Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making (Version 2.01 on arXiv)

Here is Version 2.01 of the Law on the Market Paper
From the AbstractWhat 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.  

Law on the Market? Evaluating the Securities Market Impact Of Supreme Court Decisions (Katz, Bommarito, Soellinger & Chen)

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: Do judicial decisions affect the securities markets in discernible and perhaps predictable ways? In other words, is there “law on the market” (LOTM)? This is a question that has been raised by commentators, but answered by very few in a systematic and financially rigorous manner. Using intraday data and a multiday event window, this large scale event study seeks to determine the existence, frequency and magnitude of equity market impacts flowing from Supreme Court decisions.

We demonstrate that, while certainly not present in every case, “law on the market” events are fairly common. Across all cases decided by the Supreme Court of the United States between the 1999-2013 terms, we identify 79 cases where the share price of one or more publicly traded company moved in direct response to a Supreme Court decision. In the aggregate, over fifteen years, Supreme Court decisions were responsible for more than 140 billion dollars in absolute changes in wealth. Our analysis not only contributes to our understanding of the political economy of judicial decision making, but also links to the broader set of research exploring the performance in financial markets using event study methods.

We conclude by exploring the informational efficiency of law as a market by highlighting the speed at which information from Supreme Court decisions is assimilated by the market. Relatively speaking, LOTM events have historically exhibited slow rates of information incorporation for affected securities. This implies a market ripe for arbitrage where an event-based trading strategy could be successful.

Available on SSRN and arXiv

Even The Algorithms Think Obamacare’s Survival Is A Tossup (via

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Readers will probably observe that {Marshall+} is still a work in progress (for example – my colleague noted {Marshall+} believes that Justice Ginsburg would appear to be slightly more likely to vote to overturn the ACA than Justice Thomas).  While this probably will not prove to be correct in King v. Burwell, our method is rigorously backtested and designed to minimize errors across all predictions (not just in this specific case).  This optimization question is tricky for the model and it will be the source of future model improvements. I have preached the whole mantra Humans + Machines > Humans or Machines and this problem is a good example.  The problem with exclusive reliance upon human experts is they have cognitive biases, info processing issues, etc.  The problem with models is that they generate errors that humans would not.

Anyway, the good thing about having a base model such as {Marshall+} is that we can begin to incorporate a range of additional information in an effort to create a {Marshall++} and beyond.    And on that front there is more to come …

Legal Analytics – Introduction to the Course – Professors Daniel Martin Katz + Michael J Bommarito

Here is an introductory slide deck from “Legal Analytics” which is a course that Mike Bommarito and I are teaching this semester. Relevant legal applications include predictive coding in e-discovery (i.e. classification), early case assessment and overall case prediction, pricing and staff forecasting, prediction of judicial behavior, etc.

As I have written in my recent article in Emory Law Journal – we are moving into an era of data driven law practice. This course is a direct response to demands from relevant industry stakeholders. For a large number of prediction tasks … humans + machines > humans or machines working alone.

We believe this is the first ever Machine Learning Course offered to law students and it our goal to help develop the first wave of human capital trained to thrive as this this new data driven era takes hold. Richard Susskind likes to highlight this famous quote from Wayne Gretzky … “A good hockey player plays where the puck is. A great hockey player plays where the puck is going to be.”

Announcing the All New LexPredict FantasySCOTUS – (Sponsored By Thomson Reuters)

LexPredictToday I am excited to announce that LexPredict has now launched the all new FantasySCOTUS under the direction of Michael J. Bommarito II, Daniel Martin Katz and Josh Blackman.

FantasySCOTUS is the leading Supreme Court Fantasy League. Thousands of attorneys, law students, and other avid Supreme Court followers make predictions about cases before the Supreme Court. Participation is FREE and Supreme Court geeks can win cash prizes up to $10,000 (many other prizes as well — thanks to the generous support of Thomson Reuters).

We hope to launch additional functionality soon but we are now live and ready to accept your predictions for the 2014-2015 Supreme Court Term!

Predicting the Behavior of the Supreme Court of the United States: A General Approach (Katz, Bommarito & Blackman)

SCOTUS Prediction Model
:  “Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm and reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).”

You can access the current draft of the paper via SSRN or via the physics arXiv.  Full code is publicly available on Github.  See also the LexPredict site.  More on this to come soon …

Announcing the Beta Pre-Release of Legal Language < Search the History of ANY Phrase in the Decisions of the United States Supreme Court >

In partnership with Michigan State University College of Law and Emory Law, today we announce the Beta Pre-Release of a New Web Interface – We are just getting started here with this project and anticipate many features that will be rolling out to you in the near future. Please feel free to send us your feedback / comments.


