Most investors run multi-asset class portfolios against allocation targets. For example, a certain investor may need exposure to private markets of about 30% and exposure to public markets of about 70%.

In the institutional investment world, it is common practice to manage a portfolio against asset allocation targets, which are often imposed by the board of trustees and their actuaries to meet long-term liabilities.

In the private wealth world, financial advisors use asset allocation targets/boundaries to match an individual's risk tolerance and target returns (e.g., max 30% equities).

Inevitably though, investors won't be able to run portfolios exactly on target. Different fund subscription/redemption frequencies on the public side, uncertainties surrounding capital calls and distributions on the private side, and market moves impacting the portfolio in different ways all contribute to effective asset allocation deviating from targets most of the time. Also, investors may want to intentionally deviate from targets for a brief period of time to exploit tactical dislocations in the market (e.g., increasing exposure to equity after a severe drawdown, to profit from an expected rebound).

Once a certain exposure has been decided, investors must then implement it via either passive or active instruments, which also contributes to returns.

At the end of a period, investors must ascertain which part of their performance can be attributed to asset allocation decisions (deviating from targets is an example of such a decision) vs. manager selection (a broader way of saying "how" to implement an asset allocation decision). In the case of institutional investing, providing such a breakdown is a classical reporting obligation of investment teams vis-a-vis their board of trustees.

In this article, we'll discuss how Novus allows you to measure which portion of the portfolio's performance comes from asset allocation vs. manager selection decisions. First, we'll review the formulas underlying the calculation framework. Second, we'll learn how to set things up (and configure them) on Novus. Third, we'll walk through a step-by-step reconciliation directly Novus. Finally, we'll wrap up with concluding remarks.

The math behind Novus Fund Attribution is a specific instance of the generic Brinson attribution framework. To review its principles, and see how they apply to the problem of splitting performance into asset allocation vs. manager selection decisions, check the video below.

For example, assume your board has formulated an asset allocation policy whereby

Let's denote:

and

for a given period. The allocation, selection, and interaction effect for a given category can be expressed via the following formulas:

The allocation, selection, and interaction effects add across categories to the total allocation, selection, and interaction effect at the portfolio level.

Fund Attribution models can be set up from within the Data Tab. Follow the Models sub-tab, then choose Fund Attribution among the model categories on the left. For a video walk-through, feel free to watch the video below.

The setup occurs in two steps:

Within Creation, the asset allocation policy is defined. Click on + New Model on the upper right to create a category for your asset allocation (e.g., Private Equity). Give it a name, set a benchmark, and weight by date.

In the example below, note that the weight of the asset allocation policy to the Private Equity category will be assumed to be 30% for the period spanning from May 31st, 2017 until December 31st, 2019 (where a new value sets in). Be sure to set the first date in the time-series equal to the inception date of the portfolio.

Keep in mind that, for a given date, all weights across the various categories must add up to 100%. In our case, since we only work with two categories, the weight in Public Equity must be set to 70% by May 31st, 2017. By the same token, the weight to Public Equity must be set to 75% by December 31st, 2019. Since the illustration is similar to the Figure right above, a screenshot was omitted for brevity's sake.

We now need to specify which funds are intended to express the asset allocation targets. This occurs within the Assignment tab, just right of the Creation tab in the upper left corner.

After having chosen the portfolio in the upper left corner, click on + New Assignment in the upper right corner to create a new assignment. If you want to edit an existing assignment, simply choose from the drop-down left of the portfolio name.

Assign funds to asset allocation categories via the drop-down on the right. While the portfolio aggregation nodes (e.g., 'Hedge Fund' or 'Equity Long Short' were made visible to improve the readability of the portfolio, they do not need to be assigned to asset allocation targets (or models as we call them). In fact, assigning one to them won't have any impact.

The fields representing the allocation, selection, and interaction components of Fund Attribution are fund-level fields called 'Asset Allocation', 'Stock Selection', and 'Interaction'. They are fund-level fields available under the Fund Attribution section within the field drop-down.

Watch the video below for a step-by-step walkthrough of the steps needed to represent Fund Attribution fields within Novus.

A couple of best practices (as illustrated in the video) are worth noticing:

The snapshot below features a collection of analytics leveraging Novus Fund Attribution. Further down below you'll find a video walk-through of its main features.

Fund attribution is a crucial tool for allocators willing to understand the drivers behind the performance of their portfolios. It's actionable in the sense that it gives feedback on whether the two main investment degrees of freedom in the hands of allocators (allocation and selection) have been contributors or detractors to returns. It's also a fundamental asset for reporting performance to boards overseeing the investment teams at foundations/endowments and pensions.

The discussion above has been limited to a single period (except in the snapshot referenced above), as the math for multi-period attribution requires an understanding of smoothing (see here for the Novus University article on Smoothing). It's important to note that smoothing techniques were not applied within Fund Attribution over multi-period analyses.

Since the underlying engine for Fund Attribution is the allocation-selection-interaction Brinson framework (without Fachler adjustments), curious readers are referred to specialized textbooks for a deeper understanding of Brinson-type attribution frameworks. A great resource is "Practical portfolio performance measurement and attribution" by Carl Bacon.