corporate governance through stock trading in brazil ... · voting rights (ordinary stocks, or on),...
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Corporate Governance Through Stock Trading in Brazil: Empirical Evidence with
Institutional Investors
ORLEANS SILVA MARTINS
Universidade Federal da Paraíba
LUCAS NOGUEIRA CABRAL DE VASCONCELOS
Universidade Federal da Paraíba
Abstract
This paper analyses the viability of stock trading or the threat of stock liquidation as a
mechanism to promote corporate governance, addressing its effects on abnormal returns,
information incorporation and operational performance of companies. For this, we use
property data available in the annual Reference Forms (RF) of publicly traded companies in
the Brazilian stock market. The sample included 254 stocks with data available between
December 2009 to December 2017 in Thomson Reuters Eikon and Economatica database.
The hypotheses formulated are tested with panel data regressions, based on the multiple block
holders’ governance through Theory of Multiple Blockholders of Edmans and Manso (2011).
The results indicate that the number of institutional investors is not related to abnormal
returns. Therefore, the majority of institutional investors in the Brazilian stock market may
not be informed investors. On the other hand, the number of institutional investors increases
the amount of firm-specific information into stock prices, rising stock market price efficiency.
This relationship is stronger among the preferred stocks (PN), but this mechanism is still not
valid to increase firm’ operational performance. Despite the possible increase in stock price
efficiency, the investors cannot adopt such a mechanism to exercise governance. As a
contribution, the study indicates that competition among institutional investors is important to
raise stock prices efficiency levels, even in an emerging market. Therefore, policies that allow
capital inflow, that guarantee greater property protection, that increase liquidity and that
associate managers’ salaries with the stock performance, are beneficial to reinforce the stock
markets efficiency in Brazil.
Keywords: Corporate Governance, Stock Liquidation, Stock Price Informativeness.
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1. Introduction
Imagine that after a series of negotiations, two companies A and B conclude that a
merger between them will create value. The agreement is partially closed and communicated
to the market. However, the investors of company A believe that the company will not benefit
from the agreement (e.g., the reasons are many: substantial debt rises, loss of entry barriers,
dilution of earnings per share, strategic misunderstandings, among other factors). Investors
express their dissatisfaction by selling their shares and knocking down prices considerably. In
this scenario, there is a reduction in the managers’ pay for performance, a reduction in the
price to issue new shares and an increase in the takeover risk (Edmans & Holderness, 2017).
Based on the observation of markets’ behavior, recent theories of corporate governance
affirm that it is possible to exercise control by the trading of stocks or by the threat of
liquidation (Edmans & Manso, 2011). These corporate governance theories merge with the
market microstructures literature by assuming that the trading activity of informed investors
incorporate private information into prices, increasing market efficiency and promoting higher
operational performance (Edmans & Holderness, 2017). With a few exceptions, most studies
were conducted in developed markets, with strong legal property protection and proper
disclosure requirements, there is also high liquidity and low transaction costs (Bharath,
Jayaraman, & Nagar, 2013; Edmans, Fang, & Zur, 2013; Gallagher, Gardner, & Swan, 2013).
However, emerging markets should have a degree of market efficiency and legal protection
inferior the developed markets (La Porta, Lopez-de-Silanes, Shleifer, & Vishny, 1997; La
Porta, Lopez‐de‐Silanes, Shleifer, & Vishny, 1998). In this sense, this research tests some of
the main implications of these theories and shows that they cannot be fully applied to explain
the reality of the Brazilian capital market, the largest emerging market in Latin America.
What is the reason to study in this market? The theory states that the effectiveness of
governance though trading appears as an alternative in markets where capital is pulverized or
when voting is not guaranteed for all investors (Edmans & Manso, 2011). In the Brazilian
stock market, in the higher level of B3’s corporate governance segments, the New Market,
34% of the corporate control is pulverized (KPMG, 2016). However, in the other corporate
governance segments (Level 2, Level 1 and Basic), this percentage is no more than 9%, 4%,
and 14%, respectively. Except for the New Market, where all the shares must guarantee the
voting rights (ordinary stocks, or ON), there is an ordinary stock concentration and a high
level of non-voting shares (preferred stocks, or PN) (KPMG, 2016).
The study of alternatives to voting offers evidence of the feasibility of capital allocation
in firms with a pulverized property structure. On the other hand, there are theoretical aspects
that consider the dispersed property and stock liquidity as harmful factors to the intervention
capacity. These factors would encourage short-term behavior and allow the stock liquidation
instead of bearing the cost of intervening to improve a bad company (Bhide, 1993). To the
best of our knowledge, this is the first paper that addresses the topic of corporate governance
through trading and threat of liquidation in Brazil, contributing to expanding the literature of
ownership and control in this market.
In order to make the research feasible, it was assumed that institutional investors are
informed and that their property is large enough to guarantee the intervention power
(McCahery, Sautner, & Starks, 2016). Three hypotheses were elaborated based on the
corporate governance literature (Edmans & Manson, 2011). First, following the empirical
evidence that institutional investors are informed (Hendershott, Livdan, & Schürhoff, 2015;
Collin-Dufresne & Fos, 2015; McCahery, Sautner, & Starks, 2016) and that competition for
the profit of the informational advantage can reduce abnormal returns, the first hypothesis
(H1) is established as “the greater the competition among institutional investors, the smaller
will be the abnormal stock returns”. This hypothesis has a theoretical foundation on Edmans
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and Manso (2011) and empirical base on Bharath, Jayaraman and Nagar (2013), Edmans,
Fang and Zur (2013), Gallagher, Gardner, and Swan (2013).
The competition for information would imply in a higher informational efficiency by
the rapid incorporation of information into prices. The theory of Edmans and Manso (2011)
generates the second hypothesis (H2), that “the number of institutional investors is positively
related to the incorporation of information into prices” and, as a consequence, we would have
a positive effect on operating performance, implying in the third hypothesis (H3) that “the
greater the price efficiency, the greater the company operational performance”. Empirical
evidence before the theoretical development of Edmans and Manso (2011) had already been
documented by Fang, Noe, and Tice (2009) and later by Gallagher, Gardner, and Swan
(2013). The hypothesis test results indicate that only H2 can be supported.
