a dynamic and adaptive approach to distribution planning and

52 Journal of Financial Planning | A P R I L 2 0 0 9 www.FPAjournal.org

Contributions

David M. Blanchett, CFP®, CLU, AIFA®, QPA, CFA, is a

full-time MBA candidate at the University of Chicago

Booth School of Business in Chicago, Illinois. He won the

Journal of Financial Planning’s 2007 Financial Frontiers

Award with a paper titled “Dynamic Allocation Strategies

for Distribution Portfolios: Determining the Optimal

Distribution Glide Path.”

Larry R. Frank, Sr., CFP®, a wealth advisor and author,

lives in Rocklin, California. He shifts people’s focus from

an income-centric to a wealth-centric viewpoint to help

them better understand how to live on their investments.

He can be reached at LarryFrankSr@BetterFinancial

Education.com.

Distribution planning research isentering its second generation.The first generation of distribu-

tion research provided answers to rela-tively static questions such as “what is aninitial safe withdrawal rate” and “what isthe best (constant) allocation for a distri-bution portfolio.” Recognizing that distri-bution decisions are not made only once atretirement, an expanding body of researchis exploring retirement as a more dynamicperiod, in which changes can be made assituations warrant.This paper will explore the question,

“What is a safe withdrawal rate?” not onlyinitially, but also currently. It will do so froman adaptive perspective, where the with-drawal rate is revisited annually based onthe performance of the underlying portfolioor unforeseen expenditures. It will also berevisited simultaneously with the effects ofthe dynamic relationships of (1) constantly

decreasing distribution periods as the clientages, which in turn allow for (2) an increas-ing supportable withdrawal rate with a sim-ilar probability of failure rate throughoutretirement. The study modeled the revisitsannually, but the data are displayed as five-year slices through the data for simplifica-tion of reporting purposes.

An adaptive approach to distributionplanning, where the withdrawal rate is fluidand not constant, can dramatically improvethe probability of success of a distributionstrategy. Reviewing the withdrawal rate alsoallows for the withdrawal amount to beincreased as situations warrant, whichensures that a retiree is maximizing his or

A Dynamic and Adaptive Approach toDistribution Planning and Monitoringby David M. Blanchett, CFP®, CLU, AIFA®, QPA, CFA, and Larry R. Frank, Sr., CFP®

B L A N C H E T T | F R A N K

• This paper advances the “second-generation approach” to the sustainablewithdrawal rate question.The studyevaluates the ongoing sustainability ofthe withdrawal rate that is revisitedevery year. The withdrawal rate itself(not the dollar value) is increased,decreased, or stays the same based onthe probability of failure for theremaining target distribution period.

• This adaptive approach recognizesthat sustainability decisions do notoccur just once at retirement, butshould change as situations warrantthroughout retirement.To supportongoing sustainability decisions, annualprobability of failure of the currentwithdrawal rate is presented in thispaper, summarized in five-year slicesthrough the data.

• As a person ages, this allows for slowlychanging to higher withdrawal ratesassociated with those shorter remain-ing distribution periods. For example, a

15-year distribution period is moreappropriate for an 80-year-old than fora 60-year-old retiree. Essentially, aperson “ages through the data” fromlonger distribution periods to evershorter distribution periods.

• Revisiting the withdrawal annuallyallows for higher withdrawal rates ifthe portfolio performs well, forunplanned or unforeseen additionalexpenses, or for lowering withdrawalrates if the portfolio is underperform-ing. This is done through comparisonof the current withdrawal rate tobenchmark data to evaluate the asso-ciated probability of failure rates of agiven portfolio mix and remaining dis-tribution time.

• The revisiting approach introduced inthis paper is simpler than some of thecomplex decision rules that have beenpreviously introduced, and is thereforeeasier to implement and change as theclient ages and portfolio values change.

