Rebalancing a multi-asset portfolio: A guide to the choices and trade-offs

Jacqueline Yang, CFA, Investment Strategy Analyst
Nick Samouilhan, PhD, CFA, FRM, Head of Multi-Asset Strategy – APAC
2025-12-31
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The views expressed are those of the authors at the time of writing. Other teams may hold different views and make different investment decisions. The value of your investment may become worth more or less than at the time of original investment. While any third-party data used is considered reliable, its accuracy is not guaranteed. For professional, institutional, or accredited investors only.

Key points

  • We believe the choice of a rebalancing approach should focus on the trade-off between deviations from a portfolio’s target weights and the portfolio turnover required to implement a particular approach. 
  • Using historical and simulated data, we evaluated these trade-offs, as well as the impact on equity exposure and risk-adjusted returns, for a range of calendar- and deviation-based rebalancing approaches. Results varied, sometimes widely, but in all cases it was better to rebalance than not to do so. 
  • Higher volatility and/or lower correlations in the portfolio imply a larger trade-off, while lower volatility and/or higher correlations imply a smaller trade-off. Therefore, it may be helpful to think about the trade-off as a range of potential outcomes and consider one’s comfort with the deviation versus turnover outcomes across the different scenarios. 
  • We think allocators should generally stick to a disciplined rebalancing approach and avoid trying to “time” the decision, especially when the size of the required rebalancing is large and confidence in the market view is low.

Asset allocators set strategic portfolio weights based on investment objectives, operational and investment constraints, and long-term market views. However, market movements cause those target weights to shift over time, necessitating a rebalancing process.

There are, broadly, two types of rebalancing: calendar-based approaches, which rebalance at a set cadence (monthly, quarterly, annually, etc.), and deviation-based approaches, which rebalance based on a set band of deviations from the target weights.1 Our prior research suggests that the choice of a rebalancing approach is unlikely to meaningfully improve a portfolio’s risk-adjusted return. We have found, however, that having any reasonable rebalancing approach has yielded a meaningfully higher risk-adjust return than simply allowing a portfolio to “drift” with market performance.

So, how should one select a rebalancing approach? We believe allocators are best served by comparing the trade-offs between the deviations from a portfolio’s target weights and the portfolio turnover when using different approaches. In this paper, we compare these trade-offs across a range of calendar- and deviation-based rebalancing strategies. We also discuss a number of practical issues, such as how to address illiquid assets and transactions costs, and we conclude with a framework allocators can use as a guide to set their own rebalancing process.

Calendar-based vs deviation-based approaches: How we evaluated the differences

Calendar-based approaches are helpful for resource allocation and internal alignment (i.e., with investment decisions) as the rebalancing date is known. The downside is that the maximum divergence between portfolio and target weights in the calendar period is unknown. Deviation-based approaches, on the other hand, have the benefit of directly limiting the degree of deviation from the target weights. The downside is operational uncertainty, as the timing and frequency of rebalancing are unknown.

As noted, we have found that the choice of rebalancing approach is unlikely to improve a portfolio’s risk-adjusted return. In addition, basing the choice of a rebalancing approach on risk-adjusted returns can unintentionally shift the focus to issues unrelated to the goal of rebalancing — which is to keep a portfolio at the target weights that were set based on long-term investment objectives. For example, if rebalancing is undertaken based on a discretionary view of markets, then what is actually being evaluated is the discretionary view itself.

We believe a more appropriate way to evaluate a rebalancing approach is to understand the trade-off between deviations from a portfolio’s target weights and the portfolio turnover required to implement that approach. Keeping the weights closer to target requires more frequent rebalancing (and, therefore, higher costs), while allowing for less frequent rebalancing results in greater disparity between portfolio and target weights.

To illustrate these trade-offs, we evaluated six rebalancing approaches: three calendar-based methods (monthly, quarterly, annually), two deviation-based methods (symmetric and asymmetric), and one base case of drifting (i.e., no rebalancing). For the deviation-based approaches, we used a symmetric band of deviations (i.e., rebalancing when deviations reach +/-5%) and an asymmetric band (i.e., rebalancing when deviations reach +7%/-3%).2 The asymmetric approach introduces a pro-growth bias, intended to reflect the notion that bull markets are typically much longer in duration than bear markets. For each approach, we show three evaluation perspectives: 1) the trade-off between portfolio turnover and the deviation between portfolio and target weights; 2) the range of the equity deviations; and 3) the risk-adjusted returns.3

We evaluated the approaches using both historical and simulated data. We also looked at how two key asset class characteristics — correlation and volatility — impacted the trade-offs with each approach.

