The journey towards an IBOR

In my latest contribution to the SimCorp Journal, I profiled one of three firms I worked closely with to optimize their investment operations for a high-yield (leveraged loan) strategy. The article highlighted the critical importance of an IBOR-centric operation in preserving and expressing the alpha inherent in that firms’ strategy.

A winner's story is always uplifting, but 'second place' also has some crucial lessons to impart. In this post, I describe the approach that a non-IBOR firm is now taking to enhance their investment operations.

Wisely, they did not immediately go out and buy (or build) a new investment management system to solve their problems. Such an initiative would have been premature given the scale and breadth of their global book-of-business. Instead, they first utilized analytics (specifically graph network models) to envision and create the optimal workflows within the firm that would best support their high-yield strategy and eventually other strategies as well.

Like many asset managers, the firm’s investment operations were historically organized as a collection of task-oriented activities (e.g., pricing, corporate actions, settlements, and so on), seemingly just mimicking their internal organizational chart. 'Operations', therefore, evolved into a one-size-fits-all utility for servicing the wide range of investment strategies that the front office was continually developing and releasing downstream.

After reviewing the analytics results, the firm realized that a more productive construct for their investment operations would be to assemble explicit competencies (people and systems) that are geared to proactively nurture specific investment strategies. For their high-yield strategy, they created a more focused approach that drew on particular internal groups, plus elements of their investment systems, that best supported credit and sector sensitive portfolio implementations.

The firm also learned from the analytics that these workflow threads should incorporate third-parties, such as prime brokers, sub-custodians, and industry utilities (e.g., Omgeo). Though the firm’s span of control over these entities is limited (compared to internal resources and systems), the graph network models gave them the ability to quantify and qualify the flows into and out of these functional ‘black boxes’.

Significant gains in timeliness and accuracy for tracking accruals were obtained by shunting these activities from a custodian back to an internal group at the firm. Moreover, the firm was able to benefit from a consistent reduction in the number of days to settle these loan transactions.

They are now in the process of leveraging their experience with the high-yield strategy into other complex mandates such as LDI, risk-parity, and absolute return. In effect, they are creating a number of customized workflows that course through their organization and marshal the most effective support for each specific investment strategy.

As we speak, the firm in question is still living with systems premised on an ABOR. However, their usage of analytics shone a light on aspects of their operation that were first in need of remediation. So intuitively, they have already moved the needle of operational efficiency in their favor.

The firm understands their need to implement an investment book of record architecture to maximize the preservation of alpha across the range of investment strategies that they offer. The moral of this story is that even though they knew that an IBOR was necessary, they realized early on (with the help of analytics) that an IBOR alone would not be sufficient.

Want to learn more?

Read Chito’s Journal article: New Frontiers – Operational efficiency and it’s impact on portfolio performance

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