Journal of Personal Finance - IARFC

Volume 10, Issue 1

2011

The Official Journal of the International Association of

Registered Financial Consultants

Journal of Personal Finance

Volume 10, Issue 1 3

CONTENTS

EDITOR’S NOTES .......................................................................................... 9

RESEARCH & THEORY

Public Awareness of Retirement Planning Rules of Thumb ................. 12

Robert N. Mayer, Ph.D., University of Utah

Cathleen D. Zick, Ph.D., University of Utah

Michelle Glaittli, University of Utah

Retirement planning advice commonly takes the form of rules of thumb

offered in self-help books, magazine articles, and Internet websites.

The rules provide simple answers to questions about how much to save,

how to allocate retirement investments, and how to safely draw down

retirement savings. The accuracy of these rules is hotly debated among

finance scholars, but little is known about the extent to which members

of the public are aware of these rules. This study examines awareness

of four widely-disseminated retirement rules of thumb among

employees of a large university (N=3,095). Male respondents and

those with higher levels of education are more aware of these rules than

females and people with lower levels of education, but fewer than half

of respondents are aware of even the best known of the four rules

studied. Finally, we discuss the implications of the results for financial

planning professionals.

The Demand for Financial Planning Services ........................................ 36

Sherman D. Hanna, Ph.D., Ohio State University

Based on 1998 to 2007 Survey of Consumer Finances datasets the

proportion of households reporting use of a financial planner increased

from 21% in 1998 to 25% in 2007, with an estimated increase of almost

five million households between 2004 and 2007. Multivariate analysis

shows that the likelihood of using a financial planner is strongly related

to risk tolerance, with those with low risk tolerance the least likely, and

those with above average risk tolerance the most likely to use a

financial planner, controlling for income, net worth, age, and other

factors. Those with substantial risk tolerance have significantly lower

likelihood of using a financial planner than those with above average

risk tolerance. Black households are more likely but Hispanic and

Other/Asian households are less likely than comparable White

households to use a financial planner. The likelihood of using a

financial planner increases with net worth for ranges above zero, but

also increases as net worth decreases below zero.

4 Journal of Personal Finance

©2011, IARFC. All rights of reproduction in any form reserved.

Can Dual Beta Filtering Improve Investor Performance? .................... 63

James Chong, Ph.D., California State University, Northridge

Shaun Pfeiffer, Ph.D. Candidate, Texas Tech University

G. Michael Phillips, Ph.D., California State University, Northridge

This study investigates the possibility that more efficient portfolios

may be constructed by using the dual-beta model that screens out assets

that exhibit more extreme downside risk sensitivity. Three portfolios

were constructed, using the criteria of standard CAPM beta, down-

market beta, and a combination of up-market and down-market betas.

Overall, the standard CAPM beta consistently lags the dual-betas.

When compared to the Fama-French three-factor inspired DFEOX, the

dual-betas also performed reasonably well, with the ability to contain

the downside while participating in the upside.

Safe Withdrawal Rates from Retirement Savings for Residents of

Emerging Market Countries .................................................................... 87

Channarith Meng, Ph.D. Candidate, National Graduate Institute for

Policy Studies (GRIPS)

Wade Donald Pfau, Ph.D., National Graduate Institute for Policy

Studies (GRIPS)

Researchers have mostly focused on U.S. historical data to develop the

4 percent withdrawal rate rule. This rule suggests that retirees can

safely sustain retirement withdrawals for at least 30 years by initially

withdrawing 4 percent of their savings and adjusting this amount for

inflation in subsequent years. But, the time period covered in these

studies represents a particularly favorable one for U.S. asset returns that

is unlikely to be broadly experienced. This poses a concern about

whether safe withdrawal rate guidance from the U.S. can be applied to

other countries. Particularly for emerging economies, defined-

contribution pension plans have been introduced along with under-

developed or non-existing annuity markets, making retirement

withdrawal strategies an important concern. We study sustainable

withdrawal rates for the 25 emerging countries included in the MSCI

indices and find that the sustainability of a 4 percent withdrawal rate

differs widely and can likely not be treated as safe.


Financial Planning Literature Survey .................................................. 109

Benjamin E. Fagan, MSFE, PlusPlus Inc. Shawn Brayman, M , PlusPlus Inc. ESThis study is intended to provide an environmental scan of current

research from Australia, Canada, United Kingdom and the United

States, related to financial planning/services from 2003 to July 2010.

The objective of this exercise is to try and highlight research areas

where there may be gaps. This is not intended to review the research in

any manner but rather to aggregate and document its existence in some

broad based categories. The study was carried out in two parts. To

begin with, research was collected, categorized and totalled to

determine high and low volume areas. Finally, industry practitioners

and academics were petitioned to provide their opinions. Based on our

findings, Estate Distribution Analysis, Pension Alternatives and Tax

Optimization were found to be the topics that require the most focus for

further research. Modern Portfolio Theory, General Portfolio

Management and Product Shelf were the categories that were

determined to be the most overly researched areas.



CALL FOR PAPERS

JOURNAL OF PERSONAL FINANCE

(www.JournalofPersonalFinance.com)

OVERVIEW

The new Journal of Personal Finance is seeking high

quality manuscripts in topics related to household financial

decision making. The journal is committed to providing high

quality article reviews in a single-reviewer format within 45

days of submission. JFP encourages submission of manuscripts

that advance the emerging literature in personal finance on

topics that include:

- Household portfolio choice

- Retirement planning and income distribution

- Individual financial decision making

- Household risk management

- Life cycle consumption and asset allocation

- Investment research relevant to individual portfolios

- Household credit use

- Professional financial advice and its regulation

- Behavioral factors related to financial decisions

- Financial education and literacy

EDITORIAL BOARD

The journal is also seeking editorial board members.

Please send a current CV and sample review to the editor. JPF

is committed to providing timely, high quality reviews in a

single reviewer format.

CONTACT Michael Finke, Editor

Email: [email protected]

www.JournalofPersonalFinance.com


JOURNAL OF PERSONAL FINANCE

VOLUME 10, ISSUE 1

2011

EDITOR

Michael S. Finke, Texas Tech University

ASSOCIATE EDITOR

Wade Pfau, National Graduate Institute for Policy Studies (GRIPS)

EDITORIAL ASSISTANT

Benjamin Cummings, Texas Tech University

EDITORIAL BOARD

Steve Bailey, HB Financial Resources

Joyce Cantrell, Kansas State University

Monroe Friedman, Eastern Michigan University

Joseph Goetz, University of Georgia

Clinton Gudmunson, Iowa State University

Sherman Hanna, The Ohio State University

Karen Eilers Lahey, University of Akron

Doug Lambin, University of Maryland, Baltimore County

Jean Lown, Utah State University

Angela Lyons, University of Illinois

Ruth Lytton, Virginia Tech University

Lewis Mandell, University of Washington and Aspen Institute

Yoko Mimura, University of Georgia

Robert Moreschi, Virginia Military Institute

Edwin P. Morrow, Financial Planning Consultants

David Nanigian, The American College

Barbara O‘Neill, Rutgers Cooperative Extension

Jing Xiao, University of Rhode Island

Rui Yao, University of Missouri

Tansel Yilmazer, University of Missouri

Yoonkyung Yuh, Ewha Womans University

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EDITOR’S NOTES

This first official 2011 issue of the Journal of Personal

Finance is also the first of my tenure as full-time editor

following in the footsteps of John Grable and Ruth Lytton, who

have ably guided the Journal from its inception. This is my

third issue as editor after having served as guest editor of two

previous issues. In each of those issues I have relied on an

extremely hard-working and capable group of reviewers who

have committed to providing authors a high quality, timely

manuscript evaluation. No journal can survive without the

hard work of many scholars who volunteer to improve the

quality of research in financial planning. I'd like to take this

moment to thank the reviewers for this issue, and in particular

the members of the editorial board who take on the bulk of

reviewer responsibilities.

This issue contains articles on a new approach to

portfolio construction that has been used by institutional

investors in the past, but is new to the field of individual

portfolio management. Authors James Chong, Shaun Pfeiffer

and G. Michael Phillips decompose Beta between upside and

downside covariance with the market and seek to improve

portfolio efficiency by looking for securities where Beta in

bear markets is different from Beta in bull markets. Since the

traditional Capital Asset Pricing Model assumes symmetry and

prices assets based on both upside and downside risk, an

investor could conceivably construct a portfolio of securities

that have a relatively low total Beta (less than 0.7), but have a

down-market Beta below 0.7 and an up-market Beta above 0.7.

In other words, they perform better in an up-market without



performing worse in a down market. The authors find evidence

of superior performance using this portfolio technique, which

may be particularly attractive for loss-averse investors seeking

limited downside risk.

I would also like to highlight the very interesting work of

authors Channarith Meng and Wade Pfau. Dr. Pfau's recent

work on retirement decumulation addresses the very real

possibility that stock market returns used in previous

decumulation shortfall studies, namely United States equity

returns since 1926, may be overly optimistic. Extending the

dataset into the 19th century, or simulating returns using a

bootstrap method as in this article, provides more sober

estimates of shortfall probabilities and of the optimal portfolio

share held in equities during retirement. Since the U.S. had an

unprecedented equity market run in the 20th century, Meng and

Pfau ask how investors in other countries would have fared

using the same decumulation methodology. In this issue they

focus on sustainable withdrawal rates in developing nations

and find substantial variation among countries and among

strategies. I find this research particularly compelling since, as

we are often reminded, past performance does not always

predict the future - particularly in a world where the global

capital market will have a strong influence on U.S. investors.

In "The Demand for Financial Planning Services,"

Sherman Hanna finds that the use of financial planners climbed

by five million between 2004 and 2007 and explores which

Americans are more likely to use a planner. Among his more

interesting findings are that, even independent of income and

wealth, more educated households are more likely to hire a


professional to provide financial advice. It appears that many

of those who are likely to be the most knowledgeable about

personal finance also realize that they need an expert to help

them make better financial decisions. Perhaps unsurprisingly,

single women are also more likely than single men to hire a

planner to help them with their finances.

I am looking forward to the Winter issue of the Journal of

Personal Finance and would again like to thank those who

contribute to the Journal and to the readers and the IARFC for

their support and interest in advancing the science of personal

finance.

~Michael Finke



PUBLIC AWARENESS OF RETIREMENT PLANNING

RULES OF THUMB

Robert N. Mayer, Ph.D.

University of Utah

Cathleen D. Zick, Ph.D.

University of Utah

Michelle Glaittli

University of Utah

Retirement planning advice commonly takes the form of rules of

thumb offered in self-help books, magazine articles, and Internet

websites. The rules provide simple answers to questions about

how much to save, how to allocate retirement investments, and

how to safely draw down retirement savings. The accuracy of

these rules is hotly debated among finance scholars, but little is

known about the extent to which members of the public are aware

of these rules. This study examines awareness of four widely-

disseminated retirement rules of thumb among employees of a

large university (N=3,095). Male respondents and those with

higher levels of education are more aware of these rules than

females and people with lower levels of education, but fewer than

half of respondents are aware of even the best known of the four

rules studied. Finally, we discuss the implications of the results for

financial planning professionals.

Retirement planning advice can never be simple, but it is

often simplified in the form of ―rules of thumb.‖ These rules

are offered in books and articles, websites, and television

Robert N. Mayer, Department of Family and Consumer Studies, University

of Utah, 225 South 1400 East, Salt Lake City, UT 84112-0080;

(801) 581-5771; [email protected]

The research reported in this paper was supported by a grant from the Direct

Selling Education Foundation. The authors also appreciate the help of Kara

Glaubitz and Matt Argyle in the preparation of the revised manuscript.


programs. The rules address key questions of retirement

planning: how much will I need to save, how can I reach my

retirement savings goal, and how can I make my retirement

nest egg last my entire lifetime? Clearly, using rules of thumb

cannot replace planning that takes into account the specifics of

an individual‘s situation, but these rules are the beginning of

point of retirement planning for many individuals. Hence,

regardless of the status of these rules among finance scholars

and practitioners, it is important to understand public

perception of these retirement guidelines.

The article is organized as follows. First, we situate rules

of thumb in the broader process of consumer decision making

and financial planning for retirement. Second, we describe

four common retirement rules of thumb, including the origins

and evolution of these rules. Third, we describe a research

study that examined public awareness of these four rules and

present its major findings. The study should not be interpreted

as an endorsement of these rules, only an acknowledgement of

their ubiquity in the mass media. Finally, we comment on the

implications of the study‘s findings.

Retirement Rules of Thumb in Context

Individuals who wish to be deliberative about retirement

planning can seek professional help, plan on their own, or

employ some combination of the two approaches. Despite the

availability of a variety of professional financial planners to

assist individuals with their retirement planning, only a

minority of people avail themselves of these professional

services (Certified Financial Planning Board of Standards,



2009; Elmerick, Montalto, & Fox, 2002). The majority of

people are retirement planning do-it-yourselfers, and their

planning activities may rely heavily on rules of thumb. Even

people who avail themselves of professional financial help may

use rules of thumb as a departure point for discussions with

their advisors.

The use of rules of thumb in retirement planning is

relevant to three broad topics within consumer research:

positive vs. normative, heuristics, and financial literacy. In a

presidential address to the American Finance Association, John

Y. Campbell (2006) highlighted the difference between

observed (positive) and ideal (normative) financial behavior.

Rules of thumb – however imperfect they may be – are

normative statements about what people ought to do. These

statements can be studied positively, however, by examining

the extent to which people are aware of them, properly

understand them, are aware of their limitations, and use them.

The study reported here addresses the first of these research

questions.

Given the many complex choices that people face in their

daily lives and the finite resources that can be devoted to these

choices, people often use rules of thumb, shortcuts, and other

―heuristics‖ to facilitate these decisions (Kahneman, Slovic, &

Tversky, 1982). Following these rules is designed to yield

results that, while not perfect, are satisfactory (Simon, 1956;

Schwartz, 2004). Despite the importance of rules of thumb,

little attention has been devoted to their use by consumers in

the retirement planning process.


The study of retirement rules of thumb can also be

situated within the topic of financial literacy. Literacy is often

discussed in terms of concepts that can be judged as correct or

incorrect (Hung, Parker, & Yoong, 2009; Huston, 2010;

Remund, 2010). Rules of thumb, in contrast, are only

approximations of a correct response; they may need to be

adjusted in light of individual circumstances. Nevertheless,

awareness of rules of thumb is an important, if neglected,

element of financial literacy.

Common Retirement Rules of Thumb

Retirement rules of thumb are appealing because they

provide simple and concrete guidance for addressing the

complex task of retirement planning. The centerpiece of

retirement planning is calculating how much a person will need

to fund a desired or ―comfortable‖ retirement lifestyle. Yet

only a minority (42%) of people in the 2011 Retirement

Confidence Survey conducted by the Employee Benefit

Research Institute had made this basic calculation (Helman,

Copeland, & VanDerhei, 2011). Advice dispensed by popular

financial gurus such as Suze Orman and Dave Ramsey

sidesteps this calculation by offering a simple rule of thumb:

save a certain percent (typically, 10 or 15) of income for

retirement. While easy to remember, this type of retirement

planning rule fails to provide a retirement saving target,

rationale, or method. The four rules discussed below, while

simple as well, offer more specific guidance for retirement

planning to those people wishing to follow them.



Income Replacement Ratio Rule

Retirement planning requires an estimate of how much

income will be needed to cover anticipated expenses. In this

regard, a rule of thumb is that a household needs roughly 70-

90% of its pre-retirement gross income to maintain its current

standard of living. The fact that the ratio is less than 100% is

based on the assumption that taxes decline in retirement as do

many expenses (e.g., commuting expenses, clothing purchased

for work).

The idea of an income replacement ratio rule has a long

history. An article published in July of 1965 told retirees that

50- 60% replacement income was the needed amount in

retirement (Nuccio, 1965ab). Only a few months later, the

same author revised this figure upward to between 50-75%

(Nuccio, 1965). In 1981, a self-help book supported a

replacement income of 75% (Schiller, 1981), a percent also

found in a 1993 self-help book (Williamson, 1993). A 2009

article aimed at nurses recommended a replacement income

between 60-80% (Strohfus & Schrader, 2009), and a self-help

book for ―dummies‖ suggested that the ratio might be 100%

(Benna, 2009).

Like all rules of thumb, the income replacement ratio

ignores individual differences in age and income. For

example, some people may need far more than 80% of income

in the early years of retirement as they try to catch up on the

things they always wanted to do. Their income replacement

needs could well be below 80% in their final years of life

(assuming no large out-of-pocket heath care expenses).

Similarly, income replacement needs vary by a person‘s


household income. Research by Aon Consulting and Georgia

State University (Palmer, 2008) finds that a household earning

only $30,000 per year, for example, might need to replace 90%

of more of its pre-retirement income to maintain a given

lifestyle, whereas a household earning $70,000 might only

need to replace 77% (Palmer, 2008). Research by Scholz and

Seshadri (2009) found a median optimal target replacement

rate of 75% for married couples, but the authors urged caution

in using rules of thumb due to enormous variability among

households.

20 Times Income Rule

Another rule of thumb that can be used to determine the

total amount needed for retirement is multiplying an

individual‘s current annual income or projected annual income

requirement in retirement by a particular number. Robert

Sheard popularized the number 20 in his book, Money for Life:

The 20 Factor Plan for Accumulating Wealth While You’re

Young (2000). Twenty-times-income is one of many rules that

involve multiplying current income to derive a retirement

savings goal. The author of a 1977 article in the Wall Street

Journal recommended saving ten times one‘s annual income to

produce a financially secure retirement (Moffitt, 1977). More

recently, Stein and Demuth‘s (2009) self-help book promoted a

factor of between twelve and sixteen when multiplying current

salary. In August of 2009 Money magazine told readers that it

was thirty times annual income (―Make Peace,‖ 2009). It is

likely that the escalation of the factor used in this rule is driven,

at least in part, by increasing retiree longevity.



Charles Ferrell, author of Your Money Ratios (2009),

suggests that the number by which current income is multiplied

should increase as a person ages. When a person is 50 years

old, multiplying by five may be sufficient. For someone who

is 65 years old, multiplying by twelve is more appropriate

(assuming additional income is available from Social Security).

Regardless of the specific number that is used in multiplying

current income (or required retirement income), the value of

this rule of thumb is likely to be greatest when a person is close

to retirement age.

110 Minus Age in Stocks

In addition to guidance in setting retirement savings

goals, individuals use rules of thumb to decide how to allocate

their investments across asset classes. One such rule is to ―own

your age in bonds.‖ This would mean, for instance, that a

person who is 40 years old should allocate 40 percent of his or

her investment portfolio to bonds, with the remainder going

largely to stocks. This rule is based on the assumption that a

person‘s holdings should be more conservatively invested as

they age (Lozada, 2004).

A rule that yields similar results to the own-your-age-in-

bonds rule is to subtract your age from a particular number to

determine the percent of stocks in an investment portfolio.

The most common form of this rule is 100-minus-age, although

the origins of this formulation are not known. It would suggest

that a 40-year old investor would have 60 percent of his or her

holdings in stocks and 40 percent in bonds. Over time, though,

the number used in the rule has migrated upward to 110-minus-

age or even 120-minus-age in response to increased longevity


(MarksJarvis, 2007). In August of 2008, Consumer Reports

magazine told readers that the old rule of 100 was outdated and

that 110 was now a more reasonable number to use in

determining stock allocation; but even the number of 110 was

questioned because of the increases in life expectancies

(―Deep-Six,‖ 2008). A year later Money magazine also

promoted 110 as the new number to use in stock allocation

decisions (―Make Peace…,‖ 2009).

William Bengen (1996) varies the number used in the

rule to reflect differences in risk tolerance. He proposes 115

for people with low risk tolerance, 128 for those with moderate

risk tolerance, and 140 for the aggressive investor. The

moderate risk rate formula of 128 minus age is still more

aggressive than the numbers typically publicized in the popular

media.

While the exact number may vary among formulations,

the concept behind the various number-minus rules of thumb is

embodied in target date and life-cycle mutual funds. These

funds slowly and automatically decrease stock allocation as a

person ages. Wang (2007) investigated the percent of stock

allocation provided by various target date funds and compared

these percentages to the rule of 120 minus age. His analysis

showed that these funds varied from being too high by nearly

nine percent to too low by over 21 percent. However, each

fund did have an allocation option that was within two percent

of using the rule at some point in the lifecycle.

The various number-minus-age rules are meant to apply

before retirement and beyond it. The rule encourages

substantial stock ownership during the early years of



retirement. Having too little invested in stock during

retirement can jeopardize financial security, although having

too much invested in stocks can have the same effect if there is

a major market downturn.

4% Withdrawal Rate

In addition to building a retirement nest egg, pre-retirees

need a sense of how much money they will be able to withdraw

annually during retirement without running a substantial risk of

outliving their savings. A widely-cited rule of thumb addresses

this issue: one can safely withdraw 4% per year adjusted for

inflation. For example, if an individual has a nest egg totaling

$1 million, withdrawing four percent the first year in retirement

would make $40,000 available. The second year, $41,200

could be withdrawn if the inflation rate were three percent.

The rule assumes that the unused portion of the retirement

account is allocated in an age-appropriate fashion among asset

classes.

The 4% withdrawal rule during retirement is often

associated with William P. Bengen. In 1994, Bengen wrote a

seminal paper on the safe withdrawal rates from retirement

portfolios. He concluded that a 4.1 percent withdrawal rate

over thirty years is safe for a portfolio composed of 50% stocks

and 50% intermediate government bonds (Bengen, 1994).

Increasing the share of stock in the portfolio increases the

funds that can be withdrawn but also increases the risk of

exhausting the funds before the end of thirty years.

The 4% withdrawal has generated a great deal of debate

among academics and financial practitioners (Scott, Sharpe, &

Watson, 2009). Nevertheless, Bengen‘s original formulation of


the rule appears to be reasonably accurate as a ballpark figure.

For example, Cooley, Hubbard, and Walz (1999) concluded

that a portfolio composed of 75% equities allowed for a 4-5%

withdrawal rate over thirty years. For shorter payout periods,

say, fifteen years or less, the withdrawal rate could be as high

as eight or nine percent a year. Despite any shortcomings, the

4% withdrawal rule has worked its way into many textbooks

and self-help sources of retirement advice (Armstrong & Doss,

2009; Eisenberg, 2006; Garman & Forgue, 2010).

Summary

Rules of thumb for retirement are common in the popular

media and address some of the crucial aspects of retirement

planning. Researchers do not know, however, the extent to

which pre-retirees are aware of these rules; which types of

people are most and least aware of these rules; and how these

rules are used in the process of retirement planning. The

research reported here addresses the first two of these three

important questions.

