Electronic copy available at: http://ssrn.com/abstract=1629786
1
Behavioral Portfolio Analysis of Individual Investors
1
Arvid O. I. Hoffmann
*
Maastricht University and Netspar
Hersh Shefrin
Santa Clara University
Joost M. E. Pennings
Maastricht University, Wageningen University, and University
of Illinois at Urbana-Champaign
Abstract: Existing studies on individual investors’ decision-
making often rely on observable socio-demographic
variables to proxy for underlying psychological processes that
drive investment choices. Doing so implicitly ignores

the latent heterogeneity amongst investors in terms of their
preferences and beliefs that form the underlying drivers
of their behavior. To gain a better understanding of the relations
among individual investors’ decision-making, the
processes leading to these decisions, and investment
performance, this paper analyzes how systematic differences in
investors’ investment objectives and strategies impact the
portfolios they select and the returns they earn. Based on
recent findings from behavioral finance we develop hypotheses
which are tested using a combination of transaction
and survey data involving a large sample of online brokerage
clients. In line with our expectations, we find that
investors driven by objectives related to speculation have higher
aspirations and turnover, take more risk, judge
themselves to be more advanced, and underperform relative to
investors driven by the need to build a financial
buffer or save for retirement. Somewhat to our surprise, we find
that investors who rely on fundamental analysis
have higher aspirations and turnover, take more risks, are more
overconfident, and outperform investors who rely on
technical analysis. Our findings provide support for the
behavioral approach to portfolio theory and shed new light
on the traditional approach to portfolio theory.

JEL Classification: G11, G24
Keywords: Behavioral Portfolio Theory, Investment Decisions,
Investor Performance, Behavioral Finance
*
Corresponding author: Arvid O. I. Hoffmann, Maastricht
University, School of Business and Economics,
Department of Finance, P.O. Box 616, 6200 MD, The
Netherlands. Tel.: +31 43 38 84 602. E-mail:
[email protected]
1
The authors thank Jeroen Derwall and Meir Statman for
thoughtful comments and suggestions on previous
versions of this paper. Any remaining errors are our own.
Electronic copy available at: http://ssrn.com/abstract=1629786
2
I. Introduction
The combination of increased self-responsibility for retirement
and an aging population has led a
growing number of people to become accountable for their own
financial futures. Considering

the significant impact of current investment choices on future
lifestyles (Browning and Crossley,
2001), it is important to understand how individual investors
differ when it comes to the
triangular relationship among the decisions they make, the
processes leading to these decisions,
and the resulting investment performance.
To date, our understanding of these relationships remains
limited (Wilcox, 2003), as existing
research either studies only part of this triangle (Nagy and
Obenberger, 1994) or uses observable
socio-demographic variables such as gender, age, or transaction
channel to proxy for the
underlying psychological processes that drive investors’
decision-making (Graham, Harvey, and
Huang, 2009).
2
In so doing, these studies implicitly assume that investors in
the same age
bracket, having the same gender, or using the same transaction
channel are homogenous in their
underlying psychological processes and the impact these have
on their decision-making.
Recent literature on latent heterogeneity suggests that
identifying the influence of

unobservable variables such as investors’ preferences and
beliefs is key to achieving a better
understanding of financial market participants’ choices and
behavior (Heckman, 2001; Pennings
and Garcia, 2009). Unobservable, individual-level differences
may help to explain the underlying
mechanisms of a wide variety of behavioral anomalies (Dhar
and Zhu, 2006; Graham et al.,
2
A well-known finding is women’s outperformance of men, on a
risk-adjusted basis, due to the accumulation of
transaction costs by overconfident male investors who trade
heavily (see e.g., Barber and Odean, 2000). Other
important results are that older investors have better diversified
portfolios and trade less aggressively than their
younger counterparts (Dorn and Huberman, 2005; Goetzmann
and Kumar, 2008), whereas investors switching from
phone-based to online trading are found to trade more actively,
more speculatively, and less profitably than before
the switch (Odean and Barber, 2002).
3

2009; Lee, Park, Lee, and Wyer, 2008), but to date they have
not been widely used to explain
individual investors’ decision-making or performance.
Our investigation into the role of individual differences focuses
on the following questions:
How do investors differ from each other in respect to the type
of information upon which they
rely to develop their strategies? How do investors differ from
each other in respect to their
general investing objectives and risk attitudes? To what extent
do differences among investors
impact the composition of their portfolios, trading activity, and
investment performance?
To address these questions, we develop a dynamic behavioral
theoretical framework and an
empirical study. The theoretical framework is a behavioral
extension of the traditional Euler
equation approach and combines preferences, beliefs, and other
variables that are typically
unobservable such as investors’ ambition level and risk attitude
to explain how investors make
portfolio choices.
3
As such, the framework reflects some of the essential features
of behavioral

portfolio theory (BPT) (Shefrin and Statman, 2000) and findings
from studies on overconfidence
(Barber and Odean, 2001; Kahneman and Lovallo, 1993). BPT
emphasizes the role of behavioral
preferences in portfolio selection and proposes that individual
investors’ portfolio choices and
consequently return performance reflect characteristics such as
aspirations, hope, fear, and
narrow framing. In this respect, BPT helps to explain why some
investors simultaneously buy
bonds and lottery tickets by investigating multiple objectives
(e.g., protection from poverty at
retirement and potential for a shot at riches) as well as
aspirations (Statman, 2002). Studies on
overconfidence emphasize the role of beliefs and help to explain
why some investors are overly
3
In the remainder of this paper, we refer to “observable”
variables when discussing variables that can be constructed
from secondary data, such as transaction records, and
“unobservable” variables when discussing variables that as a
general matter cannot be observed using secondary data, but
require primary data collection, such as our investor

survey. Thus, although technically the latter variables are not
“unobservable” for our sample we continue to use this
terminology throughout this paper for reasons of consistency.
4
optimistic (Barber and Odean, 2001) and develop excessively
bold forecasts (Kahneman and
Lovallo, 1993).
Our empirical study combines individual investors’ survey
responses with their trading
records to create a unique dataset combining soft and hard data
over an extended time period.
The survey allows us to directly measure investor
characteristics that typically remain
unobservable, such as their objectives and strategies. Instead of
using proxies based on, for
example, demographics, we directly measure these aspects of
investors’ underlying preferences
and beliefs (Graham et al., 2009). Together with their trading
records, this allows us to relate
investors’ decision-making processes with their observed
choices instead of inferring the first

from the latter (cf. Manski, 2004). We empirically identify
different segments of individual
investors, label and profile these segments, and compare their
return performance.
In doing so, we contribute to the literature in several ways. We
(1) characterize some of the
key ways in which individual investors differ from each other in
terms of both preferences and
beliefs, (2) develop a stylized dynamic behavioral portfolio
selection model to explain how
differences in preferences and beliefs lead to differences in
investors’ portfolio decisions, (3)
develop a series of hypotheses based on predictions stemming
from the model, and (4) present a
series of empirical findings, some of which serve to test our
hypotheses, and some of which
strike us as surprising and at odds with conventional wisdom.
Our most striking result is that
overtrading does not necessarily result in underperformance.
Rather, underperformance depends
on the circumstances. Investors with strong beliefs that stem
from using fundamental analysis
trade more frequently but still outperform investors using other
strategies.

II. Traditional Portfolio Analysis: Stylized Model
5
A traditional model of dynamic portfolio choice (Merton, 1971;
Viceira, 2001) involves an
expected utility maximizing investor choosing, at each time t,
consumption (ct) and securities (xt
= xt,1 …, xt,J) given initial wealth (W), a stochastic stream of
labor income (Lt), and stochastic
prices (qt). The standard Euler condition for this problem
involves the purchase of a marginal
unit of security j at time t, and requires that the marginal
benefit of this purchase be equal to the
marginal cost. The marginal benefit is the expected marginal
utility of consumption at time t+1
generated by the marginal investment in security j. The
marginal cost is the foregone marginal
utility of consumption at time t, as the increased expenditure on
security j comes at the expense
of less consumption at time t.
In the traditional model, the expected utility function has the

form E( t
t
(u(ct)), where is
a subjective time preference discount factor, and the expectation
is taken over a subjective
probability belief (P) which an investor associates with the
underlying stochastic process. The
Euler condition has the following form:
qt,j u/ ct = E(qt+1,j u/ ct+1) (1)
In words, purchasing an additional unit of security j at time t
reduces consumption by qj,t units,
with each unit reduction of consumption resulting in the decline
in utility at t of u/ ct. At t+1,
the additional unit of security j will result in the ability to
purchase qj,t+1 units of consumption,
whose consumption increases discounted utility by u/ ct+1. At
the optimum, the foregone
utility at t is exactly matched by the increased expected utility
at t+1.
6
The traditional model postulates that investors’ subjective

probability beliefs (P) are
objectively correct, and implicitly assumes that markets are
efficient.
4
In this setting, the purpose
of the portfolio is to manage the risk profile of the investor’s
consumption stream, based on
initial wealth (W) and stochastic labor income (L). This means
that the portfolio serves to hedge
uncertain labor income so as to smooth consumption over time.
Unless labor income is highly
volatile, most trading activity would only involve marginal
adjustments to a diversified portfolio
with the purpose of rebalancing or dealing with liquidity needs
to finance consumption.
III. Behavioral Portfolio Analysis: Stylized Model
The behavioral approach to portfolio choice emphasizes
additional motives for trading besides
rebalancing and consumption-related liquidity. These motives
are connected to a series of
phenomena documented in the behavioral literature including:
Probability weighting and reference point effects involving

gains and losses, psychophysics,
emotions, and aspirations (Kahneman and Tversky, 1979;
Lopes, 1987)
Mental accounting (Thaler, 1985; Thaler, 2000)
Ambiguity aversion (Fox and Tversky, 1995; Heath and
Tversky, 1991)
Status quo bias (Mitchell, Mottola, Utkus, and Yamaguchi,
2006).
The disposition effect (Shefrin and Statman, 1985)
The attention hypothesis (Barber and Odean, 2008)
Lack of diversification (Benartzi and Thaler, 2001; Goetzmann
and Kumar, 2008)
4
Operationally, this means that prices q can be expressed in
terms of a stochastic discount factor m according to q =
E(mx), where the expectation is formed with respect to (P).
7
Realization and evaluation utility (Barberis and Xiong, 2008)
Insufficient saving due to a lack of self-control (Shefrin and
Thaler 1988, Benartzi and