Executive Summary

Contributions

her lifetime income. As the client ages, hisor her remaining time dynamically getsshorter. The adaptive approach in thisstudy demonstrates that the withdrawalrate may be slowly increased as the clientages through management of the client’sexposure to probability of failure with hisor her current withdrawal rate and remain-ing distribution time.

Previous Research

The assumption of a constant real with-drawal amount from a portfolio is a consis-tent theme in past distribution research.The sustainable withdrawal rate is typicallydefined as a percentage of assets where aninitial amount, adjusted for inflation, isassumed to be taken from the portfolio forthe entire distribution period. For example,a 5 percent withdrawal rate from a $1 mil-lion portfolio would result in a $50,000withdrawal in year one. The withdrawal inyear two, though, would not be based on 5percent of portfolio assets; instead thewithdrawal would be $50,000 plus infla-tion. The $50,000 withdrawals, adjustedfor inflation, are typically assumed to con-tinue until the end of the distributionperiod, where the strategy would either bejudged as “passing” (that is, it was able towithstand the withdrawal for the entiredistribution period) or “failing” (in otherwords, it ran out of money).Recognizing that distribution planning is

more dynamic than just an initial with-drawal decision, a number of studies haveintroduced logic, or decision rules, to helpadvisors determine how and when toadjust a withdrawal amount over time.Guyton (2004) introduced perhaps themost well known study involving decisionrules, which were tested in a follow-uppaper by Guyton and Klinger (2006).Guyton employs a variety of rules, such asthe Portfolio Management Rule, the Infla-tion Rule, the Withdrawal Rule, and theProsperity Rule, to help an advisor deter-mine how to adjust the withdrawal overtime to ensure the ongoing sustainability ofthe portfolio.

While Guyton’s research provides valu-able insight into distribution planning, ittakes a very “one size fits all” approach todistribution planning. For example, he usesa fixed 40-year period for his study. Fortyyears is a relatively conservative estimatefor the distribution period, and eachretiree (or retired couple) will have a dis-tribution period that is unique based on hisor her unique age, health, and family his-tory. In contrast, the analysis conducted forthis paper considers nine different timeperiods (10 to 50 years in 5-year incre-ments) and takes a simpler approach toadjusting withdrawals.Bengen (2001) tested a variety of

performance-based withdrawal methodolo-gies where the distribution rate wasadjusted during retirement in response tochanging portfolio conditions. One testinvolved potentially increasing the real dis-tribution rate by 25 percent or decreasingit by 10 percent based on whether theclient was in a bull or bear market. For thispaper, the authors use a more precisemethodology than Bengen’s to determinewhether an adjustment is necessary.Bengen’s analysis was also limited to 55test “runs” due to his reliance on historicaltime series sequence data; in contrast, thispaper takes a bootstrap approach and uses100,000 runs per scenario.Pye (2001) addressed the probability that a

withdrawal amount will need to be reducedover various periods and for various with-drawal rates. Stout and Mitchell (2006) tooka similar approach to Pye where the with-drawal is potentially increased or decreasedannually, based on the likely sustainability ofthe portfolio. Stout and Mitchell’s dynamicmodel employs three types of controls—portfolio deviation thresholds, withdrawaladjustment rates, and absolute withdrawalrate limits—in order to prevent overreac-tions to short-term market movements.Stout and Mitchell note that downwardadjustments should be more immediate thanupward adjustments, and this paper incorpo-rates that concept. This paper could be seenas an extension of Stout and Mitchell’s work.Portfolio Ruin, Balancing Sequence Risk and

