Individual Investors’ Portfolio Choice and Birthplace Bias
Ted Lindblom1, Taylan Mavruk, and Stefan Sjögren
March 15, 2013
ABSTRACT
This paper examines whether the geographical proximity bias of individual investors stems
from information asymmetry or behavioral factors. We introduce the concept of ‘birthplace
bias’ and find that the proximity bias is strongest for investors who have never moved away
from the area in which they were born. The bias is stronger the longer the investor has lived
in the same geographical area. However, neither local bias nor birthplace bias demonstrates
an advantage in terms of abnormal portfolio return. This result implies that proximity bias is
not driven by information asymmetry, which may have policy implications with regard to the
confidence in financial markets.
JEL Classification: G11, G14, G15.
Keywords: birthplace bias, local bias, individual investors, information asymmetry.
1
Corresponding Author: Ted Lindblom, Phone: +46-31-786 14 97, E-mail: ted.lindblom@handels.gu.se.
Ted Lindblom, Taylan Mavruk and Stefan Sjögren are with Centre For Finance, University of Gothenburg, Box
100, SE-405 30 Gothenburg, and acknowledge the Centre’s support for this research. We thank Barbara
Bukhvalova for valuable comments and discussions. We also thank Paolo Sodini and seminar participants at
Young Scholars Nordic Finance Workshop, Stockholm for valuable discussions. We are grateful for all the
support and discussions with our Visiting Professors, Malmsten Guest Professors, Bertil Danielsson Visiting
Professors, and Centre for Finance seminar participants at the School of Business, Economics and Law,
University of Gothenburg. We would like to especially thank, Tamir Agmon, Shubhashis Gangopadhyay, Anjan
Thakor, Per Östberg, and our colleagues at the Centre for Finance for their constructive discussions and ideas.
We thank Euroclear Sweden for providing the investor data. Finally, we acknowledge financial support from
Vinnova.
1
1. Introduction
A rapidly growing empirical research field with relevance to the financing cost of firms and
hence for the efficacy of the allocation of scarce resources in an economy is concerned with
investors’ strong preference for investing domestically instead of internationally (Cooper and
Kaplanis (1994); Tesar and Werner (1995); Kang and Stulz (1997); Lewis (1999); Pastor
(2000)). In the past decade, this research also has been increasingly documenting and
analyzing households’ and individual investors’ bias toward firms located in their local
regions (Coval and Moskowitz (1999, 2001); Grinblatt and Keloharju (2001a, b); Feng and
Seasholes (2004); Ivković and Weisbenner (2005); Massa and Simonov (2006); Bodnaruk
(2009); Seasholes and Zhu (2010)). Despite theoretically grounded arguments for investors to
be well-diversified (Markowitz (1952)) and a body of empirical evidence of the benefits
thereof (Obstfeld (1994); Harvey (1995); De Santis and Gerard (1997)), previous studies on
these types of investor preferences are consistent in documenting a significant distortion of
the average portfolio of both professional investors (French and Poterba (1991); Coval and
Moskowitz (1999); Hau (2001); Baik, Kang and Kim (2010)) and individual ones (Grinblatt
and Keloharju (2001a); Massa and Simonov (2006); Bodnaruk (2009); Zhu and Seasholes
(2010)). There is far less consensus in the literature regarding the reason for the observed
biases of investors. This statement is particularly true for the geographical proximity bias
phenomenon, commonly referred to as ‘local bias’, displayed in the investors’ domestic
portfolios.
A number of theoretically derived hypotheses have been put forward to explain the existence
of local bias, and the empirical evidence is mixed. The divergent outcomes of previous
surveys may be explained partly by the ways in which local bias and its implications are
measured and assessed and partly by the limited access to adequate and/or complete data sets
2
(cf. Campbell (2006)) or, in other words, the use of more or less ‘biased’ data. In this study,
we have put particular effort into identifying and deepening our understanding of the main
drivers behind individual investors’ proximity bias to ‘accurately’ operationalize the concept
of proximity bias before we examine its generality and earnings effect. Recognizing an
unexplored dimension of the proximity bias phenomenon, we adopt and develop the concept
of ‘birthplace bias’. This concept measures and assesses to what extent individual investor
portfolios are tilted towards the stocks of firms located in the birthplace area of the investor.
For investors who remain residents of the area in which they were born, local and birthplace
bias coincides. However, these biases become differentiated as soon as the investor moves to
another area. Hence, by introducing the concept of birthplace bias, we are able to empirically
test the validity and real accuracy of the following type of aphorism: You can take an investor
out of Texas, but you cannot take Texas out of the investor.
Our analysis of individual investors and their equity portfolios is conducted using data of
high quality with respect to both accuracy and timeliness. The data span the second half of
the past decade (2006-2010) and cover a time period consisting of both strong and weak
conditions in the economy as a whole. This setting differentiates this study from many
previous surveys based on older data and on time periods in which the general economic
conditions have been rather stable or heading upwards. Our findings lend support to the
theoretical guideline proclaiming that it is beneficial for investors to aim at a well-diversified
portfolio. This result does not mean that we do not observe any proximity bias of investors.
On the contrary, as established in previous studies, the proximity bias of individual investors
is prevalent on an aggregate level. We find that the bias is stronger the longer the investor has
lived in the same geographical area. Additionally, through the adoption of the new birthplace
bias measure, we are able to ascertain that the geographical proximity bias is strongest for
3
investors who have never moved away from the area in which they were born. Notably,
neither local bias nor birthplace bias appears to pay off in terms of abnormal portfolio return.
This analysis result is statistically significant and may have policy implications for the
confidence in financial markets. In contrast to previous studies that have arrived at the
opposite result, this finding suggests that access to superior information is not the reason for
individual investors’ proximity bias. If this result means that there is no information
asymmetry between (outside) investors, this would be in accordance with the semi-strong
form of the efficient market hypothesis (EMH).
In the following sections, we explain in greater detail how the analysis is performed. In
Section 2, we briefly recapitulate the main hypotheses generated in the previous literature to
explain the rationale behind the geographical proximity bias of investors, i.e., local bias.
Thus, we also review related empirical surveys with respect to their findings, data selection
and measurement approaches. In Section 3, we present our research questions and hypothesis.
In Section 4, we present the data set used, the methods of processing the data and the steps
taken to strengthen its accuracy. This section also includes the motivations for the various
measures of proximity bias adopted in the analysis. Section 5 contains the analysis and its
results. Finally, Section 6 concludes the paper.
2. Reasons for local bias and prior empirical evidence
Two fundamental hypotheses are suggested as explanations of the local bias phenomenon.
The bias is either the result of rational or behavior-based investor decisions. The former
decision rests on some type of information-related advantage2, whereas the latter decisions
2
In particularly home bias literature also hedging is put forward as another potential rational explanation for
distorted investor portfolios (Pesenti and van Wincoop (2002)). However, in their comprehensive local bias
study, Massa and Simonov (2006) do not find evidence that supports this explanation.
4
are more or less emotionally rooted. The rational hypothesis is in turn founded on either of
two main potential drivers. As suggested by Coval and Moskowitz (2001), one of these
drivers might be that local investors have superior information compared to non-local
(remote) investors. The other driver is instead related to Merton (1987) in suggesting that
local investors are in a better position to interpret existing information. Behaviorally oriented
investor decisions also can be traced to either of two underlying explanations. Local investors
may be ‘overconfident’ about their ability to make profitable investments in proximate firms
(Barber and Odean (2000)) or they may just have very strong preferences for investing
locally without any concerns about expected earnings, let alone prospects of generating
abnormal returns (Goetzmann and Kumar (2008)).
2.1 Superior information
The first potential driver of the rational hypothesis presumes a case of information
asymmetry in which local investors receive relevant information regarding firms in their
vicinity before remote investors. Because of this information advantage, it is rational for the
local investors to tilt their portfolios toward proximate firms (with promising prospects)
because this approach would increase their likelihood of earning excess risk-adjusted
returns.3
The fact that the presence of information asymmetry has implications for the pricing of risk
capital and, ultimately, for the capital structure of the firm is nothing new per se. Information
asymmetry has given rise to several capital structure theories, such as the ‘signaling’ theory
3
An underlying assumption seems to be—at least implicitly—that ‘local information’ is bound to be positive,
which is of course an unrealistic assumption. Information on local firms can be both positive and negative,
why a distortion of the portfolio of a local investor towards remote firms instead of proximate ones may be
explained by ‘negative’ superior information about the business prospects of the latter firms, i.e. if this
portfolio generates abnormal returns. We will test this in Section 5.
5
(Ross (1977)), the ‘windows of opportunity’/‘market timing’ theory (Ritter (1991)), and the
‘pecking-order’ theory (Myers and Majluf (1984)). These theories are concerned, however,
with how information asymmetry between those ‘inside’ the firm (management) and those on
its ‘outside’ (‘new’ potential shareholders) can explain the firm’s cost of capital and financial
decision-making. The focus is primarily on the search for ‘cheap’ risk capital and/or what
role the alignment between management and the current (‘old’) shareholders plays in regard
to the creation of firm and shareholder value. Geographical proximity is not an issue in these
theories; additionally, in local bias studies, the implications for the capital structure of firms
are barely considered explicitly. The latter seems reasonable when study results fail to lend
support to that local investors have superior information, but if the results are affirmative
capital structure implications are evident. If local investors are shown to be better informed, a
new dimension would be added to the capital structure theories. This scenario would imply
that ‘old’ shareholders living in the vicinity of the firm are in a more favorable position and
are more strongly aligned with management than remote ‘old’ shareholders. Accordingly,
‘new’ potential local shareholders would likely be in a better position than potentially ‘new’
and perhaps ‘old’ shareholders living in remote areas. Whether this outlook is the case in
reality deserves to be further examined, provided that investor proximity bias is explained by
information asymmetry.
Previous empirical surveys provide evidence of over-performing as well as underperforming
local portfolios. Using the same historical data set (ranging from 1991 to 1996) on the equity
investments made by the private (household) customers of a large anonymous US brokerage
firm,4 Ivković and Weisbenner (2005) and Ivković, Sialm and Weisbenner (2008) arrive at
4
This data set is from a rather stable and growth-oriented time period of the US economy and has been the basis of many
other studies, including Odean (1998, 1999), Barber and Odean (2000, 2001), Dhar and Kumar (2001), Hong and Kumar
(2002), Coval, Hirshleifer and Shumway (2005), Dhar and Zhu (2006), and Goetzmann and Kumar (2008).
