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The Three Forms
of (Legal) Prediction
professor daniel martin katz
home | Illinois tech - chicago kent
blog | ComputationalLegalStudies
corp | LexPredict
experts, crowds & algorithms
professor michael j bommarito
Three Types of Lawyers
(as described by paul lippe)
play “whack-a-mole”, reacting to
problems by creating fear and
friction within organizations and
the impression that there is a
legal risk around every corner.
Mediocre Lawyers
can help clients shape
(perhaps distort)
external perception of risk.
Merely Clever Lawyers
design systems that
balance risk and improve
transparency, helping clients
correctly price risk internally
Great Lawyers
On Background
Associate Professor of Law
IllinoisTech - Chicago Kent
Affiliated Faculty
Stanford CodeX
Center for Legal Informatics
College of Law
Fellow
Stanford CodeX
Center for Legal Informatics
Adjunct Professor
University of Michigan
Center for Study of Complex Systems
Chief Strategy Officer
LexPredict
Chief Executive Officer
LexPredict
computationallegalstudies.com
Our
Blog
(since 2009)
@ computational
We are
#LegalInformatics
Researchers
Quantitative Legal Prediction
- or -
How I Learned to Stop Worrying and Start
Preparing for the Data Driven Future of the
Legal Services Industry
Professor Daniel Martin Katz
#LegalAnalyics
#LegalData #LegalPrediction
The United States Tax Court
Cases and Dockets
#TaxLitigation
Measuring the Complexity of the Law:
The United States Code


Daniel Martin Katz, Joshua Gubler, Jon Zelner, Michael Bommarito, Eric Provins
& Eitan Ingall, Reproduction of Hierarchy? A Social Network Analysis of the American
Law Professoriate, 61 Journal of Legal Education 76 (2011)
Legal Language Explorer
Indexing 450,000+ Cases
#FreeTheLaw
#OpenSource
#ManagingFinancialRisk
Black
Reed
Frankfurter
Douglas
Jackson
Burton
Clark
Minton
Warren
Harlan
Brennan
Whittaker
Stewart
White
Goldberg
Fortas
Marshall
Burger
Blackmun
Powell
Rehnquist
Stevens
OConnor
Scalia
Kennedy
Souter
Thomas
Ginsburg
Breyer
Roberts
Alito
Sotomayor
Kagan
1953 1963 1973 1983 1993 2003 2013
9-0 Reverse
8-1, 7-2, 6-3
19 19 19 19 19 20 20
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
- Reverse
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
-
8-1, 7-2, 6-3
9-0
19 19 19 19 19 20 20
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244
http://arxiv.org/abs/1407.6333
available at
Revise and Resubmit @ PloS One
#JudicialPrediction
#PredictingLegalOutcomes
Acyclic digraphs arise in many natural and artificial processes. Among the
broader set, dynamic citation networks represent a substantively important
form of acyclic digraphs. For example, the study of such networks includes the
spread of ideas through academic citations, the spread of innovation
through patent citations, and the development of precedent in common law
systems.
(2017 Forthcoming)
Legal Informatics
Ron DolinDaniel Martin Katz Michael Bommarito
35+ Contributors
(Edited Volume)
(Katz, Dolin & Bommarito, Editors)
#STEM + #LAW = Law’s Future
Daniel Martin Katz, The MIT
School of Law? A Perspective on
Legal Education in the 21st
Century, University of Illinois
Law Review 1431 (2014)
New York Times - August 1, 2014 

Daniel Martin Katz, an associate professor with
expertise in big data and powerful computing and their
applications to legal studies. He hopes to give his
students a leg up in a job market that seems
increasingly bleak, and to help them become “T-
shaped,” by which he means having deep knowledge —
the downward swipe of the letter T — as well as a
broadened set of abilities. So providing them with
information on seemingly arcane subjects like data
analytics can be a career builder. “Analytics plus law
gets you into a niche,” he said.
