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Journal of Personal Finance
Volume 10, Issue 1
2011
The Official Journal of the International Association of
Registered Financial Consultants
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.
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Journal of Personal Finance
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, downmarket 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, definedcontribution pension plans have been introduced along with underdeveloped 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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
5
Financial Planning Literature Survey .................................................. 109
Benjamin E. Fagan, MSFE, PlusPlus Inc.
Shawn Brayman, MES, PlusPlus 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.
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Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
7
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
Mailing Address:
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Journal of Personal Finance
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© Copyright 2011. International Association of Registered Financial
Consultants. (ISSN 1540-6717)
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Journal of Personal Finance
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Disclaimer: The Journal of Personal Finance is intended to present timely,
accurate, and authoritative information. The editorial staff of the Journal is
not engaged in providing investment, legal, accounting, financial,
retirement, or other financial planning advice or service. Before
implementing any recommendation presented in this Journal readers are
encouraged to consult with a competent professional. While the
information, data analysis methodology, and author recommendations have
been reviewed through a peer evaluation process, some material presented
in the Journal may be affected by changes in tax laws, court findings, or
future interpretations of rules and regulations. As such, the accuracy and
completeness of information, data, and opinions provided in the Journal are
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General Editorial Policy: It is the editorial policy of this Journal to only
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Journal of Personal Finance
The Financial Planning Building
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©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
9
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
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Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
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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
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Journal of Personal Finance
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 widelydisseminated 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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
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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,
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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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
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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.
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Journal of Personal Finance
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 7090% 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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
17
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.
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Journal of Personal Finance
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-inbonds 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-minusage or even 120-minus-age in response to increased longevity
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
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(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
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Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
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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
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Journal of Personal Finance
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-ofthumb 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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
23
What percent of pre-retirement income do financial
experts think retirees will need in retirement? (2030%; 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 110minus-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)
24
Journal of Personal Finance
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‖).
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
25
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) nonbenefitted 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%.
26
Journal of Personal Finance
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
Std.
Dev.
12.7
2.3
0.49
0.20
0.27
Mean
Age
Education
Benefited: Non-Exempt*
Benefited: Other*
Non-Benefited*
Age in Years
42.9
Years of Schooling
16.2
1=Non-Exempt, 0=Exempt
0.38
1=Other, 0=Exempt
0.04
1=Non-Benefited,
0.08
0=Exempt
Gender: Female
1=Female, 0=Male
0.63
Household Income
Income in $1,000s
82,223
Household Income Share Percent of Total Income
71.6
Marital Status: Married
1=Married/Cohabiting,
0.71
0=Otherwise
*The omitted group in this sequence of dummy variables are those
employees who are exempt and benefits eligible.
0.48
52,841
27.6
0.45
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
27
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
Income Replacement Ratio Rule
20-30%
50-60%
80-90%
110-120%
Don‘t know/Not sure
20-Times-Income Rule
5
10
20
30
Don‘t know/Not sure
110-Minus-Age Rule
50
80
110
140
Don‘t know/Not sure
4% Withdrawal Rule
4%
8%
12%
16%
Don‘t know/Not sure
Percent
Frequency
8.98
36.06
36.45
7.08
11.44
278
1,116
1,128
219
354
10.76
26.59
27.21
7.88
27.56
333
823
842
244
853
9.66
28.08
22.84
1.94
37.48
299
869
707
60
1,160
41.32
20.39
12.08
2.39
23.81
1,279
631
374
74
737
28
Journal of Personal Finance
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,
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
29
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
1.00
(1.02-1.03)** (1.00-1.00)
Education
1.05
1.06
1.14
1.03
(1.01-1.10)** (1.02-1.11)** (1.08-1.19)** (0.992-1.07)
Non-Exempt,
Benefits
Eligible1
Other, Benefits
Eligible1
Non-benefited1
Gender
Household
Income
(in $1000s)
Household
Income Share
Marital Status:
Married
1.00
1.01
(0.99-1.00) (1.01-1.02)**
0.83
(0.69-1.01)*
0.97
(0.79-1.19)
0.98
(0.78-1.22)
1.03
(0.86-1.24)
0.74
(0.47-1.17)
0.78
(0.47-1.28)
0.68
(0.38-1.23)
0.90
(0.58-1.38)
0.88
(0.64-1.21)
0.94
(0.68-1.31)
1.04
(0.73-1.47)
1.21
(0.90-1.63)
0.81
0.73
0.70
0.81
(0.69-0.96)** (0.62-0.87)** (0.58-0.85)** (0.69-0.95)**
1.00
(1.00-1.00)
1.00
1.01
1.01
(1.00-1.00) (1.00-1.01)** (1.00-1.01)**
1.00
(1.00-1.01)
1.00
(0.99-1.00)
1.00
(1.00-1.00)
1.00
(1.00-1.01)
1.34
0.99
(1.09-1.66)** (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**
The omitted group in this sequence of dummy variables are those
employees who are exempt and benefits eligible.
