The Inﬂuence of Investors’ Jobs on Portfolios:
Is there a Job Industry Bias?
This version: February 27, 2008
(working paper - comments welcome)
The major component of most investors’ wealth is the discounted value of
(non-tradeable) future labor income and therefore an important background
risk which should be adequately hedged by ﬁnancial portfolio risk.
We test this hypothesis empirically by using a unique dataset of 30,000 private
investors of whom we know their profession as well as their detailed portfolio
composition. Instead of lower ratios we ﬁnd that investors hold signiﬁcantly
higher ratios of equity in their job industry - a job industry bias. Investors tend
not only to ignore but to aggravate background risk in their overall wealth by
holding biased ﬁnancial portfolios.
Rational and ﬁnancially sophisticated investors better hedge their labor in-
come risk. In contrast, a short event study shows that unsophisticated in-
vestors can not expect to reduce their job industry bias by taking ﬁnancial
JEL classiﬁcation: D12, D10, G29
Keywords: Background Risk, Labor Income, Household Portfolios, Asset Allocation, Be-
E-Finance Lab & Johann Wolfgang Goethe-University, Chair of Banking and Finance,
Mertonstr. 17, 60054 Frankfurt am Main. E-Mail: firstname.lastname@example.org,
phone +49.162.2658081, fax +49.69.798.28585.
The Inﬂuence of Investors’ Jobs on Portfolios 2
The largest component and thus the largest background risk in most households’ wealth is
the discounted value of future labor income i.e. non-tradeable human capital. Theoretical
models and empirical evidence suggest that dividends on human capital (labor income)
and dividends on stocks are correlated or at least cointegrated so it is of great impor-
tance for investors to adequately hedge this background risk when choosing the ﬁnancial
portfolio to reduce risk exposure of overall wealth. Rational investors following ﬁnancial
theory should hold a lower portion of their ﬁnancial portfolio in their job industry than
peers working in other industries.
Using a dataset of almost 30,000 individual investors’ disaggregated portfolios with a
large German direct bank we are able to check empirically whether private investors act
according to ﬁnancial theory in respect of hedging labor income risk. We construct ﬁve
industries (Financial Services, IT, Health Care, Technology and Aviation) and assign per-
sons’ professions as well as securities resulting in a ﬁnal sample of 4,395 private investors
working in one of these industries. Then we compute the ﬁve industry ratios in each
investor’s equity portfolio. Instead of lower holdings - as one would expect from ﬁnancial
theory - investors exhibit much higher holdings of equity in their respective job industry
across all ﬁve industries. This bias which we shall call job industry bias is robust against
several variations and indicates that most investors not only ignore background risk in
their ﬁnancial portfolios but even aggravate it.
Further analysis shows that ﬁnancial sophistication and rationality signiﬁcantly reduce
the bias. This supports the assumption that investors have no rational motif to hold high
ratios of their job industry in ﬁnancial portfolios but simply act irrationally and deviate
from ﬁnancial theory - a common behavior that Campbell (2006) calls investment mis-
takes. Thus a job industry bias is very much in line with various ﬁndings in the behavioral
ﬁnance literature like home bias, overtrading, the disposition eﬀect and many more.
Having found that ﬁnancial sophistication reduces a job industry bias and thus sophisti-
cated investors better hedge their background risk, it is interesting to see if unsophisti-
cated investors can improve their portfolio by taking ﬁnancial advice. Regression analyses
indeed show that advised customers exhibit a lower industry bias than non-advised cus-
tomers. However a small event study among the sub-sample of investors that switched
from acting self-directed to advised enables us to isolate the eﬀect of advisors on portfo-
lios. It turns out that advised investors better hedge their background risk on their own
and not because of the inﬂuence of an advisor: The causality seems to be that better
hedged investors also take ﬁnancial advice and not that ﬁnancial advice leads to better
hedged background risks. So unsophisticated investors can not expect to improve their
The Inﬂuence of Investors’ Jobs on Portfolios 3
portfolios in regard to a better hedge of background risk by taking ﬁnancial advice.
To our knowledge this is the ﬁrst empirical study - at least for German customers - that
concentrates on job industries to analyze whether private investors consider their back-
ground risk from labor income when choosing the ﬁnancial portfolio. We contribute to the
literature on background risk and portfolio choice in providing another dimension where
investors signiﬁcantly deviate from ﬁnancial theory and make an investment mistake: Not
only ignorance but aggravation of background risks by their ﬁnancial portfolio choice.
The rest of the paper is organized as follows: Section 2 reviews related work. Section 3
describes the empirical evidence for a job industry bias as well as its quantiﬁcation and
determinants. Section 4 analyzes the impact of ﬁnancial advice on hedging labor income,
section 5 concludes.
2 Theory and literature review
2.1 Human capital and portfolio choice
For most households, (non-tradeable) human capital is the largest component of wealth
as Campbell (2006) states. So it is not surprising that Cocco et al. (2005) showed that
ignoring the eﬀects of background risk in portfolio composition generates large utility
costs for investors.
Literature on background risk like Heaton and Lucas (2000b) mostly identiﬁes three ma-
jor components of background risk: (1) Most studies (see e.g. Heaton and Lucas (1997) or
Campbell and Viceira (2002)) focus on labor income as the primary source of background
risk. (2) The eﬀects of housing on portfolio choice is still debated in the literature since
it can be seen as non-tradeable asset and therefore as background risk (see e.g. Cocco
(2005)) but also as hedge against renting cost risks as e.g. Sinai and Souleles (2005) show.
Since it is not yet clear which eﬀect housing has on background risk we exclude housing
aspects here. (3) The last major component of background risk is entrepreneurial income.
