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Home Bias
Sonu Roopam
(Second year MS)
Financial Economics
Capstone ECON 675 602

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CONTENT
1. Abstract and Introduction Page 1-3
Home bias description and reasons for existence
2. Data and Analysis Page 3-8
Studying 30 randomly chosen mutual funds to test industry
influence on portfolios
2.1 Regression Model Page 6
Σ (αij – βij)2
< Σ (βij – αj)2
2.2 Data for 30 Mutual Funds Page 8
Table 1
3. Results Page 8-11
o Figure 1
o Figure 2
o Figure 3
o Figure 4
4. Conclusions and Discussion Page 12-13
5. References Page 14-15

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Home Bias
Abstract
The paper explores the effects of the psychological bias exhibited in investor behavior called
Home Bias. The focus of this study is to show the prominence of large industries in state
directly effects the investment strategies of a mutual funds belonging to that state. During the
study we observe that mutual funds exhibit a strong and optimistic domestic home bias as well
as a bias for the state’s prominent industries in their portfolio decisions. The study looks at 30
different mutual funds located across the United States, and finds that those mutual funds
with a stronger home bias prove to yield higher returns on investments compared to their
counterparts.
INTRODUCTION
Home bias is defined as the preference for domestic equities by investors. The potential benefits
of international equity diversification are well known and have been well documented several
times. Investors consistently fail to exploit these benefits, and prefer to concentrate their
investments in the equities of their home state (Strong, Zu 1999). The United States investors
place 98% of their equity portfolios in domestic equities. Similar figures can be seen in other
regions such as: 78.5% for UK; 86.7% for Japan; and 85% for the European Union (Strong, Zu
1999).
Various attempts have been made to explain the puzzle behind home bias, but in this study we
explore the positive and/or negative impacts of the bias and the affects it has on portfolio returns.

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Second part of this study will test how the prominent industries of a region influence the
investment decisions and returns.
Possible reasons for home bias explained by Stong and Zu are: international investment
restriction on international capital flows; withholding taxes; and transaction costs. Another
reason prominent reason for the existence of home bias is that domestic investment may serve as
a hedge for large and frequent shocks to domestic income, caused by cyclical fluctuations (Tesar
and Werner 1995).
With differential information, investors prefer to incline their portfolios towards stocks that they
have more information/availability towards because better information stocks result in lower
conditional variance in their stock returns (Zhou 1998). French and Poterba (1991) also explain
that investors may simply be more optimistic about their domestic markets in their study. Overall
strong level of information and proximity of stock for investors seem to be the primary factor
causing home bias in the equity market today.
DATA AND ANALYSIS
The paper, “What Drives Home Bias? Evidence from Fund Mangers’ Views” by Torben Lutje
and Lukas Menkoff, influenced the style of the study for this paper. Their study looked at 324
fund managers in Germany. Proximity proved to be an informational advantage and higher
expected returns were confirmed.

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Description of Data
I started the study by obtaining data for mutual funds from Morningstar.com (access from Texas
A&M Libraries). The second data was obtained from the United States Census Bureau.
Morningstar explicitly describes portfolio investment within the United States and
internationally. It also displays the information regarding the distribution of portfolio of the
mutual fund for each industry. When I first started this study, I restricted the search to
Morningstar’s Fund Favorites, which gave a result of 116 funds. I further restricted to search to
only US categories that would allow to make a domestic analysis on home bias strictly in the
United States.
The Morningstar mutual fund categories that I looked at for the analysis were - Large Blend,
Large Growth, Large Value, Mid-Cap Blend, Mid-Cap Growth, Mid-Cap Value, Small Blend,
Small Growth and Small Value. After applying the categorical restriction, I was left with a list of
54 mutual funds, which were mainly located in the financial hubs of the United States therefore;
it’s wasn’t a broad range in terms of looking at industry concentration across the country. I later
changed the data set to 30 randomly chosen mutual funds located in non-financial locations. This
data set was able to explain home bias on a wider range across different states and industries
without the concentration in financial hubs.
For the state wise data from Unite States Census Bureau, I was able to estimate the industry
composition for each state by calculating annual payroll of the listed industries. The reason for
choosing annual payroll category to calculate the percentage of industry is because ‘sale, receipts