Longevity Risk

A key consideration when constructing adistribution portfolio is how much to allo-cate between equities and fixed income/cash. The long-term importance of theallocation decision has been well docu-mented by Brinson, Hood, and Beebower(1986), and more recently by Tokat, Wicas,and Kinniry (2006). The potential benefitof non-constant equity allocations for dis-tribution portfolios has been noted byBlanchett (2007).Two key risks must be addressed when

making the allocation decision: sequencerisk and longevity risk. Sequence risk is therisk, or really the implication, of startingthe distribution period in a bear market (ora market with low or negative returns).Sequence risk will affect clients differentlysince people retire at different times. Arecent study by WatsonWyatt (Watson 2008)found that retirees with a substantial portionof their assets in defined-contribution typeinvestments are especially prone toencounter sequence risk because they tendto retire during market booms (that is,when their 401(k)s are doing well). Marketbusts tend to follow market booms, whichis the type of market these retirees arelikely to face shortly after they retire (thinkmean reversion).Sequence risk is directly correlated to

the market risk of the portfolio. Therefore,more conservative portfolios with lowerequity allocations will have a lower likeli-hood of encountering sequence risk. Butmore conservative allocations increaselongevity risk, or the risk of the outlivingone’s resources.As life expectancies continue to

increase, the need to create portfolios thatcan sustain 40 or more years of inflation-adjusted withdrawals is becoming increas-ingly important. Studies by Cooley, Hub-bard, and Walz (1998); Tezel (2004);Cassaday (2006); and Guyton and Klinger(2006) all confirm the importance ofequities in order to maintain an inflation-adjusted withdrawal over a prolongedperiod. Equities are important because


www.FPAjournal.org A P R I L 2 0 0 9 | Journal of Financial Planning 53



they have historically increased the returnof a portfolio versus cash or fixed income.Return is a key driver of portfolio success;however, higher returns are typicallyaccompanied by higher variability, orstandard deviation.Higher equity allocations, therefore,

decrease longevity risk but increasesequence risk. Viewed differently, if a clientis unlucky and encounters poor initialreturns (sequence risk) during the distribu-tion period, it is likely that the withdrawalamount will need to be reduced in order for

the portfolio to survive. If a client is lucky,though, and encounters high initial returns,it is likely the withdrawal amount can actu-ally be increased. The key is revisiting thewithdrawal to determine whether it is stillreasonable given the current value of theportfolio. This is the primary concept thatwill be explored in this piece.

‘Revisiting’ Methodology

Four different equity allocations were con-sidered for the analysis because risk toler-ances differ across investors and testingonly one allocation (60/40, for example)would ignore this fact. The four differentallocations considered for the paper were20/80 (20 percent equity and 80 percentcash/fixed), 40/60, 60/40 and 80/20. The

equity piece of the allocation is split two-thirds to domestic large equity and one-third to international equity, while thecash/fixed income allocation is split evenlybetween cash and fixed income. For exam-ple, the allocation for the 60/40 portfoliowould be 40 percent domestic large blendequity, 20 percent international equity, 20percent cash, and 20 percent intermediate-term bond.¹The withdrawal is revisited each year for

this study. Based on the underlying proba-bility of failure for the portfolio, the with-

drawal amount can either beincreased by 3 percent,decreased by 3 percent, orstay the same. Note, thischange is in addition to apotential increase due toinflation. All withdrawalamounts are considered tobe in real terms, eliminatingthe effect of inflation on theanalysis. This was done bysubtracting the monthlyinflation rate, which wasdefined as the increase inthe Consumer Price Indexfor all Urban Consumers(CPI-U)², from the monthlyreturns used in the analysis.CPI-U was used as the defi-

nition of inflation because it is the mostcommon definition.The probability of failure of the with-

drawal is calculated each year based on theportfolio allocation, the number of yearsremaining in the target period, the previ-ous year’s withdrawal, and the portfoliovalue at the end of the previous year. Thewithdrawal dollar amount is decreased by 3percent if• The probability of failure for the port-folio is greater than 20 percent whenthe target end date is 20+ years away

• The probability of failure is greaterthan 10 percent when the target enddate is 11–19 years away

• The probability of failure is greaterthan 5 percent when the target enddate is 10 years or fewer away