Study Design

As part of National Consumer Protection Week 2011, the

authors collaborated with the Division of Human Resources of

a Mountain West university to create an educational event for

the university‘s more than 20,000 full-time and part-time

employees. The event took the form of an online quiz

regarding retirement planning. The quiz, which centered on 12

knowledge questions and an additional 4 questions on

retirement ―rules of thumb,‖ was available from Monday,

March 7 through Friday, March 11. As an incentive to

participate, 20 prizes were offered. In keeping with the theme



of the event, these prizes consisted of a one-time $250

contribution to a new or existing supplemental retirement

account administered for the employee by the University.1

The primary goal of our research was to examine public

awareness of four retirement rules of thumb. To place our

findings in a broader context, however, we interviewed four

professional financial advisors to get their views on the value

of retirement rules of thumb (Glaittli, 2011). The results of

these interviews are reported in the Discussion section below.

Measures

As previously indicated, there is very little research on

consumer awareness of retirement rules of thumb. An

exception is a 2008 study conducted by Metropolitan Life

Insurance Company. Among fifteen multiple-choice questions

that were meant to measure ‗retirement IQ‖ were two regarding

retirement rules of thumb. One referred to the income

replacement ratio in retirement, the other to the 4% withdrawal

rate rule. Both of these questions were used in this study, but

with the answer categories modified to create equal numerical

intervals between choices and to make one answer

unambiguously reflective of the general presentation of these

rules to the general public:

1 To increase the likelihood that participants took the quiz seriously and did

not submit answers just for the sake of winning a prize, only those people

who correctly answered 4 or more of the 12 knowledge questions in the

quiz were eligible to win a prize. These knowledge questions covered a

variety of retirement-related topics and were separate from the rule-of-

thumb questions. In the case of the knowledge questions, respondents had

an incentive to guess an answer rather than choose ―don‘t know/not sure.‖

This incentive did not exist, however, for the rule-of-thumb questions since

answers to these questions did not affect prize eligibility.


What percent of pre-retirement income do financial

experts think retirees will need in retirement? (20-

30%; 50-60%; 80-90%; 110-120%; Don‘t

know/Not sure)

To help ensure that an individual has enough money

to make savings last his or her lifetime, experts

recommend limiting the percent people withdraw

from their savings principal each year to: (4%; 8%;

12%; 16%; Don‘t know/Not sure)

Despite many references to the 20-times-income and 110-

minus-age rules in the popular press, we were unable to find

survey questions covering these two rules of thumb. We

therefore developed questions to gauge awareness of these two

rules. After pretesting to ensure clarity, the two questions were

finalized as follows:

Financial experts suggest that individuals, in order

to maintain their current standard of living during

retirement, need to save an amount of money that

equals their annual income multiplied by a certain

number. What is the number that financial experts

suggest using? (5; 10; 20; 30; Don‘t know/Not sure)

Financial experts have a simple formula for

recommending the percentage of stocks that people

should have in their investment portfolios at

different ages. This formula involves subtracting a

person‘s current age from which of the following

numbers? (50; 80; 110; 140; Don‘t know/Not sure)



Answers to these four questions were coded into two

categories: ―aware‖ and ―not aware‖ of the rule. Aware

responses were those that reflect the general consensus among

financial experts and commentators for each rule. All other

answers (with the exception of omitted answers) were coded as

not aware, as we had no way of comparing the remaining

answers in terms of their proximity to the aware response. For

example, is a person who responds that one can safely

withdraw 12% of one‘s retirement savings each year during

retirement more aware of the 4% withdrawal rule than

someone who chose ―Don‘t know/Not sure‖ as an answer?

In addition to the awareness measures of the four rules of

thumb, respondents were asked to provide information about

their basic socio-demographic characteristics. These

characteristics included age, years of education, gender, marital

status, household income, and percentage of household income

accounted for by the individual respondent. In addition,

respondents were grouped into four employment categories,

each reflecting a different university retirement plan (or

absence thereof). One group (―exempt‖) has a defined

contribution retirement plan and represents roughly half of all

respondents. A second group (―nonexempt‖) has a defined

benefit retirement plan, with a small defined contribution

component. This group comprises 38% of all respondents.

The two remaining groups are both small, one consisting of

part-time employees without a university-administered

retirement plan (―non-benefited‖) and the other composed of

respondents who could not classify themselves as exempt or

non-exempt (―other‖).


Sample Characteristics

During the five-day event period, 3,180 employees took

the quiz and submitted their answers. Of these respondents, 41

did not answer one or more of the rules of thumb questions and

were subsequently dropped from the sample. Another 44

people were dropped because they did not provide an answer to

one of the three socio-demographic questions that were coded

as categorical variables (gender, marital status, and

employment category). In the relatively small number of cases

where there was a missing value for a continuous variable (age,

education, household income, and percent of household income

earned by the respondent), missing values were coded to the

mean for that variable. Recoding missing responses to the

mean value does not bias the coefficient estimates in the

multivariate analyses but it does make the tests of statistical

significance somewhat less conservative. Taken together,

these adjustments resulted in a sample of 3095 people.

The task of comparing the final sample with the overall

population of university employees is complicated by the fact

that university-wide data on socio-demographic characteristics

are only available for full-time employees, that is, those

drawing benefits. As it turned out, very few (259) non-

benefitted employees participated in the survey. (These

employees do not have a retirement plan provided by the

university, but they are eligible to establish a supplemental

retirement account through the university.) The university‘s

Division of Human Resources estimated that approximately

10,000 benefitted employees received the email invitation to

participate in the survey, yielding a cooperation rate among

these employees of approximately 28%.



Study Results

Sample Characteristics

A complete description of the sample characteristics is

found in Table 1. Data are not available to compare the people

who took the survey and those who declined to participate.

Nevertheless, the benefitted employees who took the survey

appear to be reasonably representative of the overall population

of benefitted employees. Benefitted employees who

participated matched university-wide data for age but were

more likely than non-participants to be female and more likely

to work for the health sciences units of the university than the

non-health sciences units. Comparison data were not available

for income or marital status. Note, however, that even if the

sample were exactly representative of the university as a

whole, the results of this study would not be generalizable to

other populations. The results can only indicate trends among

Table 1

Socio-Demographic Characteristics of Sample (N = 3,095)

Variable Definition Mean Std.

Dev.

Age Age in Years 42.9 12.7

Education Years of Schooling 16.2 2.3

Benefited: Non-Exempt* 1=Non-Exempt, 0=Exempt 0.38 0.49

Benefited: Other* 1=Other, 0=Exempt 0.04 0.20

Non-Benefited* 1=Non-Benefited,

0=Exempt

0.08 0.27

Gender: Female 1=Female, 0=Male 0.63 0.48

Household Income Income in $1,000s 82,223 52,841

Household Income Share Percent of Total Income 71.6 27.6

Marital Status: Married 1=Married/Cohabiting,

0=Otherwise

0.71 0.45

*The omitted group in this sequence of dummy variables are those

employees who are exempt and benefits eligible.


the respondents in this study, a shortcoming that is common in

exploratory studies. The sample is fairly diverse in terms of

socio-demographic characteristics (e.g., age, educational

attainment, gender, and income) and therefore permits

exploration of differences among individuals in awareness of

rules of thumb.

Awareness of Rules of Thumb

The descriptive results for awareness of the four rules of

thumb are shown in Table 2. Across the four questions,

awareness of the rules of thumb was low with the modal

Table 2

Responses for Four Retirement Rules of Thumb (N = 3,095)

Response Percent Frequency

Income Replacement Ratio Rule

20-30% 8.98 278

50-60% 36.06 1,116

80-90% 36.45 1,128

110-120% 7.08 219

Don‘t know/Not sure 11.44 354

20-Times-Income Rule

5 10.76 333

10 26.59 823

20 27.21 842

30 7.88 244


110-Minus-Age Rule

50 9.66 299

80 28.08 869

110 22.84 707

140 1.94 60

Don‘t know/Not sure 37.48 1,160

4% Withdrawal Rule

4% 41.32 1,279

8% 20.39 631

12% 12.08 374

16% 2.39 74




number of rules selected that correspond to the advice of

financial experts being 1-2. Only 108 (3.5%) respondents

selected all four rules while 858 (28%) respondents identified

none of them. In none of the four cases did a majority of

respondents select the rule as it is formulated by financial

experts and commentators. Awareness was highest for 4%

withdrawal rule (41.32%) and lowest for the 110-minus-age

rule (22.8%). Percentages of the sample ranging from 11.4 to

37.5 chose ―Don‘t know/Not sure,‖ but when eliminating these

people, the number of people who misidentified a rule of

thumb typically exceeded those who had correctly identified it.

For example, 1,613 people chose an income replacement ratio

below or above ―80-90%,‖ compared to 1,128 choosing this

replacement interval.

People who were aware of one rule of thumb were more

likely to be aware of other rules of thumb, but only mildly so.

Correlations among the four questions were consistently

positive and statistically significant at the p<.0001 level, but

they were also weak. The highest association was between

awareness of the income replacement rule and awareness of the

110-minus age rule, but the correlation was only .16. Thus, it

makes sense to analyze separately the socio-demographic

predictors of awareness rather than try to create a measure that

combines awareness across the four measures.

Predictors of Awareness

Given the dichotomous nature of the dependent variables,

multinomial logit analyses were used to examine the

connection between awareness and respondent characteristics.

These characteristics were age, years of education, gender,


Table 3

Estimated Odds Ratios for the Logistic Regressions

(95% confidence interval in parentheses)

Independent

Variables

Replace.

Ratio 20 X Income 110 - Age 4% Withdraw

Age 1.021

(1.02-1.03)**

1.00

(1.00-1.00)

1.00

(0.99-1.00)

1.01

(1.01-1.02)**

Education 1.05

(1.01-1.10)**

1.06

(1.02-1.11)**

1.14

(1.08-1.19)**

1.03

(0.992-1.07)

Non-Exempt,

Benefits

Eligible1

0.83

(0.69-1.01)*

0.97

(0.79-1.19)

0.98

(0.78-1.22)

1.03

(0.86-1.24)

Other, Benefits

Eligible1

0.74

(0.47-1.17)

0.78

(0.47-1.28)

0.68

(0.38-1.23)

0.90

(0.58-1.38)

Non-benefited1

0.88

(0.64-1.21)

0.94

(0.68-1.31)

1.04

(0.73-1.47)

1.21

(0.90-1.63)

Gender 0.81

(0.69-0.96)**

0.73

(0.62-0.87)**

0.70

(0.58-0.85)**

0.81

(0.69-0.95)**

Household

Income

(in $1000s)

1.00

(1.00-1.00)

1.00

(1.00-1.00)

1.01

(1.00-1.01)**

1.01

(1.00-1.01)**

Household

Income Share

1.00

(1.00-1.01)

1.00

(0.99-1.00)

1.00

(1.00-1.00)

1.00

(1.00-1.01)

Marital Status:

Married

1.34

(1.09-1.66)**

0.99

(1.00-1.00)

0.92

(0.73-1.16)

1.07

(0.88-1.30)

χ2 131.90** 31.08** 127.47** 46.64** 1The omitted group in this sequence of dummy variables are those

employees who are exempt and benefits eligible.

** p<.05, *p<.10

marital status, household income, percentage of household

income accounted for by the individual respondent, and

employment category as it bears on type of retirement plan.

The only characteristic that predicted awareness across

all four awareness measures was the respondent‘s gender, with

men being more aware than women. Higher levels of



education predicted greater awareness for three of the

awareness measures, the exception being the 4% withdrawal

rule. Older respondents displayed greater awareness of the

income replacement ratio rule and the 4% withdrawal rule than

younger respondents, but there were no age differences for the

other two rules. Interestingly, neither household income nor

the percentage of the household income earned by the

respondent predicted awareness. It might have been expected

that people with relatively greater household incomes and who

account for the majority of their household‘s income would be

more attuned to retirement planning information, including

rules of thumb. Similarly, people with defined contribution

plans have greater responsibility for guiding their retirement

planning than those with defined benefit plans and therefore

they might have been expected to show greater awareness of

the rules of thumb. This was not the case, though.

Discussion

To put a human face on the research reported here, we

spoke with four professional financial advisors to get their

views on the value of retirement rules of thumb. Some

advisors argued that awareness of retirement rules of thumb is

an important element of financial literacy and can serve as a

conversation starter in retirement planning. These advisors

reported that clients who are aware of financial rules of thumb

have little trouble understanding that these rules need to be

customized to fit individual circumstances. One advisor felt

that the sooner her clients learn these rules, the better. Another

believed that these rules are most useful when clients are

young, that is, just beginning their financial education.


Other advisors with whom we spoke were less sanguine

about the use of rules of thumb by their clients. These advisors

reported having to spend time explaining the limitations of

these rules. One advisor believed that clients can be lulled into

a false sense of security just because they have met one or

more of these rules.

What about individuals who use retirement planning rules

of thumb without the guidance of a financial professional?

What is the likely impact of their reliance on these rules? First,

it should be noted that overall awareness of the four rules of

thumb studied here is fairly low, with only the 4% withdrawal

rule exceeding a 40% awareness threshold for the sample.

Thus, any help or harm that comes from the use of these rules

is unlikely to be widespread. Second, people with higher levels

of education tend to be more aware of the retirement rules of

thumb, suggesting that these rules are a component of financial

literacy rather than a substitute for it. Similarly, men are more

aware of the rules than women. Given that research

consistently reveals that men are more financially literate than

women (Lusardi and Mitchell, 2008; Fonseca, Mullen,

Zamarro, and Zissimopoulos, 2010), our finding again suggests

that rules of thumb are used by those who are likely be able to

assess the benefits and shortcomings of these rules.

Conclusion

Our study was exploratory in nature and as such

addresses only a few of the research questions that are raised

by the existence of retirement planning rules of thumb. As

indicated at the outset, our interest in documenting public



awareness of these rules of thumb should not be interpreted as

an endorsement of the accuracy or utility of these rules. Our

results suggest, however, that awareness of these rules –

especially among men and relatively well educated individuals

– merits additional investigation. This research can examine

awareness of these rules in a more nationally representative

sample and, more important, any relationships among

awareness, use of these rules, and retirement preparedness.

Future research might also compare awareness, understanding,

and use of retirement rules of thumb among clients of

professional financial planners versus those who plan without

professional help. To the extent that professional planners

promote client awareness and use of rules of thumb, do

planners favor conservative rules (to reduce the possibility of

being viewed as ―failures‖) or aggressive ones (to increase

commissions, fees, and other forms of remuneration)?

Regardless of whether a person works with a financial

planning professional or is a do-it-yourselfer, individuals need

to be active participants in retirement planning. Rules of

thumb, by virtue of their simplicity, may serve as

steppingstones to more sophisticated retirement planning.

As long as individuals understand the benefits and

limitations of retirement rules of thumb, efforts to educate the

public about these rules can have two types of benefits. First,

awareness of these rules can serve as building blocks of

financial literacy, especially when used in conjunction with

professional financial assistance. Second, public education

efforts can correct misperceptions about the content of

common rules of thumb. We found that many people


inaccurately describe particular rules of thumb (e.g.,

subtracting their age from 80 rather than from 110 in the rule

about asset allocation across the lifespan). If rules of thumb

are to be useful at all, they need to be the rules of thumb that

have achieved some rough consensus among scholars and

practitioners, not some misunderstanding of these rules.

References

Armstrong, F., & Doss, J.R. (2009). The retirement challenge--Will you sink

or swim? A complete, do-it-yourself toolkit to navigate your financial

future. Upper Saddle River, NJ: FT Press.

Bengen, W.P. (1994). Determining withdrawal rates using historical data.

Journal of Financial Planning, 7(4), 14-24.

Bengen, W.P. (1996). Asset allocation for a lifetime. Journal of Financial

Planning, 9(4), 58-67.

Benna, T. (2009). Managing your money all-in-one for dummies. Hoboken,

NJ: Wiley.

Campbell, J.Y. (2006). Household finance. Journal of Finance, 61(4),

1553-1604.

Certified Financial Planning Board of Standards, Inc. (2009). 2009 National

consumer survey on personal finance. Washington, D.C. Retrieved

from http://www.cfp.net/downloads/CFP_Board_2009_National_

Consumer_Survey.pdf

Cooley, P. L., Hubbard, C.M., & Walz, D.T. (1999). Sustainable

withdrawal rates from your retirement portfolio. Financial Counseling

and Advising 10(1), 40-51.

Eisenberg, L. (2006). The number. New York: Free Press.

Elmerick, S. A., Montalto, C. P., & Fox, J. J. (2002). Use of financial

planners by U.S. households. Financial Services Review, 11(3),

217-213.

Ferrell, C. (2009). Your Money Ratios. New York: Avery/Penguin.

Fonseca, R., Mullen, K.J., Zamarro, G., & Zissimopoulos, J. (2010). What

explains the gender gap in financial literacy? Rand Working Paper

762, June. Retrieved from http://www.rand.org/content/dam/rand/

pubs/working_papers/2010/RAND_WR762.pdf

Garman, E.T., & Forgue, R.E. (2010). Personal finance. Boston, MA:

Houghton Mifflin.

Glaittli, M. (2011). Retirement rules of thumb: History and assessment by

professionals, Senior Honor‘s Thesis, University of Utah, April 29.

Helman, R., Copeland, C., & VanDerhei, J. (2011). The 2011 retirement

confidence survey: Confidence drops to record lows, reflecting ‗the



new normal,‖ Issue Brief 355, March. Retrieved from

http://www.ebri.org/pdf/briefspdf/EBRI_03-2011_No355_RCS-

2011.pdf

Hung, A., Parker, A.M., & Yoong, J. (2009). Defining and Measuring

Financial Literacy. RAND Working Paper 708. September. Retrieved

from http://www.rand.org/content/dam/rand/pubs/working_papers/

2009/ RAND_WR708.pdf

Huston, S.J. (2010). Measuring financial literacy. Journal of Consumer

Affairs, 44(2), 296-316.

Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under

uncertainty: Heuristics and biases. New York: Cambridge University

Press.

Lozada, G.A. (2004). Constructing age-dependent portfolios, Journal of

Personal Finance, 3(4), 59-75.

Lusardi A. and O. Mitchell (2008). Planning and financial literacy: How do

women fare? American Economic Review, 98(2), 413-417.

Make Peace With Your Money (2009). Money, August, p.6.

MarksJarvis, G. (2007). Saving for retirement without living like a pauper

or winning the lottery. Upper Saddle River, NJ: Financial Times.

Moffitt, D. (1977, January 24). How some professional pension-planning

ideas can show how much to save for retirement. Wall Street Journal,

p. 34.

Nuccio, S. (1965a, July 8). Personal finance: Planning pensions. New York

Times, p. 39.

Nuccio, S. (1965b, December 9). Personal finance: Facing retirement. New

York Times, p. 73.

Palmer, B.A. (2008). 2008 GSU/Aon RETIRE project report. Georgia State

University. J. Mack Robinson College of Business. Retrieved from

http://rmictr.gsu.edu/Papers/RR08-1.pdf

Deep-six the ‗rule of 110‘ (2008, August). Consumer Reports Online.

Retrieved from http://www.consumerreports.org/cro/money/personal-

investing/recession-financial-advice/deep-six-the-rule-of-110/

recession-financial-advice-deep-six-the-rule-of-110.htm

?loginMethod=auto

Remund, D.L. (2010). Financial literacy explicated: The case for a clearer

definition in an increasingly complex economy. Journal of Consumer

Affairs 44(2), 279-295.

Schiller, M. K. (1981). Personal and family finance: Principles and

applications. Boston, MA: Allyn & Bacon.

Scholz, J.K., & Seshadri. A. (2009). What Replacement Rates Should

Households Use? University of Michigan Retirement Research Center,

Working Paper WP 2009-214. http://www.mrrc.isr.umich.edu/

publications/papers/pdf/wp214.pdf

Schwartz, Barry (2004). The Paradox of Choice. New York, Ecco.


Scott, J.S., Sharpe, W.F., & Watson, J.G. (2009). The 4% rule at what

price? Journal of Investment Management, 7(3), 31-48.

Sheard, R. (2000). Money for life: The 20 factor plan for accumulating

wealth while you're young. New York: HarperBusiness.

Simon, H. A. (1956). Rational choice and the structure of the environment.

Psychological Review, 63, 129–138.

Stein, B., & DeMuth, P. (2005). Yes, you can still retire comfortably! The

baby-boom retirement crisis and how to beat it. Carlsbad, CA: New

Beginnings.

Strohfus, P.K., & Schrader, V. (2009). Assisting nursing students to plan for

retirement. Nurse Educator, 34(2), 54-55.

Wang, J. (2007). Stock allocation rule: 120 minus age. Bargaineering

Personal Finance Blog. January 7. Retrieved from

http://www.bargaineering.com/articles/stock-allocation-rule-120-

minus-age.html

Williamson, G.K. (1993). Sooner than you think: Mapping a course for a

comfortable retirement. Homewood, IL: Business One Irwin.



THE DEMAND FOR FINANCIAL PLANNING SERVICES

Sherman D. Hanna, Ph.D.

Ohio State University

Based on 1998 to 2007 Survey of Consumer Finances datasets the

proportion of households reporting use of a financial planner

increased from 21% in 1998 to 25% in 2007, with an estimated

increase of almost five million households between 2004 and 2007.

Multivariate analysis shows that the likelihood of using a financial

planner is strongly related to risk tolerance, with those with low

risk tolerance the least likely, and those with above average risk

tolerance the most likely to use a financial planner, controlling for

income, net worth, age, and other factors. Those with substantial

risk tolerance have significantly lower likelihood of using a

financial planner than those with above average risk tolerance.

Black households are more likely but Hispanic and Other/Asian

households are less likely than comparable White households to

use a financial planner. The likelihood of using a financial planner

increases with net worth for ranges above zero, but also increases

as net worth decreases below zero.

The proportion of households using financial planners

has increased, but is at a relatively low level, even at high

levels of income and net worth. What factors are related to the

use of financial planners? Which types of households seem to

be underserved by financial planners? This paper uses a

combination of the 1998 to 2007 Survey of Consumer Finances

datasets to analyze the effects of household characteristics and

risk tolerance on the use of financial planners. The empirical

results are discussed in the context of normative analyses of the

Sherman D. Hanna, Consumer Sciences Department, Ohio State

University, 1787 Neil Avenue, Columbus, OH 43210; (614) 292-4584;

[email protected]


value of financial planning advice, thus providing context for

the identification of underserved segments of the population.

