Three Ways to Improve Safe Withdrawal Rates
Few problems facing financial planners have been as extensively studied as sustainable withdrawal rates (SWRs). Today, the conventional wisdom holds that a 4% SWR is reasonable, given a traditional 60/40 approach. But higher SWRs can be achieved in a number of ways, and the last chapter in the search for better deaccumulation planning is yet to be written.
Using Monte Carlo analysis, I recently examined three ways SWRs can be increased. I compared and quantified the benefits of increasing diversification beyond equities and bonds, increasing allocations to fixed income, and employing tactical asset allocation (TAA).
Let’s look at how those three alternatives work:
Increase portfolio diversification beyond equities and bonds
Much that is written about SWRs focuses on a portfolio containing only stocks and bonds. Stocks are represented by an index, such as the S&P500, and bonds are represented by an aggregate bond index, such as the Lehman/Barclay AGG. Jonathan Guyton has demonstrated that a more diversified portfolio increases SWRs, and my work has led me to similar conclusions. In particular, commodities, real estate, utilities, and gold all have the potential to increase a portfolio’s return relative to risk.
Increase fixed income allocations with TIPS or high-Beta equities
A related but less understood way to increase SWRs is to take a more radical approach to diversification. Robert Huebscher, the editor-in-chief of this publication, has proposed, for example, that a portfolio allocated between muni bonds and TIPS might be able to provide an SWR as high as the SWR from a traditional 60% stock / 40% bond portfolio. In my own work, I have explored the possibility of creating a portfolio that is overwhelmingly allocated to bonds (80%-90%), with the balance in very aggressive, high-Beta asset classes. This approach can also lead to higher SWRs while maintaining protection from a substantial melt-down in equities.
Integrate tactical asset allocation (TAA) into the portfolio plan
In simplest terms, tactical asset allocation exploits the fact that the price we pay for something matters. Michael Kitces, for example, has analyzed price-to-earnings ratios in the broader market as a way to determine SWRs more efficiently. He found that lowering the allocation to equities when price-to-earnings ratios are high (and vice versa) increased average SWR. There are other ways to integrate TAA into the SWR problem, too.
It’s not just the SWR that matters, though…
An important theme that has emerged in recent years is that we must also consider the potential for a retirement-oriented portfolio to suffer massive losses when faced with a low-probability extreme event, such as we experienced in 2007-2008. The potential loss in such an event is only partly captured by the expected volatility of the portfolio, and it is only partly offset by the aggregate exposure of the portfolio to fixed income.
In times of great market stress, correlations among equity asset classes increase across the board, reducing the effectiveness of diversification in the short term. For investors drawing income from their portfolios, these short-term events can be very costly.
The projected volatility of a portfolio is an important consideration, regardless of its apparently sustainable SWR. A portfolio with a higher SWR may not be a good bet, especially if it means incurring higher expected volatility.
The baseline case
The standard “4% rule” suggests that an investor can plan to draw 4% of the value of their portfolio in the first year of retirement, and increase that amount to keep up with inflation each year. Someone who retires with a $1 million portfolio can, for example, plan to draw $40,000 in income during the first year of retirement. Both historical analysis and projections using Monte Carlo simulations suggest that this level of income draw is very safe, albeit surely not a guarantee.
The 4% rule typically assumes a portfolio that is 60%-70% equities and 30%-40% bonds. The relative safety of the portfolio is calculated as the probability that an investor will completely run out of money after a certain number of years – this is called the failure probability. For all of the examples presented in this article, we will target a plan that provides a projected 70% chance of being able to fund the income draws for 30 years (in other words, a 30% failure probability). Different assumptions about the acceptable failure rate will lead to different SWRs, but the relative impacts of the different strategies that we present here are consistent.
The decision to benchmark to a 70% probability of funding a 30-year retirement is arbitrary. Kitces, in his historical studies, looks for the largest draw that would have survived every 30-year period starting from 1871 to 1975. On this basis, Kitces comes out with a SWR for a 60% equity / 40% bond portfolio of 4.5% with zero probability of failure on the basis of rolling 30-year periods. As Kitces notes, however, the future is not necessarily like the past. In particular, there is every reason to believe that equity risk premium will be lower in the future than it was in the past century, which was characterized by America’s global dominance. The baseline assumption that I use in my Monte Carlo simulations – that domestic stocks (the S&P500) will provide a real return of 5.3% – is consistent with a range of experts. Lower equity risk premia mean lower SWRs or higher failure rates.When I run my Monte Carlo simulation (using the Quantext Portfolio Planner) based on data available through April 2010 with a portfolio that is 60% allocated to IVV (the iShares S&P500 ETF) and 40% allocated to IEF (iShares 7-10 Year Treasury ETF), I find that a 4.2% income draw results in a failure probability of 30% with a 30-year time horizon. QPP assumes a 3% inflation rate, consistent with the historical average over the past century and with many other studies of SWRs (including Kitces, for example). A portfolio that is 70% allocated to IVV and 30% allocated to IEF can provide a slightly higher 4.25% SWR with the same failure probability than its 60/40 counterpart. These results help to explain the origins of the so-called 4% rule.