Improving Performance Measurement for Asset Managers
Performance measurement is widely seen as one area of the financial services industry that is ripe for innovation. As Wall Street & Technology previously reported, a 2013 research study by CEB TowerGroup predicted a 6.9% per annum increase in performance spending by asset managers through 2017.
The report identifies a number of drivers, pointing toward increasing investor pressures, regulatory demands, and a more sophisticated fund product landscape. At the same time, the report revealed that many firms are entrenched in their current performance systems, describing current practices as "arcane."
My experience aligns with this research. Most investment managers I encounter have a range of tactical systems that calculate performance and carry out risk analysis for specific asset classes. For example, firms may have one system for all institutional equities, another for fixed income or private equity and yet another for a wealth management business line. The genesis of these systems often comes from the needs of the front office but this desire for more performance data rests on the operational dependability and reliability of audited data generated from the middle and back office.
As a result, to calculate organizational level performance, risk, and exposure, information officers, risk officers and portfolio managers spend a significant amount of time cobbling together performance information. Pulling that information together to properly capture and report performance and risk both internally and externally is a largely manual task and prone to error. Senior team members dedicate more time gathering, normalizing and enriching their data than actually analyzing it once reconciled. Beyond creating an unnecessary distraction, these manual processes introduce an added layer of operational risk and increase the potential for misreporting.
Discussion around performance measurement innovation tends to gravitate toward the more exciting end of the spectrum, such as advances in data visualization, interactive dashboards, widgets, and mobile reporting. While these are worthy and much needed technologies, the truth is that these advances are only as effective as the underlying data. While these bells and whistles can ease the consumption of data, true improvement in performance measurement must start with the data itself and carries through to return and analysis calculations.
The utility of any performance tool relies on the quality of the data inputs to deliver consolidated, accurate, consistent and timely information across the entire enterprise. With this in place, investment teams can perform the exact risk analysis needed at all levels of the organization and slice and dice performance data as required to analyze complex fund strategies.
Given the weight that’s placed on performance returns and risk attributes by the industry, it’s critical that firms view performance data in the same way they view accounting data and their investment book of record; effectively, firms should be looking to develop a performance book of record (PBOR).
Some of the roadblocks asset managers face when it comes to measuring performance and risk that would be addressed by a PBOR include:
· Multiple sources. The calculation and delivery of performance returns based on different accounting rules, different frequency of valuations, and different levels of detail from disparate recordkeeping systems. A solution must have elegant patterns for enriching “incomplete” data for complex instruments such as derivatives and non-marketable assets, and measuring data quality to track consistency issues or errors that necessitate performance-returns recalculation or analytic data refresh.
· Multiple views. The calculation of performance across multiple clients, fund types, asset classes, and operating regions with different and discrete measurement requirements. A solution must have the flexibility to calculate different types of return such as net of fees, gross of expenses, before/after tax, and notional valuation basis in a single platform.
· Multiple methodologies. Harmonizing methodologies across different asset classes because of their unique requirements (e.g. private equity with lagged/non-lagged returns and real estate with actual versus projected cash flows). Alignment of varying historical return frequencies in support of flexible ‘to-date’ assumptions as defined by portfolio characteristics.
· Platform optimization. Supporting performance calculations, benchmark-, currency-, and hedge-relative performance attribution analysis, and ex-post and ex-ante risk on a single platform. A solution must provide a technical architecture that is scalable and flexible and able to support the increased level of data required to calculate performance at the security-level with the flexibility to support the needs of the business.
Given the increased need to produce monthly risk measures and stress tests for regulators, and increased demand by clients for detailed analysis on a strategy’s risk and return profile, there is a lot of room for improvement when it comes to the technology that supports performance measurement, attribution, and risk management in the asset management industry. New advances would not only better and more efficiently satisfy reporting requirements, but would also improve understanding of the drivers of success and aid decision-making. A performance book of record would go a long way in helping firms achieve these critical goals and manage down risk in today’s volatile global markets.
As Chief Technology Officer of Eagle Investment Systems , Marc Firenze drives the software, technology, and architecture decisions across Eagle's investment management suite and ensures that development directly supports the firm's corporate vision. With more than 20 ... View Full Bio
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