Determinants of growth in distribution portfolios

This course is no longer eligible for CE credits. Go to cecorner.ca to find eligible courses.

Background

The first serious research on determinants of portfolio growth was a study by Gary P. Brinson, Randolph L. Hood, and Gilbert L. Beebower in 1984. They analyzed data over a 10-year period, beginning in 1974, from 91 large corporate pension plans with assets of at least $100 million. It’s usually referred to as the “Brinson study.”

They later expanded their analysis to include an additional 10 years of data. The new study, “Determinants of Portfolio Performance II,” was published in the Financial Analysts Journal (January/February 1995).

They concluded that the components of a portfolio’s difference in success are: asset allocation, 93.6%; security selection, 2.5%; market timing, 1.7%; other, 2.2%. Many in the financial industry have since believed asset allocation is the holy grail of investing.

When a new account is opened, the first thing a client does is fill out a risk-assessment questionnaire. Based on their answers, he or she is then pigeonholed into one of four or five investment portfolios. This course challenges that approach.

The findings of the Brinson study cannot be applied to individual retirement portfolios for the following reasons:

• The dynamics of cash flow in a pension fund are entirely different from those in an individual retirement account. A pension fund has a continuous inflow of money over time. In an individual retirement account, inflows occur mainly during working years. After retirement, there are usually no more inflows – only outflows. Pension funds are “open-perpetual” systems; individual retirement accounts are “closed-finite” systems.
• When there’s a shortfall in a pension fund, contributions are increased to cover it. There’s usually no such opportunity with individual retirement savings.
• Once withdrawals from an individual retirement account begin, the adverse effect of “reverse dollar-cost-averaging” becomes important. In a pension fund this effect is insignificant because there’s a continuous inflow of money.
• Inflation is important for individual accounts. Withdrawals must be increased over time to maintain the same purchasing power. In pension funds, there’s no such concern: as inflation rises, salaries and pension contributions increase. Also, many pension funds have limits on how retirement payments are indexed. Individual retirees holding their own saving accounts do not have that choice—their expenses must be met.
• The Brinson study’s timeframe is 20 years and covers only one secular bullish trend—arguably the “luckiest” 20 years of the twentieth century. Such a short time frame misses significant adverse events that occur exclusively in secular bear or secular sideways market trends.

Figure 1: Time period covered in the Brinson study

The Brinson study is still valuable, as there’s no doubt asset allocation is important for a pension fund’s success. Asset allocation reduces volatility of returns to an acceptable level; the question is whether volatility of returns is the most important factor. The answer is a resounding “No.”

Sequence of returns and inflation have a far greater impact than volatility of returns. Our analysis also uses other factors not included in the Brinson study, including:

• Withdrawal rates
• Rebalancing frequency
• Portfolio costs
• Potential alpha produced by better management of investments

Figure 2: Determinants of a portfolio’s growth

Sequence of returns and inflation are outside the control of the investor or advisor. We call them the “luck factor.” We’ll refer to the others as “manageable factors.”

Methodology

We use market data from 1900 through the end of 2010, and no Monte Carlo simulators. “Aftcasting” is our term for actual market history. It displays, in one chart, the outcome of all historical asset values of all portfolios since 1900, as if a scenario starts in each one of the years between 1900 and 2000. It provides a bird’s-eye view of all outcomes, and success and failure statistics with exact historical accuracy. This precision stems from inclusion of historical equity performance, inflation rates, and interest rates (see “Data Sources”), as well as historical sequencing of all these data sets.

Consider an example. Bob, 65, is retiring and plans on withdrawing $15,000, indexed annually to inflation, until age 95. His primary concern is sustainability of his income stream. He has $350,000 in his investment portfolio and an asset mix of 40% equities and 60% fixed income.

Figure 3 shows the aftcast for this scenario. The thin, black lines represent aftcasts. There is one line starting at the left vertical axis for each year since 1900. We define the bottom decile (bottom 10%) of all outcomes as “unlucky,” the top decile (top 10%) as “lucky.” The blue line shows the median outcome where half of the scenarios are better and half worse. In this example, the probability of depletion by age 95 is 34%.

