financial advice: an improvement for worse?
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Financial Advice: An Improvement for Worse?∗
Yigitcan Karabulut†
Goethe University Frankfurt
This draft: June 17, 2013
Abstract
In this paper, I analyze the role of �nancial advisors in individual investment decisions
and ask whether �nancial advice is a reliable substitute for individuals' �nancial literacy.
I report two main �ndings. First, I �nd that individuals who tend to be �nancially less
sophisticated are more likely to consult professional advisors, which supports the notion
that �nancial advice serves as a substitute for �nancial literacy. Second, when I analyze
the impact of �nancial advice on portfolio choice, I show that, if anything, use of �nancial
advice does not improve the quality of individuals' investment decisions. For example,
I document that advised investors earn lower raw and risk-adjusted returns than self-
directed investors, even before deducting advisory fees and transaction costs. Overall, the
evidence presented in this study casts doubts on the ability of �nancial advice to serve as
an e�ective substitute for �nancial literacy.
Keywords: Financial advice, individual investors, household �nance.
JEL Codes: D12, D14, D8, D18.
∗I would like to thank Ben Craig, Andreas Hackethal, Michael Haliassos, Orcun Kaya, Isabel Schnabeland seminar participants at the 2011 European Retail Investment Conference, the 2010 Annual Meeting of theEuropean Finance Association, the 2010 Annual Meeting of the German Finance Association, 8th. Workshop onMoney, Banking and Financial Markets, the 2010 PhD Workshop of the French Finance Association, Universityof Mainz and University of Frankfurt for helpful comments and discussions.†Please address all correspondence to Yigitcan Karabulut, Goethe University Frankfurt, Department of
Money and Macroeconomics, House of Finance, Grueneburgplatz 1, 60323, Frankfurt, Germany, E-mail:karabulut@econ.uni-frankfurt.de, Phone: +49-69-798-33859. The usual disclaimer applies.
1 Introduction
In recent years individuals have become increasingly active in �nancial markets. Accordingly,
both the stock market participation rates and portfolio shares of households in risky assets have
risen signi�cantly over the last two decades (Bilias, Georgarakos, and Haliassos, 2008).1 Amajor
reason for the spread of the `equity culture' is the growing responsibility of individuals to save
for retirement (Campbell, 2006; Lusardi and Mitchell, 2007; van Rooij, Lusardi, and Alessie,
2011). In particular, demographic transitions have caused policymakers in many countries to
reform traditional bene�t pension plans and to adapt de�ned contribution schemes, which shift
the responsibility and risks of retirement �nancing to individuals (Lusardi and Mitchell, 2007,
2011). For example, Ryan, Trumbull, and Tufano (2011) report that American households held
approximately 420 billion USD in de�ned contribution plans in 1985, which had risen to 3.3
trillion USD by 2009.
Meanwhile, �nancial markets become more complex in the wake of recent �nancial market
innovations. On the one hand, the advent of more sophisticated �nancial instruments creates
the possibility of more-customized �nancial products and services, which can better suit the
needs of individuals (Lusardi and Mitchell, 2011). On the other hand, the increase in the
number of investment options makes it di�cult for individuals to invest wisely (Hortascu and
Syverson, 2004).
The increasing responsibility of individuals to make important economic decisions, coupled
with the higher sophistication of �nancial markets, raises the question of whether households
are well-equipped to cope with these decisions, including how much to save and invest, how
to allocate investments over di�erent asset classes and so on. Recent research documents that
many households do not possess a su�cient level of �nancial literacy (Lusardi and Mitchell,
2007), lack information (Guiso and Jappelli, 2006) and exhibit behavioral biases (Kahneman,
Knetsch, and Thaler, 1991), which often lead to suboptimal �nancial decisions (e.g., Barber
and Odean, 2000, 2001; Polkovnichenko, 2005; Campbell, 2006; Goetzmann and Kumar, 2008).
Indeed, individuals' limited ability to make informed decisions can incur substantial welfare
costs, not only for individuals themselves but for society at large. For example, Boeri and
Guiso (2007) and Akerlof and Shiller (2009) argue that the low �nancial literacy of the U.S.
households is one of the driving forces of the recent subprime crisis.2 Thus, an important issue is
1For a more detailed discussion about the composition of household portfolios, see, for example, the contri-butions in Guiso, Haliassos, and Japelli (2001).
2In a recent paper, Gerardi, Goette, and Meier (2010) address this issue and show that there is indeed a
1
determining how to improve the quality individuals' �nancial decisions. The literature suggests
three potential remedies to overcome the obstacles faced by individuals toward making sound
�nancial decisions: �nancial advice, �nancial literacy education and default options. In this
paper, I focus on the role of professional advice in individuals' investment decisions and ask
whether �nancial advice is a reliable substitute for individuals' �nancial literacy.3
To answer this question, I focus on three key issues. First, I analyze how the decision
to have a �nancial advisor correlates with the demographics and �nancial characteristics of
individuals. The purpose of this analysis is to gauge whether �nancial advice serves as a
substitute for �nancial literacy. Second, I investigate the relationship between �nancial advice
and portfolio performance to quantify the potential welfare e�ects of �nancial advice. Third,
to examine the channels through which �nancial advisors can a�ect portfolio performance, I
compare the portfolio choices of advised and self-directed investors along several dimensions,
including their trading behavior, diversi�cation choices and asset allocation decisions. I attempt
to address these issues using a unique database from a large German commercial bank that
includes personal characteristics, end-of-month portfolio positions, trades and information on
use of �nancial advice for each sampled individual.
I obtain the following results in this study. First, individuals who tend to be �nancially less
sophisticated are more likely to consult professional advisors, which supports the notion that
�nancial advice serves as a substitute for �nancial literacy. This �nding is intuitive because
investors with low levels of �nancial literacy are shown to be particularly in need of investment
guidance (Lusardi and Mitchell, 2007). Thus, there is considerable scope for �nancial advice
to help these individuals. At the same time, however, the fact that �nancial advice resembles
a `credence good', i.e., individuals are generally neither ex-ante nor ex-post able to assess its
quality, coupled with the multitasking of an advisor, i.e., selling and advising, can create in-
centives for �nancial advisors to exploit individuals' lack of �nancial literacy.4 The analysis of
the welfare e�ects of �nancial advice provides to some extent an indication of which of these
opposing e�ects is dominant. I observe that advised investors earn lower raw and risk-adjusted
negative association between individuals' �nancial literacy and mortgage delinquencies and defaults.3Use of �nancial advice is widespread. For example, a recent survey in the EU indicates that 80 percent of
respondents seek professional advice before purchasing any investment products (Chater, Huck, and Inderst,2010). In another survey in the U.S., Hung et al. (2008) document that 73 percent of all investors make use of�nancial advice when making investment decisions.
4See, for example, the models of Inderst and Ottaviani (2009, 2012) who formally demonstrate that thepossibility of abuse and costly incentive problems is especially severe when sales agents have to perform thedual task of promoting new customers and providing advice to them.
2
returns than self-directed investors, even before deducting advisory fees and transaction costs.
This �nding is consistent with the results of previous studies, such as Hackethal, Haliassos,
and Jappelli (2012), who show that independent �nancial advisors negatively a�ect portfolio
performance. Interestingly, I �nd that the negative e�ect of �nancial advisors is more pro-
nounced among investors with lower investment-income ratios who presumably do not correct
for the underlying con�ict of interest and take advisors' recommendations at face value. These
�ndings demonstrate the possible agency con�ict between professional advisors and individuals
with limited �nancial literacy.
Of course, individuals actively choose whether to make use of �nancial advice, which suggests
that consulting a professional advisor may re�ect an endogenous choice. For example, an
investor who realizes poor portfolio performance can make use of �nancial advice in the hope
that professionals will help him or her to improve his or her performance results. To address
this potential bias, I also use instrumental variable (IV) techniques when testing the e�ects of
�nancial advice on portfolio performance. Even after controlling for the endogeneity of �nancial
advice, I still �nd a negative e�ect of �nancial advisors on portfolio performance, although the
IV coe�cients are larger in magnitude. Overall, the bene�ts of �nancial advice, if present at
all, seem to fall along dimensions other than portfolio performance.
When I turn to other portfolio decisions, I �nd that the extent of portfolio under-diversi�cation
is greater among self-directed investors, indicating that �nancial advisors appear to moderate
both home bias and equity under-diversi�cation. The latter �nding is consistent with the view
that �nancial advisors respond to monetary incentives and promote mutual funds, which in
turn improves equity diversi�cation. Similarly, use of �nancial advice lowers the number of
trades, whereas it shows no signi�cant e�ect on account turnover. At the same, however,
I document that advised investors rebalance their portfolios less frequently, and thus, their
portfolios display a higher degree of inertia. I note that these �ndings remain the same even
after controlling for the possible endogeneity of �nancial advice. Finally, the examination of
asset allocation in advised and self-managed portfolios suggests that �nancial advisors do not
improve the asset allocation decisions of individuals. In fact, self-directed customers exhibit
better asset allocation and timing abilities than advised customers, which could partly explain
the underperformance of advised portfolios. In summary, it appears that the use of �nancial
advice does not improve the quality of individuals' investment decisions.
This paper contributes to the growing empirical literature on the role of �nancial advice in
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individual investment decisions (Bergstresser, Chalmers, and Tufano, 2009; Hackethal, Halias-
sos, and Jappelli, 2012; Mullainathan, Noth, and Schoar, 2012; Kramer, 2012). For example,
Bergstresser, Chalmers, and Tufano (2009) indirectly investigate the impact of �nancial advice
by comparing the performance and other dimensions of mutual funds sold directly to investors
with those sold through brokers. The researchers �nd little evidence that �nancial advice adds
value. In an audit study, Mullainathan, Noth, and Schoar (2012) use mystery shopping in the
U.S. to test whether �nancial advice helps biased investors to correct their �nancial decisions.
The authors conclude that �nancial advice fails to improve the investment decisions of indi-
viduals. In another paper, using administrative data, Chalmers and Reuter (2012) �nd that
use of �nancial advice contributes to lower portfolio returns and higher market risk. Overall,
consistent with these papers, the evidence in my paper casts doubt on the ability of �nancial
advice to serve as an e�ective substitute for �nancial literacy.
The remainder of the paper is organized as follows. Section 2 describes the data and
variables I use in the empirical analysis. In Section 3, I outline the identi�cation strategy.
Section 4 analyzes the question of who has a �nancial advisor, whereas Section 5 investigates
the welfare e�ects of �nancial advice by comparing advised and self-directed portfolios. In
Section 6, I analyze the possible channels through which �nancial advice a�ects individual
portfolios. Section 7 concludes the paper.
2 Data, Variable De�nitions and Descriptive Statistics
The primary database is a record of the trades and monthly portfolio positions of 3,032 indi-
vidual investors with accounts at one of the largest German commercial banks from January
2003 to October 2005. In total, there are 100,056 investor-month observations, or an average
of 33 observation months per individual, resulting in a strongly balanced panel.5
The database indicates the end-of-month portfolios of the sampled individuals and records
all of their trades at a monthly frequency. Moreover, the data also provide detailed demographic
and investor-type information of sampled individuals such as age, gender, profession, self-
reported risk preference (ranges from very safe to very risky), self-reported monthly labor
income, self-reported �nancial competence and place of residence, detailed at the parish level.6
5Although I have 34 observation months per individual in the original data, I exclude the �rst month (i.e.,January 2003) from the sample because the portfolio returns cannot be de�ned for this month.
6The original data set provides information for 5,051 individuals. Nonetheless, I had to exclude 2,019individuals (a total of 68,646 observations) from the sample due to incomplete information on labor income
4
In the Data Appendix, I provide a detailed description of all variables employed in the analysis.
The bank collects the demographic and investor-type information when an individual opens an
account at the bank and updates this information in case the bank receives any new information
from the customer in the interim.
