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Are You Incorporating Rebalancing in Your Strategic Asset Allocation Process?

Risk, Performance, and Reporting

By Chen Sui, CFA  |  January 27, 2022

Long-term investors will need to adopt next-generation strategic asset allocation practices to successfully play their part in a rapidly changing world. Truths, practices, and principles that have traditionally been accepted by long-term investors face reduced significance and potential obsolescence. We will examine the key topics and the expected changes to the way long-term investors will make investment decisions in a series of articles on the applications of multi-horizon strategic asset allocation.

Here we look at the implicit single-horizon buy-and-hold rebalancing assumption in current strategic asset allocation (SAA) practices versus a multi-horizon alternative.

Next Generation Strategic Asset Allocation (SAA)—Multi-Horizon Analysis

Most of the investment industry’s attention is focused on forecasting the expected return and risk of asset classes, often referred to as capital market assumptions (CMAs). Research teams evaluate macroeconomic trends, create models, and consult with industry participants to arrive at long-term asset-class forecasts.

By contrast, the approach for determining the optimal proportion of the asset classes to hold has largely remained unchanged since the initial ideas on mean variance optimization were published. The next generation of asset allocation tools focuses specifically on improving upon this foundation through the concept of multi-horizon analysis.

The Implied Buy-and-Hold Assumption in Current SAA Tools

Let’s examine an often overlooked aspect of asset allocation: rebalancing assumptions in current asset allocation tools. Often an efficient frontier (EF) based on investor CMAs is calculated to support the asset allocation process so investors can examine the risk and return possibilities for different allocations between asset classes.

efficient-frontier-20-year-investment-horizon

It is implicitly assumed in the return calculation that the entire 20-year investment horizon is treated as a single period for return calculation; this implies a buy-and-hold rebalancing strategy for the entire investment horizon i.e., no active rebalancing and weight changes driven purely by market movements. The risk and return figures are often annualized, further obscuring this assumption.

Is the Buy-and-Hold Assumption Flawed?

As is often the case with models, the flaw is not with the assumption itself, but with whether the interpretation and application of the results are inconsistent with the model assumptions.

In their 1995 paper Dynamic Strategies for Asset Allocation, Perold and Sharpe compared buy-and-hold rebalancing to other dynamic strategies such as constant-mix (fixed-mix), a strategy that involves rebalancing periodically to a constant set of weights. They found that buy-and-hold strategies favor strongly trending markets whereas fixed-mix strategies favor markets that show reversion/oscillation.

Most long-term investors will periodically rebalance investments towards strategic weights instead of allowing the weights to drift with market movements. This rebalancing strategy is closer to a fixed-mix strategy than buy-and-hold. Those following such a strategy should be making allocation decisions, and evaluating resulting investment performance, using risk and return metrics calculated assuming a fixed-mix rebalancing strategy.

How Do You Incorporate Rebalancing Decisions in the Strategic Asset Allocation Process?

Introducing rebalancing decisions necessitates the division of the investment horizon into multiple sub-periods; this is the very essence of multi-horizon analysis and represents the next generation of asset allocation analysis tools. A fixed-mix strategy involves rebalancing the asset class weights back to the optimal strategic allocation at the start of each sub-period.

Using an optimizer capable of multi-horizon calculations to apply investment rules at each sub-period opens a new world of possibilities for SAA analysis such as:

  • Incorporating rebalancing strategies such as fixed-mix or dynamically changing weights over time
  • Introducing CMAs that change over time—potentially a different set of CMAs for each sub-period; enabling trends, market regimes, or just more nuanced market forecasts to be incorporated
  • Multi-horizon risk statistics such as drawdown or probability of achieving certain investment outcomes
  • More sophisticated analysis of scenarios such as impacts of climate change scenarios over time

These applications are only possible due to the combination of the latest multi-period simulation and optimization techniques and will be further explored in subsequent articles.

How Does the Buy-and-Hold Strategy Compare to the Fixed-Mix?

The fixed-mix strategy has the same inputs as the buy-and-hold; the difference is that the 20-year investment horizon has been divided into 20 equal sub-periods. The optimum weight allocation is calculated for the entire investment horizon and at the start of each sub-period the weights are rebalanced to the optimum allocation. The risk and return metrics reflect the impact of this rebalancing strategy.

buy-and-hold-vs-fixed-mix-efficient-frontiers

Notable features of the two efficient frontiers:

  • In this case, the fixed-mix EF dominates the lower risk portion of the buy-and-hold EF. While it is not expected to always be the case, Perold and Sharpe noted that it depends on the expected behavior of the markets. What’s important is that there is a difference between the EFs produced by the two strategies—investors should make and evaluate their asset allocation decisions against the appropriate strategy.
  • The maximum return points for both EFs consists of the same allocation of asset classes; this is expected since the underlying inputs are the same. However, the return for the buy-and-hold EF is much larger; this is a result of the lack of rebalancing leading to a more concentrated portfolio.
  • The lowest risk points on both EFs have the same allocation of asset classes—a result of the same inputs. The difference in the expected risk between the two strategies is due again to the rebalancing; when the allocation is rebalanced to the optimal weights, the portfolio benefits more from diversification.

The key message here is that rebalancing strategy has an impact on the portfolio risk/return expectations. Those that rebalance their allocations to long-term strategic weights (fixed-mix) should be using risk/return expectations calculated with a multi-horizon approach (fixed-mix). This ensures that the SAA decision-making process and subsequent performance evaluation is performed on a basis consistent with the actual strategy execution.

What’s Next?

When thinking about strategic asset allocation, it is important to ask what are the implicit assumptions being made by the process and tools being used? Are you following a buy-and-hold, fixed-mix strategy, or more dynamic strategy? Is treating the investment horizon as a single homogenous period realistic?

In the next article, we’ll examine how allowing the optimal weights to change throughout the investment horizon (dynamic rebalancing strategy) impacts the investment characteristics.

Todor Bilarev, PhD, Senior Quantitative Researcher, Analytics and Trading Solutions, contributed to this article.

Disclaimer: 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.

Multi-Period Portfolio Optimization

Chen Sui, CFA

Principal Sales Specialist, EMEA Analytics

Mr. Chen Sui is a Principal Sales Specialist for Performance, Risk, and Portfolio Construction at FactSet. He joined as part of the acquisition of BISAM. In his current role, he brings 15 years of experience working in various aspects of the buy-side and vendor side to help guide clients through their selection processes for vendor-based technology solutions. Mr. Sui works with clients across the globe and a wide variety of buy-side institutions such as traditional Institutional Asset Managers, Asset Owners, Hedge Funds, and Wealth Management firms. Prior to FactSet, he spent time in Performance, Risk, Project, and Client servicing teams at Wilshire Associates, JP Morgan, and CQS. Mr. Sui earned a master’s degree in Mathematical Finance from the University of York and is a CFA charterholder.

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