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Journal of Financial Economics

Journal of Financial Economics 101 (2011) 641–665

0304-40

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journal homepage: www.elsevier.com/locate/jfec

Do small shareholders count?$

Eugene Kandel a,b, Massimo Massa c,b,n, Andrei Simonov d,b

a Hebrew University, Jerusalem, Israelb CEPR, United Kingdomc Finance Department, INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, Franced Michigan State University, USA

a r t i c l e i n f o

Article history:

Received 30 January 2009

Received in revised form

6 October 2010

Accepted 3 November 2010Available online 30 March 2011

JEL classification:

G11

G32

G34

Keywords:

Shareholder heterogeneity

Firm value

Corporate finance

Managerial decision making

5X/$ - see front matter & 2011 Elsevier B.V.

016/j.jfineco.2011.03.018

thank anonymous referee, Y. Amihud, S. Bas

art, L. Cohen, M. Cremers, A. Dittmar, J. Dow, R. G

, G. Huberman, C. Mayer, P. Pasquariello, T. Santo

h and seminar participants at Columbia Univers

University of Michigan, Indiana University,

ty of Vienna, and National Bureau of Economic

meeting for their useful comments and suggesti

Ivan Sundqvist for numerous helpful discussion

the data.

esponding author at: Finance Department, IN

ce, 77305 Fontainebleau Cedex, France. Tel.:

3 1 60724045.

ail address: [email protected] (M. M

a b s t r a c t

We hypothesize that age similarity among small shareholders acts as an implicit

coordinating device for their actions and, thus, could represent an indirect source of

corporate governance in firms with dispersed ownership. We test this hypothesis on a

sample of Swedish firms during the 1995–2000 period. Consistent with our hypothesis,

we find that compared with shareholders of differing ages, same-age noncontrolling

shareholders sell more aggressively following negative firm news; firms with more

age-similar small shareholders are more profitable and command higher valuation; and

an increase (decline) in a firm’s small shareholder age similarity brings a significantly

large increase (decline) in its stock price. The last effects are more pronounced in the

absence of a controlling shareholder.

& 2011 Elsevier B.V. All rights reserved.

1. Introduction

The corporate finance literature suggests thatdispersed shareholders leave the company at the mercyof the managers who can expropriate from the firm’sowners at will. This literature stresses the role of controlling

All rights reserved.

ak, S. Batthacharya,

opalan, D. Gromb, L.

s, H. Servaes and M.

ity, London Business

McGill University,

Research corporate

ons. We are grateful

s and for providing

SEAD, Boulevard de

þ33 1 60724481;

assa).

shareholders as the main monitors of managers and,therefore, as key determinants of firm value (Holmstromand Tirole, 1993; Bolton and von Thadden, 1998a, 1998b;Kahn and Winton, 1998;Noe, 2002; Faure-Grimaud andGromb, 2004). In general, small noncontrolling and non-strategic shareholders are assumed not to monitor, aseach small shareholder has little power and no incentive toengage in monitoring. At the same time, standard assetpricing models predict that the actions of every shareholder,including those of the small shareholders, could affect theprice of the stock (Hong, Kubik and Stein, 2004).

While we are not aware of any study that explicitlyshows how small shareholders can directly condition thebehavior of the manager and the firm’s corporate policies,small shareholders as a group could play a critical role.Suppose that a large number of small shareholderscoordinate the timing of their stock sales. Such actionundoubtedly has a strong effect on the stock price, aneffect that could last for a while and, hence, would

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dx.doi.org/10.1016/j.jfineco.2011.03.018

mailto:[email protected]

dx.doi.org/10.1016/j.jfineco.2011.03.018

2 In sociology this is described as generational theory. According to

Karl Mannheim, generation affect an individual’s consciousness in much

the same way as social class or culture does. ‘‘Individuals who belong to

the same generation, who share the same year of birth, are endowed, to

that extent, with a common location in the historical dimension of the

social process’’ (Mannheim, 1997 [1952], 35).This result in the formation

of particular dispositions and ‘‘certain definite modes of behavior,

feeling, and thought’’ (Mannheim, 1997 [1952], 36). Generational

influence, therefore, determines how individuals perceive, experience

and interpret the information about social world, which in turn affects

E. Kandel et al. / Journal of Financial Economics 101 (2011) 641–665642

certainly get the attention of the manager, especiallywhen his compensation is strongly linked to the stockprice. Given that the cost of coordination among manysmall shareholders is prohibitively high, this channel ofcorporate governance has been left largely unexplored bythe literature. We argue that such coordination couldarise unintentionally if small shareholders have commonfeatures that drive their information processing and stocksales. Thus, a high degree of similarity among the firm’scurrent small shareholders could translate into correlatedselling behavior that resembles the behavior of a largeshareholder, even without deliberate coordination. Thatis, small shareholder similarity could lead small share-holders to also have a role in disciplining managers,even if these shareholders are neither more strategic normore capable of monitoring management than usuallyassumed. For instance, these shareholders could be noisetraders who happen to react to news in the same waywithout any underlying model (behavioral setting), orthey could be rational traders who happen to interpretnews in the same way (differences of opinions setting).

This paper tests whether such unintentionally coordi-nated actions constitute a channel through which smallshareholders as a group can discipline managers. Thebasic threat for managers is that if they disappoint a largegroup of similar small shareholders, those shareholderswould take a ‘‘Wall Street walk’’ by selling the stock at thesame time (effectively selling like a large shareholderwould), bringing about a sharp drop in the stock price.The implicit threat of a shareholder sell-off makes it moreexpensive for equity-incentivized managers to engage invalue-reducing activities. The crucial parameter here isthe degree of coordination, which we conjecture todepend on the degree of similarity among noncontrollingshareholders.

Our argument hinges on managers’ awareness of thedegree of similarity among the firm’s shareholders. Inmost countries this information is not directly accessibleto the manager. In Sweden, however, Central SecurityRegistry (Vardepapperscentralen AB, or VPC) collects dataon holdings of Swedish companies. SIS Agarservice ABuses semiannual snapshots of these data (as well as theirown proprietary data on voting pacts, family connections,trusts, strategic shareholdings via foreign holding firms,etc.), and sells these data to firms and other interestedparties.1 This dataset contains information on practicallyall shareholders of all listed firms, including their age,location, and other demographic characteristics. We usethis unique dataset of Swedish firms to test our hypotheses.

More specifically, using the Swedish dataset for the1995–2000 period, we create measures of shareholders’similarity based on age, wealth, and location. While we donot know a priori which traits are more important, ageseems a likely candidate, as different cohorts are exposedto different fads and investment climates. For example,shareholders who have lived through a long bear marketmay react differently to information than shareholders

1 For a small subset of large firms the information is distributed on a

biweekly basis.

who have experienced only a stock market boom.2 Agesimilarity is also related to the formation of social net-works, which could facilitate the sharing of informationand opinions and thus lead to coordinated actions. Foreach firm, we measure the degree of similarity across allits individual noncontrolling shareholders.

To show that our argument has support in the data, weneed to perform the following sequence of tests. First, wemust show that more similar individual shareholders doact in a coordinated fashion in their selling decisions,especially in response to bad news, which is essential toattract the attention of managers. Next, we must showthat managers react to this information. That is, we mustprovide evidence that firms with more similar smallshareholders exhibit higher profitability. Finally, we mustshow that the markets value the similarity of smallshareholders, all else equal. We perform each of thesetests. The results suggest that small shareholder similarityacts as a (hitherto unexplored) driver of corporate valuecreation.3

We start by showing that more age-similar smallshareholders tend to sell less under normal conditions.This suggests that they tend to hold on to a stock longer.This makes them an identifiable and persistent group. Atthe same time, we show that firms with a higher percen-tage of age-similar small shareholders experience largersell-outs following bad news. A negative surprise shocktranslates into 11% higher sales for stocks with above-median homogeneity of common shareholders. As pre-dicted, this behavior is asymmetrical: Shareholder simi-larity does not play any role in shareholders’ response topositive news, and buying behavior is not affected either.This is because share ownership itself introduces theasymmetry; that is, shareholders are more likely to sellthan to buy. We do not find much effect of wealthsimilarity. In robustness tests we show that individualsmall shareholders tend to show more herding in theirinvestment decisions with investors in their own agecohort than with investors from other cohorts. Overall,these findings suggest that age-similar small shareholderstend to react more aggressively to bad news about thefirm and that this threat of unintentionally coordinatedaction is persistent.

Next, we relate small shareholder similarity to firmprofitability and value. Here we must address the poten-tial endogeneity of ownership structure (Demsetz, 1983;Demsetz and Lehn, 1985). Relying on the findings on

the range of possible forms of behavior.3 While higher similarity of small shareholders seems to create

value on average, we do not claim that it always helps companies. A

pseudo-coordinated sale based on a wrong interpretation of news could

hurt a perfectly healthy company.

E. Kandel et al. / Journal of Financial Economics 101 (2011) 641–665 643

home investment bias in Coval and Moskowitz (1999,2001), Huberman (2001), and Hong, Kubik and Stein(2008), we exploit the location of shareholders as anidentifying restriction. The availability of complete infor-mation on the location of all shareholders for each firmallows us to devise a unique identifying strategy that pinsdown the exogenous component of the shareholderstructure. We find that firms with small shareholdersimilarity that is one standard deviation greater thanthe average display 14.5% higher than average Tobin’s qwhich is about 10% of the total standard deviation ofQ. Moreover, their profit margins are 9.5% higher thanaverage [4.4% and 16%, respectively, for return on assets(ROA) and return on equity (ROE)]. When we repeat thisexercise on the subsample of investors born in the samecountry in which they reside, we obtain similar results.

Finally, we examine the effect of annual changes inshareholder similarity on subsequent stock returns. Theway the similarity measures are constructed allows us tosay something about the source of the change, especiallyfor the large changes. A large decline in similarity isunlikely to come from a random event, as this wouldimply that a large number of dissimilar shareholdersrandomly chose to sell together, which is unlikely. Thus,the larger the decline, the more likely it is that the declinewas driven by a coordinated sale of the similar share-holders. That is, similar shareholders (e.g., shareholdersbelonging to the same age group) interpreted the news ina similar way and sold the shares en block. A significantincrease in similarity is likely due to coordinated buying,i.e., an event that appealed to many similar shareholders.For example, it could be driven by an article in a publica-tion that appeals to this age group.

We show that firms that experience an increase inshareholder similarity observe positive returns. Morespecifically, a one standard deviation increase in similar-ity is associated with a 2.18% increase in annual stockreturn. Also, when we form five portfolios of firms basedon the change in the degree of shareholder similarity andcompare the performance of these portfolios over thesubsequent year, we show that a value-weighted portfoliothat is short the most-declining similarity quintile andlong the most-increasing similarity quintile generates a1.6% monthly return. The a of the four-factor model runon this portfolio is 1.6–2% per month, and it is highlystatistically significant. This result is not an anomaly asinformation on the change in similarity is not available tothe market at the time of portfolio formation, but itillustrates the magnitude of the effect.

Taken together, our findings provide evidence thatsmall shareholders could, unintentionally, have a signifi-cant governance role if they are sufficiently similar. Wecan interpret these findings in light of two non-mutuallyexclusive hypotheses. The first posits that local ownershipincreases shareholder similarity. Managers could be sub-ject to greater peer pressure from firm shareholders thatlive close by (e.g., Kandel and Lazear, 1992). The higherthe fraction of local shareholders, the more that pressurefrom the local community acts as a disciplining device. Inaddition, the more similar the local shareholders are, theless the manager (himself part of the local community)

wants to disappoint them. Local connections, social inter-actions, and local culture impose costs on cheating one’sneighbors. The alternative hypothesis posits that man-agers (or, to some extent, controlling shareholders) whoseobjective function is explicitly or implicitly linked to theequity price do not want to disappoint small shareholdersfor fear of the price impact of a massive sell-off.

Several recent papers explore the theory of discipliningmanagers by ‘‘voice through exit’’. Admati and Pfleiderer(2009) show that a large minority shareholder couldwield a certain amount of power over the manager dueto his ability to exit the company and depress its stockprice. Edmans (2009) argues that block holders monitorthe firm’s fundamental value and sell their stakes uponnegative information, thus making prices reflect funda-mental value and affecting managers’ incentives. Edmansand Manso (2011) argue that competition among smallershareholders (who hold sizable positions) can affectmanagerial decision making by coordinating exit or entryin some circumstances. In both cases, shareholdersbehave strategically, choosing to become more informedand more active depending on the scenario. We proposea complementary argument. Our results suggest theexistence of a novel channel through which shareholdercharacteristics affect the value of the firm even though theshareholders are small and act completely nonstrate-gically. In particular, small shareholder age similarityacts as a coordination device that replaces strategicconsideration. Lack of strategic consideration acts as acommitment device to sell, thus strengthening the impactof age similarity.

Miller (1977) argues that higher heterogeneity ofopinions should increase the price of the stock, as shortpositions are more expensive. An extensive recent litera-ture tests this hypothesis by using the dispersion ofanalysts’ forecasts as a proxy for opinion heterogeneity(e.g., Diether, Malloy, and Scherbina, 2002). One couldargue that higher shareholder similarity should be asso-ciated with lower heterogeneity of opinions, leading tolower stock prices for firms with more similar shareholders.The fact that we find the opposite result does not reject thishypothesis but instead indicates that the magnitude of thiseffect is lower than that of the disciplining effect.

Our paper also contributes to the literature thatrelates shareholder composition to firm performance(e.g., Morck, Shleifer and Vishny, 1988; McConnell andServaes, 1990; Himmelberg, Hubbard, and Palia, 1999;Holderness, Kroszner, and Sheehan, 1999; Franks andMayer, 2001; Franks, Mayer, and Renneboog, 2001). Whilethe extant literature focuses mostly on large/controllingshareholders, we show an important role played by smallshareholders, which indicates that the entire ownershipstructure could affect firm performance. Moreover, ourunique data yield an identifying strategy that pins downthe exogenous component of the shareholder structure,which is not an easy task in this literature.

Finally, our results contribute to the debate on theextent of disclosure of shareholders’ information. If know-ledge of shareholder characteristics positively affects amanager’s incentives, especially in firms with dispersedownership, then perhaps such information should be


widely disseminated in the U.S. and the U.K., where suchfirms are a norm. This issue requires more study.

The paper is structured as follows. In Section 2, wedevelop our testable hypotheses. In Section 3, we describethe data and the construction of our measures of similarity.Section 4 relates shareholder similarity to shareholders’ reac-tions to news. In Section 5, we relate shareholder similarityto firm profitability, and in Section 6 we study how simi-larity affects stock price. A discussion of the results is provi-ded in Section 7. A brief conclusion follows in Section 8.