The sample bank also keeps records of whether its customers make use of �nancial advice
that is o�ered by professional advisors at the bank's local branches. More speci�cally, when
opening an investment account, every bank customer is randomly assigned to a �nancial advisor
at the local branch, regardless of whether they make use of the �nancial advice provided. When
an individual investor places an order via internet, phone or fax in the bank's trading system,
the bank's information system directly shows that order to the corresponding �nancial advisor,
who makes an entry to record whether the customer's order is based on a recommendation
provided by the advisor. It is important to note that the sample bank does not use these
entries to assess the quality of advice or the performance of its advisors. Therefore, bank
advisors face no direct or indirect incentives to provide misleading information to the system.
Based on the entries of the bank employees, I calculate a �nancial advice variable for each
sampled individual that measures the degree of use of �nancial advice. This variable is cal-
culated as the ratio of advised trades relative to all trades executed by the customer over the
sample period:
Financial Advice Intensityi =
∑Tt Advised Tradesit∑T
t All Tradesit(1)
Here, i indexes the sampled individual and t refers to the monthly time period. By de�nition,
Financial Advice Intensityi is a continuous variable and can take a value between zero and one,
where zero implies self-managed accounts and one suggests full delegation of portfolio decisions
to the professional advisors.7
In addition, I create several control variables to account for the e�ects of investor biases.
First, I construct a variable to control for investor overcon�dence, which has been extensively
documented among individual investors (Odean, 1998a; Barber and Odean, 2000, 2001; Guiso
(1,500 individuals), place of residence (201 individuals) and regional variables employed as instruments in themultivariate analysis (318 individuals), respectively.
7By de�nition, the �nancial advice variable can only be computed if an investor makes a trade, i.e., thedenominator in Equation 1 is greater than zero. Otherwise, it would be missing. In approximately 81 percent of100,056 investor-month observations, I observe no trade. Therefore, to have a meaningful measure of �nancialadvice, I aggregate both the advised and non-advised trades over the entire sample period for each sampledindividual and construct the �nancial advice variable.
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and Jappelli, 2006). Investors with this biased perception tend to overestimate their own valu-
ations of the stock market and concern themselves less about the beliefs of others. Accordingly,
greater overcon�dence leads to increased trading (Barber and Odean, 2000), greater risk taking
(Odean, 1998a) and deterring individuals from seeking professional advice (Guiso and Jappelli,
2006). Following Goetzmann and Kumar (2008) and Bailey, Kumar, and Ng (2008), investor
overcon�dence is measured with a dummy variable set to one for investors who fall in the high-
est portfolio turnover quartile and the lowest risk-adjusted performance quartile as measured by
the Sharpe ratio, that is, those investors who trade the most but attain the worst performance.
In addition, I also include a male gender dummy variable in the regressions, given that male
investors are more likely to be overcon�dent (Barber and Odean, 2001).
Another psychological bias that is shown to a�ect investor behavior is the competence
e�ect (Heath and Tversky, 1991; Graham, Harvey, and Huang, 2009). The competence e�ect
posits that individuals are more willing to bet on their own judgments when their subjective
competence in an area is higher (Heath and Tversky, 1991). Thus, this investor bias is relevant
to investor behavior, particularly, to the decision to make use of �nancial advice. To account
for this e�ect, I use information on the self-perceived �nancial knowledge and skills of the bank
customers. Speci�cally, the bank asks the individuals to rate their �nancial knowledge on a
scale from 1 (very poor) to 6 (very good) when opening an account. Based on this information,
I measure investor competence with a dummy variable set to one for investors who rate their
�nancial knowledge as `very good' and zero otherwise. Finally, I de�ne a derivative dummy
that is set to one if an investor held any derivative products (e.g., options, futures, etc.) in his
portfolio during the sample period. As noted by Bailey, Kumar, and Ng (2008), this variable
serves as a good proxy for (relatively) more sophisticated individual investors who may also
have a preference for speculation.
In Panel A of Table 1, I �rst present descriptive statistics regarding the characteristics of
the investors in the �nal sample. Unless noted otherwise, the mean and the standard deviation
are always calculated on the full pooled sample. The investors are, on average, 56 years old
and have an average portfolio value of 50,901 Euro. The majority of the bank customers,
approximately 39.5 percent, are occupied as employees, whereas the share of blue-collar workers
and retirees account for 4.0 and 21.5 percent, respectively. Approximately 21 percent of the
individuals in the sample report to take above-average �nancial risk (i.e., Risky and Very risky),
whereas 18 percent of them are willing to take below-average risk (i.e., Very safe and Safe).
6
Furthermore, the mean (median) value of the �nancial advice intensity is 47.1 (50) percent,
indicating that approximately one in every two trades made by the sampled investors is based
on the recommendations of �nancial advisors.
When I turn to investor biases, I observe that 5.6 percent of the sampled individuals tend
to display overcon�dent behavior. Interestingly, the share of investors who rate their �nancial
knowledge as `very good' is also equal to 5.6 percent. Although the shares of `overcon�dent'
and `competent' investors are identical in the �nal sample, only 3 percent of the overcon�dent
investors report to a have higher subjective competence, which implies that investor overcon-
�dence does not seem to subsume the competence e�ect although both concepts are closely
related.
In the analysis of the impact of �nancial advice on portfolio choice, I �rst focus on the per-
formance aspect. More speci�cally, I analyze how �nancial-advisor-assisted accounts perform in
comparison to self-managed portfolios. To do so, I compute the monthly portfolio returns using
the modi�ed version of the Dietz (1968) measure, assuming that all dividends and interests are
paid in the middle of a given month and reinvested:
Returngrossit =
(Portfolioit − Portfolioit−1)− (Purchasesit − Salesit)Portfolioit−1 + (Purchasesit − Salesit + Cashit) · 0.5
(2)
Here, i indexes the sampled investor and t refers to the monthly time period. Returngrossit
represents the portfolio returns before deducting any transaction costs; Portfolioit is the market
value of the portfolio of investor i at the end of month t; Purchasesit and Salesit denote the
cumulative purchases and sales in month t, respectively. Finally, Cashit represents the cash
proceeds from dividends or coupons.8
Next, I calculate the portfolio returns net of transaction costs (i.e., advisory fees, trading
costs, etc.) in a manner analogous to that used to formulate Equation 2:
Returnnetit =
(Portfolioit − Portfolioit−1)− (Purchasesit − Salesit)− CostsitPortfolioit−1 + (Purchasesit − Salesit + Cashit) · 0.5
(3)
Using the net monthly returns as computed in Equation 3, I then calculate the abnormal
returns for each individual portfolio with respect to the CAPM and the four-factor model that
includes Fama and French (1993) factors and the Carhart (1997) momentum factor, respec-
8Note that the results of the portfolio performance tests presented in the next section are robust to adoptingdi�erent assumptions about the timing of transactions, i.e., at the beginning or end of a given month.
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tively:
Returnnet1it = ai + bi ·RMRFt + ei,t (4)
Returnnet1it = αi + β1i ·RMRFt + β2i · SMBt + β3i ·HMLt + β4i · UMDt + εi,t (5)
Here, ai and αi denote the one-factor alpha and four-factor alpha, respectively. Returnnet1it
is the net monthly return on the portfolio of investor i in excess of the risk-free rate, which is
captured by monthly returns on the JP Morgan 3 Month Euro Cash Index. RMRFt denotes
the excess return on the market portfolio that is approximated by the comprehensive German
CDAX Performance Index. SMBt, HMLt and UMDt correspond to monthly returns on size,
value premium and momentum portfolios, respectively. The size portfolio return (SMB) is
approximated by the di�erence in monthly returns on the small cap SDAX index and the large
cap DAX 30 index. The book-to-market portfolio return (HML) is approximated by the return
di�erence between the MSCI Germany Value Index and the MSCI Germany Growth Index.
Finally, the momentum portfolio return (UMD) is the di�erence in monthly returns between
a group of stocks with recent above-average returns and another set of stocks that displayed
below-average returns.9
To ensure that the results of the performance tests are not driven by any outliers, I winsorize
the performance measures at the �rst and ninety-ninth percentile; i.e., I set all observations
that fall beyond these tolerances to the �rst and ninety-ninth percentile values, respectively.
Panel B of Table 1 presents summary statistics for these variables.
3 Endogeneity of Financial Advice: Identi�cation Strategy
One important feature of my analysis is that I treat the decision to consult a �nancial advisor
as an endogenous choice and tackle the arising endogeneity issue in the econometric analysis. I
argue that there are two possible mechanisms through which the decision to consult a �nancial
advisor can be endogenous.
The �rst possible mechanism is the omitted variable bias. Although having a rich data
set at the individual level allows me to account for a large set of investor controls, still, some
9The group with above-average returns is de�ned as the top 30 percent of stocks from the CDAX index overthe past 11 months, and the below-average group contains the lowest 30 percent of stocks from the same indexover the same period.
8
unobserved individual characteristics such as level of social capital or trading experience can
simultaneously a�ect both the decision to seek �nancial advice and the portfolio choices of
individuals. Statistically, unobserved in�uences could induce a correlation between the �nancial
advice variable and the error term even after controlling for observable characteristics, which
would render ordinary least squares (OLS) estimates inconsistent.
Clearly, the natural way to solve this problem is to exploit the panel dimension of the data
and use panel regression techniques such as �xed e�ects or random e�ects, which would enable
me to control for omitted individual e�ects in the regressions. In my case, however, the �xed
e�ects estimator is not feasible because almost all investor controls do not vary over time, and
thus, the individual �xed e�ects would be perfectly correlated with them. Therefore, I use the
random e�ects approach, which does not have this problem.
Second, any relationship between portfolio choice and having a �nancial advisor could re�ect
`reverse' causality; that is, the decision to make use of �nancial advice can be endogenously
determined by the portfolio choices of individuals. For example, investors who exhibit poor
portfolio performance can consult �nancial advisors in the hope that professional advisors would
help them to improve their performance results (Hackethal, Haliassos, and Jappelli, 2012). This
situation would generate a spurious correlation between the left-hand side variables and the
�nancial advice variable, which would not allow me to render a causal interpretation of the
estimated coe�cient with respect to �nancial advice.
To overcome the identi�cation problem, I use an instrumental variables (IV) technique,
which is conducted in the two stage least squares (2SLS) fashion. Conceptually, there are two
conditions that a variable must satisfy to be considered a valid instrument. First, it must be
correlated with the endogenous �rst-stage variable, which is, in my case, use of �nancial advice.
This condition ensures that variations in the IV induce variations in the endogenous variable.
Second, the IV must be uncorrelated with the error term in the equation of interest, which
ensures that the latent characteristics do not contaminate the induced variations.
Keeping these caveats in mind, I instrument the �nancial advice variable with two regional
variables that are constructed from primary information regarding the 5-digit zip code of res-
idence. In particular, I use the number of psychotherapists and number of all bank branches
in a parish where the sampled individual is located.10 As I will show shortly, both of these
10The data on the number of psychotherapists in a given parish is hand-collected from the web site ofthe German Psychotherapists Association (Deutsche Psychotherapeuten Vereinigung). The information on thenumber of bank branches is provided by an independent commercial data provider. Furthermore, it is importantto note that the variable for the number of bank branches includes not only the branches of the sampled bank
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instruments meet the two criteria that I outlined above, i.e., they are highly correlated with
the �nancial advice variable and do not have any direct e�ect on the variables of interest.
The motivation for the use of the number of psychotherapists and number of bank branches
as instruments is as follows. With respect to the former, individuals living in neighborhoods
with a higher number of therapists are more likely to seek professional help or advice when
they are confronted with di�culties or challenges in their daily lives. Thus, I assume that
these people also have a higher tendency to consult professionals when they are confronted
with essential economic decisions, such as where to invest or how to allocate their savings
among alternative investment options. Therefore, my prior is that this variable is positively
associated with use of �nancial advice. With respect to the latter, investors living in parishes
with a greater concentration of banks can potentially obtain investment-relevant information
from local sources or advice from other third parties in a more convenient way, which together
could lower the tendency to seek professional �nancial advice. Therefore, I expect the number
of bank branches to be negatively associated with the decision to make use of �nancial advice.
