Prediction Markets, Shadow Trading, and Insider Risk in the Age of Algorithms: The Empirical Evidence Now Deciding Securities Cases

Joshua Mitts, David J. Greenwald Professor of Law | Columbia Law School

Joshua Mitts is the David J. Greenwald Professor of Law at Columbia Law School, where he uses advanced data science to conduct research on corporate and securities law. His primary focus is informed trading in capital markets and related topics in law and finance. Employing empirical methods including statistical analysis and machine learning, Professor Mitts studies short selling, securities lending, informed trading on cybersecurity breaches, information leakage and hedge fund activism, insider trading on corporate disclosures, and information transmission in financial markets— work that has made him a leading and frequently cited authority on the intersection of technology, trading, and market regulation.

• Education & Credentials

Professor Mitts holds a Ph.D. in Finance and Economics from Columbia Business School (2018), a J.D. from Yale Law School (2013), and a B.A. in Liberal Studies from Georgetown University (2010). His interest in data science dates back to high school, when he won the Microsoft Windows Forms Coding Hero Award for developing software for the Microsoft .NET platform.

• Recognition & Leadership

Professor Mitts joined the Columbia Law faculty in 2017 as associate professor of law and was named professor of law in 2022 before being appointed to the endowed David J. Greenwald professorship. He is a fellow of the Columbia Law School Program in the Law and Economics of Capital Markets and a member of the Center for Financial and Business Analytics at Columbia University’s Data Science Institute. A widely sought voice on market structure and regulation, he is regularly quoted in national outlets including the Associated Press, CNBC, and Bloomberg News on topics such as prediction markets, short selling, and informed trading.

• Professional Involvement

Professor Mitts frequently speaks at conferences, symposiums, and workshops, recently presenting his paper “A Legal Perspective on Technology and the Capital Markets: Social Media, Short Activism and the Algorithmic Revolution” at the New Special Study of the Securities Markets/FINRA Technology Conference. To help practitioners and scholars communicate more effectively with software engineers, he introduced the course Data and Predictive Coding for Lawyers to the Law School curriculum, and he taught at the Columbia Law Summer Program in American Law in Amsterdam in 2019. His research and commentary regularly inform public debate on emerging issues such as prediction markets and their implications for elections and securities regulation.

• Experience

Professor Mitts’s scholarship sits at the forefront of empirical corporate and securities law, applying quantitative methods to questions of market manipulation, disclosure, and the flow of information among sophisticated traders. His specialties span securities law, corporate law, financial contracts, law and finance, and empirical methods in law, and his teaching bridges the worlds of law, finance, and data science. Through his research, teaching, and public commentary—including analysis featured in litigation and policy debates over prediction markets—he has established himself as an influential scholar shaping how regulators, courts, and market participants understand technology’s growing role in the capital markets.

SESSION 1 – Betting on Inside Information: Insider Trading, Manipulation, and Corporate Risk in Prediction Markets | 12:00pm – 1:00pm

This session examines the rise of informed trading on prediction markets such as Kalshi and Polymarket, where people now bet on elections, corporate events, government actions, and pop culture. Growth on these platforms has brought a wave of trades that appear to rely on classified, corporate, or other nonpublic information. The session draws on the speaker’s own study of Polymarket contracts, which flagged more than 210,000 suspicious bets with about 70% win rates and an estimated $143 million in profits. It explains why this activity is so hard to detect, how the same markets can be moved by false rumors and coordinated trading, and who has authority to police it: the CFTC, the states, or the platforms themselves. It also covers the new risk for companies whose employees can bet on their employer’s product launches, earnings, deals, or leadership changes. Attendees will leave with a practical framework for advising platforms, companies, and individuals in this fast-moving area.  

BREAK | 1:00pm – 1:10pm

SESSION 2 – Beyond the Tipper and Tippee: Shadow Trading, 10b5-1 Plans, and How Data Analytics Is Changing Insider Trading Enforcement | 1:10pm – 2:10pm

This session addresses how insider trading law is expanding past the traditional tipper–tippee case. Regulators and plaintiffs now pursue shadow trading, meaning trades in other companies’ stock based on confidential information from one’s own company. They are also looking closely at trades made under 10b5-1 plans and at unusual trading before cybersecurity breaches and activist campaigns. At the same time, statistical and machine-learning tools can spot suspicious trading patterns that would have gone unnoticed ten years ago. The session covers the legal theories expanding liability, what the data shows about informed trading in stock markets, how regulators and litigants use trading-pattern analysis as evidence, and how that evidence can be challenged in court. Attendees will leave with concrete steps for building insider trading compliance programs that hold up under this new level of data-driven scrutiny.