- Although investors may be reluctant to add to higher-risk exposures in a market drawdown, we believe it is essential to maintain a prudent rebalancing approach.
- Our analysis of historical and simulated market drawdowns suggests that rebalancing potentially improves outcomes relative to a non-rebalanced portfolio.
- We believe investors should select the rebalancing rule that they think is most appropriate and adhere to it through all periods, including market drawdowns.
Rebalancing asset exposures is fundamental to prudent portfolio management and has long been considered a key determinant of long‑term performance. Regularly reorienting to targeted long‑term asset allocations helps ensure that all risk exposures in the portfolio are intentionally accepted. However, many investors may be reluctant to follow their normal rebalancing policies in periods of market stress, when adding to higher‑risk exposures may seem particularly unpalatable.
We believe it is essential that investors maintain a prudent rebalancing approach. Our analysis of both historical and simulated equity market drawdowns found that sticking to an investment policy’s rebalancing rule typically led to better outcomes when compared with a passive strategy of allowing portfolio exposures to drift with market movements.
In this paper, we analyze the impact of various rebalancing methods in both historical and simulated market drawdowns. We compare various rebalancing rules: two of them calendar‑based (monthly and quarterly) and two that rely on exposure bands (±2.5% and ±5.0%). Our findings suggest that during market drawdowns and subsequent price recoveries:
- Using Monte Carlo analysis, we found that all of the rebalancing rules we tested outperformed a non‑rebalanced portfolio in at least 90.9% of simulated scenarios.
- In our simulations, certain rebalancing methods potentially outperformed others during specific types of market drawdowns. However, it is impossible for investors to know in advance the type of drawdown they are experiencing.
- Our simulations suggested that there is no “silver bullet” rebalancing rule, given the multiple considerations that need to be addressed when designing and maintaining rebalancing policies.
We believe investors should select the rebalancing approach that they believe is most appropriate for them, given their own circumstances, and adhere to it through all periods, especially during market drawdowns and recoveries.
The Importance of Rebalancing
Establishing and implementing a portfolio rebalancing policy is widely believed to improve portfolio performance over full market cycles. Over rolling 10‑year periods since 1989, any of our four rebalancing methods would have outperformed a hypothetical non‑rebalanced portfolio. Figure 1 shows the average cumulative excess returns and hit rates (the percentage of all rolling periods in which the hypothetical rebalanced portfolio would have outperformed) for the various hypothetical rebalancing methods versus a hypothetical non‑rebalanced portfolio with assumed initial allocations of 60% to global equities and 40% to U.S. bonds.
The hypothetical rebalanced portfolios would have outperformed a hypothetical non‑rebalanced portfolio in a large majority of the historical 10‑year rolling periods covered in our study, ranging from an 88.0% hit rate for a monthly rebalancing rule to a 89.6% hit rate for a rule that sought to keep relative exposures within ±2.5% bands. The average margin of cumulative excess return would have ranged from 4.22 percentage points (for monthly rebalancing) to 6.07 percentage points (for a rebalancing policy based on ±5% bands).
Assuming a hypothetical starting portfolio balance of USD 1,000,000, the average improvement to ending balances from adhering to one of the rebalancing rules we tested would have ranged from USD 42,199 to USD 60,652.
Hypothetical Rebalanced vs. Non-rebalanced Portfolios1
(Fig. 1) Hit rates and average cumulative excess returns over rolling 10-year periods
January 31, 1989, through March 31, 2020.
1 Initial portfolio weights: 60% equity/40% bonds. Equities represented by the Morgan Stanley Capital International All Country World Index (MSCI ACWI); bonds by the Bloomberg Barclays U.S. Aggregate Bond Index. The results shown above are hypothetical, do not reflect actual investment results, and are not indicative of realized past or future performance. See appendix for rebalancing methodology.
Sources: MSCI and Bloomberg Index Services Limited (see Additional Disclosures); all data analysis by T. Rowe Price.