Data Sources

• Equities: The annual data of the Standard and Poor Composite Stock Price Index started in 1871. The S&P index was established in 1926, including 90 large-cap stocks and later in 1957 it was changed to hold 500 stocks. In our calculations, we use the annual index data from the tables in Chapter 26 of Robert J. Shiller’s book Market Volatility (MIT Press, ISBN 978 0262 691512,) and from Standards and Poor’s website. The Dow Jones Industrial Average was established in 1896 and the historical data is available on their website.
• Inflation: U.S. Bureau of Labor Statistics, wholesale price index for the years 1900-1913, the CPI-U after 1913.
• Interest rates: 1900–1987, courtesy of Market Volatility, by Robert J. Schiller, MIT Press, [1997], pp. 440–441, data series 4, and Federal Reserve historical data for 6-month CD rate.
• Fixed-income yield: Historical 6-month CD rate plus 0.5% yield premium, net after portfolio expenses. This represents approximately a bond portfolio with about five- to six-year average maturity, held until maturity with no capital gains or losses.

Figure 3: The aftcast of fixed $15,000 annual withdrawals, indexed to inflation, from an investment portfolio, starting capital of $350,000

Aftcasting only shows what would have happened for a specific scenario—we do not use it to make predictions. We fully agree with those who say that past events will not necessarily be repeated in the future.

We’re interested in the frequency, size and the persistency of extreme events that happened in the past. These extremes are our starting point for designing a robust retirement plan for our clients.

Let’s look at each factor in more detail.

Sequence of Returns

The sequence of returns is the direction and persistency of the volatility of returns. Mathematically, it can be defined as the first time-derivative of the volatility of returns.

Volatility of returns by itself does not necessarily cause damage to a distribution portfolio. It is the sequence of returns that can do irreparable damage. For example, someone retiring in 1987 would have experienced a significant volatility of returns—a 30% drop in the equity index during October and November of that year.

But after this drop, equity markets quickly recovered and the adverse volatility of returns was not accompanied by an adverse sequence of returns. Consequently, there was no damage to the portfolio’s longevity.

Figure 4: The effect of adverse volatility of returns, if not accompanied by an adverse sequence of returns. Retirement starting year 1987 is indicated in heavy black

Let’s look at how sequence of returns works over a four-year time horizon. We have two scenarios: In the first, the investor is lucky in the beginning—his portfolio grows by 20% in each of the first two years and then declines by 10% in each of the final two years. This is the “good start” portfolio.

In the second scenario he is unlucky in the beginning—his portfolio declines by 10% in each of the first two years and then grows by 20% in each of the final two years. This is the “bad start” portfolio.

Table 1 shows the average annual growth is identical in both cases—5%. Let’s see how these scenarios affect total growth over this 4-year time period. First, we look at an accumulation portfolio. The investor starts with $100,000 initial capital and no money is added to or withdrawn from the portfolio. Table 2 shows the portfolio’s value at the end of each year:

Table 1: Sequence of Returns

Year	Annual Growth
	Good Start	Bad Start
1	20%	-10%
2	20%	-10%
3	-10%	20%
4	-10%	20%
Average Growth:	5%	5%

Table 2: Sequence of Returns, accumulation

Year	Portfolio Value- Accumulation (initially $100,000)
	Good Start	Bad Start
1	$120,000	$90,000
2	$144,000	$81,000
3	$129,600	$97,200
4	$116,640	$116,640
Average Growth:	16.64%	16.64%

In both scenarios—good start and bad start—total growth during the four-year time period is identical: 16.64%. If no money is distributed out of the portfolio, then the sequence of returns has no effect on the final outcome.

Keep in mind MERs have to be paid out of portfolio assets. So unless a client is adding more to a portfolio than what the MER is taking out, he has, in effect, a distribution portfolio.

Let’s look at the effect of the sequence of returns on a distribution portfolio. The same investor starts with the same portfolio, experiences the same good-start/bad-start scenarios, but in this case withdraws $5,000 at the end of each year.