The withdrawal amount is increased by 3percent if the probability of failure is lessthan 5 percent. If neither of the above con-ditions is met, the distribution dollaramount does not change (except for infla-tion or deflation adjustments).The target period is defined as the length

of the assumed distribution period (30years, for example). As the portfolio pro-gresses over time, the remaining target dis-tribution period, or planning period,decreases. For example, if the target periodis 30 years, after 4 years the target periodwould be 26 years.To build a reference table where the

withdrawal rate (as percentage of currentassets) based on the equity allocation andremaining period could be determined, theprobabilities of failure were calculated foreach of the four equity allocations (20/80,40/60, 60/40, and 80/20) for periodsbetween 1 and 50 years (in one-year incre-ments) and for withdrawal rates from 0percent to 100 percent (in 1 percent incre-ments). (A sampling of the data pointsused in the reference table can be found inFigure 2 on page 56.)For the revisiting strategy, the probabil-

ity of failure was calculated for each year ofeach run of each scenario to replicate thedynamic approach an advisor would takewhen working with a retired client as mar-kets change. The probability of failure is avery fluid number that can change a greatdeal over time. As an example, Figure 1includes the probability of failure for 50runs of a Monte Carlo simulation with a 6percent initial real withdrawal rate over a30-year period for a 60/40 portfolio wherethe withdrawal is adjusted during the dis-tribution period based on the previouslydescribed methodology.The probability of failure at the begin-

ning (year zero) is the same for each of the50 Monte Carlo runs, 39.01 percent. But asthe portfolio progresses through the distri-bution period, the probability of failurechanges for each of the runs. In the aggre-gate, the probability of failure tends todecrease because the initial failure rate ishigher than the respective target probabil-

Contributions

“As the client ages, his or herremaining distribution period decreasesand the client dynamically movesthrough the ever-shortening distributionperiods. As a result, their currentbenchmark withdrawal rate andassociated probability of failureadjusts with time.”

ity of failure (20 percent). This causes thewithdrawal amount to be reduced by 3 per-cent a year until it falls within an accept-able probability of failure range. Figure 1demonstrates why it is important to regu-larly revisit the likelihood of failure for adistribution strategy, as the probability of aportfolio failing (or succeeding) is alwayschanging over time.The actual returns used for testing pur-

poses were created through a processknown as bootstrapping. This is a type ofsimulation analysis where the in-sample testperiod returns are randomly recombined tocreate annual test returns. For the analysis,monthly return information was obtainedon the four test asset classes from 1927 to2007 (81 calendar years) and randomlyrecombined to create hypothetical realannual rates of return for the analysis. Forexample, the monthly real returns for eachof the four categories for the same month(such as June 1961) would be recombinedwith monthly real returns from 11 othermonths (such as March 1930, January 1995,May 1979, and so on) to create each hypo-thetical annual real return. A benefit of thebootstrapping process is that no assump-tions need to be made about the distributionof hypothetical returns (for example, lep-tokurtic and positively skewed).Distributions from the portfolio were

assumed be taken once a year at the begin-ning of each year. Each test scenario wassubjected to a 100,000 run bootstrapMonte Carlo simulation. The simulatorused for this research was built inMicrosoft Excel by one of the authors. Theoriginal simulator built for this analysisused 10,000 runs; however, the simulatorwas expanded to accommodate more runs(from 10,000 to 100,000) due to the vari-ability in the results of the 10,000 runseries. Over two billion Monte Carlo simu-lations were performed for this analysis,the majority of which were used to createthe reference table (Figure 2 shows asample of the data points) to calculate theongoing sustainability of a given with-drawal rate.The portfolios were assumed to be held

in tax-deferred accounts and therefore anytax implications of the withdrawals areignored. Based on the bootstrappingmethodology, it is implicitly assumed thatthe portfolios are rebalanced back to theirtarget allocations monthly. Any potentialcosts associated with the rebalancing werealso ignored.Nine target distribution periods (10, 15,

