Trade matching is the foundation of the post-trade process, involving an automated comparison of the buyer and seller versions of trade attributes such as price, quantity, valuation date, etc. When the buy and seller attributes differ, the settlement process can be disrupted or fail. For both counterparties involved, failed trades can create both financial and reputational risks. But enhanced trade matching capabilities can offer more information on how and why breaks occur, which helps firms identify weaknesses and reduce risk.
Trade affirmation and trade matching are terms that have traditionally been used interchangeably, but matching describes a process that is much more robust. Trade affirmation can be as simple as a buy-side firm confirming details with a broker-dealer, or an automated process that generates a binary response; there is a match or there isn’t.
Firms that use a variety of executing brokers can use the concept of a “penalty box,” directing business away from a particular provider for a specified period to increase accountability and improve the quality of execution. Matching processes can help portfolio managers and traders compare executing brokers so they can make the best decisions about their business.
The best platforms can even leverage artificial intelligence to anticipate underlying problems that might be difficult to identify at first glance. By using matching data recorded over time and across products and markets, some matching systems can alert managers to potential problems based on past experience.
Theorem offers a Trade Match Solution
Our enhanced trade matching solution includes analytical capabilities that help clients more quickly understand where problems exist. The legacy matching process, for example, would identify four trades as unaffirmed. The user would then have the responsibility of determining the element that caused the mismatch. The enhanced matching process identifies the discrepancies and labels them with simple and clear descriptions, such as “trade price discrepancy 123 vs. 124.”
Moreover, our trade matching solution offers more sophisticated analytics that can be used after trades are affirmed to detect patterns in trading activity. Match scoring makes it possible to quantify the quality of execution. A match score can be calculated as simply the number of trades at any specified point of time that is matched or non-matched. However, the best types of match scores are expressed as a percentage, which is much more meaningful in determining the quality of execution. For Theorem clients, a score of 97.7 means that 99.7% of all trades, at the time of matching, are currently matched.
Trade matching today has evolved far beyond the simple reconciliation of trade details. Using new analytics functions, traders can monitor and improve their performance while firms can increase the efficiency and quality of their execution platforms. Better execution can cut costs and reduce risk, significant benefits that few firms would be willing to forego.
About the Author
Rebecca Baldridge, CFA, is an investment professional and financial writer with more than 20 years of experience in creating content and research for asset managers, investment banks, brokers and other financial services clients. She’s worked for some of the biggest names in the industry, including Merrill Lynch Asset Management, JP Morgan Asset Management, BNY Mellon and Franklin Templeton. Rebecca also spent 9 years as an analyst and director of equity research in Moscow, working for several Russian banks. In late 2019, she founded Quartet Communications, a boutique communications firm serving financial services clients. Her writing has been published in outlets including Pensions & Investments, MSNBC.com, Inc. magazine, and Investopedia.com. She holds a B.A. in Russian from Purdue University and an M.S. in Finance from the Krannert Graduate School of Management at Purdue.