TheLawLab.com
Legal
Tech +
Innovation
Certificate
Quantitative Methods for Lawyers
http://www.quantitativemethodsclass.com/Professor Daniel Martin Katz
Intro Class
Legal Analytics
Professor Daniel Martin Katz
Professor Michael J Bommarito II
http://www.legalanalyticscourse.com/Professor Daniel Martin Katz
Professor Michael J. Bommarito II Advanced Class
The Age of
Data
Driven
Law
Practice
It Has Already Begun ...
implication is that every
organization needs a
data strategy
(including law firms & inside counsel)
Some Examples
The Age of
Quantitative Legal Prediction
The Age of
Quantitative Legal Prediction
The Age of
Quantitative Legal Prediction
The Age of
Quantitative Legal Prediction
Quantitative Legal Prediction
- or -
How I Learned to Stop Worrying and Start
Preparing for the Data Driven Future of the
Legal Services Industry
Professor Daniel Martin Katz
Today we are going to
talk about one key
idea in prediction
There are 3 Known Ways
to Predict Something
Experts, Crowds, Algorithms
We could apply this to a
wide range of problems
For today we will apply
these approaches to the
decisions of the
Supreme Court of United States
Every year, law reviews, magazine and
newspaper articles, television and radio
time, conference panels, blog posts, and
tweets are devoted to questions such as:
How will the Court rule in particular cases?
There are only 3 ways 

to predict something
Experts
Crowds
Algorithms
Experts
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:
experts
Case Level Prediction
Justice Level Prediction
67.4% experts
58% experts
From the 68
Included
Cases
for the
2002-2003
Supreme
Court Term
these experts probably
overfit
they fit to the noise
and
not the signal
we need to
evaluate
experts and
somehow
benchmark
their
expertise
from a pure
forecasting
standpoint
the best
known
SCOTUS
predictor is
the law
version of
superforecasting
Crowds
crowds
https://fantasyscotus.lexpredict.com/case/list/
We can
generate
Crowd
Sourced
Predictions
however,
not all
members of
crowd are
made equal
we maintain
a ‘supercrowd’
which is the top n%
of predictors
up to time t
the
‘supercrowd’
outperforms
the overall
crowd
(and the best
single player)
(performance for the 2015 - 2016 term)
not
enough
crowd
based
decision
making in
(legal)
institutions
“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.”
in law
here
is our
commercial
offering
design
to
unlock
untapped
expertise
in
organizations
#Winning
Allowing
for
Frictionless
Crowdsourcing
#ManualUnderwriting
https://lexsemble.com/
https://lexsemble.com/
Algorithms
Black
Reed
Frankfurter
Douglas
Jackson
Burton
Clark
Minton
Warren
Harlan
Brennan
Whittaker
Stewart
White
Goldberg
Fortas
Marshall
Burger
Blackmun
Powell
Rehnquist
Stevens
OConnor
Scalia
Kennedy
Souter
Thomas
Ginsburg
Breyer
Roberts
Alito
Sotomayor
Kagan
1953 1963 1973 1983 1993 2003 2013
9-0 Reverse
8-1, 7-2, 6-3
19 19 19 19 19 20 20
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
- Reverse
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
-
8-1, 7-2, 6-3
9-0
19 19 19 19 19 20 20
algorithms
Online
Learning
Model
Our approach is a special version
of random forest
Black
Reed
Frankfurter
Douglas
Jackson
Burton
Clark
Minton
Warren
Harlan
Brennan
Whittaker
Stewart
White
Goldberg
Fortas
Marshall
Burger
Blackmun
Powell
Rehnquist
Stevens
OConnor
Scalia
Kennedy
Souter
Thomas
Ginsburg
Breyer
Roberts
Alito
Sotomayor
Kagan
1953 1963 1973 1983 1993 2003 2013
9-0 Reverse
8-1, 7-2, 6-3
19 19 19 19 19 20 20
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
- Reverse