How the different approaches stacked up

We evaluated the various rebalancing approaches over the 1973 – 2022 period based on monthly data and assuming a 60% equity/40% bond target asset allocation. Figure 1 shows the trade-off between the average deviation between the portfolio weights and target weights (x-axis) and the average annual turnover (y-axis). Unsurprisingly, not rebalancing (Drifting) had the highest deviation and the lowest turnover (zero), while the most frequent calendar approach (Monthly) had the lowest deviation but the highest turnover. The Quarterly and Monthly rebalancing approaches required two to four times the portfolio turnover of the other rebalancing approaches, while achieving only a slight reduction in the average deviation from target weights.

Interestingly, the Annual, Symmetric, and Asymmetric approaches provided roughly similar trade-offs between turnover and deviation. And between the two deviation-based approaches, Symmetric provided a slightly more efficient trade-off than Asymmetric (less deviation and less turnover).

Figure 1

Yied differential

Figure 2 provides a different perspective, looking at the range of the equity exposures experienced by portfolios when using the different rebalancing approaches — again, assuming a 60% target equity allocation. Note the very wide range of results from the Drifting approach, with equity exposure reaching a high of almost 85% and a low of less than 50%. Those are significant deviations and illustrative of the problems that can result from not having a rebalancing approach. The range of the Annual approach (70% – 45%) shows the potential danger of rebalancing so infrequently. The Symmetric, Quarterly, and Monthly approaches provided a narrower range than the others. Finally, note how the Drifting and Asymmetric approaches introduced a pro-risk bias to the deviations, with the equity weighting being above target on average.

Figure 2

Yied differential

Lastly, we found no significant differences in risk-adjusted returns across the rebalancing approaches (Figure 3). But as expected, the most important message is that all rebalancing approaches did better than not rebalancing.

Figure 3

Yied differential

As noted, we also compared the rebalancing approaches (again assuming a 60%/40% portfolio) with simulation analysis, using our strategic capital market assumptions (30 – 40 years) and Monte Carlo simulations assuming a normal distribution, 30-year periods with monthly observations, and 1,000 simulations. The results were consistent with the results of the historical analysis, including:

  • The most frequent calendar approach (Monthly) had the lowest deviation but highest turnover. The Quarterly and Monthly rebalancing approaches still required at least two to four times the portfolio turnover relative to the other rebalancing approaches, for only a slight reduction in deviation from target weights.
  • The Annual, Symmetric, and Asymmetric approaches again provided similar trade-offs between turnover and deviation.
  • In terms of equity exposures, wider ranges resulted from the Drifting and Annual approaches. The Symmetric, Quarterly, and Monthly approaches again provided a narrower range than the others, and the Drifting and Asymmetric approaches still resulted in an upward bias in equity deviations.
  • There were no significant differences in risk-adjusted returns across the rebalancing approaches, and all rebalancing approaches did better than not rebalancing.

The impact of correlations

The first asset class characteristic we explored was correlations between portfolio allocations. Conceptually, lower correlations should lead to larger divergences between the portfolio allocations over time, as the allocations move less in line with each other, and higher correlations should lead to smaller divergences, as the allocations move more in line with each other.

To test these assumptions, we started with the baseline results from our simulation analysis but assumed two correlation scenarios: one in which the correlation between equities and bonds is 0.5 lower and one in which the correlation is 0.5 higher. The results made clear that correlation has an important but differing impact for calendar- and deviation-based approaches. Specific data is available on request, but here we highlight the key takeaways:

Calendar-based approaches — Lower correlations led to larger deviations for calendar-based approaches, as the assets tended to move less in line with one another over time. The larger deviations also increased turnover, given that the amount needing to be rebalanced was larger. Higher correlations led to smaller deviations and less turnover as the asset classes moved more closely together.

Deviation-based approaches — Lower correlations led to more turnover as the rebalancing bands were reached more frequently, while higher correlations led to less turnover. In both cases, there was minimal difference in deviation given the constraint of the rebalancing bands.

Risk/return relationship — When correlations declined, rebalancing resulted in increased risk-adjusted returns for all rebalancing approaches, as negative correlations imply a greater rate of mean reversion — so buying a recent underperformer and selling a recent outperformer would be beneficial. Higher correlations, on the other hand, led to lower risk-adjusted returns.

In summary, portfolios with more correlated assets may be easier to manage from a rebalancing perspective, given lower deviation and turnover, than portfolios with less correlated assets, regardless of the rebalancing approach used.

The impact of volatility

The second asset class characteristic we explored was the volatility of portfolio allocations. Conceptually, we can think of lower volatility leading to lower divergences between the portfolio allocations over time as the magnitude of movements is smaller, and higher volatility creating greater divergences as the magnitude of movements is larger.