Heaton and Lucas (2000a) found empirically that it leads to lower stock holdings as these
investors realize their background risk and adjust their ﬁnancial portfolios to hedge their
entrepreneurial income. To further focus on the eﬀects of industry-speciﬁc labor income,
we exclude entrepreneurs from the sample so that labor income remains as the main com-
ponent of background risk in our analysis.
Depending on the type of labor income it can be seen as rather riskless (e.g. for civil
servants) or risky (e.g. artists) asset and there is still debate in the literature on the risk
The Inﬂuence of Investors’ Jobs on Portfolios 4
properties (Campbell (2006)). Depending on the riskiness of an investor’s labor income
it can be seen as (risky) stock or (low risk) bond investment paying dividends on human
wealth. Campbell and Viceira (2002) show that investors should adjust their ﬁnancial
portfolio to compensate for implicitly holding human wealth and conclude that human
capital can crowd out certain assets. This eﬀect is stronger for young investors as their
total wealth consists to larger parts of human capital and therefore should have stronger
impact on ﬁnancial portfolio choice.
Labor income risk and stock market risk are likely to be correlated since both are inﬂu-
enced by the same business cycle conditions and - in this speciﬁc analysis - furthermore
subject to the same industry business conditions. Unfortunately, empirical studies on
these correlations are very rare. Analyzing U.S. data from 1965 to 1994, Davis and
Willen (2000) ﬁnd both small negative as well as positive correlations between wages and
security returns in speciﬁc industries.
According to Campbell et al. (1999), these correlations increase if labor income is used as
a 1-year lagged variable so that the authors assume a stable correlation between market
returns and human capital returns in their model. In a very recent article, Benzoni et al.
(2007) looked at various research evidence on the correlation of stock market and human
capital returns. They prove empirically that the two markets are cointegrated and show
mathematically that the assumption of cointegration allows for much higher correlation
between the two markets.
Hence we can assume that labor income and stock market returns are correlated and
follow that this correlation should be even stronger if focusing on a speciﬁc industry.
2.2 Related research on job industry weights in portfolios
Related research concentrating on job industries as well as portfolio industries is very rare
which is why we try to ﬁll this gap by providing empirical evidence of private investor
behavior in this ﬁeld.
In a recent study on Swedish households, Massa and Simonov (2006) ﬁnd that investors
tilt their portfolios (relative to the Swedish stock market index) to stocks that are familiar
to them. They deﬁne familiarity as stocks with geographical and professional proximity
to the investors and stocks that have been held for a long time. So a partial result of their
analysis is that professional proximity leads to overweighting in the respective stock indus-
tries. The study is mainly focused on entrepreneurial income and geographical proximity
and the authors ﬁnd only weak evidence of a professional proximity eﬀect. Nevertheless
their ﬁndings indicate that investors do not hedge their background risk in ﬁnancial port-
folios despite theory tells us that this would decrease overall portfolio risk exposure.
The Inﬂuence of Investors’ Jobs on Portfolios 5
3 Empirical analysis
3.1 Data description
We use a unique data set of nearly 30,000 customers of a large German direct bank. The
portfolios of individual customers are available at security level dated February 2007 which
allows for a very detailed and up-to-date analysis. Table 10 shows some demographics
indicating that investors in the sample are a bit younger, more male and have higher risk
tolerance than other retail banking customers in previous German studies (see Bluethgen
et al. (2007) for example). However our sample consists of direct bank customers so that
these deviations are plausible. Furthermore all investor characteristics in our sample are
very much consistent with investors in other studies on German direct banking customers
like Glaser (2003). So our sample is not exactly representative for the typical German
retail banking customer but very much for the typical German online banking customer.
Table 10 also includes some portfolio characteristics: Dummy variables indicate if a per-
son is classiﬁed as heavy trader by the bank’s data warehouse (indicating high trade
frequency) or Eurex trader which shows that the investor is allowed to trade futures,
options and other derivatives. Deposit and cash values are computed as means over the
last six months to account for short-term ﬂuctuation. Average return p.a. and Sharpe
Ratio are computed over the last 20 months. Number of securities is a simple proxy
for na¨ diversiﬁcation following Benartzi and Thaler (2001) and Mitton and Vorkink
(2007). Other measures for diversiﬁcation are the Herﬁndahl-Hirschmann-Index (HHI)
used e.g. by Blume and Friend (1975) or Dorn and Huberman (2005), the ratio of funds
in the equity portfolio following Guiso and Jappelli (2005b), the ratio of international
equity - which is non-German equity here - in the equity portfolio following Bluethgen et
al. (2007) and the share of index funds in the portfolio.
Many securities were classiﬁed into certain industries in the original data sample already
by the bank’s internal system. Additionally, we used stock industries from vwd, a large
German market data supplier and added mutual fund industries from Feri Rating & Re-
search, a large German rating company and data supplier. As most bonds, money market
funds, and other securities cannot be assigned to a speciﬁc industry, this study focuses
on the equity portfolio of investors. The average equity share in our sample is above 85%
so that no major wealth components regarding industry allocation are ignored.
After adding security industries from the mentioned data sources, 6,325 out of 7,982 secu-
rities could be matched with an industry. The coverage is not bad regarding the fact that
many mutual funds have no clear industry focus but invest in regions or tax-optimized
The Inﬂuence of Investors’ Jobs on Portfolios 6
Investor professions and security sectors are assigned to ﬁve industries. Security sectors are
sometimes overlapping because they are extracted from three data sources and some German
descriptions have no explicit translation. Italic security sectors are excluded from the analysis
later when checking for robustness.