Thaler 2007)
A. Behavioral Euler Equation
Consider a behavioral analogue to the traditional framework,
which can capture the particular
phenomena just described. We begin with a full optimization
extension to (1), which we
subsequently interpret in terms of a quasi-optimization
analogue. Write the analogue of expected
utility as an objective function U. Let U have as its arguments
consumption stream c = [ct],
portfolio choices x = [xt], changes in portfolio positions y = [xt
– xt-1], prices q = [qt], and
probability beliefs (P). The arguments c, x, and y are random
variables, with c and x being the
objects of choice. The inclusion of x, y, and q as arguments
allows for an investor’s preferences
to reflect not only consumption, but also the performance of his
or her portfolio and the impact
of gains and losses from trading.
In the neoclassical framework, investors with predictable
streams of labor income make
small but frequent adjustments to their portfolios, by weighing

the costs of foregone marginal
current consumption associated with a marginal security
purchase against the expected marginal
future consumption so generated. Notice that the criterion
driving portfolio choice is
consumption and savings.
In the corresponding behavioral framework, the consumption-
savings feature is augmented
by additional considerations. When a behavioral investor
contemplates a marginal increase in his
or her holdings xt,j of security j at time t, (s)he adds three
additional components to the
neoclassical calculus. Those components take the form of U/
xt,j, U/ yt,j, and U/ yt+1,j. The
behavioral Euler condition is:
8
qt,j U/ ct = t+1 (qt+1,j U/ ct+1) + U/ xt + U/ yt - t+1 U/ yt+1
(2)
The term t+1 (qt+1,j U/ ct+1) is the analogue to E(qt+1,j u/
ct+1), with the summation t+1 over

the support of outcomes at t+1. The term U/ xt captures the
effects of marginal evaluation
utility, meaning the psychological feelings the investor
experiences from the value of his or her
portfolio at different points in time. Here an investor’s sense of
wellbeing at a given moment,
apart from his or her consumption, is enhanced when his or her
portfolio grows, and is
diminished when it falls. The terms U/ yt and U/ yt+1 capture
realization utility (Barberis and
Xiong, 2008), meaning the impact of trading a position. In this
respect, an investor who sells at a
gain might experience positive realization utility whereas an
investor who sells at a loss might
experience negative realization utility (cf. Thaler and Johnson,
1990).
5
The minus sign associated
with the term t+1 U/ yt+1 in (2) reflects the fact that increasing
xt reduces yt+1 = xt+1 – xt.
B. Preferences: Evaluation Utility and Realization Utility
In the stylized thought process that underlies condition (2), the
investor’s decision regarding a
security at time t balances the benefits from trading and holding

a security against the associated
costs. As in the neoclassical condition (1), increases in future
consumption appear as benefits and
decreases in current consumption appear as costs. As for the
psychological benefits that appear
on the right-hand-side of (2), consider the determinants of
evaluation utility and realization
utility.
5
Parenthetically, transaction costs can be captured by
augmenting the model to feature both bid and ask prices. In
the current framework, there is a single transaction price and so
bid-ask spreads are zero.
9
Evaluation utility reflects the emotional experience associated
with holding a position in a
security. Among the determinants of evaluation utility are three
variables described in Shefrin
and Statman (2000) and Shefrin (2008), namely SP, , and P(A).
The variable SP, known as a security-potential function, is
similar to expected utility. It is

associated with gains and losses to the value of a position, and
relates to feelings associated with
thrill-seeking, presence or absence of anxiety, and value-
expressive benefits derived from, for
example, holding stocks of socially responsible firms (Statman,
2004).
In contrast to an expected utility function, SP features rank-
dependent weights in place of
probabilities. Rank-dependent weights reflect particular
emotions, such as fear and hope.
6
Investors who are overly fearful act as if they overweight
unfavorable events relative to more
favorable events. Notably, although the probability weight
attached to an event does not vary
with portfolio decisions, the decision weight assigned to an
outcome can vary with the position
an investor takes in a security. In particular, when holding a
long position in security j, a fearful
investor will tend to overweight the probability that the return
is negative. However, should the
same investor instead hold a short position in security j, s(he)
would overweight the probability
that the return is positive.

The variable refers to an aspiration level. For example, the
investor’s aspiration might be
that the portfolio s(he) selected at t-1 be worth at least t at time
t. Correspondingly, P(At) is the
probability the investor assigns to meeting that goal. Investors
who set both high aspirations and
high probabilities of achieving those aspirations are said to be
ambitious. A key feature of the
portfolio selection framework developed by Shefrin and
Statman (2000) is that ambitious
investors take on high risk.
6
Psychological-based decision theories tend to use an inverse-S
shaped weighting function for distribution functions
(Kahneman and Tversky, 1979; Tversky and Kahneman, 1992).
This corresponds to a U-shaped weighting function
for density functions in which probabilities of extreme events
are exaggerated. .
10
The realization utility terms on the right-hand-side of (2)
embody the impacts of pride or

regret directly associated with the act of trading. Examples are
the feeling of pride associated
with selling at a gain or the feeling of regret associated with
selling at a loss.
C. Beliefs: Biases, Framing and Probability Weighting
The behavioral approach emphasizes the importance of both
preferences and beliefs. The
discussion in the previous paragraphs has emphasized
preferences. In shifting the emphasis from
preferences to beliefs, we identify three key issues. First,
investors typically have erroneous
beliefs stemming from behavioral biases. Examples of biases
are excessive optimism and
overconfidence. Excessive optimism can lead investors to
overestimate expected returns (De
Bondt and Thaler, 1985), whereas overconfidence can lead them
to underestimate risk (Barber
and Odean, 2000; Odean, 1998). In conjunction, this can lead to
forecasts which are too bold (cf.
Kahneman and Lovallo, 1993). Moreover, most individual
investors have only the vaguest
notion of how security returns are jointly distributed (Benartzi
and Thaler, 2001).

Second, because of framing effects, behavioral investors ignore
information relating to
return covariance. This is the key reason underlying the
violation of stochastic dominance in
prospect theoretic choice experiments (Kahneman and Tversky,
1979). Therefore, the beliefs
used in connection with (2) across securities might not be
compatible with a single set of beliefs
P.
7
Instead, we follow the approach of prospect theory with narrow
framing and assume that
investors’ beliefs consist of marginal distributions for each
security, which are applied to (2) on a
7
In the behavioral model, investors seek to achieve equality (2)
for each security, thereby balancing the marginal
benefits and marginal costs of increasing the amount held of
each security j. However, computing the values of
U/ ct, U/ ct+1, U/ xt,j, U/ yt,j, and U/ yt+1,j is a tall order
requiring full knowledge of the joint distribution of
all security returns. For this reason, we postulate that investors
use a heuristic approach to estimate marginal benefits
and costs, and as a result select suboptimal portfolio strategies.

11
security by security basis.
8
In particular, investors are assumed to ignore covariances when
choosing their portfolios.
Third, preferences and beliefs interact through probability
weighting. In this regard, decision
weights are applied to subjective probabilities. However, both
preferences and beliefs also
combine to impact at least two other psychological phenomena,
aversion to ambiguity and status
quo bias, a topic to which we now turn.
D. Ambiguity Aversion and Status Quo Bias
Ambiguity aversion reflects discomfort stemming from a lack of
knowledge of the underlying
probabilities (Ellsberg, 1961). For example, knowing that an urn
contains 100 balls, of which 50
are red and 50 are black is different from knowing that an urn
contains 100 balls whose color is
either black or red, but with no knowledge of the fraction of
each. Status quo bias involves the

tendency to preserve the status quo instead of to make a change
from the status quo.
Both aversion to ambiguity and status quo bias play key roles in
the portfolio issues we
analyze. Aversion to ambiguity can lead investors to hold
relatively few securities, leading for
example to x=0 for most securities. Status quo bias involves an
underlying reluctance to trade
(Samuelson and Zeckhauser, 1988), leading for example to y=0
holding much of the time.
9
8
Prospect theory takes as its starting point expected utility
theory and replaces the utility function with a value
function, probabilities with probability weights, and a single
complex optimization with a collection of simpler
optimizations. Notably, the value function and probability
weighting function are consistent across the simpler
optimizations.
9
An example that is often used to explain the relation between
status quo bias and regret avoidance is the following.
Suppose you own stock worth $1,000 in Company A and can
exchange it for $1,000 of stock in Company B. Given

your investment assessment, you choose to hold your current
shares. Your neighbor holds $1,000 in Company B
and, for reasons similar to yours, decides to switch his shares
for $1,000 of Company A. During the next six months,
the value of each person’s stock in Company A falls to $700.
Which one feels the greater regret? According to the
psychology literature, the answer is that your neighbor will
experience more regret because he took an action that
produced an unfavorable consequence, and can easily imagine
having done otherwise. In contrast, you took no
action, and so it is more difficult for you to imagine acting
differently.
12
Notably, solutions featuring zero holdings (x=0) or zero trading
(y=0) are instances of corner
solutions. Shefrin (2008) points out that behavioral preference
maps involving rank-dependent
weighting feature many kinks, and these in turn give rise to
corner solutions where marginal
conditions like (2) fails to hold with equality. Aversion to
ambiguity is manifest in a strong fear
response, the fear that a highly unfavorable event will occur if
the investor takes a decision with

limited knowledge of the underlying probabilities. Such fear can
lead an investor to view any
non-zero position in a particular security as unattractive, be that
position long or short. Formally,
this would entail U/ xt < 0 for xt > 0 and U/ xt > 0 for xt < 0
with a point of non-
differentiability (kink) at xt = 0. Similar remarks apply to status
quo bias (in respect to U/ yt).
Status quo bias does not mean that investors refrain from
trading altogether, only that other
forces must be strong enough to counter the bias. Aversion to
ambiguity and status quo bias
induce investors to hold a few securities rather than many, and
to trade intermittently rather than
continuously. Certainly, if at some point in time, the needs for
hedging, rebalancing, and
liquidity are sufficiently strong, investors will overcome status
quo bias and trade. Likewise,
investors can overcome status quo bias if they have enough
confidence in their stock picking
skills to feel little potential for regret (Kahneman, Knetsch, and
Thaler, 1991), derive sufficiently
high evaluation utility from their portfolios, or have bold
enough forecasts (cf. Kahneman and

