In investing, the main drivers of risk in institutional portfolios are typically the macro factor risk exposures. While selecting individual securities may add value on the margin, asset allocation generally steers a portfolio over the long-term.


In a study of 91 large U.S. pension plans, Brinson, Hood, and Beebower1 broke the investment management process into three simple components:

  1. Investment policy
  2. Market timing
  3. Security selection

The investment policy involves setting the strategic, long-term allocations across certain asset classes. For example, how much of my portfolio do I want in stocks versus bonds? Market timing is a more short-term, tactical decision that opportunistically may over or underweight a particular asset class, sector, or group of securities. For example, my long-term allocation to stocks is 60%, but I think there is a near-term opportunity for stocks to outperform bonds, so I’ll tactically increase the stock allocation to 65% at the expense of bonds. Finally, security selection occurs within an asset class. For example, how will I achieve my 60% long-term allocation to stocks? Should I invest in Microsoft or Google? Which stock do I think will outperform?

The authors of the study determined that investment policy dominated the other two components (market timing and security selection, or collectively “investment strategy”), with the former explaining on average 94% of the variation in the total return2 of the pension plans. They found that “although investment strategy can result in significant returns, these are dwarfed by the return contribution from investment policy—the selection of asset classes and their normal weights.”3 Therefore, it is a much more impactful, and thus important, decision to determine the investment program’s long-term allocation to stocks versus actually picking which stocks will go in the portfolio.

This study was conducted in the 1970s and 1980s, and while the markets and the world we operate in have changed, many investors still focus on adding value through investment strategy (as demonstrated by teams of research analysts and portfolio managers within institutional investment programs).

We used the returns of institutional investor pro forma portfolios4 on Venn to determine if the results of Brinson, Hood, and Beebower’s study still generally hold. Here is the Venn study set up:

  • We looked at a total of 77 master portfolios5
  • We ran a factor analysis, using the Two Sigma Factor Lens, over each portfolio’s full history6

Exhibit 1 displays the average percentage of portfolio risk that can be explained by the various risk factors in the Two Sigma Factor Lens.

Exhibit 1: Risk Contribution Breakdown of Portfolios in the Venn Study7

Source: Venn. May 2019.


Exhibit 1: Risk Contribution Breakdown of Portfolios in the Venn Study7

To summarize the results, our study found that on average 67% of portfolio risk can be explained by exposure to the Core Macro factors in the Two Sigma Factor Lens. These factors represent systematic exposure to the principal drivers of asset class returns, specifically Equity, Interest Rates, Credit, and Commodities.

If we expand to all of the macro factors in the Two Sigma Factor Lens (both Core and Secondary Macro factors), the average amount of portfolio risk explained is 71%. Therefore, broad exposure to asset class-based risks, or what Brinson, Hood, and Beebower generally consider “investment policy”, is still predominantly steering the results of these portfolios.

What about the “value-add”, or the risk that can’t be explained by risk factors? On average, the amount of risk that was unexplained by the Two Sigma Factor Lens was 21%. Does this mean that 21% of the portfolio’s risk was driven by “investment strategy” decisions like security selection? Not necessarily. While this might be part of the story, the residual could also be a function of risk factors not included in the Two Sigma Factor Lens or differences in factor construction.

However, we do know that, according to our study, any risk that is not explained by exposure to common, well-known, highly-liquid, macro asset class-based factors represents the minority of risk in this selection of institutional investor portfolios.

Conclusion

We used Venn and the Two Sigma Factor Lens to conduct a study that sought to understand whether the results from Brinson, Hood, and Beebower still generally hold, and we found that the majority of return variation in investor portfolios on Venn can be explained by systematic macro factor exposures. This implies that deciding which asset classes (or in our case, macro risk factors8 ) to include and exclude in a portfolio and the amount of exposure to each are critical to overall investment success and should be considered carefully and systematically by investors.

Venn can help investors:

  • Understand the risk drivers of their portfolio through factor-based analytics
  • Determine their go-forward return expectations for risk factors, by using either their organization’s asset class forecasts or relying on Venn-provided factor forecasts
  • Implement portfolios that reflect those forecasts and are aligned with their organization’s overall investment objectives

Contact us to learn more about Venn today.


References
1 Gary P. Brinson, L. Randolph Hood, and Gilbert L. Beebower. (1986). “Determinants of Portfolio Performance.” Financial Analysts Journal, 42(4), 39-48.
2 Calculated by taking the average of the unadjusted R-squared of the regressions of each plan’s actual total return on its stocks/bonds/cash equivalents investment policy return.
3 Gary P. Brinson, L. Randolph Hood, and Gilbert L. Beebower. (1986). “Determinants of Portfolio Performance.” Financial Analysts Journal, 42(4), 39-48.
4 The portfolios in Venn are pro forma, meaning they do not reflect the actual portfolio returns realized by organizations. They are constructed using the organization’s current investments and weights (as provided to Venn) and building a quarterly-rebalanced portfolio back in time.
5 The 77 portfolios represent all of the master portfolios, excluding “Demo” and “Default” portfolios, belonging to organizations on Venn with USD as their base currency. Master portfolios on Venn are intended to represent the organization’s complete portfolio, including all asset classes and holdings therein. However, some organizations may choose to only analyze a subset of their portfolio on Venn (or include investments for analysis that are not currently included in their real portfolio), and we did not control for this in the study.
6 The average master portfolio had a return history of 5 ½ years. We applied minimums to this history based on the frequency of portfolio returns. For example, portfolios with monthly frequency required at least 3 years of history to be included in our study.
7 Risk contribution represents the breakdown of the standard deviation of a portfolio’s returns by its exposures to the factors in the Two Sigma Factor Lens.
8 Please see the blog post titled “ Are asset classes providing you diversification? ” for more information on why Venn believes risk factors, as compared to asset classes, can help investors achieve more effective portfolio diversification.

This article is not an endorsement by Two Sigma Investor Solutions, LP or any of its affiliates (collectively, “Two Sigma”) of the topics discussed.  The views expressed above reflect those of the authors and are not necessarily the views of Two Sigma. This article (i) is only for informational and educational purposes, (ii) is not intended to provide, and should not be relied upon, for investment, accounting, legal or tax advice, and (iii) is not a recommendation as to any portfolio, allocation, strategy or investment.  This article is not an offer to sell or the solicitation of an offer to buy any securities or other instruments. This article is current as of the date of issuance (or any earlier date as referenced herein) and is subject to change without notice. The analytics or other services available on Venn change frequently and the content of this article should be expected to become outdated and less accurate over time.  Two Sigma has no obligation to update the article nor does Two Sigma make any express or implied warranties or representations as to its completeness or accuracy. This material uses some trademarks owned by entities other than Two Sigma purely for identification and comment as fair nominative use. That use does not imply any association with or endorsement of the other company by Two Sigma, or vice versa. See the end of the document for other important disclaimers and disclosures. Click here for other important disclaimers and disclosures.