Stick to the Policy Even During Market Drawdowns
Despite the potential benefits of adhering to clear portfolio rebalancing rules, investors may be tempted to abandon their rebalancing policies during market drawdowns to avoiding buying into falling markets. To examine the potential pitfalls of such an approach, we analyzed our four rebalancing methods in a sample of historical and simulated market sell‑offs.
We first examined how the various rebalancing methods would have performed in two previous market events: the bear market that followed the technology bubble of the late 1990s, and the 2007–2009 global financial crisis. As shown in Figure 2, all of the hypothetical rebalanced portfolios would have outperformed a hypothetical non‑rebalanced portfolio, on average, during and after the two historical market events.
We found considerable dispersion across the rebalancing methods in terms of both the value added and the frequency of outperformance. Moreover, while historical scenarios can be insightful, future market sell‑offs and recoveries are likely to follow different paths. This observation prompted us to expand our analysis to study a wide range of simulated scenarios using Monte Carlo analysis to understand if certain rebalancing approaches could be more effective than others in market drawdowns.
In order to capture potential differences in efficacy across the four rebalancing methods analyzed, we modeled hypothetical equity/bond portfolios across 1,000 simulated equity market drawdowns and subsequent recoveries.
We sought to examine rebalancing methods from a variety of perspectives:
- Did the rebalancing methods work across the simulations in aggregate?
- Did the results change if we parsed the simulated data into more nuanced scenarios (e.g., depths and speeds of the drawdowns and recoveries)?
We found high conviction in our answer to the first question, as the hypothetical rebalanced portfolios outperformed a hypothetical non‑rebalanced portfolio in the vast majority of our simulated downturns at meaningful levels. Specifically, Figure 3 shows the percentage of the simulated scenarios in which a hypothetical rebalanced portfolio outperformed a hypothetical non‑rebalanced portfolio. In at least 90.9% of the simulated scenarios, the rebalanced portfolio outperformed the non‑rebalanced portfolio.
Outperformance of Hypothetical Rebalanced vs. Non‑rebalanced Portfolios1
(Fig. 2) Average cumulative excess returns from market peak through trough and recovery
1 Initial portfolio weights: 60% equity/40% bonds. Equities represented by the MSCI ACWI; bonds by the Bloomberg Barclays U.S. Aggregate Bond Index. The results shown above are hypothetical, do not reflect actual investment results, and are not indicative of realized past or future performance.
See appendix for bear market peak, trough, and recovery dates as well as information on rebalancing methodology.
Sources: MSCI and Bloomberg Index Services Limited (see Additional Disclosures); all data analysis by T. Rowe Price.
While rebalancing operated in a falling market during the drawdown and in a rising market through the subsequent recovery, each method tended to outperform a hypothetical non‑rebalanced portfolio over the full cycle. Additionally, as also shown in Figure 3, the outperformance of each rebalancing method versus a hypothetical non‑rebalanced portfolio was meaningful, ranging from 1.00 to 1.53 percentage points of additional cumulative excess return versus the passively drifting non‑rebalanced portfolio, which we would view as the “rebalancing alpha.”
While we believe the aggregate results of our simulations make a strong case for rebalancing, the path‑dependent nature of equity drawdowns and recoveries merits a closer look at the dispersion of potential outcomes across a variety of scenarios. Therefore, we examined subsets of results to ensure that our findings were robust across a range of simulated bear markets and recoveries. Specifically, we studied results within two segmentations of the data:
- the depth of the simulated equity drawdown
- the duration of the overall event from drawdown through recovery.
We again found that hypothetical portfolios that were rebalanced by any of the methods we modeled consistently outperformed a hypothetical non‑rebalanced portfolio.
Simulated Performance of Hypothetical Rebalanced vs. Non‑rebalanced Portfolios1
(Fig. 3) Hit rates and average cumulative excess returns across all simulations
1 Initial portfolio weights: 60% equity/40% bonds. Equities represented by the MSCI ACWI; bonds by the Bloomberg Barclays U.S. Aggregate Bond Index. The results shown above are based on Monte Carlo simulations. See appendix for information on simulation parameters and methodology.
Sources: T. Rowe Price, MSCI, and Bloomberg Index Services Limited (see Additional Disclosures); all data analysis by T. Rowe Price.
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