Instant Return of a Time Series Plot for One or More Comma Separated Phrases.  The default search is currently interstate commerce, railroad, deed (with plots for each of the term displayed simultaneously).

Feel free to test out ANY phrase of Up to Four Words in length.

Here are just a few of our favorites:

Clear and Present Danger
Habeas Corpus
Custodial Interrogation
Due Process

In the current version, we are offering results for EVERY decision of the United States Supreme Court (1791-2005).  We plan to soon expand to other corpora including the U.S. Court of Appeals, etc.

Each of the Phrases you search will be highlighted in Blue.  If you click on these highlighted phrases you will be taken to the full list of United States Supreme Court decisions that employ this phrase:

Check out the advanced features including normalization and alternative graphing tools.

Daniel Martin Katz, Michael J. Bommarito II, Julie Seaman, Adam Candeub & Eugene Agichtein, Legal N-Grams? A Simple Approach to Track the ‘Evolution’ of Legal Language in Proceedings of Jurix: The 24th International Conference on Legal Knowledge and Information Systems (Vienna 2011) available at

Click on the Image Below and You Will Be Directed to our Presentation at 24th International Conference on Legal Knowledge and Information Systems ( Jurix 2011 – Vienna )
This offers some motivation for the project as well as a Brief Slide Based Tutorial Designed to Highlight Various Functions Available on the Site.


Michael J. Bommarito, Building Legal Language Explorer: Interactivity and Drill-Down, noSQL and SQL available at

Oyez @ Chicago Kent Releases Free OyezToday App for IPhone

Kudos to Jerry Goldman, the other folks at the Oyez Project as well as the Chicago-Kent College of Law for making this free resource available to the public!

From the description: “OYEZTODAY at IIT Chicago-Kent College of Law offers you the latest information and media on the current business of the Supreme Court of the United States. OYEZTODAY provides: easy-to-grasp abstracts for every case granted review, timely and searchable audio of oral arguments + transcripts, and up-to-date summaries of the Court’s most recent decisions including the Court’s full opinions. You will have access to all this information on your iPhone with the ability to share reactions on Facebook, Twitter, or by email. (Recordings of opinion announcements from the bench will follow when the Court releases these files to the National Archives at the start of the Court’s next Term).  Chicago-Kent is proud to provide this free service to enhance the public’s understanding of the Supreme Court and current legal controversies.”

Court Under Roberts Is Most Conservative in Decades [Via NY Times]

The Sunday New York Times features an article by Adam Liptak assessing the conservatism of Robert Court.  The article features some good coverage for some of the leading law and political science scholars who study the United States Supreme Court.  Well worth the read!

Netflix Challenge for SCOTUS Prediction?

During our break from blogging, Ian Ayers offered a very interesting post over a Freakonomics entitled “Prediction Markets vs. Super Crunching: Which Can Better Predict How Justice Kennedy Will Vote?” In general terms, the post compares the well known statistical model offered by Martin-Quinn to the new Supreme Court Fantasy League created by Josh Blackman. We were particularly interested in a sentence located at end of the post … “[T]he fantasy league predictions would probably be more accurate if market participants had to actually put their money behind their predictions (as with”  This point is well taken. Extending the idea of having some “skin in the game,” we wondered what sort of intellectual returns could be generated for the field of quantitative Supreme Court prediction by some sort of Netflix style SCOTUS challenge.

The Martin-Quinn model has significantly advanced the field of quantitative analysis of the United States Supreme Court. However, despite all of the benefits the model has offered, it is unlikely to be the last word on the question. While only time will tell, an improved prediction algorithm might very well be generated through the application of ideas in machine learning and via incorporation of additional components such as text, citations, etc.

With significant financial sum at stake … even far less than the real Netflix challenge … it is certainly possible that a non-trivial mprovement could be generated. In a discussion among a few of us here at the Michigan CSCS lab, we generated the following non-exhaustive set of possible ground rules for a Netflix Style SCOTUS challenge:

  1. To be unseated, the winning team should be required to make a non-trivial improvement upon the out-of-sample historical success of the Martin-Quinn Model.
  2. To prevent overfitting, the authors of this non-trivial improvement should be required to best the existing model for some prospective period.
  3. All of those who submit agree to publish their code in a standard programming language (C, Java, Python, etc.) with reasonable commenting / documentation.