2. Testable hypotheses

We now lay out our testable hypotheses. Our argu-ment consists of two parts. First, we hypothesize thatsimilar agents react similarly to market signals, especiallybad signals. Of particular importance to our story is thatthe selling pressure in reaction to bad news about the firmis greater for firms with a more homogenous shareholderbase. In the presence of a downward-sloping demandcurve for stocks (e.g., Shleifer and Vishny, 1986; Baker,Coval, and Stein, 2007), such selling pressure translatesinto a sharper stock price decline.

Heterogeneous shareholders use different models toevaluate news. This makes their overall reaction to badnews rather muffled. The rationale is that, if shareholdersare heterogeneous, shareholders that sell are replaced byoutside investors who become interested in the stock.This is the case even if a firm under-performs and reportsbad news. Thus, heterogeneous investors’ response to badnews could be moderate. However, if many similar smallshareholders hold similar views about the stock and valueit significantly above the current market price (i.e., theyare intramarginal), they hold the stock longer and selltogether if bad news causes them to become pessimisticabout the stock. That is, these investors’ very similaritycoordinates their actions without any strategic considera-tions on the part of any single shareholder. Clearly, such aresponse would be accompanied by significant downwardprice pressure. This commonality can manifest itself invarious ways. A simple model that illustrates just onesuch mechanism within the difference of opinions frame-work [see Kandel and Pearson (1995) and Banerjee andKremer (2008) for further details] is provided in Appendix A.These considerations lead to our first testable hypothesis.

H1. Higher similarity among small shareholders inducesmore directional volume and a sharper stock price reac-tion following negative news about the firm.

The second part of our argument is that managersmust care about shareholders’ reaction, which could bedue to peer pressure or social interactions betweenmanagers and shareholders or to the fact that managerscare about the stock price implications of disappointingtheir shareholders. Under this assumption, managers offirms with more similar small shareholders are likely toengage in fewer value-destroying activities, and control-ling shareholders are less likely to engage in self-dealing(see La Porta, Lopez-de-Silanes, Shleifer, and Vishny, 1998),thus increasing the profitability of the firm and its stock

price. Expectations of higher profitability induce higherprices. This leads to the following testable predictions:

H2. Firms characterized by greater similarity amongsmall shareholders are more profitable.

H3. Firms characterized by greater similarity amongsmall shareholders have higher market valuations.

Small shareholders are less likely to be relevant if therealready exists strong controlling ownership. First, power-ful shareholders are more likely to take a more activemonitoring role, in which case the disciplining function ofhomogeneous small shareholders is less relevant. Second,more powerful controlling shareholders might care lessabout the price implications of disappointing small share-holders. This suggests our last testable hypothesis.

H4. The impact of small shareholders’ similarity isweaker in the presence of controlling shareholders.

Miller (1977) predicts that more heterogeneous opinionsabout the stock should increase its value, as short positionsare too expensive to fully reflect negative views on thestock. If similarity is related to the information interpreta-tion, then the Miller (1977) argument suggests that highersimilarity should have a dampening effect on the stockprice, which contradicts H3. The two effects are notmutually exclusive, however, and thus we test for thedirection and the magnitude of the net effect.

One could wonder whether shareholder similarity isa proxy for board homogeneity. More homogeneousshareholders could appoint more homogenous boardmembers and, therefore, increase firm value by enhancingboard effectiveness. A substantial literature focuses on therole of similarity among different key players of anorganization—usually, managers and board members(e.g., Crawford and Sobel, 1982; Cremer, 1993;Aghionand Tirole, 1997; Dessein, 2002). For example, Van denSteen (2010) argues that homogeneity within an organi-zation ‘‘facilitates delegation and coordination, reducesmonitoring and influence activities, improves the qualityof communication, and increases effort and expectedutility’’ (Van den Steen, 2010, 1718). Blau (1977),Westphal and Zajac (1995), Bourgeois, Eisenhardt, andKahwajy (1997), Carter, Simkins, and Simpson (2003), andAdams and Ferreira (2008) analyze the impact of boarddiversity/similarity on firm value. In general, the idea isthat the main benefit of a diverse team is that ‘‘yteammembers are able to provide different perspectives onimportant issues, which may reduce the probability ofcomplacency in decision-making’’ (Adams and Ferreira,2008, 11). Diversity ‘‘may be able to add value by bringingnew ideas and different perspectives to the table’’ (Adamsand Ferreira, 2008, 24). We therefore want to control forthe possibility that shareholder similarity is correlatedwith board diversity. However, the correlation betweenour measures of age similarity and board heterogeneity isalmost zero in our sample, indicating that this effect isunlikely.


3. Data, variable construction, and methodology

We use data obtained from a number of differentsources. For each shareholder we have information onage, wealth, location, and stockholdings at the stock level.For each stock we have detailed information on thecompany and on the price, volume, and volatility at whichit trades. Below we describe these data sources, variablecreation and methodology in more detail.

3.1. Individual stockholdings

We use data on individual shareholders collected bythe Swedish Central Security Registry (VPC). The datacontain stockholdings held directly as well as those heldin the street name, including holdings of U.S.-listedAmerican Depositary Receipts (ADRs). In addition, SISAgarservice AB collects information on the ultimate ownersof shares held in trusts, foreign holding companies, and thelike (for details, see Sundin and Sundqvist, 2002). Our datacover the period 1995 through 2000. Overall, the recordsprovide information on the owners of 98% of the marketcapitalization of publicly traded Swedish companies, includ-ing their age. We have information on at least 81.6% of eachcompany’s market capitalization; for the median company,we have information on 97.9% of its equity. The dataprovided by SIS Agarservice AB are linked by StatisticsSweden with the Longitudinal INdividual DAta for Sweden(LINDA) dataset.

3.2. LINDA

LINDA is a register-based longitudinal dataset that is ajoint endeavor between the Department of Economics atUppsala University, the National Social Insurance Board(RFV), Statistics Sweden, and the Ministries of Finance andLabor. LINDA consists of a large representative panel ofhouseholds for the population over the period 1960–2000.For each year, information on all family members of thesampled individuals is added to the dataset. The samplingprocedure ensures that the data are representative for eachyear. Moreover, the same family is traced over time. Thisprovides a time series dimension that, in general, is lackingin surveys based on different cohorts polled over time.

The variables include individual characteristics (gender,age, marital status, country of birth, citizenship, year ofimmigration, place of residence at the parish level, educa-tion, profession, employment status), housing information(type and size of housing, owner, rental and occupationstatus, single-family or multi-family dwelling, year ofconstruction, housing taxation value), and tax and wealthinformation. In particular, the income and wealth taxregisters include information on labor income, capitalgains and losses, business income and losses, pensioncontributions, taxes paid, and taxable wealth. A detaileddescription of the dataset is provided by Edin andFredriksson (2000).4 The combined LINDA and sharehold-ing dataset covers the period 1995 through 2000. The

4 These data are available at http://linda.nek.uu.se/.

overall sample we use contains 1,757,406 observations. Forthe purpose of this paper, we use background informationon the parental household (heritage variables), including thesize, wealth, and income of the household during indivi-duals’ childhoods.

3.3. Firm-level information and other data

For individual security returns (including dividends)and the overall market index (SIX market index), we usethe SIX Trust database. For information on firm-levelcharacteristics we use Market Manager (MM) PartnersDatabase. These two databases are the equivalent of theCenter for Research in Security Prices and Compustat,respectively. In addition, the Market Manager Partnersdatabase contains information at the plant level, includingmunicipality location of the plant. We derive informationon firm profit margins, ROA, and ROE, as well as otherfirm variables. We also use information on analystsfollowing a firm derived from the international sectionof the International Brokers’ Estimate System (I/B/E/S)dataset. We collect data on the dispersion of analysts’forecasts as well as analysts’ forecast errors, based onthe difference between analyst earnings forecasts andearnings announcements.

3.4. Construction of the proxy for shareholder similarity

To test our hypotheses, we need proxies for share-holder similarity. A natural proxy is based on the agecohort of the shareholder. The higher is the proportion ofshareholders of the same age, the stronger is shareholdersimilarity. For each firm, we construct our proxy forsimilarity using an index of the concentration of the firm’scohorts. In particular, the degree of Shareholder Similarity

(SS) for firm j is defined as

SSj ¼XC

c ¼ 1

x2jc ,xjc ¼

PIi ¼ 1 NijcPC

c ¼ 1

PIi ¼ 1 Nijc

, ð1Þ

where Nijc is the number of shares that individual i, whobelongs to age cohort c, holds in firm j.

This measure can be interpreted as a Herfindahl indexin which the market share of cohort c in company j isgiven by the proportion of shares in the jth firm held byshareholders (denoted by i) belonging to the same agecohort, c. The intuition is that the impact of shareholdersimilarity is stronger for firms whose stocks are mostlyheld by shareholders of the same age. We consider fivecohorts: shareholders who, in 1995, were under 30 yearsof age, between 31 and 40, between 41 and 50, between51 and 60, and 61 and older. This measure ignores someaspects of the age distribution. For example, a firm with20-year-old shareholders and the oldest shareholderseach comprising 50% of the firm’s share holdings wouldhave the same Shareholder Similarity measure as a firmwith the same proportions of 30- and 40-year-old share-holders. We, therefore, complement the above measureby also using the Standard Deviation of Age Groups, whichis defined over the distribution of age group weights.

http://linda.nek.uu.se/


As a robustness check, we also use three other proxies(SSFF, SSFI, and SSL). SSFF is SS weighted by the free float(the proportion of individual shareholders in the firm),andSSFI is SS weighted by the number of individualnoncontrolling shareholders in Sweden (noncontrollingshareholders minus institutions minus foreigners). Wealso experiment with a native-based similarity measure,SSL, which is a raw measure but calculated only on thesubset of native-born individual investors [investors whowere born and still reside in the same county (lan)]. Therationale behind the use of these three variables is asfollows. The role of homogenous shareholders is differentdepending on the proportion of shareholders who actuallytrade. These could be just the free float (SSFF) as we do notexpect controlling blockholders to trade often. That freefloat is the main metric of interest for financial markets isconfirmed by the fact that major indexes are based on freefloat or have moved to it; e.g., MSCI Free indices. Alter-natively, we could argue that the key variable is the freefloat net of the institution. Such a variable would not besubject to short-term market moods. Finally, we use avariable based on the subset of native born individualinvestors located near the firm, SSL. As Hong, Kubik, andStein (2008) show, individuals tend to invest in the stocksof firms located closeby for exogenous reasons, i.e.,investors show local bias. Therefore, focusing on theseinvestors allows us to concentrate on the subset of owner-ship that is more likely to be exogenous. It is worth noting,however, that there is not a high degree of mobility inSweden and thus the decision to locate to a specific region islikely to be exogenous with respect to the characteristics ofthe company.

To control for wealth effects, we construct a measurebased on wealth, Shareholder Wealth Similarity, followingan analogous procedure as for Shareholder Similarity. Tocontrol for the effect of local bias in ownership, we useLocal Share (LS). This variable is calculated as the fractionof individual nonstrategic shareholders in the company wholive within a 50 kilometer (km) radius of the closest firmhead office or establishment with at least ten permanentemployees.5 Both in the case of wealth similarity and LS, weuse raw measures as well as weighted measures, with LSFF,LSFI, and LSL, corresponding, respectively, to SSFF, SSFI, andSSL. Table 1 presents a summary of all the variables used inour paper.

We also consider two alternative measures of concen-tration: Gini and C1. Following Stuart and Ord (1994) and

5 This approximately corresponds to the size of a Local Labor Market

Area (LLM). There are 109 Labor Market Areas in Sweden with a high

degree of economic integration as measured by commuting ties. The

average (median) population of a LLM is 81,200 (26,700). The average

(median) area is 3,770 (2,318) square kilometers (km), which corre-

sponds to a linear dimension of approximately 50 km. This consideration

leads us to focus on 50 km as our definitionof locality. Approximately

62% of the individual noncontrolling shareholders live within a 50 km

radius of a firm. To construct the locations of local establishments, we

used the location of establishments reported by MM Partners AB. While

our measure is more accurate than the one based on the location of

headquarters, this distinction affects only a few firms: 73% of firm-year

observations have only one location, and only 8% have more than five

locations.

Mills and Zandvakili (1997), and using the fact thatPCc ¼ 1 xjc ¼ 1, our measure of concentration based on the

Gini coefficient is

SSGj ¼2

ðC�1Þ

XC

c ¼ 1

c xjc�1

C

� �, ð2Þ

where groups are ordered in ascending order based on xjc.The minimum value is zero when all measurements areequal, and the theoretical maximum is one for ultimateinequality. Our last measure of concentration is based onthe fraction of shares held by the largest group,SSC1j ¼maxcðxjcÞ. These measures provide an indicationof whether what matters is just the concentration ofownership or its entire distribution.

Descriptive statistics are reported in Table 1, Panel A.In Fig. 1, we plot the frequency distribution of theage-based measure of similarity, SS, which indicates ahigh degree of variation in the sample. We also constructtests of differences in the degree of similarity acrossshareholders and localities. In Table 1, Panel B, we reportthe mean and the standard deviation of the distribution ofthe noncontrolling shareholders’ age-based similarity bythe shareholders’ parish of residence. We group share-holders into three classes: all Swedish shareholders, allshareholders who live close to a firm (local shareholders)regardless of the firm in which they invest, and allshareholders who do not live close to a firm (non-localshareholders) regardless of the firm in which they invest.We test for differences between characteristics of the threeclasses’ distributions. The results show significant differ-ences in concentration across classes of shareholders. Thatis, the degree of concentration of local shareholders in termsof age is larger than that of nonlocal shareholders and is alsodifferent from the overall national average.

It is important to note that one possible concern couldrelate to concentration per se. If there is high ownershipconcentration, then by definition the measure of ageconcentration is high. However, this is not likely in ourcase, as we focus attention on small shareholders withpotentially large variation in ages. Still, to directly addressthis issue, we also estimate a specification in which weexplicitly control for potential endogeneity by adding ameasure of concentration based on the Herfindhal index ofconcentration of the small shareholders. The (unreported)results are very similar to those reported here.

3.5. Econometric Methodology

The structure of the problem poses an econometricchallenge: the data are panel data with potentially corre-lated errors both across firms and over time. If theresiduals are correlated across observations, ordinaryleast square (OLS) standard errors are biased and under-estimate the true variability of the coefficients. Petersen(2009) shows that in the presence of firm-specific effects,standard OLS, Newey and West, and Fama and MacBethdeliver biased standard errors, while (industry or firm)clustered standard errors are unbiased. The standardapproach based on a panel estimation with firm fixedeffects also yields unbiased standard errors, but only if the

Table 1Descriptive Statistics.