Literature Review

Studies analyzing empirical patterns on use of financial

planners have not focused on the theoretical relationship

between the value of financial planning advice and risk

tolerance. Bae and Sandager (1997) reported results from

convenience samples, and found that respondents were most

interested in advice on retirement funding, investment/asset

growth, and reducing tax burden. Elmerick, Montalto, and Fox

(2002) used the 1998 Survey of Consumer Finances (SCF)

dataset to analyze the types of households that reported using a

financial planner for comprehensive advice, advice on savings

and investment, or advice on credit. They did not provide a

theoretical framework other than a brief mention of modern

portfolio theory, but noted that many households sought

comprehensive financial planning advice. They reported that

21% of households used a financial planner for some type of

advice. In their multivariate analysis, those under 35 were

more likely to use a financial planner than those 35 and older,

use of financial planners increased with education, Blacks were

more likely and Hispanics less likely than Whites to use

financial planners, unmarried female households were more

likely than married households to use financial planners, use of

financial planners increased with income to the $50,000 to

$74,999 range and then was roughly the same above that level,

and the use of financial planners increased with net worth and

also with the level of financial assets.



Chang (2005) discussed some sociological theoretical

aspects of the decision to seek help with savings and

investment decisions from a social network versus professional

help, and noted ―…although socioeconomic status should be

positively related to access … those with the most resource-

rich networks may be least likely to use them to search for

financial information because they have greater access to

alternative sources of information, such as financial

professionals.‖ Chang used the 1998 SCF to analyze the

likelihood of seeking financial advice from paid financial

professionals: financial planners, accountants, brokers and

lawyers. Chang reported that the most common source of

advice was friends or relatives, mentioned by 41% of those

who reported saving or investing, compared to about 36% who

consulted some type of paid financial professional. Chang‘s

multivariate analysis showed that use of paid financial

professionals increased with education and liquid asset level

but decreased with income, was higher for single female head

households than for married couples, higher for Black

households than for White households, but lower for Other

(Hispanic and Other/Asian combined) than for White

households, and increased with risk tolerance.

Peterson (2006) suggested that a household‘s need for

financial planning services should be related to the complexity

of its financial situation, which he stated should depend on the

number of goals, the number of financial accounts, the number

of dependents, and the level of financial resources. He noted

that the need for financial planning services must be balanced

against the cost of the services. He analyzed the 2004 SCF

dataset, and found that the use of a financial planner was


positively related to resources and the number of goals and of

accounts, but not related to the number of dependents.

Hanna and Lindamood (2010) discussed the theoretical

benefits of using a financial planner based on expected utility

analysis, and estimated the monetary value of hypothetical

ideal advice to a naïve consumer. Assuming plausible values

of risk aversion, advice that is likely to increase wealth in the

future is not valued as much as the expected wealth increase,

and those with high risk aversion (low risk tolerance) would

not place much value on such advice. However, advice that

reduces the risk of large wealth losses has very high value,

even if the probability of the loss is very low, and the value of

such advice increases substantially with risk aversion.

Consumers with very high risk aversion (very low risk

tolerance) might value such advice very highly. In one

example for a household with total wealth of $2,500,000, they

demonstrated that advice that eliminates the risk of one in a

thousand chance of a loss of 80% of household wealth would

have a value of $1,620 if relative risk aversion is very low, but

$932,709 if relative risk aversion is very high. Therefore,

those with very high risk aversion (very low risk tolerance)

should place high values on risk reducing advice.

This article analyzes a combination of the 1998 to 2007

Surveys of Consumer Finances in use of financial planners by

households, and therefore represents an advance over previous

research in testing for changes over time in the use of financial

planners. This article is also the first to test separately for the

effect of negative net worth on the use of financial planners.

By discussing the results in terms of a normative model for the



benefits of financial planning services, this article also provides

more insights into underserved segments of the U.S.

population.

Theory

Given that financial planners are paid by commissions or

fees or a combination of methods, it makes sense that a

household‘s resources, including income and assets, would

affect its demand for financial planning services. I will focus

on the use of financial planners, and ignore the interaction

between the demand for financial planning services and the

demand for financial advice from others such as bankers and

brokers, but including the demand for other types of advice

would be an obvious extension to this research.

All other things equal, those with low risk tolerance

should place a much higher value on financial planning advice

that reduces risk than those with high risk tolerance (Hanna &

Lindamood, 2010). The reverse is true for advice that

increases the expected value of wealth, but the difference in

value of such advice for low and high risk tolerance households

is much smaller than the difference in value for risk reduction

advice. Therefore, households with low risk tolerance should

have a higher demand for financial planning services than

households with high risk tolerance.

The need for financial planning services may be related

to the ability of the household to do its own planning, which is

presumably related to the complexity of its financial situation

as well as its knowledge and cognitive ability. Warschauer

(2008) discussed some major issues in financial planning, and


obviously the simpler types of households, e.g., a young one

person household with no savings or discretionary income,

might have low need for financial planning services, whereas

an older household with higher income and assets might have

higher needs. The ability of a household, in terms of

experience with its financial management, might be related to

age and cognitive ability, as well as formal learning. Education

is related to cognitive ability (Berry, Gruys, & Sackett, 2006).

However, even though a person with high cognitive ability may

be more likely to be able to manage his or her own financial

planning tasks, such a person might also be more likely to

recognize the need. As Yuh and Hanna (2010) discussed,

education might be related to being more future-oriented, and

therefore more educated households might place a higher value

on the future benefits of financial planning services.

For a particular level of net worth, complexity, and

ability of the household to manage its own finances, age may

be related to the perceived value of financial planning services

in terms of the value of future benefits, based on remaining life

expectancy. Discounting future benefits at some rate, e.g., 3%

per year, would mean that the present value of the benefits of

financial planning services would be much lower for somebody

with a 20 year remaining life expectancy than for a 30 year

remaining life expectancy, though for younger households

there would not be a large difference between a 30 year and a

40 year life expectancy. Therefore, in terms of age, there

would not be much difference between a 30 year old and a 40

year old with otherwise similar situations in terms of the

present value of future benefits of financial planning, but there

might be a substantial difference between a 60 year old and a



70 year old, as remaining life expectancies might decrease

substantially. As for single head versus couple households,

with equal abilities, the remaining life expectancies might

imply greater benefit for couple households, but task

specialization (Lindamood & Hanna, 2005) would imply that

for a given level of resources and complexity, couples would

have less need for paid financial planning services than single

head households.

Methods

Data and Variables

I use a combination of the 1998, 2001, 2004, and 2007

SCF datasets to study the demand for financial planners. For

more information about the SCF datasets and methodological

issues, see Bucks, Kennickell, Mach, and Moore (2009),

Lindamood, Hanna, and Bi (2007), and Hanna, Lindamood and

Huston (2009). The SCF dataset contains five implicates. I use

the repeated-imputation inference (RII) method to correct for

underestimation of variances due to imputation of missing data

(Montalto & Sung, 1996). The descriptive results are weighted

to represent the population proportions of households, with the

SCF population weights adjusted so that the apparent sample

size was equal to the actual sample size. In general, I follow

methods suggested by Lindamood, et al. (2007).

The dependent variable is whether a household reported

using a financial planner for information on savings or

investment decisions, and/or for borrowing or credit decisions.

One of the questions was: ―What sources of information do you

use to make decisions about saving and investments?‖ That

question, and a similar question about borrowing or credit


decisions, presented some alternatives such as

magazines/newspapers, and open-ended responses were also

coded (see discussions in Elmerick, et al., 2002 and in Chang,

2005).

The explanatory variables included in the study are age of

the head, education of the household, job status of the

household, risk tolerance, household income, presence of

children aged under 19, homeownership, and household type,

as well as the racial/ethnic self-identification of the respondent.

The racial/ethnic categories are those available in the public

datasets of the SCF, White, Black, Hispanic, and a combined

Other category which is likely to be mostly Asian/Pacific

Islander (Hanna & Lindamood, 2008). The possibility of

nonlinear effects for age makes it reasonable to include both

age and age squared to account for non-linear effects of age in

our multivariate analysis, but in the descriptive analyses (Table

2) I classify age using six categories: under age 30, age 30-39,

age 40-49, age 50-59, age 60-69, and age 70 and over.

Education may have an impact on the financial knowledge of

the household, and therefore its choices. For non-couple

households, education is based on the highest education

attained by the head, but for couple households, it is based on

the partner with the higher level of education. For instance, if a

husband‘s highest education is a high school diploma and the

wife has a bachelor degree, the education of the household is

coded as bachelor degree. Job status is based on the head for

non-couple households, and for couple households I use the

status of both the head and the partner/spouse based on the

following: if one or both are self-employed I count the

household status as self-employed, if neither is self-employed



but at least one is an employee I count the household status as

employee, if neither is employed or self-employed but neither

is of retirement age I count the household status as no work,

and if neither is employed or self-employed but at least one is

of retirement age I count the household status as retired.

Couples may make different choices and generally may have

more potential resources than single people. Having a

dependent child under the age of 19 may increase the number

of goals but also reduce the amount available for investing

(Yuh & Hanna, 2010), so it is unclear whether it will have a

positive or negative effect on the use of financial planning

services.

The income and wealth-related factors include household

income, net worth, and homeownership. Household income

and net worth are measured using natural logs to capture the

possible non-linearity of the relationship, although for our

descriptive results in Table 2, I present results using categories

of income and net worth. For values of income and net worth

equal to zero, the log of 0.01 is used. Net worth is specified as

a piecewise (Suits, Mason, & Chan, 1978) log variable to allow

for different effects for positive and for negative net worth.

Households with negative net worth are different from

households with low net worth (Chen & Finke, 1996) so I

allow for separate effects of net worth in the negative range

versus in the positive range of net worth.1

1For positive values of net worth, the log of net worth is used, and otherwise

that variable is computed as the log of 0.01. A separate variable is

computed for negative values of net worth, the log of the absolute value of

net worth, and for non-negative values of net worth, that variable is

computed as the log of 0.01.


Statistical Analysis

For descriptive results, statistical tests for differences in

the proportions of households using financial planners are

calculated allowing for the implicate structure of the SCF

datasets (SAS code is shown in Chen, 2007), with differences

calculated relative to the same reference categories used in the

multivariate analyses, e.g., the mean for Hispanic households is

compared to the mean for White households. Logistic

regression (Logit) is an appropriate technique for a multivariate

analysis of a dependent variable with a small number of levels

(Allison, 1999). As suggested by Montalto and Sung (1996),

this study uses the repeated-imputation inference (RII) method

to correct for underestimation of variances due to imputation of

missing data.2 I also created graphs to illustrate selected logit

results, along with descriptive results.3

Results

Descriptive Results

The proportion of households using a financial planner

increased from 21% in 1998 to 25% in 2007 (Table 1). Based

on the SCF sampling weight, in 2007 over 29 million

households reported using a financial planner, an increase of

almost five million households over 2001. Table 2 contains

means tests of using a financial planner by categories of

2Deaton (1997) suggested that weighting regression procedures using

endogenous weights might result in biased estimates, so I did not weight the

logistic regression. 3For the logit results, I used a transformation (Allison, 1999, p. 14) of the

estimated coefficients, e.g., for age and age squared, but applied them at the

mean levels of each corresponding descriptive category, and adjusted the

calculated likelihood so that the mean of the patterns corresponded to the

overall sample mean.



independent variables. (For income and net worth, I used mean

rates by categories for the descriptive table, even though I use

continuous variables in the logistic regression.) There are

significant differences in the likelihood of using a financial

planner by most of characteristics used in this study.

The likelihood of using a financial planner was roughly

the same for 1998, 2001, and 2004, and then it increased

significantly in 2007 to 25%. Only 11% of those who said they

were unwilling to take any risks with investments used a

financial planner, with the other levels of risk tolerance having

higher rates, with the peak rate of 33% being for ―above

average,‖ and the ―substantial‖ level having a significantly

lower rate (29%) than the rate for ―above average.‖

The proportion using a financial planner increased, then

decreased with age, from 18% for the less than 30 category to

27% for the 50 to 59 category, then decreased to 16% for the

70 and older category. Married households were the most

Table 1

Number of Households Using a Financial planner, and Percent of

All Households, by Survey Year

Year Number of Households

Using a Financial Planner

Percent of Households


1998 21,670,000 21.1%

2001 21,300,000 22.0%

2004 24,350,000 21.7%

2007 29,300,000 25.2%

Calculated by author, weighted projections from 1998, 2001, 2004, and

2007 Surveys of Consumer Finances


Table 2

Using a Financial Planner by Various Characteristics, Bivariate

Analysis, Combined 1998-2007 Datasets (Means Test)

Variable Category

% in

category

Using a

Financial

Planner

(n=17,684) Per-

cent

sig.

level1

Survey year year 1998 24.3 21.1

year 2001 25.1 20.0 .004

year 2004 25.6 21.7 .131

year 2007 25.0 25.2 .000

Risk tolerance no risk 40.7 10.8 .000

average 38.1 28.2 .000

above average 17.2 33.2

substantial 4.0 28.7 .000

Age Less than 30 13.5 17.6

30-39 19.1 23.7 .000

40-49 22.2 22.9 .000

50-59 17.8 26.9 .000

60-69 12.0 23.8 .000

70 and over 15.5 15.7 .000

Marital status married 50.0 24.6 .000

single male 14.5 19.4

single female 27.2 19.5 .842

partner 7.3 18.8 .261

Racial/ethnic status of White 75.4 23.6

respondent Black 12.8 20.8 .000

Hispanic 8.4 11.6 .000

Other/Asian 3.4 17.7 .000

Household education < high school 11.1 7.2

{for couples, high school 28.2 15.2 .000

maximum level some college 26.5 23.5 .000

of either partner} bachelor degree 20.1 28.7 .000

post-bachelor degree 14.1 35.1 .000



Table 2

Using a Financial Planner by Various Characteristics, Bivariate

Analysis, Combined 1998-2007 Datasets (Means Test)

Variable Category

% in

category

Using a

Financial

Planner

(n=17,684) Per-

cent

sig.

level1

Child<age 19 yes 43.6 22.4 .006

no 56.4 21.6

Employment status employee 61.4 23.1

{of household} self-employed 14.0 28.0 .000

retired 21.4 16.3 .000

not employed 3.2 12.9 .000

Homeowner yes 67.9 15.5 .000

No 32.1 25.1

Household income 0-23,654 24.9 11.3

23,654-46,250 25.3 17.4 .000

46,251-82,966 24.8 24.6 .000

82,967-135,242 15.0 31.7 .000

>135,242 10.0 39.2 .000

Household net worth < 0 7.4 16.2 .000

0-14,000 17.6 10.7

14,001-102,753 25.0 17.3 .000

102,754-333,200 25.0 23.1 .000

333,201-822,716 14.9 32.6 .000

> 822,716 10.1 39.5 .000

All households 100.0 22.0 1Significance test is for mean difference from reference category for each

variable. Bold is the reference category; weighted data; RII technique is

used.

likely to use a financial planner (25%), while other types of

households had rates roughly five percentage points lower.

Only 12% of households with Hispanic respondents used a

financial planner, compared to 24% of those with White

respondents, 21% of those with Black respondents, and 18% of


households with respondents choosing an ―other‖ racial/ethnic

category.

The likelihood of using a financial planner increased

steadily with education, from 7% for those with less than a

high school degree to 35% of those with a post-bachelor

degree. Having a child under 19 in the household was related to

a slightly higher rate of using a financial planner. Households

with a self-employed head or spouse had the highest rate of

using a financial planner, 28%, compared to 23% for

households with an employee, 16% for retired households, and

13% for those otherwise not employed. Homeowners were

more likely to use a financial planner than renters.

The likelihood of using a financial planner increased with

income, from 23% of households with annual incomes under

$23,654 to 39% of households with incomes over $135,242.

Over 7% of households had negative net worth. The likelihood

of using a financial planner was higher for those households

(16%) than was the likelihood for households with net worth of

zero to $14,000 (11%) and about the same as the rate for

households with net worth of $14,001 to $102,753. The rate

steadily increased net worth increases, with almost 40% of

those with net worth over $822,717 using a financial planner.

Multivariate Results

The logistic regression shows the effects of independent

variables on the likelihood of using a financial planner (Table

3). Most of the effects are similar to the descriptive patterns

shown in Table 2. Figure 1 shows the actual and calculated

likelihoods of using a financial planner by survey year. The

calculated results are based on the logit coefficients for survey



Table 3

Using a Financial Planner, Multivariate Logistic Analysis

(n=17,684)

Variable1


Coeff.2 p-val.

3 s.e. Odds ratio

Intercept -3.0562 .000 0.2459

Survey year (1998)

year 2001 -0.0742 .155 0.0522 0.928

year 2004 0.0532 .302 0.0515 1.055

year 2007 0.3506 .000 0.0505 1.420

Risk tolerance (above average)

no risk -0.8803 .000 0.0611 0.467

average -0.0767 .078 0.0436 1.009

substantial -0.2624 .001 0.0797 0.899

Age 0.0278 .000 0.0077 1.028

Age squared -0.0003 .000 0.0001 1.000

Marital status (married)

single male -0.1841 .004 0.0635 0.832

single female 0.2107 .000 0.0555 1.234

partner -0.0879 .292 0.0835 0.916

Racial/ethnic status (White)

Black 0.3013 .000 0.0695 1.352

Hispanic -0.2360 .014 0.0958 0.790

Other/Asian -0.3529 .001 0.1068 0.703

Education (< high school)

high school degree 0.3383 .003 0.1149 1.403

some college 0.6038 .000 0.1152 1.829

bachelor's degree 0.7017 .000 0.1177 2.017

post-bachelor degree 0.8276 .000 0.1196 2.288

Presence of a child < 19 -0.1564 .000 0.0421 0.855

Employment status (employee)

self employed -0.0649 .159 0.0461 0.937

no work but not retired -0.2429 .082 0.1396 0.784

retired 0.0487 .471 0.0676 1.050


Table 3

Using a Financial Planner, Multivariate Logistic Analysis

(n=17,684)

Variable1


Coeff.2 p-val.

3 s.e. Odds ratio

Income (log) [if ≤ 0, log(.01)] 0.0261 .033 0.1225 1.026

Net worth (log) [if ≤ 0, log(.01)] 0.1015 .000 0.0111 1.107

-Net worth (log) [if ≥ 0, log(.01)] 0.0950 .000 0.0135 1.100

Homeowner 0.1004 .089 0.0590 1.106

Concordance (mean) 70.2% 1Reference category in parentheses.

2Unweighted analysis combining all five implicates.

3Significance level and standard error based on RII technique.

Figure 1

Rate of Use of Financial Planner by Survey Year, and Rate

Calculated Based on Mean Values of Other Variables

Actual rates based on results shown in Table 2. Calculated rates based on

logit results shown in Table 3, at mean values of other variables.

19%

21%

23%

25%

27%

1998 2001 2004 2007

Mean by Year Calculated at Mean Values of Other Variables



Figure 2

Rate of Use of Financial Planner by Risk Tolerance, and Rate




year in Table 3, with all other variables set at the overall

sample means. In other words, the calculated results show what

the financial planner use would have been if characteristics

such as risk tolerance, income, net worth, and household

composition had not changed during the period. For both the

actual and calculated results, there was not much change for

1998, 2001, and 2004, but there was a substantial increase

between 2004 and 2007 for both the actual and calculated

likelihoods.

Figure 2 shows the actual and calculated likelihoods of

using a financial planner by risk tolerance. As with the

descriptive results, the highest likelihood of using a financial

planner is for those with above average risk tolerance, although

those with average risk tolerance were not significantly

10%

15%

20%

25%

30%

35%

no risk average above average substantial

mean by risk tolerance level

calculated at mean values of other variables


Figure 3

Rate of Use of Financial Planner by Category of Age of Head, and

Rate Calculated Based on Mean Values of Other Variables

Actual rates based on results shown in Table 2 (age of head <30, 30-39,

40-49, 50-59,60-69, 70 and over.) Calculated rates based on logit results

shown in Table 3, at mean values of other variables.

different from those with above average risk tolerance based on

the logit. Those having substantial risk tolerance were

significantly less likely to use a financial planner than those

with above average risk tolerance. As with the actual pattern

(Table 2), the logit implies that those unwilling to take any risk

were much less likely to use a financial planner than those

willing to take average or above average risk, though the

differences were somewhat reduced because of the setting of

income, net worth, and other household characteristics at the

overall sample mean.

15%

17%

19%

21%

23%

25%

27%

25 35 45 55 65 75

Mean of Age Group Calculated Based on Logit



Figure 4

Rate of Use of Financial Planner by Racial/Ethnic Category of

Respondent, and Rate Calculated Based on Mean Values of Other

Variables



Figure 3 shows the actual and calculated likelihoods of using a

financial planner by the age of the head. The combined effect

of age and age squared implies that the likelihood of using a

financial planner increases until age 42, then decreases, so the

peak is lower than the peak age range in the descriptive results,

50 to 59. Note that the calculated likelihood of using a

financial planner for those under 30 is almost as high as for

those age 30 to 39 or 40 to 49, which is because of the

assumption that the younger households had the same net

worth and other characteristics as the sample means. Both the

actual and calculated likelihoods decreased substantially from

about age 55 to age 80.

11%

13%

15%

17%

19%

21%

23%

25%

27%

White Black Hispanic Asian/other

mean by groupl calculated at mean values of other variables


Figure 5

Rate of Use of Financial Planner by Household Education, and Rate




Household education is defined by the highest education level of the head

for single head households, and the maximum education level of either

partner for couple households.

Figure 4 shows the actual and calculated likelihoods of using a

financial planner by the racial/ethnic identification of the

respondent. Unlike the actual patterns, the calculated patterns

show that households with a Black respondent would be more

likely than households with a White, Hispanic, or Other/Asian

respondent to use a financial planner, if each group had the

overall sample mean levels of net worth and other household

characteristics. Households with Hispanic respondents and

households with Other/Asian respondents would be less likely

than households with White respondents to use a financial

planner, given equal household characteristics.

5%

10%

15%

20%

25%

30%

35%

<HS HS Some college BS Post-BS

mean by education level




Figure 6

Rate of Use of Financial Planner by Household Income Category,

and Rate Calculated Based on Mean Values of Other Variables




using a financial planner by household education. Both

patterns show rates substantially increasing with education,

though the calculated pattern is less steep than the actual

pattern, because of the assumption that each group has the

overall sample means of net worth and other characteristics.


using a financial planner by household income. The effect of

income in Table 3 is statistically significant, but the magnitude

of the effect shown in the graph is small, with most of the

effect for increases from very low income to the mean of the

10%

15%

20%

25%

30%

35%

40%

　0-23,654 23,654-

46,250

46,251-

82,966

82,967-

135,242

>135,242

Income Category

mean by income category



Figure 7

Rate of Use of Financial Planner by Household Net Worth Category,

and Rate Calculated Based on Mean Values of Other Variables



lowest category (not shown in Figure 6). A household with an

annual income of $0.01 would have a calculated likelihood of

using a financial planner of 16%, assuming mean values of net

worth and other characteristics, while one with an income of

$343,455 (mean of top decile) would have a calculated

likelihood of 23%, and one with an income of $25,000,000

would have a calculated likelihood of 25%.


using a financial planner by household net worth. The

For the age, income, and net worth graphs (Figures 3, 6, and 7) the logit

results were used to calculate likelihoods at the mean levels for the

descriptive categories, e.g., the calculated likelihood for the lowest income

category in Figure 6 is for the mean income in that category, $13,437.

