The authors contribute to the literature of investment performance measurement by developing a framework to measure a manager’s asset allocation skills. They decompose the manager’s alpha into risk-based allocation, relative valuation trades, and factor-timing components.
The authors build on the generally accepted manager alpha analysis by creating a framework to analyze the value added to investment returns through active asset allocation. They decompose the asset allocation alpha into risk-based allocation, relative value trades, and factor-timing components.
Although numerous studies indicate that manager selection alphas are largely zero or possibly even negative, asset allocation decisions and asset allocation alpha are generally seen as important for overall portfolio performance. Although skill- and risk-based decompositions of manager alpha and risk-based analysis are widely used to evaluate single-asset-class portfolio performance, they do not provide sufficient information on sources of performance for a more diversified, multi-asset-class portfolio with dynamic asset allocation and stock-picking decisions.
The authors aim to fill this gap by developing a framework to better evaluate the contribution of asset allocation decisions to portfolio performance. They propose a framework that would be a great benefit to multi-asset-class portfolio managers for client reporting purposes and to manager selectors for analyzing the sources and persistence of investment returns.
The authors first define the policy portfolio as the asset allocation mix determined by the unique investor return requirements and risk constraints. Conventionally, investors take the excess return of the implemented (or actual total) portfolio over the policy portfolio and decompose the excess return into asset allocation alpha and manager selection alpha.
To better determine the actual asset allocation alpha, the authors first define another portfolio—the tactical portfolio. The tactical portfolio has the identical asset class exposure as the total (or implemented) portfolio; the difference is that it invests only in the indices assigned as benchmarks to each of the permissible asset classes. So, the tactical portfolio return minus the policy portfolio return equals asset allocation alpha. The total portfolio alpha, defined in this context as the excess return of the implemented portfolio over the policy portfolio, can be decomposed into asset allocation alpha and manager selection alpha. So, the total portfolio return minus the tactical portfolio return equals manager selection alpha.
The authors then focus on the asset allocation alpha and the three components: risk based, relative value, and dynamic. They calculate the policy portfolio factor loadings using typical indices as proxies for macro risk factors. The risk-based component of the asset allocation alpha is measured by calculating the difference between the average tactical portfolio factor loadings and the policy portfolio factor loadings and multiplying it by the average factor returns.
The relative value component measures the skill of the portfolio manager in gaining certain risk exposures via the lowest-priced asset class. For example, exposure to the growth factor can be gained through the cheapest issues among U.S., international, or emerging market stocks and high-yield corporate bonds. As valuations change, managers can dynamically shift asset allocation from overvalued to undervalued asset classes while maintaining a constant exposure to the underlying risk factor. For calculation purposes, the relative value component is the difference between the total static component and the risk-based component.
The last component of asset allocation alpha is the result of successfully timed risk-allocation decisions. Although the overall portfolio risk exposure remains constant, the allocation to the different risk factors can exploit the mean-reverting nature of risk premiums over the business cycle. The authors use the rich pricing of growth and credit factors in the expansionary phase of the business cycle as an example, in which the expected returns cannot deliver the typical risk premium and can actually turn negative. The factor-timing component is defined as the difference between the average of the factor-based returns and the average of factor loadings multiplied by the average factor return.
The authors provide a valuable tool for analyzing an important part of multi-asset-class portfolio investment performance. The framework they develop follows logically from the existing and widely used manager alpha analysis method. Most importantly, the authors work with the concept of risk exposures rather than simple asset class allocations. Overall, this research adds support for performance reporting, analysis, and ultimately, multi-asset-class portfolio manager selection activities.
Summarized by Mark Harrison, CFA, and Billyana Kuncheva, CFA. Copyright 2013, CFA Institute. Reproduced and republished from the CFA Digest with permission from CFA Institute. All rights reserved. Article originally published in the Journal of Index Investing, Spring 2013, Vol. 3, No. 4: 64-72.
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