** p<.05, *p<.10
1
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
30
Journal of Personal Finance
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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
31
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
addresses only a
by the existence
indicated at the
was exploratory in nature and as such
few of the research questions that are raised
of retirement planning rules of thumb. As
outset, our interest in documenting public
32
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
33
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.
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Journal of Personal Finance
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]
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
37
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.
38
Journal of Personal Finance
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 resourcerich 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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
39
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
40
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
41
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
42
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
43
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
44
Journal of Personal Finance
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
1
For 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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
45
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
2
Deaton (1997) suggested that weighting regression procedures using
endogenous weights might result in biased estimates, so I did not weight the
logistic regression.
3
For 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.
46
Journal of Personal Finance
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
Using a Financial Planner
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
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
47
Table 2
Using a Financial Planner by Various Characteristics, Bivariate
Analysis, Combined 1998-2007 Datasets (Means Test)
Variable
Category
% in
category
(n=17,684)
Survey year
Risk tolerance
Age
Marital status
Racial/ethnic status of
respondent
Household education
{for couples,
maximum level
of either partner}
year 1998
year 2001
year 2004
year 2007
no risk
average
above average
substantial
Less than 30
30-39
40-49
50-59
60-69
70 and over
married
single male
single female
partner
White
Black
Hispanic
Other/Asian
< high school
high school
some college
bachelor degree
post-bachelor degree
24.3
25.1
25.6
25.0
40.7
38.1
17.2
4.0
13.5
19.1
22.2
17.8
12.0
15.5
50.0
14.5
27.2
7.3
75.4
12.8
8.4
3.4
11.1
28.2
26.5
20.1
14.1
Using a
Financial
Planner
Persig.
cent
level1
21.1
20.0
.004
21.7
.131
25.2
.000
10.8
.000
28.2
.000
33.2
28.7
.000
17.6
23.7
.000
22.9
.000
26.9
.000
23.8
.000
15.7
.000
24.6
.000
19.4
19.5
.842
18.8
.261
23.6
20.8
.000
11.6
.000
17.7
.000
7.2
15.2
.000
23.5
.000
28.7
.000
35.1
.000
48
Journal of Personal Finance
Table 2
Using a Financial Planner by Various Characteristics, Bivariate
Analysis, Combined 1998-2007 Datasets (Means Test)
Using a
Financial
Variable
Category
Planner
Persig.
(n=17,684)
cent
level1
Child<age 19
yes
43.6
22.4
.006
56.4
21.6
no
Employment status
61.4
23.1
employee
{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
32.1
25.1
No
Household income
24.9
11.3
0-23,654
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
17.6
10.7
0-14,000
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
1
Significance test is for mean difference from reference category for each
variable. Bold is the reference category; weighted data; RII technique is
used.
% in
category
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
49
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
50
Journal of Personal Finance
Table 3
Using a Financial Planner, Multivariate Logistic Analysis
(n=17,684)
Variable1
Intercept
Survey year (1998)
year 2001
year 2004
year 2007
Risk tolerance (above average)
no risk
average
substantial
Age
Age squared
Marital status (married)
single male
single female
partner
Racial/ethnic status (White)
Black
Hispanic
Other/Asian
Education (< high school)
high school degree
some college
bachelor's degree
post-bachelor degree
Presence of a child < 19
Employment status (employee)
self employed
no work but not retired
retired
Using a Financial Planner
Coeff.2 p-val.3
s.e. Odds ratio
-3.0562
.000
0.2459
-0.0742
0.0532
0.3506
.155
.302
.000
0.0522
0.0515
0.0505
0.928
1.055
1.420
-0.8803
-0.0767
-0.2624
0.0278
-0.0003
.000
.078
.001
.000
.000
0.0611
0.0436
0.0797
0.0077
0.0001
0.467
1.009
0.899
1.028
1.000
-0.1841
0.2107
-0.0879
.004
.000
.292
0.0635
0.0555
0.0835
0.832
1.234
0.916
0.3013
-0.2360
-0.3529
.000
.014
.001
0.0695
0.0958
0.1068
1.352
0.790
0.703
0.3383
0.6038
0.7017
0.8276
-0.1564
.003
.000
.000
.000
.000
0.1149
0.1152
0.1177
0.1196
0.0421
1.403
1.829
2.017
2.288
0.855
-0.0649
-0.2429
0.0487
.159
.082
.471
0.0461
0.1396
0.0676
0.937
0.784
1.050
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
51
Table 3
Using a Financial Planner, Multivariate Logistic Analysis
(n=17,684)
Using a Financial Planner
Coeff.2 p-val.3
s.e. Odds ratio
Variable1
Income (log) [if ≤ 0, log(.01)]
0.0261
.033 0.1225
Net worth (log) [if ≤ 0, log(.01)]
0.1015
.000 0.0111
-Net worth (log) [if ≥ 0, log(.01)]
0.0950
.000 0.0135
Homeowner
0.1004
.089 0.0590
Concordance (mean)
70.2%
1
Reference category in parentheses.