Industry Professions Security Sectors
Fin. Services banker banks, online brokers, insurers, ﬁnancial service providers,
ﬁnancial sector, investment companies
IT computer computers/software, it-services, it, internet, internet service
scientist providers, phone/communication, it hardware,
Health Care doctor, pharma/cosmetics/gene technology, health services,
pharmacist health care, bio technology, chemistry/pharma
Technology engineer, automotive, electrics/electronics, energy technology, vehicle
technician, and machine manufacturing, aircraft construction, optical
electrician, instruments/measurement, solar energy, environment
motorcar engineering, technology, shipbuilding, semi-conductors,
mechanic nanotechnology, defense technology, bio-energy
Aviation pilot airlines
The professions and security sectors are grouped into ﬁve industries: Financial Services,
IT, Health Care, Technology and Aviation. The respective assignments are shown in table
We can not tell if the investors in our sample hold all their wealth in the securities ac-
counts in our sample or if they have considerable funds in other bank accounts. But the
size of securities accounts and cash holdings is quite close to the numbers in other studies
on German customers who could rule out deposits with other banks.
3.2 Hedging labor income risk
As shown in section 2.1, labor income and market returns are positively correlated so
that investors acting rationally and thus according to ﬁnancial theory should hedge their
industry exposure when choosing the ﬁnancial portfolio. The resulting portfolios should
have signiﬁcantly lower ratios of equity from the respective job industry than the portfo-
lios of peers working in other industries. This leads to the ﬁrst hypothesis:
The Inﬂuence of Investors’ Jobs on Portfolios 7
Hypothesis 1: Investors hold a lower portion of equity from the industry they work in
compared to investors working in other industries.
3.2.1 Job Industry Bias
To test this hypothesis empirically we accumulate the equity portfolios of all investors
working in the same industry which leads to ﬁve aggregated industry investors and im-
plicitly comprises a value-weighting of the individuals’ portfolios. Then we look at the
industry ratios in each of the ﬁve aggregated investors and build a table that displays
aggregated investors in columns and their respective ratios of industry equity holdings in
Analysis with ﬁve aggregated investors (value weighted)
Accumulating the equity portfolios of all investors that work in the same industry leads
to ﬁve aggregated industry investors and implicitly comprises a value-weighting of the
individuals’ portfolios. It should be read row by row to see that investors working in a spe-
ciﬁc industry invest more in ’their own’ industry than their peers working in the other industries.
Asset Industry Financial Serv. IT Health Care Technology Pilots Obs.
Financial Services 15.44% 5.14% 8.51% 7.73% 8.39% 329
IT 9.67% 20.96% 8.83% 13.34% 11.75% 355
Health Care 1.37% 1.74% 3.32% 1.89% 2.02% 1,206
Technology 10.85% 11.03% 8.11% 16.88% 10.11% 2,461
Aviation 0.33% 0.66% 0.44% 0.92% 7.31% 44
Total 37.65% 39.53% 29.20% 40.75% 39.59% 4,395
Table 2 shows that the ratios on the main diagonal (bold) are much higher than the other
values in the same row. The ﬁrst row for example tells us that investors working in the
Financial Services industry hold 15.44% of their equity in Financial Services assets while
investors working in other industries hold only between 5.14% and 8.51% in this industry.
Rational investors should hold less in their job industry compared to their peers so one
would expect a ratio below 8%.
Instead table 2 shows exactly the opposite: All ﬁve industry investors hold much higher
ratios in their job industry, most of them about twice as high as their peers working in
The Inﬂuence of Investors’ Jobs on Portfolios 8
For investors working in the technology industry, overweighting the job industry in the
portfolio - which we call a job industry bias - is not twice as high but still clear and large.
This might be due to the fact that this industry comprises many diﬀerent professions so
that the eﬀect is blurred since a more exact matching of professions and securities was
not possible. Still, even despite this blurring eﬀect in technology, a strong job industry
bias is obvious.
Thus an industry bias could show up stronger the more selective our industry assignment
gets. This assumption is supported when we look at a very selective industry - aviation.
It consists only of pilots and airline stocks and shows the most extreme job industry bias:
Pilots allocate ten times more equity to airline companies than their peers. The numbers
in aviation have to be interpreted with care as they are based on only 44 observations.
Nevertheless, the bias is enormously strong and further analysis will show that the eﬀect
remains strong even if we account for the small number of observations.
To tighten the above strong results, we conduct two robustness checks: First we exclude
some security classes from industry composition to make sure that the results are ro-
bust against variations in the industry deﬁnitions. Table 7 shows only marginal changes
in the results so we can assume that the job industry bias is robust for industry deﬁnition.
Analysis with individual investors (unweighted)
Ratios in this table are the equally-weighted means of all investors’ portfolios in the respective
industry. Thus small investors with usually extremer ratios compared to big diversiﬁed
portfolios enter with the same weight.
Asset Industry Financial Serv. IT Health Care Technology Pilots Obs.
Financial Services 13.11% 5.84% 6.45% 6.00% 6.45% 329
IT 11.04% 16.29% 10.70% 13.86% 9.89% 355
Health Care 1.64% 1.59% 3.07% 1.74% 1.51% 1,206
Technology 10.30% 11.85% 8.62% 15.58% 8.22% 2,461
Aviation 0.42% 0.83% 0.52% 0.86% 7.65% 44
Total 36.51% 36.40% 29.36% 38.04% 33.72% 4,395
As a second check, we calculate the matrix in table 2 without value-weighting the port-
folios. The results shown in table 3 are again only marginally weaker than the value-
weighted numbers. A possible explanation is that investors with small portfolios and only
one stock (thus 0% or 100% industry ratios) for example enter the calculation with the
The Inﬂuence of Investors’ Jobs on Portfolios 9
same weight as a EUR 100,000 investor holding a well-diversiﬁed portfolio. But still, the
eﬀect is clearly visible across all ﬁve industries indicating robustness in the results.
So we found clear and strong evidence that investors do not hold lower ratios of equity in
their job industry but much higher portions. Investors not only fail to hedge their labor
income, they even increase their overall wealth exposure to industry risk when selecting
their ﬁnancial portfolios.
A possible explanation for these high holdings in the job industry are employer stocks.
