In recent years, there have been several investigations by government agencies and follow-on civil litigations involving alleged price fixing and bid rigging of financial benchmarks. These include:
• foreign currency; • the London Interbank Offered Rate (LIBOR); • US Dollar International Swaps and Derivatives Association Fix (ISDAfix); • Euro Interbank Offered Rate (EURIBOR); • Singapore Interbank Offered Rate (SIBOR) and Swap Offer Rate (SOR); • the Australian Bank Bill Sweep Rate (BBSW); and • Central and Eastern European, Middle Eastern, and African currencies (CEEMEA).
Like matters involving manufactured (i.e., physical or tangible) products, these cases include allegations of anticompetitive conduct, but the nature of the relevant financial products and the data required for economic analysis are different than those for tangible products. For example, an antitrust case may involve a manufactured product that was sold to a customer at a specific point in time at an allegedly supra-competitive price. However, when a financial instrument is the product at issue, the information required to assess antitrust issues for a two-sided transaction often relies on institutional details underlying the parties’ trades and positions over a period.
Best practices we described include: • identifying the data systems that potentially contain data requested in the discovery phase of the case; • considering the best way to extract the relevant data, including assessing whether a targeted approach may be more efficient and effective than a full data dump; • validating the extracted data to identify potential anomalies; and • assessing how to prepare the data for analysis, including identifying key elements in the data, determining the extent to which disparate datasets should be combined, and evaluating whether there is additional useful information that can be linked together. As we noted, following these best practices provides practitioners with reliable methods to retrieve data that can be reliably used to address relevant economic questions posed by counsel and experts. These best practices remain relevant for cases involving financial products. However, there are additional considerations for data used to support economic analyses of transactions involving financial products for class certification, liability, and damages analyses. For example, determination of the relevant parties, products, and transactions in electronic databases often requires a careful examination into how each entity identifies a transaction in the data, amendments to transaction(s) during the course of the contract, and the amount and timing of any payments made by the parties. While some transactions are processed and cleared in a straightforward manner, others can involve more complexity because of varied contractual terms and subsequent amendments made by the counterparties after the initial trade. The overlay of big data is another consideration that has implications on the collection, management, and analysis of financial transactions in litigation. In certain cases, banks and other parties are asked to produce data spanning several years, across multiple systems, and comprising billions of records. As a result, data may not be unified across a bank or across trading platforms. Mergers and acquisitions, system upgrades, procedural changes at trading desks, and new product offerings can create challenges in identifying and extracting relevant data for financial instruments. In this article, we address these additional considerations and discuss best practices for lawyers and experts for the efficient and effective collection and preparation of transactional data in antitrust cases involving financial instruments. Throughout the paper, we apply the concepts we discuss to the experience of hypothetical financial institutions, “Money Bank” and “Investment Bank,” which trade financial products. www.edgewortheconomics.com | 3 Managing Financial Product Data In Antitrust Cases I. Hypothetical Transaction Involving a Financial Instrument Consider, for example, a hypothetical “plain vanilla” interest rate swap transaction between Investment Bank, an interest rate swap dealer, and Money Bank, a bank seeking to hedge its interest rate risk. For this trade, the two parties agree to exchange interest payments, with one party’s payments based on a fixed interest rate while the other party’s payments based on a floating interest rate (LIBOR). As seen in Exhibit 1, the example shows the trades specify that: • Money Bank receives fixed rate payments of 5% every six months and pays to Investment Bank floating payments of 3-month Libor (e.g., 2.5955%) every three months. • Investment Bank pays fixed rate payments of 5% every six months and receives from Money Bank floating payments of 3-month Libor every three months. While this exhibit summarizes the high-level terms of the transaction, more information is required to calculate the payments each party receives. Specifically, there is detailed documentation on the date and time of the transaction, the length of the contract (i.e., its tenor), the settlement date, and many other details that are captured by the parties. These terms are typically summarized in a term sheet. An abridged version of the term sheet for the transaction between Money Bank and Investment Bank is in Exhibit 2, below. Exhibit 2 Exhibit 1 4 | www.edgewortheconomics.com Managing Financial Product Data In Antitrust Cases The term sheet captures important information from the transaction between Money Bank and Investment Bank when they agreed to the trade. This term sheet also conveys explicit information about this specific trade but does not provide complete information about the life cycle of the transaction. In fact, there may be several trades tied to one transaction; an initial trade under one set of terms and subsequent trades and amendments that alter those terms. II. Assessing Data: Manufactured Products Versus Financial Products For cases involving financial data, it can be complicated to assess antitrust impact and damages in the context of financial instruments because of the nature of pricing and the life cycle of the transaction. This is borne out when assessing the differences in data sources used to conduct an economic analysis of manufactured products and financial products, as shown in Exhibit 3, below. Recognizing the differences and similarities between transactional data for manufactured products and financial instruments provides context for understanding the distinct nature of the data to be collected.