This table contains descriptive statistics for the sample. Panel A reports firm-level characteristics and definitions. Panel B presents the mean and standard

deviation of the age distribution of firm shareholders, focusing on the concentration, i.e., the Herfindahl index. We consider three classes of investors: all

Swedish investors, all investors who live close to a firm (local investors) regardless of the firm in which they invest, and all investors who do not live close to a

firm (nonlocal investors) regardless of the firm in which they invest. ‘‘Local’’ is defined sing a 50 kilometers radius around the business units of a firm.

Panel A: Firm-related variables

Variable Definition Mean Median Standard

deviation

Inter-

quartile

range

Market-to-Book Ratio of the market value of the company to the book value of common equity.

Data from Market Managers (MM) Partners and SIX Trust.

3.430 1.941 8.331 2.075

Tobin’s q (Market value of equity plus book value of debt) / book value of assets. Data

from MM Partners and SIX Trust.

1.543 0.957 2.262 0.954

Size Logarithm (base 10) of the market value of the company (in Swedish kronor,

SEK). Data from SIX Trust.

9.473 9.411 0.928 1.425

Leverage Ratio of book value of debt to the sum of equity and debt. Data from MM

Partners.

0.473 0.135 0.895 0.657

Employees Number of employees by eight categories: Category 1: Fewer than 1,000

employees, Category 2 : 1,000–1,499, Category 3: 1,500–1,999, Category 4:

2,000–2,999, Category 5: 3,000–3,999; Category 6: 4,000–4,999, Category 7:

5,000–9,999; Category 8: more than 10,000. Data from MM Partners.

2.669 2.775 1.345 1.767

Bid-Ask Spread Bid-ask spread of the stock price (in SEK). Data from by SIX Trust. 2.258 0.928 5.458 1.311

Return on Assets (ROA) Ratio of earnings before interest, taxes and depreciation to book value of assets.

Data from MM Partners.

0.049 0.083 0.204 0.079

Return on Equity (ROE) Ratio of earnings before interest, taxes and depreciation to book value of

equity. Data from MM Partners.

0.069 0.155 0.556 0.209

Profit Margin Ratio of net income to sales. Data from MM Partners. 0.038 0.091 0.695 0.147

Shareholder Similarity (SS)SSj ¼

PCc ¼ 1

x2jc ,xjc ¼

PIi ¼ 1 NijcPC

c ¼ 1

PIi ¼ 1 Nijc

0.414 0.369 0.157 0.149

where Nijc is the number of shares that individual i who is member of group c

holds in company j. We define the groups on the basis of age cohort (0–30, 31–

40, 41–50, 51–60, 61þ) for the sample of all, local investors (within a

50 kilometers radius of the closest firm establishment), and nonlocal investors.

Information from Central Security Registry (Vardepapperscentralen AB, or VPC)

and SIS Agarservice AB.

GINI-Based Shareholder

Similarity (SSG)

SSG is a measure of inequality, defined as the mean of absolute differences

between all pairs of groups for some measure. The minimum value is zero

when all measurements are equal, and the theoretical maximum is one for

ultimate inequality .If the xc is the fraction of shares held by members of group

c, and groups are ordered in ascending order of x, then

SSGj ¼ ð2=C�1ÞPC

c ¼ 1 cðxjc�ð1=CÞÞ , and groups are defined as for SS.

0.578 0.583 0.155 0.182

C1-Based Shareholder

Similarity (SSC1)

Measure of inequality based on share of largest group, SSC1j¼max{xjc}. 0.541 0.533 0.151 0.198

Free-Float Weighted

Shareholder

Similarity(SSFF)

Float-based measures constructed by weighting the raw measures by the free

float variable (i.e., noncontrolling shareholders)

0.284 0.273 0.098 0.136

Individual Investors’ Weighted

Shareholder Similarity

(SSFI)

Measures constructed by weighting the raw measures by the proportion of

Swedish individual shareholders in the firms (noncontrolling shareholders

minus institutions minus foreigners).

0.103 0.063 0.114 0.119

Native-Born Shareholder

Similarity (SSL)

Same as raw measure SS but calculated only on the subset of individual

investors who were born and still reside in the same county (lan).

0.416 0.375 0.176 0.182

Shareholders Wealth

Similarity

Defined as Shareholder Similarity SS, but groups were based on investors’

population wealth quintiles. Information from VPC and SIS Agarservice AB.

0.465 0.433 0.173 0.377

Standard deviation of age

groups

Defined over the distribution of age group weights. Information from VPC and

SIS Agarservice AB.

0.298 0.271 0.157 0.226

Local Share (LS) Fraction of individual nonstrategic shareholders in the company who live

within a 50 kilometers radius from closest firm establishment. Information

from VPC and SIS Agarservice AB.

0.576 0.551 0.236 0.589

Free-Float Weighted Local

Share (LSFF)

Measure constructed by weighting the raw measure LS by the free float

variable (i.e., noncontrolling shareholders)

0.413 0.391 0.195 0.259

Individual Investors’ Weighted

Local Share (LSFI)

Measure constructed by weighting the raw measure LS by the proportion of

Swedish individual shareholders in the firms (noncontrolling shareholders

minus institutions minus foreigners).

0.131 0.096 0.125 0.138

Native-Based Local Share

(LSL)

Same as raw measure LS, but calculated only on the subset of individual

investors who were born and still reside in the same county (lan).

0.588 0.563 0.247 0.599

Free Float Ratio of free float to market cap at the end of the previous calendar year.

Similar to the Morgan Stanley free-float indices we subtract blockholdings in

excess of 5% from market cap. Data from SIS Agarservice AB.

0.714 0.733 0.186 0.270


Table 1 (continued )

Panel A: Firm-related variables

Variable Definition Mean Median Standard

deviation

Inter-

quartile

range

Share of individual investors Share of market cap owned by Swedish individual investors (excluding

strategic blockholders) at the end of the previous calendar year. Information

was provided by VPC AB and SIS Agarservice AB.

0.242 0.194 0.189 0.290

Mean age Mean age of shareholders of the firm (in years). Information from VPC AB and

SIS Agarservice AB.

55.81 56.04 5.188 8.085

Mean log Wealth Mean of log10 of the portfolio wealth of firm’ investors (in thousands of SEK).

Information from VPC AB and SIS Agarservice AB.

5.925 5.951 0.364 0.472

Board Similarity1 Herfindahl index of the proportion of board members based on age group (0–

30, 31–40, 41–50, 51–60, and 61þ). Data from MM Partners.

0.424 0.375 0.164 0.111

Board Similarity2 Share of males among board members. Data from MM Partners. 0.825 0.889 0.217 0.199

Panel B: Shareholder distributional characteristics

Age-based similarity measure (Herfindal)

Statistics calculated over Mean Standard deviation t-test

All investors All local investors

All investors 0.414 0.157

All local investors 0.448 0.181 �20.29

All nonlocal investors 0.314 0.034 17.74 22.74

0.00

5.00

10.00

15.00

20.00

25.00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Freq

uenc

y (p

erce

nt)

Age-based shareholder similarity measure

Fig. 1. Plot of frequency distribution of the age-based similarity measure SS.


firm effects are permanent. This requirement is unlikelyto be satisfied in our setup. We are not aware of a methodspecifically designed to address both firm and timeeffects, and thus the choice of specification depends onthe dimension along which the bias is more severe.Petersen (2009) recommends that the researcher ‘‘addressone [dimension] parametrically (e.g., including time

dummies) and then estimate standard errors clusteredon the other dimension’’ (Petersen, 2009, 475). Given thatour main explanatory variables do not change drasticallyover time, we expect the bias to be particularly severealong the time dimension. Accordingly, we adopt a panelspecification with time fixed effects, and we cluster at theindustry level. We also experiment with time effects and

7 The effect of 6% is derived by multiplying the corresponding

coefficient (�0.38) by the standard deviation of Shareholder Age

Similarity (0.157). This corresponds to 28% of the unconditional mean

of average sales (0.22).


clustering at the firm level, as well as with a standardpanel specification with firm fixed effects and clusteringat both the time and the industry level. Given that theresults are not qualitatively different across specifications,we report only on the former specification here. Theestimation method is two-stage least squares (2SLS).

Because we expect the bias to be particularly severealong the time dimension, for the monthly specificationswe also employ a Fama and MacBeth approach withcorrection for first-order autocorrelation. As in priorliterature, we use the Fama and MacBeth approach forthe analysis of stock returns. This allows us to compareour results with other studies.

4. Shareholder similarity and reaction to news

Above we argue that if shareholders are similar, theyshould herd more in selling subsequent to bad news. Inthis section, we test whether higher age similarity amongshareholders increases the sensitivity of overall order flowto an informational shock.

As a proxy for public information shocks we consider ameasure based on earnings surprises. In particular, wedefine Surprise as the difference between forecasted earn-ings per share (EPS) and realized EPS (normalized byrealized EPS). For firms that report their annual earningsin the first part of the year, we use the forecasted earningsfrom December of the previous year. For firms that reportearnings in the second part of the year, we use June forecasts.Forecasted EPS data come from I/B/E/S International.

Next, we construct measures of buys and sells aroundearnings announcements at the firm level and regressthese measures on our proxies for shareholder similarity,our earnings surprise measure, and a set of controlvariables. To control for shareholder similarity, we inter-act Surprise with a dummy variable that takes the value ofone if similarity is above the median value and zerootherwise. As before, we consider three distinct samples:the overall sample that includes all shareholders, thesample of local shareholders, and the sample of nonlocalshareholders. Sells (Buys) are defined as the proportion ofshares sold (bought) by existing small shareholdersbetween t�1 and t relative to the number of shares heldby individual shareholders at the beginning of period t�1.

Among the control variables, we include two alterna-tive measures of board similarity: Board Similarity1 andBoard Similarity2 (defined in Table 1). We also add ameasure for the quality of corporate governance (Corpo-

rate Governance Index), which is defined similarly toGompers, Ishii, and Metrick (2003) and is based onCronqvist and Nilsson (2003).6 We further include avariable that represents the size of the firm’s free float(Share of Free Float) and a variable that proxies for theproportion of shares not held by either the controllingshareholders or the institutional shareholders (Share of

6 Specifically, this measure is the sum of four dummies that are

equal to one if there are differential share classes, controlling share-

holders have preemption rights on high-voting shares, voting restric-

tions are in place, and a voting pact exists between large shareholders.

This measure is calculated at the end of the previous fiscal year.

Individuals). The free float allows us to proxy for thepresence of blockholders (i.e., one minus free float is thecontrolling group’s holdings). Share of Free Float and Share

of Individuals refer to the shares of non-strategic share-holders and individual shareholders, respectively, at theend of the previous calendar year. The additional controlvariables are similar to those used by Gompers, Ishii, andMetrick (2003). They include Size, Leverage, Employees, asdefined in Table 1, and Dividend Yield, High-Tech Dummy,and A-list Dummy, as defined in Appendix B. We alsoinclude the quoted percentage Bid-Ask Spread calculatedin t�2; Turnover, defined as the logarithm of the ratio ofshares traded to shares outstanding at the beginning oft�2; and the stock’s Price (in Swedish Kronor, SEK) duringt�2. The results are reported in Table 2. We report theresults only for sales (the direction of trades that is ofinterest here), but we note that virtually none of therelevant variables is significant for buys. In the table wereport results for all sales, sales by local investors, andsales by nonlocal investors. We separately identify theimpact of similarity in the case of bad surprises.

From the table one can see that, in line with ourHypothesis 1, firms with more age-similar shareholdersexperience significantly fewer sales under normal condi-tions. That is, Shareholder Age Similarity is always negativeand significant [Specifications (1), (2), (3), and (4)]. Interms of economic significance, firms with homogeneityone standard deviation above the mean are tradingapproximately 6% less.7 Comparing results for the overallsample with sales by local and nonlocal investors, weconclude that the results are driven by local investors.This is an important finding, as it suggests that small localshareholders from the same age cohort tend to hold on totheir shares more than others. This reinforces the claimthat such shareholders could have an ability to influencefirm management. We do not observe a similar effect forwealth similarity.

The results indicate that sales generally increase aftera bad surprise (EPS is smaller than the consensus esti-mate) but decrease only marginally after a good surprise.This effect is driven by local investors, while nonlocalinvestors tend to play a contrarian strategy and buy aftera bad surprise. At the same time, the coefficient on theproduct of Bad Surprise and Age Similarity (specifications 2,4, and 6) shows that firms characterized by high agesimilarity among small shareholders experience signifi-cantly higher sales following a negative earnings shock: aone standard deviation increase in Shareholder Similari-

ty*Bad Surprise leads to a 14% increase in sales.8 The effectagain comes from the subsample of local shareholders

8 We calculate it as the corresponding coefficient (0.35) times the

standard deviation of Shareholder Similarity*Bad Surprise of 0.41. The

effect is economically large, as it corresponds to an increase in sales from

0.22 to 0.36. However, a more accurate comparison would be with the

conditional mean of high-similarity firms (0.22�0.06¼0.16), which

implies that sales of similar investors increase by a factor of two after

a bad surprise.

Table 2Shareholder similarity and surprise shocks.

This table reports the results of a regression of measures of sells on shareholder similarity, surprise shocks, and a set of control variables. We report the results for overall sells and sells by local and nonlocal

owners. Local owners are defined as owners who live within 50 kilometers of a firm’s closest establishment. We define sells as the percentage of shares held by individual investors that were sold by owners

between t�1 and t. That is, sells are defined as the number of shares sold by owners between t�1 and t divided by number of shares held by individual investors at t�1. We define Surprise as the difference

between forecasted earnings per share (EPS) as of the last month of the previous forecasting year and realized EPS normalized by the absolute value of realized EPS. Yearly EPS data come from Institutional

Brokers’ Estimate System (I/B/E/S). We use a semiannual frequency, December and June of each year. If the EPS release date is in the period between January to June, excess sells are based on the difference

between changes in end-of-June holdings and the holdings as of the end of December of the previous year. If the EPS release date falls between June and December, we use the difference between the end-of-

December holdings and the holdings as of the end of the following June. The measures of shareholder similarity and the control variables are as defined in Table 1 and Appendix B. Following Gompers, Ishii, and

Metrick (2003), we also include the Bid-Ask Spread, Turnover, and Price. We consider the case of an aggregate shock and the case in which we split the shock such that there is high or low similarity. In the latter

case, we interact the surprise with dummies representing the level of shareholder similarity. A dummy of high (low) similarity takes the value one if similarity is above (below) the median value of the year and

zero otherwise. The estimates are based on a panel regression with time fixed effects and (year and industry) clustered errors. t-statistics are reported in parentheses. Coefficients for the control variables

Leverage, Employees, Bid-Ask Spread, Price, High-Tech Dummy, A-list Dummy, Dividend Yield, Corporate Governance Index, Number of Shares of Free Float, and Share of Individual Investors are omitted for brevity. We

also report F-tests of equality of coefficients for low- and high-similarity surprises.