These results have two implications. First, if the presence of institutional investors
raises informational efficiency levels, then the policies that allow the capital inflows and
guarantee greater property protection are beneficial to the Brazilian stock market. Such
policies would generate significant synergies for the outsider investors, which in turn are
associated with higher levels of performance and value creation (Fang, Noe, & Tice, 2009)
and with the default risk reduction (Brogaard, Li, & Xia, 2017). The second implication is
that there is no evidence that a higher number of institutional factors leads to better
performance. Therefore, this research emphasizes that, to date, voting governance is a more
efficient form of control in Brazil, which in turn is only functional when there is legal and
property protection (Shleifer & Vishny, 1986). These results give rise to conjectures that the
lack of association between executive rewards and stock performance can mitigate potential
efficiency gains and prevent governance through trading from being effective in these firms.
This study is divided into five sections. Section 2 presents the hypotheses development,
with theories of corporate governance through trading and treats of liquidation (exit) and the
concept of stock price informativeness. Section 3 describes the methodological procedures.
Section 4 reports the empirical results and Section 5 presents the conclusion and implications
of these findings.
2. Hypotheses Development
2.1. Corporate Governance and Stock Trading
The control framework literature presents two options available to investors to ensure
firm value maximization (Hirschman, 1970): (i) they may engage to impose management
changes (known as “voice”, “vote”, or “direct intervention”), or (2) they may leave the
company, liquidating their stocks. In the first option, corporate governance is exercised by
voting. The ability to monitor the managers will depend on the number of stocks held, being
proportional to the power of “voice” (Shleifer & Vishny, 1986). The second line argues that
governance can be exercised by alternative means, such as the trading of stocks and threats of
liquidation of its holdings in the company (Edmans & Manso, 2011; Edmans & Holderness,
2017). Although diffuse property in multiple blockholders reduces the effectiveness of direct
intervention by voting, it increases the efficiency of trading as a corporate governance
mechanism (Edmans & Manso, 2011).
In this literature stream, a blockholder is a shareholder with sufficient power to induce
intervention (or threaten to do so) and who has an informational advantage. Usually, they are
institutional investors: investment banks, stock and pension funds, asset managers and hedge
funds (McCahery, Sautner, & Starks, 2016). Blockholders may buy or sell their stocks
according to the expectation of future performance. When management is inefficient,
shareholders can sell their stocks, which would lead to lower prices and lower managers’
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salaries (when based on stock performance), it also would raise takeover risks and would
undermine subsequent stock issues (Edmans & Manso, 2011).
However, because they cannot coordinate trading orders to maximize the returns for all
blockholders, these shareholders compete for the information advantage’ profit. This behavior
would increase the efficiency of the markets through the rapid incorporation of information
into prices since each blockholder would issue orders with parts of the private information.
The prices would reflect the real value of the company and, therefore, the managerial
performance (Edmans & Manso, 2011). If there were only one shareholder informed, he
would strategically limit his trading orders to hide his private information. However, when
there is competition, the fast profits maximization behavior adjusts prices faster (Holden &
Subrahmanyam, 1992; Georgakopoulos, 2017). This corporate governance mechanism
happens more because of the threat of stocks liquidation than by actual trading: the stronger
the ex-ante threat liquidation, the higher the likelihood that the manager will work to improve
his results, reducing the need for ex-post real liquidation (Edmans & Holderness, 2017).
In the empirical field, there is evidence that institutional investor trading is motivated by
private information: the stock liquidation by institutional investors is related to CEO turnover
and the fall in prices in the long run (Parrino, Sias, & Starks, 2003); the short-term
negotiations of institutional investors anticipate future returns, demonstrating the exploitation
of informational advantages (Yan & Zhang, 2009; Gallagher, Gardner, & Swan, 2013); their
trading behavior anticipates the news and the accounting results (Hendershott, Livdan, &
Schürhoff, 2015); and the days when institutional traders show abnormal returns (Collin-
Dufresne & Fos, 2015). The role of institutional investors in the price informativeness is still
an expanding field, and the results diverge. On one hand, there is evidence that institutional
investors do not incorporate information into stock prices (Piotroski & Roulstone, 2004). On
the other hand, researches demonstrate that the more significant presence of institutional
investors increases the markets efficiency with the rapid incorporation of company-specific
information (Brockman & Yan 2009; Gallagher, Gardner, & Swan, 2013; Bai, Philippon, &
Savov, 2016; Brogaard, Li, & Xia, 2017; Dang, Nguyen, Tran, & Vo, 2018).
Finally, although scarce, there is also conflicting evidence for the effectiveness of
trading as a governance mechanism. Fang, Noe, and Tice (2009) present several tests to
explain the relationship between liquidity and the increase in the value of companies. The
authors conclude that although institutional investors increase liquidity and, consequently, the
markets informational efficiency, the presence of institutional investors does not explain the
increase in the companies’ performance. Brogaard, Li, and Xia (2017) test whether the
presence of institutional investors reduces the likelihood of corporate bankruptcy by
increasing the informational efficiency provided by these investors. The authors argue that,
although it is not the best way to reduce bankruptcy, institutional investors help reduce this
risk by intervening in strategic decisions. Due to the lack of empirical evidence from Brazil,
this research explores this channel of governance in this market.
There is also evidence that countries institutional factors affect the governance capacity
of institutional investors by altering the incentives to collect and negotiate by information.
Research suggests that informed trading by institutional investors is rising in countries with
less information transparency (poor disclosure infrastructure), low media coverage, poor
corporate governance and regulatory quality (Maffett, 2012; Dang et al., 2018). In these
countries, less public information can motivate institutional investors to acquire private
information and execute profitable transactions (Maffett, 2012; Dang et al., 2018). In Brazil,
ordinary stocks (common shares, with vote rights) and preferred stocks (without vote rights)
differ between governance and property protection regimes (KPMG, 2016). Evidence at the
firm level was presented by Martins and Paulo (2014), which document that ON and PN
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stocks differ in their relation to information asymmetry. These findings justify the test in
different classes of stocks in Brazilian stock market research.