Benartzi (2001) analyzes retirement savings accounts and ﬁnds that the majority holds
large fractions (10% to 40%) of company stock. The information of investors’ employer is
not included in our dataset so we can not quantify the exact eﬀect of employer stockhold-
ing plans. On the other hand, pension plans and employer stocks are much less popular in
Germany: Skiera (2007) found in a representative study among 20,000 German households
that only 1.5% hold employer stock. Furthermore, our analysis is not based on pension
plan portfolios but trading portfolios of a large direct bank so we can safely assume that
the inﬂuence of employer stock is marginal.
Nevertheless, this explanation does not aﬀect the recommendation from ﬁnancial theory
to minimize correlations between the assets of an investor’s total wealth - regardless of
the fact that the stocks might have been bought using a stock pension plan.
Another rationale for a job industry bias could be private information on companies in
the investors’ job industry. But this private information can be positive as well as nega-
tive information on a company so that the resulting decision is to sell/short-sell or buy
a stock from the job industry. Thus private information can be ruled out as motif for
overweighting the job industry in the ﬁnancial portfolio.
So there is strong and robust evidence that investors exhibit a job industry bias; they
do not hold lower portions of their job industry but much higher portions. H1 is clearly
rejected by our portfolio data.
There are two possible reasons why the portfolio data does not conﬁrm H1. First, investors
do act rationally and the theoretical recommendation to hedge background risk with the
ﬁnancial portfolio is simply wrong. This seems unlikely given all the consistent research
that has been conducted in the area. A second possible explanation is that investors act
irrationally and not according to ﬁnancial theory and therefore deviate signiﬁcantly from
textbook recommendations. Section 3.3 will further analyze these two possibilities.
The Inﬂuence of Investors’ Jobs on Portfolios 10
Besides the fact that there is strong overweighting of the own job industry in investors’
portfolios, it would be interesting to have a clear overweight measure over all 4,395 in-
vestors in the ﬁve industries. To compute such a measure, a benchmark for every single
investor is necessary. A basis for this benchmark could be the share of the respective
industry in the market portfolio. Holding the same industry ratio as in the market port-
folio does of course not include a hedge against labor income (background) risk. However,
as there are no ﬁgures of correlations between the respective job and the asset industry
available, the task to compute a correct individual optimal ratio is almost impossible.
Nevertheless the respective industry ratio of the market portfolio represents a maximum
ratio that investors should hold in a respective industry. It serves as a good relative
measure of overweighting the own job industry or job industry bias: Investors can be
compared across job industries according to their distance to the benchmark where a
value of zero represents a job-neutral ﬁnancial portfolio i.e. investors do not hedge their
labor income risk.
But even the exact industry shares of the market portfolio are impossible to assess so that
we approximate this industry-speciﬁc ﬁgure by the average of all other customers in the
sample. To give an example, we exclude the 329 persons working in the ﬁnancial services
industry from our overall sample of 29,184 investors and see that the remaining 28,855
have on average 6.54% of their equity portfolio in the ﬁnancial services industry. Then we
take the individual ratio of equity in ﬁnancial services over all equity for each of the 329
investors working in the ﬁnancial services industry, subtract the benchmark of 6.54% and
get 329 values for overweight. This calculation is done for all ﬁve industries and 4,395
As a robustness check, we also compute the benchmark of all other investors from the
other four industries. Table 4 shows that the two benchmarks are quite close to each other
and the investors in the respective industries are on average high above the benchmark -
consistent with our previous ﬁndings.
The variable overweight is computed for all 4,395 customers and should be signiﬁcantly
below zero if investors act according to ﬁnancial theory and hedge their job industry risk.
Once more, empirical evidence is in sharp contrast to theoretical recommendations. A t-
test on the portfolio data shows that overweight is signiﬁcantly positive with a mean of 4.1
percentage points above the respective industry benchmark and a p-value of 0.0%. This
underlines previous evidence for a clear and strong job industry bias in investors portfolios.
The Inﬂuence of Investors’ Jobs on Portfolios 11
Benchmarks and industry weights
The ﬁrst column shows investors’ value-weighted portfolio ratios in their own job industry as
displayed on the main diagonal in table 2. The second column shows the unweighted ratios as
displayed on the main diagonal in table 3. Columns two and three are used to calculate the
overweight for each investor.
Asset Industry Own Industry Own Industry Benchmark of Benchmark of
(aggregated) (indiv. Investors) all other investors 4 other industries
Fin. Services 15.44% 13.11% 6.54% 6.18%
IT 20.96% 16.29% 11.72% 11.37%
Health Care 3.32% 3.07% 1.86% 1.58%
Technology 16.88% 15.58% 10.56% 9.75%
Aviation 7.31% 7.65% 0.66% 0.66%
Having built a measure for job industry bias, regressing demographics and portfolio char-
acteristics can provide further information on the question what drives a job industry bias
i.e. not only ignorance but aggravation of the background risk in overall wealth.
We include a gender dummy for male investors because a possible explanation for over-
weighting the job industry is that investors are overconﬁdent in their ability to pick
winning stocks in their job industry and gender is ”a natural proxy for overconﬁdence”
according to Barber and Odean (2001). Furthermore, behavioral ﬁnance tells us that
women generally act diﬀerently from men in their ﬁnancial decisions.
To see if investors’ age has an eﬀect on industry bias is of interest because younger and
older investors diﬀer in their implicit holdings of human capital and thus also vary in their
need to hedge labor income risk. We also include investors’ self-reported risk tolerance on
a scale of 1 (investor wants to avoid any risk) to 6 (investor accepts extremely high risk)
to see if risk-averse investors tend to better hedge their labor income risk.
To see if investors’ portfolio characteristics like deposit value (depvalue) and the share of
equity have inﬂuence on an industry bias, we also include these two variables. The share
of international equity in the equity portfolio (intdiv) serves as a measure for diversiﬁca-
tion to see if investors who care about diversiﬁcation in their ﬁnancial portfolio also better
hedge their background risk and thus gain better diversiﬁcation in their overall wealth.