Table 3: Sequence of Returns, distribution

Year	Portfolio Value – Distribution (initially $100,000, $5,000 withdrawn at the end of each year)
	Good Start	Bad Start
1	$115,000	$85,000
2	$133,000	$71,500
3	$114,700	$80,800
4	$98,230	$91,960
Total Decline:	1.77%	8.04%

When the investor had a good start, his portfolio’s total decline, including withdrawals, was 1.77%. When he had a bad start, the total decline over the same four-year time period was much larger—8.04%. This is the effect of the sequence of returns in a distribution portfolio.

Figure 5 shows the effect of starting retirement in 1937 (unlucky timing) versus 1980 (lucky timing). In both cases, the retiree starts with initial capital of $1 million and a 40% equity, 60% fixed-income asset mix, rebalanced annually. The initial withdrawal amount is $50,000 at age 65, indexed to inflation each year.

The lucky investor retired in 1980. At age 85, his portfolio grew to nearly $4 million. The unlucky investor retired in 1937. His portfolio ran out of money at age 81. Since all other factors are identical, luck must be the determinant of this difference in outcomes.

Figure 5: The importance of luck with respect to timing of the retirement

While it’s not difficult to separately analyze the effects of volatility of returns and sequence of returns, it’s beyond the scope of this course. The distinction is unnecessary for calculating the luck factor, so we combine the sequence and volatility of returns as a single line item separated by a slash.

Here are the steps for calculating the effect of the sequence/volatility of returns on a distribution portfolio:

• Isolate and exclude the effect of the variability of inflation from secular trends. We do this by using a fixed “average” inflation rate during retirement. This leaves us with variations only in the sequence of returns/volatility.
• Calculate the asset value of the portfolio over time for all years since 1900. We define the top 10% of all outcomes as the “lucky” outcome and the bottom 10% as the “unlucky” outcome.
• Calculate the median outcome, where half of the outcomes are better and half worse.
• Calculate the compound annual return (CAR) of the lucky, unlucky and median portfolios.
• Finally, the effect of the sequence of returns/volatility is half the difference between the CAR of the lucky and unlucky portfolios, divided by the CAR of the median:

LF = (CAR₉₀ – CAR₁₀) x 100%

2 x CAR₅₀

where:

LF is the luck factor CAR90 is the compound annual return of the lucky (top decile) portfolio CAR10 is the compound annual return of the unlucky (bottom decile) portfolio CAR50 is the compound annual return of the median portfolio

The luck factor measures the average difference of the compound annual returns between the extreme outcome and the median outcome, expressed as a percentage.

Example #1

Dan is 65 years old and just retired. He has $1 million in his portfolio and needs $60,000 each year, indexed to inflation. His asset mix is 40% equity and 60% fixed income, rebalanced annually.

On the equity side, he expects the index return. On the fixed-income side, he expects a return of 0.5% over the historical 6-month CD rates after all management fees. Using a 3.2% annual withdrawal increase to reflect historical average inflation, we calculate his luck factor resulting from the sequence of returns.

The chart shows the lucky, unlucky and median portfolios. We calculate compound annual returns for the lucky, unlucky and median portfolios as 8.00%, 2.83% and 5.46%, respectively. Plug these numbers into Equation #1 and calculate the luck factor resulting from the sequence/volatility of returns:

Luck factor attributable to sequence/volatility of returns = 8.00% – 2.83%

2 × 5.46%

= 47.3%

Table 4 shows the luck factor for different withdrawal rates. In all cases, the asset mix is 40% equity and 60% fixed income.

Table 4: The impact of the sequence/volatility of returns for various withdrawal rates

Initial Withdrawal Rate
0%	2%	3%	4%	5%	6%	8%	10%
Effect on Portfolio Growth Rate
46.7%	46.3%	46.5%	51.8%	50.2%	47.3%	62.2%	69.1%

Sequence of Inflation

Inflation, or more accurately, the sequence of inflation is the second most important component of the luck factor. Even a short-term inflation increase compels the retiree to withdraw higher amounts from his portfolio for the rest of his life just to maintain his purchasing power. This can deplete the portfolio prematurely.