2.2. Stock Price Informativeness
Stock price informativeness is defined as the amount of firm-specific information that is
incorporated in prices, whether public or private information (Roll, 1988). The view that
prices aggregate dispersed information among market participants goes back to Hayek (1945).
The modern version of this idea is found in Grossman and Stiglitz (1980) and Kyle (1985),
where stock markets allow the production of information in the trading process between the
informed and the other investors. Stock markets are vital in generating price signals that
signal investment allocation and investment decisions (Tobin, 1982). If prices fully reflect
fundamental values, such a process occurs through two channels: (i) capital is correctly priced
and allows for prediction of future cash flows, and (ii) this information promotes feedback
and incentives for managers to make decisions (Bond; Edmans, & Goldstein, 2012; Edmans,
Jayaraman, & Schneemeie, 2017).
The first channel is more relevant in the primary markets. If prices reflect the
fundamentals, companies acquire fair values of financing and investors are rewarded for
returns commensurate with the risk incurred. The second information channel is more relevant
in secondary markets, where the most substantial fraction of transactional activity occurs
among investors. As there is no transfer of resources to firms, prices have real consequences
when they affect decisions made by managers on the real side of the economy (Bond,
Edmans, & Goldstein, 2012). At this point, decision makers can use the information in prices
when they aggregate “bits” of information from thousands of investors, serving as a feedback
mechanism for management actions or exogenous valuation of firm performance
(Subrahmanyam & Titman, 1999; Bond, Edmans, & Goldstein, 2012). Researchers who
support this view are documented by Chen, Goldstein, and Jiang (2007), Bakke and Whited
(2010), De Cesari and Huang-Meier (2015), Bai, Philipon and Savov (2016) and Edmans,
Jayaraman and Schneemeier (2017).
From a practical point of view, there is no direct measure of price informativeness. The
most common is the prices non-synchronicity of Roll (1988) (Bai, Philippon & Savov, 2016):
if the companies’ information availability rises and so the informational efficiency, there is an
argument that the measure of goodness of fit ( ) of a market model,
, is inversely associated with the amount of firm-level information in
stock prices. The explanation for this relationship is that the stock returns are due to (i)
movements in systematic risk factors, (ii) changes in the market environment, and (iii)
company-specific movements. Therefore, greater availability of firm-specific information
would reduce the stock returns synchrony and increase the price informativeness (Roll, 1988).
In general, the researches that support this view are classified in the literature as an
information-based explanation for stock synchronicity (Dang, Moshirian, & Zhang, 2015).
Alternative proxies for price informativeness include: the Probability of Informed
Negotiation (PIN) (Easley, Hvidkjaer, & O'Hara, 2002), the information flow metric of
Llorente, Michaely, Saar & Wang (2002), the Amihud’s illiquidity index (Amihud, 2002), the
Bid-Ask Spread and the Proportion of Zero Returns (Lesmond, Ogden, & Trzcinka, 1999).
2.3. Hypotheses
Based on the evidence that institutional investors are informed (Hendershott, Livdan, &
Schürhoff, 2015; Collin-Dufresne & Fos, 2015; McCahery, Sautner, & Starks, 2016), the
information risk increases with their presence (Easley, Hvidkjaer & O'hara, 2002). However,
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because they cannot coordinate their orders to maximize the value for all, informed investors
compete for the profit of the informational advantage (Edmans & Manso, 2011; Akins, Ng, &
Verdi, 2012). This relationship generates the first hypothesis of this study:
H1 (Hypothesis of Competition for Information Advantage): The higher the
competition between institutional investors the smaller the abnormal stock returns.
This behavior would increase market efficiency through the fast incorporation of firm-
specific information into prices. The prices would reflect more closely the real value of the
company and, therefore, the performance of managers (Edmans & Manso, 2011), according to
the second hypothesis (H2):
H2 (Hypothesis of Information Production by Trading): The number of institutional
investors has a positive relationship with the incorporation of information into stock
prices.
If this type of governance is efficient, it is expected that there will be a positive effect on
the companies’ operational performance (Holmstrom & Tirole, 1993; Edmans & Manso,
2011), implying the third hypothesis (H3):
H3 (Hypothesis of Governance through Trading): The higher the efficiency of the
pricing promoted by institutional investors, the greater the operational performance of
the company.
Finally, research suggests that countries with less information transparency, low media
coverage, poor corporate governance, and reduced regulatory quality provide incentives for
institutional investors to collect and negotiate from private information (Maffett, 2012; Dang
et al., 2018). In the Brazilian stock market, at the firm level, the preferred stocks have similar
bad corporate governance disadvantages: absence of voting rights and poor tag along. In this
case, although no hypotheses are drawn, this kind of stock was separated to conduct
additional tests.
3. Method
3.1. Data
To construct the sample, as a first filter, companies with no complete data in the
Thomson Reuters Eikon and Economatica databases from years 2009 to 2017 were dropped.
The non-availability of ownership data justifies the data range (2009-2017) before the year
2009. The other variables were obtained from the Thomson Reuters Eikon database. Panel A
of Table 1 shows the sample construction process. Panel B presents the firm distribution by
sectors and panel C shows the number of firms in each year. Companies with no data to
calculate dependent and independent variables were excluded. Banks and financial institutions
were also excluded since their leverage and book-to-market values cannot be compared with
other firms. The special preferred stocks, “PNAs” and “PNBs”, were dropped because of their
very low liquidity and to avoid double counting between the simple preferred stock and
PNAs/PNBs. The final sample had a total of 254 stocks and 1719 valid observations.
Due to the data distribution, panel data techniques were used in the regression analysis.
Before estimation, to avoid outliers’ biases, each variable in every year was winsorized at the
bottom and top 2.5% of their distributions. According to the VIF test, no multicollinearity was
detected (Wooldridge, 2010). The Hausman test was adopted to establish the most appropriate
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estimator (Fixed effects vs. Random Effects). Robust standard errors were used to overcome
the heteroscedasticity and autocorrelation assumptions violations.
Table 1 – Process of sample construction, sectorial and temporal distribution.
