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.
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