All sells Sells by local investors Sells by nonlocal investors

(1) (2) (3) (4) (5) (6)

Variable Estimate t-statistics Estimate t-statistics Estimate t-statistics Estimate t-statistics Estimate t-statistics Estimate t-statistics

SS—Shareholder Age Similarity �0.38 (�4.79) �0.39 (�4.92) �0.15 (�3.45) �0.16 (�3.32) �0.09 (�1.46) �0.08 (�1.49)

Standard Deviation of Age Groups �0.18 (�1.99) �0.19 (�1.90) �0.23 (�2.18)

Shareholder Wealth Similarity �0.53 (�1.68) �0.33 (�1.02) �0.23 (�0.95) �0.10 (�0.40) �0.33 (�0.93) �0.26 (�0.72)

Good Surprise �0.05 (�1.87) �0.04 (�1.64) �0.03 (�1.69) �0.02 (�1.14) 0.00 (�0.15) 0.22 (0.32)

Bad Surprise 0.04 (3.34) 0.37 (0.50) 0.04 (3.81) 0.05 (0.09) �0.01 (�2.18) 0.00 (�0.19)

Shareholder Similarity*Bad Surprise 0.35 (2.49) 0.25 (2.66) �0.03 (�0.52)

Shareholder Wealth Similarity*Bad Surprise �0.41 (�0.52) 0.20 (0.32) �0.31 (�0.44)

Mean Wealth 0.08 (3.25) 0.01 (0.20) 0.07 (3.02) 0.00 (�0.02) 0.03 (1.03) �0.05 (�0.98)

Mean Age 0.01 (3.66) 0.01 (4.62) 0.01 (3.34) 0.01 (3.70) 0.00 (0.55) 0.00 (0.89)

Mean Age*Bad Surprise 0.01 (0.30) �0.03 (�0.87) 0.02 (0.47)

Mean Wealth*Bad Surprise 0.00 (�2.85) 0.00 (�4.19) 0.00 (�0.36)

Board Similarity1 0.03 (0.53) 0.00 (�0.01) 0.03 (0.63) 0.01 (0.23) 0.00 (0.07) �0.01 (�0.29)

Board Similarity2 �0.01 (�0.25) �0.02 (�0.81) �0.02 (�0.44) �0.04 (�0.81) 0.02 (0.59) 0.02 (0.41)

Market-to-Book 0.00 (�1.81) 0.00 (�2.16) 0.00 (�1.78) 0.00 (�2.29) 0.00 (0.25) 0.00 (0.00)

Size �0.04 (�3.18) �0.06 (�4.65) �0.02 (�1.11) �0.03 (�1.96) �0.04 (�3.00) �0.05 (�3.36)

Adjusted R2 0.471 0.528 0.414 0.477 0.283 0.311

E.

Ka

nd

elet

al.

/Jo

urn

al

of

Fina

ncia

lE

con

om

ics1

01

(20

11

)6

41

–6

65

65

0

9 We also control for the case in which shareholders ‘‘coordinate’’

their activities through voting pacts, syndicates, or other agreements as

well as pyramids and cross-shareholdings by aggregating their positions.10 The effect of 4.4% is derived by multiplying the corresponding

coefficient (0.28) by the standard deviation of Shareholder Age Similarity

(0.157). Similarly, for ROE (Profit Margin) the effect of 17% (9.5%) is

derived by multiplying the corresponding coefficient of 1.08 (0.61) by

the standard deviation of Shareholder Age Similarity.


and is highly significant. Wealth similarity does not play arole. In addition, we show that positive surprises have nostatistically significant effect on sales and that buys arecompletely unaffected by similarity. These findings sug-gest that the degree of shareholder age similarity affectsthe way investors react to negative information shocks, inline with our hypotheses.

Finally, we partition all firms into quintiles by theirdegree of small shareholder age similarity each year andconstruct a transition matrix between quintiles over time.The (unreported) results indicate strong persistence. Byfar the highest degree of persistence is found in the topquintile, with 87% of firms in the top quintile in one yearalso in the top quintile in the following year. This corrobo-rates the finding that once such similarity is established, ittends to persist unless the firm reports bad news.

5. Shareholder similarity and firm profitability

The above results show that similarity among share-holders amplifies their joint reaction to bad news. Thisis the first piece of evidence we need, as it suggeststhat managers of firms with more similar sharehol-ders could be less likely to act against the interests ofsmall shareholders. In this section, we test the jointhypothesis that managers take note of such reactionsand respond accordingly. We discuss the econometricissues in Appendix C.

We conjecture in Hypothesis 2 that if small share-holder similarity disciplines managers through the threatof a coordinated sale of stock, it should increase firmprofitability. We test this hypothesis by applying thestandard methodology (Gompers, Ishii, and Metrick,2003) and estimating

Pjt ¼ aþbSSjtþgLSjtþdCjtþejt , ð3Þ

where &jt captures the profitability of the jth firm at timet, SSjt is our proxy for similarity, and LSjt is the fraction oflocal shareholders. We use three proxies for profitability:ROA, ROE, and Profit Margin (as defined in the Table 1). Weadopt these measures to relate our results to priorliterature (e.g., Gompers, Ishii, and Metrick, 2003). Thesemeasures, though highly correlated, shed light on differ-ent facets of profitability. For example, ROE focuses onpure profitability for shareholders, ROA considers thecapital provided by debt holders, and Profit Margin

examines the degree of profitability of sales. That is, thefirst two measures represent the return on each addi-tional unit of capital, either equity or debt, while the lastmeasure represents the return on each additional unitsold. The fact that each of these measures is of similarmagnitude suggests that the effect on profitability ismostly due to an increase in operating performance asopposed to the use of leverage.

We consider both the raw measure of similarity and aweighted one. Cjt is a vector of control variables for the jthstock at time t. The control variables are the same as definedbefore. Following Gompers, Ishii, and Metrick (2003), wealso include measures of past stock returns. Return 23 is thecompounded gross return for the period between monthst�2 and t�3, Return 46 is the compounded gross return for

the period between months t�4 and t�6, and Return 712 isthe compounded gross return for the period betweenmonths t�7 and t�12. The frequency is monthly.

To test Hypothesis 4, we also interact shareholdersimilarity with a proxy for the power of controlling share-holders, namely, a dummy taking the value of one if thereexist shareholders who own more than 20% of the votes inthe firm and for whom the ratio of control to cash flowrights is greater than 1 (La Porta, Lopez-de-Silanes, Shleifer,and Vishny, 1998).9 Hypothesis 4 posits that the impact ofnoncontrolling shareholders should be weaker if controllingshareholders have a tighter grip on the firm via dual-classshares or pyramidal structures.

The results are displayed in Table 3. Panels A–C report theresults for ROA, ROE, and Profit Margin, respectively. In PanelD we report the results for changes in profitability. Theresults show that profitability is strongly affected by share-holder similarity. In particular, in line with Hypothesis 2, astrong and statistically significant positive relation is foundbetween profitability and shareholder similarity: Firms withmore similar small shareholders are more profitable. Theseresults hold across all the specifications and are robust to theuse of alternative weighting of the similarity measure. Ourresults are also statistically and economically significant.

Reported are the results for the main index of similarity(SS) and the raw measure of local share (LS), based upon OLSand 2SLS estimates. Similarity is shown to be positivelyrelated to profitability: For the 2SLS specification a onestandard deviation increase in similarity is associated with4.4% (17%, 9.5%) higher ROA (ROE, Profit Margin)10. We alsoconsider specifications based on the Gini index and the C1index of concentration. Using these alternative measuressimilarity is again found to be positively related to profit-ability: a one-standard deviation increase in similarity isassociated with 5.1% (18%, 12%) higher ROA (ROE, Profit

Margin) for C1 and 4.8% (17%, 12%) higher ROA (ROE, Profit

Margin) for Gini.We consider alternative measures of concentration:

SSL (raw measure of concentration based on the native-born investors), SSFF (concentration weighted by the freefloat), and SSFI (concentration weighted by Swedishindividual non-controlling shareholders). Similarity con-tinues to be positively related to profitability: a onestandard deviation increase in similarity is associatedwith a ROA (ROE, Profit Margin) that is 5.6% (17%, 15%)higher in the case of SSL, 4.0% (15%, 8.3% ) higher in thecase of SSFF, and 5.7% (25%, 20%) higher in the case of SSFI.

Finally, in the last two specifications we split thesample into firms without and with a dominant share-holder. We find that the positive relation between simi-larity and profitability appears only in the former sample.This is consistent with Hypothesis 4. The impact of

Table 3Shareholder similarity and operating performance.

This table reports the results of regressions of Return on Assets (ROA, Panel A), Return on Equity (ROE, Panel B), and Profit Margin (ROE, Panel C) in excess of the corresponding industry median on shareholder

similarity, fraction of local investors, board similarity measures, and a set of stock characteristics. The measures of shareholder similarity and the control variables are defined as in Table 1 and Appendix B. We

report the results for ordinary least quares (OLS) and two-stage least square (2SLS). We report the estimates based on a panel regression with year fixed effects and industry- and time-clustered errors, as we

described in Section 3 in the text. We use 848 yearly observations (number of firms ranges between 91 and 271). We also report the w2 and p-value of the Hansen overidentifying restriction and Anderson-

Rubin-Wald weak instruments robust inference test. We report separately the results for the full sample and for subsamples with and without a dominant shareholder (defined as a shareholder who has control

rights of the firm in excess of 20% and whose cash flow rights are lower than his control rights). Year fixed effects are in each of the specifications. Coefficients for Size, Leverage, Employees, Dividend Yield, Cash,

High-Tech Dummy, A-list Dummy, Share of Free Float, Share of Individuals, Corporate Governance Index, Mean_Age, and Mean Wealth are omitted for brevity. Panel D reports the results for changes in ROA, ROE, and

Profit Margin as a function of past changes in similarity measures. Estimates are calculated using a heteroskedasticity-robust estimator with industry-year fixed effects. A total of 615 observations are used in

Panel D. t-statistics are reported in parentheses.

Panel A: ROA

SS, LS SS, LS SSG, LS SSC1, LS SSL, LSL SSFF, LSFF SSFI, LSFI SS, LS SS, LS

Ols whole sample 2sls, whole sample 2sls, whole sample 2sls, whole sample 2sls, whole sample 2sls, whole sample 2sls, whole sample 2sls, firms without

dominant

shareholder

2sls, firms with

dominant

shareholder

Variable Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Shareholder

Similarity

0.06 (2.11) 0.28 (2.12) 0.31 (2.85) 0.34 (2.28) 0.32 (3.31) 0.41 (2.20) 0.50 (2.02) 0.34 (2.03) �0.10 (�0.39)

Local Share 0.01 (0.87) 0.04 (1.13) 0.03 (0.83) 0.03 (0.79) 0.05 (1.43) 0.02 (0.45) 0.23 (1.61) �0.03 (�1.29) 0.33 (3.74)

Standard

Deviation of

Age Groups

0.07 (1.43) 0.12 (1.18) 0.13 (1.32) 0.13 (1.31) 0.10 (0.95) 0.13 (0.87) 0.08 (0.30) 0.07 (1.20) 0.31 (2.44)

Shareholder

Wealth

Similarity

0.01 (0.03) �0.08 (�0.36) �0.07 (�0.33) �0.02 (�0.08) �0.23 (�1.03) �0.15 (�0.55) �0.76 (�1.90) �0.32 (�1.22) 0.33 (0.74)

Lagged ROA 0.59 (5.98) 0.64 (5.44) 0.62 (5.19) 0.64 (5.43) 0.67 (5.20) 0.63 (5.36) 0.74 (6.11) 0.54 (5.59) 0.62 (6.87)

Board Similarity1 0.04 (1.72) 0.07 (2.15) 0.06 (1.86) 0.07 (2.09) 0.05 (1.72) 0.06 (1.90) 0.07 (2.72) 0.06 (1.59) 0.07 (1.28)

Board Similarity2 0.01 (0.26) 0.05 (1.25) 0.04 (1.13) 0.04 (1.16) 0.04 (1.06) 0.05 (1.24) 0.09 (1.82) 0.06 (2.07) �0.12 (�2.21)

Adjusted R2 0.475

Anderson-Rubin-Wald test 24.43 24.43 24.43 24.43 17.18 13.95 15.18 21.04

(p-value) (0.00) (0.00) (0.00) (0.00) (0.00) (0.02) (0.00) (0.00)

Hansen J-stat 2.18 1.61 1.89 0.11 1.44 3.27 4.99 3.16

(p-value) (0.54) (0.66) (0.60) (0.99) (0.70) (0.39) (0.17) (0.37)

Panel B: ROE

Shareholder

Similarity

0.16 (2.16) 1.08 (3.41) 1.16 (3.76) 1.23 (3.35) 1.00 (3.33) 1.54 (3.37) 2.20 (2.06) 1.13 (1.98) 0.61 (0.80)

Local Share 0.07 (1.47) 0.06 (0.54) 0.03 (0.28) 0.02 (0.19) 0.10 (0.96) 0.00 (�0.03) 0.59 (1.68) 0.05 (0.57) 0.43 (2.90)

Standard

Deviation of

Age Groups

�0.01 (�0.07) 0.09 (0.35) 0.10 (0.42) 0.12 (0.48) �0.03 (�0.10) �0.03 (�0.08) 0.58 (0.86) 0.09 (0.71) 0.52 (1.73)

Shareholder

Wealth

Similarity

�0.29 (�0.39) �0.45 (�0.55) �0.44 (�0.53) �0.22 (�0.25) �0.82 (�0.99) �0.87 (�0.83) �2.46 (�1.54) �1.06 (�1.30) �0.31 (�0.32)

Lagged ROE 0.35 (3.04) 0.37 (3.28) 0.35 (3.28) 0.37 (3.47) 0.37 (3.12) 0.35 (3.18) 0.37 (5.97) 0.31 (5.49) 0.47 (2.84)

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Adjusted R2 0.349


(p-value) (0.00) (0.00) (0.00) (0.00) (0.01) (0.03) (0.05) (0.00)

Hansen J-stat 0.84 0.36 1.10 1.42 0.51 1.65 1.87 5.69

(p-value) (0.84) (0.95) (0.78) (0.71) (0.92) (0.65) (0.60) (0.13)