10%

15%

20%

25%

30%

35%

40%

<0 0-14,000 14,001-

102,753

102,754-

333,200

333,201-

822,716

>822,716

Net Worth Category

mean by net worth category




calculated likelihood of using a financial planner increases

strongly with net worth as net worth increases from zero, but it

also increases strongly as net worth becomes more and more

negative. The calculated likelihood shown for the negative net

worth category is for the mean level of net worth for that

category, -$16,032. The logit coefficient for Ln(-net worth)

implies that a household with a negative net worth of -

$300,000, would be as likely to use a financial planner as an

otherwise similar household with positive net worth of

$99,980.

Controlling for net worth, income, and other

characteristics, single headed female households are

significantly more likely than married couple and single headed

male households to use a financial planner, unlike the actual

pattern of married couple households being more likely than

single female households to use a financial planner. There is a

substantial difference between single female and single male

households in the calculated likelihood of using a financial

planner, presumably because of the greater self-confidence of

males and their reluctance to seek help.

Households with a child under 19 are less likely to use a

financial planner than otherwise similar households without a

child under 19, although the difference is small. Homeowners

are not significantly different from otherwise similar renters in

the likelihood of using a financial planner. Controlling for

other characteristics, households with employee job status are

not significantly different from those categorized as self-

employed, retired, or not working.


Implications

The substantial increase in the use of financial planners

between 2004 and 2007 may provide optimism for the financial

planning industry, but some of the patterns suggest

underserved market segments. The result that even after

controlling for income, age, and net worth, those with low risk

tolerance (unwilling to take investment risks) are less likely to

use a financial planner than those with higher risk tolerance

may seem reasonable in terms of the idea of financial planning

as portfolio management, but in terms of the theoretical results

demonstrated by Hanna and Lindamood (2010), those with low

risk tolerance but high net worth or income should place

substantially higher value on the risk management aspects of

comprehensive financial planning than should households with

high risk tolerance. Those under 30 are unlikely to use

financial planners, but the logit results suggest that level is

appropriate relative to the low net worth of young households,

especially to the extent that benefits of financial planning are

more related to protecting assets than increasing assets. The

decrease in use of financial planners by elderly households

seems reasonable in terms of decreasing future benefits

because of more limited remaining life expectancies, but for

those with substantial assets, the value of reducing risks should

still be substantial (Hanna & Lindamood, 2010). Single male

headed households also seem to be an underserved segment.

As Elmerick, et al. (2002) showed, other things equal,

households with a Black respondent are much more likely than

similar households with a White respondent to use a financial

planner for credit or borrowing decisions, and somewhat more



likely to use a financial planner for savings or savings or

investment decisions, so credit problems of Black households

might be part of the differences in terms of Black-White

differences in overall use of financial planners. The result that

households with Hispanic and with Other/Asian respondents

are significantly less likely to use financial planners than those

with White or Black respondents suggests that populations with

substantial proportions of immigrants are underserved by

financial planners. Almost 40% of Hispanics in the U.S. are

immigrants, and 67% of Asians are immigrants (U.S. Census

Bureau, 2010). Immigrants who lack familiarity with financial

planning in the United States may be a factor, but increased

marketing to these segments may be beneficial. Chatterjee

(2009) found that immigrants have lower participation in U.S.

financial markets than native-born Americans, so that

difference may also help explain the lower use of financial

planners by Hispanics and the Asian/other group in the Survey

of Consumer Finances.

The strong effect of education on the likelihood of using

a financial planner after controlling for net worth and other

characteristics suggests that less educated affluent households

may be underserved by financial planners. To the extent that

low education is related to being more present-oriented, it is

possible that those households might not value the future

benefits of financial planning highly, but presumably those

households might find financial planning by themselves to be

more challenging than more educated households.

The small negative effect of having a dependent child

under the age of 19 suggests that even though the number of


goals may be higher for those with one or more children, the

reduction in the amount available for investing outweighs that

effect.

Net worth seems to be much more important than

household income in the likelihood of using a financial

planner. Further research should consider the impact of

different components of net worth on the likelihood of using a

financial planner. However, the result that being a homeowner

does not have a significant effect in the logit suggests that the

most important variation in typical household net worth does

not matter much in whether households use a financial planner.

References

Allison, P. D. (1999). Logistic regression Using SAS: Theory and

application. Cary, NC: SAS Institute, Inc.

Bae, S. C. & Sandager, J. P. (1997). What consumers look for in financial

planners. Financial Counseling and Planning, 8(2), 9-16.

Berry, C. M., Gruys, M. L., & Sackett, P. R. (2006). Educational attainment

as a proxy for cognitive ability in selection: Effects on levels of

cognitive ability and adverse impact. Journal of Applied Psychology,

91(3), 696-705.

Bucks, B. K., Kennickell, A. B., Mach, T. L., & Moore, K. B. (2009).

Changes in U.S. family finances from 2004 to 2007: Evidence from the

Survey of Consumer Finances. Federal Reserve Bulletin, 95, A1-A55.

Chang, M. L. (2005). With a little help from my friends (and my financial

planner). Social Forces, 83(4), 1469-1498.

Chatterjee, S. (2009). Do immigrants have lower participation rates in U.S.

financial markets? International Journal of Business and Finance

Research, 3(2), 1-13.

Chen, C.C. (2007). Changes in retirement adequacy, 1995-2004:

Accounting for retirement stages. Dissertation, The Ohio State

University.

Chen, P. & Finke, M. S. (1996). Negative net worth and the life cycle

hypothesis. Financial Counseling and Planning, 7, 87-95.

Deaton, A. (1997). The analysis of household surveys: A microeconometric

approach to development policy. Baltimore, MD: Johns Hopkins

University Press.



Elmerick, S. A., Montalto, C. P., & Fox, J. J. (2002). Use of financial

planners by U.S. households. Financial Services Review, 11(3), 217-

213.

Hanna, S. D. & Lindamood, S. (2008). The decrease in stock ownership by

minority households. Journal of Financial Counseling and Planning,

19(2), 46-58.

Hanna, S. D., & Lindamood, S. (2010). Quantifying the economic benefits

of personal financial planning. Financial Services Review, 19(2), 111-

127.

Hanna, S. D. Lindamood, S., & Huston, S. J. (2009). National household

datasets for financial research: Survey of Consumer Finances.

Proceedings of the Academy of Financial Services. Retrieved from

http://www.academyfinancial.org/09Conference/09Proceedings/(2E)

Hanna, Lindamood, Huston.pdf

Lindamood, S., & Hanna, S. D. (2005). Determinants of the wife being the

financially knowledgeable spouse. Proceedings of the Academy of

Financial Services.

Lindamood, S., Hanna, S.D., & Bi, L. (2007). Using the Survey of

Consumer Finances: Methodological considerations and issues. Journal

of Consumer Affairs, 41(2), 195–214.

Montalto, C. P. & Sung, J. (1996). Multiple imputation in the 1992 Survey

of Consumer Finances. Financial Counseling and Planning, 7, 133–

146.

Peterson, B. (2006). Are households with complex financial management

issues more likely to use a financial planner? Thesis, University of

Wisconsin - Madison.

Suits, D. B., Mason, A.. & Chan, L. (1978). Spline functions fitted by

standard regression methods. Review of Economics and Statistics, 60,

132-139.

U.S. Census Bureau (2010, January). Race and Hispanic Origin of the

foreign-born population in the United States: 2007. American

Community Survey Reports.

Warschauer, T. (2008). The economic benefits of personal financial

planning. Proceedings of the Academy of Financial Services.

Yuh, Y. & Hanna, S. D. (2010). Which households think they save? Journal

of Consumer Affairs, 44(1), 70-97.


CAN DUAL BETA FILTERING IMPROVE INVESTOR

PERFORMANCE?

James Chong, Ph.D.

California State University, Northridge

Shaun Pfeiffer, Ph.D. Candidate

Texas Tech University

G. Michael Phillips, Ph.D.

California State University, Northridge

This study investigates the possibility that more efficient portfolios

may be constructed by using the dual-beta model that screens out

assets that exhibit more extreme downside risk sensitivity. Three

portfolios were constructed, using the criteria of standard CAPM

beta, down-market beta, and a combination of up-market and

down-market betas. Overall, the standard CAPM beta consistently

lags the dual-betas. When compared to the Fama-French three-

factor inspired DFEOX, the dual-betas also performed reasonably

well, with the ability to contain the downside while participating in

the upside.

Introduction and Literature Review

Individual investors appear more sensitive to investment

losses than would be predicted by neoclassical economic

preferences (Tversky & Kahneman, 1991; Ang, Chen, & Xing,

2006). This sensitivity compromises realized portfolio

performance by inducing extreme rebalancing toward safety

James Chong, Department of Finance, Real Estate, and Insurance,

California State University, Northridge, 18111 Nordhoff Street, Northridge,

CA 91330-8379; (818) 677-4613; [email protected]

The authors are grateful to the editor, Michael Finke, and an anonymous

referee for useful comments on a previous version of this paper. The usual

disclaimer applies.



following market declines (Barberis, Huang & Santos, 2001) or

by limiting exposure to equities (Siegel, 2005). Consequences

from loss aversion can significantly reduce future financial

well-being when the portfolio does not reflect this type of risk

preference. Prior research also notes that asset volatility may

not be symmetric between gains and losses (Estrada, 2007). If

certain assets exhibit more extreme declines in performance

during a market decline, it may be possible to identify these

securities ex ante in order to construct more optimal consumer

portfolios that are more attractive to individual investors. This

study investigates the possibility that more efficient portfolios

may be constructed by using a portfolio selection technique

that screens out assets that exhibit more extreme downside risk

sensitivity.

Loss Aversion

Loss aversion is defined as higher sensitivity to

investment losses than gains. Research estimates that the pain

of loss for a typical investor is roughly twice the pleasure from

an equivalent gain (Kahneman, Knetsch, & Thaler, 1990;

Tversky & Kahneman 1991). This type of risk preference

makes risky assets less appealing to the investor. Loss aversion

can be magnified by behavioral biases such as mental

accounting. For example, sensitivity to losses is shown to

increase with frequency of account evaluation (Benartzi &

Thaler, 1995). Findings from Barber and Odean (2000) suggest

that average investors turn over roughly 75% of their portfolios

each year, which supports the notion of frequent account

evaluation. Higher levels of loss aversion are associated with

less equity exposure, a desire for portfolio insurance, or some

combination of protective strategies and a reduction in equity


exposure (Berkelaar, Kouwenberg & Post, 2004). According to

Siegel (2005), loss-averse investors reduce stock holdings and

forego a substantial equity risk premium over longer

investment horizons. Additionally, Barber, Odean, and Zheng

(2000) find loss aversion, or the propensity to hold losers and

sell winners, leads to an annual reduction in portfolio returns of

roughly 3.5%. In turn, research suggests that loss aversion may

be more important than risk aversion when constructing a

portfolio (Basu, Raj & Tchalian, 2008).

Portfolio performance of a loss-averse investor can suffer

due to rebalancing into safer asset classes after poor market

returns. For example, Barberis, Huang and Santos (2001)

suggest that investors become more risk seeking following

gains and more risk averse following losses. This leads to a buy

high and sell low strategy where the investor realizes lower

than average returns over the investment horizon. Specifically,

investors fail to benefit from mean reversion that is associated

with asset prices over longer investment horizons (Debondt &

Thaler, 1985). Additionally, portfolio transactions triggered by

behavioral biases and loss aversion have been shown to reduce

returns by 1% to 5% per year versus a buy and hold strategy

(Barber et al., 2000; Barber & Odean, 2000). Cochrane (1999)

suggests that this type of portfolio rebalancing represents a

shift in investor risk preference, which sacrifices returns in

order to reduce risk.

Portfolio Construction

Investors may delegate portfolio decisions to a financial

planner in an attempt to reduce the negative effects of loss

aversion and other behavioral biases. Hanna and Lindamood



(2010) note loss prevention as one of the primary benefits

financial planners offer to their clients. The planner attempts to

construct an optimal portfolio based on the goals and unique

risk preferences of each client (Eyssell, 2003) in addition to the

individual investment attributes relative to the overall portfolio

(Markowitz, 1952). Mean-variance optimization is a process

that many planners use to derive a long-term asset allocation.

Farrelly (2006) notes that optimization involves a great deal of

art for many practitioners. In other words, practitioners

frequently constrain the optimization output in order to account

for errors in the optimization assumptions, behavioral biases

and risk preferences of the client.

Many practitioners rely on the tenets of Modern Portfolio

Theory (MPT) and Capital Asset Pricing Theory (CAPM)

when constructing investment portfolios. The notion that

investors should consider risk in portfolio decisions is central

to seminal studies in finance (Markowitz, 1952; Sharpe, 1964).

MPT defines risk as the variance of investment returns. Beta

represents risk in the CAPM framework. Beta is systematic risk

and is seen as the covariance of returns between an investment

and the market portfolio relative to the variance of returns of

the market portfolio. In short, CAPM states that the expected

return on an investment is solely a function of beta, the investor

is not compensated for bearing unsystematic risk, and high beta

stocks are expected to outperform low beta stocks in periods of

positive market returns. Additionally, CAPM suggests that

more risk-averse investors should increase the amount of risk-

free securities while maintaining the value-weighted exposure

to risky assets (Canner, Mankiw, & Weil, 1997). However,

practitioners rely on risk metrics such as beta and variance of


sub-asset classes to make asset allocation decisions. Beta is

widely used by investment advisors to measure portfolio risk

(Levy, 2010; Chan & Lakonishok, 1993). Research estimates

that 70% of practitioners use the CAPM beta as a measure of

systematic risk (Graham & Harvey, 2001).

Risk Measurements

Empirical evidence suggests that beta is an imperfect

measure of investment risk. Potential estimation error

associated with beta due to lower r-squared statistics (Eyssell,

2003) has led many researchers to estimate beta on portfolios

rather than individual securities (Blume, 1975; Fama & French,

1992). Aggrawal and Waggle (2010) find that beta varies

significantly across different financial websites. The authors

note that the deviation is due to the use of different proxies for

the market. In addition to the errors in beta, there is mixed

empirical evidence on CAPM. Early empirical evidence

supports the claims of CAPM (Jensen, 1969; Downs & Ingram,

2000). Subsequent studies, however, find many empirical

contradictions in relation to the claims of CAPM (Fama &

Macbeth, 1973; Black, Jensen, & Scholes, 1972). Research

finds a size (Banz, 1981) and value effect (Stattman, 1980;

Rosenberg, Reid, & Lanstein, 1985) are important in

explaining investment returns. Together these findings suggest

that beta is not the only factor explaining returns and

eventually lead to the formation of the three-factor model

(Fama & French, 1992). Findings that suggest beta is not

positively related to average returns over varying periods of

analysis are even more troubling to the predictions of CAPM

(Fama & French, 1992). In other words, the relationship



between beta and average returns is not reliable and the CAPM

beta may not be the best way to define risk.

Beta can remain useful to practitioners in spite of the

empirical evidence against it. The findings that contradict

CAPM suggest that the relationship between beta and average

returns is flatter than predicted by CAPM. Black (1993) notes

that beta remains useful to investors and planners as a risk

measure. Specifically, low beta investments offer higher risk-

adjusted returns than high beta investments. Other studies

suggest that beta is a useful indicator of downside risk

exposure in declining markets. Grundy and Malkiel (1996) find

that higher beta stocks consistently underperform lower beta

stocks during periods where the S&P drops by more than 10%.

Together, these findings suggest that the strength of empirical

support for beta is weaker than CAPM would suggest;

however, this does not mean beta is a useless measure of risk.

Research also provides many alternative measures of risk

for practitioners to use in portfolio design. The concern for

downside risk measures is captured in many early studies. For

example, Markowitz (1959) notes that semi-variance, or

downside deviation, is a better measure of risk than variance.

The author adds that the use of semi-variance, rather than

variance, in the optimization process can lead to better

portfolios. These suggestions are based on the idea that

investors are typically loss averse. Collectively, the concerns

associated with the traditional CAPM beta and the concept of

loss aversion has led to the suggestion that there may be better

measures of risk than beta (Chan & Lakonishok, 1993). Using

the idea of semi-variance, Estrada (2007) constructs a


downside beta. This risk measure captures the sensitivity to the

market when market returns are negative or below some

threshold such as average market returns. Galagedera (2009)

suggests that the downside beta is a better measure of

systematic risk than the CAPM beta and that the difference

between these risk measures is greatest for low volatility

portfolios. Additional research suggests that the use of

downside risk measures is better able to estimate an

appropriate amount to allocate to risky assets (Berkelaar,

Kouwenberg, & Post, 2004).

Loss Prevention Strategies

Loss avoidance is of key importance and the dual-beta

model is found to be of value in capturing the downside risk.

The same can be said for many financial planners and their

clients who are concerned with capital preservation and loss

avoidance (Bajtelsmit, 2005). However, there are many

strategies that a financial planner can employ to mitigate

portfolio losses. Asset allocation, rebalancing, the use of

derivatives, and reducing exposure to stocks are a few ways to

mitigate portfolio losses. First, note that total risk is a function

of systematic risk and unsystematic risk (Xiong, Ibbotson,

Idzorek, & Chen, 2010). Asset allocation, which includes

diversification within and across asset classes, is an approach

to reduce unsystematic risk and the potential for significant

portfolio losses (Markowitz, 1952). Correlation tightening

during market declines (Bauer, Haerden, & Molenaar, 2004)

and positive correlation between stocks and bonds over longer

investment horizons (Campbell & Ammer, 1993) are

limitations of asset allocation as a tool to mitigate portfolio

losses. Research estimates that rebalancing can reduce



volatility by roughly 1% and increase portfolio returns by

approximately 50 basis points per year (Plaxco & Arnott,

2002). Although rebalancing incurs transaction costs and taxes

the benefit of maintaining a certain risk exposure is important

to the planning profession (Daryanani, 2008). Portfolio

insurance, or rolling put options, is another loss prevention

strategy that may be considered by planners. However, Arnott

(1998) notes that the cost of this strategy can be as high as 5%

per year. Eliminating exposure to equities can reduce portfolio

shocks; however, the client forfeits the benefit of an equity risk

premium.

Our study focuses on the use of downside beta in an

attempt to construct more attractive portfolios for clients. We

acknowledge that portfolio strategies based on downside beta

should be used alongside proper asset allocation and

rebalancing criteria. Recent research shows that low beta

portfolios can provide higher returns and lower volatility than

high beta portfolios (Baker, Bradley & Wurgler, 2011). Our

study attempts to identify more efficient portfolios based on the

downside beta used in Estrada (2006) by screening out assets

that exhibit greater downside sensitivity.

Our work is closely related to that of Pettengill et al.

(1995), who provide contrary evidence to that of Fama and

French (1992), in that there is a significant relationship

between beta and returns so long as one segregates beta into its

up-market and down-market components (henceforth, referred

to as the dual-beta model).1 Our empirical findings are clear—

1Further literature on up- and down-market betas can be found in Moelli

(2007) and the references therein.


the standard CAPM beta consistently lags the dual-betas, in

terms of average daily returns and return-to-standard deviation

ratio. As such, the dual-beta model is superior to the standard

CAPM model. When compared to the Fama-French three-

factor inspired portfolio, the dual-betas also performed

reasonably well, with the ability to contain the downside while

participating in the upside. The relatively poor performance of

the traditional beta portfolio suggests the need for financial

planners to explore alternative forms of stock selection—the

Fama-French three-factor model is such an alternative but a

more tractable solution can be found in the dual-beta model.

The findings of this study advocate to financial planners, if

they have not done so already, the use of the dual-beta model

for stock selection and portfolio construction.

The paper is structured as follows. We begin by

providing a brief overview of our sample data, followed by a

description of our methodology. We then proceed to present

some results from our findings. Finally, we end with our

conclusions.

Data and Methodology

DFA Core Equity 1 Portfolio (DFEOX)

The efficacy of the Fama-French three-factor model2 has

led many to conclude that ―alpha can be elusive when

measured against the three-factor model‖ (Pollock, 2007).

2In addition to beta, Fama and French found size (i.e., the return on small

stocks minus the return on big stocks, SMB) and value (i.e., the return on

high book-to-market stocks minus the return on low book-to-market stocks,

HML) to be of significance in explaining average returns and therefore,

valid proxies for risk.



Subsequently, a new benchmark was proposed—―the new face

of indexing‖ (Fama, 2000)—where ―the goal of indexing

switches from diversification across the available stocks to

diversification across the available risk-return dimensions,‖

resulting in the creation of the Dimensional Fund Advisors

(DFA) Core Equity 1 Portfolio (DFEOX), which seeks ―to buy

the total U.S. market in proportions that provide higher

exposure to the risk premiums associated with size and value

identified by Fama and French.‖3 The DFEOX is categorized

under ―Large Blend‖ by Morningstar and therefore is deemed

an appropriate benchmark for large cap portfolio performance.

Standard CAPM Model

Although beta has been shown by Fama and French

(1992) to be an imperfect measure of investment risk, the

standard CAPM model, where beta is derived from, is still

popular among financial planners and investment professionals

and can be expressed as

( ) , (1)

where is the risk-free rate (we use the overnight U.S. Federal

funds rate as proxy), is the return on asset j, ( ) is the

observed excess return on asset j, is the estimated regression

intercept, called alpha, is the estimated excess return

on the market index (here, the S&P 500 index, SPX), and is

the unexplained portion of the model. In our paper, we estimate

the standard CAPM beta using one-year daily returns.

3http://www.dfaus.com/strategies/us-equity.html


Dual-Beta Model

The dual-beta model is an extension of the standard

CAPM model. It estimates the parameters separately for up-

market, when the daily return for the market index is non-

negative, and down-market, when the daily return for the

market index is negative. The dual-beta model can thus be

described as

( )

(

)

, (2)

where ,

, , and

are the estimated parameters for up-

market and down-market days respectively; on days

the market index did not decline and on days it did; D

is a dummy variable, which takes the value of 1 when the

market index daily return is non-negative, and zero otherwise.

If there is no asymmetry in beta, then

and

,

i.e., equations (1) and (2) are identical. As with the standard

CAPM beta, we estimate the up-market and down-market betas

using one-year daily returns.

Portfolio Construction with Standard CAPM and Dual-Beta

Models

For our analysis, we employ daily data, from January 1,

2006 to March 4, 2011, for a total of 1,350 data points.4

DFEOX was established on November 1, 2005, and for

convenience, we used January 1, 2006 as the start date.

