Corporate Governance Through Stock Trading in Brazil ... · voting rights (ordinary stocks, or ON), there is an ordinary stock concentration and a high level of non-voting shares

1

www.congressousp.fipecafi.org

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.

2


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

3


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’

4


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

5


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,

6


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

7


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

8


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


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)

9


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)

10


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)

11


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%


12


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

13


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)


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)


Within-R2 (%) 12.70% 14.48% 13.16% 16.15% 19.94% 12.76%



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

14


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),

15


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)


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)


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%



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

16


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.

17


References

Akins, B. K., Ng, J., & Verdi, R. S (2012). Investor competition over information and the

pricing of information asymmetry. The Accounting Review 87(1), 35-58. Retrieved from

https://doi.org/10.2308/accr-10157

Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects.

Journal of Financial Markets, 5(1), 31-56. Retrieved from https://doi.org/10.1016/S1386-

4181(01)00024-6

Bai, J., Philippon, T., Savov, A. (2016). Have financial markets become more informative?

Journal of Financial Economics, 122(3) p. 625–654, 2016. Retrieved from

https://doi.org/10.1016/j.jfineco.2016.08.005

Bakke, T. E., Whited, T. M. (2010). Which Firms Follow the Market? An Analysis of

Corporate Investment Decisions. The Review of Financial Studies, 23(5), 1941-1980.

Retrieved from https://doi.org/10.1093/rfs/hhp115

Banz, R. (1981).The relationship between returns and market value of common stocks.

Journal of Financial Economics, 9(1), 3-13. Retrieved from

https://doi.org/10.1016/0304-405X(81)90018-0

Bhandari, L. C. (1988). Debt/Equity Ratio and Expected Common Stock Returns: Empirical

Evidence. Journal of Finance, 43(2), 507-528. Retrieved from

https://www.jstor.org/stable/2328473

Bharath, S. T., Jayaraman, S., Nagar, V. (2013). Exit as governance: an empirical analysis.

Journal of Finance, 68(6), 2515–2547. Retrieved from https://doi.org/10.1111/jofi.12073

Bhide, A. (1993). The hidden costs of stock market liquidity. Journal of Financial

Economics, 34(1), 31-51. Retrieved from https://doi.org/10.1016/0304-405X(93)90039-E

Bond, P., Edmans, A., & Goldstein, I. (2012). The real effects of financial markets. Annual

Review of Financial Economics, 4(1), 339-360. Retrieved from

https://doi.org/10.1146/annurev-financial-110311-101826

Boone, J. (1998) Oil and gas reserve value disclosures and bid-ask spreads. Journal of

Accounting and Public Policy, 17(1). 55-84. Retrieved from

https://doi.org/10.1016/S0278-4254(97)10005-9

Brockman, P., & Yan, X. S. (2009). Block ownership and firm-specific information. Journal

of Banking & Finance, 33(2), 308–316. Retrieved from

https://doi.org/10.1016/j.jbankfin.2008.08.011

Brogaard, J., Li, D., & Xia, Y. Stock liquidity and default risk. Journal of Financial

Economics, 124(3), 2017. Retrieved from https://doi.org/10.1016/j.jfineco.2017.03.003

Chen, Q., Goldstein, I., & Jiang, W. (2007). Price informativeness and investment sensitivity

to stock price. Review of Financial Studies, 20(3), 619-650. Retrieved from

https://doi.org/10.1093/rfs/hhl024

Clarke, J., & Shastri, K. (2000) On information asymmetry metrics. SSRN Working Paper.

2000. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=251938.

Collin-Dufresne, P., & Fos, V. (2015). Do Prices Reveal the Presence of Informed Trading?

Journal of Finance, 70(4), 1555-1582. Retrieved from https://doi.org/10.1111/jofi.1226

Cordeiro, R., Machado, M. A. V. (2013). Estratégia de valor ou de crescimento? Evidências

empíricas no Brasil. Revista Brasileira de Gestão de Negócios, 15(46). Retrieved from

http://dx.doi.org/10.7819/rbgn.v15i46.1170

Dang, T. L., Dang, M. Nguyen, L., & Phan, H. L (2018). Media coverage and stock return

synchronicity around the world. Working Paper in FMA Conference – San Diego.

Retrieved from

http://fmaconferences.org/SanDiego/Papers/2018_FMA_Media_coverage_and_Stock_Pri

ce_Synchronicity.pdf.

https://doi.org/10.2308/accr-10157

https://doi.org/10.1016/S1386-4181(01)00024-6

https://doi.org/10.1016/S1386-4181(01)00024-6


https://doi.org/10.1093/rfs/hhp115

https://doi.org/10.1016/0304-405X(81)90018-0


https://doi.org/10.1111/jofi.12073

https://doi.org/10.1016/0304-405X(93)90039-E

https://doi.org/10.1146/annurev-financial-110311-101826

https://doi.org/10.1016/S0278-4254(97)10005-9



https://doi.org/10.1093/rfs/hhl024

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=251938


http://dx.doi.org/10.7819/rbgn.v15i46.1170

http://fmaconferences.org/SanDiego/Papers/2018_FMA_Media_coverage_and_Stock_Price_Synchronicity.pdf

http://fmaconferences.org/SanDiego/Papers/2018_FMA_Media_coverage_and_Stock_Price_Synchronicity.pdf

18


Dang, T. L., Moshirian, F., & Zhang. B. (2015). Commonality in news around the world.