Panel C: Profit margin

Shareholder

Similarity

0.35 (2.33) 0.61 (2.84) 0.80 (3.00) 0.81 (3.06) 0.89 (3.88) 0.85 (2.72) 1.84 (2.04) 1.82 (2.26) �1.10 (�2.14)

Local Share 0.27 (2.87) 0.19 (2.64) 0.16 (1.98) 0.16 (2.00) 0.22 (3.10) 0.21 (2.02) 0.43 (2.40) 0.35 (2.47) 0.83 (2.94)

Standard

Deviation of

Age Groups

0.06 (0.24) 0.36 (1.80) 0.39 (2.22) 0.39 (2.12) 0.30 (1.73) 0.36 (1.36) 0.54 (1.51) 0.04 (0.16) 0.16 (0.81)

Shareholder

Wealth

Similarity

0.29 (0.18) �0.15 (�0.39) �0.06 (�0.18) 0.02 (0.05) �0.59 (�1.35) �0.10 (�0.19) �0.65 (�2.03) �0.48 (�0.26) �0.72 (�0.51)

Lagged Profit

Margin

0.35 (5.83) 0.19 (3.30) 0.18 (3.60) 0.19 (3.52) 0.18 (2.95) 0.19 (3.17) 0.36 (4.35) 0.34 (5.98) 0.25 (3.33)



Adjusted R2 0.577


(p-value) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Hansen J-stat 5.91 4.59 5.17 2.22 4.07 3.97 4.73 7.43

(p-value) (0.12) (0.20) (0.16) (0.53) (0.25) (0.27) (0.19) (0.06)

Panel D: Changes in profitability

DROA DROE DProfit Margin

SS, LS SSG, LS SSC1, LS SSL, LSL SSFF, LSFF SSFI, LSFI SS, LS SS, LS

Variable Esti-

mate

t-sta-

tistics

Esti-

mate

t-sta-

tistics

Esti-

mate

t-sta-

tistics

Esti-

mate

t-sta-

tistics

Esti-

mate

t-sta-

tistics

Esti-

mate

t-sta-

tistics

Esti-

mate

t-sta-

tistics

Esti-

mate

t-sta-

tistics

D Shareholder Similarity 0.184 (2.33) 0.084 (2.12) 0.077 (1.73) 0.162 (2.09) 0.244 (2.29) 0.473 (1.99) 0.221 (2.25) 0.067 (2.54)

D Local Share 0.078 (0.60) 0.060 (0.58) 0.069 (0.69) 0.075 (0.57) 0.095 (0.57) 0.066 (0.18) 0.126 (0.75) 0.013 (0.27)

D Shareholder Wealth

Similarity

�0.056 (�0.29) �0.029 (�0.14) �0.032 (�0.16) �0.067 (�0.35) �0.090 (�0.34) �0.044 (�0.22) 0.142 (0.48) �0.280 (�1.49)

D Standard Deviation of Age

Groups

�0.123 (�1.85) �0.146 (�2.35) �0.144 (�2.28) �0.118 (�1.69) �0.151 (�1.51) �0.165 (�0.83) �0.321 (�3.23) �0.170 (�2.21)

D Mean Age 0.888 (0.64) 0.003 (0.89) 0.002 (0.86) 0.712 (0.52) 0.736 (0.50) 0.516 (0.35) 2.600 (1.21) �0.010 (�0.01)

D Mean Wealth 0.002 (0.95) �0.006 (�0.33) �0.004 (�0.39) 0.002 (0.66) 0.002 (0.75) 0.000 (�0.10) 0.007 (0.92) 0.004 (2.73)

Board Similarity1 0.038 (2.08) 0.015 (0.77) 0.016 (0.79) 0.039 (2.18) 0.039 (2.12) 0.039 (2.02) 0.050 (0.80) 0.022 (1.68)

Board Similarity2 �0.013 (�0.82) �0.020 (�1.27) �0.020 (�1.30) �0.010 (�0.64) �0.012 (�0.71) �0.010 (�0.65) �0.007 (�0.35) �0.002 (�0.15)

Adjusted R2 0.114 0.113 0.112 0.114 0.113 0.104 0.087 0.084

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11 Approximately 20.4% of the companies have non-wholly owned

subsidiaries. Removing these companies does not affect the results as

most of these are companies with strong dominant shareholder. The

data of fully owned subsidiaries versus majority owned ones are drawn

from Market Manager Partners AB.12 We also consider a specification with time effects and clustering

at the firm level, as well as a standard panel specification with firm fixed

effects and clustering at both the time and the industry levels. Given

that the results are not qualitatively different, for brevity we reportonly

our results on the specification with time fixed effects and clustering at

the industry level.


shareholder similarity should be stronger for firms with-out dominant shareholders, that is, where there is lessopportunity for an alternative source of control overmanagers. This finding implies that our results are particu-larly relevant for countries with dispersed stock ownership.

The fact that we explicitly control for external andinternal measures of governance (i.e., institutional andblockholding-based governance) as well as for boardsimilarity suggests that our measure of shareholder simi-larity is not proxying for other dimensions of governance.In fact, the standard governance measures we use do notprovide additional explanatory power. The free float orfraction of small shareholders in a firm do not directlyaffect the firm’s profitability either.

Board concentration (Board Similarity1) does have adirect positive impact on profitability. This result is robustacross specifications and different definitions of profit-ability (ROE, ROA, and Profit Margin), even though it is lesssignificant for ROE. Under the base specification, firmswith board similarity one standard deviation higher thanaverage have 3.9% (2.1%, 1.1%) higher Profit Margin (ROE,

ROA). This is consistent with extant theories on teams aswell as theories on board composition showing thatsimilarity among board members makes it easy for themto coordinate in the interest of the company.

It is important to note that the results hold in our basecase (SS, LS) as well as in the cases in which we adjust forthe free float (SSFF, LSFF), the presence of individual share-holders (SSFI, LSFI), and local-born similarity (SSL, LSL).

As an additional robustness check, we reestimate speci-fication Eq. (3) in differences. The results are reported inTable 3, Panel D. We focus on the main proxy for similarity(SS) as well as its modified version using local ownership.An increase in shareholder similarity is associated with anincrease in profits, although the results in differences areweaker than in levels.

Reported are the results for the main index of similar-ity (SS) and the raw measure of local share (LS) for thethree measures of profitability (ROA, ROE, and Profit

Margin, respectively). A one standard deviation changein similarity is associated with a 0.9% (1.0%, 0.3%) increasein ROA (ROE, Profit Margin). We also consider specifica-tions based on Gini and C1 indices of concentration. Forthese alternative measures, a one standard deviationchange in similarity increases ROA by 0.6% for the caseof C1 and by 0.6% for the case of the Gini Index.

We consider alternative measures of concentration:SSL (raw measure of concentration based on the local-born investors), SSFF (concentration weighted by the freefloat), and SSFI (concentration weighted by the Swedishindividual non-controlling shareholders). Again, a changein similarity is positively related to an increase in profit-ability, with a one-standard deviation increase in similar-ity associated with 1.1% (0.8%, 0.7%) higher ROA for thecase of SSL (SSFF, SSFI).

Overall, the findings in Table 3 suggest that firms withhigher shareholder age similarity are more profitable. Thisprovides support for the voice through exit hypothesis,with some indication of peer pressure on large owners.However, given that large changes in similarity are rareand take time to affect a manager’s behavior, in the short

run their impact on firm profitability is likely to be lesspronounced. We expect to see a much stronger immediateimpact on valuations.

6. Shareholder similarity and firm value

By disciplining managers and increasing firm’s profit-ability, higher similarity should also increase a firm’s stockprice. To test this hypothesis, we take a two-prongedapproach. We first look at the relation between levels ofsimilarity and valuation. We then examine the relationbetween changes in similarity and subsequent returns.

6.1. Shareholder similarity and stock price

We start by looking at stock prices. Following Coval andMoskowitz (2001), Gompers, Ishii, and Metrick (2003), andHong, Kubik, and Stein (2004, 2008), we regress bothmarket-to-book and Tobin’s q on the levels of age andwealth similarity at year-end, the share of local shareholders,board similarity, and a set of stock-specific characteristics. Toaddress the fact that computing measures of firm valuecould be problematic if the firm has majority ownership insubsidiaries but there is some minority interest for which itis difficult to account for, we reestimate the specification byfocusing only on firms with wholly owned subsidiaries.11

The (unreported) results are unchanged. The definitions ofmarket-to-book and Tobin’s q are provided in Table 1.

We limit the set of control variables to thosecommonly used in the literature. These include firmcharacteristics such as Size, Leverage, Employees, Dividend

Yield, Cash, High-Tech Dummy, and A-list Dummy. We alsoinclude our measures of direct and indirect governance(i.e., Corporate Governance Index, Share of Free Float, andShare of Individuals). All the variables are defined as inprevious sections. We estimate a panel specification withtime fixed effects, and we cluster the errors at theindustry level.12 The estimation method is 2SLS.

The results are reported in Table 4. In the interest ofbrevity, we report only the results based on Tobin’s q.Results based on levels are in Panel A, and results basedon changes are in Panel B. We find that, consistent withHypothesis 3, firms with higher shareholder similaritycommand a higher market valuation. The results arerobust across our alternative similarity measures. Inwidely held firms a one standard deviation increase insimilarity increases prices by more than 20% relative tothe average price in the sample. Wealth similarity has noeffect on stock prices, in line with our previous findings.

Table 4Shareholder similarity and stock valuation.

This table reports the results of the regression of Tobin’s q (Panel A) and changes in Tobin’s q (Panel B) on the level of (changes in) shareholder similarity, share of local shareholders, board similarity, and a set

of stock characteristics. The measures of shareholder similarity and the control variables are defined as in Table 1 and Appendix B. We report the results for ordinary least square (OLS) and two�stage least

square (2SLS) specifications. We use as instruments the Herfindahl index of local (50 kilometer) age�based similarity, the share of local individual investors among the individual investors, the standard

deviation of the parental capital income of the investors, and the Herfindahl index based on the parental capital income decile. Tests of the first stage are similar to those reported in Table A1 and are omitted for

brevity. We report the estimates based on a panel regression with year fixed effects and industry� and time�clustered errors, as described in Section 3 in the text. We use 848 yearly observations (number of

firms ranges between 91 and 271). We also report the w2 and p�value of the Hansen overidentifying restriction and the Anderson�Rubin�Wald weak instruments robust inference test. We report separately

the results for the full sample and for subsamples with and without a dominant shareholder (defined as a shareholder who has control rights of the firm in excess of 20% whose cash flow rights are lower than his

control rights). Year fixed effects are in all the specifications. Coefficients for Size, Leverage, Employees, Dividend Yield, Cash, High-Tech Dummy, A-list Dummy, Share of Free Float, Share of Individuals, Corporate

Governance Index, Mean Age, and Mean Wealth are omitted for brevity. Panel B reports the results for changes in Q as a function of past changes in similarity measures. Estimates are calculated using a

heteroskedasticity-robust estimator with industry-year fixed effects. A total of 615 observations are used in Panel B.

Panel A: Tobin’s q

SS, LS SS, LS SSG, LS SSC1, LS SSL, LSL SSFF, LSFF SSFI, LSFI SS, LS SS, LS

Ols whole sample 2sls, whole sample 2sls, whole sample 2sls, whole sample 2sls, whole sample 2sls, whole sample 2sls, whole sample 2sls, firms without

dominant

shareholder

2sls, firms with

dominant

shareholder


statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Estimate t-

statistics

Shareholder Similarity 0.43 (2.66) 1.46 (2.47) 1.66 (2.32) 2.35 (2.59) 1.09 (2.12) 1.99 (2.06) 4.29 (1.99) 2.14 (2.72) 0.92 (0.90)

Local Share 0.10 (0.83) 0.36 (1.60) 0.05 (0.30) 0.09 (0.46) 0.33 (1.48) 0.40 (1.15) 2.43 (1.93) 0.55 (1.66) �0.08 (�0.16)

Standard Deviation of Age

Groups

�0.34 (�1.77) �0.11 (�0.42) �0.45 (�2.00) �0.36 (�1.47) �0.18 (�0.82) �0.05 (�0.12) 0.41 (0.39) 0.15 (0.45) 0.14 (0.29)

Shareholder Wealth

Similarity

�1.06 (�1.32) �1.60 (�1.55) �2.41 (�2.82) �2.86 (�2.90) �1.40 (�1.35) �1.09 (�0.98) �10.25 (�3.63) �4.85 (�2.68) �1.26 (�0.69)

Lagged Profit Margin 0.29 (5.30) 0.28 (5.80) 0.29 (5.65) 0.28 (5.70) 0.29 (5.64) 0.29 (5.56) 0.28 (6.22) 0.25 (4.87) 0.33 (3.43)

Board Similarity1 0.03 (0.25) 0.08 (0.62) 0.06 (0.57) 0.13 (1.07) �0.02 (�0.15) 0.04 (0.30) 0.05 (0.41) 0.12 (0.40) 0.12 (0.45)

Board Similarity2 �0.12 (�1.06) �0.21 (�1.61) �0.20 (�1.34) �0.26 (�1.85) �0.19 (�1.47) �0.19 (�1.49) �0.23 (�1.57) �0.22 (�1.15) �0.38 (�1.04)

Adjusted R2 0.540


(p-value) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.00) (0.00)

Hansen J-stat 2.30 4.91 2.53 3.17 2.63 3.88 2.71 7.37

(p-value) (0.51) (0.18) (0.47) (0.37) (0.45) (0.28) (0.44) (0.06)

Panel B: Changes in Tobin’s q

SS, LS SSG, LS SSC1, LS SSL, LSL SSFF, LSFF SSFI, LSFI


D Shareholder Similarity 0.68 (2.08) 0.46 (2.10) 0.49 (1.74) 0.71 (2.35) 0.90 (1.79) 0.93 (0.82)

D Local Share �0.45 (�1.56) �0.62 (�2.13) �0.40 (�1.17) �0.75 (�2.54) �0.49 (�1.29) �1.31 (�1.57)

D Shareholder Wealth Similarity �0.47 (�1.22) �0.29 (�0.91) �0.37 (�0.99) �0.54 (�1.40) �0.36 (�0.79) �2.52 (�2.15)

D Standard Deviation of Age Groups �0.47 (�1.75) �0.36 (�1.65) �0.34 (�1.33) �0.45 (�1.85) �0.55 (�1.62) �1.31 (�1.95)

D Mean Age �6.38 (�1.61) �6.87 (�1.69) �7.02 (�1.58) �7.72 (�2.11) �5.77 (�1.43) �13.60 (�1.41)

D Mean Wealth 0.01 (1.03) 0.00 (0.12) 0.01 (0.50) 0.00 (0.18) 0.01 (1.03) 0.03 (0.73)

Board Similarity1 0.03 (0.25) 0.07 (0.51) 0.02 (0.18) 0.04 (0.31) 0.03 (0.25) 0.04 (0.28)

Board Similarity2 �0.07 (�0.54) �0.09 (�0.67) �0.07 (�0.58) �0.07 (�0.55) �0.06 (�0.51) �0.06 (�0.53)

Adjusted R2 0.232 0.233 0.230 0.233 0.227 0.223

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In particular, Panel A reports the results for the mainindex of similarity (SS) and the raw measure of local share(LS) in OLS and 2SLS specifications. We see that highersimilarity is associated with a higher Tobin’s q. For the 2SLspecification, a one standard deviation increase in simi-larity is associated with 0.23 higher Tobin’s q (a 15%increase relative to the mean and a 10% increase relativeto the standard deviation).13 Results for the Gini and C1indices are robust to the change in measure: a onestandard deviation increase in similarity is associatedwith 0.25 (0.34) higher Tobin’s q for C1 (Gini Index). Wealso consider the other measures of concentration andfind the same results. As before, we further show that,consistent with Hypothesis 4, a positive correlation existsbetween similarity and Tobin’s q only if there is nodominant shareholder. A manager of a firm with adominant shareholder is thus likely to pay less attentionto small shareholders’ actions relative to a manager of awidely held firm. This suggests that small shareholdersimilarity could be a substitute for a controlling share-holder in disciplining managers.

