Printing a total return number raises questions about what drove performance, opening avenues for thoughtful analysis and authentic performance stories. But before investment professionals can even begin to tell their story, their data systems must have the three elements of a strong foundation: being the single source of truth, accurate data, and robust datasets.
Establishing a Fully Vetted, Single Source of Truth
Performance measurement in its most elemental form is data management, a technical problem that requires an IT solution. Establishing a single source of truth for holdings, transactions, reference, and index data is a critical step, so one centralized set of input files can be created, vetted, and fully trusted to be completely accurate.
Having a centralized system avoids costly duplication of data and helps resolve enterprise level data issues that often arise from disparities in data. Similarly, processing speed from crisp programming and applied technology promotes efficient, real-time analysis.
When it comes to performance measurement systems, relying only on system accuracy and speed isn’t enough. To be effective as the single source of truth, a system must be able to withstand the intellectual rigor of portfolio management teams and their investors. If a system presents inaccurate or inconsistent data and has limited functionality, asset managers often must buy additional systems.
To serve as an enterprise solution for many portfolio management teams, the performance measurement system should be flexible and customizable with multiple performance measurement models and countless ways to slice and dice portfolio performance. Lastly, a robust system should be able to handle many different types of securities and trades, across asset classes and investment strategies.
Dirty Data? Let the System Clean It Up for You
Some performance measurement systems tolerate and accept a certain amount of inaccuracy in return measurement. Increasingly, getting close enough is not good enough. Using automation to hunt for securities with mismatches or that one incorrect return hiding in the data is the most efficient way to go for the last mile for accuracy.
The key to accuracy is tying the performance measurement system to accounting data (the book of record). A good system also systematically reviews every piece of data to verify its accuracy with a series of data-quality tests that can put a stamp of approval on good data and tell the user why a particular piece of data might be bad, or even correct it for the user. There are a number of intricacies of return measurement relating to market events and securities that require complex accounting, which can lead to inaccuracy. Systematic automation should seek and find the usual suspects and correct inaccuracy.
From there, the treasure trove of financial metrics that relate to the securities enriches the book of record and makes the data ready for performance analysis. As a result, robust systems can play a big role in anticipating, diagnosing, and correcting imperfect data, thereby cleaning it up for the user prior to performance analysis.
Terabytes of Data Translated into Authentic, Valuable Performance Stories
Once you have a single source of truth and accurate data, it’s important to also have access to robust datasets to conduct a nuanced performance analysis. Some examples include current data coverage, often across a universe of thousands of stocks for thousands of risk measures and fundamental and economic variables.
Accurately reflecting earnings, corporate actions, and metrics related to market prices is only the beginning, because as the investment world changes, so does the data. When the rise of quantitative models led firms to adopt rigorous methods for assessing fundamental and economic factor risks, performance measurement systems, client service, and even portfolio management teams became accountable for understanding and describing what drove performance via a multi-dimensional way of viewing risk and performance.
When new market forces drive investment performance and flows, such as passive investing, ESG, or risks related to COVID-19, vast new datasets must be integrated into the performance system to be able to ask and answer sophisticated and nuanced questions about what drove investment performance. These data series that must be integrated with the book of record also need to be subjected to review to ensure their accuracy. As such, the data demands of flexible, multidimensional performance measurement systems are vast, with terabytes of market content embedded in the system, all of which need to be systematically double checked, early and often.
Telling Compelling Stories Requires a Strong Foundation
Establishing a single source of truth, impeccable data accuracy, and the availability of robust and dynamic datasets and analytics are foundational to uncovering performance themes that are then translated into authentic stories that connect investors and their clients.
Strong performance measurement systems implement these foundational elements. Free from the challenges of having multiple systems, inaccurate data, and analytical limitations due to data availability, users can focus on applying their expertise with rigorous performance analysis, investment ideas, and communicating the performance story.
This blog post is for informational purposes only. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.
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