Many retirement plans are designed using an “assumed average inflation rate” between 2% and 3% for the client’s entire life. But historically, the inflation rate was less than 3% only 52% of the time. So any retirement plan that assumes an average of 3% inflation (or lower) is incongruent with experience. There’s an almost even chance inflation might be higher than that, causing the portfolio to deplete sooner than planned.

This is where the sequence of inflation comes into play: if a retirement plan is designed for a “normal” inflation rate, only a few years of “high” inflation can reduce the portfolio life significantly.

Figure 6 illustrates a hypothetical retirement plan designed to start at age 65 with initial capital of $1 million. It assumes an average portfolio growth rate of 8%. The retiree needs $66,000 per year, indexed to 3% annually until age 95. With these admittedly aggressive assumptions, the “forecast” indicates an uninterrupted income stream until age 95. This is designated on the graph as “Normal CPI Sequence.”

Now assume the retiree experiences a higher-than-“normal” CPI for the first three years of retirement. For ages 65 through 67, the annual CPI jumps to 10%; it then reverts back to 3% at age 68, and stays there for the rest of his life. This is the “Adverse CPI Sequence” on the chart. Portfolio longevity is drawn back from age 95 to 86—a 30% reduction of the original 30-year time horizon.

Figure 6: Sequence of Inflation, hypothetical case

Let’s look at two examples from the last century. Consider a retiree with an asset mix of 40% equity, 60% fixed income, and a 6% initial withdrawal rate. We’ll use historical dividend rates and assume no management fees. If this person retired at the beginning of the market crash of 1929, his portfolio would have lasted until age 89. If he retired in 1966—the beginning of a secular sideways market—his portfolio would have depleted at age 85, as shown in Figure 7.

The high inflation between 1966 and 1981 would have forced the retiree to withdraw more, eventually depleting his portfolio. The net effect of this was worse than the secular bearish trend that started in 1929, when equities lost about 80% of their value.

Figure 7: High inflation can shorten portfolio life more than the worst market crash

It gets worse. More often than not, the sequence of returns and sequence of inflation are cumulative. Many academics and financial experts claim equities traditionally beat inflation. That’s only partially true. During secular bullish trends (1921-1928, 1949-1965, 1982-1999), which make up 43% of the last century, the growth of the equity index easily covered any adverse effects of inflation (see Figure 8).

Figure 8: The performance of DJIA (index only) and inflation during secular bullish trends

But for the remaining 57% of the century, markets were either in secular bearish or secular sideways trends. The equity index was never able to overcome the effects of inflation for any of these periods. By the time the trend changed to secular bullish, there was little or no money left in the portfolios (see Figure 9).

Figure 9: The performance of DJIA (index only) and inflation during all secular bearish/sideways trends

Here’s how to calculate the luck factor created by the effect of inflation in a retirement portfolio:

• Isolate and exclude the effect of the sequence/volatility of returns. We do that by using a fixed “average” portfolio growth rate in the aftcast. This leaves us only with the historical inflation rates that vary from year to year.
• Calculate the compound annual return (CAR) of the lucky, unlucky and the median portfolios.
• Calculate the luck factor due to inflation using Equation 1.

Example #2

Marco is 65 years old, and just retired. He has $1 million in his portfolio and needs $60,000 each year, indexed to inflation. His asset allocation is 40% equities and 60% fixed income, rebalanced annually.

He assumes he’ll receive the index return, 6.7% (between the years 1900 and 2010), after all management fees. As for the fixed income side, the average return was 5.1% for the same time period.

We calculate the average return for a 40% equity/60% fixed income portfolio as 5.74% (40% of 6.7% equity growth and 60% of 5.1% fixed income growth). This is used as the portfolio growth rate; withdrawals are indexed to actual CPI annually.