The higher the quality of corporate governance(i.e., the lower the value of the index) and the higherthe proportion of controlling shareholders (i.e., the lowerthe free float), the higher are stock prices. This is con-sistent with previous findings on corporate governanceboth for the U.S. (Gompers, Ishii, and Metrick, 2003) andfor Sweden (Cronqvist and Nilsson, 2003). Taken togetherwith the previous findings, our results suggest that thelevel of corporate governance is related to the level ofstock prices. Finally, we observe a strong and persistentpositive correlation (across specifications) between stockprice and firm size, whereas we find a negative correlationbetween stock price and leverage.

In Panel B, we test whether a change in similarityinduces a change in Tobin’s q. We find that a one-standarddeviation increase in similarity raises Tobin’s q by 0.03 inthe base specification, by 0.04 for C1, and by 0.04 for theGini index. We again consider the other measures ofconcentration. A change in similarity continues to bepositively related to a change in Tobin’s q, with aone-standard deviation change in similarity inducing a0.05 (0.03, 0.01) increase in Tobin’s q for the case of SSL

(SSFF, SSFI).

6.2. Shareholder similarity and stock returns: cross-

sectional approach

We now consider stock returns. If an increase in agesimilarity increases prices, a positive (negative) change insimilarity should be positively (negatively) related tostock returns. To test this conjecture, we regress stockreturns on changes in our proxies for similarity and a setof control variables. We consider two specifications. Thefirst is a panel specification with time fixed effects anderrors clustered at the industry level. The second is aFama and MacBeth specification based on a series of

13 We calculate it as the corresponding coefficient (1.46) times the

standard deviation of Shareholder Similarity of 0.16.

monthly crosssections. Given that the results agree, inthe interest of brevity we report only the latter results tofacilitate comparison with prior literature.

The timing is as follows. Information on the composi-tion of shareholders is compiled semiannually (end ofDecember and end of June). The compilation takes time,and the data are said to be released to the market 7–8months after some of the trades were done. While we donot have the exact release dates, the release of the data islikely to be the event to which the market reacts, that is,we should not observe a stock price response to the actualchanges in the composition of small shareholders. Accord-ingly, we lag the composition variable by 6–12 months tocapture a price response. For consistency and compar-ability with the literature, we adopt a specification analo-gous to the one of Gompers, Ishii, and Metrick (2003) andCoval and Moskowitz (1999, 2001) and estimate

Rjt ¼ aþbDSSjt�1þgDLSjt�1þdCjtþejt , ð4Þ

where Rjt is the annual return of the jth stock in year t,DSSjt and DLSjt are our proxies for changes in shareholdersimilarity and in the fraction of local shareholders ofthe jth stock during year t�1, and Cjt is a vector ofcontrol variables for the jth stock at time t. The estima-tion frequency is monthly. We use raw returns, market-adjusted returns (residuals of the capital asset pricingmodel), and industry-adjusted returns (difference betweenraw returns and industry portfolio returns).

We employ the alternative measures of similarity andlocal ownership. In the interest of brevity, we report theresults for the weighted measures (SSFF, SSFI, LSFF, LSFI)and the native-born measures (SSL, LSL) only for thewhole sample. However, we report the results for boththe full sample and the subsamples with and without adominant shareholder for the SS and LS measures. We alsocontrol for board similarity measures, the quality ofcorporate governance, the firm’s free float, and the shareof individual investors. The other control variables are thesame as those used by Gompers, Ishii, and Metrick (2003)as well as those defined in Section 4.

The results are reported in Table 5. We show thatreturns are positively and significantly related to changesin shareholder similarity; moreover, their magnitude islarge. These results hold across all the specifications, bothin the case of the panel specification and the Fama andMacBeth specification. In the interest of brevity we reportonly the Fama and MacBeth results. In Panel A we reportthe results for the raw returns, in Panel B we report theresults for the market-adjusted returns, and in Panel C wereport the results for the industry-adjusted returns.

The different specifications agree, providing consistentevidence of a strong positive correlation between changesin similarity and stock returns. For the sake of brevity, wejust comment on the results based on market-adjustedreturns (Panel B). A one standard deviation increase insimilarity increases returns by 0.18% per month in thebase specification, by 0.40% in the Gini-based specifica-tion, and by 0.32 in the C1-based specification. This holdsfor other measures as well, with similar magnitudes. Asbefore, the effect on firms without a dominant

Table 5Shareholder similarity and stock returns.

This table reports the results of the regression of stock returns on changes in shareholder similarity and a set of control variables. Changes in similarity are defined from year t�1 to year t. We report the

results for raw returns, market-adjusted returns (residuals of the Capital Asset Pricing Model), and industry-adjusted returns (difference between raw firm returns and industry portfolio returns) in Panels A–C,

respectively. Measures of similarity and local share measures are defined in Table 1 and Appendix B. We report separately the results for the full sample and for subsamples with and without a dominant

shareholder (defined as a shareholder who has control rights of the firm in excess of 20% and whose cash flow rights are lower than his control rights). Other control variables are as defined in Appendix B and

Table 1. Fama and MacBeth standard errors are adjusted for first-order autocorrelation (Cochrane, 2005). We use 10,176 monthly observations (the number of firms ranges between 91 and 271). All the

estimates are multiplied by one hundred. Coefficients for Market-to-Book, Size, Leverage, Employees, Bid-Ask Spread, Price, High-Tech Dummy, A-list Dummy, Dividend Yield, Cash, Return 23, Return 46, Return 712,

Turnover, Corporate Governance Index, Share of Free Float, and Share of Individuals are omitted for brevity.

Panel A: Raw returns, Fama and MacBeth estimates

Whole sample No dominant

shareholder

Dominant

shareholder

SS, LS SSG, LS SSC1, LS SSL, LSL SSFF, LSFF SSFI, LSFI SS, LS SS, LS


statistics

Estimate t-

statistics

Estimate t-statistics Estimate t-statistics Estimate t-statistics Estimate t-

statistics

Estimate t-

statistics

Estimate t-statistics


D Local Share �1.87 (�0.53) �1.27 (�0.32) �0.41 (�0.11) �1.99 (�0.49) �2.18 (�0.44) 0.15 (0.02) 3.11 (0.51) 5.22 (0.94)


Similarity

2.23 (0.71) �4.51 (�0.37) �5.27 (�0.42) 2.25 (0.84) 2.29 (0.71) �7.70 (�0.62) �0.38 (�1.92) �1.58 (�0.51)

D Standard Deviation of

Age Groups

3.24 (1.03) 3.42 (0.85) 2.81 (0.74) 3.46 (1.07) 3.60 (1.16) �2.40 (�0.58) 5.63 (1.32) 2.94 (0.44)

D Mean Age �0.01 (�0.07) 0.04 (0.39) 0.01 (0.11) �0.01 (�0.10) 0.01 (0.08) �0.08 (�0.64) �0.15 (�0.91) 0.11 (0.54)

D Mean Wealth 4.56 (2.36) 3.32 (1.95) 3.57 (2.08) 4.51 (2.29) 4.35 (2.19) 3.01 (1.93) 6.68 (3.19) 7.40 (2.55)



Panel B: Market�adjusted returns, Fama and MacBeth estimates


D Local Share 0.03 (0.01) �4.01 (�0.93) �2.61 (�0.62) �0.13 (�0.24) �0.54 (�0.10) 2.65 (0.26) 3.09 (0.44) 3.61 (0.52)


Similarity

�3.19 (�0.26) �10.72 (�0.93) �6.18 (�0.58) �1.75 (�0.59) �4.13 (�0.33) �1.57 (�0.12) �20.62 (�1.10) �6.03 (�0.19)

D Standard Deviation of

Age Groups

�4.11 (�1.05) �6.77 (�1.71) �5.19 (�1.33) �3.89 (�0.97) �4.19 (�1.08) �4.03 (�1.04) 2.84 (0.53) �0.89 (�0.16)

D Mean Age 0.02 (0.20) �0.05 (�0.66) �0.04 (�0.42) 0.04 (0.29) 0.05 (0.41) 0.02 (0.20) �0.08 (�0.53) 0.16 (0.89)

D Mean Wealth 2.17 (1.50) 0.64 (0.38) 1.32 (0.84) 1.92 (1.69) 1.99 (1.34) 1.93 (1.33) 3.54 (2.09) 5.58 (2.15)



Panel C: Industry-adjusted returns, Fama and MacBeth estimates


D Local Share �2.13 (�0.50) �4.34 (�1.09) �2.57 (�0.65) �4.29 (�0.82) �5.64 (�0.94) �15.57 (�1.27) 0.58 (0.07) 3.95 (0.57)

D Shareholder

WealthSimilarity

�13.79 (�1.26) �4.00 (�0.37) �10.24 (�1.02) �9.42 (�1.49) �14.88 (�1.31) �11.44 (�1.00) �26.40 (�1.11) �0.99 (�0.05)

D Standard Deviationof

Age Groups

�4.79 (�1.22) �6.91 (�1.71) �6.73 (�1.68) �5.48 (�1.05) �5.09 (�1.28) �5.66 (�1.41) 2.73 (0.52) �4.13 (�0.58)

D Mean Age �0.11 (�0.87) �0.03 (�0.34) �0.07 (�0.70) �0.08 (�0.66) �0.08 (�0.61) �0.12 (�0.89) �0.19 (�1.00) �0.01 (�0.04)

D Mean Wealth 1.67 (1.08) 0.06 (0.04) �0.08 (�0.05) 1.46 (0.77) 1.50 (0.96) 1.33 (0.81) 4.10 (1.51) 4.07 (1.15)


Board Similarity2 �1.46 (�1.12) �2.27 (�1.69) �2.38 (�1.77) �1.25 (�0.99) �1.56 (�1.16) �1.22 (�0.89) 1.41 (1.25) �5.03 (�1.59)

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shareholder is 0.38% per month, compared to a rathersmall 0.03% for firms with a dominant shareholder.

The fact that the results also hold for industry-adjustedreturns suggests that we are not just picking up aspurious correlation due to some unobserved relationbetween the industries in which the firms operate andownership type. Also, these results, coupled with the factthat the control variables include alternative governancemeasures and board similarity measures, suggests thatshareholder similarity is not proxying for an externalmeasure of governance (i.e., governance index), an inter-nal measure of governance (i.e., blockholders, insiders, orcontrolling shareholders), or board similarity. Shareholdersimilarity is thus shown to have its own distinct role inaffecting stock returns. In contrast, local ownership doesnot seem to directly impact stock returns in a consistentway. If we look at the other control variables, we seethat the Corporate Governance Index, Share of Free Float,and Share of Individual Shareholders appear to be unrelatedto returns. Moreover, these control variables do notappear to provide additional explanatory power in ourspecification. However, they do have significant explana-tory power if used independently instead of together(Cronqvist and Nilsson, 2003). This could suggest thatpart of the explanatory power generally attributed tothese variables could, in fact, be due to their relation toshareholder similarity. That is, firms with better govern-ance could be firms with higher shareholder similarityand not vice versa. Overall, our findings in Table 5 suggestthat an increase in small shareholder age similarity ispositively related to subsequent returns. This supportsour Hypothesis 3.

It is important to stress that these findings also holdfor the locals-only age similarity measure. In principle,however, it should be just the unexpected change in agesimilarity that drives returns.14 We address this issue bydecomposing the change in similarity into its unexpectedand expected components. To do so, we use an econo-metric procedure to decompose the change into anexpected and an unexpected component. The expectedvalue is derived by projecting the change on a set ofpredetermined information variable that are used by themarket to get expectations about the future value of thestate variables in the economy; i.e., change in similarity ofownership. In particular, we assume that changes insimilarity can be decomposed into changes that aredriven by changes in the following observable variables:Size, Book-to-Market, Leverage, Employees, liquidity vari-ables, industry and listing dummies, etc. The residuals ofthe regression of changes in shareholders’ similarity onthe set of observables are used as unexpected changes insimilarity. The results (not reported) are very similar,suggesting that the impact of changes in similarity onstock prices is mostly related to unexpected shocks tosimilarity. That is, the degree of shareholder similarityappears to be priced, with only unexpected changes inshareholder similarity inducing stock price changes.

14 We thank Y. Amihud for pointing this out to us.

6.3. Shareholder similarity and stock returns: portfolio

approach

Finally, we propose a simple investment strategy togauge the effect of the change in age similarity on returns.We form five portfolios based on the annual change insimilarity: From Portfolio 1, consisting of firms belongingto the quintile with the highest decline in similarity, toPortfolio 5 consisting of firms belonging to the quintilewith the highest increase in similarity. The ranking isbased on year t�1. We follow these portfolios for oneyear (t) and then form new portfolios. We considerequally weighted as well as value weighted portfolios.We also form a zero-investment portfolio that takes ashort positioning Portfolio 1 and a long position inPortfolio 5. This is not an implementable strategy as thequintile portfolios could not have been formed given theunavailability of the data at the time. As a result, thisexperiment does not provide evidence of price anomalies.Instead, it simply provides a way to gauge the economicimpact of small shareholder age similarity.

