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Visibility into trade data is vital as style drift swirls around AMs

Compressed margins and fear of disruption by tech giants is driving asset managers to search for new ways to raise revenue, reduce costs, and develop new funds and products. But these trends also imply a significant amount of unpredictability and operational turmoil that some managers’ IT teams are unprepared for.

Pressures on asset managers are broad and growing. In reacting to compressed margins today and expectation of disruption by tech giants in the longer term, firms are frequently searching for new ways to raise revenue, reduce costs, and develop new funds and products that speak to the change amongst their clients—whether evolving requirements at the institutional level or changing demography and geography targeted by wealth management units. That push continues to drive a new level of innovation, as well as a steady stream of mergers and acquisition activity, and both are exciting trends, upending a part of financial services typically known for its staid stability. But they also imply a significant amount of unpredictability and operational turmoil that some managers’ IT teams are unprepared for.

This battle has also played out in a puzzling market environment, defined by low volatility and low interest. It has demanded creative thinking in the search for yield and in the construction of new funds. At the top of the priority list—for firms and clients alike—is efficiency and, as a result, we have seen a predictable rise of increasingly diverse opportunities to passively invest, particularly via exchange-traded funds (ETF) wrappers. Enabled by artificial intelligence-driven selection, portfolio rebalancing, and other automation gains, this “Smart Beta” revolution arrived with a bang, leaving more actively managed funds bearing similar performance outcomes to explain their higher commissions. Much of the innovation work and M&A mentioned above has chased gains in this lucrative, scalable passive realm.

A few years on, things have cooled down, reverting a little back to the norm. A more holistic view of efficiency is now the goal and, specifically, there is a growing acceptance that the mix of the passive- and active-category instruments should be optimized, rather than over-weighting to one or the other. As portfolio managers accept this style (or strategy) drift as a kind of new normal, they face a number of questions to consider. These include the precision of a given instrument in achieving the desired exposure or hedge; potential market impacts; liquidity availability from market makers; and of course, the all-in transaction cost of trade execution—including the nontrivial expense of handling back-end processing for exotics, fixed income, and credit. All of this must be digested and parameterized for it to work.

Counting the Consequences

Constructing those points of comparison—and managing the data intake required to do so—is a tough task. It has seen many managers take up a recent refresh of their risk modeling and portfolio management platforms, in an effort to better establish asset pricing as well as push as much of the transaction cost analysis (TCA) as close to pre-trade as possible. They are likewise using market data to do a closer review of style drift itself—whether from a product issuers’ perspective (i.e., how persuasive is a new ETF proving?) or the end-user perspective (how much does the portfolio sway back and forth over time; is that sway permissible and cost-effective?). The consequences are significant, from evaluating a large tactical investment’s intricacies, to refining the logic behind a firm’s investment operations writ large. As the Smart Beta wave proved, billions of dollars can hinge upon getting it right.

Of course, all of this places new onus on the technology stack beneath. Lots of ground has been gained by using cloud-based infrastructure and AI to support new passive investment products. Internally, the new platforms being installed demand more configuration flexibility and reference data to bring together the modeling and analysis input increasingly contributed from different teams—quants, risk, portfolio managers, trading ops—throughout the enterprise. Still, many managers are also finding that as they build (or buy) new styles and themes into the portfolio, traditional functions like trade data reconciliation have a huge role to play.

Transaction data management is foundational for the security master (or investment book of record)—the single source of truth that then feeds risk and portfolio management. Any adjustment in style, be it subtle or substantial, must reflect both present holdings as well as an optimal future state. Therefore, the ability to reconcile new, unfamiliar types of products—with accurate positional data available in real time, and incorporation of vital ancillary information such as underlying product composition or tenors—is vital to being able to oversee, assess, and improve composition of the portfolio. Likewise, the reconciliation data platform should enable and interact with, rather than hinder, greater efforts toward automation and deployment of AI. And again, today this goes just as easily for a large fund issuer as it does a traditional asset manager or even institutional investor.

Styles may change, and the next investment innovation is always coming, but in one crucial operational sense, things remain very much the same: reconciliation must match the sophistication of the investment process, or the entire thing risks falling behind.


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