The dummies for heavy trader and Eurex trader are not included because these persons
are only a small fraction of the sample and exhibit high correlation with the variable risk
The Inﬂuence of Investors’ Jobs on Portfolios 12
So our simple model is speciﬁed:
Overweight = α + β1 male + β2 age + β3 risk + β4 depvalue + β5 equityshare + β6 intdiv +
The results of the regression are displayed in table 5 on page 14. Because two more vari-
ables will be added later, factor loadings and p-values are noted in the ﬁrst of the three
Table 5 shows that gender has no signiﬁcant eﬀect on background risk hedging; men and
women are equally aﬀected by a job industry bias. So overconﬁdence in stockpicking
abilities in the job industry does not seem to be the main reason for high holdings of job
industry stock. Interestingly, older investors show more industry bias. With increasing
age, the negative eﬀect of overweighting the job industry diminishes because the human
capital shrinks and the need to hedge labor income risk also decreases. Nevertheless it
seems unlikely that older investors consider this fact. The best explanation is probably
that older persons worked for a longer time in their industry and invest in companies they
are familiar with.
The signiﬁcant negative inﬂuence of risk tolerance on overweighting is surprising as one
would suspect that investors with low risk tolerance should try harder to hedge their labor
income risk than investors willing to bear higher risks. On the other hand this is just
one more piece of evidence indicating that most investors do not understand the need to
hedge their labor income.
The measure of geographical diversiﬁcation is highly signiﬁcant and reduces a job industry
bias. It is plausible that investors who care and know about the importance of diversi-
ﬁcation in their ﬁnancial portfolios are also more likely to perceive the need to diversify
their background risk.
The positive and highly signiﬁcant loading of β5 demonstrates that portfolio structure is
an important determinant of job industry bias. Investors holding high ratios of stocks
and stock mutual funds in their portfolios tend to overweight their own job industry.
Summing up we ﬁnd in this ﬁrst regression that age and equity share in investors portfolios’
drive the probability to overweight the job industry while diversiﬁcation motives and risk
tolerance reduce it. In the following sections, two more explaining variables will be added
to get a clearer picture of the eﬀects.
The Inﬂuence of Investors’ Jobs on Portfolios 13
3.3 Rationality, education and sophistication
This section analyzes the question whether investors’ rationality and sophistication aﬀect
their job industry bias. The result will shed more light on the reason why H1 could not
be conﬁrmed: Is ﬁnancial theory mistaken or do investors act irrational and thus not
according to ﬁnancial theory?
We take a look at a sub-sample of 962 investors that hold a Dr. (Ph.D.) or Professor’s
degree indicating the highest level of education. These persons are likely to better under-
stand complex relations between diversiﬁcation, risk, and hedging. Furthermore, working
in academia for years should also increase the rationality of these persons so that we take
high education as a proxy for rationality.
So if H1 could not be conﬁrmed because investors do not act rationally and thus according
to ﬁnancial theory, the subsample of 962 Dr.’s and Prof.’s should exhibit a signiﬁcantly
lower job industry bias than the other 3,433 investors. This leads to a second hypothesis:
Hypothesis 2: Investors holding a Dr. or Prof. degree act more according to ﬁnancial
theory and thus have less job industry bias than the other investors in the sample.
A t-test shows that the individual overweights for the group of Dr.’s and Prof.’s (avg.
2.19%) are with a p-value of 0.1% lower than the overweights of investors who are not
Dr.’s or Prof.’s (avg. 4.62%). To check this result for robustness against the assumed
normal distribution, we conduct a median diﬀerence test and a Wilcoxon Mann-Whitney
test. Both non-parametric tests conﬁrm with a p-value of 0.0% that Dr.’s and Prof.’s
have lower job industry bias in their portfolios than their peers.
Additionally, we add a dummy variable ’DrProf’ to our regression model to see if the
eﬀect remains if we control for the other variables. The estimation is displayed in column
2 of table 5 and shows that the dummy has negative loading and is highly signiﬁcant with
a p-value of 0.1%. Hence H2 is supported by comparison of two subsamples as well as a
As most of the Dr.’s and Prof.’s work in the Health Care industry, there are not enough
observations in the other industries to set up a matrix like in tables 2 or 3 to demonstrate
the eﬀect. Nevertheless the above tests indicate that more rational investors have a sig-
niﬁcantly lower job industry bias.
So ﬁnancial theory behind H1 is not challenged and the rejection of H1 is due to irrational
behavior of investors which is very much in line with household ﬁnance and behavioral
The Inﬂuence of Investors’ Jobs on Portfolios 14
Determinants of a job industry bias
Risk tolerance is reported by investors themselves when opening their account on a scale
from 1 to 6. Share of international equity in the investor’s equity portfolio is a measure for
diversiﬁcation. The dummy variable DrProf comprises only persons with a Dr. or Prof. degree.