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and shipment’ category is not complete for some of the industries in states. Annual payroll helps
to evaluate how much states are invested in each industry and later comparing the percentage of
industry in state to percentage of industry in U.S. becomes relatively clear and easier to explain.
Data Analysis
Regression Model
∗ αij - % of industry j in the state i
∗ αj - % of industry j in the US
∗ βij - % of company i’s investment in industry j
Home Bias present if Σ (αij – βij)2
< Σ (βij – αj)2
To compile all the data for analysis, first it’s important to find out the location of each mutual
fund. The information regarding the headquarters of the mutual fund has to be obtained from the
company website. Then through Morningstar, I listed each funds investment percentage in
United States stock. The range is approximately from 58.11% to 110.56% stock investment in
the US. This alone shows a very strong domestic bias. Mutual funds invest heavily in US
equities, exhibiting the first signs of home bias for this study.
The next step was to calculate the highest investment by industry in the fund’s portfolio. This
information is available on Morningstar.com under the portfolio tab for any given mutual fund.
In the next step, I calculated the percentage of that same industry of the home state of the mutual
fund (by annual payroll). The last step is to calculate the percentage of that industry in the US

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economy (also by payroll). Using the equation: Σ (αij – βij)2
< Σ (βij – αj)2 .
If proven true then
there is home bias towards preference of investment in industry depending on the state of the
mutual fund and the prominence of that industry in the state.
For example, White Oak Select Growth (WOGSX) is a Colorado based mutual fund that places
34.94% of its investments in the technology industry (largest investment of the mutual fund).
Colorado’s technology industry is 13.85% of the state economy alone. The technology industry
of the United States only comprises of 10.98%. This shows CO has higher percentage of the
technology industry compared to the national percentage of the technology industry, influencing
mutual funds in CO to invest in the technology industry and thereby causing a home bias.
When applying the equation Σ (αij – βij)2
< Σ (βij – αj)2
, WOGSX displays the result 4.45% <
5.74%, which shows the latter being significantly higher and thereby proves there is a strong
home bias present.
The Table 1 on Page 8 lists the 30 mutual funds by ticker name, name of the mutual funds, the
Morningstar category they belong to, state they are located in, the annual return on the portfolio,
the largest industry in the state, highest investment of mutual fund by industry, percentage of
investment of the same industry in the state and then United States. The last three columns are
the regression and weather the mutual fund exhibits a home bias or not.

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Table 1. Data for 30 Mutual Funds
RESULTS
1. After running the regression, 22 out the 30 mutual funds exhibit a home bias. That means
73.33% of the data is biased towards the prominent industries of its home state, which
influences the portfolio choice and distribution. Also, those mutual funds that show a
home bias yield a return of 7.45% on their portfolio as compared a return of 4.95% by the
non home bias mutual funds (figure 2). We can conclude from these results that home
bias is present on both a national and state level for the mutual funds and also, that home
bias is a positive investment strategy producing profitable returns on portfolios. Figure 1
and Figure 2 demonstrate the above-mentioned points.