6
the conclusion that individual investors benefit from information asymmetry when investing
locally, whereas Zhu (2002) and recently Seasholes and Zhu (2010) and Nofsinger and
Varma (2011) arrive at the opposite conclusion. In addition to studying the equity holdings of
households (as is commonly performed in these types of studies), Seasholes and Zhu (2010)
also analyze individual investors’ trading on a transactions basis. Regardless of analysis,
these researchers find that local investors do not appear to have superior information with
regard to proximate stocks. Even when the analysis also incorporates small local stocks not
included in the S&P 500 index, their results still suggest that local investors tend to buy and
sell the ‘wrong’ local stocks. The fact that local buys also underperform local sells in local
markets is regarded as strong evidence for rejecting the rational (information asymmetrydriven) hypothesis.5
A few empirical surveys of individual investors’ geographical proximity bias in other regions
and countries are based on rather recent data sets. Among these studies, Feng and Seasholes
(2004) and Seasholes, Tai, and Yang (2011) examine whether local bias pays off in mainland
China, whereas Bodnaruk (2009) examines local bias in Sweden. Although the results
obtained by Seasholes et al. (2011) indicate that local bias does not pay off, the other two
studies provide evidence of abnormal returns on local portfolios as suggested by the rational
hypothesis. Bodnaruk (2009) even finds that his results are consistent after checking for
investors that changed their residence.6 His measurement of the proximity bias of individual
investors differs from the majority of previous surveys, in which the location of firm
headquarters relative to the residence of the investor determines whether the firm is local.
5
Seasholes and Zhu (2010) acknowledge that their sample is subject to geographical selection biases but that the sample
includes very small portfolios and therefore is 25 percent larger than the sample used by Ivković and Weisbenner (2005).
6
The study by Feng and Seasholes (2004) covers a limited time period (1999 to 2000) of individual investors’ trade through
a large Chinese brokerage firm, in which actual returns are regressed on net trades. Seasholes et al. (2011) use more recent
brokerage records of a Chinese securities firm established in 17 different regions. Bodnaruk (2009) is based on a larger
data set displaying the stock holdings of almost all Swedish individual investors between 1995-2001.
7
Bodnaruk also considers the location of other firm establishments such as production
facilities. Although this factor should increase the likelihood of investors owning local stocks,
Bodnaruk does not report any significant differences.
2.2 Familiarity
The second potential driver of the rational hypothesis for explaining the geographical
proximity bias phenomenon is closely related to the information asymmetry driver in that
information on proximate firms is supposed to play a central role even though all investors
have access to this information at the same time. This driver rests on a bounded-rationality
type of assumption that local investors are, in comparison to remote investors, more familiar
with some subset of firms in their vicinity, giving them a comparative advantage in
interpreting any new information released by these firms to all investors (cf. Merton (1987)).
Hence, investors who place a disproportionally high share of their capital in proximate firms
are expecting that this scenario should pay off and lead to excess risk-adjusted returns in their
portfolio, as in the case of information asymmetry. This expectation is also what Massa and
Simonov (2006) find when they analyze the nature of familiarity and the way that this
concept affects the portfolio choice of individual investors in Sweden.7 However, not all
investor portfolios are distorted toward proximate firms because not all local investors are
qualified to accurately interpret the value of available information. Korniotis and Kumar
(2008) argue that investors with cognitive abilities are more likely to access better
information because of social networks, efficient information gathering and the capability to
learn from earlier mistakes. These researchers find that investors with high cognitive ability
7
Massa and Simonov (2006) complement most of the data used by Bodnaruk (2009) with longitudinal data on the investors
concerning income, bank accounts, real estate and other types of wealth as well as demographic and family characteristics.
8
earn significantly higher returns than those with low cognitive ability. The importance of
investor sophistication is also supported by the analysis results of Bodnaruk (2009).
2.3 Behavioral explanations
Theoretically, it is difficult to justify investors’ local portfolio decisions by rational
explanations in terms of superior information or capabilities to interpret available
information—at least in the long run. If investors believe in information asymmetry or
interpretation skills, the most rational strategy would be to mimic local portfolios and, hence,
to earn abnormal returns. An overall portfolio comprised of local portfolios from different
areas will therefore earn more than one from a single local area. In that respect, the rational
explanation for the local bias puzzle should be that an investor faced with two equal
investments chooses local stocks out of pure preferences. The alternative explanation to this
proximity bias phenomenon is instead behavior oriented.
The behaviorally rooted hypothesis seeks to explain local bias from a less rational standpoint
by adopting a financial behavior framework. In such a framework, investors’ preferences are
assumed to be driven by psychological, cultural and/or emotional factors such as investor
hubris, common language, and the feeling of belonging and familiarity in terms of ‘knowing
of’ rather than having true ‘knowledge about’ proximate firms (Grinblatt and Keloharju
(2001b); Huberman (2001)).
On one hand, previous empirical surveys on the behavior-based proximity bias of investors
indicate that this bias is induced by overconfidence among local investors (Barber and Odean
(2000); Bailey et al.. (2008); Bailey et al.. (2010); Korniotis and Kumar (2008)). Local
investors overestimate either the quality of (private) information or their ability to interpret
9
available information. Prior empirical evidence lends support to the view that individual
investors suffer from their overconfidence because it leads to excessive trading and highly
distorted portfolios. Using the same historical data on individual investors in the US (see
footnote 4), Barber and Odean (2000) find that investors trading frequently are on average
generating a much lower return than those trading infrequently.
On the other hand, previous empirical research also documents that investors’ proximity bias
reflects sensation desire (Grinblatt and Keloharju (2009)), cognitive abilities (Korniotis and
Kumar (2008)), high IQ (Grinblatt et al.. (2011, 2011a)), sophistication and other investor
characteristics such as age, gender, culture, language, wealth, experience, and perceived
competence (Grinblatt and Keloharju (2001); Goetzmann and Kumar (2008); Graham,
Harvey, and Huang (2006)). In principle, these studies show that the behavior hypothesis to a
large extent explains the proximity bias of investors who have a low understanding of the
benefits of portfolio diversification. These investors are likely to have low cognitive abilities
and low sophistication and, overall, their portfolios significantly underperform relevant
benchmarks. This result means that the local distortion observed in these investors’ portfolios
is not driven by any yield expectations let alone any expectations of excess return. The
behaviorally rooted hypothesis is instead suggesting that the distortion of investors’ portfolios
towards proximate firms should be viewed as a consequence of investor decisions made by
the ‘heart’ rather than by the ‘brain’.
3. Research Questions and Hypotheses
The mixed results of previous empirical studies lead us to conclude that the overall research
question still remains to be answered: “Is the proximity bias of individual investors explained
by the rational hypothesis (‘brain’) or the behavior hypothesis (‘heart’)?” By introducing the
10
concept of birthplace bias, we can explore this question more in depth. If it turns out that the
proximity bias of individual investors is not only a question of local bias but is also about
where the investor was born (and thus actually time dependent as suggested by Bodnaruk
(2009)), it would be possible to compare and examine the differences between the portfolio
returns of local investors born in the area in which they live and local investors born
elsewhere. We know from Pool et al. (2012) that remote institutional investors tend to invest
more in their home (where they ‘grew up’) area; however, no previous study has compared
whether the proximity bias of individual investors differs between investors born in the area
in which they live and investors who are residents in that area but were born elsewhere. We
use a step-wise approach to explore the question of whether birthplace bias matters and break
down the overall research question into three sub-questions. We formulate our first subquestion (SQ1) as follows:
SQ1. Do investors who are living in their birth district hold portfolios that are more tilted
toward local firms than investors who were born elsewhere?
If we find that birthplace does not matter in the portfolio choice of local individual investors,
the first sub-question would not require any further analysis. The opposite finding would, on
one hand, imply that local individual investors are emotionally attached to their place of birth
when investing in proximate stocks. We know from empirical studies in other areas that
people tend to have a positive view of their birthplace and that ‘place identity’ is a wellinvestigated phenomenon in research fields outside of proximity bias (see, e.g., Cuba and
Hummon (1993) and Reade (2001)). The non-financial literature on corporate social
responsibility (CSR) also implicitly recognizes this phenomenon, assuming that noneconomic factors such as social expectations and legitimacy influence firm behavior (Moir
(2001)). On the other hand, to conclude that birthplace bias is emotionally driven may be
11
premature. It may not be the single explanation or an explanation at all for such proximity
bias. If these individual investors earn abnormal returns on their local portfolios, birthplace
bias would instead appear to be information driven. Because previous findings on the ‘gains’
of local bias of individual investors are mixed, we formulate the first hypothesis (H01) in null
form as follows:
H01. Investors who live in their birth district and are proximity biased do not earn
abnormal returns on their local portfolios.
The case in which the correlation between the proximity bias and the birthplace of investors
is significantly positive and does pay off would be in line with the rational (information
asymmetry-driven) hypothesis. If this correlation does not pay off, the case would instead be
more in accordance with the behavior-oriented hypothesis.
Investigating identity and social preferences, Wann, Tucker and Schrader (1996) find that
sports fans’ support of their home team prevails for a long time after moving to a new area,
even if the new location is distant from this team’s catchment area. The distinction between
investors’ local bias and birthplace bias makes it possible to examine whether this type of
affection is also related to investors’ proximity bias by comparing the birthplace bias of
investors living in the district in which they were born with the birthplace bias of investors
who have moved out of their birth district and now live elsewhere. We therefore formulate
our second sub-question (SQ2) as follows:
SQ2. How does the birthplace bias of investors who have moved into another district
compare with the bias of investors living in the area in which they were born?
12
On one hand, if birthplace bias is prevalent and, similar to the support of a home team,
prevails after an individual investor has moved from the birth district, this implies that the
investor is taking other than pure financial considerations into account when investing
capital. On the other hand, closeness can be defined not only in terms of geographical
proximity but also in terms of social network distances. The investor’s social network in the
birth district may still persist and thus may be of importance even if the investor lives
elsewhere. This possibility implies that the positive birthplace bias of an investor living in
another district may be in accordance with the rational hypothesis suggesting that the investor
still has access to superior information on firms located in the birth district. Hence, we cannot
rule out a rational explanation related to either access to superior information or an
interpretational advantage (cf. familiarity). Investors who are undiversified and invest in
firms located in their birth district may create such information advantages through their
social networks in the vicinity of the firm (cf. Pool et al.. (2012) on institutional investors).
To test whether remote investors who hold portfolios distorted toward firms in their birth
district are able to utilize an information advantage, perhaps originating from their social
network and other connections to the birth district, we formulate the corresponding second
hypothesis (H02) in null form as follows:
H02. Remote investors do not earn abnormal returns on their birthplace-oriented
portfolios.
Bodnaruk (2009) finds that the (distance-based) local bias of the average investor did
increase over time after changing residence, implying a reallocation of the investor portfolio.
In addition to examining whether movers become locally bias in their ‘new’ district by
reallocating their portfolios to include a greater share of local stocks relative to the market,
we can also discern if birthplace bias decreases over time after an investor has moved into
13
another district. In the first stage, we can distinguish when in time the eventual reallocations
are made over a four-year period. In the second stage, we can extend the analysis beyond that
time horizon by controlling for the investors’ place of birth, which allows us to identify those
investors who, prior to 2006, did move to the district in which they are currently living.
Hence, we formulate the third sub-question (SQ3) as follows:
SQ3.
How are the portfolios of investors who have moved from one district into
another affected over time?
One aspect related to the third sub-question is the way in which the risk-adjusted portfolio
earnings of the investors are affected by their change of ‘county’ residence. If birthplace bias
contains information other than familiarity, such as an interpretational advantage, it may
explain why investors hold tilted portfolios toward proximate firms. Similarly, if investors
believe that they are able to obtain (positive) private information about the stocks of firms
located in the new district, investors most likely will increase their proximity bias toward
firms in this district over time. We therefore formulate the third and final null-hypothesis
(H03) as follows:
H03. Investors who move into another district and increase their proximity bias do not
earn abnormal returns on their local portfolios.
