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Aligning Risk and Return: Incorporating Alpha Factors into Risk Model Decomposition

Risk, Performance, and Reporting

By Russell Smith  |  July 25, 2019

There are several ways in which active portfolio managers can demonstrate the value they add to the investment process using different performance attribution models. One of the most common themes is standardization, either in the form of an attribution model (i.e. a Brinson-based framework) or in the use of a commercially available risk model within a risk-based performance attribution framework.

But it is that latter point that often raises questions.

For a highly skilled systematic investor, it is unlikely that an alpha strategy can readily be found within a commercially available risk model. Instead, such a strategy would likely follow a proprietary signal that is developed following a period of research.

While often a key source of skill, it is this very “uniqueness” that can cause problems for fund managers as the unique nature of the alpha renders it difficult to evidence. While a Brinson-based attribution framework could be used (i.e. by grouping the fund by bins based on a proprietary alpha score) this can be difficult to interpret for investors who are used to consuming attribution reports based on more traditional country/sector-based grouping schemas. Furthermore, as we have previously highlighted, where several decisions are made within the investment process, a classic Brinson-style decomposition does not provide a satisfactory method of aggregation across the decisions.

For a manager who follows a proprietary alpha strategy, this presents a dilemma. Should they use a Brinson-based framework, which can lead to issues of interpretation, or use a risk-based performance attribution model which may contradict the manager’s core strategy?

To address this problem, we have considered how firms can incorporate their alpha strategies into both a risk and performance measurement framework.

Defining Alpha

Our first step was to develop our own alpha signal, using the backtesting framework within FactSet’s factor research application, Alpha Testing.

Incorporating the Alpha into the Risk Model

In an effort to demonstrate the level of risk and return that can be attributed to a proprietary alpha signal, and accordingly the skill of the manager in developing this signal, we need a framework through which this signal can be readily incorporated.

Portfolio Construction and Alpha Measurement

Select the right portfolio construction technique, build two portfolio and determine the benefit, if any, of using a custom risk model. To address this, we answered four broad questions. Could the use of a custom risk model lead to:

  • Improved efficiency in the portfolio construction process
  • Improved ex post risk analysis
  • Improved attribution of ex ante risk
  • Improved ex post performance attribution

Find out the result of our research by downloading the white paper below.

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Russell Smith

Vice President, Senior Sales Specialist, Portfolio Risk and Quantitative Analytics

Mr. Russell Smith is Vice President, Senior Sales Specialist in Risk and Quantitative Analytics at FactSet. In this role, he is one of FactSet's experts for Portfolio Risk and Quantitative Analytics and has spent the last eight years specializing in workflows and solutions for quantitative analytics including but not limited to factor research, portfolio construction, optimization, and risk and factor attribution. Starting as a Consultant in 2008, he has spent two years working with some of FactSet’s largest buy-side clients across the UK and Ireland before joining the Analytics team in 2010. Mr. Russell earned an LLB from Nottingham Trent University.


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.