All values were computed using heteroskedasticity-robust standard errors. VIF values below
1.08 and the correlation coeﬃcients in table 8 demonstrate that the values are not aﬀected by
(1) (2) (3)
Overweight Overweight Overweight
Gender (male) 0.0105 0.00803 0.00702
(0.260) (0.390) (0.450)
Age 0.000585** 0.000673** 0.000723**
(0.048) (0.024) (0.015)
Risk tolerance -0.00613** -0.00613** -0.00653***
(0.014) (0.014) (0.009)
Deposit value in TEUR 0.0000311 0.0000386 0.0000475*
(0.230) (0.130) (0.069)
Share of equity in portfolio 0.0905*** 0.0903*** 0.0827***
(0.000) (0.000) (0.000)
Share of int. equity in equity portfolio -0.175*** -0.174*** -0.172***
(0.000) (0.000) (0.000)
DrProf (dummy) -0.0202*** -0.0195***
Advised (dummy) -0.0282***
Constant 0.0372 0.0389 0.0478**
(0.110) (0.094) (0.042)
Observations 4389 4389 4389
R2 0.09 0.09 0.10
Robust p values in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The Inﬂuence of Investors’ Jobs on Portfolios 15
4 Financial Advice and background risk
Empirical studies (see e.g. Dorn and Huberman (2002) or Guiso and Jappelli (2005a))
found that education and wealth are good proxies for ﬁnancial sophistication. Looking
at the portfolio values in the sub-sample of Dr.’s and Prof.’s compared to the other 3,433
investors shows that Dr.’s an Prof.’s are with an average of 98,455 EUR much wealthier
than the other investors who have an average of 69,283 EUR in their portfolios. So it
is reasonable that the group of Dr.’s and Prof.’s seems to be not only more rational but
also more ﬁnancially sophisticated than their fellow investors and thus exhibit a lower job
industry bias, i.e. better hedge the job industry risk in their portfolios.
This leads to the interesting question for alternatives for unsophisticated investors. Does
professional ﬁnancial advice better integrate background risk in investors’ portfolios and
reduce a job industry bias? Financial advisors should consider their clients’ background
risk and thus hedge the labor income risk in the ﬁnancial portfolios:
Hypothesis 3: Financial advisors consider their clients’ background risks which leads to
lower ratios of equity from the respective job industry in advised portfolios.
To test this hypothesis, we again split our 4,395 customers in two sub-samples of 604
advised and 3,791 non-advised persons. Then we conduct a t-test and ﬁnd that the vari-
able overweight is with a p-value of 0.0% lower for advised customers (avg. -0.2%) than
for non-advised customers (avg. 5.8%). Again we also conduct a median-diﬀerence and
Wilcoxon Mann-Whitney test to relax the assumption of normally distributed diﬀerences
between the groups and conﬁrm the results of the t-test with p-values of 0.0%.
Furthermore we add a dummy for advised in our regression model and see in column 3
of table 5 that the dummy variable is with a p-value of 0.0% highly signiﬁcant and has a
negative eﬀect on overweighting the job industry. This supports the ﬁnding that ﬁnancial
advice signiﬁcantly reduces a job industry bias.
So the results indicate that ﬁnancial advisors adjust their clients’ portfolios to hedge their
respective labor income risk and H3 can be conﬁrmed.
However the lower industry bias of advised clients is not necessarily due to the advisors’
capabilities: It is also possible that sophisticated investors realize the need to hedge their
background risk themselves, thus do not exhibit a job industry bias but decide to take
ﬁnancial advice. This could be due to a lack of time to deal with their ﬁnancial aﬀairs
in detail, for example. So the problem is that we can not rule out that the 604 advised
investors better hedge labor income risk regardless of their decision to take ﬁnancial ad-
The Inﬂuence of Investors’ Jobs on Portfolios 16
4.2 Event study
Econometric methods like the usage of instrumental variables or (propensity score) match-
ing ﬁrst seem appropriate to shed light on this issue. However, concerning instrumental
variables, it is very diﬃcult to identify and measure an instrumental variable in the
dataset. The matching method is common in medical or economic literature and is mainly
used to avoid a self-selection bias in managerial literature.
Regarding the very case of ﬁnancial advice the question is not if more sophisticated in-
vestors self-select themselves to take ﬁnancial advice but rather the chronology of events:
Neither method can distinguish between investors holding well-diversiﬁed portfolios who
decided to take ﬁnancial advice and investors holding poorly diversiﬁed portfolios that
took ﬁnancial advice and because of that now also exhibit well-diversiﬁed portfolios. It
can not be ruled out that the advised customers held well-diversiﬁed portfolios already
before taking advice. Thus these methods are not appropriate to reliably determine the
eﬀect of ﬁnancial advice.
Fortunately, our sample allows us to deal with this special endogeneity or causality prob-
lem in a very elegant way. The data comprises not only the disaggregated deposit holdings
in February 2007 but also monthly holdings from 01/2000 to 09/2007. Furthermore we
have the change dates of investors that decided to stop acting self-directed and take ﬁnan-
cial advice in the time between 02/2006 and 07/2007. So we are able to conduct a small
but selective event study. Even a strongly reduced sample size should suﬃce to support
or doubt the ﬁnding that ﬁnancial advisors signiﬁcantly reduce investors’ job industry
The selectiveness of this small subsample allows for a clear isolation of the advisor’s eﬀect:
It enables us to analyze the portfolios of the same persons before and after the decision
to take ﬁnancial advice.1
Out of the 4,395 investors working in one of the selected industries, 58 changed once
from self-directed to advised. Investors that changed their advice-status more than once
between 02/2006 and 07/2007 have been excluded form the study.
To see if the portfolios exhibit a lower job industry bias after taking advice, two points in
time ( ’pre’ and ’post’) are selected. Then the value for overweight before taking advice
(pre) is subtracted from the value after taking advice (post) and should be signiﬁcantly
negative if ﬁnancial advisors substantially reduce a job industry bias in their clients’ port-
folios. To check the results for robustness against changes in the time window, pre- and
Our dataset comprises more than 700 persons that switched from self-directed to advised and is
subject of another paper focusing on various eﬀects of ﬁnancial advice. Here we only consider investors
working in one of our ﬁve selected industries and the eﬀect on overweight.
The Inﬂuence of Investors’ Jobs on Portfolios 17
post date are varied. The month of the decision to take advice is denoted 0 (and thus
individual for every investor) while pre- and post months are denoted with negative or
positive month numbers, respectively.