The results are presented in Table 6. In Panel A, wereport the results for the value-weighted portfolios, and inPanel B we report the results for the equally-weightedportfolios. To control for risk, we estimate a marketmodel, a Fama and French (1992) model, and the four-factor model of Carhart (1997). The zero-investmentportfolio yields a monthly a of 2.04% (1.64%) per monthfor the value-weighted (equal-weighted) portfolio. It isalso worth mentioning that the (unreported) raw returnson the zero-investment value-weighted (equal-weighted)portfolios are 1.6% (1%) per month.

We reiterate that this large excess return does notrepresent an anomaly, as it is not a return on an invest-able strategy. Instead, it provides us a sense of themagnitude of the effect that changes in similarity haveon stock valuation. In other words, these excess returnsare just the reflection of a one-time change in valuations.For investors to be able to make money, they should knowthe changes in investors’ demographics in real time. Thisis, however, not possible. SIS Agarservice AB uses semi-annual snapshots of this data coming from VPC (as well asits own proprietary data on voting pacts, family connec-tions, trusts, strategic shareholdings via foreign holdingfirms, etc.) and sells this data to firms and others. The dataare coming with significant delay for most firms. Thedelay can be as long as 8–9 months, and by then most ofthe effect has dissipated.

7. Discussion

Thus far, we focus our discussion on concentrationmeasures. Gini is used as an alternative proxy for con-centration. However, the Gini index is also an index ofinequality of distributions. The fact that the results con-tinue to go through when we replace the Herfindahl indexof concentration with the Gini index suggests that theway ownership is distributed matters. But if distributionmatters, differences in age groups could play a role, inwhich case concentration could be driven by one specificage class; e.g., old investors. We, therefore, ask whether

Table 6Portfolios based on changes in similarity.

This table report results of Fama-French-Carhart analysis of quintile portfolios sorted on Shareholder Simularity (SS). We use the SIX Trust database and

the Market Manager Partners database to construct market (MKTRF), size (SMB), value (HML), and momentum (UMD) factors for Sweden. The SIX Trust

database contains stock prices and dividend payments. The Market Manager Partners database reports company accounting variables. The market risk

factor is the return on the market (SIX index) net of yield on 3-month Swedish treasuries. In constructing size and value factors we follow Fama and

French (1992); when constructing momentum we follow Carhart (1997). Panel A (B) reports the results for value (equally) weighted portfolios. We report

the results for one factor, three factors (Fama and French), and four factors (Fama-French-Carhart). We also report the results for the difference between

extreme portfolios. Regressions are based on 60 monthly observations. All estimates are multiplied by one hundred.

Panel A: Value-weighted portfolios

Interceptt MKTRF HML SMB UMD Adjusted R2

Portfolio 1 �1.334 92.21 �2.026 1.159 15.404 0.788

(large decreases in SS) (�3.75) (12.88) (�0.28) (0.16) (0.92)

Portfolio 2 �0.237 100.68 �4.316 �17.072 �48.092 0.761

(�0.62) (13.19) (�0.55) (�2.26) (�2.69)

Portfolio 3 �0.292 88.14 11.161 6.682 10.448 0.812

(�1.03) (15.39) (1.90) (1.18) (0.78)

Portfolio 4 0.548 109.88 5.333 �13.057 7.021 0.809

(1.46) (14.55) (0.69) (�1.75) (0.40)

Portfolio 5 0.710 78.44 20.474 5.798 21.683 0.774

(large increases in SS) (2.55) (13.96) (3.56) (1.04) (1.65)

Long Portfolio 5, Short Portfolio 1 1.997 �26.542 0.114

(4.07) (�2.93)

2.047 �13.572 20.355 4.345 0.257

(4.55) (�1.50) (3.51) (0.49)

2.044 �13.765 22.500 4.640 6.279 0.245

(4.50) (-1.51) (2.40) (0.51) (0.29)

Panel B: Equally-weighted portfolios

Portfolio 1 �0.675 111.522 �10.882 2.814 �6.108 0.896

(large decreases in SS) (�2.13) (18.02) (�1.65) (0.41) (�0.41)

Portfolio 2 �0.146 104.453 �3.780 �14.600 �24.469 0.886

(�0.52) (19.12) (�0.65) (�2.43) (�1.85)

Portfolio 3 �0.439 103.369 6.044 2.403 16.935 0.911

(�1.69) (20.42) (1.12) (0.43) (1.38)

Portfolio 4 0.334 98.279 4.164 �12.714 �11.635 0.878

(1.24) (18.73) (0.75) (�2.21) (�0.92)

Portfolio 5 0.938 81.962 4.257 22.638 24.735 0.849

(large increases in SS) (3.06) (13.71) (0.67) (3.45) (1.71)

Long Portfolio 5, Short Portfolio 1 1.456 �27.049 0.130

(2.81) (�3.14)

1.618 �28.146 4.841 17.792 0.161

(3.13) (�2.81) (0.69) (1.62)

1.613 �29.559 15.140 19.824 30.843 0.171

(3.14) (�2.94) (1.42) (1.80) (1.27)


age similarity responds in the same way regardless of theage class of the investors. To test whether this is the case,we break the analysis down based on the largest agegroup in a given firm.15 We find that all the age groupsreact similarly. We also examine whether the degree ofconcentration of different age groups affects firm profit-ability (ROA, ROE, and Profit Margin),market-to-book, orTobin’s q. In this case, the effect seems to weakly prevailamong the eldest (older than 61 years) and youngest(younger than 40 years) investors.

Overall, our findings suggest that firms with highersmall shareholder age similarity are more profitable anddisplay higher market values. These results hold regard-less of the quality of internal or external firm governanceor of the degree of board similarity. That is, shareholder

15 Due to the fact that the youngest group is rather small (about 2%

of the firm-year observations), for the purpose of this analysis we merge

the two youngest groups.

similarity is not proxying for measures of governance orfor board similarity, but rather has a distinct and impor-tant impact on stock prices.

Our results differ from those based on theories relatingdifferences in beliefs to stock prices in the presence ofshort-sale constraints (Miller, 1977; Chen, Hong, andStein, 2002; Diether, Malloy, and Scherbina, 2002). Thesetheories posit that, given shareholders with pessimisticviews are reluctant to participate in the stock market,stock prices are a function of the beliefs of relatively moreoptimistic shareholders. This implies that a higher dis-persion of opinions (i.e., lower similarity) would lead toan increase in stock prices. However, the channeldescribed by these theories is entirely market-based. Thatis, no direct impact on managerial behavior is assumed.Our results provide evidence in favor of a separatechannel based on the reaction of managers.

In contrast, our results are consistent with the recentfindings of Boot and Thakor (2004) and Dittmar andThakor (2007) on equity issuance decisions. These authors


argue that firm value is increasing in shareholder-man-ager alignment and show that managers use equity tofinance projects when they believe that shareholders’views about project payoffs are more aligned with theirs,thus maximizing the likelihood of agreement with share-holders. Their focus is on the sunny side of shareholder-manager alignment, that is, on the ability of managers toexploit alignment as if it were an option. In our context,we focus more on the dark side of this alignment, that is,on the disciplining mechanism of shareholders who votewith their feet and hence can drive the stock price down.Our study is also distinct in the sense that the mechanismwe inspect is based on informal coordination of smallshareholders, whereas their logic pertains more to a largeshareholding block. However, the spirit of our paper issimilar as we can consider shareholder similarity to beone of the mechanisms that aligns managers’ and smallshareholders’ interests. Put differently, our proxy forconcentration can be seen as an indirect proxy for interestalignment between management and shareholders. Thehigher the concentration, the more the firm is induced toalign its interests with those of its shareholders. The factthat our results are much stronger in the case of firmswithout a dominant shareholder supports this view. Smallshareholders should matter less in the presence of acontrolling blockholder as in this case managers are likelyto give less weight to the voice of small shareholders.

The fact that our results are not driven by one age group(e.g., old shareholders) does not necessarily mean that allshareholders interpret the news the same way. If this werethe case, then similarity would not matter, because for allpractical purposes all shareholders are similar. However, thisargument would require that all news events are the same;i.e., they are unidimensional and the regression captures thisdimension. However, as is known from the literature ondifferences of opinions (e.g., from Mayshar, 1983; Varian,1985; Harris and Raviv, 1993; Kandel and Pearson, 1995; andthe subsequent literature), people do not interpret informa-tion the same way, which implies that news have more thanone dimension. Much empirical evidence on that emergedin the last 15 years. Given that people interpret informationdifferently, and that news are multidimensional, it is reason-able that the difference between the interpretations of theyoung and old shareholders depends on both the news in

Young clu

(25%)

Young consider as bad news, old do not (50%) Price goes

Old consider as bad news, young do not (50%) No effec

Fig. 2. Matrix of negative earnings surprise and firm types. The

itself and the additional relevant information that research-ers do not observe.

Consider a simplified thought experiment for illustra-tion purposes. Assume for simplicity that one-half of allthe negative earnings surprises we identify are perceivedby the young shareholders as bad news, but the oldshareholders fully expected them, and, thus, do notconsider them news because of the other information. Inthis case, young shareholders will react, while the oldshareholders will not react to them. For the other half ofthe negative earnings surprises, the older shareholdersconsider them as bad news, while the younger treat themas nonevents. In this case, old shareholders will react,while the young shareholders will not react to them.

Also, assume that there are three types of firms, with acluster of similar young shareholders, with a cluster ofolder shareholders, and without clusters. Fig. 2 depicts amatrix of negative earnings surprise and firm types. Thecells indicate the price reaction in the firm-event space.

Let us assume that an econometrician could separate thesample along the event types (the rows) and estimate aspecification in which the price effect is a function of thedegree of similarity and similarity interacted with age. Boththe level effect of similarity and its interaction with agewould be significant. Higher similarity by itself would beassociated with a bigger price drop, but for it would be theyounger age group times similarity that would have a muchlarger coefficient. Similarly, in the lower row, the old agewould be associated with the high similarity impact.

Unfortunately, we cannot partition the sample alongunobservable (to us) dimensions of news (or its perceptionby the different age groups). In our analysis, we are forced toadopt an econometric methodology based on a pooledregression. The use of this methodology would producethe following outcome in our simplified example: The ageeffects would exactly cancel in the interaction, but thesimilarity effect would remain. In fact, the use of a pooledspecification provides a significantly downward-biased esti-mate of the similarity effect.

This implies that, even if we observe that for similarearnings surprises different age groups react similarly, stillwe cannot draw the conclusion that they react similarly tothe same information events, as characterized by all signalsreleased along with it.

Firms

ster

(25%) (50%)

down

t Price goes down

Old cluster No cluster

No effect No effect

No effect

cells indicate the price reaction in the firm-event space.


Overall, our findings complement prior work by pro-viding a view of corporate governance in which thedistribution of shareholders has a direct impact on firmvalue by conditioning the decision of managers and,hence, indirectly influencing the stock price. This viewsheds new light on the determinants of stock pricesand corporate decisions. Specifically, unlike in standardcorporate theory, our results suggest that managers’decisions are directly affected by the investment decisionsof shareholders, as opposed to direct monitoring. Further,unlike in standard asset pricing theory, our results sug-gest that a firm’s stock price is indirectly determined bymanagers’ reactions to shareholder pressure.

8. Conclusion

In this paper, we study how a firm’s shareholdingstructure affects its financial and operating performance.We argue that the degree of shareholder similarity affectsfirm value. In particular, similar shareholders are betterable to informally coordinate their actions, especiallysales of stocks following negative news about the firm,and thus similar shareholders can play a monitoring rolethat translates into higher profitability and higher stockprices.

We test this hypothesis by using a novel dataset contain-ing information on all the shareholders for each firm inSweden over the past decade. We construct a proxy forsimilarity based on age. We show that similar shareholdersreact in the same way to firm news and that greatersimilarity among shareholders increases firm profitabilityand returns. This evidence is consistent with the existenceof a channel through which the distribution of shareholdersaffects firm value. Our findings thus raise a new set ofquestions in asset pricing and corporate finance regardingthe path dependency of shareholder composition and itseffects on management and firm valuation.

Appendix A. A simple model

This is a two-period model. Let X0 be the unknownvalue of the security in period zero and X1 be the value ofthe same security in period one. We assume that thebeliefs of investor i about the security value at time t arenormally distributed with means xt,i and precisions Ht,i.Using constant absolute risk aversion (CARA) utility func-tion with risk tolerance r, we can postulate that thedemand functions of investor i for shares of this securityare [ignoring hedging demands, but, as Banerjee andKremer (2008) show, this does not affect prices]:

n0,iðP0Þ ¼ ðx0,i�P0ÞrH0,i and n1,iðP0Þ ¼ ðx1,i�P0ÞrH1,i: ð5Þ

We assume for simplicity that Hi,t¼Ht8i and that thesupply of shares per capita, S, does not change acrossperiods.

We consider two scenarios. In the first scenario, thereexists a continuum of investors, whose type is denoted byi. We denote the population means by xt. Then, theequilibrium prices are

P0 ¼ x0�S=ðrH0Þ and P1 ¼ x1�S=ðrH1Þ: ð6Þ

We follow a simple model of differences in opinions asin Kandel and Pearson (1995). It assumes that followingan arrival of a public signal L different types of investorsinterpret it differently. Formally, we assume that agent i

believes that L is a biased signal of the fundamental value,where the bias is mi.

We further assume, as in Kandel and Pearson (1995),that all agents agree on the precision of the signal, whichis denoted by h. The relation between the prior beliefs andthe posterior beliefs is derived by applying Bayes rule:

x1,i ¼ ðH0�0,iþhðL�miÞÞ=H1, where H1 ¼H0þh: ð7Þ

We assume for simplicity that the average bias is zero,i.e., Emi¼0. Then we can represent the price change as afunction of the signal interpretation when the populationis distributed continuously:

P1�P0 ¼ x1�S=ðrH1Þ�x0þS=ðrH0Þ¼ðh=H1Þ½L�x0þS=ðrH0Þ�:

ð8Þ

The price change consists of two components. One isproportional to the deviation of the public signal from theprior mean, (h/H1)[L�x0], and could be positive or nega-tive depending on the sign of the deviation. The second isthe price increase of hS/(rH1H0) due to higher precision ofthe investor’s beliefs following the signal arrival.