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Figure 1
Figure 2
2. Another point that I came across while analyzing the results was the YTD Return % also
plays an important role in the determination of home bias. I noticed some returns were
0
20
40
60
80
Home
Bias
No
Home
Bias

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lower for those mutual funds that invested in the industries that have been described as
the second or third largest industries in their state in comparison those funds that invested
in the biggest industries of their state.
Out of the 30, those 22 mutual funds that demonstrate a home bias were further divided
into two categories: those that highest investment was in the largest industry of their
home state, and those that the highest investment in the second or third largest industry of
their home state. After the calculations, the former subcategory had a return of 9.04%
while the latter had a return of 6.19% on an average (Figure 3 and Figure 4). From this
result, it can be implied that significantly higher returns would be achieved if the degree
of home bias were strong.
Figure 3
Home
Bias
Largest
investment
is
in
largest
industry
=
9.04%
Largest
Investment
in
second
or
third
largest
industry
=
6.19%

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Figure 4
3. The last result of the study was a rather interesting one; we find that those states with
relatively smaller economies are the ones that exhibit weaker home bias towards industry
prominence as well as weaker domestic home bias. Mutual funds located in states such as
Nebraska, Rhode Island and Oklahoma show little or no home bias in their investment
strategies, compared to mutual funds located in larger economies such as California,
Texas, and Massachusetts. The reason behind this result may be traced back to the basics
of home bias – home bias is a psychological bias and exists because of better access to
information and availability of stocks in the home market. But if a small economy has
only a few available stocks to invest in, or lower impact of industry prominence in a state
on it’s people, investors will look for options elsewhere i.e. outside their state and even in
international markets. This creates a chance for investors to break their inclination
towards home bias and diversify and expand their portfolios.

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CONCLUSION AND DISCUSSION
Conclusion
The results of this study lend support to the existence of the home bias strategy in domestic
markets. Although many papers and studies previously questioned and criticized home bias, in
this study it’s apparent that when looking at a small sample size of randomly selected mutual
funds, home bias is a profitable and optimistic plan. When leaning towards prominent industries
of a state for investment ideas for mutual fund portfolio, returns are significantly higher. The
study also reveals that a stronger home bias would yield high average returns on portfolios.
While this is true for the sample size of 30 mutual funds, the results may be different on a larger
scale.
Home bias is also weaker for smaller economies due to smaller industries in those states, and
thereby having a weaker influence on investor behavior.
After the data interpretation of this study, it’s can be concluded that home bias is a high risk, but
high profit investment plan for mutual funds. Investment in domestic equities based on highly
prominent industries for higher returns is a justification for domestic and regional home bias.
Discussion
The next step of the study would require an in depth analysis of the mutual fund’s management
company and advisors of the fund. Home bias can be predicted from the location of the
management company and personal and educational background of the advisor of the fund.

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Market and mutual fund risks can also affect the degree of profits of home bias. If we take into
account the market beta and compare to the mutual fund, returns will be adjusted.
This paper is a lays out a strong foundation for next steps for the study of positive effects of
home bias.

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References:
1. Coval, Joshua D. and,Tobias J. Maskowtz. “Home Bias at Home: Local Equity
Preference in Domestic Portfolios.” The Journal of Finance. (1999) 1-29. Print.
2. Graham, John R., Campbell R. Harvey, and Hai Huang. “Investor Competence,
Trading Frequency, and Home Bias.” National Bureau of Economic Research.
(2005) 1-32. Print.
3. French, K. R., and Porteba, J. M. “Investor Diversification and International
Equity Markets.” American Economic Review (1981) 22-226. Print.
4. Kilka, Michael and Martin Weber. “Home Bias in International Stock Return
Expectations.” Journal of Psychology and Financial Markets. (2000) 176-192.
Print.
5. Lüthe, Torben and Lukas Menkhoff. “What Drives Home Bias? Evidence from
Fund Managers' Views.” University of Hannover. (2004) 1-25. Print.
6. http://www.Morningstar.com. Mutual Funds. September 2014
7. http://www.census.gov/en.html. Business and Industry. 2012. September 2014
8. Tessar, L. L., and Werner, I. M. “Home Bias and High Turnover.” Journal of
International Economics. (1995) 14, 467-492. Print.

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9. Zhou, C. “Dynamic Portfolio Choice and Asset Pricing with differential
Information.” Journal of Economic Dynamics and Control. (1998) 22, 1027,
1051. Print.