Although both of these factors are likely to be exogenous with respect to portfolio per-
formance and the investment choices of individuals, a careful reader could be skeptical of the
validity of these instruments. More speci�cally, these factors may have served as a proxy for
another regional variable that has a direct e�ect on the variables of interest. For example, some
unobserved regional characteristics correlated with the instruments, such as the level of social
capital (e.g., trust levels) in the parish, can potentially a�ect both the portfolio choices of the
individuals and their tendency to seek �nancial advice. This situation is problematic, however,
because the instruments may thus not be orthogonal to the error process. To overcome, or
at least to minimize, the severity of this problem, I include regional dummy variables that
account for possible latent regional characteristics and capture the direct e�ects of the location
of individuals.
The excluded instruments, i.e., number of psychotherapists and bank branches in a given
parish, are assumed to be correlated with the �nancial advice variable but uncorrelated with
the error term in the equation of interest. To further ensure the quality of these instruments,
I perform two speci�cation tests from the �rst-stage regressions, in which the �nancial advice
variable is regressed on the full set of instruments. First, as I over-identify each speci�cation to
achieve stronger identi�cation, I am able to use over-identi�cation tests such as the Hansen-J
but also the branches of all banks in a given parish.
10
statistic, which allow me to test for the statistical exogeneity of the instruments. Indeed, I
observe that the Hansen-J test statistics for over-identi�cation fail to reject the null of valid
instruments, suggesting that the instruments satisfy the orthogonality condition (χ2-test=0.868;
p-value=0.351).
In �nite samples, however, having valid instruments is not su�cient to ensure the consistency
of the IV approach (Staiger and Stock, 1997). In addition, the excluded instruments have to
correlate strongly with the endogenous �rst-stage variable. Accordingly, I �nd that the joint-
test of signi�cance for the excluded instruments exceeds Stock and Yogo (2002)'s critical values
for 10 percent maximal size distortion (F -statistics=24.54; p-value<0.01), which indicates that
the excluded instruments are strongly correlated with the �nancial advice variable and thus do
not su�er from a weak instrument problem.11 Overall, the speci�cation tests imply that the
instruments appear to meet the two necessary conditions.
4 Who seeks Financial Advice?
I begin the analysis by investigating the determinants of seeking �nancial advice. To do so,
I construct an indicator variable for having a �nancial advisor. In particular, I de�ne an
individual investor as advised if more than half of his trades are based on the recommendations
of the bank advisors and zero otherwise.12 Using probit regressions, I then model having a
�nancial advisor as a function of various investor characteristics such as age, profession, investor
biases, risk preference and monthly salary.
My proposed model is reported in Table 2. I report the marginal e�ects rather than the
original probit coe�cients, along with heteroscedasticity-consistent standard errors. As shown
in column (i), male and high-income investors are less likely to consult �nancial advisors when
making investment decisions. These �ndings are consistent with the existing literature that
shows that the levels of �nancial literacy is particularly lower among females and low-income
individuals (Campbell, 2006; Lusardi and Mitchell, 2007). In other words, the lower demand
of high-income and male investors for �nancial advice supports the notion that �nancial advice
serves as a substitute for the �nancial literacy of individuals. Next, I observe that the demand
11For a comprehensive discussion on the weak instrument problem, see, for example, Stock and Yogo (2002)and Stock, Wright, and Yogo (2002).
12The choice of the threshold value (i.e., 0.50) is motivated by the fact that 0.50 is equal to the sample medianof the �nancial advice intensity variable (see Equation 1). This enables me to have two equal-sized groups. Inote that I have tried various speci�cations with di�erent threshold values and I obtain qualitatively similarresults.
11
for �nancial advice increases with the willingness to take �nancial risk and age. Each of these
e�ects is economically highly signi�cant. Regarding the latter, ceteris paribus, individuals
who are older than 60 are 23.2 percentage points more likely to seek �nancial advice than
younger investors in the under-30 age group. With respect to profession, all of the estimated
coe�cients are positive (relative to the omitted group of `Retiree'), except the dummy variable
for being an employee. One possible interpretation that is consistent with this �nding is the
opportunity costs argument. For example, the possible time and opportunity costs associated
with managing a portfolio may be higher for individuals who are occupied as executive employee.
Therefore, these individuals may delegate portfolio decisions to professionals to minimize the
arising opportunity costs.
In this respect, I document that male, younger and high-income individuals more likely to
bet on their own judgments and manage their portfolios on their own. When I turn my attention
to the e�ects of investor biases, I �nd that the demand for �nancial advice is signi�cantly
lower among biased investors - those scoring high on overcon�dence and the competence e�ect.
The adverse e�ect of overcon�dence is in line with existing evidence indicating that these
investors concern themselves less about the opinions of others and thus are less willing to rely
on information or recommendations provided by professionals (Barber and Odean, 2001; Guiso
and Jappelli, 2006). Similarly, the negative association between the competence e�ect and
having a �nancial advisor is also intuitive. As noted previously, the competence e�ect posits
that people with this biased perception are more willing to bet on their own judgments (Heath
and Tversky, 1991; Graham, Harvey, and Huang, 2009). The results shown in Table 2 support
this conjecture. I observe a signi�cant negative e�ect of competence on the decision to have a
�nancial advisor. In particular, investors with higher perceived competence are 10.1 percentage
points less likely to be matched with a �nancial advisor. Finally, I �nd that those investors
who invest in particularly risky products (i.e., derivatives) are also less willing to demand for
�nancial advice.
In speci�cation (ii) in Table 2, I regress the �nancial advice dummy on the full set of in-
struments, including the two regional variables; number of psychotherapists and bank branches
in the parish. I �nd that both instruments exhibit the predicted signs, and the coe�cient
estimates are both statistically and economically signi�cant. The demand for �nancial advice
is greater for individuals who live in neighborhoods with a higher number of therapists and a
lower number of bank branches. In other words, investors who are likely to seek professional
12
help from psychotherapists and those who are less likely to have access to investment-relevant
information from local sources or advice from other third parties tend to make use of �nancial
advice.
In summary, the evidence described in this section reveals two interesting facts about the
determinants of �nancial advice. First, �nancial advisors tend to be matched with those in-
vestors who are likely to be less �nancially sophisticated, suggesting that �nancial advice serves
as a substitute for �nancial literacy. Second, investor biases such as overcon�dence and the
competence e�ect appear to be important factors that deter individuals from seeking profes-
sional advice, even though �nancial advisors can potentially insulate those individuals from
these behavioral biases. In the following, I turn to the e�ects of �nancial advice on individual
portfolios to test whether �nancial advice is an e�ective substitute for �nancial literacy.
5 Portfolio Performance and Financial Advice
In this section, the portfolio performance of advised investors is compared to that of self-
directed investors, with the goal of identifying whether use of �nancial advice contributes to
improvements in portfolio performance.
First, I focus on the e�ects of �nancial advisors on net monthly portfolio returns as de�ned
in Equation 3. Table 3 reports the estimation results, along with standard errors clustered
at the zip-code level. Estimates of four di�erent speci�cations are reported in the table. Re-
gression (i) shows the pooled OLS results without time and region �xed e�ects; (ii) shows the
pooled OLS results with time and region �xed e�ect; (iii) shows the results of a random e�ects
estimator that accounts for unobserved investor heterogeneity; and (iv) shows the results of
the instrumental variable regressions. These regressions, and all those that follow, also include
suppressed dummies for monthly time and regions.
Clearly, the main variable of interest in my model is the degree of �nancial advice that is
calculated as the ratio of advised trades to all trades made by the investor. Interestingly, I �nd
an adverse e�ect of professional �nancial advice on the net monthly portfolio returns, regardless
of whether controlling for time and region �xed e�ects; unobserved individual heterogeneity or
the possible endogeneity of �nancial advice. The moderating e�ect of �nancial advisors is both
statistically and economically signi�cant at the one-percent level in all four speci�cations. For
example, based on the OLS coe�cient, a one unit increase in the degree of �nancial advice is
13
associated with a decrease of 11 basis points in the monthly returns, which corresponds to a
reduction of 1.32 percent in the annual rate of return. However, these results need to be inter-
preted with caution, given that use of �nancial advice can re�ect an endogenous choice. When
I correct for endogeneity, I also �nd qualitatively similar results, although the IV coe�cient is
larger in magnitude.
It is also worth brie�y discussing some of the other controls in the regressions and their
importance. First, and not surprisingly, I �nd that investor overcon�dence is negatively as-
sociated with monthly returns. The negative e�ect of overcon�dence on portfolio returns is
consistent with the �ndings reported in the literature that such investors trade securities too
frequently and thus exhibit poor performance (Barber and Odean, 2000, 2001). I also �nd that
income-rich individuals tend to realize higher portfolio returns, perhaps due to their higher lev-
els of �nancial literacy (Campbell, 2006; Lusardi and Mitchell, 2007). With respect to the risk
tolerance of investors, all of the estimated coe�cients are positive and statistically signi�cant
(relative to the omitted group of `Very Safe'), implying that portfolio returns increase with the
risk tolerance of investors. Finally, I observe a signi�cant negative e�ect of age on portfolio
returns. For example, individual investors who are older than 60 earn on average 3.12% lower
returns annually than the investors in the under-30 age group. This �nding is interesting. On
the one hand, older individuals are likely to have more investment experience that they have
accumulated over time (Korniotis and Kumar, 2011). On the other hand, aging is likely to have
an adverse e�ect on the cognitive abilities of people (Agarwal, Driscoll, Gabaix, and Laibson,
2009; Christelis, Japelli, and Padula, 2010). Because I am not able to control for those e�ects
separately, the age variable is likely to capture these confounding e�ects. Hence, the negative
coe�cient on age suggests that age-driven increases in investment experience seem to be o�set
by the age-driven decline in the ability of investors to make e�ective investment decisions.
One natural explanation why collaboration with professional advisors negatively a�ects port-
folio returns is the fees paid to bank for �nancial advice. For example, Bergstresser, Chalmers,
and Tufano (2009) note that brokered-channel mutual fund customers pay more than twice as
much loads and fees as direct-channel customers. In other words, the possible bene�ts of �nan-
cial advice can be partially or entirely o�set by its direct costs. Consistent with this conjecture,
in an unreported analysis, I �nd that advised customers incur signi�cantly higher transaction
costs than self-directed investors (59.24 Euro versus 43.74 Euro; t-statistics=7.741).13 To ad-
13The transaction costs are computed on a monthly frequency and contain fees paid to banks for �nancialadvice, trading costs and portfolio management fees.
14
dress this possibility, I next investigate the e�ect of �nancial advice on gross portfolio returns
as de�ned in Equation 2. The right-hand-side variables are exactly the same as in Table 3, and
the dependent variable is now gross monthly returns before deducting any transaction costs.
As reported in Table 4, both the OLS and the IV regressions yield negative and statistically
signi�cant estimates for the use of �nancial on gross portfolio returns; however, as before the
IV estimate is greater in magnitude. In other words, the transaction costs explanation is not
supported by the data: the advised investors earn lower returns even before deducting any
transaction costs.
The evidence described so far suggests that advisor-assisted portfolios underperform self-
managed portfolios. However, the �rst moment of portfolio returns has yet to be explored. Put
di�erently, �nancial advice could still create value by reducing investors' exposure to portfolio
risk, thus increasing the risk-adjusted returns.