Lovallo, 1993).
Bold forecasts stem from conviction, a combination of
familiarity, strong opinions, and
confidence, just the opposite of ambiguity. Consider some of
the findings in the psychology
literature about the influence of information on decision
makers’ degree of conviction. An often
cited study of horse race handicappers by Slovic and Corrigan
(1973) analyzes how confidence
and accuracy change as functions of the amount of racing sheet
information. Accuracy increases
13
with the amount of information, until a point of information
overload is reached, after which it
slightly declines (Oskamp, 1965). However, confidence
increases steadily with the amount of
information (Locander and Hermann, 1979). Hahn et al. (1992)
confirmed that decision quality is
an outcome of both time pressure and information load.
The findings in Heath and Tversky (1991) on the determinants
of ambiguity aversion

provide further insight into the drivers of conviction. They
show that ambiguity aversion is
reduced by a sense of familiarity and expertise. Fox and
Tversky (1995) establish that the degree
of ambiguity aversion in a particular choice increases when
decision makers contrast the choice
with a situation in which he or she has more knowledge, or
someone else has more knowledge.
E. Setting the Stage for Hypotheses Development
To set the stage for the development of our hypotheses, we
recapitulate some of the interpretive
features in the stylized behavioral Euler approach embodied
within condition (2). In respect to
preferences, consider (2) to be a dynamic extension of the
mental accounting version of BPT in
Shefrin and Statman (2000). Here securities are evaluated
relative to goals defined by aspiration
levels and success probabilities, with each mental account and
associated aspiration level
corresponding to a different goal (cf. Das et al., 2010).
Examples of the types of goals we
consider in the remainder of the paper relate to capital growth,
retirement saving, hobby, and

speculation (cf. Lewellen, Lease, and Schlarbaum, 1980). In
this paper, we refer to these types
of goals as “objectives.”
As in BPT, condition (2) pertains to two points in time.
However, unlike BPT, (2) contains
terms pertaining to realization utility, which impacts trading
behavior. In addition, the relative
strength of the different terms in (2) is assumed to reflect the
general nature of different types of
14
goals. For example, we think it reasonable to assume that
realization utility is stronger for
investors whose primary objective is thrill seeking than
investors whose primary objective in
investing involves savings for retirement.
In respect to beliefs, we interpret the model as if investors are
quasi-rational, relying on
subjective marginal distributions rather than on joint return
distributions. These distributions are
all implicitly conditional. To this end, our paper focuses on the
source of the conditioning.
Examples include the media (financial news), past prior prices

(technical analysis), and financial
variables (fundamental analysis) (cf. Lease, Lewellen, and
Schlarbaum, 1974). In this paper, we
refer to these types of information sources as “strategies.”
As a system, (2) is more akin to a consumer choice model than a
mean-variance portfolio
model. In this respect, securities are selected and held for their
attributes, and their contribution
to satisfying needs (cf. Wilcox, 2003). Just as each consumer
purchases only a small subset of
available products, so do behavioral investors hold only a small
subset of available securities, at
least directly. The determinants of which securities are held at
any time reflect the interaction
among ambiguity aversion, status quo bias, and boldness of
beliefs, as in the “bold forecasts,
timid choices” framework of Kahneman and Lovallo (1993). In
addition, the quasi-rational
feature of (2) might involve investors holding different types of
securities, not because they value
diversification, but because they have a taste for variety.
Although variety might mimic
diversification, investors ignore covariance information in (2),
and so do not value diversification

per se.
In the next section we develop a series of hypotheses, based on
the behavioral Euler
condition (2) and some related assumptions. Our first major
assumption is that (2) features the
“bold forecasts, timid choices” property, in which forecasts
need to be sufficiently bold to
15
overcome status quo bias. Our second major assumption is that
investors implement (2) in a
manner similar to making consumer choices, meaning that they
do not value diversification per
se, although they might have a taste for variety in their security
holdings. Our additional
assumptions pertain to the manner in which evaluation utility,
realization utility, and aspiration
variables vary across investor objectives, and confidence and
accuracy vary across investor
strategy. We develop these additional assumptions in the next
section, where we use condition
(2) to describe how variation in boldness across investor
strategies, and variation in aspirations

across investor objectives predicts variation in trading patterns
and associated returns.
IV. Hypotheses
Overconfidence pertains to beliefs, and status quo bias pertains
to preferences. The behavioral
approach emphasizes the psychological features associated with
both preferences and beliefs. In
this paper, we focus on the role of investment objectives as a
reflection of investor preferences,
and the role of investment strategy as a reflection of investor
beliefs. In this section, we develop
hypotheses about the impact of both strategies and objectives.
Our hypotheses relate to individual differences across the
spectrum of investors. In this
regard, overconfidence leads some investors to trade too much,
while status quo bias leads other
investors to trade too little (Goetzmann and Kumar, 2008;
Rantapuska, 2006). Overconfidence
leads investors’ forecasts to be excessively bold, while status
quo bias leads to timid choices and
inaction (Kahneman and Lovallo, 1993). As discussed below, in
our framework, both features

can operate simultaneously with the result that investors trade
only intermittently, when their
beliefs are sufficiently bold to outweigh status quo bias.
16
Status quo bias is strong. Mitchell et al. (2006) provide
evidence that 80% of participants in
401(k) accounts initiate no trades in a two-year period, and an
additional 11% make only one
trade. Therefore, few investors in their sample rebalance.
Similarly, Ameriks and Zeldes (2004)
find that 50% of the investors in their sample do not rebalance
over a nine year period. In a
related vein, Choi et al. (2008) find that 80% of investors in
401(k) plans maintain the plan’s
default savings contribution and investment option.
Investors who trade rarely if ever lie at one end of the spectrum.
At the other end of the
spectrum lie investors who trade on a daily basis. Barber et al.
(2009) report that 17% of traders
in Taiwan are day traders. For most day traders, overconfidence
is strong. In the main, our

hypotheses deal with investors lying in the middle of the
spectrum, where status quo bias and
overconfidence operate in tension.
Active trading stems from conviction. An overconfident
investor with sufficiently high
conviction in his or her stock picking skills will tend to
overcome status quo bias and engage in
frequent trading (cf. Kahneman and Lovallo, 1993). In respect
to beliefs, our hypotheses for
active traders focus on the nature of the information upon which
investors rely, and the degree to
which that information generates conviction. We suggest that
variation in investors’ trading
activity will be influenced by the nature of the information upon
which their trading strategies
depend. If investors possess information that generates high
conviction, the resulting
overconfidence leads to bolder forecasts. Bolder forecasts are
able to overcome investors’ status
quo bias that would otherwise cause timid choices and inaction.
This relationship is a key feature
of the hypotheses we develop below, especially in respect to
trading strategies.

A. Investment Strategies
17
First, compare investors who rely on fundamental analysis as a
strategy with those who rely on
technical analysis. Investors using fundamental analysis
examine all underlying conditions
relevant for future stock price developments. Besides financial
statements, these include
economic, demographic, and geopolitical factors. In contrast,
investors relying on technical
analysis only study the stock price movements themselves,
believing that historical data provides
indicators for future stock price developments.
To us, this suggests that fundamental analysis typically involves
more information than
technical analysis (cf. Shleifer and Summers, 1990). Investors
relying on fundamental analysis
are therefore more likely to become more familiar with the
firms they follow than investors
relying on technical analysis. After all, fundamental analysis
serves to focus primary attention on
details pertaining to the firms themselves, whereas technical

analysis focuses attention on price
patterns generated by firms’ stocks. This focus on firm
fundamentals instead of the kind of
pattern recognition tasks inherent in technical analysis leads us
to conclude that familiarity bias
will tend to be stronger by investors relying on fundamental
analysis than investors relying on
technical analysis.
In the language of Kahneman and Lovallo (1993), investors who
rely on fundamental
analysis are more inclined to adapt an “inside view” (Kahneman
and Lovallo, 1993) and become
overconfident than those who rely on technical analysis, as
confidence is an increasing function
of the amount of information (Locander and Hermann, 1979).
We hypothesize that as a result
their forecasts become bolder and they more easily overcome
status quo bias, leading to less
timid choices. Thus, based on condition (2) we expect investors
who rely on fundamental
analysis to trade more frequently than those who rely on
technical analysis, ceteris paribus. In
short:

18
H1: Relative to investors relying on technical analysis,
investors relying on fundamental analysis
will form bolder beliefs, and their greater overconfidence will
induce them to trade more
frequently.
Apart from a very small segment of highly skilled investors who
hold concentrated portfolios
(Barber, Lee, Liu, and Odean, 2009; Goetzmann and Kumar,
2008), overtrading typically leads
to underperformance due to the accumulation of transaction
costs (Barber and Odean, 2000). As
there is no a priori reason to expect that investors using
fundamental analysis are more skilled
than investors using other strategies, we expect:
H2: Relative to investors relying on technical analysis,
investors relying on fundamental analysis
will earn lower risk and style adjusted returns.
Second, compare investors who rely on fundamental analysis

with those relying on their
intuition. In the behavioral framework, investors do not place
high intrinsic value on
diversification. In the spirit of prospect theory’s isolation effect
(mental accounting, narrow
framing) (Kahneman and Tversky, 1979), investors act as if
they implement condition (2) on a
security-by-security basis, rather than as part of an integrated
optimization.
10
As a result, status
10
Prospect theory is a boundedly rational theory of choice
involving maximization of a weighted value function.
The maximization does not typically correspond to a full
optimization, as complex decision tasks are often
simplified into smaller subtasks with important information
about the subtasks being omitted. In this respect, the
value function used to make decisions corresponds to a “proxy”
of the decision maker’s utility function. Decision
makers rely on proxies because they lack the ability required to
compute utility. The use of proxies featuring
omissions can result in suboptimal choice, of which a notable

example is the selection of stochastically dominated
risks. A key feature of prospect theory is that the value function
and weighting function are common across decision
tasks. In the present analysis, think of equation (2) featuring a
proxy for the expected utility terms, in which the
omitted information involves the contribution to utility from
securities other than j. If we follow the prospect theory
assumption, then the proxy will be the same across securities.
19
quo bias will typically lead to underdiversification. Ceteris
paribus, (2) implies that investors
holding more securities will tend to be those with stronger
convictions in their stock picking
skills and in possession of better and more information which
leads them to make bolder
forecasts (Kahneman and Lovallo, 1993). Only in these cases,
will investors be able to overcome
status quo bias and be willing to invest in multiple stocks and
thus make less timid choices.
As discussed previously, it is likely that the latter features
correlate with reliance on
fundamental analysis. As such, investors who rely on

fundamental analysis will tend to hold a
larger number of different stocks in their portfolios than other
investors.
11
Conversely, investors
who only rely on intuition, and therefore less information, will
tend to have less conviction
regarding their stock picking skills for most securities and their
status quo bias leads them to
make timid choices. As a result, these investors may be biased
towards a small(er) number of
stocks with which they are familiar (Huberman, 2001).
Goetzmann and Kumar (2008) point out
that as investors increase the number of stocks in their
portfolios, they tend to choose stocks
which co-move, thereby depriving themselves of the benefits of
diversification. Moreover, to
avoid feelings of regret (Kahneman et al., 1991) investors
relying on intuition will exhibit a
strong status quo bias and hold fewer securities in their
portfolios. In short:
H3: Investors relying on fundamental analysis will hold a larger
number of different stocks in

their portfolio than investors relying on intuition.
B. Investment Objectives
11
The common proxy assumption implies that at the origin, the
decision to trade is determined by whether or not the
net benefit is sufficient to overcome the obstacle imposed by
the kink, with the latter being common across
securities.
20
Investment objectives are imbedded in investors’ preferences.
Aspiration levels constitute an
important component of objectives. A key implication of
behavioral portfolio theory is that
investors whose goals involve high aspirations act as if they
have a high tolerance for risk,
implying that investors who set high aspiration levels in
combination with an associated high
probability of achieving those levels, will tend to choose risky
portfolios (Shefrin and Statman,
2000). Risky portfolios are portfolios that are more exposed to

market risk and overweight small
firms (Barber and Odean, 2001). Hence we hypothesize:
H4: Investors with higher aspiration levels have higher risk
profiles than investors with lower
aspiration levels.
H5: Investors with higher risk profiles will hold riskier
portfolios (i.e. with higher exposure to
the market and small-firm factors) than investors with lower
risk profiles.
As previously discussed, because of familiarity bias, investors
who rely on fundamental analysis
are likely to have high conviction in their stock picking skills.
In addition to leading them to
make bold forecasts, we suggest that familiarity also leads them
to be more ambitious than
investor whose beliefs feature more ambiguity. This is because
ambiguity involves uncertainty
about P(A), the probability of achieving the aspiration level. In
turn, ambiguity aversion induces
pessimism about P(A), which results in less risk taking. This
leads to the following hypothesis:

