Since the early 2010s, investigators and antitrust regulators have focused on communications among market-making traders in online chatrooms as part of criminal and civil investigations. Traders have often used these forums to post quotes, negotiate trades, and share market information, and competition authorities have cited materials from these chats as evidence to support claims of anticompetitive conduct. But because transcripts of these communications can run into the millions of pages, it is often prohibitively difficult to analyze them and extract the relevant content manually.
Recently, however, powerful data science methods – in particular, machine learning (ML) and natural language processing (NLP) algorithms – have emerged; these algorithms can process vast quantities of text and identify complex linguistic patterns in order to obtain pertinent information without sacrificing analytical precision. In the article “Lessons from recent banking litigation, four Analysis Group consultants – Managing Principal Samuel Weglein, Principal Chris Feige, Vice Presidents Hadrien Vasdeboncoeur and Ilona Mostipan – explain these new tools and describe in detail the ways in which they can help identify relevant evidence with greater precision than has previously been the case. Using blinded examples from recent litigation, the authors offer guidance for using these NLP and ML algorithms, as well as practical steps for organizing, structuring, and analyzing large datasets.
“Lessons from recent banking litigation” is published on Westlaw Today, as well as in Westlaw Journal Derivatives, Westlaw Journal Antitrust, and Westlaw Journal Computer & Internet.
Associated People
Chris Feige
Mr. Feige specializes in the areas of finance, securities, and financial markets. He has worked on and managed a range of securities and valuation projects in the UK and Europe. Mr. Feige has been appointed as expert in Dutch court to provide valuation and securities claims reports in support of Steinhoff’s global securities settlement, and gave evidence in the Dutch Enterprise Chamber regarding the valuation of Getir. He has also managed teams evaluating shareholder reliance and disclosure materiality and estimating counterfactual share prices in UK Financial Services and Markets Act (FSMA) Section 90A litigation matters. Mr. Feige has supported experts analyzing the volume of false and spam accounts on Twitter, Twitter’s information security infrastructure, Twitter’s data privacy and compliance with a US Federal Trade Commission (FTC) consent decree, and share price and valuation issues on behalf of Twitter in Twitter v. Musk in which Elon Musk eventually purchased Twitter at his initial offer price. In cases involving alleged market manipulation in the foreign exchange (FX) and IBOR markets, he has analyzed trade data and evaluated alleged manipulation strategies. Mr. Feige worked on USA v. Richard Usher, et al., and the Foreign Exchange Class Antitrust Litigation, analyzing FX trade and chat data, as well as competition issues; preparing experts for testimony at trial; and providing data analyses and consulting support to counsel throughout the projects. He has also worked on a range of international arbitration cases, including valuation, damages, and competition analyses. In addition, he has developed complex valuation models, including discounted cash flow models, and analyzed asset-backed securities, collateralized debt obligations, and other securitized products in support of expert testimony in a number of bankruptcy and damages matters. Mr. Feige has also worked on a number of international arbitrations valuing defaulted sovereign debt, expropriated oil fields, and retail operations. His work has been published in several industry journals.