This table displays the diﬀerence in industry overweights of 58 investors before (pre) and after
(post) each of them decided to take ﬁnancial advice. If advisors reduce their clients’ job industry
bias, the value ’overweight post - pre’ should be signiﬁcantly negative. The diﬀerence is computed
for several points in time to see if results are robust to window changes.
Months Overweight Signiﬁcance Investors
Pre Post Pre Post Post - Pre p-value (Obs.)
-1 1 2.55% 5.06% 2.51% 9.33% 58
-1 2 2.55% 5.13% 2.57% 9.08% 58
-1 3 2.67% 1.81% -0.86% 89.34% 55
-1 4 3.17% 2.56% -0.61% 90.31% 53
-2 1 3.70% 5.02% 1.32% 27.97% 57
-2 2 3.70% 5.07% 1.37% 27.49% 57
-2 3 3.87% 2.04% -1.84% 89.33% 54
-2 4 4.43% 2.81% -1.62% 86.74% 52
-3 1 3.81% 5.02% 1.21% 29.94% 57
-3 2 3.81% 5.07% 1.26% 29.48% 57
-3 3 4.09% 2.04% -2.06% 90.66% 54
-3 4 4.66% 2.81% -1.85% 89.09% 52
Table 6 does not show the expected signiﬁcant negative overweight diﬀerences. In fact,
the industry bias seems to increase slightly after taking ﬁnancial advice and then de-
crease after the third month. Furthermore, almost all values are not signiﬁcant - only
two out of 15 values show signiﬁcance at the 10% level. Because of these insigniﬁcances
we can not say that the job industry bias rises after taking advice. However table 6
shows that there is no signiﬁcant reduction in home bias after taking ﬁnancial advice and
this should be the case if advisors correctly consider the background risk of their clients.
The positive levels of overweight also rule out that we see no eﬀect because these 58 in-
vestors do not have a job industry bias. It can also be ruled out that certain customers
have been marked as adviced in the dataset by the bank despite they have no or rare
contact with a ﬁnancial advisor: These persons actively decided to take ﬁnancial advice,
signed a respective contract and speak regularly to their personal advisor (no callcenters).
So we ﬁrst found in section 4.1 that advised investors exhibit lower job industry bias. A
second analysis to isolate the eﬀect of ﬁnancial advice showed that advisors do not reduce
their clients job industry bias. The evidence indicates that the group of advised investors
The Inﬂuence of Investors’ Jobs on Portfolios 18
better hedge their background risk on their own and - independently - decided to take ﬁ-
nancial advice that does not mitigate their job industry bias. This could be because these
sophisticated investors do not have enough time to take care of their ﬁnancial aﬀairs and
seek support from a ﬁnancial advisor for example.
In the end, H3 can not be conﬁrmed - the lower bias in advised investors’ portfolios can
not be credited to the advisors but to the investors themselves. Thus ﬁnancial advisors
do not adequately consider their clients’ background risks in ﬁnancial portfolios.
This study dealt with the question whether the industry an investor works in inﬂuences
his or her ﬁnancial portfolio allocations. Rational agents should act according to ﬁnancial
theory and hedge their labor income (background) risk in their ﬁnancial portfolios. Hu-
man capital is the major component in most households’ wealth and dividends on human
capital - labor income - are correlated to stock market returns as the literature shows.
Thus rational investors should hold a lower portion of their ﬁnancial portfolio in their job
industry than peers working in other industries.
Based on profession and portfolio data of almost 30,000 persons, we constructed ﬁve in-
dustries and allocated professions of 4,395 investors and securities, mainly stocks and
stock mutual funds with a clear industry focus. Analyzing the 4,395 portfolios we found
that investors allocate not less assets in their job industry but much more. This job in-
dustry bias in investors’ portfolios is in sharp contrast to ﬁnancial theory and provides
one more case where investors do not act rationally and bear unnecessary high risk.
We further showed that ﬁnancial sophistication, proxied by education and wealth, signif-
icantly reduces the bias. Financially sophisticated investors better hedge the background
risk in their ﬁnancial portfolios which is in line with prior research ﬁnding that sophisti-
cated investors achieve better performance.
Furthermore we studied if unsophisticated investors can improve their poor situation and
reduce their job industry bias by taking ﬁnancial advice. A regression indeed showed that
advised investors have lower job industry bias than non-advised investors. However we
could isolate the eﬀect of advisors in a little event study and found that advised investors
better hedge their background risk in their portfolios anyway and not because the advisor
told them. This leads to the unpleasant conclusion that unsophisticated investors can not
adequately hedge their background risk and can not improve their situation by taking
professional ﬁnancial advice.
The Inﬂuence of Investors’ Jobs on Portfolios 19
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The Inﬂuence of Investors’ Jobs on Portfolios 21
Robustness check for ﬁve aggregated investors
This is a robustness check to table 2 and diﬀers in the asset industry composition:
Phone/Communication, IT Hardware and IT Services are excluded from IT and Bio-Energy is
excluded from Technology.
Asset Industry Financial Serv. IT Health Care Technology Pilots Obs.
Financial Services 15.44% 5.14% 8.51% 7.73% 8.39% 329
IT 5.15% 12.94% 4.53% 6.42% 7.70% 355
Health Care 1.37% 1.74% 3.32% 1.89% 2.02% 1,206
Technology 10.84% 10.99% 8.09% 16.83% 10.11% 2,461
Aviation 0.33% 0.66% 0.44% 0.92% 7.31% 44
Total 33.13% 31.47% 24.89% 33.80% 35.54% 4,395
Correlations of socio-demographic variables
This table displays the pairwise correlation coeﬃcients of exogenous variables used in the
regression analysis on page 14. All correlation coeﬃcients are below 20%, all VIFs are below
1.08 so the variables are free of multi-collinearity.
Gender Age Risk Deposit Equity Intern. DrProf Advised
toler. value share diversif.
