Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making

What happens when the Supreme Court of the United States decides a case impacting one or more publicly-traded firms? While many have observed anecdotal evidence linking decisions or oral arguments to abnormal stock returns, few have rigorously or systematically investigated the behavior of equities around Supreme Court actions. In this research, we present the first comprehensive, longitudinal study on the topic, spanning over 15 years and hundreds of cases and firms. Using both intra- and interday data around decisions and oral arguments, we evaluate the frequency and magnitude of statistically-significant abnormal return events after Supreme Court action. On a per-term basis, we find 5.3 cases and 7.8 stocks that exhibit abnormal returns after decision. In total, across the cases we examined, we find 79 out of the 211 cases (37%) exhibit an average abnormal return of 4.4% over a two-session window with an average |t|-statistic of 2.9. Finally, we observe that abnormal returns following Supreme Court decisions materialize over the span of hours and days, not minutes, yielding strong implications for market efficiency in this context. While we cannot causally separate substantive legal impact from mere revision of beliefs, we do find strong evidence that there is indeed a "law on the market" effect as measured by the frequency of abnormal return events, and that these abnormal returns are not immediately incorporated into prices.

Transcript

1. daniel martin katz michael bommarito tyler soellinger james ming chen Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making
2. Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making daniel martin katz edu | Illinois tech - chicago kent blog | ComputationalLegalStudies corp | LexPredict.com tyler soellinger james ming chen michael bommarito edu | Illinois tech - chicago kent blog | ComputationalLegalStudies corp | LexPredict.com home | michigan state law corp | LexPredict.com home | michigan state law blog | jurisdynamics.blogspot.com
3. Thank you very much for allowing us to present our work
4. Thank you very much for allowing us to present our work
5. I would like to start with a quick overview of some allied work before getting to the paper for today … https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726 Access Paper Here
6. In allied work, we have explored judicial prediction from a variety of different angles …
7. The Three Forms of (Legal) Prediction
8. Experts, Crowds, Algorithms The Three Forms of (Legal) Prediction
9. In so much as prediction is the task in question … #MachineLearing is the method du jour
10. It is not necessarily ML alone but rather some ensemble of experts, crowds + algorithms
11. 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
13. predicting the decisions of the Supreme Court of the United States #SCOTUS
14. Experts
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. experts
17. Case Level Prediction Justice Level Prediction 67.4% experts 58% experts From the 68 Included Cases for the 2002-2003 Supreme Court Term
18. these experts probably overfit
19. they fit to the noise and not the signal
21. if this were finance this would be trading worse than S&P500
22. #NoiseTrading
23. #BuffetChallenge
24. #BuffetChallenge
25. like many other forms human endeavor law is full of 
 noise predictors …
26. from a pure forecasting standpoint
27. the best known SCOTUS predictor is
29. the law version of superforecasting
31. Crowds
32. not enough crowd based decision making in institutions
34. “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.”
35. crowds
39. https://fantasyscotus.lexpredict.com/case/list/ We can generate Crowd Sourced Predictions
40. not all members of crowd are made equal
41. we maintain a ‘supercrowd’ which is the top n of predictors up to time t-1 (i.e. a Condorcet Jury)
42. the ‘supercrowd’ outperforms the overall crowd (and even the best single player)
43. 7000+ players 600,000+ predictions From 2011-2017 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3085710 Crowdsourcing Accurately and Robustly Predicts Supreme Court Decisions
45. Brief Aside About Crowd Sourced Prediction #LegalCrowdSourcing
46. (most pundits did not identify as a serious candidate him until mid-January 2017) Neil Gorsuch was #1 o n o u r F a n t a s y Platform 12 Days after Donald Trump was elected President (i.e Nov 20)
47. #FantasySCOTUS
49. Algorithms
50. 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.
51. 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 * dkatz3@kentlaw.iit.edu 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.
53. We call this a ‘general’ model of #SCOTUS Prediction 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 * dkatz3@kentlaw.iit.edu 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.
54. Not just interested in accuracy over a short time window 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 * dkatz3@kentlaw.iit.edu 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.
55. A locally tuned model will typically lead to overfitting as the dynamics shift 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 * dkatz3@kentlaw.iit.edu 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.
56. We want a model that is robust to a large number of known dynamics … 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 * dkatz3@kentlaw.iit.edu 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.
57. 243,882 28,009 Case Outcomes Justice Votes Current Version of #PredictSCOTUS 1816-2015
58. Experts, Crowds, Algorithms
59. expert forecast crowd forecast learning problem is to discover how to blend streams of intelligence algorithm forecast ensemble method ensemble model