The lucky, unlucky and median portfolios are indicated on the aftcast chart above. We calculate the compound annual returns for the lucky, unlucky and median portfolios as 8.04%, 2.77% and 5.50%, respectively. Plug these numbers into Equation 1 and calculate the luck factor that’s attributable to inflation:

Luck factor attributable to inflation = 8.04% – 2.77%

2 × 5.50%

= 47.9%

Table 5 shows the luck factor resulting from inflation for various withdrawal rates. In all cases, the asset mix is 40% equity and 60% fixed income.

Table 5: The impact of inflation for various withdrawal rates

Initial Withdrawal Rate
0%	2%	3%	4%	5%	6%	8%	10%
Effect on Portfolio Growth Rate
0.0%	12.3%	23.3%	33.2%	40.9%	47.9%	59.2%	59.5%

Asset Allocation

Take a 65-year old investor, just retired. His retirement savings are valued at $1 million. He needs to withdraw $60,000 each year, indexed to actual inflation. On the equity side, he expects an average of 2% dividend yield, and pays 0.5% management fees.

Let’s look at how his portfolios would have performed if he were to start his retirement in any of the years between 1900 and 2000. We aftcast six different asset mixes:

Table 6: The impact of asset allocation for various withdrawal rates

Asset Mix (Equity / Fixed Income)	Probability of Depletion by Age 95	Median Portfolio depleted at Age
100% Equity	68%	87
80 / 20	67%	87
60 / 40	74%	87
40 / 60	78%	86
20 / 80	91%	86
100% Fixed Income	95%	87

Table 6 shows that neither the median portfolio life, nor the probability of depletion improved significantly by changing the asset allocation. While asset allocation is an effective tool to limit the volatility of returns, its effect on the sequence of returns is generally insignificant in decumulation portfolios, where the median portfolio life is shorter than the age of death used in the plan.

With that observation, we can now calculate the impact of asset allocation. We figure out the difference in compound annual returns (CAR) of the median portfolio for the asset mix with the best and the worst CAR as described in Example 3.

Example #3

Bob, 65, just retired. He has $1 million in savings and needs $30,000 each year, indexed to inflation. His equities grow the same as the S&P500 index. He rebalances his asset mix annually if equities deviate by more than 3%.

Based on market history, the compound annual return (CAR) of the median portfolio for various asset mixes are as follows:

	Asset Mix
	(Equity / Fixed Income)
	0/100	20/80	30/70	40/60	50/50	60/40	70/30	80/20	100/0
CAR, (median)	4.98%	4.92%	4.98%	5.07%	5.28%	5.41%	5.16%	5.10%	4.22%

Based on market history, for this example the highest growth rate was 5.41% and the lowest 4.22%. If Bob makes the worst asset allocation decision, the maximum penalty, in absolute terms, is a 1.19% difference in CAR. In relative terms, the difference is 28.2%, calculated as 1.19% divided by 4.22%.

Table 7 shows the impact of asset allocation for various withdrawal rates.

Table 7: The impact of asset allocation on portfolio growth

Initial Withdrawal Rate
0%	2%	3%	4%	5%	6%	8%	10%
Effect on Portfolio Growth Rate
29.0%	25.9%	28.2%	33.0%	36.8%	27.5%	19.3%	7.2%

Rebalancing Frequency

Many investment professionals believe frequent rebalancing is better. It’s supposedly done to reduce portfolio volatility. But does it? And how does it impact portfolio longevity?

Volatility has two components. The first is short-term, random fluctuations. Every second, every minute, every day, some event happens somewhere in the world that influences investor psychology. As investors make trading decisions, markets move up or down. If we agree with the notion that price movements within a one-year time horizon are mostly random, then we can’t expect a significant reduction in volatility by rebalancing more than once annually. So, you can rebalance as often as you want—even daily—but it won’t reduce random volatility.

The second component of volatility occurs over the longer term. Markets respond to the collective expectation of investors and a trend forms. Rebalancing can reduce volatility only if it is done after an observable trend. Our analysis shows that the 4–year U.S. Presidential election cycle is the shortest market cycle with an observable trend that we can work with. Example 3 shows the impact of rebalancing frequency.