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
-
8-1, 7-2, 6-3
9-0
19 19 19 19 19 20 20
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244
http://arxiv.org/abs/1407.6333
available at
Revise and Resubmit @ PloS One
we have developed an
algorithm that we call
{Marshall}+
random forest
Benchmarking
since 1953
+
Using only data
available prior to
the decision
Mean Court Direction [FE]
Mean Court Direction 10 [FE]
Mean Court Direction Issue [FE]
Mean Court Direction Issue 10 [FE]
Mean Court Direction Petitioner [FE]
Mean Court Direction Petitioner 10 [FE]
Mean Court Direction Respondent [FE]
Mean Court Direction Respondent 10 [FE]
Mean Court Direction Circuit Origin [FE]
Mean Court Direction Circuit Origin 10 [FE]
Mean Court Direction Circuit Source [FE]
Mean Court Direction Circuit Source 10 [FE]
Difference Justice Court Direction [FE]
Abs. Difference Justice Court Direction [FE]
Difference Justice Court Direction Issue [FE]
Abs. Difference Justice Court Direction Issue [FE]
Z Score Difference Justice Court Direction Issue [FE]
Difference Justice Court Direction Petitioner [FE]
Abs. Difference Justice Court Direction Petitioner [FE]
Difference Justice Court Direction Respondent [FE]
Abs. Difference Justice Court Direction Respondent [FE]
Z Score Justice Court Direction Difference [FE]
Justice Lower Court Direction Difference [FE]
Justice Lower Court Direction Abs. Difference [FE]
Justice Lower Court Direction Z Score [FE]
Z Score Justice Lower Court Direction Difference [FE]
Agreement of Justice with Majority [FE]
Agreement of Justice with Majority 10 [FE]
Difference Court and Lower Ct Direction [FE]
Abs. Difference Court and Lower Ct Direction [FE]
Z-Score Difference Court and Lower Ct Direction [FE]
Z-Score Abs. Difference Court and Lower Ct Direction [FE]
Justice [S]
Justice Gender [FE]
Is Chief [FE]
Party President [FE]
Natural Court [S]
Segal Cover Score [SC]
Year of Birth [FE]
Mean Lower Court Direction Circuit Source [FE]
Mean Lower Court Direction Circuit Source 10 [FE]
Mean Lower Court Direction Issue [FE]
Mean Lower Court Direction Issue 10 [FE]
Mean Lower Court Direction Petitioner [FE]
Mean Lower Court Direction Petitioner 10 [FE]
Mean Lower Court Direction Respondent [FE]
Mean Lower Court Direction Respondent 10 [FE]
Mean Justice Direction [FE]
Mean Justice Direction 10 [FE]
Mean Justice Direction Z Score [FE]
Mean Justice Direction Petitioner [FE]
Mean Justice Direction Petitioner 10 [FE]
Mean Justice Direction Respondent [FE]
Mean Justice Direction Respondent 10 [FE]
Mean Justice Direction for Circuit Origin [FE]
Mean Justice Direction for Circuit Origin 10 [FE]
Mean Justice Direction for Circuit Source [FE]
Mean Justice Direction for Circuit Source 10 [FE]
Mean Justice Direction by Issue [FE]
Mean Justice Direction by Issue 10 [FE]
Mean Justice Direction by Issue Z Score [FE]
Admin Action [S]
Case Origin [S]
Case Origin Circuit [S]
Case Source [S]
Case Source Circuit [S]
Law Type [S]
Lower Court Disposition Direction [S]
Lower Court Disposition [S]
Lower Court Disagreement [S]
Issue [S]
Issue Area [S]
Jurisdiction Manner [S]
Month Argument [FE]
Month Decision [FE]
Petitioner [S]
Petitioner Binned [FE]
Respondent [S]
Respondent Binned [FE]
Cert Reason [S]
Mean Agreement Level of Current Court [FE]
Std. Dev. of Agreement Level of Current Court [FE]
Mean Current Court Direction Circuit Origin [FE]
Std. Dev. Current Court Direction Circuit Origin [FE]
Mean Current Court Direction Circuit Source [FE]
Std. Dev. Current Court Direction Circuit Source [FE]
Mean Current Court Direction Issue [FE]