Panel A: Sample Construction Process
Steps Number of Stocks Stock-year
All stocks available in the databases between 12/2009 to 12/2017 562 5058
Stocks without data for dependent variables (214) (1926)
Stocks without data for dependent variables (51) (459)
Banks and other financial services companies (24) (216)
Special preferred stocks (PNAs and PNBs) (19) (171)
Final sample 254 2286
Final sample of ordinary stocks (with vote right) 194 1746
Final sample of preferred stocks (without vote right) 60 540
Panel B: Sample sectorial distribution a
Sect. A B C D E F G H I J Total
N 41 73 17 12 33 10 12 6 7 43 254
% 16.1 28.7 6.7 4.7 13.0 3.9 4.7 2.4 2.8 16.9 100.0
Panel C: Sample yearly distribution b
Year 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
N 176 181 185 199 200 201 189 189 199 1719
% 10.2 10.5 10.8 11.6 11.6 11.7 11.0 11.0 11.6 100.0
Source: Research Data (2019).
Note: a Sectors: A = Industrial goods, B = Cyclical consumption, C = Non-cyclical consumption, D = Financial
and others (banks and insurers are not included). E = Basic materials, F = Oil, Gas and Biofuels, G = Health, H =
Information technology, I = Utility, J = Telecommunications.
Table 2 shows the descriptive statistics separated into ordinary stocks (Panel A) and
preferred stocks (Panel B). A typical ordinary stock (ON) have an annual 3.5% bid-ask spread
( ), is monitored by 4-5 analysts, is a growth company with low systematic risk
( ) and has approximately 4 institutional investors. The preferred stocks (PN)
already have a 3.3% bid-ask spread ( ), are monitored by 2-3 analysts, is a low-beta
growth company and has approximately 3 institutional investors.
Table 2 – Descriptive statistics of the sample.
Variable Mean Median SD. Min. Max. N
Panel A – Sample of ordinary stocks
Financial Leverage 0.425 0.426 0.237 0.000 0.994 1319
Bid-Ask Spread 0.035 0.032 0.021 0.000 0.483 1319
Analyst Coverage 4.834 3.000 5.145 0.000 18.000 1319
ln(Analyst Coverage) 1.239 1.386 1.100 0.000 2.944 1319
Ownership dispersion -0.486 -0.463 0.292 -1.000 0.000 1319
Market-to-Book 2.489 1.433 4.836 0.052 91.219 1319
ln(Market-to-Book) 0.321 0.359 1.046 -2.941 4.513 1319
N. of Institutional Investor 3.809 4.000 2.675 0.000 26.000 1319
ln(N of Institutional Investor) 1.402 1.609 0.626 0.000 3.295 1319
Price non-synchronicity 2.647 2.183 1.963 -0.024 9.775 1319
Return on Assets 0.075 0.075 0.104 -0.902 0.605 1319
Abnormal Returns (%) 0.088 0.000 0.577 -1.232 4.834 1319
Systematic risk 0.581 0.531 0.419 -1.871 3.903 1319
Size (Market Capitalization) 14.474 14.632 1.788 7.812 19.751 1319
ROA Volatility 0.037 0.020 0.075 0.000 1.000 1319
Panel B – Sample of preferred stocks
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Financial Leverage 0.408 0.411 0.268 0.000 0.995 400
Bid-Ask Spread 0.033 0.028 0.035 0.000 0.519 400
Analyst Coverage 2.987 0.000 4.968 0.000 16.000 400
ln(Analyst Coverage) 0.729 0.000 1.063 0.000 2.833 400
Ownership dispersion -0.438 -0.404 0.313 -1.000 0.000 400
Market-to-Book 1.294 0.613 3.291 0.052 38.467 400
ln(Market-to-Book) -0.465 -0.488 1.017 -2.941 3.649 400
N. of Institutional Investor 3.820 3.000 3.425 0.000 25.000 400
ln(N of Institutional Investor) 1.317 1.386 0.755 0.000 3.258 400
Price non-synchronicity 3.414 2.985 2.477 -0.024 9.775 400
Return on Assets 0.066 0.067 0.103 -0.482 1.007 400
Abnormal Returns (%) 0.029 -0.041 0.470 -1.215 2.309 400
Systematic risk 0.506 0.464 0.594 -7.058 1.981 400
Size (Market Capitalization) 13.157 13.153 2.371 8.032 18.896 400
ROA Volatility 0.034 0.021 0.050 0.000 0.487 400
Source: Research Data (2019).
3.2. Institutional Investors Identification
First, to construct the institutional investors’ ownership variables, it is necessary to
identify them. The name and ownership percentages of the 30 largest investors in each
company (with and without voting rights) was used to identify the institutional investors. The
data comes from Economatica, one of the most popular financial database company in Brazil.
Since the institutional shareholder’s CNPJ (the Brazilian CNPJ number is equivalent to the
US Employer Identification Number) were not available in the database, the names of the
investors were cross-referenced with Economatica’s fund database. After this identification
process, several banks, pension funds, and foreign resource managers were left unidentified.
Therefore, in order to avoid underestimation of the number of institutions, keywords were
used to separate companies, funds, and banks apart from individuals. The keywords were
formed around the variations of the term’s “corporations”, “funds”, and “investment
managers”. Table 3 shows all the keywords used for the identification.
Table 3 – Keywords for the identification of institutional investors.
Keywords for the identification of institutional investors
Group Institutional
Investors Keywords
#1 Investment Funds a
FIA, Banco, Bank, Banc, Equity, Equities, Hedge, Asset, Management,
Capital, Clube de, Foundation, FI Mult, Research, Group, Fund, Ações,
Investment, Investments, Investimentos, Gestão, Cred, Previ, Petros,
Previdência, Pension, Partners, FIP, Insurance.
Foreign Investors b Llc, Llp, GmbH, Ltd, Inc, B.V, BV, Corporation.
#2 Holdings Participações, Partic, Holding, Empreendimentos.
#3 Other Companies c SA, S.A, S/A, Ltda.
#4 Government Governo, Sec de Est, Estado, União, Estadual, BNDES, BNDESPar.