In Table 5, I investigate the e�ects of �nancial advice on risk-adjusted returns. Speci�cations
(i) and (ii) use a one-factor alpha as the performance measure, whereas I use a four-factor alpha
as the dependent variable in regressions (iii) and (iv).14 Consistent with prior evidence, I �nd
that use of �nancial advice also reduces risk-adjusted returns. Both the OLS and the IV
coe�cients are negative and statistically signi�cant at the one-percent level. This remains true
for both risk-adjusted performance measures. It is not unexpected that the recommendations
of the �nancial advisors in my sample do not produce persistent positive alphas for their
clients. In fact, most existing studies show that professional money managers do not consistently
outperform passive benchmarks (Jensen, 1968; Malkiel, 1995; Desai and Jain, 1995). However, it
is still somewhat surprising that �nancial advisors do not even contribute to better performance
relative to self-directed customers.15
Finally, I investigate whether the e�ect of �nancial advice varies across individuals with po-
tentially di�erent incentives. To address this issue, I split the sample into two subsamples based
on the investment-income ratio of individuals. Following Graham, Harvey, and Huang (2009), I
14Over the sample period, I calculate a one-factor alpha and a four-factor alpha for each customer portfo-lio separately based on a single-factor and a four-factor model as described in Equation 4 and Equation 5,respectively. This reduces my sample size to 3,032, i.e., to the number of individual investors in the �nalsample. Therefore, I use cross-sectional OLS and IV regressions in analyzing the e�ects of �nancial advice onrisk-adjusted returns.
15I acknowledge that these results need to be interpreted with caution. For instance, I ignore the possibletime and opportunity costs associated with managing a portfolio due to the di�culty of estimating these costs.Consider, for example, the time and opportunity costs of managing a portfolio exceeding both direct andindirect costs of �nancial advice. In this case, collaboration with advisors could still be better than the casewhen investors have to incur high costs in managing their own portfolios.
15
�rst calculate the investment-income ratio by dividing the time-series average of portfolio value
by labor income and then de�ne the investors who fall above (below) the cross-sectional median
value as high- (low-) incentive investors. Table 6 shows the e�ect of bank advisors on portfolio
performance for each subgroup separately. Both the OLS and IV regressions yield strong and
negative estimates for use of �nancial advice among low-incentive investors, whereas I �nd no
signi�cant e�ect of �nancial advice for high-incentive investors, i.e., higher investment-income
ratio. One possible interpretation that is consistent with this �nding is that high-incentive
investors may expend more e�ort, for example, in information search and acquisition processes
when making investment decisions. Accordingly, these investors can �lter out possible `poor'
recommendations of banks advisors. By contrast, low-incentive investors may conduct only a
very limited search and take advisors' recommendations at face value (also not correcting for
the underlying con�ict of interest), which can possibly lead to poor portfolio performance.
In summary, I �nd no evidence that use of �nancial advice contributes to better account
performance. On the contrary, investors who manage their accounts on their own realize better
performance results. However, it is not clear through which channel �nancial advisors in�uence
portfolio performance negatively. To address this issue, I next analyze the e�ects of �nancial
advice on investment mistakes and the dynamic asset allocation decisions of individual investors.
6 Investment Mistakes, Asset Allocation and Financial Ad-
vice
In this section, I analyze the possible channels through which �nancial advisors can a�ect the
portfolio performance of individuals. First, I analyze the impact of �nancial advice on investors'
diversi�cation choices. Then, I turn to the e�ect of professional advisors on the trading behavior
of individuals. Finally, I examine the asset allocation recommendations of �nancial advisors
and assess their market timing abilities.
6.1 Portfolio Diversi�cation and Financial Advice
Standard models of portfolio choice suggest that investors should hold diversi�ed portfolios
to eliminate non-compensated idiosyncratic risk. In reality, however, individual investors are
typically underdiversi�ed (Barber and Odean, 2000; Polkovnichenko, 2005; Goetzmann and
Kumar, 2008) and do not diversify internationally (French and Poterba, 1991; Kang and Stulz,
16
1997; Coval and Moskowitz, 1999; Bailey, Kumar, and Ng, 2008). There is thus considerable
scope for �nancial advisors to improve portfolio diversi�cation.
To assess the impact of �nancial advisors on the diversi�cation choices of individual in-
vestors, I focus on two diversi�cation measures. The �rst measure is the share of mutual funds
in the stock portfolio, which captures the extent of equity portfolio diversi�cation. The second
diversi�cation measure is the percentage of domestic securities in the equity portfolio, which
measures the extent to which investors exhibit a home bias.
Because I de�ne the measures of diversi�cation only for stockholders, the regression esti-
mates can be a�ected by selection bias. Therefore, I use the procedure proposed by Heckman
(1979) to counter the potential bias due to sample selection. Speci�cally, I begin by estimating
a probit model, where the decision to invest in stocks is modeled as a function of observable
investor characteristics, monthly time e�ects and regional controls. To develop this model,
following Vissing-Jorgensen (2002), I use the one-lagged Euro amount invested in stocks as the
excluded variable. The use of this exclusion restriction is motivated by stock market entry
costs.16 Then, I compute the inverse Mills ratio (IMR) from the selection equation and add
this ratio to the corresponding estimation model of portfolio diversi�cation.
I begin, in columns (i) to (iii) of Table 7, by testing the e�ects of �nancial advice on equity
portfolio diversi�cation. In all three speci�cations, I �nd that the coe�cient estimates of
the �nancial advice variable is positive and statistically signi�cant, which suggests that advised
investors hold better diversi�ed portfolios. In fact, the positive contribution of �nancial advisors
to equity diversi�cation is not surprising, given the monetary incentives of advisors to promote
mutual funds. Speci�cally, �nancial advisors may be rationally responding to their incentives
and directing their clients to mutual funds to receive higher commissions, which in turn improves
equity diversi�cation.
A similar perspective on the role of �nancial advice applies to improving international
diversi�cation. Both the OLS and the IV coe�cients are statistically signi�cant at the one-
percent level, and the IV coe�cient is larger in magnitude, -0.498 versus -0.186. In other words,
�nancial advisors seem to encourage cross-border investments, and thus, equity home bias is
less pronounced for advised investors.17
16For a detailed discussion about the e�ects of entry costs on stock market participation, see, for example,Vissing-Jorgensen (2002) and Haliassos and Michaelides (2003).
17One can argue that bank advisors promote foreign investments presumably because international ordersare associated with higher commissions. However, the sampled bank charges a �xed commission rate for bothdomestic and international stocks, which does not support this explanation. In particular, the sampled bank
17
Controlling for other variables, income-rich individuals are more likely to own international
equity securities. The positive association between international diversi�cation and income is
consistent with the information-costs-based explanation that the �xed costs of learning about
foreign securities may deter individuals from investing in international securities (Kang and
Stulz, 1997). I also �nd that the coe�cients for investor competence are positive and highly
statistically signi�cant, indicating that investors who perceive themselves to be more competent
tend to avoid foreign stocks. In fact, the adverse e�ect of investor competence is somewhat
puzzling and does not support the view that investors with higher perceived knowledge and
skills are more willing to invest in foreign assets (Graham, Harvey, and Huang, 2009). Finally,
I observe that home bias increases with the risk tolerance of individuals, which is consistent
with the predictions of Goetzmann and Kumar (2008).
6.2 Trading Behavior and Financial Advice
Financial theory provides two competing perspectives on trading activity. The �rst hypothesis
suggests that rational agents either should not trade for speculative reasons (Milgrom and
Stokey, 1982) or they should only trade when the marginal bene�t of the trade equals or
exceeds its marginal costs, increasing their expected utility (Grossman and Stiglitz, 1980). An
alternative perspective is found in the overcon�dence models of Odean (1998b) and Gervais and
Odean (2001). According to these models, investors will trade to their detriment. Using data
from a large U.S. discount broker, Barber and Odean (2000, 2001) test these distinct theories
and show that individual investors overtrade to their detriment because of high transaction
costs.
Hence, a priori, �nancial advisors can have two opposing e�ects on the trading behavior
of individuals. On the one hand, use of professional advice can limit excessive trading and
associated transaction costs because professionals are shown to be less subject to investor
biases (List, 2003; Haigh and List, 2005). On the other hand, the fact that �nancial advisors
generate higher trading commissions through the purchases of their clients can create incentives
for advisors to increase their trading frequency (Shapira and Venezia, 2001). In Table 8, I
investigate which of these e�ects is stronger.
I begin by testing the e�ects of professional advice on the trading frequency. In columns
charges a commission rate of 100 basis points for purchases or sales of all stocks, regardless of whether they aredomestic or foreign.
18
(i) to (iii), the dependent variable is the natural logarithm of monthly number of trades. The
regression results in the �rst three columns suggest that the e�ect of �nancial advice on trading
frequency is negative and statistically signi�cant, indicating that trading frequency decreases
with use of �nancial advice. This result supports the notion that �nancial advisors seem to
limit overtrading. However, given that sales commissions depend on trading volume rather
than number of trades, �nancial advisors can face incentives to increase account turnover.
To address this possibility, in columns (iv)-(vi), I investigate the relation between �nancial
advice and portfolio turnover. Following Barber and Odean (2001), I calculate the monthly
portfolio turnover as the sum of one half of the monthly purchase turnover and one half of the
monthly sales turnover. When I turn my attention to portfolio turnover, I observe that �nancial
advice displays no signi�cant e�ect on account turnover. This remains true when I control for
unobserved individual heterogeneity and the possible endogeneity of the use of �nancial advice.
In summary, the possible positive and negative e�ects of �nancial advice on individual trading
behavior tend to cancel out each other.
With respect to behavioral biases, I �nd strong evidence that portfolio turnover increases
with both overcon�dence and investor competence. Each of these e�ects is economically signif-
icant. Based on the IV coe�cient, the monthly turnover in overcon�dent investors' accounts
is 344 basis points more than that in unbiased investors' accounts. Similarly, investors who
feel more competent trade 240 basis points more than those who do not exhibit a competence
e�ect, which con�rms the �ndings of (Graham, Harvey, and Huang, 2009). Of the other control
variables, I observe that younger, income-rich and less risk-averse investors also turn over their
portfolios more frequently.
The results presented so far suggest that �nancial advisors lower the number of trades,
whereas they show no signi�cant e�ect on portfolio turnover. In fact, investors with a target
risk level should periodically rebalance their portfolios to maintain the desired risk pro�le,
for example, in response to passive changes in their portfolios or to changes in demographic
characteristics (e.g., aging).18 Based on the evidence presented in Table 8, a natural question
to ask is whether advised portfolios are characterized by inertia. To answer this question, I
next investigate the relation between �nancial advice and the portfolio rebalancing decisions of
individuals.
18As I noted earlier, the empirical literature based on administrative data �nds evidence of overtrading (e.g.,Odean (1998a); Barber and Odean (2000)), whereas another strand of research that focuses on survey data andretirement accounts documents substantial inertia in household portfolios (Agnew, Balduzzi, and Sunden, 2003;Bilias, Georgarakos, and Haliassos, 2010).
19
First, I need to establish an appropriate de�nition of portfolio rebalancing. Following Al-
varez, Guiso, and Lippi (2012), I use a broad notion of portfolio rebalancing. In particular, I
assume that a portfolio rebalancing occurs when there is a net sale in one asset class and a
net purchase in another. Likewise, in their model, Bonaparte, Cooper, and Zhu (2012) also
attribute the simultaneous purchases and sales of two di�erent asset classes to the rebalancing
purposes of households. Furthermore, to keep the analysis simple, I restrict my attention to two
broad asset classes, (i) risky assets (including stocks, retail derivatives and real estate funds)
and (ii) risk-free assets (including direct and indirect bonds, cash and money market funds).