The above scenario implicitly assumes that each per-son updates his beliefs independently of others. We nowtest what happens when a group of investors makes thesame decisions. We consider a second scenario in whichthe percentage y of the population is distributed as before,but there is also a homogeneous group of size (1�y),whose members have identical beliefs and interpret thesignal the same way: xt,g and mg (which is not necessarilyequal to zero at any given time). The individual demandsare determined as before, but the aggregate demandfunction consists of the continuum and the group. Theequilibrium prices every period are

P0 ¼ yx0þð1-yÞx0,g�S=ðrH0Þ and P1 ¼ yx1þð1-yÞx1,g�S=ðrH1Þ:

ð9Þ

We can again represent the price change as a functionof the public signal interpretation:

P1�P0 ¼ ðh=H1Þ½L�yx0�ð1-yÞx0,g�ð1-yÞmgþS=ðrH0Þ�: ð10Þ

As in the previous case, we have a component asso-ciated with the signal deviation from the prior mean,(h/H1)[L�yx0�(1�y)x0,g], and a component associatedwith the increase in the precision, hS/(rH1H0). However,unlike the previous case, there is an additional compo-nent, (�h/H1)(1�y)mg, which captures the difference(from the population mean) in the interpretation of thenews by the members of the group.

If mg is large and positive, which means that the groupsinterprets the news as bad, prices decline; if mg is largeand negative, then the group drives prices up. In bothcases the effect is due to the fact that all the members ofthe group interpret the information the same way. Theimpact increases with the size of the group, (1�y).

The main prediction of this simple model is that whenshareholders, even if small, are homogeneous, they reactto the (negative) news in a similar way and this would


magnify their impact on price. In fact, the effect on theprice could be similar to the one of a single big share-holder. This is just one channel through which highersimilarity of the shareholders can translate into a largerpotential impact on price.

Appendix B. Definitions of additional variables

A-list Dummy is equal to one if the company is listed inthe A-list of Stockholm Stock Exchange at t. Data areprovided by SIX Trust.

Cash is the logarithm (base 10) of liquid assets in thebalance sheet (thousands of SEK). Data are provided byMM Partners.

Corporate Governance Index is based on Cronqvist andNilsson (2003), defined similarly to Gompers, Ishii, andMetrick (2003), is the sum of four dummies for differ-ential share classes, preemption rights on high-votingshares, voting restrictions, and voting pact among largeshareholders.

Dividend Yield is the dividend yield of stock; that is,ratio of the dividends paid in the previous year and theend-of-year share price. Data are provided by SIX Trust.

High-Tech Dummy is equal to one if the companybelongs to a high-tech industry. Data are provided byMM Partners.

Market-adjusted returns are residuals of CAPM withexcess returns on SIX Trust market index used asbenchmark.

Price is the price (in SEK) of a share. Data are providedby SIX Trust.

Raw Returns is defined as ln [(PtþDt)/Pt�1], where P issplit-adjusted price and D is dividend (both in SEK). Dataare provided by SIX Trust.

Return23, Return46, and Return712 denote compoundedgross return for the period between months t�2 and t�3,t�4 and t�6, and t�7 and t�12, respectively.

Turnover is the logarithm of the ratio of shares tradedto shares outstanding. Data are provided by SIX Trust.

Appendix C. Econometric issues

The endogenous nature of the ownership structure(Demsetz, 1983; Demsetz and Lehn, 1985) may induce aspurious correlation between shareholder similarity andfirm’s profitability and value. For example, more profit-able firms could attract more similar shareholders. Toaddress this issue, we adopt an instrumental variablespecification that exploits the similarity of all local share-holders as instrument.

Individuals have been shown to invest more in compa-nies headquartered close to where they live (Huberman,2001; and Coval and Moskowitz, 1999, 2001) or located intheir home country (Bhattacharya and Groznik, 2008). Stockmarket participation as well has been related to that of theindividual’s community (Guiso, Sapienza, and Zingales,2004; Feng and Seasholes, 2004; Brown, Ivkovic, Smith,and Weisbenner, 2008). These findings suggest that thelocation of the shareholders around the firm is a variablethat is largely exogenous to the main observable

characteristics of the firm we want to study, i.e., profitabilityand stock returns, but a variable that affects investorbehavior. We, therefore, use this feature to implement aninstrumental variables estimation in which we project thefirm’s shareholders’ similarity, on the similarity of theshareholders living close to the firm. The idea behind thisidentifying restriction is that the firm’s shareholder struc-ture is in large part determined by its location. That is, thedegree of similarity of the firm’s shareholders is directlyrelated to the degree of similarity of the shareholdersresiding in its locale.

For example, consider two firms: A is located inGoteborg and B is in Malmo. We expect the degree ofshareholder similarity of firm A to be related to the degreeof similarity of Goteborg residents, and the similarity offirm B to be related to the similarity of Malmo residents.In other words, historic, social, and cultural reasonsdetermine the age distribution of local residents, whilehome bias induces them to invest in local firms. So, ifthere is a predominance of older people in Malmo, dueperhaps to its relatively mild climate or better health andwelfare services, this should be reflected in a predomi-nance of older shareholders in the firms located in Malmo.

This identifying restriction requires the following threeelements to be true. First, shareholders in Sweden areaffected by local bias. This is the case in Sweden, as shownby Massa and Simonov (2006) and Bodnaruk (2009). Sec-ond, different localities have significantly different distribu-tional characteristics for the shareholders living there. And,as shown in Table 1, Panel B, significant differences existbetween shareholders residing in different localities.

The third element is that firms inherit the shareholderdistributional characteristics of the localities in whichthey are located. Testing this point is trickier, as itrequires showing this difference at the firm level. Thatis, the age distribution of the shareholders of the firmshould be related to the age distribution of all the share-holders living close to it, and not related to the agedistribution of shareholders in other locales. To test this,we regress our proxies of Shareholder Similarity and Local

Share defined at the individual firm level on the sameproxies of similarity of all local and nonlocal shareholders,as well as the share of local individual shareholdersamong all Swedish individual shareholders. We employthe same control variables as in the main specifications.This guarantees that our instrumented proxies are uncor-related with other variables in our specifications. We alsouse the Corporate Governance Index, the Free Float, and theShare of Individuals, as well as the two measures of boardsimilarity. Given the potential correlation between localand nonlocal similarity, we also consider an orthogona-lized measure of nonlocal similarity constructed byregressing local on nonlocal similarity and taking theresiduals. Results are reported in Table A1. They show astrong statistical relation between the firm and locationsimilarity levels, which is robust across specifications. Inparticular, a one standard deviation increase in theaverage similarity of the shareholders in the locality,defined in terms of the head office or closest firm estab-lishment with at least ten permanent employees, raisesthe average shareholder similarity from 0.41 to 0.48 (17%

Table A1Instrumenting measures of similarity and local share.

This table reports the results of regressions of alternative measures of Shareholder Similarity and Local Share on the set of explanatory variables and instruments. We use as instruments the Herfindahl index of

local (50 kilometers) and nonlocal age-based investor similarity, the share of local individual investors among Swedish individual investors, the standard deviation of the parental capital income of investors,

and the Herfindahl index based on the parental capital income decile. We also report the Adjusted R2, the Partial R2 of the excluded instruments, and Shea’s Partial R2 of excluded instruments. The F-test of the

excluded instruments, the Kleibergen-Paaprk LM statistic, and the Kleibergen-Paaprk-Wald F statistic are reported as well. We report the critical value of the Kleibergen-Paaprk-Wald F-statistic weak

instrument test (based on Andrews and Stock, 2005) in brackets. We use 848 yearly observations (the number of firms ranges between 91 and 271). The coefficient on the standard deviation of parental income

in 1970 is multiplied by 100,000. We report results for the specification with lagged ROA. The other specifications (lagged ROE, Profit Margin) are similar and are omitted for brevity. Coefficients for Leverage,

Employees, Dividend Yield, Cash, High-Tech Dummy, A-list Dummy, Share of Free Float, Share of Individuals, Corporate Governance Index, Standard Deviation of Age groups, Shareholders Wealth Similarity, Mean Age, and

Mean Wealth are omitted for brevity.

Shareholder Similarity

(SS)

Local Share (LS) Shareholder Similarity

(SSFF)

Local Share (LSFF) Shareholder Similarity

(SSFI)

Local Share (LSFI)


Local share of investors �0.06 (�1.99) �0.03 (�0.41) �0.04 (�2.52) 0.00 (�0.06) �0.01 (�0.95) 0.02 (0.91)

Herfindahl of parental decile of capital income in 1970 0.07 (4.87) �0.06 (�3.42) 0.04 (5.40) �0.03 (�2.82) 0.02 (4.25) 0.00 (�0.40)

Standard deviation of parental capital income in 1970 0.00 (5.92) 0.00 (3.18) 0.00 (6.50) 0.00 (3.54) 0.00 (7.12) 0.00 (3.14)

Age Herfindahl all nonlocal investors 0.05 (0.91) �0.35 (�2.37) 0.04 (0.97) �0.19 (�1.93) 0.19 (2.33) �0.43 (�2.35)

Age Herfindahl all local investors 0.39 (4.58) �2.56 (�14.35) 0.24 (3.66) �1.75 (�13.92) 0.37 (2.83) �2.03 (�5.90)

Lagged ROA �0.02 (�1.32) 0.05 (2.24) 0.01 (0.29) 0.12 (2.36) �0.01 (�3.67) 0.02 (1.77)

Board Similarity1 0.04 (1.16) �0.14 (�2.43) 0.05 (1.95) �0.14 (�3.19) 0.01 (0.69) �0.05 (�1.94)

Board Similarity2 0.02 (0.62) 0.01 (0.25) 0.01 (0.71) �0.01 (�0.27) 0.00 (�0.33) 0.00 (�0.09)

Size 0.02 (1.84) �0.06 (�4.82) 0.01 (1.18) �0.05 (�4.87) 0.00 (1.82) �0.02 (�3.89)

Year fixed effects Yes Yes Yes Yes Yes Yes

Adjusted R2 0.561 0.554 0.568 0.637 0.798 0.805

F-test of excluded instruments 14.57 122.41 16.06 126.77 17.39 30.63

(p-value) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Partial R2 of instruments 0.110 0.453 0.092 0.419 0.126 0.395 0.110

Shea partial R2 0.074 0.304 0.062 0.285 0.099 0.311 0.074

Underidentification test (Kleibergen-Paap rk LM statistic) 77.95 44.206 40.851

(p-value) (0.00) (0.00) (0.00)

Weak identification test (Kleibergen-Paap rk Wald F-statistic) 29.39 14.02 11.33

Critical value of the test [8.78] [8.78] [8.78]

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of its cross-sectional mean)16 for the base Shareholder

Similarity; from 0.28 to 0.32 (12% of the mean) for thefree-float measure, SSFF, and from 0.10 to 0.15 (46% of itsmean) for SSFI. The effect of similarity of nonlocal inves-tors on our firm measures is either absent (for SS and SSFF

corresponding coefficients are not significant), or muchsmaller in economic magnitude (for SSFI) than the effectof local shareholders similarity.17 The effects of boardsimilarity and size are also not significant. For the Local

Share of investors in the firm, the strongest (and negative)effect comes from local shareholder similarity and size. Theseresults suggest that the shareholding structure of a firm isaffected by the firm’s locale. We, therefore, adopt the averagesimilarity of the local shareholders as well as the share oflocal individual shareholders as our main instruments.

For robustness, we also use as additional instruments thestandard deviation of the parental capital income of theshareholders and the Herfindahl index based on the parentalcapital income decile.18 These describe the distribution ofthe capital and labor income of the shareholders’ parentalfamily when the shareholders were between ten and 15years old. We argue that these variables should help toexplain current concentration and dispersion of shareholders.The intuition is that parental characteristics affect education,upbringing, culture, and the overall view of the world and,therefore, also the person’s attitude toward investing. Thisimplies that variations in parental characteristics should helpexplain variation in the current choice of the investors andindirectly in their incentive to concentrate their holdings. Forexample, if all the investors from a specific area shared thesame type of parental wealth and income, they could havereceived a similar upbringing and similar values that later inlife could have induced them to focus in similar type ofstocks. That is, old wealth could have started investing earlierand so could have a bigger tilt toward old value stocks, whilerecent wealth could have focused on more recent stocks thathave become more known at the time they have startedaccumulating money and investing.

Good instruments should be correlated with the endo-genous explanatory variables but orthogonal to any otheromitted characteristics. That is, the instruments should beuncorrelated with the dependent variable of interestthrough any channel other than their effect via theendogenous explanatory variables. In other words, thecorrelation between the residuals of the second stage andthe instruments should be null.

16 Standard deviation of similarity of investors in locality is 0.181

(see Table 1, Panel B), corresponding coefficient from Table A2, specifi-

cation (1) is 0.39, and total effect is 0.39*0.181¼0.07. This corresponds

to about a 17% increase in the unconditional mean of the similarity

measure (0.414).17 The effect for non-local investors is equal to 0.19*0.034¼0.006

versus 0.05 for local investors’effect.18 To construct these variables, we trace individual shareholders via

the LINDA dataset to their parental household (or their own if they were

adults in 1970). Then we build, at the firm level, Herfindahl indexes and

standard deviations of labor and capital income of the parental families

of the shareholders. These are either in monetary values (1970 SEK) or in

terms of the decile of the labor and capital income distribution they

belonged to. We also calculate the Herfindahl index based on locality of

the shareholders’ parents in 1970. These variables are meant to proxy for

the determinants that have induced the shareholders to invest.

To test whether our instruments are correlated to thevariable of interest, in Table A1, we report the R2 of thefirst stage in the instrumental variable regression as wellas the partial R2’s, the Shea partial R2’s, and the F-tests ofthe instruments in such a regression. The partial R2 of ourinstruments in general exceeds 10%, and exclusion testshows that instruments are jointly significant (F-statisticsis in general greater than 10, p-values are essentially 0).We also report Kleibergen-Paap tests for under-identifica-tion and for weak instruments. Kleibergen-Paap-WaldF-statistics is higher than the critical values (reported inbrackets) rejecting the hypothesis of weak instruments.These tests show that the instruments are not weak in thesense of Staiger and Stock (1997). For example, the test ofweak instruments ranges from an F-value of 29.39 in thecase of the base shareholder similarity (SS) to 14.02 and11.33 for the other two measures (SSFF, SSFI), respectively.

To test for the lack of correlation with the dependentvariable of interest, in Tables 3 and 4, we report the Hansentest of over identification.19 In all the specifications, thediagnostics show that the instruments, while stronglystatistically correlated with the endogenous proxy of inter-est, do not affect the dependent variable of interest througha channel other than their effect via the endogenousexplanatory variables.

One final issue could be that the age structure of thepopulation reflects the degree of past local economicdevelopment and could be spuriously correlated withthe firm’s performance. To alleviate this concern, we useinformation related to place of birth of individual inves-tors. We use our native based similarity and local sharemeasures (SSL and LSL) defined just for the investors thatwere born and still reside in the county of birth. Investorsborn in the area did not move there just for companyrelated reasons. The results we obtain for this measure arevery close to the one obtained for raw measure. Thisgreatly reduces the concern of spurious correlation.

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