2
Unweighted analysis combining all five implicates.
3
Significance level and standard error based on RII technique.
1.026
1.107
1.100
1.106
Figure 1
Rate of Use of Financial Planner by Survey Year, and Rate
Calculated Based on Mean Values of Other Variables
27%
25%
23%
21%
19%
1998
Mean by Year
2001
2004
2007
Calculated at 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.
52
Journal of Personal Finance
Figure 2
Rate of Use of Financial Planner by Risk Tolerance, and Rate
Calculated Based on Mean Values of Other Variables
35%
30%
25%
20%
15%
10%
no risk
average
above average
substantial
mean by risk tolerance level
calculated at 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.
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
53
Figure 3
Rate of Use of Financial Planner by Category of Age of Head, and
Rate Calculated Based on Mean Values of Other Variables
27%
25%
23%
21%
19%
17%
15%
25
35
45
Mean of Age Group
55
65
75
Calculated Based on Logit
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.
54
Journal of Personal Finance
Figure 4
Rate of Use of Financial Planner by Racial/Ethnic Category of
Respondent, and Rate Calculated Based on Mean Values of Other
Variables
27%
25%
23%
21%
19%
17%
15%
13%
11%
White
mean by groupl
Black
Hispanic
Asian/other
calculated at 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.
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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
55
Figure 5
Rate of Use of Financial Planner by Household Education, and Rate
Calculated Based on Mean Values of Other Variables
35%
30%
25%
20%
15%
10%
5%
<HS
HS
Some college
BS
Post-BS
mean by education level
calculated at 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.
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.
56
Journal of Personal Finance
Figure 6
Rate of Use of Financial Planner by Household Income Category,
and Rate Calculated Based on Mean Values of Other Variables
40%
35%
30%
25%
20%
15%
10%
0-23,654
23,65446,250
46,25182,966
82,967135,242
>135,242
Income Category
mean by income category
calculated at 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.
Figure 5 shows the actual and calculated likelihoods of
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.
Figure 6 shows the actual and calculated likelihoods of
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
57
Figure 7
Rate of Use of Financial Planner by Household Net Worth Category,
and Rate Calculated Based on Mean Values of Other Variables
40%
35%
30%
25%
20%
15%
10%
<0
0-14,000
14,001102,753
102,754333,200
333,201- >822,716
822,716
Net Worth Category
mean by net worth category
calculated at 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.
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%.
Figure 7 shows the actual and calculated likelihoods of
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.
58
Journal of Personal Finance
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 selfemployed, retired, or not working.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
59
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
60
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
61
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.
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Elmerick, S. A., Montalto, C. P., & Fox, J. J. (2002). Use of financial
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Hanna, S. D. & Lindamood, S. (2008). The decrease in stock ownership by
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Hanna, S. D., & Lindamood, S. (2010). Quantifying the economic benefits
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Hanna, S. D. Lindamood, S., & Huston, S. J. (2009). National household
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Hanna, Lindamood, Huston.pdf
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financially knowledgeable spouse. Proceedings of the Academy of
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Lindamood, S., Hanna, S.D., & Bi, L. (2007). Using the Survey of
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Montalto, C. P. & Sung, J. (1996). Multiple imputation in the 1992 Survey
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146.
Peterson, B. (2006). Are households with complex financial management
issues more likely to use a financial planner? Thesis, University of
Wisconsin - Madison.
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U.S. Census Bureau (2010, January). Race and Hispanic Origin of the
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Warschauer, T. (2008). The economic benefits of personal financial
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©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
63
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 threefactor 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.
64
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
65
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
66
Journal of Personal Finance
(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 riskfree 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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
67
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
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Journal of Personal Finance
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 riskadjusted 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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
69
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
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Journal of Personal Finance
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—
1
Further literature on up- and down-market betas can be found in Moelli
(2007) and the references therein.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
71
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 threefactor 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).
2
In 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.
72
Journal of Personal Finance
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
(
where
,
)
(1)
is the risk-free rate (we use the overnight U.S. Federal
funds rate as proxy),
is the return on asset j, (
observed excess return on asset j,
intercept, called alpha,
) is the
is the estimated regression
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.
3
http://www.dfaus.com/strategies/us-equity.html
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
73
Dual-Beta Model
The dual-beta model is an extension of the standard
CAPM model. It estimates the parameters separately for upmarket, when the daily return for the market index is nonnegative, and down-market, when the daily return for the
market index is negative. The dual-beta model can thus be
described as
(
)
(
)
,
where
,
,
, and
(2)
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
4
These data were provided by MacroRisk Analytics from their database.
74
Journal of Personal Finance
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 threefactor model.
5
As the process is initiated at the beginning of each quarter, there is no lookahead bias.
6
The 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.