Because I am interested in the e�ects of �nancial advice on the simultaneous portfolio
decisions of individuals, I require an empirical model that allows me to estimate the joint
probability of trading risky and risk-free assets in opposite directions. For this purpose, I
employ a bivariate probit model. Table 9 reports the marginal e�ects for the joint probability
of trading risky and risk-free assets. Columns (i) and (iii) report the joint probability of
selling risky assets and buying risk-free assets, whereas Columns (ii) and (iv) present the joint
probability of buying risky assets and selling risk-free assets. Note that, in speci�cations (iii)
and (iv), I control for the possible endogeneity of �nancial advice by replacing the �nancial
advice variable with its �tted value from the instrumental variable regression.19
Table 9 presents the estimation results. I report the marginal e�ects rather than original
probit coe�cients, along with standard errors clustered at the zip-code level. Both the OLS
and IV estimates yield a negative and signi�cant e�ect of �nancial advice on the rebalancing
decisions of individuals. Based on the IV estimates, ceteris paribus, a unit increase in the �nan-
cial advice intensity decreases the probability of portfolio rebalancing by 4.1 to 7.4 percentage
points, indicating that advised portfolios display a higher degree of inertia.
One possible explanation for the adverse e�ect of advisors on portfolio rebalancing is the
trail commissions attached to mutual funds. Speci�cally, investment �rms pay brokers (�nan-
cial advisors) trail commissions for every subsequent year the customer holds their products.
Accordingly, these commissions could create incentives for advisors not to encourage portfolio
rebalancing that may lead to portfolio inertia.
Controlling for other investor characteristics, I observe that high-income investors tend to
rebalance their portfolios more frequently. This result is consistent with the adjustment costs
19In the portfolio rebalancing regressions, I restrict my attention to those individuals who hold risky assetsin their portfolios. Risky assets include stocks (both direct and indirect), retail derivatives (i.e., certi�cates),derivatives and mixed mutual funds. Therefore, I have a total of 68,251 observations in these regressions.
20
explanation of Bonaparte, Cooper, and Zhu (2012), who argue that higher transaction costs
associated with stock trading contribute to infrequent portfolio adjustments. With respect
to behavioral biases, I �nd that both overcon�dence and investor competence increase the
probability of rebalancing portfolios. This �nding is also consistent with the positive e�ect of
these biases on trading frequency and account turnover. Put di�erently, it seems that investors
with these biased perceptions do not only randomly buy and sell securities (i.e., churning) but
they also tend to deliberately rebalance their portfolios (Calvet, Campbell, and Sodini, 2009).
6.3 Asset Allocation, Market Timing and Financial Advice
Finally, I compare the asset allocation decisions of advised and self-directed investors. As
noted by Bergstresser, Chalmers, and Tufano (2009), professionals can create value by providing
superior asset allocation recommendations. In other words, �nancial advisors could raise (lower)
the portfolio weights among di�erent asset classes prior to a rise (fall) in the market, which
would help individuals to market-time (Bollen, 2001).
To analyze this possibility, following the procedure described in Bergstresser, Chalmers, and
Tufano (2009), I investigate the asset allocation recommendations of �nancial advisors at the
aggregate level. Speci�cally, I restrict my attention to the stock, bond and cash holdings of in-
dividuals, which are assumed to represent their entire portfolios. Then, I calculate the portfolio
weight of each asset class in the aggregated advised and self-managed portfolios correspond-
ingly. The choice of these asset classes is dictated by the availability of relevant benchmark
indices that are used in the computation of monthly portfolio returns, which allows me to isolate
the possible e�ects of individual security selection. In particular, I use the MSCI World Index,
Barclays European Aggregate Bond Index and monthly Euribor rates as benchmark indices for
stock, bond and cash holdings, respectively. To evaluate the market timing abilities of �nancial
advisors, I compare, at the aggregate level, the cumulative value of 1 Euro over 34 months
invested at the beginning of the observation period and rebalanced periodically in each month
according to the corresponding portfolio weights in the advised and self-managed portfolios.
Figure 1 depicts the cumulative value of 1 Euro invested using the advised and self-managed
asset allocation weights. The results show that the asset allocation weights of self-directed in-
vestors leads to a signi�cantly higher cumulative wealth than those of advised investors (1.14
Euro versus 0.964 Euro). When the mean monthly returns of aggregated portfolios are con-
trasted, I observe that self-managed portfolios realize 43.92 basis points, whereas advised in-
21
vestors achieve -9.424 basis points returns per month. The di�erence is also statistically highly
signi�cant (t-statistics=4.078; p-value<0.001). Similarly, when the volatility of returns is con-
sidered and risk adjustment has been carried out, I �nd that self-managed portfolios produce
a signi�cantly higher Sharpe ratio than the advised portfolios (0.28 versus -0.06 per month).
As a whole, this simple exercise implies that the involvement of �nancial advisors does
not lead to superior asset allocation, which could potentially explain the underperformance of
advised portfolios relative to self-directed portfolios.
7 Conclusion
In this paper, I analyze the role of �nancial advice in individual investment decisions and ask
whether �nancial advice is a reliable substitute for individuals' �nancial literacy.
I begin the analysis by showing that individuals who tend to be �nancially less sophisti-
cated seem to be matched with �nancial advisors, which supports the view that professional
�nancial advice serves as a substitute for �nancial literacy. In fact, this �nding suggests that
there is a large scope for �nancial advisors to improve individual �nancial decisions because
these individuals are particularly in need of investment guidance. Nevertheless, the credence
good characteristic of �nancial advice, coupled with the multitasking of an advisor, can create
incentives for �nancial advisors to exploit individuals' lack of �nancial literacy.
To investigate which of these e�ects is dominant, I next turn to the welfare e�ects of �nancial
advice by contrasting advised and self-managed portfolios. I document that advised investors
earn lower raw and risk-adjusted returns than self-directed investors even before I deduct the
advisory fees and transactions costs. This �nding reveals the possible agency con�ict between
�nancial advisors and individuals with limited �nancial literacy.
To analyze the possible channels through which �nancial advisors a�ect portfolio perfor-
mance, I �nally compare the portfolio choices of advised and self-directed investors along sev-
eral dimensions, including their trading behavior, diversi�cation choices and asset allocation
decisions. I �rst show that �nancial advisors improve portfolio diversi�cation by encouraging
cross-border investments and investments in mutual funds. The latter �nding supports the
idea that �nancial advisors respond to monetary incentives and promote mutual funds. Fur-
thermore, the use of �nancial advice does not show any signi�cant e�ect on portfolio turnover,
whereas it lowers the likelihood of portfolio rebalancing. Finally, I show that use of �nancial ad-
22
vice does not improve the asset allocation decisions of investors. On the contrary, self-managed
portfolios exhibit better asset allocation and market timing than advised portfolios, which can
partly explain the underperformance of advised portfolios.
Overall, the presented evidence in this paper casts doubts on the ability of professional
advice to serve as a reliable substitute for �nancial literacy.
23
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28
Table 1: Summary Statistics
Mean Median Standard deviation No of Obs
A. Explanatory variables
Financial advice intensity 0.471 0.500 0.416 100,056Investor biases:Investor overcon�dence 0.056 0.000 0.231 100,056Derivative dummy 0.026 0.000 0.160 100,056Investor competence 0.056 0.000 0.230 100,056Demographics:Male 0.474 0.000 0.499 100,056Retiree 0.215 0.000 0.411 100,056Housewife 0.093 0.000 0.290 100,056Student 0.060 0.000 0.237 100,056Employee 0.394 0.000 0.489 100,056Executive employee 0.028 0.000 0.166 100,056Blue-collar worker 0.041 0.000 0.197 100,056Age<30 0.065 0.000 0.246 100,05630≤Age<45 0.201 0.000 0.401 100,05645≤Age<60 0.252 0.000 0.434 100,05660≤Age 0.482 0.000 0.500 100,056Income band I 0.230 0.000 0.421 100,056Income band II 0.417 0.000 0.493 100,056Income band III 0.254 0.000 0.435 100,056Income band IV 0.099 0.000 0.298 100,056Individual risk preference:Very safe 0.068 0.000 0.252 100,056Safe 0.122 0.000 0.327 100,056Conservative 0.289 0.000 0.453 100,056Balanced 0.311 0.000 0.463 100,056Risky 0.120 0.000 0.325 100,056Highly risky 0.091 0.000 0.287 100,056Instruments:No of bank branches in the parish (in ln) 3.885 3.818 1.432 100,056No of psychotherapists in the parish (in ln) 1.153 1.099 0.889 100,056
B. Explained variables
Net monthly returns (in ln) 0.005 0.001 0.034 100,056Gross monthly returns (in ln) 0.006 0.002 0.034 100,056CAPM Alpha 0.006 0.004 0.008 100,056Four-factor Alpha 0.005 0.003 0.007 100,056Monthly portfolio turnover 0.031 0.000 0.098 100,056Number of trades (in ln) 0.193 0.000 0.432 100,056Share of domestic securities in equities 0.427 0.328 0.408 63,963Share of mutual funds in equities 0.540 0.642 0.436 63,963
29
Table 2: Who seeks Financial Advice?
Financial Advice Dummy Financial Advice Dummy(i) (ii)
Male -0.0293*** -0.0295***(0.00327) (0.00327)
Overcon�dence -0.0478*** -0.0470***(0.00652) (0.00652)
Investor Competence -0.101*** -0.0984***(0.00756) (0.00757)
Derivative Dummy -0.216*** -0.216***(0.0114) (0.0114)
Employee -0.00147 7.84e-05(0.00369) (0.00369)
Executive Employee 0.0829*** 0.0833***(0.00975) (0.00973)
Worker 0.105*** 0.102***(0.00800) (0.00800)
Housewife 0.0292*** 0.0303***(0.00576) (0.00576)
Student 0.151*** 0.152***(0.00951) (0.00951)
30≤Age<45 0.122*** 0.126***(0.00917) (0.00916)
45≤Age<60 0.170*** 0.173***(0.00949) (0.00949)
60≤Age 0.232*** 0.237***(0.00929) (0.00929)
Income Band II 0.00291 0.00257(0.00406) (0.00406)
Income Band III -0.00735 -0.00755(0.00466) (0.00466)
Income Band IV -0.0175*** -0.0167***(0.00641) (0.00641)
Safe 0.107*** 0.108***(0.00710) (0.00712)
Conservative 0.151*** 0.151***(0.00630) (0.00631)
Balanced -0.00729 -0.00599(0.00644) (0.00645)
Risky 0.0747*** 0.0757***(0.00731) (0.00732)
Very Risky 0.0219*** 0.0215***(0.00789) (0.00789)
Number of psychotherapists in the parish 0.00508**(0.00200)
Number of bank branches in the parish -0.0104***(0.00117)
Observations 100,056 100,056
Note. The table presents the probit estimates of consulting �nancial advisors. Marginal e�ects are reportedrather than original probit coe�cients. Heteroscedasticity-robust standard errors are reported in the paren-thesis. Three stars denote signi�cance at 1 percent or less; two stars denote signi�cance at 5 percent or less;one star denotes signi�cance at 10 percent or less.