H6: Investors relying on fundamental analysis will have the
highest aspirations and risk profiles.
21
The behavioral framework links investments objectives to
trading behavior. In this regard,
investors saving for retirement or building a financial buffer
and investors who invest to
speculate or exercise a hobby lie at opposite ends of a
continuum. For investors who mainly
invest as a hobby or to speculate, the second and third term in
(2) loom large, as these relate to
the benefits from (anticipated) evaluation utility (Barberis and
Xiong, 2008) and thrill seeking
(Grinblatt and Keloharju, 2006). To experience these positive
emotions such investors will trade
more frequently than other investors. Hence we hypothesize:
H7: Investors who invest primarily as a hobby or to speculate
will trade more frequently than
investors whose primary investment objective is to build a
financial buffer or save for retirement.

Additionally, investors who mainly invest as a hobby or to
speculate might have very high
conviction, make bold forecasts, tolerate risk, and set ambitious
targets. Indeed, recent literature
shows that investors who trade to entertain themselves (Dorn
and Sengmueller, 2009) or to
speculate – essentially seeing stocks as a lottery ticket
providing a shot at riches (Statman, 2002)
– have higher aspirations and take more risk relative to
investors who do not associate investing
with gambling (Kumar, 2009). In contrast, investors whose
primary investment objective is to
build a financial buffer or save for retirement are likely to have
lower aspirations and choose
more conservative portfolios. In short:
H8: Investors whose primary investment objective is to build a
financial buffer or save for
retirement have lower aspirations and take less risk than
investors who invest primarily as a
hobby or to speculate.

22
V. Data and Methods
The analyses in this paper draw on transaction records of all
clients and questionnaire data
obtained for a sample of clients of the largest online broker in
The Netherlands. Due to trading
restrictions, we exclude accounts owned by minors (age <18
years). We also exclude accounts
with a beginning-of-the-month value of less than €250 and
accounts owned by professional
traders to ensure we deal with active accounts owned by
individual investors. Imposing these
restrictions leaves 65,325 individual accounts with over 9
million trades from January 2000 until
March 2006.
A. Brokerage Records
Opening positions as well as complete transaction records are
available for all prospective
participants of the survey, regardless whether they choose to
participate or not, allowing us to

control for sample selection bias. The typical record consists of
an identification number, an
account number, transaction time and date, buy/sell indicator,
type of asset traded, gross
transaction value, and transaction commissions.
B. Survey Sampling and Selection
In 2006, we designed and performed an online survey amongst
all clients of the online broker. In
total, 6,565 clients completed the questionnaire. To prevent
biased responses, the purpose of the
survey was framed in a neutral way and no reference to the
objective of the study at hand was
made. In the call to participate, respondents were requested to
“provide their opinion of the
online broker”. Brokerage clients who participated in the survey
could win a personal computer
23
that was raffled amongst respondents who fully completed the
questionnaire. Amongst other
questions, the questionnaire probed for investors’ preferences as
reflected in their investment

objective, beliefs as reflected in their investment strategies,
aspiration level as reflected by their
ambition level, risk-taking behavior as reflected in the risk
profile of their current investment
portfolio, and their sophistication as reflected in their self-
categorization into novice, advanced,
or very advanced investor classes. Figure 1 provides an
overview of the questions we used.
After matching transaction records with questionnaire data, a
sample of 5,500 clients and
corresponding accounts remain for which both hard
(transaction) and soft (survey) data are
available and which have an account history of at least 36
months.
[Figure 1 about here]
C. Descriptive Statistics
In Table 1 (Panel A) we report descriptive statistics for the
respondents to the investor survey.
We also report these descriptives for the non-respondents to test
for selection bias (Panel B).
Of the sample of 5,500 investors for which both accounting and
survey data is available,

58% is male and the mean age is about 50 years. The mean
(median) number of total trades over
the sample period is 76.45 (30.00). Average (median) monthly
turnover is about 42% (11%). The
average (median) portfolio value is €45,915 (€15,234).
Combining the average portfolio value
with a total portfolio value of €50,000-€60,000 for the average
Dutch investor (Bauer,
Cosemans, and Eichholtz, 2009) indicates that our average
client invests more than three-fourth
of his or her total investment portfolio at this particular online
broker, showing that we do not
investigate investors’ “play accounts” (Goetzmann and Kumar,
2008) but deal with
24
representative and serious investor accounts. In fact, 40.8% of
our survey respondents only hold
an investment account at this particular broker. Of the
respondents who do also hold an
investment account at another broker, 51.6% indicate that this
comprises less than half of their
total portfolio. As a robustness check, we compare the results of
investors who only invest

through this particular broker with those who also have another
brokerage account but find no
significant differences regarding our hypotheses. Following
Seru, Shumway, and Stoffman
(2008) we measure experience by the number of months an
investor has been trading since
account opening. The results in Table 1 show that the mean
(median) experience is about 40.21
(39.00) months. As compared to recent findings by Odean and
Barber (2000) and Goetzmann
and Kumar (2008) our investors’ portfolios are better
diversified, although still far from well-
diversified. The mean (median) number of stocks held by our
investors is 6.57 (4.00) while the
mean (median) Herfindahl-Hirschmann Index (HHI) is 27.78%
(21.14%). Comparing the HHI
with the normalized HHI (HHI*) indicates that investors’
portfolio weights are not uniformly
distributed. Rather, investors distribute their overall portfolio
value unevenly over different
assets. Mean (median) monthly returns over the period 2000-
2006 are -0.30% (0.30%). On
average, the respondents to the investor survey are relatively
risk-seeking, with a mean (median)

score of 5.31 (6.00) (1=very defensive, 7=very speculative).
A comparison between the respondents to the investor survey
(Panel A) with non-
respondents (Panel B) shows that relative to non-respondents,
the respondents feature more
females, are older, transact more frequently, have higher
portfolio values, are more experienced,
better diversified (hold a larger number of different stocks and
have a lower HHI), obtain a better
monthly return performance, and take more risk (all p<0.00).
Although the differences between
survey respondents and non-respondents are relatively small,
they suggest that the sample of
25
clients which completed the investor survey tend to be
relatively sophisticated investors with a
sizeable portfolio which adds to the relevance and importance
of the study at hand.
[Table 1 about here]
D. Measuring Investor Performance

Investor performance is defined as the monthly change in
market value of all securities in an
investor’s account (Bauer et al., 2009). End-of-the-month
account value is net of transaction
costs the investor incurred during the month. As performance is
measured on a monthly basis,
assumptions have to be made considering the timing of deposits
and withdrawals of cash and
securities. To be conservative, we assume that deposits are
made at the start of each month and
withdrawals take place at the end of each month. Analyses with
the assumption that deposits and
withdrawals are made halfway during the month yield similar
results. Hence, we calculate net
performance as
)(
)(
1
1
itit
itititnet

it
DV
NDWVV
R , (3)
where Vit is the account value at the end of month t, NDWit is
the net of deposits and withdrawals
during month t, and Dit are the deposits made during month t.
Gross performance is obtained by adding back transaction costs
incurred during month t,
TCit, to end-of-the-month account value,
)(
)(
1
1
itit
ititititgross
it
DV
TCNDWVV
R . (4)
Only direct transaction costs (commissions) are considered. We
do not add back any indirect

transactions costs (market impact and bid-ask spreads). The
trades of most individual investors
are relatively small, making market impact costs unlikely.
Moreover, Keim and Madhavan
26
(1998) show that bid-ask spreads may be imprecise estimates of
the true spread, as trades are
often executed within the quoted spread.
E. Attributing Investor Performance
To obtain investors’ abnormal performance, we attribute the
returns on investor portfolios to
different risk and style factors using the Carhart (1997) four-
factor model. This model adjusts
investor returns for exposure to market (RMRF), size (SMB),
book-to-market (HML), and
momentum (UMD) factors. Following Bauer et al. (2009), we
construct these factors for the
Dutch market, as our sample of investors mainly invests in
Dutch securities.
12
The market return

in the RMRF factor is represented by the return on the MSCI
Netherlands equity index. All
factor-mimicking portfolios are constructed according to the
procedure by Kenneth French.
13
The following time series model is estimated to obtain risk and
style adjusted returns:
K
k
itk tikiit
FR
1
. (5)
In this model Rit represents the excess return on investor i’s
portfolio, βik is the loading of
portfolio i on factor k, and Fkt is the month t excess return on
the k’th factor-mimicking portfolio.
The intercept αi measures abnormal performance relative to the
risk and style factors. The factor
loadings indicate whether a portfolio is tilted towards market
risk or a particular investment
style.

F. Segmenting Investors
12
In terms of volume (value) 95% (85%) of all trades are
transactions in Dutch securities. This suggests the
presence of a home bias among Dutch investors, which has
previously been documented by French and Poterba
(1991) for the US, UK, and Japan and by Karlsson and Norden
(2007) for Sweden. Hence, we find that Dutch
versions of the factor-mimicking portfolios lead to a better
model fit than do international factors.
13
See
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_lib
rary.html.
27
We group the 5,500 investors for which we obtained both hard
transaction and soft survey data
into groups based on their preferences or beliefs. Investment
objectives pertain to preferences,
whereas strategies pertain to beliefs.