4. Data and Analysis Models
Our survey spans the second half of the first decade of the 21th century, i.e., from July 2006
to June 2010. The analysis is based on semi-annual investor data obtained from the Security
Register Center of Euroclear Sweden, which includes all individual investors’ stock holdings
14
in listed firms whose headquarters are in Sweden.8 The data are restricted to the
stockholdings of domestic and foreign individual investors born in 1991 or earlier. The firms
are listed mainly on the OMX (large, mid and small cap) exchange, although some firms are
listed on the alternative minor stock markets9. The analysis therefore covers a total of 2.1
million investors and 628 listed firms (and 698 listed stocks), with an average of 1.8 million
investors and 490 firms (and 529 stocks) present in each period.
The Euroclear data set displays the number of stocks held in each firm by the investor. This
information is completed with market prices obtained mainly from Datastream. Whenever
stock prices are missing or incorrect in Datastream, we have instead used stock market
information provided in the periodical microfilms of Dagens Industri, a daily Swedish
financial newspaper similar to The Economist. We also use the personal data (personal
identification numbers, postal zip codes, and nationalities) of investors and the organizational
data regarding the firm. By using postal zip codes with five digits (the highest detail level),
we can distinguish the location of each investor and firm with very high accuracy.10
The definition of local bias is crucial for the identification of proximity-biased investors and
for the assessment of the financial effects thereof. In the seminal work of Coval and
Moskowitz (1999) that introduced and coined the concept, local bias (LB) is defined as the
value-weighted average distance between the location (determined by its latitude and
longitude degrees) of an investor (fund manager) and the corresponding locations of the firm
headquarters of the stocks in the investor’s portfolio in relation to the value-weighted
8
Euroclear’s register also includes other public and private firms that have affiliated their shares/financial instruments to the
organization’s system as well as the holdings of institutional investors.
9
These markets include First North, Aktietorget, NGM Equity, NGM Nordic MTF, and Göteborgslistan .
10
In principle, it is possible to track investors and firm headquarters to even a specific part of a Swedish town or a city.
15
distance to the market (benchmark) portfolio. A positive
investor
in Equation 1 classifies
as local biased, whereas a value of zero or less implies that the investor is to be
regarded as unbiased and distal biased, respectively:
∑
(
)
(1)
where,
local bias of investor ,
proportion of stock in the relevant market (benchmark) portfolio to investor ,
proportion of stock in the portfolio (of n stocks) of investor ,
distance between investor to stock , and
value-weighted distance from investor to the market portfolio
This definition aims to identify investors who hold a portfolio distorted towards proximate
firms. It makes clear that the distance from investor to the firm headquarters of stock then
only matters when the stock’s weight (
(
) in the investor’s portfolio deviates from its weight
∑
) in the market portfolio. However, as
∑
, Equation 1 collapses to
giving distance a disproportionally large impact whenever the
) in the investor’s portfolio is greater than its weight (
) in the
market portfolio. Assume three investors (1,2,3) living at the same distance units (
) 5, 10,
weight of a distant stock (
20, 50 and 100 (a distance unit = x miles) from the headquarters of the firms in the market.
Assume further the following stock weights in the market (benchmark) portfolio (
25, 25, 20, and 5 percent (i.e., ∑
10, and 5 percent,
) and in the investors’ portfolios:
45, 30, 0, 0, and 25 percent, and
Inserting in Equation 1 gives the following
11
): 25,
35, 25, 25,
0, 30, 50, 20, and 0 percent.
:s: 18.9, -27.4 and 3.2 percent, respectively.11
1 – (0.35 5+0.25 10+0.20 25+0.10 50+0.05 100)/(0.25 (5+10+20)+0.20 50+0.05 100) = 1 – 19.25/23.75 = 18.9%.
1 – (0.45 5+0.30 10+0.25 100)/23.75) = 1 – 30.25/23.75 = -27.4%.
16
That investor 1 is classified as local biased seems reasonable in both an information
asymmetry and familiarity context, but the classifications of the other two investors appear
less obvious. Despite the fact that nearly half of the portfolio of investor 2 is invested in firms
located closest to the investor, investor 2 is classified as being even more distal biased than
investor 1 is regarded as being local biased. Moreover, investor 3 is classified as local biased
despite not having invested in firms located at the closest distance to the investor. The
definition is therefore unsuitable for examining whether the distortion of investors’ portfolios
is explained by the rational hypothesis or the behavior hypothesis.12
Our study uses a non-distance-based definition of geographical proximity bias applied in a
number of previous empirical surveys. This definition is community oriented and based on
the administrative division of the country studied into separate jurisdictional regions or
districts such as the different states in the US (Nofsinger and Varma (2011)), the provinces in
China (Feng and Seasholes (2004)), and the various Bundesländers in Germany (Baltzer,
Stolper, and Walter (2011)). An alternative definition also adopted in previous surveys,
including Coval and Moskowitz (2001), is built on ideas similar to the gravity models in
localization decision literature and is, in that respect, also distance-based. Similar to the
community- oriented definition, proximity bias is in this definition restricted only to the
geographical area surrounding the investor and/or the firm. This area takes the shape of a
circle, which puts either the investor or the firm at its center point. The circle area is
determined by the applied radius. The early surveys use a radius of either 100 km (Coval and
Moskowitz (2001); Grinblatt and Keloharju (2001)), whereas the radius has been increased to
250 miles in later studies (Ivković and Weisbenner (2005); Ivković et al. (2008); Seasholes
1 – (0.30 10+0.50 20+0.20 50)/23.75) = 1 – 23.0/23.75 = 3.2%.
12
Coval and Moskowitz (2001) themselves adopt another distance-based definition for examining whether information
asymmetry explains the proximity bias phenomenon.
17
and Zhu (2010)13; Seasholes et al. (2011)). This approach is problematic because the longer
the radius, the larger the geographical area defined as local.14 This area is in fact sixteen times
larger when the radius is quadrupled from 100 km to 250 miles (≈ 400 km).15
The community-oriented definition adopted in this study rests on the idea that socioeconomic
and local market conditions are likely to be more homogeneous within the borders of a
community. The dispersion of information may, for instance, incur less friction inside a
community than across its borders, making distance less relevant. Less friction may also be
the case regarding social network-related activities, the degree of investor familiarity in terms
of both knowledge about and ‘knowing of’ local firms, and other behavioral factors including
the sharing of history, heritage and common values in the community, which strengthen the
feeling of belonging. An analysis of investor proximity bias based on this type of definition
has the potential to recognize and capture regional differences within a country that might be
‘hidden’ or disregarded in an analysis based on a distance-oriented definition of local bias.
Evidently, there also may be significant socioeconomic (and market) differences within an
administrative region or district. Such differences can ‘create’ borders within the community
that motivates a further division into smaller administrative units such as municipalities or
(even portions of) cities. However, striving for homogeneity is a balancing act. Driven too
far, the ‘marginal cost’ will exceed the marginal benefit of having a large number of
13
14
15
Seasholes and Zhu (2010) conduct robustness checks also using 100 km and 100 miles.
Grinblatt and Keloharju (2001, p.1066) find that “distance influences the investment behavior of institutions much less
than households”. Their regression results suggest that distances longer than 100 km have a low impact on the investment
behavior of individual investors. Moreover, these researchers find that distance only matters when the Helsinki area (in
which most firm headquarters and investor residences in Finland are located) was excluded from the analysis.
Just as the recommended firm localization is dependent on certain parameters when using a location decision-based
gravity model, the appropriate radius of a local bias study is likely to be largely dependent on how densely populated
(both concerning residents and number of firms) the resulting local bias-defined gravity area is and also on the
infrastructure and topography of the area as well as the characteristics of both investor portfolios/wealth and firms in
terms of size and geographic dispersion (cf. García and Norli (2010)).
18
communities.16 This finding suggests that a further division into smaller units should only
concern those administrative regions or districts within which (it is reason to believe that)
other community borders matter most.17
The community units in this study are determined by county borders. The main reasons for
using counties as a basis for the analysis are threefold:
i. The administrative division of the country studied into counties has a long history. It
was established more than two centuries ago (i.e., already in 1810) and has since then
been maintained for the majority of the counties.18 A few counties have been subject
to reorganizations in 1968 and 1997/98, and today there are 21 counties.
ii. The counties are characterized by being self-governed to a certain extent as well as by
having a cultural and historical heritage. In each county, a county council is elected for
a period of four years. The county council makes decisions regarding county taxes and
fees to govern the county’s main responsibility areas: health care, education and
culture, which includes museums, theatres and county music.
iii. Until 1991, all newborns were assigned two digits in their personal identification
number identifying the hospital and thereby the county in which they were born. This
identification provides an opportunity to also control for investor birthplace bias.
The analysis departs from the administrative county division in Sweden before 1997.19 This
division is fully in line with the registration of newborns into one of 25 counties until 1991.
16
17
18
19
It would, for instance, increase the likelihood that some communities will include very few, if any, observations.
It may be reasonable to give special consideration to highly densely populated regions or districts with respect to both
firms and investors (cf. the local areas determined by distance and/or gravity models).
The first counties were already established in 1634.
Since 1998 there are 21 administrative communities referred to as counties, but we use the prior division to enable
robustness checks and to control for any differences between the proximity bias of individual investors in urban and rural
areas. In the 1998 division, the greater areas of the second and third largest cities are not counties of their own.
19
The greater area of the capital and the third largest city are here counties of their own, but the
greater area of the second largest city consists of the fractions of three neighboring counties.
In the analysis, we have ‘merged’ these fractions into a fictitious county, making the areas of
the three neighboring counties somewhat smaller.20 After these adjustments, the analysis is
based on 26 domestic counties (districts) of which three are classified as urban areas and the
remaining 23 as rural areas. In accordance with their postal zip code in the data set, firms as
well as individual investors not living abroad are localized to one of these districts.21
When measuring the local bias of the individual investors in the various districts, we follow a
similar logic as the one launched by Coval and Moskowitz (1999). The distance variable
(
) in Equation 1 is, however, changed to a dummy variable taking the value = 1 if the firm
headquarters of stock
is located in the same district as the residence of investor . In
accordance with Seasholes and Zhu (2010), the local bias of investor
is then computed as
the difference between the share of local stocks in the portfolio of the investor (
∑
) and the weight of local stocks in that district’s benchmark (market) portfolio
divided by the latter22:
∑
∑
(
)
∑
(2)
where
local bias of investor in district ,
share of district
(local) stocks in the portfolio of investor ,
20
Evidently, the ‘created’ district of the greater area of the second largest city does not fully match the prior birth
registration. We therefore have assumed that investors born in any of the three neighboring counties were born in the
district (including the created greater city area district) in which they are residents at the first time they ‘appear’ as an
individual investor in the Euroclear data set. In case the individual investor did live elsewhere at that time, depending on
the two ‘birth’ digits, we refer the investor to either the greater city area or to one of three artificial birth districts.
21
Investors living abroad are categorized as either living in another Nordic country, in other parts of Europe or elsewhere.