Note: a Words associated with banks, asset managers and pension funds; b Words associated with foreign
companies; c Acronyms associated with limited liability company.
3.3. Institutional Investor Number and Dispersion
After the identification, to test the research hypotheses, two metrics were adopted: (i)
the number of institutional investors ( ) and (ii) the competition over information variable
( ), proposed by Akins, Ng and Verdi (2012). Equation 1 defines the variable .
(1)
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The number of institutions ( ) also does not consider the ownership dispersion.
Therefore, the competition proxy for information proposed by Akins, Ng and Verdi (2012)
was adopted as an alternative metric (Equation 2). Where, for each stock i in year t: is the
proxy for competition for information; is the total institutional shareholding; is the
institutional investor ownership j in stock i in year t.
(2)
3.4. Information Flow and Performance Variables
Following the literature, the price non-synchronicity was adopted as a measure of price
informativeness. In this research, the calculation was made following the recommendations of
Chan and Hameed (2006) for the emerging markets. For each stock i in year t, the following
steps were followed: (i) employ the regression model of Equation 3, by ordinary least squares
(OLS) with daily data; (ii) extract the adjusted coefficient of the goodness of fit ( ), and (ii)
according to literature, use a logistic transformation in the variable (Equation 4). Where, is
the simple daily return of stock i in day d; is the daily return for the Brazilian market
portfolio proxy, the IBrX-100 (the widest market index for Brazil); is the adjusted
coefficient of the goodness of fit of the Equation (3); is the error term; is the price
non-synchronicity of each stock i in year t.
(3)
(4)
As an alternative proxy, Boone’s (1998) low-frequency bid-ask spread proxy was
adopted. Based on Kyle’s (1985) model, the bid-ask spread should reduce as the number of
informed investors trading simultaneously increases. The bid-ask spread was adopted as a
proxy for price informativeness by Gallagher, Gardner, and Swan (2013). Equation 5 exposes
the variable. Where, is the proxy for the bid-ask spread; is the maximum stock
price i on day d; is the minimum stock price i on day d. is the number of days with valid
observations valid for stock i in year t (i.e., ).
(
(5)
Similar to Chen, Goldstein, and Jiang (2007), the Return on Assets ( ) was adopted
as an operational performance metric, as in Equation 6.
(6)
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In order to measure whether institutional investors are informed, information risk was
defined as the abnormal return of each stock i in year t (Collin-Dufresne & Fos, 2015; Klein,
Maug, & Schneider, 2017), according to Equation 7. Where, for each stock i in year t:
is the abnormal returns over the market index; and and are the simple stock returns
and the market returns of the IBrX-100, respectively.
(7)
3.4. Control variables
This section shows the control variables adopted in the multivariable regression analysis
that could influence the independent information variables ( and ). Table 4
describes the control variables and the surveys that adopted them. In the regression test of H1,
the following phenomena were controlled: (i) systematic risk ( ), to control market risk
(Sharpe, 1964); (ii) firm size ( ), because of exposure to the size risk factor of Banz
(1981); (iii) the market-to-book ( ), because of exposure to the value risk factor of Fama
and French (1992); (iv) financial leverage ( ), because of the effect of financial leverage
on returns; and (v) analysts’ coverage ( ), because there is evidence that their
performance reduces the cost of capital (Girão, 2016).
For the regression test of H2, the following phenomena were controlled: (i) firm size
( ), since the larger firms represent a considerable part of the market indices and,
consequently, greater synchronicity, and large firms also present less information asymmetry
(Clarke & Shastri, 2000, Easley, Hvidkjaer, & O'hara, 2002, Martins & Paulo, 2014); (ii)
market-to-book ( ), because the strong association with the value risk factor of Fama and
French (1992); (iii) financial leverage ( ), because the greater exposure to idiosyncratic
volatility (Ferreira & Laux, 2007); (iv) profits’ volatility ( ), since firms with more
volatile profits have a greater returns’ dispersion (Morck, Yeung & Yu, 2000); and (v)
analysts’ coverage ( ), because the evidence that analysts are related to the incorporation
of information (Chan & Hameed, 2006). For the same reasons, considering Chen, Goldstein
and Jiang (2007), in the regression test of H3, the following phenomena were controlled: (i)
Firm size ( ); (ii) market-to-book ( ) and (iii) Financial Leverage ( ).
Table 4 - Control variables description.
Controls Variable definition Main References
Systematic risk
( ) Calculated with the market model with
one-year daily returns.
(Sharpe, 1964; Morck, Yeung, & Yu,
2000).
Firm size
( ) ln of firm market capitalization
(Banz, 1981; Clarke & Shastri, 2000;
Easley, Hvidkjaer, & O’Hara, 2002;
Martins & Paulo 2014)
Firm leverage
( ) Total debt to total capital (Fernandes & Ferreira 2009)
Market-to-Book
( ) Market value of equity to book value of
equity (Clarke & Shastri, 2000).
Profit volatility
( ) Sample standard deviation of quarterly
ROAs over the last three years (Morck, Yeung, & Yu, 2000)
Analyst’ Coverage
( ) The number of analysts who follow the
company.
(Piotrosk & Roulstone, 2005; Chan &
Hameed, 2006; Girão, 2016)
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4. Results
This section shows the multivariate regressions results. As the data are in the
longitudinal format, the specification tests (Hausman) pointed to the need to control
unobservable effects invariant over time and between individuals. The H1 test is discussed in
section 4.1. The H2 and H3 tests are presented in sections 4.2 and 4.3.
4.1. Competition between Institutional Investors and Abnormal Returns
In order to test the first hypothesis (H1), the regression model of Equation 8 was
adopted. Where, for each stock i in year t, is the abnormal return variable, represents
the multiple institutional investors competition: and . are the variables
associated with returns: Systematic Risk ( ), Size ( ), Market-to-book ( ),
Financial Leverage ( ) and Analyst Coverage ( ).
(8)
Table 5 shows the regressions coefficients of Equation 8 to test the Hypothesis H1.