Example #4

Steve, 65, is retiring this year. He’s put aside $1 million for retirement, and has an asset mix of 40% equity and 60% fixed income. He needs $50,000 each year, indexed to inflation. He takes his withdrawal from the fixed income portion of his portfolio.

Let’s see the impact of rebalancing for different market trends.

Retiring into a bearish trend – 1929

The chart below shows Steve’s portfolio value if he had retired at the start of 1929—the beginning of a secular bear market. At the market bottom of 1932, his portfolio experiences a smaller loss with rebalancing every four years versus every year. The portfolio that’s rebalanced every four years provides Steve with 28 years of income. The portfolio rebalanced annually would run out of money after 21 years. Rebalancing every four years on the presidential election year increased the portfolio’s life by a respectable 38%.

Retiring into a Bullish Trend – 1921

The chart below shows the portfolio’s value if Steve had retired in 1921—the beginning of last century’s first secular bull market. If he rebalanced every four years at the end of the U.S. presidential election year, after 30 years Steve would be $1 million richer than if he rebalanced annually. Volatility was about the same in each case.

Retiring into a Sideways Trend – 1966

The chart below shows Steve’s portfolio value if he had retired at the beginning of the secular sideways trend that occurred between 1966 and 1981. It demonstrates that there was no perceivable difference in the portfolio’s value when rebalanced every four years on the presidential election year versus annually. Portfolio volatility was essentially identical.

The aftcast shows:

• The volatility was about the same whether the portfolio was rebalanced annually or once every four years on the presidential election year
• In secular bull markets, rebalancing too often stunted portfolio growth
• In secular bear markets, rebalancing too often compounded losses
• In sideways markets, it did not matter how often rebalancing occurred; the portfolio’s life varied slightly at random

The analysis shows rebalancing at the end of each U.S. presidential election year gave the best results because it synchronized with the high point of the cycle. It also reduced the effect of the adverse sequence of returns. Rebalancing at any other frequency or at any other time within the cycle did not add as much value.

We measure the impact of the frequency of rebalancing by observing the difference in the compound annual returns (CAR) of the median portfolios for both annual and presidential cycle rebalancing scenarios.

For example, at a 4% withdrawal rate the CAR of the median portfolio with annual rebalancing is 5.21%. When rebalancing is done once every four years at the end of the presidential election year, CAR becomes 5.41%. The impact of using less frequent rebalancing is 3.8%, calculated as 5.41% less 5.21%, divided by 5.21%.

Table 8 shows the impact of rebalancing frequency on the CAR for a portfolio consisting of 40% equity and 60% fixed income. The rebalancing threshold is 3%, i.e. rebalancing occurs only if the asset mix deviates by more than 3%.

Table 8: The impact of rebalancing frequency on portfolio growth at various withdrawal rates

Initial Withdrawal Rate
0%	2%	3%	4%	5%	6%	8%	10%
Effect on Portfolio Growth Rate
1.9%	6.1%	12.2%	3.8%	8.5%	5.9%	5.8%	2.2%

Keep in mind that markets were in secular sideways trends for more than half of the last century. When we average the impact of optimum rebalancing frequency over the entire century, it may appear to have a small influence. But it has a much larger positive impact in extreme markets, and this is valuable for the retiree.

Fund Management Skills – Added Alpha

Exceptional fund managers can outperform the market for limited periods of time. This excess return over the index return is called alpha. To account for this potential outperformance, we use an alpha of 2%. We are not suggesting that a skillful manager can outperform the index by 2% year after year; it’s a possible upper limit for calculation purposes.

For example, with a 4% withdrawal rate the CAR of the median portfolio with index return is 5.21%. When alpha is increased to 2%, then CAR becomes 6.13%. The impact of added alpha is 17.7%, calculated as 6.13% less 5.21%, divided by 5.21%.

Table 9: The impact of outperforming the index by 2%

Initial Withdrawal Rate
0%	2%	3%	4%	5%	6%	8%	10%
Effect on Portfolio Growth Rate
15.7%	16.8%	14.8%	17.7%	18.1%	15.1%	14.8%	14.0%

Portfolio Costs

Portfolio expenses can significantly reduce portfolio longevity or impede its long-term growth. Like inflation, its effect is not readily apparent in the short term.

