Jordan Milev, PhD

Dr. Milev's work relates to internal investigations, government and regulatory enforcement actions, consumer and securities class actions, M&A litigation and shareholder disputes, private commercial litigation, and domestic and international arbitration.

Dr. Milev's regulatory enforcement work relates to assisting counsel with conducting internal investigations, presenting analysis in regulatory investigations, and testifying in criminal and civil enforcement proceedings. In market competition and antitrust, his work has involved the analysis of financial markets, benchmark price fixing, and market manipulation. In securities class actions, he has analyzed and testified on issues relating to class certification, loss causation, and damages. He has analyzed the price impact of news and financial information in over 100 matters. In consumer class actions, his work has addressed false advertising claims, consumer choice, and consumer harm. In private and commercial litigation, he has performed analyses in numerous disputes and opined on pricing, earnings, valuation, and damages.

Dr. Milev's experience covers a variety of industries, including oil and gas, mining, healthcare and pharmaceuticals, technology, media, financial services, insurance, manufacturing, construction, and real estate. Dr. Milev advises and presents on issues relating to digital cryptocurrencies, biometric information, and value losses due to cyber incidents and data privacy breaches. Dr. Milev has advised boards of directors and senior management on issues of risk, stress testing, and activist investor and competitor activity, including suspected market manipulation.

Dr. Milev is a frequent speaker and subject matter expert at industry and academic conferences and at events providing CLE credit to attorneys. He coauthored the chapter on covered bonds in the 7th edition of the Handbook of Mortgage-Backed Securities published by Oxford University Press and edited by Frank Fabozzi.

Dr. Milev's doctoral dissertation used artificial intelligence techniques to develop a machine learning system for the analysis of stock price reactions to earnings announcements. He has taught microeconomics and econometrics courses at Yale University.