According to the analysis of the estimated coefficients, it is not possible to affirm that the
competition between institutional investors ( and ) significantly reduces the abnormal
returns. Girão (2016) reported similar results for the Brazilian stock market: there is no
relation between the number of institutional and the reduction of the cost of capital in Brazil.
According to the author, it is possible that not all institutional investors are actually informed
investors. These results contrast with the findings of Gallagher Gardner and Swan (2013) for
the Australian market. The authors found that the competition over information reduce the
bid-ask spread.
Table 5 – Number of institutional investors and abnormal return.
Independent
Variables
Dependent Variable: eRet
HSub1: β1 < 0
ON & PN
Stocks
(1)
ON
Stocks
(2)
PN
Stocks
(3)
ON & PN
Stocks
(4)
ON
Stocks
(5)
PN
Stocks
(6)
II 3.295
(0.69)
5.910
(1.11)
-4.587
(-0.49)
-0.082
(-1.01)
-0.042
(-0.42)
-0.082
(-0.66)
Beta 9.205 *
(1.95)
11.061 *
(1.83)
7.185
(1.11)
9.332 **
(1.99)
11.271 *
(1.87)
7.864
(1.14)
ln(Size) 9.511 **
(2.26)
9.296 *
(1.89)
16.111 **
(2.60)
9.645 **
(2.31)
9.368 *
(1.92)
16.456 ***
(2.72)
ln(M/B) 32.187 ***
(6.61)
36.152 ***
(6.57)
16.145 **
(2.37)
32.137 ***
(6.68)
35.977 ***
(6.61)
15.787 **
(2.24)
Debt -0.767 ***
(-4.04)
-0.913 ***
(-3.97)
-0.176
(-0.67)
-0.763 ***
(-4.03)
-0.903 ***
(-3.94)
-0.183
(-0.71)
ln(Analy) -22.478 ***
(-4.34)
-24.226 ***
(-4.63)
-15.017
(-1.09)
-22.309 ***
(-4.29)
-23.804 ***
(-4.51)
-14.094
(-1.04)
Fixed Eff. (i, t) Yes Yes Yes Yes Yes Yes
N. Observations 1,719 1,319 400 1,719 1,319 400
N. Individuals 254 194 60 254 194 60
Within-R2 (%) 23.08% 28.38% 16.64% 23.10% 28.25% 16.60%
Source: Research Data (2019).
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Notes: In each column, standard-errors are robust in the presence of heteroscedasticity (Rogers, 1993) and serial
correlation are reported in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.
The controls followed what is commonly documented in the national asset pricing
literature: Beta was positive in all regressions, and presented a positive relation,
similar to the Brazilian studies (Cordeiro & Machado, 2013), but in disagreement with the US
market findings (Fama & French, 1992). The presented a negative sign, in agreement
with the literature of the leverage effect (Bhandari, 1988). The investment analyst’s coverage
was negative allowed to state that its performance reduces the informational risk in the
Brazilian stock market (Girão, 2016).
4.2. Institutional Investors and the price informativeness
The regression model of Equation 19 was adopted to test the H2. Where, for each stock
i in year t, is the information efficiency proxy: and . represents the
multiple institutional investors competition: and . is the vector of control
variables: Size ( ), Market-to-book ( ), Financial Leverage ( ), ROA
volatility ( ) and Analyst Coverage ( ). It is expected that ( ) have positive
coefficients with and negative with .
(9)
Table 6 presents the estimated coefficients of the regression of Equation 9. It can be
seen that in the complete sample (column 1 and 4), when the dependent variable is , it
is not possible to reject the hypothesis that the competition of institutional investors ( ) do
not incorporate private information into stock prices. However, we have evidence that they
reduce the bid-ask spreads (column 4). In the ON stocks sample, loses statistical
significance. It can be seen that the signs reported in the complete sample come from PN
stocks: in columns (3) and (6), there are evidence that the higher the number of institutional
investors ( ) the greater the incorporation of private information into stock prices , either by
increasing or by reducing the bid-ask spread ( ). Thus, there is firm-level
support for the empirical evidence presented by Maffet (2012) and Dang et al. (2018) that
there is a higher positive association between institutional ownership and stock liquidity in
countries with opaque information environments or with poor institutional characteristics. In
the Brazilian stock market, the PN stocks have characteristics that are consistent with the
lower transparency and weak property rights (Leal, Silva, & Valadares, 2002).
As for controls, the size’s coefficient was negative in all samples. This relationship was
expected for , and it indicates that larger firms have greater weight in market indices
(Gul, Kim, & Qiu, 2010). For the bid-ask spread regression, the negative coefficient of size
( ) indicates that the largest companies have lower information asymmetry and lower
illiquidity. The market-to-book ( ) is positive in the complete sample and for
common stocks (ON), but it is not significant for preferred stocks (PN). These coefficients
suggest that stocks with high growth potential (i.e., high Market-to-Book) are less
synchronized with the rest of the market (Gul, Kim, & Qiu, 2010).
Finally, analysts’ coverage ( ) presented a negative coefficient for the complete
sample and for the ON stocks. At first glance, the positive relationship between the number of
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analysts and the return synchronicity is totally counterintuitive, but it is largely documented in
the literature. The most widely accepted explanation for this relationship is that analysts
disseminate much more news about industry and markets than firm-specific information
(Piotroski & Roulstone, 2004; Chan & Hameed, 2006; Feng, Hu & Johansson, 2016).
However, there is evidence that the greater human capital held by “star analysts” reduces the
returns synchronicity due to the production of firm-specific information (Xu, Chian, Jiang, &
Yi, 2013). At this point, the domestic market is likely to be dominated by analysts who issue
general industry/market information.
Table 6 - Institutional Investors and the information flow.
