5 Lessons Learned: Insights from the TT and Foley Regulatory Roundtable
What are the top five challenges for compliance teams in today’s rapidly evolving regulatory environment? We posed this question to a cross-section of industry experts from the trading and legal communities at a recent panel discussion co-hosted with Foley & Lardner, who provide legal counsel on global regulations for securities, derivatives and other financial products. Ahead of XLoD Global – New York, we’re sharing some of the big themes from the panel and how TT’s trade surveillance software can support trading firms in their compliance operations.
1. Data normalization is a challenge for all, from exchanges to high-frequency traders (HFTs).
For trading firms, data normalization and reconciliation are among the most significant challenges in maintaining data integrity, with a number of firms having recently been fined by regulators for trading activity that they simply hadn’t realized wasn’t being surveilled.
Kevin Coleman, CCO at Optiver, shared the market maker’s perspective on the challenges involved with being a global organization where trading is interconnected across the world through various markets and market structures. While many firms, including Optiver, centralize their compliance and surveillance functions, data normalization is not easy on a global scale. Integrating siloed trading systems and market data sources is resource-intensive, and a “one-size-fits-all” approach doesn’t always work for entirely different markets and regions. Coleman calls this “data ingestion variability”—and tackling this can require significant resourcing and development.
TT Trade Surveillance’s new Audit Trail Report can provide reassurance that trading activity isn’t being missed. It allows users to download a comprehensive record of all events sent to TT and verify that all of the firm’s data is being captured and surveilled.
2. Alternative data use is a gray area for trade surveillance.
An increasingly common issue in trade surveillance and data reconciliation is the use of alternative data in trading strategies—particularly around understanding what data is being used and where it is sourced from. Alternative data is data gathered from non-traditional sources and can be anything from cellphone tower pings, weather metrics and satellite imagery to client trading behavior data. Used by many traders to get an edge, it is increasingly being picked up and recognized by regulators.
One key issue is that many firms do not necessarily know how advanced their traders’ utilization of alternative data sources has become, and, given the gray area between what is considered public and non-public data, it is difficult to know what should be surveilled—and even what counts as insider trading.
The advice from Foley’s Securities Enforcement & Litigation Partner Jim Lundy is that alternative data is here to stay—and it’s evolving, so compliance teams need to have policies, procedures and controls in place and ensure that alternative data sources are being appropriately included in their surveillance systems. Compliance officers have a duty to know what data their traders are using. Likewise, if onboarding a new alternative data source, Lundy advised, “Make sure you know what that means, then you can enhance your data surveillance, knowing what those sources are.”
3. Cross-product manipulation is the ideal candidate for artificial intelligence (AI) and machine learning innovation.
Detecting and preventing cross-product and cross-market manipulation are challenges for all trading firms. It is essentially impossible for chief compliance officers (CCOs) to know how every single instrument is correlated across every exchange, market and asset class, so calibrating and tuning a model to all these specific correlated markets is difficult.
This is where there is room for technological innovation, in particular around machine learning and AI. One example is in using machine learning to create features and models that will be better at identifying cross-product manipulation, fine-tuning them to collaborate with traders and sift through the noise.
TT continues to innovate in the use of machine learning, which includes powering cross-product spoofing models that support automated spoofing, collusive spoofing and more. The models create a synthetic instrument when there is overlapping trading activity in the correlated products, then cluster and score the trading activity in mathematical similarity to regulatory case data, helping compliance officers identify suspicious behavior across instruments, products and markets.
4. 24/7 trading is the industry’s next big compliance challenge.
With Coinbase having launched 24/7 trading of Bitcoin and Ethereum futures contracts in May and many prop firms already employing established “chase the sun” trading models, exchanges and compliance teams are having to catch up. While foreign exchange (FX) and cryptocurrencies are already there, other asset classes are moving to an expanded trading day. Our panel agreed that the challenge is around how we comply with supervisory obligations when traders are active 24/7, and that all firms need to be cognizant and sensitive of the risks, particularly around infrastructure, alerts and price swings.
Matt Lisle, Chief Compliance Officer (Futures Division) at Wedbush, also highlighted that longer trading hours can lead to a choppier, less liquid market, which in turn can lead to more manipulation as markets are easier to “push around.” On a more positive note, Lisle and the panel were confident that the market will eventually be able to move to 24/7 settlement mechanisms and provide the necessary infrastructure, given that the momentum is already there.
5. Surveillance challenges for market participants are not all the same—though everyone shares a duty to prevent market manipulation.
When thinking about the perspective of a futures commission merchant (FCM) vs. a prop trader with regard to trade surveillance and compliance, we have to be mindful of the different roles these firms play and their relationships with clients and traders, as well as the impact on their ability to surveil trading activity.
The CME Market Regulation Advisory Notice from July 1, 2024 advised that all parties, from employees to members and agents, have a duty to take reasonable measures to prevent violations of the exchange rules. However, the key distinction here is around the definition of “reasonable measures.” The notice states that supervisory programs used to detect and deter violations should be commensurate with the party’s business, model, structure and geographical coverage.
Lisle and Coleman discussed their own individual circumstances as CCOs with respect to these “reasonable measures,”—specifically how challenging it is for CCOs to identify and understand each unique trading strategy. Lisle shared the FCM perspective, highlighting that Wedbush’s trading clients come from all types of firm, from retail brokerages to agricultural hedgers. With so many different trading strategies in play, the challenge is in achieving transparency and understanding of each. Conversely, for Coleman, a prop firm can “at least look their traders in the eye, understand their strategies and collaborate with them.”
One way to mitigate the challenge of monitoring such a wide range of trading activity is to use a comprehensive, global trade surveillance solution like TT Trade Surveillance, which is a hybrid system providing both out-of-the-box and configurable models, so that alerting can be tailored to the needs of the firm.
As the regulatory environment continues to evolve, the challenges facing CCOs and law and compliance professionals more generally also grow in complexity. From the sheer volume of data needing to be monitored to the move to longer trading days and weeks, trade surveillance systems are working harder than ever to keep up.
TT’s multi-asset trade surveillance platform is trusted across the industry and meets the rigid requirements of the world’s top financial institutions. Get in touch to find out how you can leverage our state-of-the-art trade surveillance models to identify potentially suspicious behavior and reduce the risk of regulatory inquiries.