Z-Score Current Court Direction Issue [FE]
Std. Dev. Current Court Direction Issue [FE]
Mean Current Court Direction [FE]
Std. Dev. Current Court Direction [FE]
Mean Current Court Direction Petitioner [FE]
Std. Dev. Current Court Direction Petitioner [FE]
Mean Current Court Direction Respondent [FE]
Std. Dev. Current Court Direction Respondent [FE]
0.00781
0.00205
0.00283
0.00604
0.00764
0.00971
0.00793
TOTAL 0.04403
Justice and Court Background Information
Case Information
0.00978
0.00971
0.00845
0.00953
0.01015
0.01370
0.01190
0.01125
0.00706
0.01541
0.01469
0.00595
0.02014
0.01349
0.01406
0.01199
0.01490
0.01179
0.01408
TOTAL 0.22814
Overall Historic Supreme Court Trends
0.00988
0.01997
0.01546
0.00938
0.00863
0.00904
0.00875
0.00925
0.00791
0.00864
0.00951
0.01017
TOTAL 0.12663
Lower Court Trends
0.00962
0.01017
0.01334
0.00933
0.00949
0.00874
0.00973
0.00900
TOTAL 0.07946
0.00955
0.00936
0.00789
0.00850
0.00945
0.01021
0.01469
0.00832
0.01266
0.00918
0.00942
0.00863
0.00894
0.00882
0.00888
Current Supreme Court Trends
TOTAL 0.14456
Individual Supreme Court Justice Trends
0.01248
0.01530
0.00826
0.00732
0.01027
0.00724
0.01030
0.00792
0.00945
0.00891
0.00970
0.01881
0.00950
0.00771
TOTAL 0.14323
0.01210
0.00929
0.01167
0.00968
0.01055
0.00705
0.00708
0.00690
0.00699
0.01280
0.01922
0.02494
0.01126
0.00992
0.00866
0.01483
0.01522
0.01199
0.01217
0.01150
TOTAL 0.23391
Differences in Trends
Total Cases Predicted
Total Votes Predicted
7,700
68,964
Justice Prediction
Case Prediction
70.9% accuracy
69.6% accuracy
From 1953 - 2014
Version 2.0 is 1791 - 2015
Small Taste of How
Our Algorithm Works …
Breiman (1984) sets forth
the CART algorithm
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:
Given Some Data:
(X1, Y1), ... , (Xn, Yn)
Now We Have a New Set of X’s
We Want to Predict the Y
Form a BinaryTree that
Minimizes the Error
in each leaf of the tree
CART
(Classification & RegressionTrees)
Observe the Correspondence
Between the Data andTrees
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
Adapted from Example
By Mathematical Monk
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
Adapted from Example
By Mathematical Monk
We want to build an
approach which can
lead to the proper
classification (labeling)
of new data points
( ) that are dropped
into this space
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
Adapted from Example
By Mathematical Monk
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
Adapted from Example
By Mathematical Monk
L e t s B e g i n t o
Partition the Space
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
1 2
1
2
Adapted from Example
By Mathematical Monk
L e t s B e g i n t o
Partition the Space
split 1
(a)
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
1 2
1
2
Adapted from Example
By Mathematical Monk
This Split Will Be
Memorialized in theTree
split 1
(a)
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
1 2
1
2
Adapted from Example
By Mathematical Monk
We Ask the Question is
Xi1 > 1 ? - with a binary
(yes or no) response
split 1
(a)
Xi1 > 1 ?
YesNo
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
1 2
1
2
Adapted from Example
By Mathematical Monk
If No - then we are in zone (a) ...
we tally the number of zeros and ones
Using Majority Rule do we assign a
classification to this rule this leaf
split 1
(a)
Xi1 > 1 ?
YesNo
(0,5)
Classify as 1
zone (a)
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
1 2
1
2
Adapted from Example
By Mathematical Monk
Here we Classify as a 1 because
(0,5) which is 0 zero’s and 5 one’s
split 1
(a)
Xi1 > 1 ?
