Attribution analysis is commonly used to provide insights into the performance impact of the investment decisions that are made within an investment portfolio over a period of time. Different attribution techniques are mainstream for different types of portfolios, like Brinson style or factor attribution for equity portfolios or fixed income attribution for fixed income portfolios. In the performance measurement industry, a lot of attention is typically given to the details of the different attribution models. Setting up an attribution is often a tedious task with many different options to keep in mind.
In this insight I will explain the three steps that we believe everyone that sets up an attribution should keep in mind, independent of the type of portfolio under investigation.
Step 1: Fundamental definition choices
The first step is defining the portfolio and benchmark side of the attribution and the type of attribution that you would like to use. Is a Brinson style attribution adequate in my situation, or do I need to use more advanced techniques such as factor or fixed income attribution? Do I want to take a top-down, bottom-up or hybrid calculation approach?
Often these choices are related to the mandate of the fund under investigation. The benchmark is the official benchmark set for the fund and the model is related to the investments in the fund explaining the impact of the decisions made by the manager. However, from a risk management perspective it is becoming more common to also evaluate portfolios against other benchmarks or even other portfolios to get an understanding of other sources of risks in the portfolio.
Step 2: Attribution hierarchy
The choice of the attribution hierarchy is important both from a calculation perspective and from a reporting perspective. From a reporting perspective, the hierarchy determines the segments (rows) within the report. So it is important to think about which levels should appear in your report, e.g. countries, sectors, individual securities, etc..
The hierarchy also has implications for the calculations. For models based on the decomposition of the security returns for instance, the hierarchy determines the aggregation levels of the difference in the contribution of the return driver in the portfolio and the benchmark. The two examples below illustrate how the hierarchy can provide valuable insights in the return drivers of the portfolio's (out)performance.
- Currency Management
A good example of how the choice of the hierarchy can provide valuable insights is to show the impact of currency on the portfolio by separating out the difference between the local and base return of the securities and aggregate them to show the impact of the individual currencies. This provides valuable insight in the currency positioning of the portfolio and helps to understand the sources of the portfolio’s (out)performance. Did the performance happen because the companies did well or because the currency that the companies are listed in did well? If the manager manages this dimension with currency forwards, a further separation between those currency forwards and the other investments provides a valuable insight into the implementation and the impact of the hedge.
Including ESG in the investment decision process is becoming increasingly popular. Often this is implemented by the use of an exclusion list or by engagement where companies get a couple of years to improve on the specific Environment, Social or Governance issue. This can be included in the performance evaluation as well, by separating out the companies on the exclusion list and thus showing the contribution of the excluded companies to the benchmark return.
Alternatively, for top-down attribution models the hierarchy determines the allocation levels, explaining the added value of the different allocation decisions. If a top-down approach is taken in managing the portfolio, a manager can take multiple allocation decisions. An example could be a global equity portfolio manager who first allocates money to different regions, then allocates to different sectors within the regions, and lastly tries to select the best performing companies within each sector. The outperformance on the lowest level of the hierarchy then reflects the skill to select the right companies.
Hybrid models can be used to combine the bottom-up and top-down approaches, e.g. to first measure the impact of the allocation decisions to different regions in a fixed income portfolio before evaluating the outperformance of the regions using fixed income effects based on the yield curve for the specific region.
Step 3: Calculation details
In the last step all the specific and detailed calculation settings can be specified. There are many different specific options, e.g. which components should be included in the return (fees, taxes, transaction costs, etc.). Also the configuration of look through relations that should be applied in the analysis can affect the results, not forgetting model-specific settings such as how to handle benchmark or portfolio-only segments in a Brinson analysis that need to be addressed.
Those details are the playing field of the performance measurement expert, since it allows for very specific tailored analysis. However, using default settings can help to create efficient workflows and also provide less experienced analysts with the comfort to create new attribution analyses. Using templates for different types of attributions can also be helpful for managing multiple attributions for different funds at the same time. Ideally such templates should cover most choices of the 3 steps, so that only the portfolio and benchmark still need to be specified in order to create a new attribution.
By capturing the choices within the three steps in templates, many attributions can be managed simultaneously through an intuitive and efficient workflow.
If you want to find out more about how we support this in our solution, please have a look at our PEARL page.