ABSTRACT: 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
Nice coverage of the research in this area and our multi year research agenda attached to forecasting using the three known streams of intelligence (experts, crowds & algorithms).
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!
I enjoyed collaborating with Paul Lippe for this short article in the ABA Journal New Normal column. We make 10 predictions about Watson’s application into the legal industry (some short term and some longer term) and preview some of our specific collaboration applying IBM Watson in the legal industry. Suffice to say there is much more to come …
Abstract: “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).”
It is a wrap for #ReInventLaw NYC 2014. We finished up with just over 800 folks in attendance for this free, public facing event at the Cooper Union (~725 at the peak of the day according to the security guards who were keeping the count). As the conference co-organizer, I want to thank all of our speakers for speaking, all of our sponsors for sponsoring and all of our attendees for attending!
There are many interesting changes underway within the legal industry. Many of the participants (both speakers and attendees) are part of the innovator / early adopter segment. It was great to connect with everyone. I hope to continue the conversation. More importantly, I look forward to working together to help build the future …
From the Abstract: “This Article proposes a novel and provocative analysis of judicial opinions that are published without indicating individual authorship. Our approach provides an unbiased, quantitative, and computer scientific answer to a problem that has long plagued legal commentators. Our work uses natural language processing to predict authorship of judicial opinions that are unsigned or whose attribution is disputed. Using a dataset of Supreme Court opinions with known authorship, we identify key words and phrases that can, to a high degree of accuracy, predict authorship. Thus, our method makes accessible an important class of cases heretofore inaccessible. For illustrative purposes, we explain our process as applied to the Obamacare decision, in which the authorship of a joint dissent was subject to significant popular speculation. We conclude with a chart predicting the author of every unsigned per curiam opinion during the Roberts Court.” <HT: Josh Blackman>