A LY T I C S ‘ I N V E R S E ’ P R O B L E M B -S C H O O L CO M P S C I P H Y S I C S P R E D I C T I O N
  78. Andrew D. Martin, Kevin M. Quinn, Theodore W. Ruger &

    Pauline T. Kim, Competing Approaches to Predicting Supreme Court Decision Making, 2 Perspectives on Politics 761 (2004). “the best test of an explanatory theory is its ability to predict future events. To the extent that scholars in both disciplines (social science and law) seek to explain court behavior, they ought to test their theories not only against cases already decided, but against future outcomes as well.”
  79. T H E R E I S G R O

    W I N G I N T E R E ST I N T H E P R E D I C T I O N C E N T R I C A P P R OAC H
  80. “There are two cultures in the use of statistical modeling

    to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown …. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.” Leo Breiman, Statistical modeling: The two cultures (with comments and a rejoinder by the author), 16 Statistical Science 199 (2001) Note: Leo Breiman Invented Random Forests
  81. 3 5 5 S C I E N C E

    6 3 2 4 3 F E B R UA R Y 2 0 1 7 S P E C I A L I S S U E O N P R E D I C T I O N
  82. P R E D I C T I O N

    I S N OT N E C E S S A R I LY # M L A LO N E B U T R AT H E R S O M E E N S E M B L E O F E X P E RT S , C R O W D S + A LG O R I T H M S
  83. http://www.sciencemag.org/news/ 2017/05/artificial-intelligence-prevails- predicting-supreme-court-decisions Professor Katz noted that in the long

    term …“We believe the blend of experts, crowds, and algorithms is the secret sauce for the whole thing.” May 2nd 2017
  84. crowd forecast learning problem is to discover how to blend

    streams of intelligence algorithm forecast ensemble method ENSEMBLE MODEL we can use machine learning methods and metadata such as case topic, lower court as well as crowd metadata to ‘learn’ the conditional weights to apply to the input signals
  85. When we would present this work on #SCOTUS Prediction folks

    would ask us “why do I care about marginal improvements in prediction ? “
  86. Well at a very minimum — if you could predict

    the cases you could perhaps trade on them in the relevant securities market …
  87. In other words, given our ability to offer forecasts of

    judicial outcomes, we wondered if this information could inform an event driven trading strategy ?
  88. Myraid Genetics “Myriad employs a number of proprietary technologies that

    permit doctors and patients to understand the genetic basis of human disease and the role that genes play in the onset, progression and treatment of disease.”
  89. Myraid Genetics “Myriad was the subject of scrutiny after it

    became involved in a lengthy lawsuit over its controversial patenting practices” which including the patenting of human gene sequences ....
  90. Initial Media Reports Early Afternoon “In early afternoon trading Thursday,

    Myriad shares were up 5.4 percent, or $2.36, at $35.73.”
  91. ONE OBVIOUS CHALLENGE IS THE PROSPECT THAT THIS INFORMATION IS

    ALREADY INCORPORATED INTO THE PRICE OF THE RELEVANT SECURITY #EfficientMarketHypothesis #Fama #EMH
  92. IN ALLIED FIELDS OF HUMAN ENDEAVOR, THERE ARE FAIRLY RAPID

    MARKET RESPONSES TO CHANGES IN THE INFORMATION ENVIRONMENT
  93. THIS ALL PRESUPPOSES A RIGOROUS INFORMATION AND MODELING ENVIRONMENT —

    THAT IS GENERALLY LACKING IN QUESTIONS OF LEGAL PREDICTION #QuantitativeLegalPrediction #LegalAnalytics #FinLegalTech
  94. Market Incorporation Hypothesis Are judicial decisions already reflected in the

    share price ? (If this were true - we would rarely see market move post decision)
  95. How General Are These Specific Examples? Theoretical + Empirical Questions

    (In other words, is this a general phenomenon ?)
  96. What is the nature of the signal incorporation environment ?

    (In other words, what are the dynamics associated with does the market response ?) Theoretical + Empirical Questions
  97. (1) Coding / PreProcessing (2) Candidate LOTM Events (3) Formal

    Evaluation Using CAPM (market model of returns) (4) Evaluate Speed of Incorporation and Related Informational Dynamics
  98. (1) Coding / PreProcessing We reviewed and coded 1,363 total

    cases decided over the period in questions. We asked a simple question - could this case plausibly impact a publicly traded security ?
  99. All Data & Code is Available Here^ ^Other than the

    WRDS Data which is *not* open source but can be obtained from Wharton https://github.com/mjbommar/law-on-the-market https://wrds-web.wharton.upenn.edu/wrds/
  100. Abnormal Returns Common approach is to use index as baseline

    and seek to identify statistically significant deviations from that baseline We want to isolate the effect of the event from other general market movements
  101. This Paper Leverages 5 Minute Data -5 Days, +5 Days

    A KEY POINT much higher frequency than most papers in literature
  102. In conclusion, we believe that this research raises many questions

    and justifies a range of future work in the area
  103. Future Work Real Trading Strategy Analysis Other Classes of Litigation

    Events 8k’s and Docket Arbitrage Higher and Lower Order Analysis Litigation Reserves, etc.