20, 25, 30, 35, 40, 45, and 50 years) andnine real distribution rates (4, 5, 6, 7, 8, 9,10, 11, and 12 percent) were tested for thefour different equity allocations (20/80,40/60, 60/40, 80/20), for a total of 324dynamic scenarios. Selecting the appropri-ate initial distribution period is typically afunction of the planned length of the dis-tribution period. For example, if you useage 95 as the base mortality date for allretirees (this methodology is discussed in apaper by the authors titled “In Search ofthe Numbers,” currently unpublished),then for a client 65 years old the initial dis-tribution period would be 30 years. As thatclient ages, his or her remaining distribu-tion period decreases and the clientdynamically moves through the ever-shortening distribution periods. As aresult, their current benchmark with-drawal rate and associated probability offailure adjusts with time.

Results: Static Withdrawals for Comparison

Before reviewing the potential benefits ofrevisiting a distribution portfolio seeFigure 2, which illustrates for baselinecomparison purposes the probabilities offailure for a static distribution strategy.After reviewing Figure 2, it is possible to

understand why 4 percent has widely beennoted as the safe initial withdrawal rate. Theprobability of failure for a static 4 percentwithdrawal rate for a 60/40 portfolio over a30-year distribution period was only 4.07percent, and only 2.01 percent for a 20/80portfolio. Viewed differently, approximately1 of every 25 clients who take $40,000 ayear from a $1 million initial portfolio(adjusted for inflation) is likely to run out ofmoney during the 30-year period. Even for a50-year distribution period the probability offailure for a 4 percent initial withdrawal ratefor a 60/40 portfolio was only 16.91 percent.Higher withdrawal rates, such as 6 percent,are commonly viewed as too aggressivebecause the probability of failure is muchhigher (such as 39.01 percent for a 60/40portfolio with a 30-year distribution period).But not everyone retires precisely at age 65(age 95 minus 30 years of distributions),and a 6 percent withdrawal is an incrediblyconservative withdrawal for a 15-year distri-bution period.

Results: Dynamic Distributions



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Figure 2 includes a sampling of the infor-mation used to create the reference tableto determine the ongoing success rateswhen testing the dynamic strategies. As anexample, if a 60/40 portfolio with 20 yearsremaining in its target period had a value

of $800,000 and a $40,000 real with-drawal, the withdrawal rate, as a percent-age of current assets, would be 5 percent($40,000/$800,000), which corresponds toa probability of failure of 2.07 percent.Because the probability of failure at thispoint is less than 5 percent, the withdrawal

amount for the next year would beincreased by 3 percent to $41,200 (from$40,000). If, however, the portfolio valuewas only $500,000, the withdrawal ratewould be 8 percent ($40,000/$500,000).Because this corresponds to a probability offailure that is greater than 10 percent

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(actually 55.25 percent), the withdrawalamount for the following year would needto be reduced by 3 percent, from $40,000to $38,800. As a reminder, this calculationwas performed for each year for each ofthe 100,000 runs for each of the 100 differ-ent scenarios.But when the withdrawal amount is

revisited on an ongoing basis, as it likelywould be when working with an advisor,the actual real withdrawal amount receivedby a client will likely change based on theperformance of the underlying portfoliodue to market forces. Figure 3 illustratesthe results of the five different percentileslices from a $1 million portfolio over asample 30-year distribution period. Theinitial withdrawal rate is assumed to be 6percent ($60,000 from $1 million), thetarget period is 30 years, and the portfolioallocation is 60/40. The withdrawalamounts are based on those runs that sur-vived the entire distribution period.As is evident in Figure 3, the range of

potential withdrawals changed over time,primarily based on the performance of theunderlying portfolio—or viewed differ-ently, the luck of the retiree. For example,based on the information in Figure 3, andthe revisiting methodology discussed previ-ously, those unlucky retirees (in the 95th