While the investors in our sample typically have only one
investment objective (e.g., saving
for retirement, building a financial buffer, speculate, exercise a
hobby), they combine different
strategies to attain this objective (e.g., combining financial
news with intuition or professional
advice).
14
Therefore, we use univariate sorting to distinguish different
segments of investors
based on investment objective (cf. Kumar, Page, and Spalt,
2009), but have to use cluster
analysis to discern segments based on investment strategy (Hair,
Anderson, Tatham, and Black,
1998).
The univariate sorting results indicate five segments of
investors based on their dominant
investment objective. These segments are labeled Capital
Growth, Hobby, Saving for
Retirement, Speculation, and Building Financial Buffer.
To distinguish segments of investors based on investment
strategy, we use a non-hierarchical
cluster analysis following Hair, Anderson, Tatham and Black
(1998).

15
The cluster analysis
groups together investors with similar scores on certain
(combinations of) strategies. In
particular, differences between segments in terms of scoring are
maximized and within segments
minimized (Punj and Stewart, 1983). This procedure leads to six
segments, which are labeled
14
In its original specification, behavioral portfolio theory
(Shefrin and Statman, 2000) is a static framework that
describes an investors’ portfolio as consisting of multiple
layers, each layer corresponding to a particular investment
goal or objective. In this paper, we incorporate elements of
behavioral portfolio theory into a dynamic framework to
develop hypotheses about investors’ trading behavior on an
ongoing basis, focusing on investors’ most important
investment objective.
15
Nonhierarchical clustering procedures are less susceptible to
outliers in comparison to hierarchical clustering
procedures. In addition, unlike hierarchical clustering, K-means
clustering as used in this study is able to analyze

large data sets as this procedure does not require prior
computation of a proximity matrix of the distance/similarity
of every case with every other case (Punj and Stewart, 1983).
28
Financial News, Financial News and Intuition, Intuition,
Technical Analysis, Fundamental
Analysis, and Financial News, Intuition, and Professional
Advice.
Table 2 reports descriptive statistics for these segments in
regard to a number of observable
variables, while Table 3 does the same for the unobservable
variables. Observable variables are
variables which can be constructed from the secondary data
(transaction records) as obtained
from the online brokerage firm. Unobservable variables cannot
be constructed using secondary
data, but require primary data as obtained by our investor
survey.
[Tables 2 and 3 about here]
VI. Profiling Investor Segments

This section profiles the different segments of investors as
obtained previously using a
combination of observable and unobservable variables.
A. Segments based on Investment Objectives
Table 2 shows that male investors are especially well
represented in the segments Hobby (0.62)
and Speculation (0.64). The latter segments also contain the
youngest (47.31 and 48.61 years,
respectively) investors, whereas those in the segment
Speculation also trade most heavily during
the sample period (99.25 times). Monthly turnover is highest in
the segment Speculation
(78.87%) and lowest in the segment Saving for Retirement
(26.44%). Investors in the segment
Capital Growth have the largest portfolio value (€62,646) while
Hobby investors have the
smallest accounts in terms of value (€24,139). Investors Saving
for Retirement are most
29
experienced (43.49 months) and best diversified both in terms

of number of stocks and the HHI
(holding 7.57 different stocks and having a HHI of 24.39%)
while investors in the segment
Speculation are least experienced (34.47 months) and less
diversified (5.63 different stocks, HHI
is 30.38%).
16
The profiles of the segments Speculation and Hobby thus
obtained, containing
younger male investors who overtrade and underdiversify, are in
line with recent findings on
speculative trading as gambling (Kumar, 2009) or entertainment
(Dorn and Sengmueller, 2009).
Table 3 demonstrates that the segment Speculation has the
highest score on ambition level
(3.52), the most speculative risk profile (5.80), reports to have
the lowest percentage of novice
investors (24.13%), and the highest percentage of advanced
(59.74%) and very advanced
(15.48%) investors, respectively. Together with the high
turnover and dominance of males in this
segment, these findings confirm and enrich earlier work that
finds that especially male investors
are subject to overconfidence and trade excessively (Barber and

Odean, 2001). Additionally,
these findings confirm the prediction by Statman (2002) that
investors who perceive investing as
playing the lottery may have particularly high aspiration levels
and be subject to overconfidence.
Not surprisingly, we find that investors in the segment Saving
for Retirement have lower
ambition levels (3.26), a less speculative risk profile (4.98) and
are more modest about their level
of sophistication (only 7.64% of this group of investors judge
themselves to be very advanced).
B. Segments based on Investment Strategy
Table 2 shows that the fraction of males is highest (0.64) in the
segment Fundamental Analysis
and lowest in the segments Financial News and Financial News,
Intuition, and Professional
Advice (both 0.55). The number of trades during the sample
period is highest for investors in the
segment Fundamental Analysis (106.09 times) and lowest in the
segment Intuition (59.80 times).
16
The tables also report HHI*, which measures the difference in

HHI relative to equal weighting.
30
The previous combination of gender and turnover is consistent
with earlier work by Barber and
Odean (2001) who find that relative to women, men are
overconfident and trade heavily. The
combination of using fundamental analysis and excessive
trading is in line with our expectations
that especially investors who feel they have more complete
information are likely to make bold
forecasts and overcome their status quo bias, leading to less
timid choices in terms of transaction
frequency. The average age is highest (51.05) in the segment
Financial News, Intuition, and
Professional Advice and lowest (48.40) in the segment
Intuition. Monthly turnover is highest
(46.63%) in the segment Financial News and Intuition and
lowest (36.20%) in the segment
Technical Analysis. The segment Fundamental Analysis has the
highest portfolio value (€72,509)
while the segment Intuition has the lowest portfolio value
(€31,379). Investors in the segment

Financial News are most experienced (41.93 months) while
those in the segment Technical
Analysis are least experienced (37.34 months). We also find
interesting differences between
segments with regard to portfolio diversification. The segment
Fundamental Analysis is best
diversified (8.05 different stocks, HHI is 25.68%), while the
segment Intuition has the worst
diversification (5.68 different stocks, HHI is 30.56%). These
investors may have less conviction
in their capabilities as they have less complete information,
resulting in forecasts that are more
conservative and not sufficiently bold to overcome their status
quo bias, leading to timid choices
(cf. Kahneman and Lovallo, 1993).
Table 3 demonstrates that investors in the segment
Fundamental Analysis have the highest
ambition level (3.43), while investors in other segments, such as
Intuition (3.09) and Financial
News (3.10) have more modest ambitions. In line with the
previous results, investors in the
segment Fundamental Analysis have the most speculative risk
profile (5.52), whereas investors
in the segment Financial News have the least speculative risk

profile (5.09). Finally, whereas the
31
segments Fundamental Analysis (16.53%) and Technical
Analysis (10.13%) have the highest
percentage of investors who regard themselves to be very
advanced, these numbers are
considerably lower in the other segments, reaching a minimum
in the segment Financial News
(3.95%). The lower score of the latter category of investors
indicates that they may be less likely
to be overconfident about their own abilities. Instead of trying
to make an independent estimate
of a company’s attractiveness using, for example, fundamental
or technical analysis, they rely on
widely available financial news to make their investments.
VII. Performance per Investor Segment
In this section we compare the raw returns and alphas of the
different segments of investors as
previously identified. We expect important differences between
segments in terms of

performance due to the previously identified differences with
respect to observable (e.g.,
turnover, age, transaction frequency, and portfolio
diversification) as well as unobservable
variables (ambition level, risk profile, sophistication) and the
predictions of the behavioral
portfolio framework. Table 4 reports the investment
performance per investor segment.
[Table 4 about here]
A. Segments based on Investment Objectives
Panel A of Table 4 shows that the segment Speculation has the
worst raw return (gross), while
the segment Capital Growth does best. The average investor in
the segment Speculation loses
0.38% per month in gross terms, whereas the average investor in
the segment Capital Growth
gains 0.68% per month.
32
The right hand side of Panel A shows that the performance
difference between the different

segments of investors widens when transaction costs are taken
into account. The return of the
segment Speculation incurs the most transaction costs, which is
intuitive considering this
segment’s high turnover. The raw net return of this segment is
now -2.22% per month, whereas
the performance of the segment Capital Growth is still positive
with 0.22% per month.
After also adjusting for both risk and style tilts, the segment
Capital Growth still achieves
the best performance with a net alpha of -0.40%, whereas the
segment Speculation remains the
worst performer with a net alpha of -1.28%. The latter result is
in line with the observable and
unobservable characteristics of the investors in this segment.
Investors whose objective is to
speculate have high ambition levels, high risk profiles, high
turnover, and judge themselves to be
very advanced. These characteristics are typical for
overconfident investors who overtrade and
consequently underperform (Barber and Odean 2001). In
addition, the factor loadings show that
these investors are heavily investing in small cap stocks, which
may be a risky strategy in

combination with the lower levels of diversification we find for
this segment.
B. Segments based on Investment Strategy
Panel B of Table 4 shows that the segment Technical Analysis
has the worst raw return (gross),
while the segment Financial News and Intuition does best,
closely followed by Fundamental
Analysis. The average investor in the segment Technical
Analysis gains only 0.07% per month in
gross terms, whereas the average investor in the segment
Financial Analysis and Intuition gains
0.86% and Fundamental Analysis 0.76% per month,
respectively.
The right hand side of Panel A shows that when transaction
costs are taken into account the
segment Technical Analysis remains the worst performer and
the segment Financial News and
33
Intuition stays the best. The raw net return of the segment
Technical Analysis becomes negative
at -0.92% per month, while the performance of the segment
Financial News and Intuition stays

mildly positive at 0.13%.
This pattern also remains the same after adjusting for risk and
style tilts, although the
difference between segments now narrows. The segment
Financial News and Intuition
achieves the best performance with a net alpha of -0.46%,
closely followed by the segment
Fundamental Analysis, which obtains a net alpha of -0.47%. The
segments Technical Analysis
and Financial News, Intuition, and Professional Advice are the
worst performers, having a net
alpha of -0.73% and -0.71% per month, respectively. The
superior performance of the segments
Financial News and Intuition and Fundamental Analysis is
interesting and suggests some stock-
picking skills.
17
After all, these investors’ stock choices must be good enough
to overcome the
detrimental effect of the relatively high level of transactions of
these segments. The inferior
performance of the segment Financial News, Intuition, and
Professional Advice is remarkable

and suggests that the advice of investment professionals may
not be very helpful for the
performance of individual investors, but is associated with a
relatively high number of
transactions and turnover. Finally, the inferior performance of
the segment Technical Analysis
illustrates the limited usefulness of past stock market
information for future return performance.
VIII. Testing of Hypotheses
This section reports the results of testing the hypotheses of the
behavioral portfolio framework as
presented in section IV. To determine whether investment
objectives and strategies result in
significant differences between investors regarding their
investment behavior and return
17
It should be noted, however, that the alphas are negative across
all groups.
34
performance we employ a series of t-tests and ANOVA’s (Hair