7
In 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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
75
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 timevarying correlation matrix,
is a diagonal matrix comprising
the square root of the diagonal elements of , and
is
̅
(  t 1t 1 )
,
(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
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Journal of Personal Finance
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 betagenerated 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).
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
77
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
1,350
1,350
1,350
1,350
Panel B: First Period (1/1/06 – 3/8/09)
Mean
-0.0321%
-0.0278%
Sample
-0.0212%
-0.0550%
Median
0.0293%
0.0000%
0.0011%
0.0000%
Standard Deviation
1.2255%
1.2758%
1.4621%
1.6778%
Maximum
10.0472%
9.3606%
8.6777%
11.0165%
Minimum
-7.0931%
-7.0606%
-8.0649%
-9.3727%
-0.0262
-0.0218
-0.0145
-0.0328
0.9134
0.9425
0.9467
1.0000
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%
Standard Deviation
0.7937%
0.8145%
1.0215%
1.3975%
Maximum
3.8441%
4.3498%
5.0254%
7.3028%
Minimum
Ratio*
Correlation
Sample
-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
520
520
520
520
Sample
*
Ratio = Mean return divided by standard deviation.
78
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
79
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
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Journal of Personal Finance
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-tostandard 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 FamaFrench 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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
81
Table 2
Summary Statistics of Average Transactions (Costs) per Quarter
Beta < 0.7
Dbeta < 0.7
Portfolio Components
72
84
Dbeta< 0.7,
Ubeta > 0.7
29
No. of Transactions
82
101
44
Transaction Costs ($)
Cost as % of NAV
574
707
308
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.
82
Journal of Personal Finance
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 downmarket 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 downmarket 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,
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
83
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 upand 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.
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Volume 10, Issue 1
87
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: 81-3-64396225; [email protected]
88
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
89
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
90
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
91
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 worstcase 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
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Journal of Personal Finance
Table 1
Population Aged 60 and over and Life Expectancy at Birth
Population Aged 60+
(% of Total Population)
Life Expectancy at Birth
(Years)
Country
1970- 2010- 20501970
2010
2050
1975
2015
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
93
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
94
Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
95
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
96
Journal of Personal Finance
Table 2
Summary Statistics
Real Fixed
Income
Returns
Argentina
Corr.
Between
Stocks &
Period
Fixed
Std.
Std.
Std.
Income
Mean
Mean
Mean
Dev.
Dev.
Dev.
Assets
1992-2009 11.5 37.8
3.6 6.4
7.2 8.1
-0.15
Brazil
1995-2009
19.1
47.8
Chile
1988-2009
18.0
China
1993-2009
4.7
Columbia
Czech
Republic
Egypt
1993-2009
Hungary
Real Stock
Returns
Country
Inflation
9.5
7.3
11.0 15.6
0.30
29.5
3.4
3.4
8.4
6.9
-0.09
45.9
-0.2
3.8
4.9
7.3
0.31
18.7
41.3
4.4
3.4
11.6
7.2
-0.59
1995-2009
11.7
30.4
-1.0
1.6
4.5
3.4
0.56
1995-2009
30.0
62.6
1.3
5.3
7.3
5.0
0.09
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
2.1
2.2
9.4
9.9
-0.14
34.2 49.4
0.19
Poland
1994-2009
2.0
34.3
Russia
South
Africa
Sri Lanka
1995-2009
14.4
60.0
1993-2009
10.4
22.8
3.7
2.4
6.9
2.5
-0.06
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
-9.9 11.5
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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
97
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
98
Journal of Personal Finance
Table 3
Sustainable Withdrawal Rates
% Failure
% Failure
Within 30
Within 30
1st
5th 10th
Country
Min.
years at 4% years at 5%
%ile %ile %ile
Withdrawal Withdrawal
Rate
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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
99
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
100
Journal of Personal Finance
Table 4
Number of Sustainable Years for Various Withdrawal Rates
4% Withdrawal Rate
5% Withdrawal Rate
1st
5th 10th
1st
5th 10th
Min.
Min.
%ile %ile %ile
%ile %ile %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.
Country
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
101
Figure 1
Asset Allocation Providing Maximum Sustainable Withdrawal Rate for
100
10th percentile
90
5th percentile
1st percentile
Percentage Allocation to Stocks
80
Minimum
70
60
50
40
30
20
10
RUS
TUR
MEX
LKA
JOR
CHN
PER
EGY
CZE.
IND
PAK
HUN
ARG
IDN
PHL
THA
MYS
ISR
POL
KOR
MAR
ZAF
CHL
COL
BRA
0
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
102
Journal of Personal Finance
Figure 2
Sustainable Withdrawal Rates across Distribution of Stock
Allocations with 5 Percent Failure Probability
8
BRA
7
Sustainable Withdrawal Rate
6
CHL
COL
ZAF
5
IDN
4POL
MAR
KOR
ISR
ARG
THA
MY S
PHL
IND
EGY
MEX
TUR
PAK
JOR
3
CHN
PER
HUN
CZE.