We construct three separate portfolios for comparison to

DFEOX. The portfolio construction and rebalancing processes

4These data were provided by MacroRisk Analytics from their database.



are initiated at the beginning of each quarter,5 using a buy-list

of stocks in the S&P 500 index.6 The criteria we impose on the

choice of stocks are a standard CAPM beta of less than 0.7,7

down-market beta of less than 0.7, and the combination of

down-market beta of less than 0.7 and up-market beta of

greater than 0.7. The portfolio is then constructed with equal

weighting on the stock components.

The rationale for these various beta models is an attempt

to capture various characteristics of the market. The standard

CAPM beta (henceforth referred as Beta) is one of the most

popular measures of investment risk, and as such, we also

employ it here. We are taking a conservative approach and

impose a filter of Beta that is less than 0.7. The down-market

beta (referred to as Dbeta) criterion of less than 0.7 is taking on

a risk-averse stance only when the market goes down. The

combination beta of Dbeta of less than 0.7 and up-market beta

(Ubeta) of greater than 0.7 (referred to as Combination) is to

ensure conservatism on down-market days but acquire more

risk on up-market days. Lastly, DFEOX is our large cap

performance benchmark, which utilizes the Fama-French three-

factor model.

5As the process is initiated at the beginning of each quarter, there is no look-

ahead bias. 6The Fama-French three-factor model is applied to the total market, which

is defined as companies listed on the NYSE, AMEX, and NASDAQ Global

Market System. By restricting ourselves to only S&P 500 stocks, we are

limiting the effectiveness of our portfolio. 7In theory, market beta equals 1. However, Chong and Phillips (2009) found

that the median beta of stocks listed on the New York Stock Exchange is

0.7.


Conditional Volatilities and Correlation

Estimating conditional volatilities and correlations, with

the GARCH (1,1) and DCC (1,1) models respectively, has

become almost standard practice in finance. With the GARCH

(1,1) model (Bollerslev, 1986), an asset‘s conditional variance

( ) can be expressed as

(3)

subject to With the DCC (1,1)

model (Engle, 2002), the time-varying covariance matrix is

expressed as , where is the diagonal matrix of

GARCH (1,1) volatilities,

is the time-

varying correlation matrix, is a diagonal matrix comprising

the square root of the diagonal elements of , and is

( 11 tt ) , (4)

where is the unconditional covariance and a and b are

scalars. The coefficients of (3) and (4) are estimated by the

maximum likelihood procedure using the BFGS algorithm.

Results

A graphical illustration of how the various beta models

performed in relation to DFEOX is presented by Figure 1. We

begin at $1 on January 1, 2006 and end on March 4, 2011. For

our sample period, the Combination beta had the highest

cumulative wealth of $1.3974. This was followed by DFEOX

($1.2465), Dbeta ($1.2298), and Beta ($1.1401). Prior to the

financial crisis, the various portfolios tracked each other

closely, with separation between the portfolios occurring at

approximately December 2008. Further, we note that none of



Figure 1

Cumulative Wealth

the beta-generated portfolios plummeted as did DFEOX during

the financial crisis. DFEOX reached its trough on March 9,

2009 ($0.5560) but made a surge in the remainder of our

sample period, eventually surpassing Beta and Dbeta. This

would suggest that DFEOX has higher volatility than the beta-

generated portfolios.

In Table 1, Panel A, we report summary statistics of

returns and risk for the whole sample period. The results (for

mean daily return, standard deviation) confirmed somewhat our

analysis of Figure 1. Although the Combination beta has the

second highest volatility, this is offset by its returns, resulting

in the highest return-to-standard deviation ratio (0.0255)

among the portfolios. Coming in second is Dbeta (0.0193),

whose ratio exceeded that of DFEOX (0.0182).


Table 1

Summary Statistics of Returns and Risk

Beta < 0.7 Dbeta < 0.7 Dbeta < 0.7,

Ubeta > 0.7

DFEOX

Panel A: Whole Period (1/1/06 – 3/4/11)

Mean 0.0156% 0.0216% 0.0334% 0.0288%

Median 0.0439% 0.0285% 0.0263% 0.0835%

Standard Deviation 1.0812% 1.1222% 1.3115% 1.5787%

Maximum 10.0472% 9.3606% 8.6777% 11.0165%

Minimum -7.0931% -7.0606% -8.0649% -9.3727%

Ratio* 0.0144 0.0193 0.0255 0.0182

Correlation 0.8984 0.9263 0.9312 1.0000

Sample 1,350 1,350 1,350 1,350

Panel B: First Period (1/1/06 – 3/8/09)

Mean -0.0321% -0.0278% -0.0212% -0.0550%

Median 0.0293% 0.0000% 0.0011% 0.0000%


Maximum 10.0472% 9.3606% 8.6777% 11.0165%

Minimum -7.0931% -7.0606% -8.0649% -9.3727%

Ratio* -0.0262 -0.0218 -0.0145 -0.0328

Correlation 0.9134 0.9425 0.9467 1.0000

Sample 830 830 830 830

Panel C: Second Period (3/9/09 – 3/4/11)

Mean 0.0917% 0.1005% 0.1205% 0.1626%

Median 0.0804% 0.0794% 0.0561% 0.0988%


Maximum 3.8441% 4.3498% 5.0254% 7.3028%

Minimum -3.0903% -3.0619% -3.4971% -4.8949%

Ratio* 0.1155 0.1234 0.1180 0.1164

Correlation 0.8741 0.9011 0.8985 1.0000

Sample 520 520 520 520

*Ratio = Mean return divided by standard deviation.



Figure 2

Conditional Correlation with DFEOX, using the DCC (1,1) Model

Further analysis is undertaken by separating the sample

period in two, at the point when DFEOX was at its lowest (see

Figure 1, when DFEOX was at $0.5560 on March 9, 2009).

This allows us to assess the portfolio structure prior to and

during the financial crisis (Panel B of Table 1) and subsequent

recovery (Panel C of Table 1), while also ensuring the

robustness of our findings.

Prior to March 9, 2009, all portfolios experienced loss

(Panel B of Table 1). Even though the various beta-generated

portfolios were highly correlated with DFEOX, with

correlation coefficients in excess of 0.9, their average daily

returns differed—the Combination beta registered average

daily returns of -0.0212% while DFEOX‘s average daily

returns was -0.0550%. On closer examination of their


Figure 3

Conditional Volatility with the GARCH (1,1) Model

conditional correlations (Figure 2), sizeable fluctuations in

correlation are evident, which explains the phenomenon of

high correlation accompanied by vastly differing returns. It is

also apparent from Figure 3 (and corroborated by Table 1,

Panel B) that DFEOX has consistently higher volatility, and

consequently inferior return-to-standard deviation ratio, than

beta-generated portfolios.

Post-March 2009 witnessed a surge by DFEOX with an

average daily return of 0.1626%. However, associated with

higher return was higher volatility relative to other portfolios;

unlike pre-March 2009, the difference in volatility between

DFEOX and the other portfolios is much greater (Figure 3).

Accordingly, the return-to-standard deviation ratio of DFEOX

lagged those of Dbeta and Combination beta. Dbeta, with



relative low volatility, had a superior ratio over Combination

beta (0.1234 vs. 0.1180) despite a lower average daily return

(0.1005% vs. 0.1205%).

Overall, the standard CAPM beta consistently lags the

dual-betas, in terms of average daily returns and return-to-

standard deviation ratio. As such, the dual-beta model, which

segregates the traditional beta into its up- and down-market

components, is superior to the standard CAPM model. When

compared to the Fama-French three-factor inspired DFEOX,

the dual-betas also performed reasonably well, with the ability

to contain the downside while participating in the upside. This

augurs well for the dual-beta model, which is considerably

simpler to implement and explain to clients than the Fama-

French three-factor model.

Limitations

This study‘s main objective is to investigate the

possibility that more efficient portfolios may be constructed by

using the dual-beta model that screens out assets that exhibit

more extreme downside risk sensitivity. However, there are

questions left unanswered. For instance, accounting for

transaction costs in establishing and maintaining a stock-only

portfolio may result in reduced efficacy of the dual-beta model.

In Table 2, we provide summary statistics of average

transactions (and transaction costs) per quarter. The

Combination beta has the lowest average number of

transactions per quarter. This is intuitive since there are fewer

stocks that meet the criteria imposed by this model. Assuming

an investor executes stock trades via a discount brokerage


Table 2

Summary Statistics of Average Transactions (Costs) per Quarter

Beta < 0.7 Dbeta < 0.7 Dbeta< 0.7,

Ubeta > 0.7

Portfolio Components 72 84 29

No. of Transactions 82 101 44

Transaction Costs ($) 574 707 308

Cost as % of NAV 0.06% 0.07% 0.03%

(e.g., Scottrade at $7 per stock trade), the transaction costs

incurred by the Combination model, in absolute terms and as a

percentage of net asset value (NAV), are the lowest of the

(dual-)beta models. On the other hand, the Dbeta model suffers

from the highest average transaction costs.

While the transaction costs mentioned above may be

reasonable for a financial planner, they may be exorbitant for

an individual investor, in which case, employing exchange

traded funds (ETFs) would be an alternative strategy, given

that commission-free ETFs are being offered by brokers (e.g.,

Scottrade, Charles Schwab). Of course, for more institutional

activities, prime brokerage operations allow for extremely

inexpensive trades.

Summary and Conclusion

This article has sought to provide a review of the standard

CAPM model and the dual-beta model available for stock

The transaction costs as a percent of net asset value is dependent on the

amount under management, which we assumed to be $1 million. A separate analysis using ETFs (not shown, but available from the authors

on request) showed improved performance for all (dual-)beta models over

their stock-only counterparts.



selection and their effectiveness. Three portfolios were

constructed, using the criteria of standard beta less than 0.7,

down-market beta less than 0.7, and the combination of down-

market less than 0.7 and up-market greater than 0.7. The

criteria used are especially appropriate for a loss-averse

investor, who has a higher sensitivity to investment losses than

gains. Thus, this article could be viewed as having provided

evidence on the effectiveness of loss prevention strategies in

stock selection. Further, in the quest for wealth enhancement

and loss prevention, a strategy of combining up- and down-

market betas was employed with success.

In addition to standard and dual-betas, we chose a

performance benchmark inspired by the Fama-French three

factor model, the DFA Core Equity 1 Portfolio (DFEOX).

Recall that Fama and French (1992) found, in addition to beta,

size (i.e., the return on small stocks minus the return on big

stocks) and value (i.e., the return on high book-to-market

stocks minus the return on low book-to-market stocks) to be of

significance in explaining average returns and therefore valid

proxies for risk. It is therefore a worthwhile exercise to

compare portfolios formed via a (dual-)beta filter with

DFEOX.

The relatively poor performance of the traditional beta

portfolio suggests the need for financial planners to explore

alternative forms of stock selection. While the Fama-French

three-factor model is such an alternative, a more tractable

solution is the dual-beta model. The findings of this study

advocate to financial planners, if they have not done so already,


the use of the dual-beta model for stock selection and portfolio

construction.

Interestingly, this study finds that a simple down-market

beta scheme produces a portfolio with relatively low variance

while generating positive returns. Such models are simple and

can be estimated using a spreadsheet (the combination of up-

and down-market betas is only slightly more involved), thus

potentially rendering considerably more complex and

cumbersome models, such as the Fama-French three-factor

model, hardly worth the additional effort.

References

Aggrawal, P., & Waggle, D. (2010). The Dispersion of ETF Betas on

Financial Websites, The Journal of Investing, 19(1), 13-24.

Ang, A., Chen, J., & Xing, Y. (2006). Downside Risk, Review of Financial

Studies, 21(4), 1767-1794.

Arnott, R. (1998). Options and Protective Strategies, The Journal of

Investing, 7(2), 16-22.

Baker, M., Bradley, B., & Wurgler, J. (2011). Benchmarks as Limits to

Arbitrage: Understanding the Low-Volatility Anomaly, Financial

Analysts Journal, 67(1), 40-56.

Banz, R. (1981). The Relationship between Return and Market Value of

Common Stocks, Journal of Financial Economics, 9, 3-18.

Barber, B. & Odean, T. (2000). Trading is Hazardous to Your Wealth: The

Common Stock Investment Performance of Individual Investors, The

Journal of Finance, 55(2), 773-806.

Barber, B., Odean, T., & Zheng, L. (2000). The Behavior of Mutual Fund

Investors, Working paper.

Barberis, N., Huang, M., & Santos, T. (2001). Prospect Theory and Asset

Prices, Quarterly Journal of Economics, 116(1), 1-53.

Basu, S., Raj, M., & Tchalian, H. (2008). A Comprehensive Study of

Behavioral Finance, Journal of Financial Service Professionals, 62(4),

51-62.

Bajtelsmit, V.L. (2005). Personal Finance: Skills for Life. Hoboken, NJ:

Wiley.

Bauer, R., Haerden, R., & Molenaar, R. (2004). Asset Allocation in Stable

and Unstable Times, The Journal of Investing, 13(3), 72-80.



Benartzi, S., & Thaler, R. (1995). Myopic Loss Aversion and the Equity

Risk Premium Puzzle, The Quarterly Journal of Economics, 110(1),

73-92.

Berkelaar, A., Kouwenberg, R., & Post, T. (2004). Optimal Portfolio

Choice under Loss Aversion, The Review of Economics and Statistics,

86, 973-987.

Black, F. (1993). Beta and Return, The Journal of Portfolio Management,

20, 8-18.

Black, F., Jensen, M., & Scholes, M. (1972). The Capital Asset Pricing

Model: Some Empirical Tests. Studies in the Theory of Capital

Markets, 79-122.

Blume, M. (1975). Betas and Their Regression Tendencies, The Journal of

Finance, 30(3), 785-795.

Bollerslev, T. (1986). Generalized Autoregressive Conditional

Heteroscedasticity, Journal of Econometrics, 31, 307-327.

Campbell, J., & Ammer, J. (1993). What Moves the Stock and Bond

Markets? A Variance Decomposition for Long-term Asset Returns, The


Canner, N., Mankiw, G., & Weil, D. (1997). An Asset Allocation Puzzle,

The American Economic Review, 87(1), 181-191.

Chan, L. & Lakonishok, J. (1993). Are the Reports of Beta‘s Death

Premature? The Journal of Portfolio Management, 19(4), 51-62.

Chong, J., & Phillips, G.M. (2009). The Performance of Safe Portfolios

during Stressful Times: How Beta Failed, Why New Measures are

Needed. Financial Planning and Analysis Corporate Round Table,

Financial Management Association, October 21.

Cochrane, J. (1999). Portfolio Advice for a Multifactor World, Economic

Perspectives, 23(3), 59-79.

Daryanani, G. (2008). Opportunistic Rebalancing: A New Paradigm for

Wealth Managers, Journal of Financial Planning, 21(1), 48-61.

Debondt, W., & Thaler, R. (1985). Does the Stock Market Overreact? The


Downs, T., & Ingram, R. (2000). Beta, Size, Risk, and Return. The Journal

of Financial Research, 23(3), 245-260.

Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of

Multivariate Generalized Autoregressive Conditional

Heteroscedasticity Models, Journal of Business & Economic Statistics,

20, 339-350.

Estrada, J. (2007). Mean-Semivariance Behavior: Downside Risk and

Capital Asset Pricing, International Review of Economics and Finance,

16, 169-185.

Eyssell, T. (2003) What‘s the Proper Beta? Financial Advisors and the

‗Two-Beta Trap‘, Journal of Financial Planning, 16(9), 54-57.

Fama, E.F. (2000). The New Indexing. Retrieved from

http://www.dfaus.com/2009/05/the-new-indexing.htm


Fama, E., & French, K. (1992). The Cross-Section of Expected Stock

Returns, The Journal of Finance, 47(2), 427-465.

Fama, E., & MacBeth, J. (1973). Risk, Return, and Equilibrium: Empirical

Tests, The Journal of Political Economy, 81(3), 607-636.

Farrelly, T. (2006) Asset Allocation for Robust Portfolios, Journal of

Investing, 15(4), 53-63.

Galagedera, D. (2009). An Analytical Framework for Explaining Relative

Performance of CAPM Beta and Downside Beta, International Journal

of Theoretical and Applied Finance, 12(3), 341-358.

Graham, J. & Harvey, C. (2001). The Theory and Practice of Corporate

Finance: Evidence from the Field. Journal of Financial Economics, 60,

187-243.

Grundy, K., & Malkiel, B. (1996). Reports of Beta‘s Death Have Been

Greatly Exaggerated, The Journal of Portfolio Management, 22(3),

36-44.

Hanna, S., & Lindamood, S. (2010). Quantifying the Economic Benefits of

Personal Financial Planning, Financial Services Review, 19(2),

111-127.

Jensen, M. (1969). The Pricing of Capital Assets, and the Evaluation of

Investment Portfolios, The Journal of Business, 42(2), 167-247.

Kahneman, D., Knetsch, J., & Thaler, R. (1990). Experimental Tests of the

Endowment Effect and the Coase Theorem, Journal of Political

Economy, 98(6), 1325-1348.

Levy, H. (2010). The CAPM is Alive and Well: A Review and Synthesis,

European Financial Management, 16(1), 43-71.

Markowitz, H. (1952). Portfolio Selection, The Journal of Finance, 7,

77-91.

Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of

Investments (Wiley, Yale University Press, 1970, Basil Blackwell,

1991).

Morelli, D. (2007). Beta, Size, Book-to-Market Equity and Returns: A

Study Based on UK Data, Journal of Multinational Financial

Management, 17, 257-272.

Pettengill, G., Sundaram, S., & Mathur, I. (1995). The Conditional Relation

between Beta and Returns, Journal of Financial and Quantitative

Analysis, 30, 101-116.

Plaxco, L., & Arnott, R. (2002). Rebalancing a Global Policy Benchmark,

The Journal of Portfolio Management, 28(2), 9-22.

Pollock, S.L. (2007). Using Three-Factor Theory to Assess Investment

Performance, Journal of Financial Planning, 20(6), 66-75.

Rosenberg, B., Reid, K., & Lanstein, R. (1985). Persuasive Evidence of

Market Inefficiency, Journal of Portfolio Management, 9, 18-28.

Sharpe, W. (1964). A Theory of Market Equilibrium under Conditions of

Risk, The Journal of Finance, 19(3), 425-442.



Siegel, J. (2005). Perspectives on the Equity Risk Premium, Financial


Stattman, D. (1980). Book values and stock returns, The Chicago MBA: A

Journal of Selected Papers, 4, 25-45.

Tversky, A. & Kahneman, D. (1991). Loss Aversion and Riskless Choice:

A Reference Dependent Model, Quarterly Journal of Economics, 107,

1039-1061.

Xiong, J., Ibbotson, R., Idzorek, T., & Chen, P. (2010). The Equal

Importance of Asset Allocation and Active Management, Financial



SAFE WITHDRAWAL RATES FROM RETIREMENT

SAVINGS FOR RESIDENTS OF EMERGING MARKET

COUNTRIES

Channarith Meng, Ph.D. Candidate

National Graduate Institute for Policy Studies (GRIPS)

Wade Donald Pfau, Ph.D.

National Graduate Institute for Policy Studies (GRIPS)

Researchers have mostly focused on U.S. historical data to develop

the 4 percent withdrawal rate rule. This rule suggests that retirees

can safely sustain retirement withdrawals for at least 30 years by

initially withdrawing 4 percent of their savings and adjusting this

amount for inflation in subsequent years. But, the time period

covered in these studies represents a particularly favorable one for

U.S. asset returns that is unlikely to be broadly experienced. This

poses a concern about whether safe withdrawal rate guidance from

the U.S. can be applied to other countries. Particularly for

emerging economies, defined-contribution pension plans have

been introduced along with under-developed or non-existing

annuity markets, making retirement withdrawal strategies an

important concern. We study sustainable withdrawal rates for the

25 emerging countries included in the MSCI indices and find that

the sustainability of a 4 percent withdrawal rate differs widely and

can likely not be treated as safe.

Introduction

What is the safe withdrawal rate from un-annuitized

retirement savings that will provide the most retirement income

for retirees without exhausting their savings? Potential retirees

must answer this question to know if their expected spending

Channarith Meng, National Graduate Institute for Policy Studies (GRIPS),

7-22-1 Roppongi, Minato-ku, Tokyo 106-8677, Japan; Phone: 81-3-6439-

6225; [email protected]



needs can be reasonably supported from their savings. When

the withdrawal rate is too high, retirees are vulnerable to the

risk of income shortfalls and poverty at later ages. A low

withdrawal rate, on the other hand, may lead retirees to

sacrifice the opportunity of a higher sustainable living

standard.

Recent interest in addressing this issue has resulted in a

growing literature. Using various simulation techniques

including historical overlapping, bootstrapping, and Monte

Carlo simulations, researchers have developed a variety of

rules and strategies in the hope of giving retirees appropriate

guidelines for their retirement planning. A range of withdrawal

rates have been recommended along with asset allocation

strategies to safely sustain retirees for a required number of

years. Among numerous studies, the 4 percent withdrawal rule

has been widely accepted as a safe sustainable withdrawal rate,

and it has become an established baseline for testing other

approaches.

In the pioneering study for this field, Bengen (1994)

suggests that an initial withdrawal rate of 4 percent adjusted for

inflation in subsequent years should be safe and sustainable for

at least 30 years. He further recommends a starting allocation

to stocks between 50 and 75 percent. In subsequent research,

Bengen (1996) indicates that a 4 percent withdrawal rate is

sustainable even when the proportion of stocks in the portfolio

is gradually reduced over time. Bengen (1997) includes small

capitalization stocks into the portfolio mix and finds a notable

increase in the sustainable withdrawal rate. In his latest


research, Bengen (2006) indicates that even 5 percent can be

safely sustainable under certain conditions.

Other studies also give support for the sustainability of 4

percent or higher withdrawal rates. Cooley, Hubbard, and Walz

(2011), updates earlier findings to show that when using

historical simulations, a 50/50 portfolio for stocks and bonds

provides a 96 percent historical success rate for a 4 percent real

withdrawal rate over 30 years. The success rate increases to

100 percent when increasing the share of stocks to 75 percent.

Monte Carlo simulations by Ameriks, Veres, and Warshawsky

(2001) indicate that a 4.5 percent real withdrawal rate is

possible with an 8.3 percent chance of exhausting money in 30

years. Tezel (2004), using historical simulations, finds that 4.5,

5.5, and 6.5 percent real withdrawal rates work for time

horizons of 30, 20, and 10 years, respectively, with the chance

of exhausting money during retirement below 8 percent.