60. expert forecast crowd forecast learning problem is to discover how to blend streams of intelligence algorithm forecast ensemble method ensemble model via back testing we can learn the weights to apply for particular problems
61. All of this is the path which led us to this paper …
63. Law on the Market
64. When we would present this work on #SCOTUS Prediction folks would ask us “why do I care about marginal improvements in prediction ? “
65. Well at a very minimum — if you could predict the cases you could perhaps trade on them in the relevant securities market …
66. In other words, given our ability to offer forecasts of judicial outcomes, we wondered if this information could inform an event driven trading strategy ?
67. http://arxiv.org/abs/1508.05751 available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726
68. We call this idea “Law on the Market” LOTM
70. “Law on the Market” LOTM
71. A Motivating Example Myriad Genetics NASDAQ: MYGN Market Cap of ~$3 billion+
72. 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.”
73. 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 ....
75. June 13, 2013 Supreme Court Offers this Decision ~10:05am
76. Initial Media Reports and Initial Trading 11:48am
77. Initial Media Reports Early Afternoon “In early afternoon trading Thursday, Myriad shares were up 5.4 percent, or $2.36, at $35.73.”
78. Final Media Reports
79. Final Media Reports
80. 9:30am Open
81. 10:00am SCOTUS
82. 11:05am MYGN Trades UP
83. 2:15pm MYGN is Off its Daily Peak but still up
84. Day 1 Close MYGN is Off Nearly 10% from Open and 20% from Daily High
85. Day 2 the Sell Off Continues
86. A Good Time to Buy an Option :)
89. So these examples represent a form of existence proof …
90. But perhaps they are rare and anachronistic cases …?
92. One obvious challenge is the prospect that this information is already incorporated into the price of the relevant security #EfficientMarketHypothesis #Fama #EMH
93. In allied fields of human endeavor, there are fairly rapid market responses to changes in the information environment
94. This all presupposes a rigorous information and modeling environment — that is historically lacking for questions of legal prediction #QuantitativeLegalPrediction #LegalAnalytics #FinLegalTech
96. Theoretical + Empirical Questions
97. Market Incorporation Hypothesis Are judicial decisions already reflected in the share price ? (If this were true - we would rarely see market move post decision)
98. How General Are These Specific Examples? Theoretical + Empirical Questions (In other words, is this a general phenomenon ?)
99. 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
101. Methods
102. (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
103. (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 ?
104. 1,363 total cases reviewed in our sample, we identified 211 candidate LOTM cases plausibly affecting one or more firms or sectors. (these included parties to the case but also third parties) (2) Candidate LOTM Events
105. 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/
106. (3) Formal Evaluation Using CAPM (market model of returns)
107. 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
108. Formalization of CAPM in Appendix A
109. Formalization of CAPM in Appendix A
110. Formalization of CAPM in Appendix A
111. Implementation ‘erer’
112. From a methods standpoint, our approach is to estimation is extremely standard (The Higher Frequency data is the interesting part of the paper)
113. Interday studies, however, face a difficult and unavoidable tradeoff: either a researcher must rely upon relatively few data points, or they must collect data over longer durations in order to generate a sufficient statistical sample. Nearly every event study in law,and the vast majority of analysis in finance, leverages data with at most daily frequency.
114. This Paper Leverages 5 Minute Data -5 Days, +5 Days much higher frequency than most papers in literature
115. SOME RESULTS
116. Summary Results
117. Market Cap
118. Some Additional Cases
119. (4) Evaluate Speed of Incorporation and Related Informational Dynamics
120. Speed of Information Incorporation
121. This is very slow … Perhaps the real action is in the options market ?
122. Relationship Between Oral Argument and Decision
123. (1) We follow the convention (Fama French) and use log returns (2) If you believe in Fama French than we give you the Pearson but we also show the non-parametric as well (aka Spearman) (3) Pearson and Spearman offer a similar story
124. No Clear Directional Effect Some Level of is magnitude / volatility based impact
125. Yet only as a precursor to future movement (volatility) but not the direction of that future movement Oral argument appears to update the information space From a trading standpoint, this counsels a volatility straddle but the case for more direct options is less clear
126. In conclusion, we believe that this research raises many questions and justifies a range of future work in the area
127. Future Work Real Trading Strategy Analysis Other Classes of Litigation Events 8k’s and Docket Arbitrage Higher and Lower Order Analysis Litigation Reserves, etc.
128. Real Trading Strategy Analysis Not Enough to be Able to Predict … Need to track real or synthetic trades
129. Other Classes of Litigation Events 8k’s and Docket Arbitrage
130. Litigation Funding
131. Other Classes of Litigation Events Litigation Funding and Reserves Litigation Reserves
132. Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making daniel martin katz edu | Illinois tech - chicago kent blog | ComputationalLegalStudies corp | LexPredict.com tyler soellinger james ming chen michael bommarito edu | Illinois tech - chicago kent blog | ComputationalLegalStudies corp | LexPredict.com home | michigan state law corp | LexPredict.com home | michigan state law blog | jurisdynamics.blogspot.com