30
Table 3: Financial Advice and Net Portfolio Returns
Net Monthly ReturnsOLS Estimates OLS Estimates Random E�ects Estimates IV Estimates
(i) (ii) (iii) (iv)
Financial Advice Intensity -0.00110*** -0.00110*** -0.00110*** -0.03222***(0.0003) (0.0003) (0.0003) (0.0122)
Male 0.00035 0.00036 0.00036 -0.00072(0.0002) (0.0002) (0.0002) (0.0005)
Overcon�dence -0.00619*** -0.00623*** -0.00623*** -0.00722***(0.0004) (0.0004) (0.0004) (0.0006)
Investor Competence 0.00197*** 0.00197*** 0.00197*** -0.00143(0.0006) (0.0006) (0.0006) (0.0015)
Derivative Dummy 0.00218*** 0.00226*** 0.00226*** -0.00232(0.0008) (0.0008) (0.0008) (0.0021)
Employee 0.00101*** 0.00104*** 0.00104*** 0.00043(0.0003) (0.0003) (0.0003) (0.0004)
Executive Employee 0.00022 0.00023 0.00023 0.00149*(0.0007) (0.0007) (0.0007) (0.0009)
Worker 0.00067 0.00072 0.00072 0.00356***(0.0006) (0.0006) (0.0006) (0.0013)
Housewife 0.00036 0.00035 0.00035 0.00138***(0.0003) (0.0003) (0.0003) (0.0005)
Student -0.00155** -0.00157** -0.00157** 0.00244(0.0007) (0.0007) (0.0007) (0.0017)
30≤Age<45 -0.00112 -0.00114 -0.00114 0.00202(0.0008) (0.0008) (0.0008) (0.0014)
45≤Age<60 -0.00176** -0.00177** -0.00177** 0.00300(0.0008) (0.0008) (0.0008) (0.0020)
60≤Age -0.00257*** -0.00260*** -0.00260*** 0.00361(0.0008) (0.0008) (0.0008) (0.0025)
Income Band II 0.00070** 0.00066** 0.00066** 0.00103***(0.0003) (0.0003) (0.0003) (0.0003)
Income Band III 0.00137*** 0.00131*** 0.00131*** 0.00115***(0.0003) (0.0003) (0.0003) (0.0003)
Income Band IV 0.00108** 0.00104** 0.00104** 0.00122***(0.0005) (0.0005) (0.0005) (0.0005)
Safe 0.00055 0.00059 0.00059 0.00433***(0.0004) (0.0004) (0.0004) (0.0015)
Conservative 0.00283*** 0.00289*** 0.00289*** 0.00786***(0.0004) (0.0004) (0.0004) (0.0020)
Balanced 0.00445*** 0.00453*** 0.00453*** 0.00383***(0.0004) (0.0004) (0.0004) (0.0005)
Growth 0.00702*** 0.00707*** 0.00707*** 0.00954***(0.0005) (0.0005) (0.0005) (0.0011)
Speculative 0.00813*** 0.00822*** 0.00822*** 0.00898***(0.0006) (0.0006) (0.0006) (0.0007)
Intercept 0.00219*** -0.01849*** -0.01849*** -0.00887**(0.0008) (0.0010) (0.0010) (0.0039)
Time Fixed E�ects No Yes Yes YesRegional Fixed E�ects No Yes Yes Yes
Hansen J-statistics 0.868p-value 0.3514F-test for excluded instruments 24.54Observations 100,056 100,056 100,056 100,056
Note. The table presents the regression estimates of portfolio returns. The dependent variable is net portfolio returns. Column(i) presents the pooled OLS estimates without time and region �xed e�ects; Column (ii) reports the pooled OLS estimates withtime and region �xed e�ects; Column (iii) reports random e�ects estimator that accounts for unobserved investor heterogeneity,and �nally, Column (iv) presents the second stage of TSLS estimates. Heteroscedasticity-robust standard errors are reportedin the parenthesis. Three stars denote signi�cance at 1 percent or less; two stars denote signi�cance at 5 percent or less; onestar denotes signi�cance at 10 percent or less.
31
Table 4: Financial Advice and Gross Portfolio Returns
Gross Monthly ReturnsOLS Estimates OLS Estimates Random E�ects Estimates IV Estimates
(i) (ii) (iii) (iv)
Financial Advice Intensity -0.00102*** -0.00103*** -0.00103*** -0.03606***(0.0003) (0.0003) (0.0003) (0.0122)
Male 0.00045* 0.00046* 0.00046* -0.00077(0.0003) (0.0003) (0.0003) (0.0005)
Overcon�dence -0.00486*** -0.00490*** -0.00490*** -0.00602***(0.0003) (0.0003) (0.0003) (0.0006)
Investor Competence 0.00196*** 0.00195*** 0.00195*** -0.00187(0.0006) (0.0006) (0.0006) (0.0015)
Derivative Dummy 0.00225*** 0.00234*** 0.00234*** -0.00283(0.0009) (0.0009) (0.0009) (0.0021)
Employee 0.00105*** 0.00108*** 0.00108*** 0.00040(0.0003) (0.0003) (0.0003) (0.0004)
Executive Employee -0.00002 -0.00001 -0.00001 0.00140(0.0007) (0.0007) (0.0007) (0.0009)
Worker 0.00069 0.00074 0.00074 0.00394***(0.0007) (0.0007) (0.0007) (0.0013)
Housewife 0.00015 0.00017 0.00017 0.00133**(0.0003) (0.0003) (0.0003) (0.0005)
Student -0.00176** -0.00178** -0.00178** 0.00274(0.0007) (0.0007) (0.0007) (0.0017)
30≤Age<45 -0.00106 -0.00109 -0.00109 0.00247*(0.0008) (0.0008) (0.0008) (0.0014)
45≤Age<60 -0.00207** -0.00208** -0.00208** 0.00329(0.0008) (0.0008) (0.0008) (0.0020)
60≤Age -0.00305*** -0.00307*** -0.00307*** 0.00391(0.0008) (0.0008) (0.0008) (0.0025)
Income Band II 0.00047 0.00043 0.00043 0.00084***(0.0003) (0.0003) (0.0003) (0.0003)
Income Band III 0.00103*** 0.00098*** 0.00098*** 0.00080**(0.0003) (0.0003) (0.0003) (0.0003)
Income Band IV 0.00064 0.00062 0.00062 0.00082*(0.0005) (0.0005) (0.0005) (0.0005)
Safe 0.00037 0.00042 0.00042 0.00463***(0.0003) (0.0003) (0.0003) (0.0015)
Conservative 0.00275*** 0.00282*** 0.00282*** 0.00841***(0.0003) (0.0003) (0.0003) (0.0020)
Balanced 0.00459*** 0.00468*** 0.00468*** 0.00389***(0.0003) (0.0003) (0.0003) (0.0005)
Growth 0.00720*** 0.00727*** 0.00727*** 0.01005***(0.0005) (0.0005) (0.0005) (0.0011)
Speculative 0.00880*** 0.00891*** 0.00891*** 0.00975***(0.0006) (0.0006) (0.0006) (0.0007)
Intercept 0.00388*** -0.01661*** -0.01661*** -0.00578(0.0008) (0.0011) (0.0011) (0.0039)
Time Fixed E�ects No Yes Yes YesRegional Fixed E�ects No Yes Yes Yes
Hansen J-statistics 1.518p-value 0.3514F-test for excluded instruments 23.980Observations 100,056 100,056 100,056 100,056
Note. The table presents the regression estimates of portfolio returns. The dependent variable is gross portfolio returns. Column(i) presents the pooled OLS estimates without time and region �xed e�ects; Column (ii) reports the pooled OLS estimates withtime and region �xed e�ects; Column (iii) reports random e�ects estimator that accounts for unobserved investor heterogeneity,and �nally, Column (iv) presents the second stage of TSLS estimates. Heteroscedasticity-robust standard errors are reportedin the parenthesis. Three stars denote signi�cance at 1 percent or less; two stars denote signi�cance at 5 percent or less; onestar denotes signi�cance at 10 percent or less.
32
Table 5: Financial Advice and Risk-Adjusted Returns
One-Factor Alpha Four-Factor AlphaOLS Estimates IV Estimates OLS Estimates IV Estimates
(i) (ii) (iii) (iv)
Financial Advice Intensity -0.00109*** -0.01410** -0.00098*** -0.01692**(0.0003) (0.0070) (0.0003) (0.0077)
Male 0.00051* 0.00002 0.00041 -0.00018(0.0003) (0.0004) (0.0003) (0.0005)
Overcon�dence -0.00552*** -0.00604*** -0.00434*** -0.00499***(0.0004) (0.0007) (0.0004) (0.0007)
Investor Competence 0.00200*** 0.00066 0.00140** -0.00026(0.0007) (0.0011) (0.0007) (0.0012)
Derivative Dummy 0.00234** 0.00021 0.00208** -0.00051(0.0010) (0.0015) (0.0008) (0.0015)
Employee 0.00115*** 0.00080* 0.00106*** 0.00066(0.0003) (0.0004) (0.0003) (0.0005)
Executive Employee 0.00008 0.00052 -0.00085 -0.00031(0.0008) (0.0010) (0.0007) (0.0011)
Worker 0.00083 0.00196* 0.00020 0.00162(0.0007) (0.0011) (0.0006) (0.0011)
Housewife 0.00034 0.00043 0.00007 0.00022(0.0004) (0.0005) (0.0004) (0.0006)
Student -0.00157* -0.00007 -0.00157** 0.00027(0.0008) (0.0014) (0.0008) (0.0014)
30≤Age<45 -0.00095 0.00047 -0.00105 0.00067(0.0009) (0.0012) (0.0008) (0.0013)
45≤Age<60 -0.00204** 0.00005 -0.00209** 0.00044(0.0009) (0.0015) (0.0009) (0.0016)
60≤Age -0.00311*** -0.00049 -0.00279*** 0.00041(0.0009) (0.0017) (0.0008) (0.0018)
Income Band II 0.00046 0.00061 0.00037 0.00053(0.0003) (0.0004) (0.0003) (0.0004)
Income Band III 0.00119*** 0.00108** 0.00115*** 0.00100**(0.0003) (0.0005) (0.0003) (0.0005)
Income Band IV 0.00077 0.00058 0.00079* 0.00060(0.0005) (0.0006) (0.0005) (0.0006)
Safe 0.00023 0.00165 0.00036 0.00212*(0.0004) (0.0011) (0.0004) (0.0012)
Conservative 0.00299*** 0.00491*** 0.00239*** 0.00479***(0.0004) (0.0012) (0.0004) (0.0014)
Balanced 0.00508*** 0.00455*** 0.00430*** 0.00371***(0.0004) (0.0006) (0.0004) (0.0007)
Growth 0.00805*** 0.00898*** 0.00652*** 0.00770***(0.0006) (0.0010) (0.0005) (0.0010)
Speculative 0.00975*** 0.00985*** 0.00794*** 0.00811***(0.0007) (0.0009) (0.0006) (0.0009)
Intercept 0.00393*** 0.00782*** 0.00298*** 0.00766***(0.0010) (0.0023) (0.0009) (0.0025)
Time Fixed E�ects No No No NoRegional Fixed E�ects Yes Yes Yes Yes
Hansen J-statistics 0.388 0.166p-value 0.533 0.6834F-test for excluded instruments 4.484 4.368Observations 3,032 3,032 3,032 3,032
Note. The table presents the regression estimates of risk-adjusted returns. In Column (i) and (ii), the dependent variable isone-factor alpha and it is four-factor alpha in Column (iii) and (iv). Column (i) and (iii) present the pooled OLS estimates withtime and region �xed e�ects and Column (ii) and (iv) report the second stage of TSLS estimates. Heteroscedasticity-robuststandard errors are reported in the parenthesis. Three stars denote signi�cance at 1 percent or less; two stars denote signi�canceat 5 percent or less; one star denotes signi�cance at 10 percent or less.