LKA
2
1
RUS
0
0
10
20
30
40
50
60
Percentage Allocation to Stocks
70
80
90
100
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
103
Figure 3
Probability of Failure for 4% Withdrawal Rate by Stock Allocation
100
90
80
RUS
70
Probability of Failure
CHN
60
50
LKA
40
JOR
30
PAK
20
10
CZE.
TUR
PHL
IND
HUN
POL
ISR
BRA
COL
ZAF
0KOR
0
MY S
THA
IDN
CHL
MAR
EGY
10
20
MEX
PER
ARG
30
40
50
60
Percentage Allocation to Stocks
70
80
90
100
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
104
Journal of Personal Finance
Figure 4
Probability of Failure for 5% Withdrawal Rate by Stock Allocation
100
90
80
RUS
CHN
POL
Probability of Failure
70
60
LKA
JOR
50
PHL
40
PAK
ISR
30
IND
KOR
THA
TUR
MAR
CZE.
MY S
HUN
20
ARG
EGY
10
MEX
PER
IDN
ZAF
0BRA
0
CHL
COL
10
20
30
40
50
60
Percentage Allocation to Stocks
70
80
90
100
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
105
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
106
Journal of Personal Finance
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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
107
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©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
109
FINANCIAL PLANNING LITERATURE SURVEY
Benjamin E. Fagan, MSFE
PlusPlus Inc.
Shawn Brayman, MES 
PlusPlus 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
110
Journal of Personal Finance
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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
111
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
112
Journal of Personal Finance
articles), and Holistic Planning (75 articles). See Appendix C
for a complete listing of articles per category.
Table 1
Articles per Main Category
Category
Retirement Planning
Portfolio Management
Behavioural Finance
Business Practices
Investment Planning
Other Planning
Estate Planning
Insurance Planning and Risk Management
Cash Flow & Liability Mangement
Holistic Planning
Tax Planning
Regulatory & Compliance
Total
Articles
419
317
246
157
148
144
132
126
108
75
58
48
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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
113
Articles Produced
Figure 1
Articles produced from 2003-2010 related to financial planning
450
400
350
300
250
200
150
100
50
0
2010 estimated
2003 2004 2005 2006 2007 2008 2009 2010
Year
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
Publication Source
0.1
0.15
0.2
0.25
0.3
Percent of Total
0.35
0.4
0.45
0.5
0.05
0
20.13%
47.18%
1.11%
1.26%
1.34%
1.63%
2.01%
2.30%
2.60%
2.82%
3.49%
3.71%
6.17%
6.91%
9.58%
10.85%
11.22%
12.85%
Risk Management
Journal of Risk and Insurance
Canadian Tax Journal
Benefits Quarterly
The Journal of Wealth…
Journal of Business Ethics
The CPA Journal
Journal of Accountancy
Insurance: Mathematic &…
The Journal of Portfolio …
Journal of Personal Finance
Journal of Financial Planning …
Financial Services Review
Journal of Family and …
Journal of Financial Service …
Financial Analysts Journal
Journal of Financial Planning
Other (361 others)
Figure 2
Publication source of financial planning related articles from 2003-2010
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Journal of Personal Finance
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
115
publications from ‗Insurance: Mathematics & Economics‘ and
the ‗Journal of Business Ethics.‘
Table 2
Geographic Source of Research
Country
Articles
% Total
1,584
80.1%
United Kingdom
168
8.5%
Netherlands
United States
114
5.8%
Canada
61
3.1%
Australia
19
1.0%
Switzerland
10
0.5%
7
0.4%
15
0.8%
Germany
Other
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.
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Journal of Personal Finance
Table 3
Frequency of Authorship
# of Papers Authored
1
2
3
4
5
6
7
8
9
10 or more
Total
Authors
2,192
286
80
40
20
14
9
4
6
7
2,658
%
82.47%
10.76%
3.01%
1.50%
0.75%
0.53%
0.34%
0.15%
0.23%
0.26%
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
117
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 subcategories 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:
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Journal of Personal Finance

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
©2011, IARFC. All rights of reproduction in any form reserved.