YesNo
(0,5)
Classify as 1
zone (a)
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
1 2
1
2
Adapted from Example
By Mathematical Monk
Using a Similar Approach Lets
Begin to Fill in the Rest of theTree
split 1
(a)
Xi1 > 1 ?
YesNo
(0,5)
Classify as 1
zone (a)
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0
1 2
1
2
Adapted from Example
By Mathematical Monk
split 1
(a)
Xi1 > 1 ?
YesNo
(0,5)
Classify as 1
zone (a) Xi2 > 1.45 ?
No Yes
split 2
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0split 1
split 2
split 3
1 2 2.2
1
2
Xi1 > 1 ?
(0,5)
Xi2 > 1.45 ?
(4,1)(2,3)
Classify as 1
Classify as 1 Classify as 0
(a)
zone (a)
1.45
YesNo
Adapted from Example
By Mathematical Monk
No
(b)
(c)
zone (b) zone (c)
YesNo
Yes
Xi1 > 2 ?
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0split 1
split 2
split 3
split 4
1 2 2.2
1
2
Xi1 > 1 ?
(0,5)
Xi2 > 1.45 ?
Xi1 > 2.2 ?
(1,4)(5,0)(4,1)(2,3)
Classify as 1
Classify as 1 Classify as 0
(a)
zone (a)
1.45
YesNo
Adapted from Example
By Mathematical Monk
No
(b)
(c)
(d)
(e)
zone (b) zone (c)
YesNo YesNo
Yes
zone (d)
Classify as 0 Classify as 1
zone (e)
Xi1 > 2 ?
Okay Lets Add Back the ( )
which are new items
to be classified
For simplicity sake there
is one in each zone
We Will Use theTree Because
theTree Is Our Prediction Machine
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0split 1
split 2
split 3
split 4
1 2 2.2
1
2
Xi1 > 1 ?
(0,5)
Xi2 > 1.45 ?
Xi1 > 2.2 ?
(1,4)(5,0)(4,1)(2,3)
Classify as 1
Classify as 1 Classify as 0
(a)
zone (a)
1.45
YesNo
Adapted from Example
By Mathematical Monk
No
(b)
(c)
(d)
(e)
zone (b) zone (c)
YesNo YesNo
Yes
zone (d)
Classify as 0 Classify as 1
zone (e)
Xi1 > 2 ?
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0split 1
split 2
split 3
split 4
1 2 2.2
1
2
Xi1 > 1 ?
(0,5)
Xi2 > 1.45 ?
Xi1 > 2.2 ?
(1,4)(5,0)(4,1)(2,3)
Classify as 1
Classify as 1 Classify as 0
(a)
zone (a)
1.45
YesNo
Adapted from Example
By Mathematical Monk
No
(b)
(c)
(d)
(e)
zone (b) zone (c)
No Yes YesNo
Yes
zone (d)
Classify as 0 Classify as 1
zone (e)
Xi1 > 2 ?
1
0
1
1
1
0
0
0
0
0
1
1 1
1
0
0
1
1
1
1
0
01
0
Xi1
Xi2
0split 1
split 2
split 3
split 4
1 2 2.2
1
2
Xi1 > 1 ?
(0,5)
Xi2 > 1.45 ?
Xi1 > 2.2 ?
(1,4)(5,0)(4,1)(2,3)
Classify as 1
Classify as 1 Classify as 0
(a)
zone (a)
1.45
YesNo
Adapted from Example
By Mathematical Monk
No
(b)
(c)
(d)
(e)
zone (b) zone (c)
No Yes YesNo
Yes
zone (d)
Classify as 0 Classify as 1
zone (e)
1
1
1
0 1
0
Xi1 > 2 ?