et al., 1998). Detailed results are
provided in Tables 2-4.
As predicted by H1, investors relying on fundamental analysis
are more overconfident than
those relying on technical analysis as reflected by the higher
proportion of fundamental traders
who report to be either “advanced” (t(1584) = 5.64, p < 0.00) or
“very advanced” (t(1584) =
3.78, p < 0.00) and the substantially larger proportion of
technical traders who report to be
“novice investors” (t(1584) = 9.05, p < 0.00). Additionally, as
predicted, trading frequency is
higher (t(1584) = 3.54, p < 0.00) for the more overconfident
fundamental traders than for the less
overconfident technical traders.
Surprisingly, we have to reject H2, as despite their frequent
trading, the risk and style
adjusted return performance of fundamental traders is actually
higher than those of technical
traders (t(1584) = 2.06, p = 0.04). These results show that
overtrading does not necessarily result
in underperformance (cf. Barber and Odean, 2000). Rather,
underperformance depends on the

circumstances. In this case, we distinguish between traders
relying on fundamental versus
technical analysis. We find that although fundamental investors
trade more, they may not be
“overconfident” in the traditional sense, as their high level of
confidence is actually warranted by
a detailed insight in the underlying economic fundamentals and
their frequent trading leads to
higher returns even after accounting for transaction costs. These
investors may learn by trading,
leading to superior returns (cf. Glaser and Weber, 2007;
Nicolosi, Peng, and Zhu, 2009).
We accept H3: Investors relying on fundamental analysis are
better diversified than
investors relying solely on their intuition as represented by the
larger number of different stocks
that are held by the former group (t(1486) = 6.07, p < 0.00) and
their lower HHI score (t(1420) =
3.83, p < 0.00). This result is in line with the discussion above,
as relative to other investors,
35
investors relying on fundamental analysis use more information,
which we hypothesize generates

more conviction with respect to their stock-picking skills.
Investors relying on fundamental
analysis instead of intuition are more likely to be more
sophisticated and therefore less impacted
by behavioral biases (Dhar and Zhu, 2006), such as regret
(Kahneman et al., 1991) and status
quo bias (Samuelson and Zeckhauser, 1988), which can be
related to under-diversification.
Our tests confirm H4: Investors with higher aspirations (above-
median ambition level) take
more risk (t(5709) = 5.71, p < 0.00) as reflected by their risk
profile (M = 5.37) than investors
with lower aspirations (below-median ambition level) (M =
5.04). As such, we confirm a key
feature of portfolio selection under the behavioral framework
developed by Shefrin and Statman
(2000): ambitious investors are more comfortable taking on high
risk.
We also confirm H5: The portfolios of investors with above-
median risk profiles have higher
exposure (t(2152) = 7.16, p < 0.00) to the market factor (M =
1.41) than investors with below-
median risk profiles (M = 1.21). Also, investors with higher risk
profiles invest more (t(2152) =

3.64, p < 0.00) in small caps (M = .71) than investors with
below-median risk profiles (M = .59).
Thus, investors with a higher tolerance for risk also select more
risky portfolios as indicated by
the respective factor loadings of their portfolios.
In line with H6, investors relying on fundamental analysis have
the highest aspirations as
reflected by their ambition levels (F(5, 5452) = 17.35, p < 0.00)
and take the most risk as
reflected in their risk profile (F(5, 5258) = 7.70, p < 0.00). The
latter result may be surprising
from a “noise trader” perspective (Black, 1986) in which traders
relying on technical analysis are
the ones taking most risk, but is in line with a behavioral
portfolio framework in which investors
with the most information have the highest convictions in their
stock picking skills, make bolder
36
forecasts, and set the most ambitious goals. To achieve these
high aspirations, they ultimately
take more risk than other investors (cf. Fisher and Statman,
1997).

18
Our tests confirm H7: Investors whose primary investment
objective is to speculate or
exercise their hobby trade more frequently (F(4, 5495) = 9.32, p
< 0.00) than investors who
invest primarily to build a financial buffer or save for
retirement.
Finally, the evidence confirms H8: Investors whose primary
investment objective is to build
a financial buffer or save for retirement have lower aspirations
(F(4, 5453) = 17.38, p < 0.00)
and take less risk (F(4, 5259) = 36.99, p < 0.00) than investors
who invest primarily as a hobby
or to speculate.
IX. Conclusions
Recent work (Barber et al., 2009) shows individual investors’
tendency to underperform relative
to the market. To date, variables which are relatively easy-to-
observe such as age, gender, and
transaction channel have been used to explain this
underperformance and are used as proxies for

typically unobservable psychological biases such as
overconfidence, loss aversion, and
familiarity. To the best of our knowledge, the existing literature
has not directly measured these
biases using consumer behavior methods such as investor
surveys (Graham et al., 2009). Neither
has the existing literature positioned its findings of
underperformance in a behavioral portfolio
framework by employing underlying variables which are less
easy-to-observe such as investment
objective and strategy.
18
Behavioral portfolio theory acknowledges that different
layers/parts of investors’ portfolios are connected to
different intrapersonal risk profiles (Shefrin and Statman,
2000).
37
In this paper we use a unique dataset involving 5,500
individual investors which contains
both “hard” accounting and “soft” survey data. We use these
data to identify segments of

investors based on their dominant investment objectives and the
investment strategies they use.
These investor segments are subsequently profiled using a
combination of observable and
unobservable characteristics. Finally, the cross-sectional return
performance of different
segments is analyzed and our behavioral hypotheses tested.
Our explanation for differences in return performance between
different segments of
investors is novel in that instead of using proxies, we use a
survey to measure directly investors’
underlying behavioral tendencies and psychological biases. We
obtain data on a variety of
variables which typically remain unobservable and combine this
with a selection of observable
variables. As such, we profile investor segments and test the
hypotheses of our behavioral
portfolio framework. In doing so, we contribute to the
emerging, but limited body of literature
investigating latent heterogeneity in finance (Heckman, 2001).
Our results might be useful for policy makers, as they show that
“the usual suspects” of
individuals who trade excessively might differ from the actual
culprits. We find that investors

using fundamental analysis actually trade more than investors
relying on technical analysis,
which contrasts with the common belief but fits a behavioral
portfolio framework. To the extent
that fundamental investors “think” they know the underlying
fundamentals that drive stock prices
but actually do not, there is a clear target group for educational
incentives that has not received
the attention it deserves until now. These investors may be
provided with questionnaires and
self-administered investment quizzes to evaluate their true
knowledge about market
fundamentals and tailor-made education offered by government
agencies or financial authorities.
38
Figure 1: variables constructed from survey responses
Variables Answer categories
Investment objective
What is your most important investment objective with
the investment portfolio at this brokerage firm?

1 - Capital growth: achieve a higher expected
return than on a savings account
2 - Hobby: interest in stock market
3 – Saving for retirement: being able to stop
working on an earlier age
4 – Speculation: try to profit from short-term
developments on the stock market
5 – Building financial buffer: building a
financial buffer for future expenses
Investment strategy
Which strategies do you use as a basis for your
investment decisions (multiple answers possible)?
1 – Financial news: I base my investment
decisions on financial news
2 – Intuition: I base my investment decisions on
my personal intuition
3 – Technical analysis: I base my investment

decisions on technical analysis
4 – Fundamental analysis: I base my investment
decisions on fundamental analysis
5 – Professional advice: I base my investment
decisions on the professional advice from an
investment advisor
6 – Tips from others: I base my investment
decisions on tips from others such as friends or
family.
7 – Other
Ambition level
How ambitious do you consider yourself to be? 1 – I am not
ambitious
2 – I am a bit ambitious
3 – I am moderately ambitious
4 – I am quite ambitious
5 – I am very ambitious
Risk profile

Investors answer a set of questions, measuring their
sensitivity for losses, time horizon, and subjective
probabilities of chance events. This leads to a
categorization between 1 and 7.
1 – Saving (no investment in (risky) equity)
2 – Very defensive
3 – Defensive
4 – Careful
5 – Offensive
6 – Speculative
7 – Very speculative
Investor Sophistication
What kind of investor do you consider yourself to be? 1 – A
novice investor
2 – An advanced investor
3 – A very advanced investor
39

Table 1: descriptive statistics
A: 5,500 Respondents of the Investor Survey Mean Std. Dev 5th
Pctl 25th Pctl Median 75th Pctl 95th Pctl
Gender (male =1) 0.58
Age in 2006 (years) 49.70 12.73 28.00 40.00 50.00 59.00 70.00
Trades (#) 76.45 132.00 1.00 9.00 30.00 83.00 311.00
Turnover (%) 42.40 121.00 0.00 3.89 10.99 31.48 173.05
Portfolio value (€) 45,915 142,576 1,057 5,321 15,234 42,406
166,840
Experience (months) 40.21 20.91 9.00 22.00 39.00 60.00 72.00
Number of stocks held 6.57 7.39 1.00 2.00 4.00 8.00 20.00
HHI (%) 27.78 23.28 1.10 9.80 21.14 39.73 78.42
HHI* (%) 17.20 21.55 0.16 4.06 9.06 20.74 70.69
Monthly Net Returns -0.003 0.059 -0.071 -0.010 0.003 0.010
0.041
Risk Profile (1-7) 5.31 1.61 2.00 4.00 6.00 7.00 7.00
B: 59,825 Non-Respondents of the Investor Survey Mean Std.
Dev 5th Pctl 25th Pctl Median 75th Pctl 95th Pctl
Gender (male =1) 0.61***