610
539
502
404
347
708
203
559
450
663
209
353
Behavioural Finance
Other Planning
Cash Flow & Liability Management
Tax Planning
Retirement Planning
Holistic Planning
Insurance Planning & Risk Management
Business Practices
Investment Planning
Regulatory & Compliance
Portfolio Management
More
Estate Planning
Category
Table 5
Importance Weight of Main Categories
421
163
438
242
274
85
266
161
157
126
189
157
Less
774
372
1101
692
833
288
974
508
561
628
728
767
Magnitude
-68
46
225
208
285
118
442
186
247
376
350
453
Net
9%
12%
20%
30%
34%
41%
45%
37%
44%
60%
48%
59%
7
4
9
9
10
3
12
4
6
11
6
7
-0.85
1.42
5.11
6.95
9.75
16.12
16.71
17.03
18.13
20.47
28.04
38.22
Con- Topics in Importance
sensus Category
Weight
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119
120
Journal of Personal Finance
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
Estate Distribution Analysis
Pension Alternatives
Tax Optimization
Holistic Planning vs. Modular
Succession Planning
Debt Management
Non-traditional Families
Divorce Planning
Needs on Disability
Dependents with Special Needs
Importance
Weight
73.50
86.70
72.35
55.15
81.76
82.14
47.09
53.83
39.76
36.48
Articles
1
13
7
0
22
24
6
14
3
6
Importance
Rank
61.18
57.65
54.72
46.51
41.62
38.88
36.27
34.90
32.17
28.10
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
121
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
Modern Portfolio Theory
General Portfolio Management
Product Shelf
Portfolio Analytics
RRIF/LIF/PRRIF
Real Estate/Mortgages
General Investment Planning
Property & Casualty Insurance
Marketing
Socially Responsible Investing
Importance
Weight
Articles
Importance
Rank
-28.40
-35.37
-14.75
-5.88
-5.28
-3.08
-1.33
-0.39
0.00
0.00
46
33
34
46
26
54
12
23
6
108
-24.25
-24.08
-10.28
-5.02
-2.97
-2.84
-0.42
-0.20
0.00
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.
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Journal of Personal Finance

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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1







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
123
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Journal of Personal Finance
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
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1






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
125
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Journal of Personal Finance




Dependents with Special Needs
Islamic Financial Planning
International Planning
Econometric Assumptions
Appendix C: Total Articles per Sub-Category
Category
Socially Responsible
Investing
Annuities
Articles
Consumer Attitudes
88
Asset Allocation
General Retirement
Planning
Professional Issues
71
Category
Econometric
Assumptions
Portfolio Objectives
Post-Modern Portfolio
Theory
Divorce Planning
66
Personal Income Tax
13
59
13
Financial Literacy
57
Real Estate/Mortgages
Specialized Financial
Planning
Mortality
54
Pension Alternatives
Stochastic vs.
Deterministic
International Planning
54
Tactical vs. Strategic
11
51
Business Models
11
Risk Tolerance
47
10
Portfolio Analytics
46
Modern Portfolio Theory
46
Investment Liquidity
Self-Managed
Financial Planning
Use of Investment
Policy
General Investment
Planning
43
Government Benefits
40
Charitable Giving
Sustainable Withdrawal
Rates
39
General Behavioural
Finance
Gifting
39
Income Profile
Demographics
39
General Tax Planning
38
General Holistic Planning
36
General Estate Planning
35
108
95
Articles
Tax Optimization
General Business
Practices
Marketing
Principal-Agent
Problem
Non-traditional
Families
©2011, IARFC. All rights of reproduction in any form reserved.
16
15
14
14
12
12
9
7
7
7
6
6
6
6
6
6
Volume 10, Issue 1
127
Total Articles per Sub-Category (cont.)
Category
General Insurance & Risk
Management
Product Shelf
General Portfolio
Management
Articles
Category
Articles
Client Relationships
33
Needs on Death
31
Dependants with
Special Needs
Use of Leverage
Guaranteed Minimum
Products
Capital Gains
Harvesting
Will Review
IRA, Distributions
30
Fee Structure
4
Long Term Care
Active vs. Passive
Management
Rebalancing
29
Goal Visioning
4
29
Corporate Tax
3
26
3
RRIF/LIF/PRRIF
26
Estate Taxes
25
Debt Management
24
Savings Behaviour
Property & Casualty
Insurance
Succession Planning
24
Needs on Disability
Term vs. Permanent
Insurance
Buy-Sell
Estate Distribution
Analysis
Key Man
23
Lending Metrics
1
22
Aggregation
0
Litigation & Compliance
22
Client Reporting
0
Employee Benefits
21
0
Best Practices
21
Ethics
20
Business Planning
18
Education Planning
18
Information Technology
17
Critical Illness
Practice Succession
Planning
Cost of Compliance
Holistic Planning vs.
Modular
Job change/loss
Islamic Financial
Planning
Healthcare
16
35
34
33
6
5
4
4
4
2
2
1
1
0
0
0
0
0
128
Journal of Personal Finance
Appendix D: Survey
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
129
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
9
Deanna L. Sharpe
6
9
Neal E. Cutler
6
Steven Haberman
Doris R MacKenzie
Ehrens
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
5
Roger G. Ibbotson
5
Lars Grüne
7
Andrew J.G. Cairns
Dorothy C. Bagwell
Durband
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
7
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Journal of Personal Finance
Appendix F: Importance Ranking Opinion Poll - Survey
Results
Areas That Require More Focus
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
Insurance Planning & Risk
Management
Cash Flow & Liability
Management
Tax Optimization
100
15%
97
15%
94
14%
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%
Sub-Category
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
131
Areas That Require More Focus (cont.)
