Attribution Analysis

Attribution analysis is a sophisticated method for evaluating the performance of a portfolio or fund manager. Also known as “return attribution” or “performance attribution,” it attempts to quantitatively analyze aspects of an active fund manager’s investment selections and decisions—and to identify sources of excess returns, especially as compared to an index or other benchmark.

For portfolio managers and investment firms, attribution analysis can be an effective tool to assess strategies. For investors, attribution analysis works as a way to assess the performance of fund or money managers

What is Attribution Analysis?

Attribution analysis, also known as “return attribution” or “performance attribution,” is an evaluation tool used to explain and analyze a portfolio’s performance against a particular benchmark. It is used to identify sources of excess returns from a firm or fund manager’s active investment decisions.

Components of Attribution Analysis

Attribution analysis compares the return generated by a particular portfolio with that of a portfolio that has been benchmarked for evaluation. It involves comparing different components of the portfolio, which are the result of investment decisions made by the portfolio manager.

The most important factors for effective attribution analyses are portfolio allocation, asset/security selection, and the interaction of these effects – as outlined by the BHB (Brinson, Hoover, and Beebower) model – which is one of the commonly used performance attribution methods.

1. Allocation Effect

The allocation effect refers to the returns generated by allocating portfolio weights to specific segments, sectors, or industries. For example, a portfolio may consist of 20% allocated to assets in the technology sector, 50% to the utility sector, and 30% to the transport sector. The weights are then compared with a benchmark portfolio.

If the portfolio weighs a sector higher than the benchmark, it is described as an overweight
If the portfolio weighs a sector lower than the benchmark, it is described as an underweight

Ideally, the aim of a portfolio manager or investment decision-maker is to place a higher weight on sectors that perform well (i.e., overweight the sectors) and place a lower weight on sectors that are bad investments (i.e., underweight the sectors).

To quantify the effect of allocation decisions, the sector weights and returns in the portfolio are compared to those in the benchmark portfolio, and the arithmetic difference in returns is the effect of the portfolio manager’s decisions regarding asset allocation.

2. Selection Effect

The selection effect refers to the impact of the selection of specific stocks or securities within a segment on the portfolio’s overall return.

A positive selection effect occurs when the portfolio return from a particular segment is greater than the benchmark return from the same segment.
A negative selection effect occurs when the portfolio return from a particular segment is lower than the benchmark return from the same segment.

Since the analysis compares returns from equivalent segments in the portfolio against the benchmark, the excess (or lost) returns are attributed to selections within segments, which are made by the portfolio manager.

Caveat: It is important to note that stock selection is unrelated to allocation in that it is not affected by segment weights, but simply by decisions regarding specific securities that are included in the portfolio.

3. Interaction Effect

The interaction effect is the combination of the selection and allocation effect. If the portfolio allocation outweighs and outperforms the benchmark, the interaction effect is positive, and vice versa. The interaction effect is essentially the cumulative effect created by asset allocation, security selection, and other investment decisions made by the portfolio manager.

However, it is important to note that the interaction effect is not easily attributable due to the fact that it is a mathematical consequence of the allocation and selection effects rather than an actively made investment decision.

What Makes a Benchmark Valid?

Measurable
Investable
Appropriate
Owned
Unambiguous
Reflective of the current investment landscape

Importance of Attribution Analysis

For portfolio managers – An effective tool to assess strategies

Attribution analysis can be used to separate the selection and allocation effects. The selection effect reflects the quality and ability to pick the right securities at the right time. It allows managers to reflect on the entire investment decision-making process and gives them opportunities to improve.

It is also used to evaluate the performance of employees at an asset management firm. If an analyst or employee recommended overweighting a particular sector or buying a particular stock, the returns can be attributed to their performance, and it can be used to reward them and motivate them further.

For investors – An effective tool to assess the performance of fund managers

For investors and clients of portfolio managers, attribution analysis is an important method to assess how a portfolio manager has performed and whether they have adhered to their investment strategies and styles.

For example, a portfolio manager may have underweighted the right sector but has picked high-return stocks, and it shows that they are better at selection than allocation. Similarly, a portfolio manager may agree to build a risk-averse portfolio. Examining the allocation and selection strategies can increase transparency and assure investors that the risk-averse strategy is being implemented.

FAQs

How Does Attribution Analysis Work?

Attribution analysis works by looking at three distinct things:

Fund assets and allocation
The fund manager’s investment style
Market timing

The goal is to explain why a portfolio or fund performs differently from the benchmark it’s being measured against. You’re looking for the difference between the returns of the portfolio versus the returns of the benchmark. The benchmark itself may be an index, such as the Dow Jones Industrials, that includes a range of assets, similar to the ones in the fund or portfolio.

The fund manager’s selection of investments, their overall investment style and the market timing in which they’re buying and selling investments can all influence how wide or narrow the gap is between returns.

Attribution Analysis and Fund Alpha?

Alpha is the excess returns a fund generates. In other words, alpha shows how well a fund is able to beat the market. If you’re investing in managed funds with the goal of beating the market, attribution analysis can give you an idea of how likely a particular fund is to do that.

The most important thing to keep in mind here is that you need to choose benchmarks that reflect the makeup of the fund you’re analyzing. It wouldn’t make sense, for example, to compare a large-cap fund to an index that includes only small-cap companies. Doing so won’t give you a true sense of whether the fund is capable of beating the market since the fund doesn’t correlate to the index.

Once you understand what a fund is likely to produce in excess returns compared to an appropriate benchmark, you can then take a closer look at the fund itself. For example, you might go through each asset within the fund to see how well it performed and how many winners or losers there are. If just a handful of assets are generating the bulk of returns, then you have to consider how well that fits your overall risk tolerance and investment style.

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