percentile or the worst 1 in 20), would seetheir initial $60,000 withdrawal reduced to$39,210 by the 30th year. But those luckyretirees in the fifth percentile (or the best 1in 20) would see their initial $60,000withdrawal increased to $121,968 by the30th year. The median expected withdrawalat the 30th year was $82,133.Revisiting the withdrawal amount also

reduced the likelihood of failure versususing a static withdrawal amount. Anexample of this is included in Figure 4,which is based on the same assumptionsfor Figure 3. Sequence risk is best con-trolled by evaluating the current with-drawal rate, since declining markets pushthe current withdrawal rate up. (Sequencerisk is always present for all retirees whotake a higher withdrawal associated withhigher probability of failure.) Time does

not cure sequence risk unless near-termrising market values (lucky retiree) reducethe current withdrawal rate such that theprobability of failure is now lower.It is important to note that using the

revisiting approach is going to result in

clients who take the same initial with-drawal rate (say 5 percent) ending up withvery different withdrawal amounts duringthe distribution period, depending ontheir actual markets experienced. To givethe reader a better idea of the distribution



of withdrawal amounts using the revisitingstrategy, the withdrawal amounts at thetarget end dates for the 95th percentile(worst 1 in 20), 90th percentile (worst 1 in10), 80th percentile (worst 1 in 5), 50th

percentile (median), and 20th percentile

(best 1 in 5) are included in Appendices1–5. The corresponding probabilities offailure for each of the scenarios isincluded in each appendix to help thereader easily reference the probability ofthat revisiting strategy surviving the target

distribution period.Revisiting, or adjusting, the withdrawal

amount throughout the distribution periodreduced the probability of failure signifi-cantly. A static real withdrawal amount,based on a 6 percent initial distribution (or


Contributions

$60,000 from a $1 million portfolio), had a39.01 percent probability of failure at 30years, while the probability of failure forthe revisited strategy was only 9.83 per-cent. Figure 5 includes the probabilities offailure for the same scenarios in Figure 2;however, unlike Figure 2, the probabilitiesof failure for Figure 5 incorporate the revis-iting methodology where the withdrawalamount was increased, decreased, or keptthe same based on the ongoing probabilityof success for the portfolio. The revisitedstrategy also had a consistently lower prob-ability of failure as seen in Figure 6.Some readers may question how it is

possible to have both a lower probability offailure and a higher median withdrawalamount when revisiting is used. Thisoccurs for two reasons. First, the with-drawal amount was reduced with poor

portfolio performance. Based on the dataused to develop Figures 3 and 4, 88.47 per-cent of the runs had withdrawal amountsless than the initial $60,000 at year 5,69.70 percent at year 10, 55.31 percent atyear 15. Reducing the withdrawal amountas situations warranted better enabled theportfolio to survive the entire distributionperiod if the market returns were low.Second, the dispersion of the endingaccount values was much tighter for therevisited methodology than the constantapproach. The revisiting methodologyensures that the withdrawal amount is tai-lored to the underlying portfolio; if theportfolio performs well the withdrawalincreases, if the portfolio performs poorlythe withdrawal decreases. Contrast thisdynamic approach with the constant with-drawal approach, where the same with-

drawal is taken regardless of the underly-ing portfolio value.It is worth noting that the probability of

failure actually increased for some of themore conservative scenarios. For example,the probability of failure for a 4 percentdistribution for a 20/80 portfolio over 25years based on the constant methodologywas only .05 percent, yet was 3.54 percentbased on the revisit methodology. The pri-mary reason for the increase was that aprobability of failure of less than 5 percentwas deemed acceptable when there wereten or fewer years to the target end datewhen determining whether to adjust thewithdrawal. For this scenario (4 percentwithdrawal, 20/80 portfolio, 25 year distri-bution period), the 95th percentile with-drawal amount (or worst 1 in 20) at the25th year was $52,834. The failure rate in