Age 0.053 1
Risk tolerance 0.071 -0.016 1
Deposit value 0.000 0.184 0.075 1
Equity share 0.042 -0.005 0.007 -0.099 1
Int. diversiﬁcation 0.001 -0.035 0.076 0.011 -0.080 1
DrPorf -0.082 0.120 0.002 0.116 -0.023 0.040 1
Advised -0.041 0.071 -0.047 0.121 -0.181 0.092 0.054 1
The Inﬂuence of Investors’ Jobs on Portfolios 22
Professions of the investors in the dataset
This table shows the professions of the respective number of all investors in the dataset. The
bold professions were used to construct ﬁve industries, the other professions were deleted from
the dataset. From investors working in one of the ﬁve industry, portfolios with less than EUR
100 in February 2007 were excluded so that the ﬁnal sample consists of 4,395 investors: 329
in Financial Services, 355 in IT, 1,206 in Health Care, 2,461 in Technology and 44 working in
Profession Investors Profession Investors
White-collar worker 9,845 Field staﬀ 69
Retiree 2,198 Bookkeeper 69
Engineer 2,120 Unemployed 68
Self-employed 2,064 Educator 62
Student 1,571 Salesman 56
Oﬃcial 1,557 Designer 53
Doctor 1,200 Assistant 52
Manager 850 Military / civilian service 52
Merchant 821 Farmer 50
Oﬃce administrator 499 Pilot 46
Housewife 472 Lecturer 44
Public servant 465 Artist 44
Teacher 447 Restaurateur 40
Computer scientist 377 Motorcar mechanic 39
Blue-collar worker 368 Fitter 33
Banker 366 Machine operator 30
Business economist 349 Cabinetmaker 30
Advisor 348 Photographer 27
Solicitor 279 Orderly 25
Technician 277 Locksmith 24
Business consultant 187 Musician 23
Electrician 175 Lab assistant 21
Tax advisor 172 Printer 18
Architect 158 Cook 18
Independent 136 Gardener 16
Industrial clerk 125 Hairdresser 15
Craftsman (master) 119 Warehouseman 14
Jurist 108 Sharper 13
Craftsman 101 Painter 13
Physicist 99 Sportsman 10
Pharmacist 82 Bricklayer 9
Nurse 77 Customer advisor 8
Apprentice 72 Masseur 8
The Inﬂuence of Investors’ Jobs on Portfolios 23
Risk tolerance is reported by the investors when opening an account on a scale from 1 (low) to 6
(high). The digital variable Dr./Prof. comprises only persons holding a Dr. or Prof. title. Share
of int. equity in the investor’s equity portfolio is a measure for diversiﬁcation following Bluethgen
et al. (2007). Deposit and cash values are means over the last six months. Average return p.a.
and Sharpe Ratio are computed over the last 20 months. The Herﬁndahl-Hirschmann-Index
(HHI) is a measure for diversiﬁcation where lower numbers indicate higher diversiﬁcation.
All 5 industries Financial Services IT
(n = 4,395) (n = 329) (n = 355)
mean median mean median mean median
Gender (male) 89.83% 100% 75.08% 100% 88.45% 100%
Age 45.98 45 41.12 40 41.28 40
Risk tolerance 4.42 5 5.25 6 4.51 5
Dr./Prof. (dummy) 21.89% 0% 0.91% 0% 5.35% 0%
Advised (dummy) 13.74% 0% 10.03% 0% 11.27% 0%
Heavy trader (dummy) 13.11% 0% 19.76% 0% 12.11% 0%
Eurex trader (dummy) 3.69% 0% 17.02% 0% 3.10% 0%
Deposit value (EUR) 105,837 68,613 102,283 61,351 88,132 65,089
Cash (EUR) 37,116 18,444 34,631 16,314 31,571 18,013
Equity share 87.59% 100% 84.79% 99.16% 87.39% 100%
Average return p.a. 12.31% 12.52% 9.77% 11.01% 13.19% 12.74%
Sharpe Ratio 0.88 0.99 0.77 0.91 0.90 0.98
No of Securities 12.42 10 11.52 9 11.10 9
HHI 9.25% 1.89% 10.42% 2.24% 10.52% 2.18%
Fund ratio in equity 39.39% 27.79% 36.43% 21.21% 42.48% 32.34%
Share of int. equity 49.88% 51.28% 47.11% 46.07% 49.57% 51.99%
Share of index funds 6.86% 100% 9.74% 0% 7.32% 0%
Pharma Technology Aviation
(n = 1,206) (n = 2,279) (n = 44)
Gender (male) 80.60% 100% 96.38% 100% 97.73% 100%
Age 47.70 46 46.53 45 42.32 40
Risk tolerance 4.41 4 4.30 4 4.52 5
Dr./Prof. (dummy) 68.49% 100% 4.59% 0% 2.27% 0%
Advised (dummy) 16.92% 0% 13.08% 0% 11.36% 0%
Heavy trader (dummy) 12.19% 0% 12.76% 0% 15.91% 0%
Eurex trader (dummy) 3.23% 0% 2.23% 0% 2.27% 0%
Deposit value (EUR) 126,642 75,935 99,102 66,735 81,734 62,821
Cash (EUR) 45,077 18,552 34,476 18,647 29,890 17,024
Equity share 87.33% 100% 88.07% 100% 90.55% 100%
Average return p.a. 12.28% 12.27% 12.47% 12.99% 16.25% 13.15%
Sharpe Ratio 0.92 1.04 0.87 0.98 0.98 0.81
No of Secs 13.29 10 12.31 10 12.64 10
HHI 8.44% 1.67% 9.41% 1.89% 3.54% 1.90%
Fund ratio in equity 46.78% 43.75% 35.73% 21.11% 39.35% 38.01%
Share of int. equity 53.16% 55.43% 48.62% 49.05% 53.84% 59.37%
Share of index funds 6.79% 0% 6.41% 0% 8.90% 0%