Panel A – Institutional Investors and the information flow ( )
Independent
Variables
Dependent Variable: Dependent Variable:
H2: β1 > 0 b H2: β1 < 0
ON & PN
Stocks
(1)
ON
Stocks
(2)
PN
Stocks
(3)
ON & PN
Stocks
(4)
ON
Stocks
(5)
PN
Stocks
(6)
ln(nII) 0.036
(0.40)
-0.067
(-0.67)
0.365 **
(2.18)
-0.231 **
(-2.39)
-0.148
(-1.41)
-0.461 *
(-1.89)
ln(Size) -0.401 ***
(-3.34)
-0.344 **
(-2.45)
-0.477 **
(-2.62)
-0.565 ***
(-5.72)
-0.601 ***
(-5.30)
-0.332 **
(-2.61)
ln(M/B) 0.298 **
(2.39)
0.313 **
(2.18)
0.113
(0.67)
-0.013
(-0.17)
0.042
(0.47)
-0.273 **
(-2.09)
Debt -0.000
(-0.11)
-0.006
(-1.28)
0.016 *
(1.71)
0.016 *
(1.90)
0.012
(1.64)
0.029
(1.14)
VROA -0.265
(-0.44)
-0.487
(-0.78)
0.427
(0.21)
0.458
(0.87)
0.593
(1.02)
-1.243
(-0.78)
ln(Analy) -0.500 ***
(-3.70)
-0.562 ***
(-3.66)
-0.179
(-1.10)
-0.135
(-1.26)
-0.157
(-1.31)
-0.007
(-0.03)
Fixed Eff. (i, t) Yes Yes Yes Yes Yes Yes
N. Observations 1,719 1,319 400 1,719 1,319 400
N. Individuals 254 194 60 254 194 60
Within-R2 (%) 12.34% 14.32% 13.39% 16.52% 20.12% 13.67%
Panel B – Institutional Investors and the information flow ( )
Independent
Variables
Dependent Variable: Dependent Variable:
H2: β1 > 0 b H2: β1 < 0
ON & PN
Stocks
(1)
ON
Stocks
(2)
PN
Stocks
(3)
ON & PN
Stocks
(4)
ON
Stocks
(5)
PN
Stocks
(6)
hII -0.004 **
(-2.38)
-0.003
(-1.48)
-0.006 *
(-1.99)
-0.000
(-0.29)
-0.000
(-0.13)
-0.002
(-0.36)
Fixed Eff. (i, t) Yes Yes Yes Yes Yes Yes
Within-R2 (%) 12.70% 14.48% 13.16% 16.15% 19.94% 12.76%
Source: Research Data (2019).
Notes: In each column, standard-errors are robust in the presence of heteroscedasticity (Rogers, 1993) and serial
correlation are reported in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.
Panel B exposes the test with the alternative variable . For this variable, the results
are conflicting with those exposed for : the larger the dispersion the smaller the predicted
value of the incorporation flow metric ( ). At this point, there is a puzzle: while the
greater number of institutional investors ( ) increases the incorporation of information, the
greater dispersion ( ) reduces it. One possible explanation is that competition among
institutional investors is essential to improve the market efficiency, but according to
McCahery, Sautner, and Starks (2016), most institutional investors claim that the threat of
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liquidation is only valid when they have at least 5-10% of the company’s stocks. In this
scenario, it is possible that extreme fragmentation of ownership may mitigate the strength of
this kind of governance.
The results of Table 6 support the hypothesis that institutional investors incorporate
firm-specific information into stock prices and increases price efficiency. This relationship is
even more evident in PN stocks, where investors cannot use direct intervention (“voice
governance”) and have unconventional ways to exercise governance, such as the sale of
stocks or the threat of liquidation. These findings support the international literature that
institutional investors are an essential part of the financial markets and that their presence
raises the levels of informational efficiency (Bai, Philippon, & Sevov, 2016). In line with the
cross-country findings of Maffet (2012) and Dang et al. (2018), the effect of informational
efficiency gain is more significant on shares with lower ownership rights (PN stocks),
probably because informed trading opportunity.
4.3. Institutional Investors and Operational Performance
In this section, it is analyzed whether institutional investors are important to the
operational performance of firms. As discussed previously, two aspects support the increase
in performance due to the greater incorporation of information into prices (i.e., by
“voice/vote” vs. “trading/threat of stock liquidation”). The panel data specification tests
(Hausman test) pointed to the use of fixed effects in individuals and in time (not tabulated).
The regression tested is shown in Equation 10. Where, is the return on assets; are the
variables or ; is the vector of control variables for the operation
performance regression.
(10)
Similar to the methodology used by Fang, Noe and Tice (2009), the sample was
segmented into four sub-samples according to the information efficiency levels: low
(below the median) and high (equal to or above the median). The
same pattern was followed for the bid-ask spread ( ). The intuition is that if
institutional investors increase price efficiency and this relationship is motivated by the
governance through trading, it is expected that the number of institutions will be more
relevant for performance in firms with high information incorporation ( ) and low bid-
ask spread ( ). However, Panel A and B of Table 7 demonstrate the irrelevance of
the number ( ) and the dispersion ( ) of institutional investors in the firm’s profitability
(the hII was not tabulated). According to the tests, it is not possible to affirm that the gains of
efficiency due to the greater incorporation of information ( ) and the reduction of the
bid-ask spread ( ) imply in grater operational performance gains ( ).
These findings were similar to the evidence on Fang, Noe, and Tice (2009) that
institutional investors do not increase market liquidity and do not increase the performance of
companies in the US market. On the other hand, the results diverge from the findings of
Gallagher, Gardner, and Swan (2013) for the Australian market. The failure to observe the
institutional investors impact on performance raises some conjectures: (i) it is possible that
the efficiency gain promoted by the institutional investors does not have a significant effect
on the firms’ performance because there are no efficient contracts that align managers'
remuneration to stock returns (Edmans, Fang, & Zur, 2013). According to Hofmeister (2018),
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the firms listed in the New Market (the segment of higher corporate governance in Brazil)
have a payment ratio of R$ 4.5 thousand for a percentage point of shareholder return, while in
firms listed in other segments, the remuneration is not sensitive to stock return. The New
Market rules do not permit the issuance of stocks without voting rights, which implies that the
preferred stocks (PN) of this research are stocks outside the New Market segment and,
consequently, without managers’ remuneration linked to stock performance; (ii) the
performance gain can occur in long-term periods not addressed in this research.
Table 1 – Incorporation of Information, Institutional Investors and Performance.
