Attribution Analysis

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Attribution analysis is a method of evaluating the performance of a portfolio or a fund manager against a benchmark, such as an index or a market segment. The sources of excess returns, or alpha, produced by the manager's active investing choices can be identified. 

It is also possible to utilize attribution analysis to evaluate the effects of numerous variables on desired outcomes like customer usage, retention, or satisfaction, such as marketing efforts, customer support activities, or the introduction of new services.

Attribution analysis is primarily composed of three elements: market timing, security selection, and asset allocation. The distribution of the portfolio's assets among various asset classes, sectors, geographies, or styles is referred to as asset allocation. The selection of specific securities within each asset class or market sector is referred to as security selection. Timing the purchase and sale of securities in accordance with market trends or projections is referred to as market timing.

A benchmark that embodies the passive investment approach must be used to compare the results of the portfolio in order to do an attribution analysis. The active return, or discrepancy between the returns of the portfolio and those of the benchmark, can be broken down into a number of components that show the relative contributions of the various components to the excess returns. For example, one can use the Brinson-Fachler model to calculate the attribution effects of asset allocation and security selection:

• Asset allocation effect = Sum of (Portfolio weight - Benchmark weight) x (Benchmark return - Total benchmark return)
• Security selection effect = Sum of (Portfolio weight x (Portfolio return - Benchmark return))
• Interaction effect = Sum of ((Portfolio weight - Benchmark weight) x (Portfolio return - Benchmark return))
• Active return = Asset allocation effect + Security selection effect + Interaction effect

The asset allocation effect quantifies the degree to which the portfolio's performance is impacted by the asset weights' departure from the benchmark weights. The security selection effect calculates how much the returns on the stocks in the portfolio deviate from the returns on the securities in the benchmark. The interaction effect quantifies the degree to which decisions regarding asset allocation and security selection interact to affect the performance of the portfolio.

The market timing effect can be measured by comparing the portfolio's returns with those of a benchmark that adjusts for market movements. For example, one can use the Treynor-Mazuy model to calculate the market timing effect:


Market timing effect = Beta x (Market return - Risk-free rate) x (Market return - Portfolio return)

The market timing effect quantifies the degree to which the portfolio's performance is affected by its exposure to market risk and its propensity to foresee market alterations. When the market performed well, the portfolio's exposure to market risk increased; conversely, when the market performed poorly, the exposure to market risk declined.

Both portfolio managers and investors should consider attribution analysis. Attribution analysis can aid portfolio managers in assessing their investment methods, determining their strengths and limitations, and explaining their value proposition to their clients. Attribution analysis can assist investors in understanding how their portfolio managers produce returns, evaluating their risk-adjusted performance, and contrasting them with other managers or benchmarks.

Attribution analysis is not without limitations, however. Some of the challenges of attribution analysis include:

• Selecting a benchmark that accurately captures the goals, limitations, and investing universe of the portfolio
• Incorporating currency impacts, transaction costs, taxes, fees, and more variables that have an impact on the returns of the portfolio
• Dealing with complexity that may not be accommodated by straightforward attribution models, such as non-linear relationships, multi-factor models, dynamic strategies, leverage, derivatives, and other difficulties
• Using caution when interpreting the findings and being aware of each attribution model's assumptions and restrictions

Although attribution analysis is a helpful tool for assessing and explaining portfolio performance, it shouldn't be applied in isolation or without the appropriate context. Along with other performance evaluation techniques like risk analysis, style analysis, factor analysis, scenario analysis, and peer group analysis, attribution analysis should be used to evaluate performance.