Again, we started with the baseline scenario from our simulation analysis, but this time we assumed two scenarios: one in which volatility of both equities and bonds was 25% lower and one in which volatility was 25% higher. Like correlation, volatility had an important but differing impact for the calendar- and deviation-based approaches. Specific data is available on request, but the key takeaways include the following:

Calendar-based approaches — Lower volatility led to smaller deviations from target weights, as it resulted in smaller swings in asset values over time (i.e., the magnitude of their relative returns was smaller). The smaller deviations also helped to reduce turnover in the calendar-based approaches given that the amount needing to be rebalanced would be lower. On the other hand, higher volatility led to larger deviations and higher turnover over time.

Deviation-based approaches — Lower volatility led to less turnover as the rebalancing bands were not reached as frequently, while higher volatility resulted in more turnover. In both cases, there was minimal difference in deviation given the constraint of the rebalancing bands.

Risk/return relationship — Unsurprisingly, lower volatility improved risk-adjusted returns, holding all else equal, and higher volatility led to lower risk-adjusted returns.

In summary, portfolios with less volatile assets may be easier to manage from a rebalancing perspective, given lower deviation and turnover, than portfolios with more volatile assets, regardless of the rebalancing approach used.

Additional thoughts on liquidity, costs, and other complexities

Illiquid assets — Illiquid assets introduce an important constraint to the rebalancing process as they tend to be, by definition, difficult if not impossible to resize (a challenge many allocators faced in 2022 with the denominator effect). This can be particularly problematic when allocating to more volatile and less correlated illiquid asset classes. With this in mind, we think allocators should consider sizing allocations to illiquid assets in an “acceptable range” and not at a particular weight. The width of this range would be a function of the volatility and correlation of the liquid assets relative to those of the illiquid assets. The midpoint of the range can be viewed as the target weight, but allocators must be comfortable deviating from it over time.

A further complication arises when the illiquid assets are close to (or above) the top of the acceptable range, making it difficult to maintain the desired absolute and relative exposure to liquid assets as they are “crowded out” by illiquid assets. We think planning ahead for how the portfolio will adapt if the size of the liquid exposures shrinks can help mitigate the risk. One method is to develop a liquid beta equivalent exposure for each private allocation and rebalance to a set of total plan (liquid plus illiquid) beta exposures, with the liquid portfolio carrying the rebalancing burden. The downside of this approach is that the liquid proxy may shrink or even disappear at extremes. A stress-test approach that considers the rebalancing impact of a significant beta sell-off on the liquid portfolio (i.e., how much selling of liquid assets into market weakness is tolerable, what rebalancing tolerances should apply, etc.) can also be an effective way to determine an appropriate illiquid allocation size.

Higher transaction costs — Rebalancing is not free, as each transaction incurs a cost, directly reducing the value (i.e., the performance) of the portfolio. Higher costs should directly bias the trade-off between deviation and turnover toward accepting larger deviations. In turn, this may bias high-transaction-cost portfolios toward deviation-based rebalancing approaches, perhaps with wider rebalancing bands. If there are substantial variations in transaction costs for different allocations (i.e., some exposures incur higher costs to rebalance than others), then allocators can consider a similar approach to the one discussed above for illiquid assets, with an “acceptable range” as opposed to a particular weight.

Active managers — We view rebalancing as predominantly a beta decision, but recognize that in practice the rebalancing decision often involves managers, not asset classes. While the broad dynamics of manager rebalancing are similar to asset class rebalancing, there are a few nuances worth noting. For example, rebalancing to maintain equilibrium between multiple active managers within the same asset class (e.g., emerging market equity) often means selling the outperformer to add to the underperformer, even though this may feel counterintuitive. However, where managers exhibit “lumpy” profiles (extended periods of outperformance/underperformance) or where the allocator’s confidence in an underperforming manager is waning, rebalancing may not be advised, as the winning manager may keep winning. In general, we think allocators likely underappreciate the benefits of rebalancing and may be too quick to lose confidence in an underperforming manager, but ultimately the key to success is having a process for making these determinations.

Rebalancing with flows — While many portfolios experience continual inflows or outflows, we don’t think this should change the broad dynamics of the rebalancing decision. There is often uncertainty around the timing and size of the flows, so basing rebalancing on the flow dynamic would effectively “outsource” the rebalancing decision — not an ideal approach given the impact rebalancing can have on performance.

That said, predictable flows may be useful as a natural rebalancing mechanism. For deviation-based rebalancing approaches, predictable flows can help reduce the number of times a portfolio needs to be rebalanced. For calendar-based approaches, when the flows occur outside of the set rebalancing schedule, they can reduce the intra-period deviations experienced by the portfolio. In either case, this may effectively reduce transaction costs directly associated with rebalancing.