Spitzer, Strieter, and Singh (2007) also find that a 4.4 percent

real withdrawal rate with 50 percent stocks can be used with a

10 percent chance for failure within 30 years. These studies

also find importance for allocating a high proportion to stocks

in the portfolio mix. Terry (2003), on the other hand, suggests a

negative relationship exists between stock allocations and

withdrawal rates. Studies by Pye (2000), Guyton (2004),

Guyton and Klinger (2006), Robinson (2007), Spitzer, Strieter,

and Singh (2007), Spitzer (2008), and Stout (2008) also

explore various decision rules for variable withdrawal

strategies to achieve higher initial withdrawal rates without

harming the overall chances for success. Scott, Sharpe, and

Watson (2009) suggest that using financial derivatives could



support a higher spending rate than offered by the 4 percent

rule.

While much of the existing literature supports the safety

of the 4 percent withdrawal rate or even higher rates, the

conclusions are usually based on the data for U.S. asset returns

since 1926. This covers a particularly fortuitous time period for

the U.S. that is unlikely to be attained over a regular basis by

any country. Blanchett and Blanchett (2008) acknowledge that

past market conditions may not suitably represent what will

happen in the future. They note that, based on the average

expected forecast for future stock returns from a variety of

sources, the future real returns for a 60/40 portfolio of stocks

and bonds in the U.S. can be expected to be between 1 and 2

percentage points less than historical averages. Dimson, Marsh,

and Staunton (2004) also argue that looking at the past U.S.

data for future predictions will lead to ―success bias.‖ This

expectation of lower future stock returns in the U.S. is also

noted by Bogle (2009) and Krugman (2005). Overall,

conclusions reached by previous studies may provide overly

optimistic recommendations about future sustainable

withdrawal rates, which could therefore jeopardize retirement

spending at later ages.

Very few studies about safe withdrawal rates consider

countries other than the U.S. Pfau (2010) is one exception that

includes 17 developed market economies. The study shows that

the U.S. enjoyed consistently low inflation, and high returns

and low volatility on stocks and bonds, relative to other

countries. With historical simulations, his results show that

only 4 countries including Canada, Sweden, Denmark, and the


U.S. could attain a maximum worst-case withdrawal rate

exceeding 4 percent for a 30-year retirement duration. This

calculation does not include account fees and assumes that

retirees in each year had the perfect foresight to choose the best

performing asset allocation. He also finds that the best worst-

case maximum withdrawal rates occur with stock allocations of

at least 48 percent for all countries except Switzerland. These

findings, in addition to the potentially weaker performance of

future market returns, pose a concern about the wide

applicability of the 4 percent rule.

Estimating sustainable withdrawal rates is of particular

importance for less developed economies with limited annuity

markets and growing reliance on defined-contribution pension

plans. In many of these countries, existing defined-benefit

pension funds provide limited coverage for the population. As

well, worldwide trends of decreasing fertility and increasing

lifespans are leading to increasingly aging populations. Table 1

summarizes these demographic trends for the countries

included in this study, showing how the percentage of the

population aged 60 and over is rapidly growing from an

average of 7.6 percent in 1970, to 11.5 percent in 2010, to a

projected 25.7 percent in 2050. Related to this, life expectancy

at birth has grown from an average of 61.3 in the early 1970s to

a projected 79.5 by the 2050s. At the same time that

populations are aging, the traditional network of having

extended families support their elderly members, which is so

important in emerging market countries that Holzmann and

Hinz (2005) include it as the fourth pillar of the old-age

support network in the World Bank‘s revised pension

framework, is being threatened by reduced family sizes and



Table 1

Population Aged 60 and over and Life Expectancy at Birth

Country

Population Aged 60+

(% of Total Population) Life Expectancy at Birth

(Years)

1970 2010 2050 1970-

1975

2010-

2015

2050-

2055

Argentina 10.7 14.6 25.0 67.2 76.1 81.4

Brazil 5.6 10.3 29.0 59.5 74.0 79.9

Chile 7.8 13.2 30.3 63.7 79.3 83.3

China 6.6 12.3 33.9 64.6 73.8 79.7

Colombia 5.5 8.6 23.7 61.7 74.0 79.7

Czech Republic 18.0 21.8 34.2 70.3 77.9 82.9

Egypt 5.4 8.0 20.2 51.7 73.5 79.7

Hungary 17.2 22.5 32.2 69.4 74.7 80.5

India 5.5 7.6 19.1 50.8 66.0 74.4

Indonesia 5.5 8.2 25.5 53.4 70.0 78.2

Israel 10.4 14.8 22.5 72.6 82.0 86.8

Jordan 5.2 5.8 18.2 62.6 73.6 79.1

Korea 5.4 15.7 38.9 63.2 80.7 85.1

Malaysia 5.4 7.7 20.4 64.9 74.6 80.3

Mexico 5.6 9.0 25.8 62.6 77.2 82.3

Morocco 6.2 8.2 24.2 53.0 72.5 79.3

Pakistan 5.8 6.4 15.8 54.6 65.8 72.7

Peru 5.6 8.8 22.7 55.5 74.3 79.9

Philippines 4.9 5.7 15.3 61.4 69.2 77.1

Poland 12.8 19.2 35.3 70.6 76.4 81.4

Russia 11.9 17.8 31.2 69.0 69.2 76.3

South Africa 5.5 7.4 14.8 53.7 53.8 65.8

Sri Lanka 5.9 12.3 27.4 64.1 75.2 80.7

Thailand 5.3 12.9 31.8 61.0 74.4 80.1

Turkey 6.1 9.0 26.0 51.3 74.3 80.0

Average 7.6 11.5 25.7 61.3 73.3 79.5

The data is based on the medium-variant projection.

Source: Population Division of the Department of Economic and Social

Affairs of the United Nations Secretariat, World Population

Prospects: The 2010 Revision


increased labor mobility. Increasingly, elderly will be left to

fend for themselves. Pfau and Atisophon (2009) provide as

case study about these demographic trends for Thailand, a

country which is working to create a defined-contribution

National Pension Fund to supplement the existing rudimentary

defined-benefit system. Whitehouse (2007) provides details

about pension reform in many different countries, indicating

how defined-contribution pensions have now become

commonplace in Latin America, the Caribbean, Eastern

Europe, and Central Asia. As such, the issue of sustainable

retirement spending in emerging market countries is quite

important. To the best of our knowledge, we are providing the

first attempt to address this issue for emerging market

economies in a stochastic framework that incorporates

volatility and probabilities of success for retirement withdrawal

strategies. We investigate both the applicability of the widely

accepted 4 percent withdrawal rule, as well as the issue of asset

allocation during retirement.

Data and Methodology

This study uses data from a variety of sources available

through the end of 2009. Returns on domestic stocks for the 25

countries are obtained from the MSCI Stock Indices. They are

calculated as the annual percentage change at year end for the

MSCI Standard Core Gross Indices. We also use domestic

currency deposit rates, taken from the International Monetary

Fund‘s International Financial Statistics (IFS), to represent the

local fixed income returns. Two exceptions are that we use the

central bank discount rate for India and Jordan in 1988-89 and

the call money rate for Pakistan. Also, for Poland, we made



adjustments to match recent and earlier deposit rates after a

change in the methodology of reporting deposit rates in 2002.

Inflation rates are also taken from the IFS. We use the longest

available time period of data for each country, except that we

drop the periods of extreme hyperinflation in Argentina and

Brazil. Analysis is based on the real returns for stocks and

deposit rates. Even though we would also like to consider

short-term and long-term government debt, such data is not

available for many of the emerging countries.

Unlike Pfau (2010), which could consider historical

simulations with 109 years of data for each developed market

country, we use a bootstrapping Monte Carlo approach with the

limited historical data for emerging markets. Annual in-sample

returns are randomly selected with replacement to form

hypothetical multi-year simulation periods for asset returns.

We simulate 10,000 hypothetical asset return paths for retirees

in each country. For each simulation, we optimize across the

two domestic assets, finding the fixed asset allocation that

provides the highest sustainable withdrawal rate for 30 years.

This is called the perfect foresight assumption, and it provides

an overly optimistic assessment for sustainable withdrawal

rates. To correct for this, we also investigate how sustainable

withdrawal rates vary by asset allocation. We consider 21

possibilities for fixed asset allocations, ranging in 5 percentage

point increments from 0 to 100 percent stocks, with the

remainder allocated to bank deposits. We assume a fixed

retirement duration of 30 years to be analogous with previous

studies. Modifying this assumption is simple, and most studies

find that sustainable withdrawal rates decrease, but at a

decreasing rate, as the retirement duration increases. Other


assumptions include no deductions for administrative fees,

annual rebalancing to the targeted asset allocations, and no

taxes.

We assume that the annual account withdrawal is set as a

percentage of the accumulated portfolio at the retirement date.

Since we have adjusted our data to eliminate the impact of

inflation, our resulting withdrawal rates are expressed in terms

of real purchasing power. Constant withdrawals are made at the

start of each year. The remaining account balance, divided

among the two assets, then grows or shrinks by that year‘s

asset returns, and at the end of the year the portfolio is

rebalanced to the target asset allocation. If the withdrawal

pushes the account balance to zero, the withdrawal rate was too

high and the portfolio failed to be sustainable for 30 years. We

calculate the maximum sustainable withdrawal rate for each

simulation.

Results

Table 2 provides summary statistics for asset returns and

inflation for the available time periods in 25 emerging market

economies. Asset returns are provided in real terms after

removing the effects of inflation. The returns for stocks and

fixed income assets vary across countries. Stocks provide

double-digit average returns for all countries except China,

Israel, Jordan, Morocco, and Poland. However, stock volatility,

as measured by standard deviation, tends also to be very high.

Standard deviations for real stock returns were under 30

percent in only 4 of the 25 countries. On the other hand, fixed

income assets tend to provide lower average returns and risks



Table 2

Summary Statistics

Country Period

Real Stock

Returns

Real Fixed

Income

Returns

Inflation Corr.

Between

Stocks &

Fixed

Income

Assets Mean

Std.

Dev. Mean

Std.

Dev. Mean

Std.

Dev.

Argentina 1992-2009 11.5 37.8 3.6 6.4 7.2 8.1 -0.15

Brazil 1995-2009 19.1 47.8 9.5 7.3 11.0 15.6 0.30

Chile 1988-2009 18.0 29.5 3.4 3.4 8.4 6.9 -0.09

China 1993-2009 4.7 45.9 -0.2 3.8 4.9 7.3 0.31

Columbia 1993-2009 18.7 41.3 4.4 3.4 11.6 7.2 -0.59

Czech

Republic 1995-2009 11.7 30.4 -1.0 1.6 4.5 3.4 0.56

Egypt 1995-2009 30.0 62.6 1.3 5.3 7.3 5.0 0.09

Hungary 1995-2009 18.4 47.6 0.8 2.7 10.4 7.6 -0.23

India 1993-2009 13.9 39.8 1.2 2.6 6.8 3.0 0.04

Indonesia 1988-2009 23.9 67.3 4.6 5.9 11.2 11.1 0.09

Israel 1993-2009 8.9 30.2 2.8 2.8 5.0 4.3 0.34

Jordan 1988-2009 6.7 29.6 1.0 5.2 5.5 6.1 0.20

Korea 1988-2009 10.7 37.4 2.8 1.9 4.6 2.2 0.04

Malaysia 1988-2009 12.0 35.1 1.8 1.5 2.9 1.3 0.06

Mexico 1988-2009 18.6 34.6 -1.2 7.2 17.7 23.7 0.26

Morocco 1998-2009 7.9 22.8 2.6 1.6 1.9 1.1 -0.30

Pakistan 1993-2009 16.5 53.6 0.3 3.3 8.6 4.6 0.16

Peru 1993-2009 21.0 38.0 -0.4 7.0 8.3 11.9 0.04

Philippines 1988-2009 10.8 44.1 1.7 2.4 7.4 3.6 -0.08

Poland 1994-2009 2.0 34.3 2.1 2.2 9.4 9.9 -0.14

Russia 1995-2009 14.4 60.0 -9.9 11.5 34.2 49.4 0.19

South

Africa 1993-2009 10.4 22.8 3.7 2.4 6.9 2.5 -0.06

Sri Lanka 1993-2009 12.7 55.8 -0.1 4.1 10.3 4.7 0.45

Thailand 1988-2009 15.1 51.0 2.5 2.9 3.8 2.3 0.07

Turkey 1988-2009 39.1 120.6 2.0 8.4 52.1 31.2 0.04

Source: Own calculations using data described in Data and Methodology

section.


in these countries. Fixed income assets provide real returns

under 5 percent in all countries except Brazil. Real average

returns are even negative for some countries. At the same time,

Russia is the only country which experienced fixed income

asset volatility above 10 percent. For inflation, average rates

were above 10 percent in Brazil, Columbia, Hungary,

Indonesia, Mexico, Russia, Sri Lanka, and Turkey. Table 2 also

includes the correlations between stocks and fixed income

assets. The correlation coefficients are small and even negative

in eight cases, implying potential diversification benefits.

Table 3 provides simulation results for sustainable

withdrawal rates over 30 years at various distribution

percentiles. The distributions are based on whichever asset

allocation provides the highest withdrawal rate over 30 years in

each simulation. In the worst-case scenario, only retirees in

Brazil, Colombia, South Africa, Chile, Morocco, and Korea

could sustain a 4 percent withdrawal rate, and retirees in 12

countries could not sustain a 3 percent withdrawal rate. In

Egypt, Peru, Jordan, China, Sri Lanka, Turkey, Mexico, and

Russia, the highest withdrawal rate for the worst-case scenario

is lower than 2 percent.

Focusing on the worst-case scenario from 10,000

simulations may be criticized as overly pessimistic or risk

averse, and the table also provides withdrawal rates at the 1st,

5th

, and 10th

percentiles. These percentiles provide the

withdrawal rates which can be sustained for 30 years with a 1

percent, 5 percent, and 10 percent chance of failure,

respectively. However, Terry (2003) argues that when dealing

with irreplaceable assets and uncertainties, even a 1 percent



Table 3

Sustainable Withdrawal Rates

Country Min. 1st

%ile

5th

%ile

10th

%ile

% Failure

Within 30

years at 4%

Withdrawal

Rate

% Failure

Within 30

years at 5%

Withdrawal

Rate

Brazil 5.00 6.23 7.08 7.59 0 0

Columbia 4.95 5.53 5.91 6.18 0 0

South Africa 4.40 4.81 5.10 5.30 0 3.2

Chile 4.34 5.05 5.93 6.66 0 0.9

Morocco 4.09 4.40 4.55 4.65 0 26.6

Korea 4.08 4.32 4.51 4.62 0 29.5

Israel 3.64 4.03 4.27 4.41 0.8 32.5

Poland 3.60 3.86 4.00 4.09 5.0 74.5

Malaysia 3.53 3.87 4.08 4.23 2.7 27.1

Thailand 3.35 3.92 4.22 4.39 1.7 26.7

Indonesia 3.26 4.19 4.90 5.36 0.6 5.9

Philippines 3.14 3.59 3.84 3.98 11.3 43.4

Argentina 3.06 3.78 4.29 4.60 2.1 18.8

Hungary 2.92 3.40 3.74 4.06 9.0 22.7

India 2.91 3.38 3.68 3.89 12.5 30.1

Pakistan 2.41 2.79 3.09 3.33 24.0 39.3

Czech Republic 2.38 2.58 2.83 3.21 19.4 30.0

Egypt 1.85 3.14 4.06 4.82 4.8 11.4

Peru 1.84 3.09 4.19 5.17 4.0 9.3

Jordan 1.81 2.53 2.93 3.18 34.5 54.9

China 1.80 2.37 2.62 2.75 65.1 78.1

Sri Lanka 1.73 2.34 2.65 2.82 40.5 55.5

Turkey 1.62 2.55 3.23 3.73 13.4 25.8

Mexico 1.39 2.62 3.91 5.02 5.4 9.9

Russia 0.06 0.17 0.30 0.41 74.1 78.8

Assumptions include perfect foresight, a 30-year retirement duration, no

administrative fees, annual inflation adjustments, and annual rebalancing.

Results are based on 10,000 simulations using bootstrapping with

replacement.

Source: Same as Table 2.


probability of failure is excessively high. Fullmer (2008) also

argues that downside risk is a painful aspect of risk and is more

unbearable after retirement when options such as continuing to

work have declined. At the 1st percentile (i.e. 99 percent chance

of success), sustainable withdrawal rates exceed 4 percent in 8

out of 25 countries: Brazil, Columbia, South Africa, Chile,

Morocco, Korea, Israel, and Indonesia. If a 5 percent failure

rate is accepted, a 4 percent withdrawal rate is sustainable in 14

countries. With a 5 percent failure rate, a withdrawal rate of 7

percent is possible in Brazil, and it is almost 6 percent in

Columbia and Chile, and 5 percent in South Africa. However,

in 5 countries even a 3 percent withdrawal rate was not

sustainable. The number of countries with withdrawal rates

exceeding 4 percent increases to 16 with a 10 percent failure

rate, but this leaves 9 countries with sustainable rates below 4

percent even with a 10 percent chance of failure.

The last two columns of Table 3 show the percentage of

failures with fixed withdrawal rates of 4 and 5 percent. With

the 4 percent withdrawal rate, 4 countries experience failures in

more than 25 percent of cases, while 15 countries experience

this outcome with a 5 percent withdrawal rate.

Table 4 shows the number of years for which 4 and 5

percent withdrawal rates are sustainable at various percentiles.

In the worst case, all countries except Russia find 4 percent and

5 percent to be sustainable for at least 10 years. The number of

sustainable years increases when a higher chance for failure is

accepted. As well, there tends to be a large drop in the number

of sustainable years when the withdrawal rate increases from 4



Table 4

Number of Sustainable Years for Various Withdrawal Rates

Country

4% Withdrawal Rate 5% Withdrawal Rate

Min. 1st

%ile

5th

%ile

10th

%ile Min.

1st

%ile

5th

%ile

10th

%ile

Brazil >50 >50 >50 >50 29 >50 >50 >50

Columbia >50 >50 >50 >50 29 44 >50 >50

Chile 36 >50 >50 >50 24 33 >50 >50

South Africa 35 47 >50 >50 24 29 33 37

Korea 31 35 38 40 23 25 26 27

Morocco 31 36 38 40 23 25 27 27

Israel 27 31 34 36 20 23 24 26

Poland 26 29 30 31 20 22 23 23

Malaysia 25 29 32 34 20 22 23 24

Thailand 24 30 34 37 19 22 24 26

Indonesia 23 38 >50 >50 18 24 33 46

Philippines 23 27 29 31 18 20 22 23

Argentina 22 29 38 46 17 21 26 29

Hungary 22 25 28 31 18 20 21 23

India 21 25 28 30 17 20 21 22

Czech Republic 19 20 21 23 16 17 17 18

Pakistan 19 21 23 25 15 17 18 20

Peru 16 23 37 >50 13 17 23 38

Sri Lanka 16 19 21 22 13 15 17 18

China 15 19 20 21 13 15 17 17

Egypt 15 23 33 >50 13 17 22 29

Jordan 15 20 23 24 13 16 18 19

Turkey 14 19 24 29 12 15 18 21

Mexico 12 18 31 >50 10 14 20 32

Russia 6 9 10 11 6 8 9 10

Assumptions include perfect foresight, no administrative fees, annual inflation

adjustments for withdrawals, and annual rebalancing.

>50 means at least 50 years of sustainability.

Source: Same as Table 2.


Figure 1 Asset Allocation Providing Maximum Sustainable Withdrawal Rate for

Various Failure Probabilities

percent to 5 percent, especially for Brazil, Colombia, Chile,

and South Africa.

Figure 1 shows the asset allocations that achieved the

perfect foresight maximum sustainable withdrawal rates shown

for Table 3. Interestingly, for most countries the optimums

occur with a low proportion of stocks. This contrasts with the

Pfau (2010) study for developed markets, which found that the

stock allocation which provided the highest withdrawal rate

was at least 50 percent in 16 of 17 countries. For the more

volatile emerging market countries, from the minimums to the

10th

percentiles of the simulations, the optimums occur with

0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge A

llocation t

o S

tocks

BR

A

CO

L

ZA

F

CH

L

MA

R

KO

R

ISR

PO

L

MY

S

TH

A

IDN

PH

L

AR

G

HU

N

IND

PA

K

CZ

E.

EG

Y

PE

R

JO

R

CH

N

LK

A

TU

R

ME

X

RU

S

10th percentile

5th percentile

1st percentile

Minimum



Figure 2

Sustainable Withdrawal Rates across Distribution of Stock

Allocations with 5 Percent Failure Probability

stock allocations below 30 percent for all countries except

Chile, the Czech Republic, Egypt, Peru, and Mexico.

Figure 2 illustrates the distribution of sustainable

withdrawal rates across stock allocations for each country with

a 5 percent probability of failure. For each country‘s

distribution, the highest withdrawal rate attained is labeled with

the country‘s name code. In the case of ties, the smallest stock

allocation is labeled. The highest withdrawal rates are achieved

with 30 percent or less stock allocations for all countries except

Chile, Peru and Mexico, where the highest withdrawal rates

occur with 50, 55, and 80 percent stock allocations,

respectively. Strikingly, 19 out of the 25 countries achieve the

highest sustainable withdrawal rates with stock allocations of

0 10 20 30 40 50 60 70 80 90 1000

1

2

3

4

5

6

7

8

ARG

BRA

CHL

CHN

COL

CZE.

EGY

HUNIND

IDN

ISR

JOR

KOR

MYS

MEX

MAR

PAK

PER

PHLPOL

RUS

ZAF

LKA

THA

TUR

Percentage Allocation to Stocks

Susta

inable

Withdra

wal R

ate


Figure 3

Probability of Failure for 4% Withdrawal Rate by Stock Allocation

15 percent or less. The distribution of stock allocations has a

downward sloping trend for many countries, noticeably when

stock allocations rise above 20 percent. Allocating a high

proportion to stocks does more harm than good for sustainable

withdrawal rates in these emerging market countries.

Finally, Figures 3 and 4 show the probability of failures

with 4 and 5 percent withdrawal rates, respectively, across the

range of stock allocations. Again, for each country‘s

distribution, the lowest probability of failure is labeled by the

country‘s name code. The distributions of failure probabilities

exhibit a convex shape (or roughly U-shaped) for many

countries. This pattern is more apparent when the withdrawal

rate is 5 percent. There is a large drop in the probability of

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

ARG

BRA CHL

CHN

COL

CZE.

EGY

HUN

IND

IDNISR

JOR

KOR

MYS

MEX

MAR

PAK

PER

PHL

POL

RUS

ZAF

LKA

THA

TUR


Pro

babili

ty o

f F

ailu

re



Figure 4

Probability of Failure for 5% Withdrawal Rate by Stock Allocation

failure when stocks are initially introduced, but the marginal

drop decreases to a minimum. Then the failure probabilities

increase for higher stock allocations.