As mentioned previously, firms with headquarters abroad have already been removed.
22
Seasholes and Zhu (2010) do not calculate the individual local bias explicitly but rather the average local bias of all
individual investors.
20
dummy variable = 1 if stock is located in the same district
value-weighted share of district
as investor , else = 0, and
stocks in investor ’s benchmark (market) portfolio.
The investors’ birthplace bias can only be computed for domestic investors. After dividing
foreign and domestic individual investors into two separate groups, we distinguish in which
of the 26 districts each domestic investor was born.23 After minor modifications, Equation 2
can also be used to compute the birthplace bias (
now the dummy variable
which investor
(
)
) of each domestic investor , although
equals 1 if stock is located in the same district
was born. Hence, the birthplace bias of investor
, where
is given by:
the share of stocks in the portfolio of investor
headquarters located in the birthplace district
of the investor, and
share of stocks with firm headquarters located in the birth district
as the one in
with firm
the value-weighted
of investor
in the
benchmark (market) portfolio of the investor.
Similar to the distance market-weighted local bias measure defined and used by Coval and
Moskowitz (1999), the community-derived market-weighted proximity bias measures based
on Seasholes and Zhu (2010) are binary. This quality is sufficient for conducting the
hypothesis tests but not for answering the three sub-questions in full. In principle there is not
any defined limit as to how large the community-derived proximity (local or birthplace) bias
measure
of an investor can be. The value range of this measure is -
(The corresponding range of the former distance market-weighted local bias measure is
instead the reverse, i.e., -
.). Because of this distribution asymmetry in
existing proximity bias measures, it is not possible to exactly determine—let alone
compare—the magnitude of investors’ local bias and/or birthplace bias. With a relative
23
Some investors may be referred to an artificial birth district (see footnote 19).
21
community proximity bias measure, it is possible to distinguish which investor is more biased
amongst the investors living (or born) within the same district at a certain point in time, but
not over time and space.
Seasholes and Zhu (2010) first compute the unweighted share of local stocks in the portfolio
of an average investor
∑
and then deduct the value-weighted share of local
stocks in the relevant benchmark (market) portfolio
∑
Thereafter, the researchers divide the residual by the latter, i.e.,
of this investor.24
, to obtain what
we consider the unweighted relative local bias of the average investor. The problem is that
this ratio is largely affected by differences in district market weights (
) between densely
and sparsely populated districts. The greater the divergence between the various district
benchmark weights, the more ambiguous this relative type of local bias measure will be.
Consequently, the measure can neither serve as a valid reference point for the proximity bias
of a particular investor in a certain district nor be added and averaged over different districts.
However, Seasholes and Zhu (2010) also report the ‘absolute difference’ between local
stocks in the unweighted average investor portfolio
benchmark portfolio
and local stocks in the average
as a complementary measure of local bias. On an aggregate level,
this measure of investor proximity bias is much less affected by divergences in benchmark
weights than their corresponding relative measure. When seeking the answers to the first two
sub-questions (SQ1 and SQ2), the proximity bias of the average individual investor is
therefore calculated in ‘absolute’ terms as displayed in Equation 3:
∑
∑
(3)
where,
24
In Seasholes and Zhu (2010) there is no superscript (u) attached to A because the researchers do not make any distinction
with respect to whether the share of local stocks in the investor portfolio or the market portfolio is value-weighted or
unweighted.
22
unweighted average investor local bias in absolute terms,
unweighted share of local stocks in portfolio of the average investor, and
value-weighted share of local stocks in the average benchmark portfolio.
The unweighted ‘absolute’ average investor birthplace bias (
Both
and
) is computed accordingly.
may be used to analyze changes over time and thus to seek answers to the
remaining third sub-questions (SQ3). However, because these proximity bias measures are
only partially weighted,25 the actual size of the portfolio of the individual investor is not taken
into account when computing the proximity distortion of the average investor portfolio. Small
investors are given the same weight as large investors. This approach may lead to either
lower (underestimated) or higher (overestimated) average investor proximity biases in
comparison to the ones derived with value-weighted averages, taking into account how large
of a share of the total capital invested by all individual investors living (born) in district
is placed in the stocks of local (‘birthplace’) firms. Equations 4 and 5 demonstrate the way in
which fully value-weighted proximity bias measures on both the district level and the country
level are derived by adjusting Equation 3:
∑
(4)
∑
∑
(5)
where,
value-weighted average investor local bias in absolute terms in district ,
value-weighted average investor local bias in absolute terms (country level),
invested capital by investor (in district ) in the total portfolio of the investor,
25
The relevant benchmark portfolio to each investor
and
, respectively) are value-weighted averages of proximate
stocks in the benchmark portfolio to investor , whereas the averages of the share of proximate (local/birthplace related)
∑
stocks in the portfolios of individual investors (∑
respectively) are unweighted averages.
23
value-weighted share of local stocks in the average investor portfolio in district , and
value-weighted share of local stocks in the average investor portfolio (country level).
The corresponding value-weighted birthplace biases of the average investor on both district
and country levels are derived accordingly, allowing us to fully explore and seek answers to
the sub-questions (SQ1 – SQ3) derived in Section 3. The related hypotheses (H01 – H03) on
whether investor proximity bias pays off are analyzed and tested using several measures of
returns, including one-factor alphas (over the value-weighted market portfolio that is
constructed by using all the stocks in our data), and Fama and French (1993) 3-factor alphas.
This approach provides the model in Equation 6:
(6)
where,
return on the portfolio of investor in period ,
risk-free rate in period ,
return on the market portfolio in period ,
difference in returns of small- and large-capitalized stocks in period , and
difference in the returns of high and low book-to-market stock in period .
The size and value portfolios are reconstituted by partitioning all the stocks into two size
portfolios26 (small and large) and three value portfolios27 (value, neutral, and growth), ranked
by market value and book-to-market value ratio. The constituents of each of the six portfolios
26
The top 50 percentile of stocks ranked by market value are partitioned into ‘large’ portfolios and the bottom 50 percentile
of stocks ranked by market value are partitioned into ‘small’ portfolios.
27
The top 30 percentile of companies ranked by book-to-market value ratio are partitioned into ‘value’ portfolios, the bottom
30 percentile of companies ranked by book-to-market value ratio are partitioned into ‘growth’ portfolios, and the middle
40 percentile of companies ranked by book-to-market value ratio are partitioned into ‘neutral’ portfolios.
24
(small value, small neutral, small growth, large value, large neutral, and large growth) are
weighted by their market value.
Because our semi-annual investor data also contain hand-collected information on the stocks
listed on minor stock markets, the holding period returns and alphas are calculated by
marking investors’ positions (that are open) in the market at the end of the relevant sample
period by using semi-annual observations. By proceeding in this way, we assume that we can
evaluate the portfolios of investors by their long-horizon holding period returns. Thus, we
assume that each of these factor benchmark characteristics can be a proxy for market
mispricing and can reflect models of risk. The deviation from these benchmarks should
therefore measure the abnormal performance of investors’ holdings. If these benchmarks
capture market mispricing, then our analyses describe the ability of proximity-biased
investors to outperform any profits they earn based upon well-diversified portfolios and any
other return predictors.
Although the measurement of long-horizon holding period returns is common in previous
empirical studies (see, e.g., Coval et al. (2005); Seasholes and Zhu (2010)), this method can
cause statistical problems because the returns may not be independent observations
(Seasholes and Zhu (2010)). To overcome this pitfall, we implement the Newey-West
correction to our standard errors and take into account serial correlation up to three lags.
In line with previous empirical studies, we observe that many investors hold only one (almost
every second investor) or two stocks (the median) in their portfolio. These stocks tend to have
been issued by nationwide firms such as Telia, H&M and Ericsson. The former firm
originates from the state-owned telecom monopoly that was privatized and listed in 2000,
25
whereas the two latter firms are on average the largest stocks in terms of market cap during
the time period studied. Instead of ‘dropping’ these firms, we examine the performance of the
average portfolio through a subsample, only including investors holding portfolios containing
more than six stocks (the 90th percentile of investors in our data).
It seems reasonable to assume that investor sophistication is positively correlated with the
size of the portfolio and that individual investors are likely to be more sophisticated the more
stocks their portfolio contains. To examine whether the proximity distortion of portfolios of
more sophisticated investors is likely to reflect their access to superior information to a larger
extent than other investors, we examine the performance of the portfolios belonging to the
subset of investors within the 99th percentile, i.e., investors with portfolios containing more
than 21 stocks. In addition, we evaluate the portfolio returns conditional upon the ‘strength’
of the portfolio distortion of the individual investor.28
The ‘strength’ of the individual investor’s proximity bias is derived through ‘normalization’
of the distributional properties of the proximity bias measure in Equation 2. If the share of
local stocks in the portfolio of investor
in district
stocks from this district in the market portfolio (
of the investor (
(
), i.e.,
) is greater than the proportion of
, the calculated local bias
) is adjusted and recalculated with a ‘skewness’ factor
a normalized local bias measure
( –
as displayed in Equation 7 (the investors’
) into
-
values are adjusted accordingly):
28
We have performed a battery of additional robustness analyses (untabulated), which are available on request. These
include: 1) a comparison and examination of the performance of total, proximate, and remote investor portfolios, 2) an
evaluation of the portfolios of investors who exhibit greater local bias than the average investor in each holding period, 3)
an examination of the difference in the portfolio returns of investors moving in and out of their birthplace district, and 4) a
test of abnormal returns in the portfolios of investors moving in and out of urban/rural areas, respectively.
26
(
)(
(
)
)
(
(
)
)
for
(7)
else
where,
normalized local bias of investor in district .
These adjustments in the investor proximity bias measure results in normalized proximity
bias measures (
) that are constrained to the value range: -
. This approach
makes it possible to assess and compare the magnitude of individual investor biases (on a 100
percentage scale) across the country (i.e., between districts) and to distinguish the districts in
which the equity portfolios of individual investors are especially distorted toward proximate
or remote firms, respectively. In addition to comparing the ‘strength’ of the proximity bias of
individual investors in different districts, we can also examine whether and to what extent the
investors change their portfolios over time and whether the ‘strength’ of investor proximity
bias has any effect on portfolio returns.
5. Analysis and Results
5.1. The average local bias of individual investors
Table I provides the answer to the first sub-question (SQ1): “Do investors who are living in
their birth district hold portfolios that are more tilted toward local firms than investors who
were born elsewhere?” The table displays the average proximity bias of individual investors
both in terms of the unweighted absolute local bias (
Equation 3 and the corresponding value-weighted bias (
) obtained in accordance with
) obtained using Equation 5.
Table I confirms the findings reported in previous empirical studies (Massa and Simonov
(2006) and Bodnaruk (2009)) that the portfolios of individual investors in Sweden, as in
many other countries (see review in Section 2), are on average tilted towards local stocks.
27
Individual investors demonstrate a 7.7 % value-weighted (
) absolute local bias on
average over the nine periods studied.29 In each period, the local bias measures are positive.
A more detailed analysis based on Equation 4 (not shown in Table 1) validates, with very few
exceptions, the fact that individual investors are on average also local biased on the district
level. These results are stable over time and in accordance with previous empirical findings—
at least on an aggregate level.
