Performance / Return Attribution via Brinson-Hood-Beebower Model

Summary

In this tutorial, the concept of performance attribution is explored using the Britain Hood B Battle Model. The video demonstrates how to decompose a portfolio’s excess returns into three key components: allocation effect, selection effect, and interaction effect. The process includes calculating portfolio and benchmark returns, followed by the application of formulas to analyze sector allocation and stock selection. By assessing the fund's performance against expectations, viewers can understand the drivers behind excess returns. The video emphasizes the importance of return attribution in fund analysis and portfolio management, offering key insights for investors and analysts.

Takeaways

• 😀 Performance attribution is crucial for understanding how portfolio returns are achieved and identifying the factors contributing to the overall return.
• 😀 The Britain Hood b battle model helps decompose excess returns into three components: allocation effect, selection effect, and interaction effect.
• 😀 Portfolio return and benchmark return are calculated using the formula: weight of asset * return of asset. This can be easily done in Excel using the SUMPRODUCT function.
• 😀 The excess return is the difference between the portfolio return and the benchmark return, which helps in understanding the fund's performance relative to the benchmark.
• 😀 Allocation effect measures the impact of sector allocation decisions on performance. It compares the fund's allocation to that of the benchmark and multiplies by the benchmark's return for each sector.
• 😀 Stock selection effect evaluates how well the fund selected assets within each sector compared to the benchmark. A positive selection effect indicates good stock picking.
• 😀 Interaction effect quantifies the combined effect of allocation and stock selection, capturing the joint impact of these decisions on overall return.
• 😀 The decomposition of excess return into these components helps to determine whether the fund's returns are in line with its stated philosophy and investment strategy.
• 😀 Understanding the fund's investment process, team structure, and style is important for evaluating whether the return attribution matches expectations.
• 😀 Return attribution analysis is just one part of fund evaluation; it should be used alongside other factors, including fund philosophy, to assess the true performance drivers.
• 😀 In the case of a stock selection-focused fund, positive stock selection effects and negative allocation effects should align with the fund's expected performance based on its strategy.

Q & A

What is the purpose of return attribution in fund analysis?

Return attribution is used to assess how a fund's returns are generated by analyzing the impact of different factors like sector allocation and stock selection. It helps investors understand the sources of excess returns compared to a benchmark.

What are the key learning outcomes of the video?

The key learning outcomes are: 1) Understanding the formulas for portfolio and benchmark returns, 2) Decomposing excess returns into allocation, selection, and interaction effects, 3) Analyzing whether fund returns align with expectations, and 4) Gaining familiarity with the Britain Hood B-Bar model.

How are portfolio and benchmark returns calculated?

Portfolio and benchmark returns are calculated by using the formula: sum of (Weight of Asset × Return of Asset). In Excel, the SUMPRODUCT function is used to calculate these returns by multiplying the weights and returns of the assets in the portfolio or benchmark.

What is the allocation effect in return attribution?

The allocation effect measures the impact of the fund's sector weights relative to the benchmark. If the fund is over-weighted in sectors that perform well, the allocation effect is positive.

How is the selection effect calculated?

The selection effect is calculated by taking the difference between the fund's asset return and the benchmark's asset return for each sector, and then multiplying that difference by the benchmark's sector weight.

What is the interaction effect in return attribution?

The interaction effect captures the combined impact of the allocation and selection effects. It is calculated by multiplying the difference in sector weights between the fund and the benchmark by the difference in returns between the fund and the benchmark for each sector.

What does a positive allocation effect indicate?

A positive allocation effect indicates that the fund's sector allocation was favorable, meaning that the sectors the fund was over-weighted in performed well compared to the benchmark.

How does the Britain Hood B-Bar model help in fund performance analysis?

The Britain Hood B-Bar model helps decompose a fund's excess returns into allocation, selection, and interaction effects. This decomposition provides insights into the factors driving a fund's performance compared to its benchmark.

Why is it important to analyze a fund's philosophy and investment process in return attribution?

Analyzing a fund's philosophy and investment process helps investors understand the fund manager's approach to generating returns. It also provides context for interpreting return attribution and assessing whether the results align with expectations.

What is the main takeaway from the example of the sector allocation fund?

The main takeaway is that the excess return of a sector allocation fund primarily comes from the allocation effect, which indicates that the fund’s performance was driven by favorable sector weighting, even though stock selection may have been less effective.