Age in 2006 (years) 45.92*** 12.28 27.00 37.00 45.00 55.00
67.00
Trades (#) 44.41*** 104.00 0.00 2.00 10.00 38.00 210.00
Turnover (%) 33.10*** 189.00 0.00 0.50 4.50 17.26 128.51
Portfolio value (€) 28,253*** 163,483 542 2,289 7,158 21,703
106,459
Experience (months) 34.21*** 23.02 2.00 13.00 31.00 55.00
72.00
Number of stocks held 6.24*** 7.11 1.00 2.00 4.00 8.00 19.00
HHI (%) 35.99*** 20.71 1.00 21.64 36.81 47.29 76.05
HHI* (%) 25.85*** 26.00 0.01 7.63 17.41 32.31 91.09
Monthly Net Returns -0.02*** 0.095 -0.016 -0.023 -0.002 0.010
0.043
Risk Profile (1-7) 4.83*** 1.86 2.00 3.00 5.00 7.00 7.00
This table presents descriptive statistics for a sample of 65,325
investor accounts at a Dutch online broker. We split
the sample into 5,500 investors who participated in our investor
survey and 59,825 who did not. The sample period
is from January 2000 to March 2006. The variables are defined
as follows: Gender refers to the fraction of accounts
hold by a male investor only. Age is the age in years of the
main account holder. Trades is the total number of stock

trades per account during the sample period. Turnover is the
average of the value of all stock purchases and sales in
a given month divided by the beginning-of-the-month account
value. Portfolio value is the average market value of
all assets in the investor’s portfolio. Experience is the number
of months an investor has been trading. Number of
stocks held refers to the number of different stocks an investor
has in portfolio at the end of the sample period. HHI
refers to the Herfindahl-Hirschmann Index value for an
investors’ portfolio at the end of the sample period (the HHI
is defined as the sum of the squared portfolio weights of all
assets. For the purpose of the HHI calculations, mutual
funds are assumed to consist of 100 equally-weighted, non-
overlapping, positions). HHI* refers to the normalized
index: (H – (1/N)) / (1 – (1/N)). Comparing HHI with HHI*
makes clear how different the value from the index is
from uniform weights. Monthly net returns is the average raw
return per month corrected for transaction costs. Risk
profile refers to the self-reported riskiness of investors’
portfolios (1=very defensive, 7=very speculative). The table
shows for each variable the mean, median, and standard
deviation, as well as 5
th
, 25
th

, 75
th
, and 95
th
percentile
values. If there is a statistically significant difference between
attribute means reported for the two samples (survey
respondents and non-respondents), it is noted by asterisks in the
mean columns of the non-respondent sample. The
mean comparison tests allow for different variances within the
two groups. ***/**/* indicate that the means are
significantly different at the 1%/5%/10% level.
40
Table 2: descriptives per investor segment – observable
variables
Segments based on investment objective Gender (male=1) Age
in 2006 (years) Trades (#) Turnover (%) Portfolio value (€)
Experience (months) # Stocks held HHI (%) HHI* (%)
Capital Growth (N=2422) 0.56 50.99 79.62 35.61 62,646 41.88
7.27 25.32 15.66

Hobby (N=1395) 0.62 47.31 65.20 43.43 24,139 39.18 5.43
32.25 19.28
Saving for Retirement (N=353) 0.53 49.85 75.33 26.44 49,359
43.49 7.57 24.39 15.46
Speculation (N=688) 0.64 48.61 99.25 78.87 33,579 34.47 5.63
30.38 17.69
Building Financial Buffer (N=642) 0.55 50.85 65.13 35.46
45,915 40.53 6.39 28.82 19.21
P-value of F-test 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
(7.68)*** (21.83)*** (9.32)*** (20.03)*** (18.19)***
(20.15)*** (12.21)*** (15.52)*** (5.63)***
Segments based on investment strategy Gender (male=1) Age in
2006 (years) Trades (#) Turnover (%) Portfolio value (€)
Experience (months) # Stocks held HHI (%) HHI* (%)
Financial News (N=963) 0.55 50.64 67.04 44.71 38,992 41.93
5.69 28.27 17.96
Financial News and Intuition (N=1235) 0.58 50.44 82.49 46.63
57,227 39.87 7.39 26.99 16.34
Intuition (N=1442) 0.59 48.40 59.80 39.89 31,379 41.06 5.68
30.56 18.69
Technical Analysis (N=878) 0.58 49.35 79.11 36.20 39,470
37.34 6.11 26.82 16.39
Fundamental Analysis (N=708) 0.64 49.45 106.09 43.10 72,509
41.12 8.05 25.68 15.74

Financial News, Intuition, and Professional Advice (N=274)
0.55 51.05 84.81 46.46 47,705 38.16 7.81 25.21 16.18
P-value of F-test 0.01 0.00 0.00 0.40 0.00 0.00 0.00 0.00 0.07
(2.99)** (5.82)*** (13.76)*** (1.03) (10.39)*** (5.98)***
(12.68)*** (4.37)*** (2.02)*
This table presents descriptive statistics for a sample of 5,500
investor accounts at a Dutch online broker regarding a number
of observable variables. We split the sample into
5 (6) segments using univariate sorting (cluster analysis) on
investment objective (strategy). N refers to the number of
investor accounts within each segment. The sample
period is from January 2000 to March 2006. The variables are
defined as follows: Gender refers to the fraction of accounts
hold by a male investor only. Age is the age in
years of the main account holder. Trades is the total number of
stock trades per account during the sample period. Turnover is
the average of the value of all stock purchases
and sales in a given month divided by the beginning-of-the-
month account value. Portfolio value is the average market
value of all assets in the investor’s portfolio.
Experience is the number of months an investor has been
trading. Number of stocks held refers to the number of different
stocks an investor has i n portfolio at the end of the
sample period. HHI refers to the Herfindahl-Hirschmann Index
value for an investors’ portfolio at the end of the sample period
(the HHI is defined as the sum of the squared

portfolio weights of all assets. For the purpose of the HHI
calculations, mutual funds are assumed to consist of 100
equally-weighted, non-overlapping, positions). HHI*
refers to the normalized index: (H – (1/N)) / (1 – (1/N)).
Comparing HHI with HHI* makes clear how different the value
from the index is from uniform weights. The table
shows for each variable the mean. We report the p-value of F-
tests to detect significance differences between segments,
reporting the F-ratio between brackets. ***/**/*
indicate that the means are significantly different between the
segments at the 1%/5%/10% level.
41
Table 3: descriptives per investor segment – variables which are
typically unobservable
Segments based on investment objective Ambition (1-5) Risk
Profile (1-7) Novice Investor (%) Advanced Investor (%) Very
Advanced Investor (%)
Capital Growth (N=2422) 3.21 5.15 37.57 54.50 7.31
Hobby (N=1395) 3.16 5.54 44.44 49.17 5.87
Saving for Retirement (N=353) 3.26 4.98 37.39 54.67 7.64
Speculation (N=688) 3.52 5.80 24.13 59.74 15.84
Building Financial Buffer (N=642) 3.15 5.05 39.88 54.98 4.67

P-value of F-test 0.00 0.00 0.00 0.00 0.00
(17.38)*** (36.99)*** (20.80)*** (5.70)*** (20.08)***
Segments based on investment strategy Ambition (1-5) Risk
Profile (1-7) Novice Investor (%) Advanced Investor (%) Very
Advanced Investor (%)
Financial News (N=963) 3.10 5.09 46.52 49.12 3.95
Financial News and Intuition (N=1235) 3.30 5.24 35.79 56.60
7.21
Intuition (N=1442) 3.09 5.43 48.06 46.12 5.48
Technical Analysis (N=878) 3.31 5.29 34.85 53.99 10.13
Fundamental Analysis (N=708) 3.43 5.52 15.25 67.80 16.53
Financial News, Intuition, and Professional Advice (N=274)
3.34 5.27 31.75 62.77 4.74
P-value of F-test 0.00 0.00 0.00 0.00 0.00
(17.35)*** (7.70)*** (54.11)*** (22.70)*** (23.97)***
This table presents descriptive statistics for a sample of 5,500
investor accounts at a Dutch online broker regarding a number
of unobservable variables. We split the sample
into 5 (6) segments using univariate sorting (cluster analysis)
on investment objective (strategy). N refers to the number of
investor accounts within each segment. The sample
period is from January 2000 to March 2006. The variables are

defined as follows: Ambition refers to the self-reported
ambition level of an investor (1=not ambitious, 5=very
ambitious). Risk profile refers to the self-reported riskiness of
investors’ portfolios (1=very defensive, 7=very speculative).
Novice/advanced/very advanced investor refers to
the self-reported “sophistication” of investors and reports the
percentage of investors per segment in each of the three
categories. The table shows for each variable the mean.
We report the p-value of F-tests to detect significance
differences between segments, reporting the F-ratio between
brackets. ***/**/* indicate that the means are significantly
different between the segments at the 1%/5%/10% level.
42
Table 4: investment performance per investor segment
Gross Returns Net Returns
A: Segments based on investment objective
1 2 3 4 5 P-value of F-test 1 2 3 4 5 P-value of F-test
Raw Return 0.0068 0.0034 0.0065 -0.0038 0.0057 0.00
(5.88)*** 0.0022 -0.0064 0.0003 -0.0222 0.0003 0.00
(25.02)***

Alpha (Carhart) -0.0040 -0.0066 -0.0061 -0.0128 -0.0054 0.00
(13.51)***
Factor Loadings:
RMRF 1.22 1.40 1.19 1.63 1.34 0.00 (19.50)***
SMB 0.57 0.75 0.56 0.86 0.66 0.00 (9.73)***
HML 0.22 0.24 0.21 0.27 0.21 0.48 (0.88)
UMD -0.03 -0.05 0.01 0.00 -0.05 0.28 (1.28)
Adj. R
2
(%) 64.45 58.65 64.42 56.53 63.12 0.00 (16.46)***
B: Segments based on investment strategy
1 2 3 4 5 6 P-value of F-test 1 2 3 4 5 6 P-value of F-test
Raw Return 0.0041 0.0086 0.0025 0.0007 0.0076 0.0012 0.00
(3.65)*** -0.0027 0.0013 -0.0054 -0.0092 0.0003 -0.0065 0.00
(6.49)***
Alpha (Carhart) -0.0057 -0.0046 -0.0058 -0.0073 -0.0047 -
0.0071 0.00 (4.29)***
Factor Loadings:
RMRF 1.31 1.32 1.33 1.24 1.30 1.32 0.60 (0.73)
SMB 0.67 0.67 0.67 0.57 0.55 0.70 0.08 (2.00)*
HML 0.26 0.24 0.21 0.20 0.22 0.17 0.19 (1.48)

UMD 0.00 -0.06 -0.06 0.02 -0.01 0.03 0.00 (4.00)***
Adj. R
2
(%) 62.70 63.58 61.92 58.52 63.69 63.61 0.00 (3.95)***
This table presents investment performance per investment
segment. We report raw gross returns, raw net returns, and
alphas and factor loadings based on net returns. For
panel A the numbers 1-5 refer to the following investor
segments: 1=Capital growth, 2=Hobby, 3=Saving for retirement,
4=Speculation, 5=Building financial buffer. For
panel B the numbers 1-6 refer to the following investor
segments: 1=Financial news, 2=Financial news and intuition,
3=Intuition, 4=Technical analysis, 5=Fundamental
analysis, 6=Financial news, intuition, and professional advice.
We report the p-value of F-tests to detect significance
differences between segments, reporting the F-ratio
between brackets. ***/**/* indicate that the means are
significantly different between the segments at the 1%/5%/10%
level.
43
Reference List
Ameriks, J. and S. Zeldes (2004). How Do Household Portfolios

Vary With Age? Working
Paper, Columbia University.
Barber, B. M., Y-T Lee, Y-J Liu, and T. Odean (2009). Just
How Much Do Individual Investors
Lose by Trading? Review of Financial Studies, 22(2), 609-32.
Barber, B. M. and T. Odean (2000). Trading is Hazardous to
Your Wealth: the Common Stock
Investment Performance of Individual Investors. The Journal of
Finance, 55(2), 773-806.
Barber, B. M. and T. Odean (2001). Boys Will be Boys: Gender,
Overconfidence, and Common
Stock Investment. The Quarterly Journal of Economics, 1 261-
92.
Barber, B. M. and T. Odean (2008). All That Glitters: The
Effect of Attention and News on the
Buying Behavior of Individual and Institutional Investors.
Review of Financial Studies, 21
785-818.
Barberis, N. and W. Xiong (2008). Realization Utility. NBER
Working Paper No.14440.
Bauer, R., M. Cosemans, and P. M. A. Eichholtz (2009). Option
Trading and Individual Investor
Performance. Journal of Banking and Finance, 33(4), 731-46.