Timing the rebalancing — Allocators sometimes try to “time” the rebalancing process (e.g., make a discretionary decision to ignore a rebalancing signal), especially when markets are volatile and rebalancing can have a large impact on performance. It may be useful to think of this decision as being a function of 1) the size of the required rebalancing (which drives the magnitude of the impact of the rebalancing decision); 2) the confidence the allocator has in their near-term market views; and 3) the tolerance for getting the decision wrong. In general, when the size of the rebalancing is large, the confidence in the market view is low, and the tolerance for getting the decision wrong is also low, we believe the allocator should adhere to a disciplined rebalancing policy.

A rebalancing framework

In our analysis, the outcomes of all the rebalancing approaches were clearly superior to the Drifting approach (i.e., no rebalancing). But which rebalancing process is the right one? Given allocators’ differing operational, investment, and governance objectives, there is no “one size fits all” answer. Instead, we offer the following framework to help guide this decision.

Step I: Select type of approach: Calendar-based or deviation-based

The first step is to choose between a calendar-based and a deviation-based approach. The deviation versus turnover trade-off can be similar between calendar-based and deviation-based approaches, depending on the frequency of the calendar-based approach and the size of the rebalancing bands for the deviation-based approach. For example, our analysis suggests that a +/-2% deviation-based approach has a similar deviation versus turnover trade-off to a quarterly calendar-based approach.

Therefore, the decision between calendar-based and deviation-based approaches should focus on operational and governance characteristics (Figure 4). For many asset owners, a standardized calendar-based rebalancing schedule will be easiest to implement. The trade-off for implementation simplicity is that it is impossible to know in advance what the divergence between portfolio and target weights will be in any given period. (In addition, those very sensitive to trading frequency and transaction costs may find that even an annual rebalancing schedule is too frequent, which will likely lead them to prefer a deviation-based approach with bands wider than +/-5%.)

On the other hand, those that are most sensitive to deviations from target will likely prefer deviation-based approaches. However, allocators pursuing this approach must be willing to take on more operational complexity in exchange for more certainty on the size of the deviations. In particular, they will need a process to track deviations on a continuous (day-by-day) basis and a process for initiating rebalancing trades whenever the deviation bands are breached.

Figure 4

Yied differential

Step II: Select frequency (calendar-based) or band size (deviation-based)

Once the type of rebalancing approach has been determined, the next step is to select the frequency for calendar-based approaches or the band size for deviation-based approaches (Figure 5). This is where the trade-off comes in. If an allocator has lower transaction costs and/or higher asset liquidity in the portfolio and wishes to put more focus on reducing deviations, then that would indicate a bias toward higher frequency for calendar-based approaches and smaller bands for deviation-based approaches; in other words, approaches that accept higher turnover to seek smaller deviations. On the other hand, if an allocator already faces higher transaction costs and/or has lower asset liquidity in the portfolio and wishes to put more focus on cost reduction (transactional or operational), that would indicate a bias toward lower frequency for calendar-based approaches and larger bands for deviation-based approaches; in other words, approaches that accept larger deviations to seek lower turnover. Ultimately, larger deviations mean portfolios that may run with a volatility or market beta that is well above (or below) the long-term average. Each allocator will have to gauge their own comfort with those types of deviations.

Figure 5

Yied differential

It is also important to consider the effect of correlations and volatility on the trade-off between turnover and deviation. Higher volatility and/or lower correlations in the portfolio imply a larger trade-off, while lower volatility and/or higher correlations imply a smaller trade-off. Therefore, it may be helpful to think about the trade-off as a range of potential outcomes and consider one’s comfort with the deviation versus turnover outcomes across the different scenarios. For example, for a monthly rebalancing approach, if volatility is 25% higher than the baseline scenario, average annual turnover in our analysis would be about 5% higher and average deviation would be about 0.25% higher. So, an allocator focused on limiting turnover may want to choose a less frequent rebalancing interval if they are concerned about entering a higher-volatility regime. 


1Rebalancing triggers for deviation-based approaches can be based on deviation from target weights or tracking error versus the benchmark. We have chosen the former for simplicity. But when a portfolio includes numerous asset classes, it may be easier to use a tracking error-based system rather than defining different deviation ranges for each asset class. | 2For the symmetric method, the portfolio would rebalance to the 60% equity target if the equity allocation rose to 65% or higher or fell to 55% or lower. For the asymmetric method, the portfolio would rebalance to the 60% equity target if the equity allocation rose to 67% or higher or fell to 57% or lower. | 3Portfolio turnover is analyzed based on average annual turnover, which is calculated as the aggregate percent of portfolio reallocated from rebalancing over the given period divided by the number of years in that period. | Portfolio deviation is calculated as the absolute value of the equity over/underweight relative to target and is analyzed based on average deviation over the given period with monthly observations. | Range of equity deviations is analyzed based on monthly observations of the equity allocation weight over the given period, plotted at the max, 75th percentile, median, 25th percentile, and min. | Risk-adjusted return is calculated as annualized return divided by annualized volatility over the given period.

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