For present purposes we’ll roll all portfolio costs into a single fixed-rate percentage—1.5% of the value of equity holdings. This includes management fees, trading costs, portfolio expenses, commissions, account fees, etc. If portfolio costs are higher, the impact will be higher than our analysis shows; the opposite holds if costs are lower.

Table 10 shows the impact of portfolio costs on CAR for an asset mix of 40% S&P500 and 60% fixed income.

Table 10: The impact of paying 1.5% portfolio costs for the equity holdings

Initial Withdrawal Rate
0%	2%	3%	4%	5%	6%	8%	10%
Effect on Portfolio Growth Rate
17.7%	17.3%	14.4%	21.9%	17.3%	12.7%	15.0%	15.6%

Combining it all: Determinants of Portfolio Growth

After calculating the effect of each of these elements of portfolio growth, we prorate all percentages to add to 100%. We then combine them in Table 11. Figure 10 depicts the same information graphically.

Table 11: Determinants of a portfolio’s growth for various withdrawal rates

	Initial Withdrawal Rate
	0%	2%	3%	4%	5%	6%	8%	10%
	Determinants of Portfolio Growth
Luck Factor:
Sequence/Volatility of Returns	42%	37%	33%	32%	29%	30%	35%	41%
Inflation	0%	10%	17%	21%	24%	31%	34%	36%
Total Luck Factor	42%	47%	50%	53%	53%	61%	69%	77%
Manageable Factors:
Asset Allocation	26%	21%	20%	20%	21%	17%	11%	4%
Rebalancing	2%	5%	9%	2%	5%	4%	3%	1%
Fund Management Skills	14%	13%	11%	11%	11%	10%	8%	9%
Portfolio Costs	16%	14%	10%	14%	10%	8%	9%	9%
Total Manageable Factors	58%	53%	50%	47%	47%	39%	31%	23%

Figure 10: Determinants of a portfolio’s growth for various withdrawal rates

By far the most important factor across all withdrawal rates is the sequence/volatility of returns. When withdrawal rates are below sustainable, the next-most-important factor is asset allocation. As withdrawal rates increase and portfolio life shortens, the importance of asset allocation diminishes and is replaced by the effect of inflation as the second most important factor.

The challenge for advisors is not whether they should invest in domestic markets or globally, in small-caps or large-caps, stocks or bonds, gold or real estate, ETFs or mutual funds. It’s how to manage clients’ “luck” during retirement. If clients’ withdrawals are beyond sustainable (usually 3.6% at age 65, 5% at age 75), advisors should mitigate risk by using guaranteed income (life or variable annuities). Only then can they turn to manageable factors and try to optimize them.

Here is a summary of our guidelines for managing luck:

Guideline # 1. No Income Required. If the client doesn’t need any income from his portfolio assets during retirement, then don’t worry about the luck factor. Look instead at asset allocation and cost reduction. Diversification, which falls under asset allocation, is also very important.

Guideline # 2. Unsustainable Income Required. If withdrawals are larger than sustainable, try to bring them to below or near sustainable. Strategies include reducing expenses, delaying retirement, creating income from other assets (renting part of the home or cottage), or working part-time. If the client follows this advice and withdrawals come closer to sustainable, then we suggest buying guaranteed income to reduce the effect of the luck factor.

Guideline # 3. Sustainable Income Required. If withdrawals are less than sustainable, do a stress test (see our CE course, “Stress Testing Your Retirement Plan: ‘How Big is My Cushion?’ ”). If the outcome is still in the “green zone” (see our CE course, “Lifelong Retirement Income: The Zone Strategy”) then follow Guideline #1. Otherwise, discuss Guideline #2.

Now that you’ve finished reading, complete the exam to receive your CE credits. If your score is 85% or higher, send an e-mail to jim@retirementoptimizer.com with your name and proof of your score (a screen shot will do) to get a free retirement calculator based on aftcasting, and a free pdf copy of Jim Otar’s 525-page book, Unveiling the Retirement Myth.