Benartzi, S. and R. H. Thaler (2001). Naive Diversification
Strategies in Defined Contribution
Savings Plans. American Economic Review, 91(1), 79-98.
Black, F. (1986). Noise. The Journal of Finance, 41(3), 529-43.
Browning, M. and T. F. Crossley (2001). The Life-Cycle Model
of Consumption and Saving.
Journal of Economic Perspectives, 15(3), 3-22.
Carhart, M (1997). On Persistence in Mutual Fund Performance.
The Journal of Finance, 52 57-
82.
Choi, J., J. Beshears, D. Laibson, and B. Madrian (2008). The
Importance of Default Options for
Retirement Savings Outcomes: Evidence from the United States,
in Lessons from Pension
Reform in the Americas, S. J. Kay and T. Sinha, eds. Oxford
University Press.
Das, S., H. Markowitz, J. Scheid, and M. Statman (2010).
Portfolio Optimization with Mental
Accounts. Journal of Financial and Quantitative Analysis,
45(2), 311-334.
De Bondt, W. F. M. and R. H. Thaler (1985). Does the stock
market overreact? The Journal of

Finance, 40(3), 793-805.
Dhar, R. and N. Zhu (2006). Up Close and Personal: Investor
Sophistication and the Disposition
Effect. Management Science, 52(5), 726-40.
Dorn, D. and G. Huberman (2005). Talk and Action: What
Individual Investors Say and What
They Do. Review of Finance, 9 437-81.
Dorn, D. and P. Sengmueller (2009). Trading as Entertainment?
Management Science, 55(4),
591-603.
Ellsberg, D. (1961). Risk, Ambiguity, and the Savage Axioms.
Quarterly Journal of Economics,
74(4), 643-69.
Fisher, K. L. and M. Statman (1997). Investment Advice from
Mutual Fund Companies. Journal
of Portfolio Management, 24(1), 9-25.
Fox, C. R. and A. Tversky (1995). Ambiguity Aversion and
Comparative Ignorance. The
Quarterly Journal of Economics, 110(3), 585-603.
French, K. R. and J. Poterba (1991). Investor diversification and
international equity markets.
American Economic Review, 81 222-6.

Glaser, M. and M. Weber (2007). Why Inexperienced Investors
do not Learn: They do not Know
their past Portfolio Performance. Finance Research Letters, 4
203-16.
44
Goetzmann, W. N. and A. Kumar (2008). Equity Portfolio
Diversification. Review of Finance,
12 433-63.
Graham, J. R., C. R. Harvey, and H. Huang (2009). Investor
Competence, Trading Frequency,
and Home Bias. Management Science, 55(7), 1094-106.
Grinblatt, M. and M. Keloharju (2006). Sensation Seeking,
Overconfidence, and Trading
Activity. NBER Working Paper No.12223.
Hahn, M., R. Lawson, and Y. G. Lee (1992). The Effects of
Time Pressure and Information Load
on Decision Quality. Psychology and Marketing, 9(5), 365-78.
Hair, J. F., R. E. Anderson, R. L. Tatham, and W. C. Black
(1998). Multivariate Data Analysis.
Upper Saddle River, New Jersey: Prentice Hall.

Heath, C. and A. Tversky (1991). Preference and Belief:
Ambiguity and Competence in Choice
under Uncertainty. Journal of Risk and Uncertainty, 4 5-28.
Heckman, J. J. (2001). Micro Data, Heterogeneity, and the
Evaluation of Public Policy: Nobel
Lecture. Journal of Political Economy, 109(4), 673-748.
Huberman, G. (2001). Familiarity breeds investment. Review of
Financial Studies, 14(3), 659-80.
Kahneman, D., J. L. Knetsch, and R. H. Thaler (1991).
Anomalies: The Endowment Effect, Loss
Aversion, and Status Quo Bias. Journal of Economic
Perspectives, 5(1), 193-206.
Kahneman, D. and D. Lovallo (1993). Timid Choices and Bold
Forecasts: A Cognitive
Perspective on Risk Taking. Management Science, 39(1), 17-31.
Kahneman, D. and A. Tversky (1979). Prospect theory: an
analysis of decision under risk.
Econometrica, 47 263-91.
Karlsson, A. and L. Norden (2007). Home sweet home: Home
bias and international
diversification among individual investors. Journal of Banking
and Finance, 31 317-33.
Keim, D. and A. Madhavan (1998). The Cost of Instututional

Equity Trades. Financial Analysts
Journal, 54 50-69.
Kumar, A. (2009). Who Gambles in the Stock Market? The
Journal of Finance, Forthcoming.
Kumar, A., J. Page, and O. Spalt (2009). Religious Beliefs,
Gambling Attitudes, and Financial
Market Outcomes. Working Paper.
Lease, R. C., W. G. Lewellen, and G. G. Schlarbaum (1974).
The Individual Investor: Attributes
and Attitudes. The Journal of Finance, 29(2), 413-33.
Lee, H.-J., J. Park, J.-Y. Lee, and R. S. Wyer (2008).
Disposition Effects and Underlying
Mechanisms in E-Trading of Stocks. Journal of Marketing
Research, 45(3), 362-78.
Lewellen, W. G., R. C. Lease, and G. G. Schlarbaum (1980).
Portfolio Design and Portfolio
Performance: The Individual Investor. Journal of Economics
and Business, 32(3), 185-97.
Locander, W. B. and P. W. Hermann (1979). The Effect of Self-
Confidence and Anxiety on
Information Seeking in Consumer Risk Reduction. Journal of
Marketing Research, 16(May),
268-74.

Lopes, L. (1987). Between Hope and Fear: The Psychology of
Risk. Advances in Experimental
Social Psychology, 20 255-95.
Manski, C. F. (2004). Measuring Expectations. Econometrica,
72(5), 1329-76.
Markowitz, H. M. (1952). Portfolio Selection. The Journal of
Finance, 7 77-91.
Merton, R. C. (1971). Optimum Consumption and Portfolio
Rules in a Continuous-Time Model.
Journal of Economic Theory, 3 373-413.
Mitchell, O., G. Mottola, S. Utkus, and T. Yamaguchi (2006).
The Inattentive Participant:
Portfolio Trading Behavior in 401(k) Plans. Working paper,
Wharton.
45
Nagy, R. A. and R. W. Obenberger (1994). Factors Influencing
Individual Investor Behavior.
Financial Analysts Journal, 50(4), 63-8.
Nicolosi, G., L. Peng, and N. Zhu (2009). Do Individual
Investors Learn from their Trading
Experience? Journal of Financial Markets, 12 317-36.

Odean, T. (1998). Volume, Volatility, Price, and Profit When
All Traders Are above Average.
The Journal of Finance, 53(6), 1887-934.
Odean, T. and B. M. Barber (2002). Online Investors: Do the
Slow Die First? Review of
Financial Studies, 15(2), 455-87.
Oskamp, S. (1965). Overconfidence in Case-Study Judgments.
Journal of Consulting
Psychology, 29(3), 261-5.
Pennings, J. M. E. and P. Garcia (2009). Risk & Hedging
Behavior: The Role and Determinants
of Latent Heterogeneity. The Journal of Financial Research,
Forthcoming.
Punj, G. and D. W. Stewart (1983). Cluster Analysis in
Marketing Research: Review and
Suggestions for Application. Journal of Marketing Research,
20(May), 134-48.
Rantapuska, E. (2006). Essays on Investment Decisions of
Individual and Institutional Investors.
Helsinki School of Economics: Doctoral Dissertation.
Samuelson, W. and R. Zeckhauser (1988). Status Quo Bias in
Decision Making. Journal of Risk

and Uncertainty, 1(1), 7-59.
Seru, A., T. Shumway, and N. Stoffman (2008). Learning by
Trading. Working Paper,
University of Chicago and University of Michigan.
Shefrin, H. (2008). A Behavioral Approach to Asset Pricing.
Boston: Elsevier.
Shefrin, H. and M. Statman (1985). The Disposition to Sell
Winners too Early and Ride Losers
Too Long: Theory and Evidence. The Journal of Finance, 40
777-90.
Shefrin, H. and M. Statman (2000). Behavioral Portfolio
Theory. The Journal of Financial and
Quantitative Analysis, 35(2), 127-51.
Shleifer, A. and J. O. Summers (1990). The Noise Trader
Approach to Finance. Journal of
Economic Perspectives, 4(2), 19-33.
Slovic, P. and B. Corrigan (1973). Behavioral Problems of
Adhering to a Decision Policy. Talk
presented at The Institute for Quantitative Research in Finance,
May 1, Napa, CA.
Statman, M. (2002). Lottery Players / Stock Traders. Financial
Analysts Journal, 58(1), 14-21.
Statman, M. (2004), What Do Investors Want? Journal of

Portfolio Management, 153-161.
Thaler, R. H. (1985). Mental Accounting and Consumer Choice.
Marketing Science, 4(3), 199-
214.
Thaler, R. H. (2000). Mental Accounting Matters, in Choices,
Values, and Frames, D.
Kahneman and A. Tversky, eds. New York: Cambridge
University Press, 241-68.
Thaler, R. H. and E. J. Johnson (1990). Gambling With the
House Money and Trying to Break
Even: The Effects of Prior Outcomes on Risky Choice.
Management Science, 36(6), 643-60.
Tversky, A. and D. Kahneman (1992). Advances in Prospect
Theory: Cumulative Representation
of Uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323.
Viceira, L. M. (2001). Optimal Portfolio Choice for Long-
Horizon Investors with Nontradable
Labor Income. The Journal of Finance, 56(2), 433-70.
Wilcox, R. T. (2003). Bargain Hunting or Star Gazing?
Investors' Preferences for Stock Mutual
Funds. Journal of Business, 76(4), 645-63.