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the 24th year of this strategy was only .01percent. In other words, the revisitedapproach resulted in a higher lifetime with-drawal amount, which is arguably eachretiree’s objective, and virtually every runthat failed did so in the last withdrawal year.Figure 6 compares the table data from

Figures 2 and 5 for the portfolio composi-tion 60/40 (other portfolios would yieldsimilar figures) for the withdrawalamounts from 4 percent to 8 percent forthe 20- to 50-year periods. This figure illus-trates the gap between Revisited (RV)withdrawal rates, which have lower proba-bility of failure rates relative to Fixed (F)withdrawal rates, which is why the RVcolumns are to the right of the F columns.In reality, people withdraw dollar

amounts from their portfolios. Withoutchanging those dollar amounts (except forincreasing them for inflation), the with-drawal rate is still constantly changing dueto the dynamic factor of fluctuating portfo-

lio market values. Advisors are able tobenchmark and compare their client’s cur-rent withdrawal rate (current dollar with-drawal amount divided by the current dis-tribution portfolio market value) to Figures3 and 6 to obtain an idea what the client’scurrent withdrawal probability for successor failure may be. This is especially impor-tant during market declines where portfo-

lio values are less, which forces a higherwithdrawal rate from the portfolio.A second dynamic factor is the effect of

aging where distribution periods are, infact, dynamically and continually shrink-ing. An initial withdrawal rate for 35 yearsremaining, then 34, 33, and so on, is quitedifferent from a sustainable withdrawalrate when the retiree has 10 years remain-ing. Withdrawal rates tend to be linearwhen aligned for distribution periods from20 to 40 years (ages 55 to 75) versus para-bolic when aligned for periods under 20years (ages 76 and older).

‘Safety’ of 4 Percent and Early Versus LaterWithdrawal Strategies

Distribution planning is not a “one size fitsall” exercise. Each client and retiree willhave different needs that are going to influ-ence the sustainable real withdrawal ratedecision. Past research on adaptive strate-

gies has noted that 4 percentis likely too conservative anestimate for an initial with-drawal rate, generally sug-gesting a higher withdrawalamount. Being able to takehigher withdrawals earlierversus later has raised thestrategy of trying to reversethis timing, or “smoothing”withdrawal rates over theentire distribution period.Observe in the previous fig-ures that,given similar proba-bility of failure rates, a higherwithdrawal ratecorrelateswith shorter distri-bution periods, and viceversa. Attempting to take a

higher withdrawal rate early in retirementwith the intention of changing to a lowerwithdrawal rate later in retirement attemptsto reverse these findings. Considerations:• It has been difficult to assess what rateto use early on, unless the advisor hasrelative probability of failure rates forall the choices.

• Smoothing strategies require the

client to have the ability to cut expen-ditures during poor markets. This isdifficult to explainunless the advisorhas relative probability of failures ofthe client’s current withdrawal rate(current annual withdrawal divided bythe current portfolio value).

• Higher initial withdrawal rates resultin still higher current withdrawal rateseven when the portfolio value declineswith poor markets (sequence risk).

• Portfolio value volatility accentuatesthe sale of more shares. The higherthe smoothing rate over a sustainablerate, the more the relative number ofshares are needed to be sold (negativedollar cost averaging effect) versus thenon-smoothed rate.

• The negative dollar-cost averagingeffect has led to the strategy of placingthe first few years of distributions intocash or more conservative portfolios/buckets.

• Because the total value supporting dis-tributions includes these conservativebuckets, this strategy is essentiallyshifting the overall portfolio to onemore conservative.

• Figures 2 and 5 provide probabilities offailure rates for different portfolio compo-sitions for different withdrawal periods.