Panel A – levels, Institutional Investors and Firm’ Operation Performance
Independent
Variables
Dependent Variable: ROA
H2: β1 > 0 a
Sample: High ( ≥ Median) Sample: Low ( < Median)
ON & PN
Stocks
(1)
ON
Stocks
(2)
PN
Stocks
(3)
ON & PN
Stocks
(4)
ON
Stocks
(5)
PN
Stocks
(6)
ln(nII) -0.284
(-0.31)
0.577
(0.45)
-1.445
(-1.23)
0.390
(0.65)
0.245
(0.34)
1.066
(1.16)
ln(Size) 2.535 **
(2.56)
1.711
(1.42)
4.145 **
(2.71)
3.088 ***
(3.66)
3.160 ***
(3.44)
2.471
(1.23)
ln(M/B) 0.264
(0.25)
0.643
(0.48)
-0.344
(-0.26)
3.100 ***
(4.03)
3.233 ***
(3.98)
3.304 *
(2.06)
Debt -0.087
(-1.42)
-0.065
(-0.82)
-0.123 *
(-1.92)
-0.025
(-0.63)
-0.021
(-0.52)
-0.036
(-0.35)
Fixed Eff. (i, t) Yes Yes Yes Yes Yes Yes
N. Observations 724 491 233 995 828 167
N. Individuals 127 88 39 127 106 21
Within-R2 (%) 8.69% 6.66% 18.54% 26.44% 30.88% 17.73%
Panel B – Bid-Ask ( ) levels, Institutional Investors and Firm’ Operation Performance
Independent
Variables
Dependent Variable: ROA
H2: β1 > 0 b
Sample: High Bid-Ask ( ≥ Median) Sample: Low Bid-Ask ( < Median)
ON & PN
Stocks
(1)
ON
Stocks
(2)
PN
Stocks
(3)
ON & PN
Stocks
(4)
ON
Stocks
(5)
PN
Stocks
(6)
ln(nII) 0.271
(0.34)
1.064
(1.02)
-1.279
(-1.13)
-0.138
(-0.19)
-0.8237
(-0.99)
1.991
(1.37)
ln(Size) 2.917 ***
(3.83)
2.598 ***
(3.04)
4.097 ***
(2.79)
4.481 ***
(2.97)
4.539 ***
(3.03)
2.375
(0.69)
ln(M/B) 1.758 **
(2.18)
2.410 ***
(2.65)
-.337
(-0.26)
0.553
(0.39)
4.539
(0.32)
4.009
(0.69)
Debt -0.058
(-1.63)
-0.075 *
(-1.92)
0.004
(0.07)
-0.068
(-0.88)
-0.009
(-0.10)
-0.239 ***
(-3.63)
Fixed Eff. (i, t) Yes Yes Yes Yes Yes Yes
N. Observations 887 710 177 832 609 223
N. Individuals 127 102 25 127 92 35
Within-R2 (%) 18.35% 19.51% 18.40% 13.50% 16.84% 15.66%
Source: Research Data (2019).
Notes: In each column, standard-errors are robust in the presence of heteroscedasticity (Rogers, 1993) and serial
correlation are reported in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively. a β1> 0 was expected in cases where is greater than the median; b β1> 0 was expected in cases where bid-
ask spread ( ) is less than the median.
About the control variables, large companies with a larger market-to-book ( )
and less leveraged ( ) are the most profitable. These coefficients are expected since
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growth firms have greater investment opportunities (Fama & French, 1995) and more
profitable firms have higher equity financing (Frank & Goyal, 2009).
5. Conclusions and Implications
This study analyzed whether theories of governance through trading or threat of
liquidation are valid in the Brazilian stock market. Three hypotheses were developed to guide
the empirical analysis. The central assumption of corporate governance through trading
theories is that investors are informed and compete for the informational advantage profit –
this assumption was tested in the first hypothesis of this research (H1). Such a negotiation
raises price efficiency by incorporating hitherto private information into prices (H2). This
mechanism would make the pricing system more aligned with company fundamentals and
would lead to higher future performance – this is the third hypothesis of the study (H3). To
identify informed investors, institutional investors were classified as holders of information
advantages.
The findings can be summarized in three points: (i) the number of institutional is not
related to returns. One possible explanation is that most of the sample institutions may have
no information advantage and are passive investors who do not compete for information; (ii)
the greater number of institutions increases the incorporation of firm-specific information to
prices, thus contributing to the increase of pricing efficiency. This relationship is even more
evident in PN shares, whose ownership rights are reduced; (ii) even incorporating more
information, it is not possible to affirm that this mechanism raises the firms’ performance.
This result can be explained by the weak link between stock performance and the executives’
pay-for-performance remuneration. So, PN stocks, which have a greater possibility of
information efficiency gains, are issued by firms whose stock prices do not promote change in
managers’ salaries.
These results have two main implications. The first one is that the presence of
institutional investors is vital to raise the stock price efficiency levels, either through the
greater incorporation of information or the reduction of the bid-ask spread. Policies that allow
capital inflows and ensure greater property protection are beneficial for enhancing the
efficiency of markets. The second implication is directly associated with the first: there is no
evidence that the greater amount of institutional implies an increase in the real performance of
the firms. Despite the possible increase in stock price efficiency, these investors cannot adopt
such a mechanism to exercise governance.
These results give rise to conjectures that the lack of association between executive
rewards and stock performance can mitigate potential efficiency gains and prevent trading or
the threat of liquidation from being an effective governance mechanism in these firms.
Therefore, in the case of companies with a dispersion property structure, it is advisable to
adopt measures that increase the stock liquidity and the adoption of an executive payment
linked to the stock performance, as proposed by Edmans, Fang and Zur (2013). These actions
may strengthen governance though trading mechanisms in the Brazilian stock market.
Finally, this study has some limitations. The database adopted to study the ownership
structure of Brazilian companies did not provide precise identifications such as the
identification number of the firms (CNPJ). This limitation has been mitigated by the use of
keywords associated with institutional investors. However, this research decision may raise
criticism about the effectiveness of this identification method and its replicability. It is
emphasized that no shareholder whose name was available on the Reference Forms (RF) was
left unclassified.
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