Experts, Crowds, Algorithms
For most problems ...
ensembles of these streams
outperform any single stream
Humans
+
Machines
Humans
+
Machines
>
Humans
+
Machines
Humans
or
Machines
>
Ensembles come in
various forms
Here is a well known example
Poll Aggregation is one form of
ensemble where the learning question is
to determine how much weight (if any)
to assign to each individual poll
poll weighting
A Visual Depiction of
How to build an
ensemble method in our
judicial prediction example
expert crowd algorithm
ensemble method
learning problem is to discover when to use a given stream of intelligence
expert crowd algorithm
via back testing we can learn the
weights to apply for particular problems
ensemble method
learning problem is to discover when to use a given stream of intelligence
{Marshall}+
algorithm
expert
crowd
algorithm
{Marshall}+ improvement
will likely come from
determining the optimal
weighting of experts,
crowds and algorithms
for various types of cases
ERISA cases
thus
might look like this
Patent cases
perhaps
might look like this
Search/Seizure cases
while
could look like this
this is one slice of our
research effort …
Given our ability to offer
forecasts of judicial
outcomes, we wondered
if this information could
inform an event-driven
trading strategy ?
Paper Released
August 24, 2015
http://arxiv.org/abs/1508.05751
available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726
We call this idea
“Law on the Market”
(LOTM)
A Motivating Example
Myriad Genetics
NASDAQ: MYGN
Market Cap of ~$3 billion+
Myriad 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.”
Myriad 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 ....
June 13, 2013
Supreme Court
offers this
decision
~10:05am
Initial Media
Reports and
Initial Trading
11:48am
Initial Media
Reports
Early
Afternoon
“In early afternoon trading
Thursday, Myriad shares
were up 5.4 percent, or
$2.36, at $35.73.”
Final Media
Reports
Final Media
Reports
-0.050
-0.025
0.000
0.025
AverageCumulativeAbnormalReturns
NASDAQ: MYGN
Pegged to S&P 500
(Market Model)
June 12
-0.050
-0.025
0.000
0.025
AverageCumulativeAbnormalReturns
NASDAQ: MYGN
Pegged to S&P 500
(Market Model)
June 12
9:30am
-0.050
-0.025
0.000
0.025
AverageCumulativeAbnormalReturns
10:00am ET
NASDAQ: MYGN
Pegged to S&P 500
(Market Model)
June 12
9:30am
June 13
-0.050
-0.025
0.000
0.025
AverageCumulativeAbnormalReturns
10:00am ET
11:40am ET
NASDAQ: MYGN
Pegged to S&P 500
(Market Model)
June 13June 12
9:30am
-0.050
-0.025
0.000
0.025
AverageCumulativeAbnormalReturns
10:00am ET
1:20pm ET
11:40am ET
NASDAQ: MYGN
Pegged to S&P 500
(Market Model)
June 13June 12
9:30am
-0.050
-0.025
0.000
0.025
AverageCumulativeAbnormalReturns
10:00am ET
1:20pm ET
11:40am ET
NASDAQ: MYGN
Pegged to S&P 500
(Market Model)
June 13June 12
9:30am
2:15pm ET
-0.050
-0.025
0.000
0.025
AverageCumulativeAbnormalReturns
10:00am ET
1:20pm ET
11:40am ET
NASDAQ: MYGN
Pegged to S&P 500
(Market Model)
June 13June 12
9:30am
2:15pm ET
Close
Paper Released
August 24, 2015
lots of litigation decisions
are just a version of this basic idea
law = finance
this is a part of the
industry where you
need rigorous
#LegalUnderwriting
but lots of litigation decisions
are actually implicit litigation finance
(or self insurance)
#fin(legal)tech
however most implicit litigation
finance is not based upon 

rigorous underwriting …
law =! finance
(but it will)
we expand on this theme in this presentation
http://computationallegalstudies.com/2015/10/fin-legal-tech-laws-future-from-finances-past-katz-bommartio/
TheLawLab.com
FinLegalTechConference.comNovember 4, 2016
A Few Plugs …
LexPredict.com
ComputationalLegalStudies.com
BLOG
Michael J. Bommarito II
@ mjbommar
computationallegalstudies.com
lexpredict.com
bommaritollc.com
university of michigan center for the study of complex systems@
Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@

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