[Insert Table I]
The picture is different on the individual level (untabulated). In reality, less than one-third of
individual investor portfolios are tilted towards local stocks. This lower degree of portfolio
distortion on the individual level is likely to be explained by the fact that approximately half
of the firms are headquartered within the capital district. In this district, more than two-thirds
of the individual investors’ portfolios are distorted toward proximate firms. Because earlier
research does not report the proportion of individual investors who invest in a locally
distorted portfolio we cannot make any comparisons with other studies on the individual
level.
A novelty in relation to previous research is that Table I makes a distinction between the
average local biases of the individual investors living in their birth district and those living in
a district other than the one in which they were born. Using this separation, we can make
comparisons that help to answer the first sub-question. The two measures of proximity bias
presented in the table appear to provide results that are not in conformity with each other. On
one hand, the table shows that in each period the value-weighted absolute local bias (
29
We simply calculate the overall averages from Table I.
28
) of
the average individual investor who lives in the birth district is significantly greater than the
corresponding local bias of investors living in the same district but born elsewhere.30 The
value-weighted absolute local biases are on average 8.9 % and 4.7 %, respectively, over the
nine periods. This result implies that in terms of the capital invested on the district level,
investors who were born in the district in which they live invest proportionally larger
amounts in local firms than other investors. On the other hand, the unweighted absolute local
bias measure (
), which is adopted from Seasholes and Zhu (2010), displays statistically
insignificant results. This measure is not adjusted for different portfolio sizes and implies that
the place of birth does not matter for the average individual investor. However, a more
detailed analysis (not tabulated) reveals that in a clear majority of the 26 districts, the
unweighted absolute local bias of the average individual investor living in a district is greater
in each period if the investor was born in that district. This situation is the case in three out of
four districts including the capital district. The latter result is important because the capital
district is both the largest district and the district that displays the highest averages of all
districts in terms of unweighted absolute local bias (
). Many rural residents have moved
into urban districts over the years, particularly into the capital district. This factor affects the
unweighted absolute local bias averages displayed in Table I. When excluding the capital
district from the calculation, these biases become greater in each period for the average
investor living in the birth district (not tabulated). This finding suggests an affirmative answer
to the first sub-question, i.e., individual investors who are living in their birth district are on
average holding portfolios that are more locally distorted than the portfolios of other
investors living in the same district but born elsewhere (the value-weighted absolute local
30
The survey covers the total population but its results are treated as if it is a sample and, thus, tested statistically. The
observed difference in average local biases (measured in terms of
) of individual investors born in the district and
born elsewhere is shown to be statistically significant at the 1% level.
29
bias is 4.2 % (significantly) larger for individual investors who are living in their birth
district).
5.2. The average birthplace bias of individual investors
Table II presents the results related to the second sub-question (SQ2): “How does the
birthplace bias of investors who have moved into another district compare with the bias of
investors living in the area in which they were born?” The individual investors appear to
show birthplace bias in each period. On average, the value-weighted absolute birthplace bias
is 6.2 % over the nine periods. The birthplace bias of the average investor tends to be lower
when the investor lives in a district other than the birth district. In all nine periods, the
absolute birthplace bias of investors living in the district in which they were born is on
average significantly greater than the bias of those living elsewhere. The overall valueweighted absolute birthplace bias over the nine periods is on average 8.9 % for investors
living in the birth district and 1.5 % for investors who moved into another district. The result
is statistically significant (at the 1 % level) regardless of whether the measurement of average
birthplace bias is based on unweighted (
) or value-weighted (
) absolute birthplace
bias measures. The result is also shown to be valid on the district level (not tabulated). The
answer to sub-question two (SQ2) is thus straightforward: the birthplace bias of investors
who have moved into another district is lower than the corresponding bias of investors living
in the area in which they were born.
[Insert Table II]
One interesting observation is that the birthplace bias of investors living in a district other
than their birth district is on average significantly (at the 1 % level) lower than their current
local bias in the district in which they live (Over the nine periods, the average value-weighted
absolute local bias and birthplace bias for individuals who moved into another district are
30
4.7 % and 1.5 %, respectively). As seen in Tables I and II, this observation appears to be
stable over time and is present in all nine periods.
5.3. The average proximity bias of individual investors who moved to another district
The answers to the first two sub-questions appear to align with Bodnaruk (2009), who
reported that the proximity bias of investors is time dependent. The finding that the birthplace
bias of investors living in the area in which they were born is on average greater than the
local bias of other investors living in this area implies that the local bias of an investor is
greater the longer the investor has been living in a district. The question of whether the
proximity bias of investors really is time dependent is not fully proven, however. An answer
to the third sub-question (SQ3): “How are the portfolios of investors who have moved from
one district into another affected over time?” requires us to study the development of the
proximity biases of those investors who moved into another district in the second period (i.e.,
in December 2006) and remained in this district in all subsequent periods.
[Insert Table III]
Table III depicts the development of the average proximate biases of ‘moving’ individual
investors both in unweighted and value-weighted terms. The table also shows the absolute
average tilt (referred to as the counterfactual bias) of the investor portfolios toward the stocks
of firms headquartered within the district to which the investor will move in the next period
(see the first row in Table III). Clearly, the counterfactual bias of the average individual
investor is lower than the investor’s proximity biases in the coming years (2.03 % and
2.73 %, respectively, of the average absolute value-weighted local bias over the eight
periods). This result is fully in accordance with the geographical proximity bias phenomenon.
The increasing averages over the entire time period studied of both the unweighted and
particularly the value-weighted absolute local biases of the individual investors suggest, in
31
line with Bodnaruk (2009), that the phenomenon is also time dependent. A more detailed
analysis reveals that this time dependency is still prevalent when excluding those individual
investors who did move back to the district in which they were born (not tabulated), albeit the
increase in the average local bias of the remaining individual investors is then less
pronounced. Another observation that is not shown in Table III is that the average birthplace
bias of investors who did move away from their birth district to another district appears to
decline over time. Finally, it is also revealed that the number of individual investors holding
portfolios that are distorted toward proximate firms increased by more than ten percent
(11.1 %) over the four years studied. These results are also stable when controlling for
investors who move to and from the capital district. Accordingly, the answer to the third
question is that the average individual investor who moves into another district tends to
increase the share of local stocks in their portfolio over time.
The answers to the three questions are consistent because our results support the existence of
proximity bias both in terms of local bias (as has been observed in many previous empirical
surveys) and birthplace bias. However, it still remains to discern and examine the reason for
the proximity bias of the average individual investor. As seen below, we report the results
from the tests of the three hypotheses outlined in Section 3. We test for abnormal returns
using one-factor alphas (the CAPM model) and Fama and French (1993) three-factor alphas.
In connection with these tests, we also perform robustness checks with respect to small
portfolios in terms of stocks included, investor sophistication and the ‘strength’ of individual
investor proximity bias.
32
5.4. Testing the abnormal returns of local and birthplace biased portfolios
The three hypotheses are tested using the localization of the firm’s headquarters as the
‘analysis unit’. This approach is in accordance with the majority of empirical surveys of
proximity bias and also appears to be reasonably well aligned with the rational information
asymmetry-driven hypothesis. Because vital firm decisions are made by top management,
any leakage of business information stems primarily from the headquarters of the firm. The
familiarity concept also appears to fit well with the choice of headquarters because official
press releases are provided by firm headquarters. However, an investor’s interest in a
particular firm might also originate from living close to another establishment of the firm,
such as a production plant or a sales office. Although this possibility is considered in some
surveys, these studies tend to arrive at the conclusion that the analysis results do not
differentiate significantly from the ones based on firm headquarters as the only localization
point (cf. Massa and Simonov (2006); Bodnaruk (2009)). This conclusion also appears to be
valid for studying the behavior-oriented hypothesis of the proximity bias phenomenon. On
one hand, the likelihood is greater that an investor’s share of local stocks will be larger when
more firms are classified as local. On the other hand, this situation also means that widely
established firms will be classified as local in many communities, leading to a greater
likelihood of a larger share of ‘local’ stocks in the value-weighted benchmark (market)
portfolio of relevance to this investor as well. Consequently, when taking other firm
establishments into account, it is not certain whether investors’ portfolios will be classified as
more or less distorted toward proximate firms. In the analysis, the likely effect of local
investors’ increased recognition of the stock of a firm with a local establishment and its
headquarters in another community will be blurred by the recognition of that stock by other
investors and, particularly, by those living in the community where the headquarters of the
firm is located. This situation makes it both rational and reasonable to use the location of firm
33
headquarters and investor residence as the analysis units when examining whether individual
investors’ proximate bias pays off.
Table IV shows the results obtained when testing the first hypothesis (H01): “Investors who
live in their birth district and are proximity biased do not earn abnormal returns on their
local portfolios.” In the first section of the table (Panel A), it is clearly shown that the local
portfolios of all individual investors living in their birth district did on average underperform,
by 1.67 basis points, during the time period studied. The investors holding portfolios distorted
toward proximate firms (
> 0 %) underperformed even more (-1.85 bps.), showing greater
negative risk-adjusted returns than the average investor living in the birth district. These
results are statistically significant and suggest that alphas are significantly negative.
However, because our hypothesis is a one-tailed test, testing whether alphas are significantly
positive, H01 cannot be rejected. Certainly, the reported results in Panels B and C include
overperforming portfolios, but these are total portfolios and regard both the average
individual investor’s portfolios containing more than six and 21 stocks, respectively, and the
corresponding proximity-biased investor living in the birth district. It should also be noted
that these portfolios display negative risk-adjusted returns in the three-factor model,
indicating that the positive alphas in the total portfolios of individual investors are not
because of systematic deviations from the market portfolio. The risk in the positive alpha
portfolios that investors bear and CAPM does not measure is captured in the three-factor
model. Moreover, the local investments of these more ‘diversified’ investors’ portfolios do
not show abnormal returns on average. Hence, it is not the investments in proximate firms
that cause the positive or less negative (three-factor alpha) results.
[Insert Table IV]
34
Using the normalized local bias measure derived in Equation 7, we also test whether the
‘strength’ of proximity bias pays off by examining the risk-adjusted returns on highly
distorted portfolios toward proximate firms (defined as
> 50 %) held by individual
investors living in their birth district (not tabulated). These portfolios on average also do not
generate abnormal returns. The average individual investor that lives in the birth district does
not appear to earn abnormal returns on local portfolios regardless of sophistication. The first
hypothesis (H01: α ≤ 0) cannot be rejected.
Table V presents the results from testing the second hypothesis (H02): “Remote investors do
not earn abnormal returns on their birthplace- oriented portfolios.” These results are similar
to the ones obtained when testing H01. Investors’ birthplace-oriented portfolios exhibit
negative risk-adjusted average returns (-2.19 bps.). For birthplace-biased investors (
>
0 %), the alpha is even more negative (-2.44 bps.). For investors with a portfolio of more than
six stocks, the negative returns on birth district investments are poorer regardless of whether
CAPM (-3.08 bps.) or the three-factor model is used (-4.05 bps.). The investors’ portfolios
that are distorted toward birth district firms also generate larger negative alphas than their
total birth district portfolios (-4.04 bps. vs. -3.08 bps.).