Moreover, the minimum probability of failure for a 4

percent withdrawal rate occurs at points where stock

allocations are less than 50 percent for most countries, except

Czech Republic, Mexico, Peru, and Russia. It is not surprising

that when the withdrawal rate increases to 5 percent, minimum

probabilities of failure move to higher stock allocations, since

more risk is needed to fund higher withdrawals. Even though

more stocks are needed to increase withdrawals to 5 percent,

still only 6 countries experience optimal stock allocations of

more than 50 percent. These results improve the robustness of

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

ARG

BRA CHL

CHN

COL

CZE.

EGY

HUN

IND

IDN

ISR

JOR

KOR

MYS

MEX

MAR

PAK

PER

PHL

POL

RUS

ZAF

LKA

THATUR


Pro

babili

ty o

f F

ailu

re


our previous findings that, differently from developed

countries, high stock allocations are not appropriate for

maintaining sustainable withdrawals with emerging market

assets.

Conclusion

Numerous studies based on U.S. data exist to help

retirees plan safe and sustainable withdrawals from their

retirement savings. In the existing literature, the well-known

finding is that an annual 4 percent inflation-adjusted

withdrawal rate over 30 years is considered safe for retirees

with a stock allocation above 50 percent. However, this

widely-accepted rule-of-thumb is not necessarily applicable for

the situation in other countries. For emerging market

economies, this issue is quite important, as annuity markets are

not developed and recent pension reforms are moving toward

defined-contribution pension plans in which retirement income

management is handled individually by retirees. Therefore,

guidelines about sustainable withdrawal rates are needed.

Our study, based on the 25 emerging market economies

included in the MSCI indices, finds that the sustainability of

the 4 percent withdrawal rule is questionable in many cases.

Using the bootstrapping approach, our results show that, in the

worst-case scenario, only retirees in 6 out of 25 countries could

sustain their 30 years of withdrawals with 4 percent. Even with

a 5 percent chance of failure, 4 percent is not sustainable in 11

countries, and even 3 percent is not sustainable in 5 countries.

Moreover, our study indicates that the optimal asset

allocation for providing the highest withdrawal rates with a low



chance for failure occur at low stock allocations for most

emerging market economies, in contrast to previous studies on

developed economies. Though a higher proportion of stocks

increases the chance of success at higher withdrawal rates,

higher withdrawal rates are also accompanied by increased

failure probabilities. To attain a 4 or 5 percent withdrawal rate,

a portfolio mix composed of less than 50 percent stock is

needed for most of the countries in our sample.

The bootstrapping approach used here provides a way to

incorporate volatility into the issue of retirement planning,

which gives a more realistic picture than using fixed rates of

returns for these financial assets. However, the approach is far

from perfect. This study makes an implicit assumption that past

patterns in financial markets are reflective of the type of

situation these countries will face in the future. Further

developments may help to reduce the financial market

volatility in these countries, but that is not yet clearly going to

be the case. Given these uncertainties, the findings of this

research suggest that retirement saving will be very important

in emerging markets as the 4 percent rule is not reliable. For

the most part, asset allocations should be lower as well for

these retirees, suggesting that appropriate asset allocations for

developed and emerging market countries may be quite

different. This issue is deserving of greater research focus in

the future, as citizens of emerging market countries cannot rely

on the results of retirement planning studies conducted for the

U.S. case.


References

Ameriks, J., Veres, R., & Warshawsky, M. J. (2001). Making retirement

income last a lifetime. Journal of Financial Planning, 14, 12, 60-76.

Bengen, W. P. (1994). Determining withdrawal rates using historical data.

Journal of Financial Planning, 7(1), 171-180.

Bengen, W. P. (1996). Asset allocation for a lifetime. Journal of Financial

Planning, 9(4), 58-67.

Bengen, W. P. (1997). Conserving client portfolios during retirement, part

III. Journal of Financial Planning, 10(6), 84-97.

Bengen, W. P. (2006). Baking a withdrawal plan ‗Layer Cake‘ for your

retirement clients. Journal of Financial Planning, 19(8), 44-51.

Blanchett, D. M. & Blanchett, B. C. (2008). Data dependence and

sustainable real withdrawal rates. Journal of Financial Planning, 21(9),

70-85.

Bogle, J. C. (2009). Enough: True measure of money, business, and life.

New Jersey: John Wiley and Sons.

Cooley, P. L., Hubbard, C. M., & Walz, D. T. (2011). Portfolio Success

Rates: Where to Draw the Line. Journal of Financial Planning, 24(4),

48-58.

Dimson, E., Marsh, P., & Staunton, M. (2004). Irrational optimism.

Financial Analysts Journal, 60(1), 15-25.

Fullmer, R. K. (2008). The fundamental differences in accumulation and

decumulation. Journal of Investment Consulting, 9(1), 36-40.

Guyton, J. T. (2004). Decision rules and portfolio management for retirees:

Is the ‗safe‘ initial withdrawal rate too safe? Journal of Financial

Planning, 17(10), 54-61.

Guyton, J. T. & Klinger, W. J. (2006). Decision rules and maximum initial

withdrawal rates. Journal of Financial Planning, 19(3), 49-57.

Holzmann, R. & Hinz, R. (2005). Old Age Income Support in the 21st

Century: An International Perspective on Pension Systems and Reform.

World Bank: Washington, DC.

Krugman, P. (2005). Confusions about social security. The Economist’s

Voice, 2(1), 1-8.

Pfau, W. D. (2010). An international perspective on safe withdrawal rates

from retirement savings: The demise of the 4 percent rule? Journal of

Financial Planning, 23(12), 52-61.

Pfau, W. D., & Atisophon, V. (2009). The impact of the National Pension

Fund on the suitability of elderly pensions in Thailand. Asian Economic

Journal, 23(1), 41-63.

Pye, G. B. (2000). Sustainable investment withdrawals. Journal of Portfolio

Management, 26(4), 73-83.

Robinson, C. D. (2007). A phased-income approach to retirement

withdrawals: A new paradigm for a more affluent retirement. Journal

of Financial Planning, 20(3), 44-56.



Scott, J.S., Sharpe, W. F., & Watson, J. G. (2009). The 4% rule – At what

price? Journal of Investment Management, 7(3).

Spitzer, J. J. (2008). Retirement withdrawals: an analysis of the benefits of

periodic ―midcourse‖ adjustments. Financial Services Review, 17(1),

17-29.

Spitzer, J. J., Strieter, J. C., & Singh, S. (2007). Guidelines for withdrawal

rates and portfolio safety during retirement. Journal of Financial

Planning, 20(10), 52-59.

Spitzer, J. J., Strieter, J. C., & Singh, S. (2008). Adaptive withdrawals.

Journal of Investing, 17(2), 104-113.

Stout, R. G. (2008). Stochastic optimization of retirement portfolio asset

allocations and withdrawals. Financial Services Review, 17(1), 1-15.

Terry, R. L. (2003). The relation between portfolio composition and

sustainable withdrawal rates. Journal of Financial Planning, 16(5),

64-72.

Tezel, A. (2004). Sustainable retirement withdrawals. Journal of Financial

Planning, 17(7), 52-57.

Whitehouse, E. (2007). Pensions Panorama: Retirement-Income Systems in

53 Countries. World Bank: Washington, DC.


FINANCIAL PLANNING LITERATURE SURVEY

Benjamin E. Fagan, MSFE

PlusPlus Inc.

Shawn Brayman, MESPlusPlus Inc.

This study is intended to provide an environmental scan of current

research from Australia, Canada, United Kingdom and the United

States, related to financial planning/services from 2003 to July

2010. The objective of this exercise is to try and highlight research

areas where there may be gaps. This is not intended to review the

research in any manner but rather to aggregate and document its

existence in some broad based categories. The study was carried

out in two parts. To begin with, research was collected,

categorized and totalled to determine high and low volume areas.

Finally, industry practitioners and academics were petitioned to

provide their opinions. Based on our findings, Estate Distribution

Analysis, Pension Alternatives and Tax Optimization were found

to be the topics that require the most focus for further research.

Modern Portfolio Theory, General Portfolio Management and

Product Shelf were the categories that were determined to be the

most overly researched areas.

Introduction

To provide an unbiased review of financial planning

research, different search methods from several sources were

employed, followed by an assessment from industry

professionals. The two main search methods used were to

Shawn Brayman, PlusPlus Inc.,55 Mary St. Suite #200, Lindsay, Ontario,

Canada K9V 5Z6; (705) 324-8001 ext. 306; [email protected]

The authors wish to thank the FPSC Foundation for sponsoring the research

that is highlighted in this report. The FPSC Foundation is a charitable

organization that promotes and disseminates research for the benefit of the

public, financial planners, academia and industry. www.fpscfoundation.ca



contact stakeholders in the financial planning industry directly

and have them submit their research and to conduct an

environmental scan using academic databases and search

engines. Six hundred and two (602) institutions were directly

contacted and asked to submit research. This number included

572 universities, 3 journals, 19 financial planning associations

and 12 other industry related institutions from Canada, the

United States, the United Kingdom, New Zealand and

Australia.

Description of Review

The environmental scan was accomplished by searching

for different keywords in academic databases over the study‘s

timeframe. ProQuest was used, at the University of Waterloo,

to perform the search across a collection of databases (for a

complete listing of the databases used, see Appendix A). The

results of this methodology could be potentially biased due to

the selection of keywords. To reduce this bias each category,

subcategory, and the general term ―financial planning‖ were

used as keywords.

Once collected, the research was grouped into specific

categories and sub-categories that had been previously selected

by PlanPlus and the FPSC Foundation (see Appendix B). This

was done based on the titles of the research if they contained

certain keywords. Also the abstract was reviewed if further

scrutiny was necessary. Because of the nature of the search

methodology, duplicates were common and were removed.

From 2003 through 2010 the literature search returned 1,978

papers and articles from 379 different sources.


It is difficult to make any assessment of whether or not

there are research gaps based solely on article volume. To

provide some cross-validation, industry professionals were

asked to provide their feedback of areas in financial planning

that currently require the most attention and those that have

been overly researched (in their opinion). This survey was

directed electronically to the same 572 universities mentioned

above, distributed in paper form at the FPSC Professional

Development Day in Canada as well as limited distribution at

the Financial Planning Association Conference in Denver. Both

events where held in October 2010. Individual emails were also

directed to several hundred planning professionals and

executives and a mailing was carried out by the FPSC to all

registered CFP® certificate holders in Canada. Based on the

feedback from 743 industry professionals an importance

ranking was established for each research category. The

importance ranking, in conjunction with article volume, was

then used to determine specifically what areas of research are

in need of focus or potentially receive too much focus. The

ranking methodology can be found in Appendix H.1.

Literature Scan

Total Published Articles by Topic/Category

In total, 1,978 literary articles were sourced in the

literature survey (as presented in Table 1). The breakdown of

each category can be seen in the following table. The most

active categories were Retirement Planning (419 articles),

Portfolio Management (317 articles) and Behavioural Finance

(246 articles). The least active areas of research were

Regulatory & Compliance (48 articles), Tax Planning (58



articles), and Holistic Planning (75 articles). See Appendix C

for a complete listing of articles per category.

Table 1

Articles per Main Category

Category Articles

Retirement Planning 419

Portfolio Management 317

Behavioural Finance 246

Business Practices 157

Investment Planning 148

Other Planning 144

Estate Planning 132

Insurance Planning and Risk Management 126

Cash Flow & Liability Mangement 108

Holistic Planning 75

Tax Planning 58

Regulatory & Compliance 48

Total 1,978

Publication Trends over Time

Over the past several years we have seen substantial

growth in the production of financial planning literature. In

2003 less than 200 articles were published. This has grown

each year to an estimated nearly 400 articles in 2010 (as shown

in Figure 1). Please note that the 2010 estimate is based on a

linear extrapolation from the articles collected through July of

that year. The vast majority of journals searched included

articles from 2003 or earlier so we feel this trend is not

significantly biased by the availability of the data.


Figure 1

Articles produced from 2003-2010 related to financial planning

Sources for Financial Planning Research

The literature search encompassed articles from 379

scholarly journals, magazines and other publication sources.

Figure 2 shows, as a percentage of the total 1,982 articles, the

publication sources that have published the most research

related to financial planning since 2003. The majority of

articles have primarily come from, Journal of Financial

Planning (20%), Financial Analysts Journal (13%), Journal of

Financial Service Professionals (11%), Journal of Family and

Economic Issues (11%) and the Financial Services Review

(10%).

Geographic Source of Research

The source of the research is largely driven by the

location of the journal which published it, as it was not possible

to determine the domicile of authors. As is evident in Table 2,

the United States clearly dominates the source of research. The

Netherlands was an unexpected surprise largely as a result of

2010 estimated

0

50

100

150

200

250

300

350

400

450

2003 2004 2005 2006 2007 2008 2009 2010

Art

icle

s P

rod

uce

d

Year



47

.18

%

20

.13

%

12

.85

%

11

.22

%

10

.85

%

9.5

8%

6.9

1%

6.1

7%

3.7

1%

3.4

9%

2.8

2%

2.6

0%

2.3

0%

2.0

1%

1.6

3%

1.3

4%

1.2

6%

1.1

1%

00

.05

0.1

0.1

50

.20

.25

0.3

0.3

50

.40

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0.5

Oth

er

(36

1 o

thers

)

Jou

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Fin

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l P

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an

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aly

sts

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l

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l S

erv

ice …

Jou

rna

l of

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mily

an

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Fin

an

cia

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ices

Rev

iew

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rna

l of

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l P

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Pers

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ath

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…

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ua

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Ca

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ax J

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sura

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Percen

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ota

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Publication SourceFig

ure

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Pu

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nin

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ela

ted a

rtic

les

from

200

3-20

10


publications from ‗Insurance: Mathematics & Economics‘ and

the ‗Journal of Business Ethics.‘

Table 2

Geographic Source of Research

Country Articles % Total

United States 1,584 80.1%

United Kingdom 168 8.5%

Netherlands 114 5.8%

Canada 61 3.1%

Australia 19 1.0%

Switzerland 10 0.5%

Germany 7 0.4%

Other 15 0.8%

Authors of Research

Although not central to the scope of this research, and in

some cases difficult to consolidate as a result of different

variations of an author‘s name, we felt that the distribution of

authorship was an interesting by-product of this scan.

Based on the total of 1,978 papers there were a total of

4,220 authors or co-authors. After some attempt to clean up the

data for consistent naming, we arrived at 2,658 unique authors.

As can be seen in Table 3, the vast majority of authors

published only one paper that appeared in our scan with just

under 1% of authors publishing 7 or more papers.

Also compiled is a list of researchers who have authored

8 or more articles in Table 4. John E. Grable and Moshe A.

Milevsky were found to have the most contributions, each with

17, followed by J. Timothy Lynch at 15. For a more extensive

list, please see Appendix E.



Table 3

Frequency of Authorship

# of Papers Authored Authors %

1 2,192 82.47%

2 286 10.76%

3 80 3.01%

4 40 1.50%

5 20 0.75%

6 14 0.53%

7 9 0.34%

8 4 0.15%

9 6 0.23%

10 or more 7 0.26%

Total 2,658

Table 4

Most Articles by Author

Articles Researcher Articles Researcher

17 John E. Grable 9 Angela C. Lyons

17 Moshe A. Milevsky 9 Willi Semmler

15 J.Timothy Lynch 9 David Blake

13 William Reichenstein 9 Amin Mawani

11 Sherman D. Hanna 8 Barbara O'Neill

11 Sharon A. DeVaney 8 Michael S. Finke

10 John J. Spitzer 8 Michael J. Roszkowski

9 Deanna L. Sharpe 8 Dennis C. Reardon

9 Neal E. Cutler

Opinion Survey on the Need for Additional Research

The second stage of the research was to try and develop a

weighting for the perceived need of additional research in the

various topic areas which could then be combined with the


basic inventory of published research. The approach was to

develop a simple two-question survey (Appendix D) using the

same research categories used to group the papers from the

research scan. The approach was to ask respondents to list the

three topics they felt were most in need of additional research

and the three topics they felt were already overly researched.

From the results of the survey, the categories and sub-

categories were separately ranked.

The survey was distributed to academics and industry

professionals as outlined in the initial description. This

resulted in 743 responses of which 120 were partial answers

where respondents selected items they felt required more

research but did not feel anything was ―over researched.‖ We

found that although the instructions indicated to select the 3

more important items most respondents selected far more. Of

the 84 options the 743 respondents selected 5,547 categories or

an average of 7.47 items they felt needed more research. The

623 respondents who chose an option about ―overly

researched‖ selected 2,679 or an average of 4.3 topics (see

Appendix F for results).

As a result of the much stronger expression of topics

requiring additional research, the overall weighting results in a

much longer list of topics where more research is desired. We

feel this is reflective of the actual belief and intent of the

respondents.

We felt that the design of the opinion survey would

provide a number of interesting perspectives on the various

topics presented:



Magnitude of interest in the topic, either positive or

negative.

Specific responses on each topic.

Like the literature scan itself, the raw results are difficult

to use. As an example, both Post-Modern Portfolio Theory and

Job Change/Loss had 40 respondents that listed them as topics

requiring more research, but there were 24 people who felt that

Post-Modern Portfolio Theory was over-researched and only 9

who felt Job Change/Loss was over-researched. We therefore

created a ―weighting factor‖ (see Appendix G that outlines the

methodology) that looked at the overall interest in the topic

based on total responses, the net difference on the responses as

either positive or negative, and the degree of consensus on the

topic as well as the volume of articles that were tracked during

the literature survey. We felt that topic areas with high

consensus of opinion provided a stronger reading of a topic‘s

appropriateness (Importance Weight).

In Table 5 – Importance Weight, you can see the

weighting of specific topics based on the combined factors of

the consensus level. The Importance Weight only considers

the opinions of the respondents, while in the following section

Importance Rank further considers the volume of articles

collected in each category. A 100% Consensus would mean all

votes on that topic were consistent for More or Over

Researched and the net score for the topic which indicated the

magnitude of the opinion on that topic area. The categories

towards the top of the table indicate that ‗More Research‘ is

needed. The categories towards the bottom indicate ‗Less

Research‘ is needed. If the Importance Weight is near zero, no


Table

5

Imp

ort

an

ce W

eig

ht

of

Ma

in C

ate

go

ries

Cate

go

ryM

ore

Less

Mag

-

nit

ud

eN

et

Co

n-

sen

su

s

To

pic

s in

Cate

go

ry

Imp

ort

an

ce

Weig

ht

Esta

te P

lan

nin

g610

157

767

453

59%

738.2

2

Beh

av

iou

ral F

inan

ce

539

189

728

350

48%

628.0

4

Oth

er

Pla

nn

ing

502

126

628

376

60%

11

20.4

7

Cash

Flo

w &

Lia

bilit

y M

an

ag

em

en

t404

157

561

247

44%

618.1

3

Tax

Pla

nn

ing

347

161

508

186

37%

417.0

3

Reti

rem

en

t P

lan

nin

g708

266

974

442

45%

12

16.7

1

Ho

listi

c P

lan

nin

g203

85

288

118

41%

316.1

2

Insu

ran

ce P

lan

nin

g &

Ris

k M

an

ag

em

en

t559

274

833

285

34%

10

9.7

5

Bu

sin

ess P

racti

ces

450

242

692

208

30%

96.9

5

Inv

estm

en

t P

lan

nin

g663

438

1101

225

20%

95.1

1

Reg

ula

tory

& C

om

plian

ce

209

163

372

46

12%

41.4

2

Po

rtfo

lio

Man

ag

em

en

t353

421

774

-68

9%

7-0

.85



strong consensus can be determined. (See Appendix G for

more information on the calculation methodology and data.)

Combining Perceived “Need” and Literature Scan

Each category was assigned an Importance Weight based

on feedback from industry professionals as outlined above.

This was then combined with the volume of articles (as in

Appendix G) to arrive at an Importance Rank. In simple terms

we created a normal distribution of the number of articles per

topic and then rated the topic based on its percentile ranking in

the distribution.

In Table 6 we display the 10 topics that scored highest

using our methodology as requiring more research

concentration. They are listed by their importance rank. Estate

Distribution Analysis requires the most focus, followed by

Pension Alternatives and Tax Optimization. For the complete

list of topics that resulted in a net belief that additional research

was required, see Appendix H.1.

Table 6

Categories Requiring the Most Focus

Sub-Category Importance

Weight Articles

Importance

Rank

Estate Distribution Analysis 73.50 1 61.18

Pension Alternatives 86.70 13 57.65

Tax Optimization 72.35 7 54.72

Holistic Planning vs. Modular 55.15 0 46.51

Succession Planning 81.76 22 41.62

Debt Management 82.14 24 38.88

Non-traditional Families 47.09 6 36.27

Divorce Planning 53.83 14 34.90

Needs on Disability 39.76 3 32.17

Dependents with Special Needs 36.48 6 28.10


Table 7 shows the areas of research that require less

focus based on the methodology outlined herein. We

determined the most overly researched topic to be General

Portfolio Management, followed by Modern Portfolio Theory

and Portfolio Analytics. See Appendix H.2 for the complete list

of topics where opinion rated it as overly researched.

Table 7

Categories Requiring Less Focus

Sub-Category Importance

Weight Articles

Importance

Rank

Modern Portfolio Theory -28.40 46 -24.25

General Portfolio Management -35.37 33 -24.08

Product Shelf -14.75 34 -10.28

Portfolio Analytics -5.88 46 -5.02

RRIF/LIF/PRRIF -5.28 26 -2.97

Real Estate/Mortgages -3.08 54 -2.84

General Investment Planning -1.33 12 -0.42

Property & Casualty Insurance -0.39 23 -0.20

Marketing 0.00 6 0.00

Socially Responsible Investing 0.00 108 0.00

Discussion of Findings

The mandate of this engagement and research project was

to provide a literature scan of research published in the field of

financial planning to help FPSC Foundation evaluate potential

areas of sponsorship in the future based on possible gaps.

The raw results quantifying the research in specific

topic areas meet the original scope of the engagement

with 1,978 research papers categorized.



The additional ―filter‖ based on the opinions of

various professionals in the industry combined with

the number of articles, or lack thereof, provides more

insight into the areas of research that require more or

less focus.

Although the weighting algorithm we developed is

somewhat arbitrary, we feel it provides a documented,

supportable methodology to align the different factors

to provide more focused guidance to FPSC

Foundation.

References

Grable, John E., (2006). Personal Finance, Financial Planning, and

Financial Counseling Publication Rankings. Journal of Personal

Finance, 5(1), 68-78.

Israelsen, Craig L., & Hatch, Shannon. (2005). Proactive Research: Where

Art Thou? Financial Counseling and Planning, 16(2).