Respondents
% 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
General Insurance & Risk
Management
Term vs. Permanent Insurance
52
8%
51
8%
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%
Sub-Category
132
Journal of Personal Finance
Areas That Require More Focus (cont.)
Respondents
% 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
Stochastic vs. Deterministic
Forecasting
Professsional Issues
33
5%
32
5%
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%
Sub-Category
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
133
Importance Ranking Opinion Poll - Survey Results (cont.)
Areas That Require Less Focus
Respondents
% 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
Insurance Planning & Risk
Management
Tax Planning
80
12%
77
12%
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
Cash Flow & Liability
Management
Risk Tolerance
46
7%
45
7%
45
7%
Demographics
45
7%
Product Shelf
44
7%
Rebalancing
43
7%
Needs on Death
41
6%
Marketing
38
6%
Fee Structure
38
6%
Sub-Category
134
Journal of Personal Finance
Areas That Require Less Focus (cont.)
Respondents
% of
Category
Total
37
6%
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%
Sub-Category
General Insurance & Risk
Management
Government Benefits
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
135
Areas That Require Less Focus (cont.)
Respondents
% 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
Stochastic vs. Deterministic
Forecasting
Business Planning
14
2%
13
2%
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%
Sub-Category
136
Journal of Personal Finance
Areas That Require Less Focus (cont.)
Respondents
% of
Category
Total
Dependents with Special Needs
6
1%
Employee Benefits
4
1%
International Planning
3
0%
Sub-Category
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.
©2011, IARFC. All rights of reproduction in any form reserved.
Volume 10, Issue 1
137
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.
138
Journal of Personal Finance
Estate Distribution Analysis
Pension Alternatives
Tax Optimization
Holistic Planning vs.
Modular
Succession Planning
Debt Management
Non-traditional Families
Divorce Planning
Needs on Disability
Dependents with Special
Needs
Sustainable Withdrawal
Rates
Personal Income Tax
Guaranteed Minimum
Withdrawal
Cost of Compliance
General Behavioural Finance
International Planning
Business Planning
Estate Taxes
Best Practices
Buy-Sell
General Estate Planning
Job change/loss
Will Review
Savings Behaviour
Long Term Care
Goal Visioning
Healthcare
Ethics
Use of Investment Policy
Capital Gains Harvesting
Lending Metrics
Critical Illness
Corporate Tax
Importance
Rank
Article
Percentile
Articles
Importance
Weight
Consensus
Net
Magnitude
Less
Sub-Category
More
Appendix H.1: Categories Requiring More Research
90 6 96 84 88% 73.50 1 0.1677 61.18
111 9 120 102 85% 86.70 13 0.3351 57.65
121 20 141 101 72% 72.35 7 0.2436 54.72
83 11 94 72 77% 55.15
0 0.1567 46.51
129 19 148 110 74% 81.76 22 0.4910 41.62
125 17 142 108 76% 82.14 24 0.5267 38.88
63 6 69 57 83% 47.09 6 0.2298 36.27
86 13 99 73 74% 53.83 14 0.3516 34.90
60 8 68 52 76% 39.76 3 0.1911 32.17
52
6
58 46 79% 36.48
6 0.2298 28.10
147 12 159 135 85% 114.62 39 0.7700 26.36
127 46 173 81 47% 37.92 13 0.3351 25.22
79 23 102 56 55% 30.75
66
135
37
66
74
79
37
155
40
76
86
73
60
47
73
71
60
42
54
48
17
58
2
14
16
22
7
47
9
30
25
14
21
11
24
29
23
13
22
20
83
193
39
80
90
101
44
202
49
106
111
87
81
58
97
100
83
55
76
68
49
77
35
52
58
57
30
108
31
46
61
59
39
36
49
42
37
29
32
28
59%
40%
90%
65%
64%
56%
68%
53%
63%
43%
55%
68%
48%
62%
51%
42%
45%
53%
42%
41%
28.93
30.72
31.41
33.80
37.38
32.17
20.45
57.74
19.61
19.96
33.52
40.01
18.78
22.34
24.75
17.64
16.49
15.29
13.47
11.53
4 0.2035 24.49
0
7
12
18
25
21
2
35
0
4
24
29
4
16
20
7
4
1
0
3
0.1567
0.2436
0.3189
0.4200
0.5445
0.4731
0.1791
0.7122
0.1567
0.2035
0.5267
0.6144
0.2035
0.3854
0.4553
0.2436
0.2035
0.1677
0.1567
0.1911
©2011, IARFC. All rights of reproduction in any form reserved.