[Insert Table V]
Panel A clearly shows that the birthplace portfolios of remote individual investors did on
average underperform during the time period studied regardless of whether the investor was
holding a portfolio tilted toward firms in the birth district. These results remain the same
when testing for a very high birthplace bias (
> 50 %) of these investors (not tabulated).
The total portfolios of individual investors holding more than six stocks in their portfolios
(see Panel B) and ‘sophisticated’ investors (see Panel C) did overperform, however. These
35
positive abnormal returns are not the result of the adoption of a birthplace-biased investment
strategy. The three factor model displays no abnormal returns. Moreover, the more
‘diversified” investors appear to lose even more on their birth district stocks (alphas are -.3.26
bps vs. -3.08 bps.). The alphas are also negative (-4.53 bps. vs. -4.04 bps.) when the more
diversified investors exhibit birthplace bias in their birth district portfolios. Hence, the
investment of remote individual investors in their birth district appears on average not to be
information driven. Our second hypothesis (H02: α ≤ 0) also cannot be rejected.
The results of the testing of the third and final hypothesis (H03): “Investors who move into
another district and increase their proximity bias do not earn abnormal returns on their local
portfolios”, are displayed in Table VI. The pattern is similar to the results of the testing of
the first two hypotheses; the individual investors who have moved into a new district at the
end of 2006 and remained in this district during the subsequent semi-annual periods until the
end of 2010 do not appear to have earned any abnormal returns on their average local
portfolio. The risk-adjusted returns on these portfolios are on average either significantly
negative or insignificantly small irrespective of whether the individual investors exhibit local
bias, hold slightly more diversified portfolios (-2.25 bps) or are sophisticated in terms of
holding a seemingly well-diversified portfolio (-7.06 bps). The risk-adjusted returns even
appear to be negatively correlated with sophistication.
[Insert Table VI]
The picture does not change when testing for the ‘strength’ of local bias; highly distorted
local portfolios (
> 50 %) also underperformed (not tabulated). These results should be
expected given the previous hypotheses test results. Accordingly, the third hypothesis (H03: α
≤ 0) also cannot be rejected.
36
5.5 Robustness analyses
To test the robustness of our results we explore the relation between the proximity bias and
the abnormal return by performing two additional performance measures: the ex post Treynor
ratio and the portfolio alphas using the calculated portfolio betas. An added benefit of this
approach is that the use of calculated beta for each stock that can provide robustness against
any structural instability that plagues our CAPM forecasting regressions to estimate the
portfolio alphas and the betas. Indeed, our application appears supportive of the previous
evidence demonstrated in our Tables.
To calculate the unconditional ex post Treynor ratio we follow the standard procedure of dividing the
excess return on the individual portfolio for the sample period by the portfolio’s systematic risk, the
portfolio beta for that period. Thus, the ex post Treynor ratio is:
. We define
the excess return on a portfolio of risky assets as the difference between the 6-month holding-period
return on the portfolio of risky assets and the return on a Swedish Treasury bill expiring at the end of
the 6 month period. The beta,
for each stock, is defined as dividing the covariance between the
stock return and the market return by the variance of the market portfolio,
portfolio beta,
. The
is defined as weighting the stock beta by the percentage of the individual portfolio
represented by holding the stock at the end of the period,
∑
In addition to the Treynor Ratio, we calculate the alpha as the difference between the expected return
on the portfolio and the required return on the portfolio using the calculated portfolio betas:
.
(8)
We test whether the portfolio alphas are significantly larger than zero (one-sided test) and then
compare the results with the evidence that are obtained in Tables IV, V, and VI. Table VII presents
37
our findings that pertain to the robustness of our underlying forecasting relations between the local
bias and the abnormal return.
[Insert Table VII]
Panel A of Table VII demonstrates the values of the Treynor ratio, and the alphas for i) the
total and the local portfolios for individuals who live in the birthplace, and ii) the total and the
local portfolios for individuals who live in the birthplace and are local biased. Panel B of
Table VII demonstrates the same analyses as Panel A but for the distal birthplace investors.
Panel C demonstrates the same analyses as Panel A but for the investors who moved into
another district and increase their local bias. Thus the analyses in Panel C focus on changes in
investor holdings and in local bias thereby the analyses eliminates the passive investors from
the sample. Moreover, the table demonstrates the values of the Treynor ratio, and the alphas
for portfolios including between 0 and 6 stocks, between 6 and 21 stocks, and larger than 21
stocks. T-statistics are used to test whether alphas are significantly larger than zero.
Table VII shows that the local portfolios in all the panels and all the samples have a negative
Treynor ratio and a negative alpha. Moreover, the alphas in all the local portfolio samples are
not significantly larger than zero. In Panel A, we observe small positive Treynor ratios and
alphas in the total portfolios including between 6 and 21 stocks and total portfolios including
larger than 21 stocks. However, the alphas are not statistically larger than zero. In Panel B, all
the values of the Treynor ratios and the alphas are negative and the alphas are not
significantly larger than zero. In Panel C, we observe the small positive Treynor ratio and
alpha values in the total portfolios. Notably, the alphas are not statistically larger than zero.
These results are consistent with the evidence that are demonstrated in Tables IV, V, and VI
and arrive at an overall conclusion that the average individual investor, regardless of living in
38
the birthplace or moved into another district and regardless of his/her financial sophistication,
does not appear to earn abnormal returns.
6. Conclusions
The analysis supports the findings of previous empirical research on the geographical
proximity bias phenomenon by documenting that the portfolios of individual investors are, on
average, tilted toward the stocks of firms headquartered in the local area. Our study rests on a
community-based definition of proximity bias, allowing an extended analysis covering an
unexplored dimension of the phenomenon through the concept of birthplace bias. Based on
this analysis, we conclude that the average investor’s proximity bias grows stronger the
longer the investor has lived within the same community and is strongest for investors who
have never moved from their birthplace area. This result implies that the proximity bias of
individual investors is time-dependent.
First, the birthplace bias of investors living in the community in which they were born is on
average shown to be significantly stronger than the average birthplace bias of remote
investors. Second, investors who moved to another community during the time period studied
on average gradually increased their holdings of the stocks of local firms in that community.
Third and finally, the birthplace bias of moving investors on average declined markedly the
period after they moved from their birthplace district, but thereafter the bias appeared to
stabilize and remain unchanged even if the investor moved to yet another district other than
the birth district.
The hypothesis tests disclose that the distortion of the average investor’s portfolio towards
proximate firms did not generate any abnormal return during the time period studied. None of
39
our three null-hypotheses can be rejected. This result indicates that local investors have
neither access to superior information nor any advantage in interpreting existing information.
Hence, in answering the overall research question, we resort to the behavioral hypothesis to
explain the observed proximity biases of individual investors.
The difference between the average birthplace bias of investors living where they were born
and those living elsewhere suggests that the geographical proximity bias phenomenon is
likely to be explained mainly by overconfidence among local investors. It appears as though
the average investor has overestimated either the access to qualitative private information on
proximate firms or the ability to interpret available business information. An emotionally
oriented explanation appears less sensible given the comparatively lower average birthplace
bias of remote investors. In that respect, investors differ from sports team supporters.
Notably, the lower average birthplace bias of remote investors does not rule out the
possibility that the geographical proximity bias phenomenon is explained by other behavioral
factors. We find, for instance, that remote investors are on average more birthplace biased
than they are counterfactual biased, implying that individual investors’ proximity bias might
be contextually dependent and more or less latent until being ‘activated’ if the investor moves
into the birthplace district again. However, this finding can also be linked to the
overconfidence of investors, i.e., they overestimate the ‘value’ of information from their
social network in the birth district.
This study suggests future research and further examination of the reasons behind the
geographical proximity bias phenomenon. One obvious avenue of research occurs within the
behavior finance area, in which our study results can serve as a point of departure for
exploratory surveys of the birthplace bias of individual investors. Another possible avenue of
40
research concerns the development of appropriate bias measures. In our study, we have used
a non-distance-based, community-oriented definition of geographical proximity bias. Using
this definition, we have come one step further in measuring the proximity bias of individual
investors because this definition rests on the idea that socioeconomic and local market
conditions are likely to be more homogeneous within the borders of a community. At the
same time, we have sidestepped the issue of determining the appropriate radius length of
gravity models in distance-based definitions. In previous research that adopts a gravity
model, the analysis is based on a fixed radius length determined without consideration of the
specific features of the gravity area created. We argue that this distance-based definition of
local bias could be further explored and suggest the challenging assignment of developing a
multi-gravity model consisting of geographical proximity bias areas (circles) of various
radius lengths depending on the specific characteristics of the areas. Such a model will have
the potential to combine the strengths of the community-oriented and distance-based
definitions to create a more fine-tuned measure of the proximity bias of individual investors
for distinguishing areas in which proximity bias is more prevalent or less so.
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Table I
The average local bias of individual investors
The semi-annual investor data are obtained from the Security Register Center of Euroclear Sweden and stretch
from July 2006 to June 2010. This table shows results from analysis of holdings-based calendar-time portfolios.
The first column indicates the population and sub-samples in our data. The second column shows the semiannual period that is studied. The third column reports the number of individual investors in each investor group
over the period, and finally, the fourth and the fifth columns report the average un-weighted (
) and valueweighted (
) absolute local bias, respectively.
Local Bias % .
All the individuals
Live in the birthplace
Moved into the district
All the individuals
Live in the birthplace
Moved into the district
All the individuals
Live in the birthplace
Moved into the district
All the individuals
Live in the birthplace
Moved into the district
All the individuals
Live in the birthplace
Moved into the district
All the individuals
Live in the birthplace
Moved into the district
All the individuals
Live in the birthplace
Moved into the district
All the individuals
Live in the birthplace
Moved into the district
All the individuals
Live in the birthplace
Moved into the district
Period
June 2006
June 2006
June 2006
December 2006
December 2006
December 2006
June 2007
June 2007
June 2007
December 2007
December 2007
December 2007
June 2008
June 2008
June 2008
December 2008
December 2008
December 2008
June 2009
June 2009
June 2009
December 2009
December 2009
December 2009
June 2010
June 2010
June 2010
# of Investors
1,693,562
975,634
717,928
1,712,682
1,070,739
641,943
1,796,790
1,053,805
742,985
1,761,706
1,030,762
730,944
1,747,061
1,020,946
726,115
1,742,562
1,015,350
747,024
1,747,098
1,017,589
729,509
1,728,846
1,002,994
725,852
1,709,072
990,540
718,532
45
4.4
4.5
4.3
4.1
4.0
4.1
4.6
4.5
4.8
4.8
4.7
4.9
4.9
4.9
5.0
4.6
4.6
4.5
4.3
4.4
4.2
4.7
4.8
4.7
5.2
5.2
5.1
6.8
7.9
4.4
7.2
8.4
4.5
7.9
8.9
5.2
8.3
9.5
5.3
8.5
9.7
5.7
7.6
8.7
4.4
7.2
8.4
4.0
7.6
8.9
4.3
8.2
9.5
4.9
Table II
The average birthplace bias of individual investors
The semi-annual investor data are obtained from the Security Register Center of Euroclear Sweden and stretch
from July 2006 to June 2010. This table shows results from analysis of holdings-based calendar-time portfolios.