Sequence risk can be managed by review-ing current withdrawal rates to ensure theyare still prudent given the relevant timeremaining. As time remaining is reduced byclient aging dynamics, the withdrawal ratemay increase over time. How to determine aclient’s time remaining is based on using acommon mortality-base age as discussed inthe white paper by the authors titled “InSearch of the Numbers.”But each retiree can potentially incur

market declines at any time. Controllingthe risk of having to reduce a retiree’s with-drawals is a function of setting the currentwithdrawal rate lower, rather than higher,at any given point. Benchmarking the cur-rent withdrawal rate provides the ability toassess the probability of failure over time.A client can reduce the likelihood theywould need to reduce their withdrawals,



“Sequence risk can be managed byreviewing current withdrawal rates toensure they are still prudent given therelevant time remaining. As timeremaining is reduced by client agingdynamics, the withdrawal rate mayincrease over time.”

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hence cut their expenses, by using a with-drawal rate appropriate for the timeremaining as well as a lower current with-drawal rate relative to other rates possiblefor that time frame remaining. In otherwords, higher rates are generally possiblefor smaller distribution periods (such as 20years) versus longer distribution periods(such as 40 years).

Conclusion

Because it is impossible to predict withcertainty the exact path each of yourclients will take during retirement, anadaptive approach should be used whendetermining the appropriate withdrawalamount from a distribution portfolio. Pastdistribution research has been based pri-marily on the assumption where a con-stant, inflation-adjusted withdrawal istaken from a portfolio for the length of thedistribution period, regardless of theunderlying portfolio. The static methodol-ogy ignores the dynamic needs of clients,market fluctuations, and client responsesto those fluctuations, where the ongoingvalue provided by advisors who regularlymeet with clients to ensure the future suc-cess of the distribution strategy rests withan ability to benchmark the client’s proba-bility of success or failure. Revisiting thewithdrawal can materially improve theprobability of success for a distributionportfolio and, therefore, is an essentialcomponent of any distribution plan.

Endnotes

1. Data definitions:a. Intermediate-term bond: defined asthe return on the Moody’s SeasonedAaa Corporate Bond Yield, assuming aten-year duration. Data obtained fromthe St. Louis Federal Reserve: http://research.stlouisfed.org/fred2/.

b. Cash: defined as the yield on thethree-month Treasury bill. Secondary

Market Rate data obtained fromTradetools.com (1927-1933) and theSt. Louis Federal Reserve (1934-2006): http://research.stlouisfed.org/fred2/.

c. Domestic large blend equity: definedas the return on the “Big Neutral”portfolio based on the 2×3 portfolioreturn information publicly availableon Kenneth French’s Web site: http://mba.tuck.dartmouth.edu/pages/fac-ulty/ken.french/data_library.html.

d. International equities: defined as thereturn on the Global Financial DataWorld ex-USA Return Index, dataobtained from Global Financial Datafrom January 1927 to December 1969and the return on the MSCI EAFEStandard Core Net USD from January1970 to December 2007.

Because pure historical data is used for-this analysis, as is common among distri-bution research, the authors would cau-tion the reader that if future returns arelower than historical returns, the actualresult of a distribution portfolio may bematerially different from what thisresearch suggests.

2. Data obtained from the Bureau of LaborStatistics.

References

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Cooley, Phillip L., Carl M. Hubbard, andDaniel T. Walz. 1998. “Retirement Sav-ings: Choosing a Withdrawal Rate thatis Sustainable.” Journal of the AmericanAssociation of Individual Investors 20(February): 16–21.

Guyton, Jonathan T. 2004. “Decision Rulesand Portfolio Management for Retirees:Is the ‘Safe’ Initial Withdrawal Rate TooSafe?” Journal of Financial Planning 17,10 (October): 54–61.

Guyton, Jonathan T. and William J. Klinger.2006. “Decision Rules and MaximumInitial Withdrawal Rates.” Journal ofFinancial Planning 19, 3 (March): 49–57.

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a dynamic and adaptive approach to distribution planning and

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