24.40
23.24
21.39
19.60
17.03
16.95
16.79
16.62
16.54
15.90
15.87
15.43
14.96
13.73
13.48
13.34
13.14
12.73
11.36
9.33
Volume 10, Issue 1
139
28
23
27
31
6
14
39
18
6
4
12
17
44
55
8
108
73
84
90
26
48
106
60
107
23
40
50
146
52
27
30
28
14
20
28
24
95
15
16
16
58
48%
37%
36%
31%
54%
42%
26%
40%
89%
65%
40%
32%
40%
25.04
9.99
10.71
8.71
7.54
8.33
7.40
9.60
84.35
9.78
6.40
5.12
23.04
Articles
Consensus
Importance
Rank
80
50
57
59
20
34
67
42
101
19
28
33
102
Net
8.17
Magnitude
36 12 48 24 50% 12.00 12 0.3189
Less
Article
Percentile
Stochastic vs. Deterministic
Forecasting
Client Relationships
Gifting
Tactical vs. Strategic
Client Reporting
Key Man
Investment Liquidity
Fee Structure
Information Technology
Financial Literacy
Employee Benefits
Income Profile
Principal-Agent Problem
Risk Tolerance
Specialized Financial
Planning
Litigation & Compliance
Portfolio Objectives
General Insurance & Risk
Management
Needs on Death
General Tax Planning
Econometric Assumptions
Aggregation
Charitable Gains
Active vs. Passive
Management
Holistic Planning
Rebalancing
Professsional Issues
Demographics
Term vs. Permanent
Insurance
General Business Practices
Government Benefits
IRA, Distributions
More
Sub-Category
Importance
Weight
Categories Requiring More Research (cont.)
33
6
11
0
1
10
4
17
57
21
6
6
47
0.6809
0.2298
0.3031
0.1567
0.1677
0.2877
0.2035
0.4026
0.9389
0.4731
0.2298
0.2298
0.8637
7.99
7.69
7.47
7.35
6.27
5.94
5.89
5.74
5.16
5.15
4.93
3.94
3.14
63 47 75% 35.06 54 0.9209
2.78
62 39 101 23 23%
83 60 143 23 16%
5.24 22 0.4910
3.70 15 0.3683
2.67
2.34
63 35 98 28 29%
8.00 35 0.7122
2.30
6.50
8.70
3.27
2.33
7.69
0.6482
0.7562
0.3854
0.1567
0.7700
2.29
2.12
2.01
1.97
1.77
4.57 29 0.6144
1.76
65
112
28
14
36
39
72
16
7
16
104
184
44
21
52
26
40
12
7
20
25%
22%
27%
33%
38%
75 51 126 24 19%
31
38
16
0
39
55 32 87 23 26% 6.08 36
53 37 90 16 18% 2.84 26
43 8 51 35 69% 24.02 59
65 42 107 23 21% 4.94 39
64 53 117 11
54 46 100
39 32 71
15 12 27
9%
8 8%
7 10%
3 11%
0.7272
0.5622
0.9490
0.7700
1.66
1.25
1.23
1.14
2 0.1791
0.85
0.64 6 0.2298
0.69 40 0.7834
0.33 30 0.6315
0.49
0.15
0.12
1.03
140
Journal of Personal Finance
Article
Percentile
Importance
Rank
0.06
0.05
0.04
0.02
0.02
0.00
0.00 108 0.9999
0.00
32 32 64
0
0%
Importance
Weight
0.8991
0.4200
0.1567
0.9983
0.9994
0.9851
Consensus
26 4 15% 0.62 51
50 2 4% 0.08 18
21 1 5% 0.05 0
93 29 31% 9.04 88
56 38 68% 25.79 95
133 1 1% 0.01 71
Net
11
24
10
32
9
66
Magnitude
15
26
11
61
47
67
Articles
Mortality
Education Planning
Islamic Financial Planning
Consumer Attitudes
Annuities
Asset Allocation
Socially Responsible
Investing
Less
Sub-Category
More
Categories Requiring More Research (cont.)
Modern Portfolio Theory
General Portfolio
Management
Product Shelf
Portfolio Analytics
RRIF/LIF/PRRIF
Real Estate/Mortgages
General Investment
Planning
Property & Casualty
Insurance
Marketing
Socially Responsible
Investing
Importance
Rank
Article
Percentile
Articles
Importance
Weight
Consensus
Net
Magnitude
Less
Sub-Category
More
Appendix H.2: Categories Requiring Less Research
35 96 131 -61 47% -28.40 46 0.8537 -24.25
47 125 172 -78 45% -35.37 33 0.6809 -24.08
14
57
51
29
43
86
77
44
57
143
128
73
-29
-29
-26
-15
126 145 271 -19
10 13 23
51%
20%
20%
21%
-14.75
-5.88
-5.28
-3.08
34
46
26
54
0.6967
0.8537
0.5622
0.9209
-10.28
-5.02
-2.97
-2.84
7% -1.33 12 0.3189 -0.42
-3 13% -0.39 23 0.5088 -0.20
37 37 74
0
0%
0.00
6 0.2298
0.00
32 32 64
0
0%
0.00 108 0.9999
0.00
©2011, IARFC. All rights of reproduction in any form reserved.