The Three Forms of (Legal) Prediction: Experts, Crowds and Algorithms — Professors Daniel Martin Katz & Michael J. Bommarito

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Econometrics (hereinafter Causal Inference) versus Machine Learning

Perhaps some hyperbolic language in here but the basic idea is still intact … for law+economics / empirical legal studies – the causal inference versus machine learning point is expressed in detail in this paper called “Quantitative Legal Prediction.”  Mike Bommarito and I have made this point in these slides, these slides, these slides, etc.   Mike and I also make this point on Day 1 of our Legal Analytics Class (which really could be called  “machine learning for lawyers”).

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Philip Tetlock: A Short Course in Superforecasting (Edge Master Class)

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Do Supreme Court Decisions Move Markets? (via WSJ Law Blog)

Screen Shot 2015-08-26 at 9.41.45 AMAccess the summary from the Wall Street Journal Law Blog and download the paper from SSRN.

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Predicting Major Geopolitical Events (via Vox)

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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

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John Holland, Computer Scientist, Psychologist, and Complexity Scientist (1929-2015)

Screen Shot 2015-08-20 at 9.48.02 PMMike and I had the great pleasure of spending several years at the University of Michigan Center for the Study of Complex Systems where John Holland spent a fair amount of his time.  He was a very giving person and of course – a true genius!  Rest in peace.

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Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures

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Your Lawyer May Soon Ask This AI-Powered App for Legal Help (via Wired)

“I thought back to that big problem lawyers face in their day-to-day work, and how it impacts regular people,” Ovbiagele tells WIRED. “I thought we should apply the capabilities of machine learning to tackle this problem and make things better for lawyers and for clients.”

“LegalRank can figure out which results get preferential results, whether that’s prioritizing a case that has more citations, knowing that a Supreme Court case should rank higher than a local decision, and other nuances.”

A subsidiary of (world’s largest) global law firm Dentons, NextLaw Labs, has signed ROSS Intelligence as its first portfolio company.

Full disclosure – I am a member of the advisory board of NextLaw Labs

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Interview with Stephen Wolfram on AI and the Future (GigaOM)

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Novel and Topical Business News and their Impact on Stock Market Activities (via arXiv)

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SlideRule – MOOC Aggregator with Learning Paths

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The Indigo Legislation Platform

HT: RC Richards

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Ideological Locations of the 2016 Presidential Contenders (via VoteView)

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Using Technology and System Design to Improve the Dodd-Frank Resolution Planning Requirement (+ Better Manage Complexity)

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This past week, I had the pleasure of participating in a half day closed door session with about ~40-50 folks from the financial services industry including several of the world’s finest law firms, representatives from SIFI and non-SIFI financial institutions as well as folks from IBM Watson and LegalOnRamp (a Watson eco-system partner).

The specific subject was RRP – the resolution planning / living wills requirement under Dodd Frank.  Former Congressman Barney Frank provided opening remarks and joined the group for the balance of the half day session.  Paul Lippe and I discussed our recent paper on Resolution Planning that was published in Banking Perspective (The Journal of The Clearing House).

As we argue in the paper, the ‘too big to fail argument’ is not really that intellectually forceful.  The question – properly posed – is what to do about complexity and the management of complex systems.  The complex and interdependent nature of the banking ecosystem is the feature that really challenges efforts to develop robust regulatory / management structures.  This would be true even if existing financial institutions were made smaller.

Our conversation was about how to use technology and system redesign to confront and manage wide scale complexity.  The resolution planning challenge should not just be focused upon clearing the existing regulatory hurdle but actually can be an opportunity for organizations to build better financial/legal information infrastructure (ultimately leading to an internet of contracts or more broadly an internet of legal things).  In building a better financial/legal information infrastructure, banks will be better positioned to manage/properly price risk.

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Thomson Reuters/CodeX 2015 Legal Tech Open Innovation Challenge – Docket Analytics

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