Appendices

Appendix A: Databases Used in Search

ABI/INFORM Globabl

ABI/INFORM Trade & Industry

Canadian Research Index

CBCA Complete

ProQuest Dissertations & Theses (PQDT)

ProQuest Asian Business and Reference

ProQuest Dissertations and Theses - UK & Ireland

ProQuest European Business

Appendix B: Survey Categories

Investment Planning

General Investment Planning

Portfolio Objectives


Portfolio Analytics

Use of Investment Policy

Tax Optimization

Aggregation

Client Reporting

Rebalancing

Guaranteed Minimum Products

Tax Planning

General Tax Planning

Personal Income Tax

Corporate Tax

Capital Gains Harvesting

Estate Planning

General Estate Planning

Will Review

Estate Distribution Analysis

Succession Planning

Charitable Giving

Estate Taxes

Gifting

Power of Attorney for Property Review

Power of Attorney for Personal Care Review

Insurance Planning & Risk Management

General Insurance & Risk Management

Needs on Death

Needs on Disability

Critical Illness

Long Term Care

Term vs. Permanent Insurance

Property & Casualty Insurance

Key Man

Buy-Sell

Pricing



Cash Flow & Liability Management

Real Estate/Mortgages

Debt Management

Lending Metrics

Income Profile

Savings Behaviour

Portfolio Management

General Portfolio Management

Modern Portfolio Theory

Post-Modern Portfolio Theory

Active vs. Passive Management

Tactical vs. Strategic

Asset Allocation

Socially Responsible Investing

Retirement Planning

General Retirement Planning

RRIF/LIF/PRRIF

IRA, Distributions

Investment Liquidity

Pension Alternatives

Government Benefits

Healthcare

Annuities

Mortality

Employee Benefits

Sustainable Withdrawal Rates

Stochastic vs. Deterministic Forecasting

Business Practices

General Business Practices

Information Technology

Product Shelf

Recruitment

Marketing


Fee Structure

Best Practices

Business Models

Practice Succession Planning

Cost of Compliance

Professional Issues

Holistic Planning

General Holistic Planning

Demographics

Holistic Planning vs. Modular

Behavioural Finance

General Behavioural Finance

Client Relationships

Goal Visioning

Consumer Attitudes

Risk Tolerance

Financial Literacy

Self-Managed Financial Planning

Regulatory & Compliance

Litigation & Compliance

Ethics

Principal-Agent Problem

Other Planning

Specialized Financial Planning

Business Planning

Education Planning

Other Accumulation Goals

Multi-Goal vs. Modular

Average vs. Graduated Tax

Divorce Planning

Terminal Illness

Non-traditional Families

Job change/loss



Dependents with Special Needs

Islamic Financial Planning

International Planning

Econometric Assumptions

Appendix C: Total Articles per Sub-Category

Category Articles Category Articles

Socially Responsible

Investing 108

Econometric

Assumptions 16

Annuities 95 Portfolio Objectives 15

Consumer Attitudes 88 Post-Modern Portfolio

Theory 14

Asset Allocation 71 Divorce Planning 14

General Retirement

Planning 66 Personal Income Tax 13

Professional Issues 59 Pension Alternatives 13

Financial Literacy 57 Stochastic vs.

Deterministic 12

Real Estate/Mortgages 54 International Planning 12

Specialized Financial

Planning 54 Tactical vs. Strategic 11

Mortality 51 Business Models 11

Risk Tolerance 47 Investment Liquidity 10

Portfolio Analytics 46 Self-Managed

Financial Planning 9

Modern Portfolio Theory 46 Use of Investment

Policy 7

General Investment

Planning 43 Tax Optimization 7

Government Benefits 40 General Behavioural

Finance 7

Charitable Giving 39 Gifting 6

Sustainable Withdrawal

Rates 39 Income Profile 6

Demographics 39 General Business

Practices 6

General Tax Planning 38 Marketing 6

General Holistic Planning 36 Principal-Agent

Problem 6

General Estate Planning 35 Non-traditional

Families 6


Total Articles per Sub-Category (cont.)

Category Articles Category Articles

General Insurance & Risk

Management 35

Dependants with

Special Needs 6

Product Shelf 34 Use of Leverage 5

General Portfolio

Management 33

Guaranteed Minimum

Products 4

Client Relationships 33 Capital Gains

Harvesting 4

Needs on Death 31 Will Review 4

IRA, Distributions 30 Fee Structure 4

Long Term Care 29 Goal Visioning 4

Active vs. Passive

Management 29 Corporate Tax 3

Rebalancing 26 Needs on Disability 3

RRIF/LIF/PRRIF 26 Term vs. Permanent

Insurance 2

Estate Taxes 25 Buy-Sell 2

Debt Management 24 Estate Distribution

Analysis 1

Savings Behaviour 24 Key Man 1

Property & Casualty

Insurance 23 Lending Metrics 1

Succession Planning 22 Aggregation 0

Litigation & Compliance 22 Client Reporting 0

Employee Benefits 21 Critical Illness 0

Best Practices 21 Practice Succession

Planning 0

Ethics 20 Cost of Compliance 0

Business Planning 18 Holistic Planning vs.

Modular 0

Education Planning 18 Job change/loss 0

Information Technology 17 Islamic Financial

Planning 0

Healthcare 16



Appendix D: Survey


Appendix E: Listing Authors with Five or More Articles

Articles Researcher Articles Researcher

17 John E. Grable 6 Michael F. Rogers

17 Moshe A. Milevsky 6 Katherine A. Hesse

15 J.Timothy Lynch 6 Murray S. Anthony

13 William Reichenstein 6 Dale L. Domian

11 Sherman D. Hanna 6 Tahira K. Hira

11 Sharon A. DeVaney 6 Vorris J. Blankenship

10 John J. Spitzer 6 Steven Haberman

9 Deanna L. Sharpe 6 Doris R MacKenzie

Ehrens

9 Neal E. Cutler 6 Raimond H. Maurer

9 Angela C. Lyons 6 Kevin Dowd

9 Willi Semmler 5 Joseph W. Goetz

9 David Blake 5 Jinkook Lee

9 Amin Mawani 5 Karen Eilers Lahey

8 Barbara O'Neill 5 Cazilia Loibl

8 Michael S. Finke 5 Yoon G. Lee

8 Michael J. Roszkowski 5 Lance Palmer

8 Dennis C. Reardon 5 Meir Statman

7 Russell N. James III 5 Richard D. Landsberg

7 Jinhee Kim 5 Michael D. Everett

7 Jean M. Lown 5 John Y. Campbell

7 Robert W. Faff 5 Alistair M. Nevius

7 April K Caudill 5 Ronald F. Duska

7 Stephen M. Horan 5 Michael S. Gutter

7 Andrew J.G. Cairns 5 Roger G. Ibbotson

7 Dorothy C. Bagwell

Durband 5 Lars Grüne

7 E.Thomas Garman 5 Sandeep Singh

6 Dennis T. Jaffe 5 Philip L. Cooley

6 So-Hyun Joo 5 Benoit Sorhaindo

6 Sandra Timmermann 5 Vickie L. Hampton

6 David Blanchett 5 Virginia R. Young



Appendix F: Importance Ranking Opinion Poll - Survey

Results

Areas That Require More Focus

Sub-Category Respondents

% of

Category

Total

Estate Planning 137 21%

Retirement Planning 134 20%

Behavioural Finance 129 20%

Sustainable Withdrawal Rates 118 18%

Investment Planning 109 17%

Debt Management 108 16%

Succession Planning 107 16%

Tax Planning 104 16%

Personal Income Tax 100 15%

Insurance Planning & Risk

Management 97 15%

Cash Flow & Liability

Management 94 14%

Tax Optimization 93 14%

Pension Alternatives 93 14%

Financial Literacy 88 13%

Risk Tolerance 83 13%

Estate Distribution Analysis 78 12%

Divorce Planning 75 11%

Holistic Planning vs. Modular 74 11%

Savings Behaviour 72 11%

Long Term Care 70 11%

Guaranteed Minimum Withdrawal 69 11%

Best Practices 65 10%

Active vs. Passive Management 65 10%

Client Relationships 63 10%

Ethics 62 9%

Use of Investment Policy 61 9%

Portfolio Objectives 58 9%

Will Review 58 9%


Areas That Require More Focus (cont.)


% of

Category

Total

Business Planning 58 9%

Fee Structure 57 9%

Estate Taxes 56 9%

Consumer Attitudes 56 9%

Non-traditional Families 56 9%

Demographics 55 8%

Capital Gains Harvesting 54 8%

Cost of Compliance 54 8%

Business Practices 53 8%

Goal Visioning 52 8%

Asset Allocation 52 8%


Management 51 8%

Term vs. Permanent Insurance 50 8%

Specialized Financial Planning 50 8%

Needs on Disability 49 7%

Holistic Planning 48 7%

Client Reporting 47 7%

Needs on Death 47 7%

Litigation & Compliance 47 7%

Portfolio Analytics 46 7%

Dependents with Special Needs 46 7%

Portfolio Management 44 7%

Healthcare 42 6%

Tactical vs. Strategic 42 6%

Business Models 41 6%

Gifting 40 6%

Critical Illness 39 6%

Lending Metrics 39 6%

RRIF/LIF/PRRIF 39 6%

Regulatory & Compliance 39 6%



Areas That Require More Focus (cont.)


% of

Category

Total

Annuities 38 6%

Rebalancing 37 6%

Corporate Tax 37 6%

Other Planning 36 5%

Job change/loss 36 5%

Government Benefits 35 5%

International Planning 35 5%

Information Technology 34 5%

Principal-Agent Problem 33 5%

Stochastic vs. Deterministic

Forecasting 32 5%

Professsional Issues 32 5%

Marketing 31 5%

Charitable Gains 29 4%

Buy-Sell 29 4%

Post-Modern Portfolio Theory 29 4%

Socially Responsible Investing 27 4%

Income Profile 26 4%

Investment Liquidity 25 4%

Econometric Assumptions 25 4%

Real Estate/Mortgages 22 3%

Modern Portfolio Theory 22 3%

Education Planning 20 3%

Key Man 18 3%

Employee Benefits 14 2%

IRA, Distributions 13 2%

Mortality 13 2%

Aggregation 12 2%

Property & Casualty Insurance 12 2%

Product Shelf 12 2%

Islamic Financial Planning 12 2%


Importance Ranking Opinion Poll - Survey Results (cont.)

Areas That Require Less Focus


% of

Category

Total

Investment Planning 146 22%

Portfolio Management 127 19%

Modern Portfolio Theory 102 16%

Portfolio Analytics 90 14%

Regulatory & Compliance 84 13%

RRIF/LIF/PRRIF 80 12%

Insurance Planning & Risk

Management 77 12%

Tax Planning 70 11%

Asset Allocation 67 10%

Retirement Planning 64 10%

Portfolio Objectives 63 10%

Term vs. Permanent Insurance 58 9%

Behavioural Finance 57 9%

Active vs. Passive Management 53 8%

Personal Income Tax 48 7%

Estate Planning 48 7%

Real Estate/Mortgages 47 7%

Litigation & Compliance 46 7%

Business Practices 46 7%

Cash Flow & Liability

Management 45 7%

Risk Tolerance 45 7%

Demographics 45 7%

Product Shelf 44 7%

Rebalancing 43 7%

Needs on Death 41 6%

Marketing 38 6%

Fee Structure 38 6%



Areas That Require Less Focus (cont.)


% of

Category

Total


Management 37 6%

Government Benefits 34 5%

Consumer Attitudes 33 5%

Socially Responsible Investing 32 5%

Will Review 31 5%

Holistic Planning 31 5%

Use of Investment Policy 30 5%

Client Reporting 30 5%

Client Relationships 30 5%

Gifting 28 4%

Savings Behaviour 27 4%

Guaranteed Minimum Withdrawal 25 4%

Ethics 25 4%

Tactical vs. Strategic 25 4%

Capital Gains Harvesting 24 4%

Post-Modern Portfolio Theory 24 4%

Education Planning 24 4%

Corporate Tax 23 4%

Critical Illness 23 4%

Best Practices 22 3%

Tax Optimization 21 3%

Charitable Gains 20 3%

Goal Visioning 20 3%

Succession Planning 19 3%

Estate Taxes 18 3%

Debt Management 18 3%

Principal-Agent Problem 18 3%

Other Planning 18 3%

Cost of Compliance 17 3%




% of

Category

Total

Econometric Assumptions 17 3%

Property & Casualty Insurance 16 2%

Information Technology 16 2%

Long Term Care 15 2%

Lending Metrics 14 2%

Income Profile 14 2%

IRA, Distributions 14 2%

Investment Liquidity 14 2%


Forecasting 13 2%

Business Planning 13 2%

Divorce Planning 13 2%

Islamic Financial Planning 12 2%

Healthcare 11 2%

Annuities 11 2%

Mortality 11 2%

Sustainable Withdrawal Rates 11 2%

Business Models 11 2%

Holistic Planning vs. Modular 10 2%

Pension Alternatives 9 1%

Specialized Financial Planning 9 1%

Aggregation 8 1%

Needs on Disability 8 1%

Professsional Issues 8 1%

Job change/loss 8 1%

Estate Distribution Analysis 7 1%

Key Man 7 1%

Buy-Sell 7 1%

Financial Literacy 6 1%

Non-traditional Families 6 1%





% of

Category

Total

Dependents with Special Needs 6 1%

Employee Benefits 4 1%

International Planning 3 0%

Appendix G: Importance Ranking Methodology

To determine which research categories were deemed to

need more or less research by industry professionals a ranking

algorithm was developed. First, a weight was established for

each category based on the level of consensus and volume of

category selection. To arrive at their importance rank, the

weights were then multiplied by that categories position in the

cumulative normal distribution, based on the number of

articles. The methodology has some slight variation depending

on whether it needed more or less research. The rank for

Estate Distribution Analysis is calculated as follows:

Category = Estate Distribution Analysis

More research = MR = 90

Less research = LR = 6

Magnitude = MR + LR = Mag = 96

Net = MR – LR = Net = 84

Consensus = C = |Net/Mag| = |84/96| = 87.5%

Importance Weight (Category Total Estate Planning) = C x

Net/Topics in Category = 0.59 x 453/7 = 38.22

Importance Weight (Sub-Categories) = C x Net = 0.875 x 84 =

73.5

*Note that the weight becomes negative for

categories that need less research.


Average number of articles per category = µ = 22.51

Standard deviation of articles = σ = 22.32

Number of articles in category = = 1

Percentile of Cumulative Normal Distribution = Percentile =

(

) = 16.77%

Importance Rank = Weight x (1 – Percentile) = 61.18

*Note the weight is multiplied by Percentile rather

than (1 - Percentile) when the weight is negative to

generate a higher ranking for categories with many

articles.



Appendix H.1: Categories Requiring More Research

Sub-Category

Mo

re

Les

s

Mag

nit

ud

e

Net

Co

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nsu

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Imp

ort

ance

Wei

gh

t

Art

icle

s

Art

icle

Per

cen

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Imp

ort

ance

Ran

k

Estate Distribution Analysis 90 6 96 84 88% 73.50 1 0.1677 61.18

Pension Alternatives 111 9 120 102 85% 86.70 13 0.3351 57.65

Tax Optimization 121 20 141 101 72% 72.35 7 0.2436 54.72

Holistic Planning vs.

Modular 83 11 94 72 77% 55.15 0 0.1567 46.51

Succession Planning 129 19 148 110 74% 81.76 22 0.4910 41.62

Debt Management 125 17 142 108 76% 82.14 24 0.5267 38.88

Non-traditional Families 63 6 69 57 83% 47.09 6 0.2298 36.27

Divorce Planning 86 13 99 73 74% 53.83 14 0.3516 34.90

Needs on Disability 60 8 68 52 76% 39.76 3 0.1911 32.17

Dependents with Special

Needs 52 6 58 46 79% 36.48 6 0.2298 28.10

Sustainable Withdrawal

Rates 147 12 159 135 85% 114.62 39 0.7700 26.36

Personal Income Tax 127 46 173 81 47% 37.92 13 0.3351 25.22

Guaranteed Minimum

Withdrawal 79 23 102 56 55% 30.75 4 0.2035 24.49

Cost of Compliance 66 17 83 49 59% 28.93 0 0.1567 24.40

General Behavioural Finance 135 58 193 77 40% 30.72 7 0.2436 23.24

International Planning 37 2 39 35 90% 31.41 12 0.3189 21.39

Business Planning 66 14 80 52 65% 33.80 18 0.4200 19.60

Estate Taxes 74 16 90 58 64% 37.38 25 0.5445 17.03

Best Practices 79 22 101 57 56% 32.17 21 0.4731 16.95

Buy-Sell 37 7 44 30 68% 20.45 2 0.1791 16.79

General Estate Planning 155 47 202 108 53% 57.74 35 0.7122 16.62

Job change/loss 40 9 49 31 63% 19.61 0 0.1567 16.54

Will Review 76 30 106 46 43% 19.96 4 0.2035 15.90

Savings Behaviour 86 25 111 61 55% 33.52 24 0.5267 15.87

Long Term Care 73 14 87 59 68% 40.01 29 0.6144 15.43

Goal Visioning 60 21 81 39 48% 18.78 4 0.2035 14.96

Healthcare 47 11 58 36 62% 22.34 16 0.3854 13.73

Ethics 73 24 97 49 51% 24.75 20 0.4553 13.48

Use of Investment Policy 71 29 100 42 42% 17.64 7 0.2436 13.34

Capital Gains Harvesting 60 23 83 37 45% 16.49 4 0.2035 13.14

Lending Metrics 42 13 55 29 53% 15.29 1 0.1677 12.73

Critical Illness 54 22 76 32 42% 13.47 0 0.1567 11.36

Corporate Tax 48 20 68 28 41% 11.53 3 0.1911 9.33


Categories Requiring More Research (cont.)

Sub-Category

Mo

re

Les

s

Mag

nit

ud

e

Net

Co

nse

nsu

s

Imp

ort

ance

Wei

gh

t

Art

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Art

icle

Per

cen

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Imp

ort

ance

Ran

k


Forecasting 36 12 48 24 50% 12.00 12 0.3189 8.17

Client Relationships 80 28 108 52 48% 25.04 33 0.6809 7.99

Gifting 50 23 73 27 37% 9.99 6 0.2298 7.69

Tactical vs. Strategic 57 27 84 30 36% 10.71 11 0.3031 7.47

Client Reporting 59 31 90 28 31% 8.71 0 0.1567 7.35

Key Man 20 6 26 14 54% 7.54 1 0.1677 6.27

Investment Liquidity 34 14 48 20 42% 8.33 10 0.2877 5.94

Fee Structure 67 39 106 28 26% 7.40 4 0.2035 5.89

Information Technology 42 18 60 24 40% 9.60 17 0.4026 5.74

Financial Literacy 101 6 107 95 89% 84.35 57 0.9389 5.16

Employee Benefits 19 4 23 15 65% 9.78 21 0.4731 5.15

Income Profile 28 12 40 16 40% 6.40 6 0.2298 4.93

Principal-Agent Problem 33 17 50 16 32% 5.12 6 0.2298 3.94

Risk Tolerance 102 44 146 58 40% 23.04 47 0.8637 3.14

Specialized Financial

Planning 55 8 63 47 75% 35.06 54 0.9209 2.78

Litigation & Compliance 62 39 101 23 23% 5.24 22 0.4910 2.67

Portfolio Objectives 83 60 143 23 16% 3.70 15 0.3683 2.34


Management 63 35 98 28 29% 8.00 35 0.7122 2.30

Needs on Death 65 39 104 26 25% 6.50 31 0.6482 2.29

General Tax Planning 112 72 184 40 22% 8.70 38 0.7562 2.12

Econometric Assumptions 28 16 44 12 27% 3.27 16 0.3854 2.01

Aggregation 14 7 21 7 33% 2.33 0 0.1567 1.97

Charitable Gains 36 16 52 20 38% 7.69 39 0.7700 1.77

Active vs. Passive

Management 75 51 126 24 19% 4.57 29 0.6144 1.76

Holistic Planning 55 32 87 23 26% 6.08 36 0.7272 1.66

Rebalancing 53 37 90 16 18% 2.84 26 0.5622 1.25

Professsional Issues 43 8 51 35 69% 24.02 59 0.9490 1.23

Demographics 65 42 107 23 21% 4.94 39 0.7700 1.14

Term vs. Permanent

Insurance 64 53 117 11 9% 1.03 2 0.1791 0.85

General Business Practices 54 46 100 8 8% 0.64 6 0.2298 0.49

Government Benefits 39 32 71 7 10% 0.69 40 0.7834 0.15

IRA, Distributions 15 12 27 3 11% 0.33 30 0.6315 0.12



Categories Requiring More Research (cont.)

Sub-Category

Mo

re

Les

s

Mag

nit

ud

e

Net

Co

nse

nsu

s

Imp

ort

ance

Wei

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t

Art

icle

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Art

icle

Per

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Imp

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ance

Ran

k

Mortality 15 11 26 4 15% 0.62 51 0.8991 0.06

Education Planning 26 24 50 2 4% 0.08 18 0.4200 0.05

Islamic Financial Planning 11 10 21 1 5% 0.05 0 0.1567 0.04

Consumer Attitudes 61 32 93 29 31% 9.04 88 0.9983 0.02

Annuities 47 9 56 38 68% 25.79 95 0.9994 0.02

Asset Allocation 67 66 133 1 1% 0.01 71 0.9851 0.00


Investing 32 32 64 0 0% 0.00 108 0.9999 0.00

Appendix H.2: Categories Requiring Less Research

Sub-Category

Mo

re

Les

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Mag

nit

ud

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Net

Co

nse

nsu

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Imp

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Wei

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Art

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Art

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Per

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Imp

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Ran

k

Modern Portfolio Theory 35 96 131 -61 47% -28.40 46 0.8537 -24.25

General Portfolio

Management 47 125 172 -78 45% -35.37 33 0.6809 -24.08

Product Shelf 14 43 57 -29 51% -14.75 34 0.6967 -10.28

Portfolio Analytics 57 86 143 -29 20% -5.88 46 0.8537 -5.02

RRIF/LIF/PRRIF 51 77 128 -26 20% -5.28 26 0.5622 -2.97

Real Estate/Mortgages 29 44 73 -15 21% -3.08 54 0.9209 -2.84

General Investment

Planning 126 145 271 -19 7% -1.33 12 0.3189 -0.42

Property & Casualty

Insurance 10 13 23 -3 13% -0.39 23 0.5088 -0.20

Marketing 37 37 74 0 0% 0.00 6 0.2298 0.00


Investing 32 32 64 0 0% 0.00 108 0.9999 0.00

Documents

Journal of Personal Finance - IARFC