The first column indicates the population and sub-samples in our data. The second column shows the semiannual period that is studied. The third column reports the number of individual investors in each investor group
over the period, and finally, the fourth and the fifth columns report the average un-weighted absolute (
) and
value-weighted birthplace bias
, respectively.
Birthplace Bias %
All individuals
Live in the birthplace
Moved to another district
All individuals
Live in the birthplace
Moved to another district
All individuals
Live in the birthplace
Moved to another district
All individuals
Live in the birthplace
Moved to another district
All individuals
Live in the birthplace
Moved to another district
All individuals
Live in the birthplace
Moved to another district
All individuals
Live in the birthplace
Moved to another district
All individuals
Live in the birthplace
Moved to another district
All individuals
Live in the birthplace
Moved to another district
Period
June 2006
June 2006
June 2006
December 2006
December 2006
December 2006
June 2007
June 2007
June 2007
December 2007
December 2007
December 2007
June 2008
June 2008
June 2008
December 2008
December 2008
December 2008
June 2009
June 2009
June 2009
December 2009
December 2009
December 2009
June 2010
June 2010
June 2010
46
# of Investors
1,587,350
975,634
611,716
1,712,682
1,070,739
641,943
1,687,424
1,053,322
634,102
1,654,922
1,029,890
625,032
1,641,761
1,019,827
621,934
1,637,886
1,014,030
623,856
1,642,283
1,015,793
626,490
1,625,352
1,001,088
624,264
1,607,200
988,501
618,699
3.2
4.5
1.4
3.0
4.0
1.2
3.4
4.5
1.5
3.5
4.7
1.5
3.5
4.9
1.4
3.3
4.6
1.2
3.0
4.4
0.9
3.4
4.8
1.1
3.7
5.2
1.3
5.6
7.9
2.0
5.9
8.4
1.7
6.3
8.9
1.7
6.7
9.5
1.8
6.5
9.7
1.4
6.0
8.7
1.2
5.8
8.4
1.3
6.2
8.9
1.5
6.6
9.5
1.3
Table III
Development of the proximity bias of those investors that did move to another district
The semi-annual investor data are obtained from the Security Register Center of Euroclear Sweden and stretch
from July 2006 to June 2010. This table shows results from analysis of holdings-based calendar-time portfolios.
The table reports the average un-weighted,
and value-weighted,
absolute local bias and the average
un-weighted
, value-weighted
absolute birthplace bias for investors that did move to another district
in the second period (612) and thereafter remained in this district in all other periods. Statistics are shown in
each period. Two different statistics are obtained for the first period (606). The first row is the counterfactual
bias indicating the absolute average bias towards firms located in the district to which investor is moving the
next period. The remaining rows report the average bias in investors’ current local and birth districts.
Local Bias %
Counterfactual ALB
’Old’ district
’New’ district
’New’ district
’New’ district
’New’ district
’New’ district
’New’ district
’New’ district
’New’ district
Period # of Investors
606
9170
606
9170
612
8824
706
8885
712
8928
806
8968
812
9014
906
9061
912
9113
1006
9170
Birthplace Bias %
# of Investors
2.21
2.43
2.40
3.03
3.20
3.17
2.87
2.52
3.03
3.44
47
2.03
1.34
2.03
2.05
2.80
2.72
2.87
2.50
3.25
3.62
8523
8188
8250
8289
8329
8375
8419
8473
8523
2.59
2.74
3.32
3.41
3.29
2.88
2.47
2.86
3.16
1.91
1.81
2.30
2.29
1.93
2.28
2.03
2.21
2.62
Table IV
Results from alpha analyses for investors living in the birth district
This table shows results from the analysis of holdings-based calendar-time portfolios of investors living in their
birth district. The dependent variable is the excess return of investors’ total portfolio when the performance of
total portfolios is analyzed and it is the excess return of the local portion of investors’ portfolio when the
performance of local portfolios is analyzed. In CAPM regressions, the independent variable is the valueweighted excess market return for all stocks in our data. In Fama and French (1993) 3-factor regressions,
independent variables, the difference in returns of small and large capitalized portfolios,
, and the
difference in the returns of high and low book-to-market portfolios,
, are also included. In the regressions,
the dependent variables are at the investor level while the independent variables vary in each period. The
regressions are run for the portfolios of investors living in their birthplace (columns 2 to 5) and for the portfolios
of investors who are local biased, (
), (columns 6 to 9). Panel A shows results for all portfolios. Panel
B reports results from portfolios with more than six stocks (90 th percentile in the data), and finally Panel C
displays results from portfolios with more than 21 stocks (99 th percentile in the data). Beta is tested against one
whereas alpha and the other risk factors are tested against zero. T-statistics are based on Newey west standard
errors, which are presented within parenthesis. Stars *,**,***, indicate the two-tailed test significance levels at
the 10%, 5%, and 1%, respectively.
Panel A: Portfolio performance of all the investors who live in their birth district
Portfolios
Y: Excess return on
Local
Total
Local
Total
Variable
Alpha
-0.0167***
-0.0190***
-0.0327***
-0.0314***
Newey west Std. Err.
(0.0003)
(0.0001)
(0.0004)
(0.0002)
R_m-Rf
1.0066***
1.0758***
0.9870***
1.0502***
Newey west Std. Err.
(0.0015)
(0.0007)
(0.0016)
(0.0008)
SMB
-0.0082***
-0.0363***
Newey west Std. Err.
(0.0006)
(0.0003)
HML
-0.0716***
-0.0551***
Newey west Std. Err.
(0.0009)
(0.0004)
No. of obs.
2,612,679
9,159,142
2,316,461
8,186,236
Local
Total
Local
Total
-0.0185***
(0.0003)
1.0060***
(0.0016)
-0.0047***
(0.0003)
0.9609***
(0.0014)
2,245,334
2,249,569
-0.0358***
(0.0004)
0.9785***
(0.0017)
-0.0104***
(0.0006)
-0.0724***
(0.0009)
1,992,120
-0.0202***
(0.0003)
0.9370***
(0.0016)
0.0001
(0.0006)
-0.0627***
(0.0009)
1,995,216
Panel B: Portfolio performance of the investors who live in their birth district and have more than 6 stocks in their portfolios
Portfolios
Y: Excess return on
Local
Total
Local
Total
Local
Total
Local
Variable
Alpha
-0.0187***
0.0017***
-0.0247***
-0.0035***
-0.0262***
0.0035***
-0.0364***
Newey west Std. Err.
(0.0005)
(0.0002)
(0.0007)
(0.0003)
(0.0005)
(0.0004)
(0.0007)
R_m-Rf
1.1328***
1.0633***
1.1480***
1.0804***
1.1527***
1.0453***
1.1575***
Newey west Std. Err.
(0.0027)
(0.0014)
(0.0030)
(0.0016)
(0.0031)
(0.0022)
(0.0035)
SMB
-0.0172***
0.0032***
-0.0185***
Newey west Std. Err.
(0.0011)
(0.0006)
(0.0013)
HML
-0.0514***
-0.043***
-0.0634***
Newey west Std. Err.
(0.0016)
(0.0008)
(0.0019)
No. of obs.
536,369
997,012
477,629
888,283
374,002
375,217
334,099
Panel C: Portfolio performance of the investors who live in their birth district and have more than 21 stocks in their portfolios
Portfolios
Y: Excess return on
Local
Total
Local
Total
Local
Total
Local
Variable
Alpha
-0.0199***
0.0032***
-0.0255***
-0.0006
-0.0292***
0.0022**
-0.0396***
Newey west Std. Err.
(0.0012)
(0.0007)
(0.0016)
(0.0009)
(0.0015)
(0.0009)
(0.0020)
R_m-Rf
1.1945***
1.1030***
1.2030***
1.1189***
1.2139***
1.1010***
1.2136***
Newey west Std. Err.
(0.0069)
(0.0037)
(0.0076)
(0.0041)
(0.0083)
(0.0052)
(0.0091)
SMB
-0.0304***
-0.0053***
-0.0315***
Newey west Std. Err.
(0.0029)
(0.0015)
(0.0034)
HML
-0.0465***
-0.0355***
-0.0634***
Newey west Std. Err.
(0.0041)
(0.0022)
(0.0049)
No. of obs.
60,267
89,058
54,224
80,138
40,635
40,755
36,676
48
Total
-0.0009
(0.0005)
1.0620***
(0.0024)
0.0069***
(0.0009)
-0.0368***
(0.0013)
334,921
Total
-0.0020
(0.0012)
1.1131***
(0.0057)
-0.0062***
(0.0021)
-0.0354***
(0.0031)
36,754
Table V
Results from alpha analyses for distal birthplace investors
This table shows results from analysis of holdings-based calendar-time portfolios of distal birthplace investors.
The dependent variable is the excess return of investors’ total portfolio when the performance of total portfolios
is analyzed and it is the excess return of the birth district portion of investors’ portfolio when the performance of
birthplace portfolios is analyzed. In CAPM regressions, the independent variable is the value-weighted excess
market return for all stocks in our data. In Fama and French (1993) 3-factor regressions, independent variables,
the difference in returns of small and large capitalized portfolios,
, and the difference in the returns of high
and low book-to-market portfolios,
, are also included. In the regressions, the dependent variables are at the
investor level while the independent variables vary in each period. The regressions are run for remote birthplace
investors (columns 2 to 5) and for the portfolios of investors who are birthplace biased,
> 0%, (columns 6 to
9). Panel A shows results for all portfolios. Panel B reports results from portfolios with more than six stocks
(90th percentile in the data), and finally Panel C displays results from portfolios with more than 21 stocks (99 th
percentile in the data). Beta is tested against one whereas alpha and the other risk factors are tested against zero.
T-statistics are based on Newey west standard errors, which are presented within parenthesis. Stars *,**,***,
indicate the two-tailed test significance levels at the 10%, 5%, and 1%, respectively.
Panel A: Portfolio performance of all the distal birthplace investors
Portfolios
Y: Excess return on
>0%,
Birth d.
Total
Birth d.
Total
Birth d.
Total
Birth d.
Total
-0.0219***
(0.0004)
1.0374***
(0.0023)
-0.0110***
(0.0001)
0.9998
(0.0008)
-0.0071***
(0.0004)
0.9819***
(0.0023)
6,737,332
-0.0256***
(0.0002)
0.9761***
(0.0009)
-0.0136***
(0.0003)
-0.0614***
(0.0005)
6,000,961
-0.0244***
(0.0004)
1.0387***
(0.0025)
1,065,853
-0.03863***
(0.0006)
1.0182***
(0.0026)
-0.0176***
(0.0009)
-0.0787***
(0.0014)
948,574
879,789
883,746
-0.0420***
(0.0006)
1.0123***
(0.0028)
-0.0211***
(0.0010)
-0.0778***
(0.0015)
783,338
-0.0213***
(0.0005)
0.9621***
(0.0025)
-0.0080***
(0.0009)
-0.0614***
(0.0014)
786,729
Variable
Alpha
Newey west Std. Err.
R_m-Rf
Newey west Std. Err.