33
Table 6: Financial Advice and Risk-Adjusted Returns: Investors by Incentives
Four-Factor AlphaOLS Estimates IV Estimates
Low-Incentive High-Incentive Low-Incentive High-Incentive(i) (ii) (iii) (iv)
Financial Advice Intensity -0.00045 -0.00121*** -0.01329 -0.01852**(0.0004) (0.0004) (0.0104) (0.0080)
Male 0.00008 0.00070* -0.00044 0.00021(0.0003) (0.0004) (0.0006) (0.0006)
Overcon�dence -0.00311*** -0.00560*** -0.00185 -0.00808***(0.0004) (0.0006) (0.0013) (0.0015)
Investor Competence 0.00222*** 0.00100 0.00131 -0.00220(0.0007) (0.0014) (0.0011) (0.0023)
Derivative Dummy 0.00140 0.00302** -0.00060 0.00046(0.0010) (0.0013) (0.0019) (0.0018)
Employee 0.00073** 0.00137*** 0.00022 0.00128**(0.0003) (0.0004) (0.0007) (0.0006)
Executive Employee -0.00106 -0.00056 -0.00211 0.00129(0.0007) (0.0011) (0.0015) (0.0017)
Worker -0.00025 0.00033 0.00134 0.00174(0.0007) (0.0010) (0.0017) (0.0015)
Housewife -0.00018 0.00046 0.00002 -0.00001(0.0004) (0.0006) (0.0006) (0.0012)
Student -0.00020 -0.00134 0.00109 0.00033(0.0009) (0.0011) (0.0018) (0.0016)
30≤Age<45 0.00065 -0.00112 0.00113 0.00108(0.0010) (0.0011) (0.0016) (0.0016)
45≤Age<60 -0.00040 -0.00166 0.00069 0.00100(0.0011) (0.0011) (0.0019) (0.0018)
60≤Age -0.00006 -0.00292*** 0.00120 0.00050(0.0010) (0.0011) (0.0019) (0.0020)
Income Band II -0.00007 0.00011 0.00042 0.00066(0.0003) (0.0006) (0.0006) (0.0009)
Income Band III 0.00083** 0.00071 0.00100* 0.00100(0.0004) (0.0006) (0.0005) (0.0009)
Income Band IV 0.00112* -0.00021 0.00076 0.00061(0.0006) (0.0008) (0.0009) (0.0011)
Safe -0.00067 0.00114** 0.00093 0.00279**(0.0005) (0.0005) (0.0017) (0.0014)
Conservative 0.00096** 0.00391*** 0.00288 0.00608***(0.0005) (0.0005) (0.0018) (0.0015)
Balanced 0.00245*** 0.00586*** 0.00262*** 0.00412***(0.0005) (0.0005) (0.0009) (0.0012)
Growth 0.00514*** 0.00803*** 0.00623*** 0.00868***(0.0007) (0.0007) (0.0014) (0.0012)
Speculative 0.00656*** 0.00913*** 0.00641*** 0.00914***(0.0008) (0.0008) (0.0010) (0.0013)
Intercept 0.00175 0.00221* 0.00656 0.00697***(0.0012) (0.0012) (0.0043) (0.0026)
Time Fixed E�ects No No No NoRegional Fixed E�ects Yes Yes Yes Yes
Hansen J-statistics 0.388 0.166p-value 0.533 0.6834F-test for excluded instruments 4.484 4.368Observations 1,516 1,516 1,516 1,516
Note. The table presents the regression estimates of risk-adjusted returns across di�erent investor groups. The dependentvariable is four-factor alpha. Column (i) and (iii) present the pooled OLS estimates with time and region �xed e�ects andColumn (ii) and (iv) report the second stage of TSLS estimates. Heteroscedasticity-robust standard errors are reported in theparenthesis. Three stars denote signi�cance at 1 percent or less; two stars denote signi�cance at 5 percent or less; one stardenotes signi�cance at 10 percent or less.
34
Table7:
FinancialAdviceandPortfolioDiversi�cation
ShareofMutualFundsin
Equities
ShareofDomesticStocksin
Equities
OLSEstimates
Random
E�ects
Estimates
IV
Estimates
OLSEstimates
Random
E�ects
Estimates
IV
Estimates
(i)
(ii)
(iii)
(iv)
(v)
(vi)
FinancialAdviceIntensity
0.24840***
0.26184***
1.02912***
-0.18603***
-0.18824***
-0.49882***
(0.0211)
(0.0206)
(0.1026)
(0.0211)
(0.0209)
(0.0843)
Male
-0.03791**
-0.03619**
-0.01458***
0.03556**
0.03320**
0.02621***
(0.0172)
(0.0170)
(0.0053)
(0.0168)
(0.0167)
(0.0043)
Overcon�dence
-0.03684
-0.00445
-0.05078***
-0.02794
-0.00703
-0.02236
(0.0544)
(0.0511)
(0.0146)
(0.0582)
(0.0520)
(0.0137)
InvestorCompetence
-0.17365***
-0.19421***
-0.08503***
0.05524**
0.07135***
0.01974*
(0.0239)
(0.0238)
(0.0133)
(0.0259)
(0.0259)
(0.0112)
DerivativeDummy
-0.18478***
-0.19389***
-0.06318***
0.05323
0.06397*
0.00451
(0.0306)
(0.0307)
(0.0181)
(0.0348)
(0.0349)
(0.0150)
Employee
-0.04831**
-0.04724**
-0.04449***
0.03558*
0.02800
0.03405***
(0.0206)
(0.0201)
(0.0050)
(0.0202)
(0.0200)
(0.0041)
ExecutiveEmployee
-0.02530
-0.02834
-0.08427***
-0.05407
-0.04552
-0.03045***
(0.0457)
(0.0464)
(0.0138)
(0.0426)
(0.0434)
(0.0107)
Worker
-0.04932
-0.03886
-0.13763***
0.02610
0.01266
0.06148***
(0.0582)
(0.0558)
(0.0170)
(0.0546)
(0.0539)
(0.0145)
Housewife
-0.04885
-0.04346
-0.07853***
0.00748
0.00200
0.01938***
(0.0336)
(0.0327)
(0.0092)
(0.0334)
(0.0322)
(0.0073)
Student
0.06787
0.05786
-0.08919***
-0.02163
-0.01592
0.04129**
(0.0533)
(0.0514)
(0.0236)
(0.0540)
(0.0519)
(0.0195)
30≤Age<45
-0.04156
-0.03434
-0.11707***
0.04603
0.04446
0.07628***
(0.0461)
(0.0457)
(0.0145)
(0.0492)
(0.0486)
(0.0122)
45≤Age<60
0.01130
0.01943
-0.12494***
0.00404
-0.00176
0.05862***
(0.0478)
(0.0474)
(0.0209)
(0.0499)
(0.0493)
(0.0175)
60≤Age
0.01777
0.02678
-0.14983***
0.02077
0.00812
0.08792***
(0.0485)
(0.0481)
(0.0244)
(0.0510)
(0.0506)
(0.0203)
IncomeBandII
-0.02958
-0.03128
-0.06046***
0.03642
0.03057
0.04879***
(0.0241)
(0.0234)
(0.0071)
(0.0231)
(0.0230)
(0.0058)
IncomeBandIII
-0.07472***
-0.07946***
-0.09586***
0.06952***
0.06829***
0.07799***
(0.0265)
(0.0259)
(0.0069)
(0.0257)
(0.0255)
(0.0056)
IncomeBandIV
-0.12702***
-0.11127***
-0.15155***
0.10983***
0.08900***
0.11966***
(0.0344)
(0.0339)
(0.0086)
(0.0349)
(0.0343)
(0.0072)
Safe
-0.01421
0.00560
-0.09374***
-0.00273
-0.03581
0.02913*
(0.0631)
(0.0576)
(0.0194)
(0.0634)
(0.0596)
(0.0165)
Conservative
-0.09330*
-0.09818*
-0.18297***
0.06693
0.06340
0.10286***
(0.0525)
(0.0505)
(0.0176)
(0.0552)
(0.0539)
(0.0154)
Balanced
-0.11942**
-0.13310***
-0.09729***
0.07789
0.07543
0.06902***
(0.0525)
(0.0508)
(0.0130)
(0.0545)
(0.0540)
(0.0122)
Growth
-0.16164***
-0.16670***
-0.19146***
0.11451**
0.10529*
0.12646***
(0.0554)
(0.0538)
(0.0139)
(0.0571)
(0.0568)
(0.0127)
Speculative
-0.27684***
-0.26989***
-0.26072***
0.16305***
0.15334***
0.15659***
(0.0561)
(0.0541)
(0.0136)
(0.0597)
(0.0585)
(0.0126)
IMR
-0.13770***
-0.03874***
-0.08040***
0.09201***
0.02608***
0.06905***
(0.0224)
(0.0072)
(0.0138)
(0.0218)
(0.0092)
(0.0119)
Intercept
0.74861***
0.75936***
0.46964***
0.30621***
0.31196***
0.41593***
(0.0714)
(0.0698)
(0.0408)
(0.0788)
(0.0775)
(0.0344)
Tim
eFixedE�ects
Yes
Yes
Yes
Yes
Yes
Yes
RegionalFixedE�ects
Yes
Yes
Yes
Yes
Yes
Yes
HansenJ-statistics
1.537
2.764
p-value
0.2151
0.1
F-test
forexcludedinstruments
71.95
71.95
Observations
61,936
61,936
61,936
61,936
61,936
61,936
Note.Thetablepresents
theregressionestim
atesofportfoliodiversi�cation.In
the�rstthreecolumns,thedependentvariableistheshare
ofmutualfundsin
riskyassets.Thedependentvariablein
thelast
three
columnsistheshare
ofdomestic
securitiesin
riskyassets.Column(i)and(iv)presentthepooledOLSestim
ateswithtimeandregion�xede�ects;Column(ii)and(v)report
therandom
e�ects
estim
atorthat
accounts
forunobservedinvestorheterogeneity,andColumn(iii)and(vi)presentthesecondstageofTSLSestim
ates.
Heteroscedasticity-robust
standard
errors
are
reportedin
theparenthesis.
Threestars
denote
signi�canceat1percentorless;twostars
denote
signi�canceat5percentorless;onestardenotessigni�canceat10percentorless.
35
Table8:
FinancialAdviceandTradingBehavior
NumberofTrades
PortfolioTurnover
OLSEstimates
Random
E�ects
Estimates
IV
Estimates
OLSEstimates
Random
E�ects
Estimates
IV
Estimates
(i)
(ii)
(iii)
(iv)
(v)
(vi)
FinancialAdviceIntensity
-0.02034**
-0.02034**
-0.37148**
-0.00188
-0.00188
-0.04350
(0.0082)
(0.0082)
(0.1557)
(0.0011)
(0.0011)
(0.0359)
Male
-0.00752
-0.00752
-0.01979***
0.00040
0.00040
-0.00106
(0.0069)
(0.0069)
(0.0062)
(0.0009)
(0.0009)
(0.0014)
Overcon�dence
0.10115***
0.10115***
0.08994***
0.03573***
0.03573***
0.03441***
(0.0167)
(0.0167)
(0.0080)
(0.0025)
(0.0025)
(0.0023)
InvestorCompetence
0.28093***
0.28093***
0.24258***
0.02856***
0.02856***
0.02401***
(0.0296)
(0.0296)
(0.0195)
(0.0043)
(0.0043)
(0.0043)
DerivativeDummy
0.21642***
0.21642***
0.16466***
0.01045*
0.01045*
0.00432
(0.0427)
(0.0427)
(0.0265)
(0.0058)
(0.0058)
(0.0057)
Employee
-0.00280
-0.00280
-0.00964**
-0.00065
-0.00065
-0.00146
(0.0088)
(0.0088)
(0.0046)
(0.0011)
(0.0011)
(0.0010)
ExecutiveEmployee
0.01905
0.01905
0.03316***
-0.00007
-0.00007
0.00160
(0.0292)
(0.0292)
(0.0122)
(0.0029)
(0.0029)
(0.0025)
Worker
-0.03929**
-0.03929**
-0.00727
0.00075
0.00075
0.00455
(0.0162)
(0.0162)
(0.0157)
(0.0029)
(0.0029)
(0.0037)
Housewife
0.01571
0.01571
0.02734***
-0.00093
-0.00093
0.00044
(0.0144)
(0.0144)
(0.0075)
(0.0018)
(0.0018)
(0.0016)
Student
-0.01945
-0.01945
0.02585
-0.00517*
-0.00517*
0.00019
(0.0216)
(0.0216)
(0.0217)
(0.0027)
(0.0027)
(0.0050)
30≤Age<45
-0.01831
-0.01831
0.01736
-0.00709***
-0.00709***
-0.00286
(0.0213)
(0.0213)
(0.0175)
(0.0027)
(0.0027)
(0.0041)
45≤Age<60
0.00974
0.00974
0.06356**
-0.00563*
-0.00563*
0.00075
(0.0217)
(0.0217)
(0.0251)
(0.0029)
(0.0029)
(0.0058)
60≤Age
0.03203
0.03203
0.10200***
-0.00773***
-0.00773***
0.00056
(0.0215)
(0.0215)
(0.0318)
(0.0027)
(0.0027)
(0.0073)
IncomeBandII
0.00464
0.00464
0.00877**
0.00079
0.00079
0.00128
(0.0082)
(0.0082)
(0.0039)
(0.0012)
(0.0012)
(0.0009)
IncomeBandIII
0.03422***
0.03422***
0.03241***
0.00187
0.00187
0.00166*
(0.0096)
(0.0096)
(0.0043)
(0.0013)
(0.0013)
(0.0010)
IncomeBandIV
0.08823***
0.08823***
0.09026***
0.00716***
0.00716***
0.00740***
(0.0183)
(0.0183)
(0.0065)
(0.0023)
(0.0023)
(0.0014)
Safe
-0.01603
-0.01603
0.02621
-0.00138
-0.00138
0.00363
(0.0123)
(0.0123)
(0.0195)
(0.0018)
(0.0018)
(0.0045)
Conservative
0.03723***
0.03723***
0.09334***
0.00224
0.00224
0.00889
(0.0121)
(0.0121)
(0.0254)
(0.0017)
(0.0017)
(0.0059)
Balanced
0.04355***
0.04355***
0.03562***
0.00325*
0.00325*
0.00231
(0.0129)
(0.0129)
(0.0062)
(0.0018)
(0.0018)
(0.0015)
Growth
0.09106***
0.09106***
0.11892***
0.00571**
0.00571**
0.00902***
(0.0169)
(0.0169)
(0.0138)
(0.0022)
(0.0022)
(0.0032)
Speculative
0.11531***
0.11531***
0.12380***
0.01536***
0.01536***
0.01637***
(0.0206)
(0.0206)
(0.0080)
(0.0035)
(0.0035)
(0.0019)
Intercept
0.06107**
0.06107**
0.16964***
0.02965***
0.02965***
0.04252***
(0.0256)
(0.0256)
(0.0499)
(0.0038)
(0.0038)
(0.0115)
Tim
eFixedE�ects
Yes
Yes
Yes
Yes
Yes
Yes
RegionalFixedE�ects
Yes
Yes
Yes
Yes
Yes
Yes
HansenJ-statistics
1.537
2.764
p-value
0.2151
0.1
F-test
forexcludedinstruments
24.540
24.540
Observations
100,056
100,056
100,056
100,056
100,056
100,056
Note.Thetable
presents
theregressionestim
atesofportfoliodiversi�cation.In
the�rstthreecolumns,
thedependentvariable
isthenumberoftradesin
logs.