SMB
Newey west Std. Err.
HML
Newey west Std. Err.
No. of obs.
Panel B: Portfolio performance of the distal birthplace investors who have more than 6 stocks in their portfolios
Portfolios
Y: Excess return on
>0%,
Birth d.
Total
Birth d.
Total
Birth d.
Total
Birth d.
Total
-0.0308***
(0.0007)
1.1813***
(0.0042)
0.0019***
(0.0003)
1.0524***
(0.0015)
0.0022***
(0.0006)
1.0510***
(0.0032)
856,203
-0.0026***
(0.0003)
1.0701***
(0.0016)
0.0034***
(0.0006)
-0.0388***
(0.0009)
762,658
-0.0404***
(0.0009)
1.2096***
(0.0049)
249,419
-0.0405***
(0.0010)
1.1880***
(0.0047)
-0.0360***
(0.0017)
-0.0681***
(0.0025)
222,352
165,208
166,455
-0.0535***
(0.0012)
1.2052***
(0.0054)
-0.0417***
(0.0020)
-0.0768***
(0.0029)
147,427
-0.0013*
(0.0008)
1.0655***
(0.0035)
-0.0028**
(0.0013)
-0.0328***
(0.0019)
148,384
Variable
Alpha
Newey west Std. Err.
R_m-Rf
Newey west Std. Err.
SMB
Newey west Std. Err.
HML
Newey west Std. Err.
No. of obs.
Panel C: Portfolio performance of the distal birthplace investors who have more than 21 stocks in their portfolios
Portfolios
Y: Excess return on
>0%,
Birth d.
Total
Birth d.
Total
Birth d.
Total
Birth d.
Total
-0.0326***
(0.0019)
1.2209***
(0.0106)
0.0017**
(0.0007)
1.1045***
(0.0039)
0.0013
(0.0013)
1.1101***
(0.0076)
79,287
-0.0019**
(0.0009)
1.1210***
(0.0043)
-0.0048***
(0.0016)
-0.0348***
(0.0023)
71,414
-0.0453***
(0.0023)
1.2567***
(0.0130)
32,982
-0.0420***
(0.0025)
1.2224***
(0.0117)
-0.0492***
(0.0044)
-0.0659***
(0.0063)
29,795
20,683
20,778
-0.0583***
(0.0031)
1.2514***
(0.0143)
-0.0514***
(0.0054)
-0.0782***
(0.0077)
18,679
-0.0028
(0.0018)
1.1224***
(0.0084)
-0.0087***
(0.0031)
-0.0357***
(0.0045)
18,730
Variable
Alpha
Newey west Std. Err.
R_m-Rf
Newey west Std. Err.
SMB
Newey west Std. Err.
HML
Newey west Std. Err.
No. of obs.
49
Table VI
Results from alpha analyses for investors who moved into another district and increase their local bias
This table shows results from analysis of holdings-based calendar-time portfolios of investors who moved in to
another district in the second period and remained in this district for 7 periods. The dependent variable is the
excess return of investors’ total portfolio when the performance of total portfolios is analyzed and it is the
excess return of the local portion of investors’ portfolio when the performance of local portfolios is analyzed. In
CAPM regressions, the independent variable is the value-weighted excess market return for all stocks in our
data. In Fama and French (1993) 3-factor regressions, independent variables, the difference in returns of small
and large capitalized portfolios,
, and the difference in the returns of high and low book-to-market
portfolios,
, are also included. In the regressions, the dependent variables are at the investor level while the
independent variables vary in each period. The regressions are run for the portfolios of investors who moved
into another district (columns 2 to 5) and for the portfolios of investors who are local biased, (
),
(columns 6 to 9). Panel A shows results for all portfolios. Panel B reports results from portfolios with more than
6 stocks (90th percentile in the data), and finally Panel C displays results from portfolios with more than 21
stocks (99th percentile in the data). Beta is tested against one whereas alpha and the other risk factors are tested
against zero. T-statistics are based on Newey west standard errors, which are presented within parenthesis. Stars
*,**,***, indicate the two-tailed test significance levels at the 10%, 5%, and 1%, respectively.
Panel A: Portfolio performance of all the investors who moved into another district and increase their local bias
Portfolios
Y: Excess return on
Local
Total
Local
Total
Local
Total
Variable
Alpha
-0.0005
0.0066**
-0.0290***
-0.0178***
-0.002
0.0101***
Newey west Std. Err.
(0.0031)
(0.0026)
(0.0039)
(0.0036)
(0.0033)
(0.0032)
R_m-Rf
0.8963***
0.8894***
0.8704***
0.8765***
0.8908***
0.8547***
Newey west Std. Err.
(0.0176)
(0.0149)
(0.0179)
(0.0166)
(0.0189)
(0.0180)
SMB
0.0414***
0.0457***
Newey west Std. Err.
(0.0067)
(0.0062)
HML
-0.1168*** -.10751***
Newey west Std. Err.
(0.0096)
(0.0090)
No. of obs.
18,427
21,495
18,258
19,064
15,769
15,780
Local
Total
-0.0308***
(0.0041)
0.8637***
(0.0191)
0.0441***
(0.0071)
-0.1168***
(0.0103)
15,698
-0.0179***
(0.0040)
0.8287***
(0.0183)
0.0528***
(0.0068)
-0.1111***
(0.0099)
15,705
Panel B: Portfolio performance of the investors who moved into another district, increase their local bias, and hold more than six stocks
Portfolios
Y: Excess return on
Local
Total
Local
Total
Local
Total
Local
Total
Variable
Alpha
-0.0106*
0.0121**
-0.0207***
0.0085
-0.0225***
0.0105**
-0.0318***
0.0085
Newey west Std. Err.
(0.0060)
(0.0047)
(0.0076)
(0.0064)
(0.0067)
(0.0052)
(0.0084)
(0.0066)
R_m-Rf
1.0877*
1.0665**
1.0920***
1.0909***
1.1377***
1.0534
1.1368***
1.0578
Newey west Std. Err.
(0.0342)
(0.0270)
(0.0348)
(0.0297)
(0.0379)
(0.0297)
(0.0386)
(0.0304)
SMB
-0.0027
0.0213***
-0.0133
0.0193*
Newey west Std. Err.
(0.0130)
(0.0111)
(0.0145)
(0.0114)
HML
-0.0543***
-0.0349***
-0.0475**
-0.0105
Newey west Std. Err.
(0.0183)
(0.0157)
(0.0203)
(0.0160)
No. of obs.
3279
3710
3193
3344
2252
2257
2213
2216
Panel C: Portfolio performance of the investors who moved into another district, increase their local bias, and hold more than 21 stocks
Portfolios
Y: Excess return on
Local
Total
Local
Total
Local
Total
Local
Total
Variable
Alpha
-0.0500***
-0.0035
-0.0647***
0.0081
-0.0706***
0.0051
-0.0850***
0.0269
Newey west Std. Err.
(0.0184)
(0.0184)
(0.0235)
(0.0246)
(0.0180)
(0.0252)
(0.0228)
(0.0320)
R_m-Rf
1.0343
1.3230***
1.0378
1.3585***
1.1612
1.4026***
1.1545
1.4168***
Newey west Std. Err.
(0.1062)
(0.1053)
(0.1093)
(0.1141)
(0.1035)
(0.1451)
(0.1051)
(0.1474)
SMB
0.0371
-0.0010
-0.0380
-0.0375
Newey west Std. Err.
(0.0400)
(0.0421)
(0.0382)
(0.0536)
HML
-0.0680
0.0252
0.0492
0.0831
Newey west Std. Err.
(0.0550)
(0.0587)
(0.0518)
(0.0727)
No. of obs.
267
300
257
277
187
187
184
184
50
Table VII
Treynor ratio and alpha analyses
This table shows the values of the ex post Treynor ratio and the alpha for i) individuals who live in the
birthplace, Panel A, ii) distal birthplace investors, Panel B, and iii) investors who moved into another district
and increase their local bias, Panel C. Results are also presented for the samples of the total and the local
portfolios as well as the portfolios including between 0 and 6 stocks, between 6 and 21 stocks, and larger than
21 stocks. The Treynor ratio is defined as the excess return on the individual portfolio for the sample period
divided by the portfolio’s systematic risk, the portfolio beta for that period. The alpha is defined as the
difference between the expected return on the portfolio and the required return on the portfolio using the
calculated portfolio betas. T-statistics are used to test whether alphas are significantly larger than zero (onesided test) and presented in the table.
Panel A: Results for
investors living in the birth district
Portfolios Treynor Ratio Alpha t-stat
Total
-0.088
-0.02
-64.2
Local
-7.187
-0.017
-39.4
LB>0%
Total
-0.08
-0.009
-35.3
Local
-0.149
-0.019
-70.6
Panel B: Results for
distal birthplace investors
Treynor Ratio Alpha
t-stat
Total
-0.089
-0.01
-75.1
Birht D.
-8.456
-0.02
-49.1
BPB>0
Total
-0.095
-0.01
-26.1
Birht D.
-0.201
-0.02
-53.9
Panel C: Results for investors who moved into
another district and increase their local bias
Treynor Ratio Alpha
t-stat
Total
-0.098
0.0009
0.653
Local
-7.263
-0.005
-1.76
LB>0%
Total
-0.092
0.006
2.09
Local
-0.165
-0.005
-1.63
Portfolios including between 0 and 6 Stocks
Total
-0.107
-0.024
-74.4
Local
-7.58
-0.018
-55.6
LB>0%
Total
-0.109
-0.011
-37.4
Local
-0.147
-0.019
-59.2
Total
Birht D.
BPB>0
Total
Birht D.
-0.111
-8.68
-0.02
-0.02
-68
-38.1
-0.127
-7.569
0.0003
-0.002
0.203
-0.67
-0.132
-0.188
-0.01
-0.02
-26.8
-40.9
Total
Local
LB>0%
Total
Local
-0.129
-0.174
0.006
-0.001
1.953
-0.33
Portfolios including between 6 and 21 Stocks
Total
0.018
-0.002
-7.22
Local
-5.135
-0.014
-28.9
LB>0%
Total
0.024
0.0007
1.81
Local
-0.139
-0.021
-38.1
Total
Birht D.
BPB>0
Total
Birht D.
0.019
-7.471
-0.002
-0.02
-6.32
-29.2
0.0332
-6.039
0.003
-0.011
1.363
-2.29
0.021
-0.224
-0.001
-0.03
-1.59
-35.2
Total
Local
LB>0%
Total
Local
0.041
-0.122
0.004
-0.016
0.874
-2.72
Portfolios including 21 stocks and more
Total
0.015
-0.002
-3.19
Local
-3.367
-0.011
-8.95
LB>0%
Total
0.014
-0.002
-2.73
Local
-0.227
-0.019
-12.38
Total
Birht D.
BPB>0
Total
Birht D.
0.016
-5.964
-0.003
-0.02
-4.17
-12.1
0.007
-4.161
0.006
-0.016
0.667
-1.1
0.013
-0.333
-0.003
-0.04
-2.28
-14.7
Total
Local
LB>0%
Total
Local
0.009
-0.241
-0.005
-0.037
-0.33
-2.39
51