Thedependentvariable
inthelast
threecolumns
isthemonthly
turnover.
Column(i)and(iv)presentthepooledOLSestim
ateswithtimeandregion�xede�ects;Column(ii)and(v)report
therandom
e�ects
estim
atorthataccounts
forunobservedinvestor
heterogeneity,andColumn(iii)and(vi)presentthesecondstageofTSLSestim
ates.
Heteroscedasticity-robust
standard
errors
are
reportedin
theparenthesis.
Threestars
denote
signi�canceat1percentorless;two
stars
denote
signi�canceat5percentorless;onestardenotessigni�canceat10percentorless.
36
Table 9: Financial Advice and Portfolio Rebalancing
SellR, BuyRF BuyR, SellRF SellR, BuyRF BuyR, SellRF
Bivariate Probit Estimates Endogeneity-corrected Bivariate Probit
(i) (ii) (iii) (iv)
Financial Advice Intensity -0.00373*** -0.00346*** -0.0408*** -0.0740***(0.000749) (0.000969) (0.0131) (0.0182)
Male -0.000710 -0.00114 -0.00186** -0.00334***(0.000594) (0.000783) (0.000739) (0.00101)
Overcon�dence 0.00849*** 0.0140*** 0.0103*** 0.0172***(0.00302) (0.00395) (0.00339) (0.00443)
Investor Competence 0.0167*** 0.0223*** 0.00745** 0.00652*(0.00242) (0.00325) (0.00319) (0.00347)
Derivative Dummy 0.00392* 0.0100*** -0.00221 -0.00297(0.00224) (0.00359) (0.00205) (0.00272)
Employee -0.000869 -0.00147* -0.00122* -0.00216**(0.000689) (0.000892) (0.000704) (0.000909)
Executive Employee -7.62e-06 -0.000487 0.00326 0.00572(0.00143) (0.00220) (0.00244) (0.00363)
Worker -0.00380*** -0.00484*** -0.000529 0.00259(0.00108) (0.00178) (0.00237) (0.00419)
Housewife 0.000248 0.000614 0.00180 0.00374*(0.00114) (0.00150) (0.00144) (0.00200)
Student -0.00232* -0.00130 0.00496 0.0164*(0.00137) (0.00227) (0.00425) (0.00853)
30≤Age<45 3.82e-05 -0.000691 0.00436* 0.00736*(0.00159) (0.00250) (0.00255) (0.00434)
45≤Age<60 0.00103 0.000459 0.00889** 0.0153**(0.00165) (0.00252) (0.00394) (0.00651)
60≤Age 0.00281* 0.00353 0.0116*** 0.0203***(0.00164) (0.00260) (0.00380) (0.00612)
Income Band II 0.000737 0.000245 0.00210** 0.00272**(0.000896) (0.00104) (0.00103) (0.00119)
Income Band III 0.00264*** 0.00436*** 0.00335*** 0.00568***(0.000996) (0.00126) (0.00106) (0.00133)
Income Band IV 0.00562*** 0.0101*** 0.00685*** 0.0127***(0.00152) (0.00220) (0.00172) (0.00255)
Safe 2.09e-05 0.00316 0.00556 0.0169**(0.00228) (0.00311) (0.00438) (0.00714)
Conservative 0.00318 0.00765*** 0.0102** 0.0226***(0.00215) (0.00262) (0.00404) (0.00626)
Balanced 0.00427* 0.00776*** 0.00390* 0.00699***(0.00225) (0.00253) (0.00226) (0.00247)
Growth 0.00724** 0.0108*** 0.0111*** 0.0182***(0.00320) (0.00359) (0.00414) (0.00513)
Speculative 0.00684** 0.0116*** 0.00696** 0.0117***(0.00339) (0.00409) (0.00346) (0.00416)
Time Fixed E�ects Yes Yes Yes YesRegional Fixed E�ects Yes Yes Yes Yes
Observations 68,251 68,251 68,251 68,251
Note. The table presents the regression estimates of portfolio inertia. The table reports the marginal e�ects.Column (i) and (iii) present the joint probability of selling risky asset and buying risk-free assets; Column(ii) and (iv) report the joint probability of buying risky assets and selling risk-free assets. Heteroscedasticity-robust standard errors are reported in the parenthesis. Three stars denote signi�cance at 1 percent or less;two stars denote signi�cance at 5 percent or less; one star denotes signi�cance at 10 percent or less.
37
Figure 1: Asset Allocation, Market Timing, and Financial Advice
Note: This �gure presents the value of 1 euro invested in the beginning of observation period and rebalancedregularly based on the aggregate asset weights of advised and self-directed customers. In order to isolate thepossible security selection e�ects, I employ benchmark indices for the considered three asset classes. I useMSCI World index, Barclays European Aggregate Bond index, and Euribor rates benchmark indexes forequity, bond and cash investments, respectively.
38
A Appendix
Representativeness of the Sample
Table A.1: Portfolio Composition
Mean Standard Deviation No of Obs
Share of direct stocks 0.167 0.303 100,056Share of stock mutual funds 0.163 0.267 100,056Share of single bonds 0.091 0.218 100,056Share of bond mutual funds 0.120 0.231 100,056Share of mixed mutual funds 0.007 0.053 100,056Share of real estate funds 0.227 0.320 100,056Share of cash 0.086 0.229 100,056Share of derivatives 0.000 0.009 100,056Share of retail derivatives 0.048 0.142 100,056Share of other securities 0.039 0.133 100,056
To examine the representativeness of my sample, I compare the portfolio holdings of in-
vestors in my sample with the portfolio of an average German investor with an investment
account as reported by the German Federal Statistics O�ce. The mean single stocks share
(0.167) and stock mutual fund share (0.163) in my sample are almost identical to the portfolio
holdings of an average German investor who invests 18 percent in single stocks and 16 percent
in stock mutual funds, respectively. Similarly, the weight of risk free assets (i.e., cash, bonds)
in my sample accounts for 29.7 percent, whereas it equals 36 percent in the average German in-
vestor's portfolio. In summary, investors in my sample closely resemble the German individual
investor population along many important dimensions.
39
TableA.2:DataAppendix
Variable
De�nitionandsource
Financialadviceintensity
Ratioofadvised
trades
toalltrades
madebyaninvestor
Investorovercon�dence
Dummyvariablesetto
1forinvestors
whofallin
thehighestportfolio
turnover
quartileandthelowestrisk-adjusted
perform
ance
quartile(G
oetzm
annandKumar,2008)
DerivativeDummy
Dummyvariablesetto
1forinvestors
whoheldderivativeproductsduringthesampleperiod
Investorcompetence
Dummyvariablesetto
1forinvestors
whorate
their�nancialknow
ledgewith
`verygood'(6)onascalewith6possiblechoices
Male
Dummyvariableforbeingamale
Retiree
Dummyvariableforbeingaretiree
Housewife
Dummyvariableforbeingahousewife
Student
Dummyvariableforbeingastudent
Employee
Dummyvariableforbeinganem
ployee
Executiveem
ployee
Dummyvariableforbeinganexecutiveem
ployee
Blue-collarworker
Dummyvariableforbeingablue-collarworker
IncomebandI
Dummyvariableforhavingamonthly
laborincomebelow
1875Euro
IncomebandII
Dummyvariableforhavingamonthly
laborincomebetween1875and2625Euro
IncomebandIII
Dummyvariableforhavingamonthly
laborincomebetween2625and3875Euro
IncomebandIV
Dummyvariableforhavingamonthly
laborincomeabove3875Euro
Verysafe
Dummyvariableforself-reported
risk
preference
"verysafe"
Safe
Dummyvariableforself-reported
risk
preference
"safe"
Conservative
Dummyvariableforself-reported
risk
preference
"conservative"
Balanced
Dummyvariableforself-reported
risk
preference
"balanced"
Risky
Dummyvariableforself-reported
risk
preference
"risky"
Highly
risky
Dummyvariableforself-reported
risk
preference
"highly
risky"
Number
ofbankbranches
intheparish
Number
ofallbankbranches
inagiven
5-digitzipcode(Source:
Acommercialdata
provider)
Number
ofpsychotherpistsin
theparish
Number
ofpsychotherapistsin
agiven
5-digitzipcode(Source:
GermanPsychotherapistsAssociationWeb
Site)
Net
monthly
portfolioreturns
Monthly
portfolioreturnsascomputedbythemodi�ed
Dietz
measure
net
oftransactioncosts
Gross
monthly
portfolioreturns
Monthly
portfolioreturnsascomputedbythemodi�ed
Dietz
measure
before
transactioncosts
CAPM
Alpha
Portfolioalphawithrespectto
theCAPM
Four-factoralpha
Portfolioalphawithrespectto
thefour-factormodelthatincludes
FamaandFrench
(1993)andtheCarhart(1997)factor
Monthly
portfolioturnover
Thesum
ofone-halfthemontlypurchase
turnover
andone-halfthemonthly
salesturnover
(Barber
andOdean,2001)
Number
oftrades
Monthly
number
ofallbuyandsalestrades
madebyaninvestor
Share
ofdomesticsecurities
inequities
RatioofGermanstocksandstock
mutualfundsto
totalstocks
Share
ofmutualfundsin
equities
Ratioofmutualfundsto
totalstocks
40
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