Dang, T. L., Nguyen, T. H., Tran, N. T. A., & Vo, T. T. A. (2018). Institutional Ownership

and Stock Liquidity: International Evidence. Asia-Pacific Journal of Financial Studies,

47(1), 21–53. Retrieved from https://doi.org/10.1111/ajfs.12202

De Cesari, A., & Huang-Meier, W (2015). Dividend changes and stock price informativeness.

Journal of Corporate Finance, 35(4). 1-17. 2015. Retrieved from

https://doi.org/10.1016/j.jcorpfin.2015.08.004

Easley, D., & O’Hara, M. (2004). Information and the cost of capital. The Journal of Finance,

59(4), 1553-1583, 2004. Retrieved from https://doi.org/10.1111/j.1540-

6261.2004.00672.x

Easley, D., Hvidkjaer, S., & O’Hara, M. (2002) Is Information Risk a Determinant of Asset

Returns? Journal of Finance, 57(5), 2185-2221. Retrieved from

http://www.jstor.org/stable/3094509

Edmans, A., & Holderness, C. G. (2017). Blockholders: A Survey of Theory and Evidence. In

Hermalin, B., & Weisbach, M. The Handbook of Corporate Governance, (1ª Ed), v. 1.

Elsevier.

Edmans, A., & Manso, G. (2011). Governance Through Trading and Intervention: A Theory

of Multiple Blockholders. The Review of Financial Studies, 24(7), 2395-2428. Retrieved

from https://doi.org/10.1093/rfs/hhq145

Edmans, A., Fang, V. W., & Zur, E. (2013). The Effect of Liquidity on Governance. The

Review of Financial Studies, 26(6), 1444-1482. Retrieved from

https://doi.org/10.1093/rfs/hht012

Edmans, A., Jayaraman, S., & Schneemeier, J. (2017). The source of information in prices

and investment-price sensitivity. Journal of Financial Economics, 126(1). 74-96.

Retrieved from https://doi.org/10.1016/j.jfineco.2017.06.017

Fama, E. F., & French, K. (1992). The Cross‐Section of Expected Stock Returns. Journal of

Finance, 47(2). Retrieved from https://doi.org/10.2307/2329112

Fama, E. F., & French, K. R. (1995). Size and book-to-market factors in earnings and returns,

Journal of Finance, 50(1), 131–155. 1995. Retrieved from https://doi.org/10.1111/j.1540-

6261.1995.tb05169.x

Fang, V. W., Noe, T. H. & Tice, S. (2009). Stock market liquidity and firm value. Journal of

Financial Economics, 94(1), 150–169. Retrieved from


Feng, X., Hu, N., & Johansson, J. C. (2016). Ownership, analyst coverage, and stock

synchronicity in China. International Review of Financial Analysis, 45, 79–96. Retrieved

from https://doi.org/10.1016/j.irfa.2016.02.002

Fernandes, N., & Ferreira, M. A. (2009). Insider Trading Laws and Stock Price

Informativeness. The Review of Financial Studies, 22(5), 1845-1887. Retrieved from


Ferreira, M. A., & Laux, P. A. (2007). Corporate Governance, Idiosyncratic Risk, and

Information Flow. Journal of Finance, 62(2),951-989, 2007. Retrieved from

https://doi.org/10.1111/j.1540-6261.2007.01228.x

Frank. M. Z., & Goyal, V. K (2009). Capital Structure Decisions: Which Factors Are Reliably

Important? Financial Management, 38(1), 1-37. Retrieved from

https://doi.org/10.1111/j.1755-053X.2009.01026.x

Gallagher, D. R., Gardner, P. A., & Swan, P. L. (2013). Governance through Trading:

Institutional Swing Trades and Subsequent Firm Performance. Journal of Financial and


https://doi.org/10.1111/ajfs.12202

https://doi.org/10.1016/j.jcorpfin.2015.08.004

https://doi.org/10.1111/j.1540-6261.2004.00672.x

https://doi.org/10.1111/j.1540-6261.2004.00672.x


https://doi.org/10.1093/rfs/hhq145

https://doi.org/10.1093/rfs/hht012


https://doi.org/10.2307/2329112

https://doi.org/10.1111/j.1540-6261.1995.tb05169.x



https://doi.org/10.1016/j.irfa.2016.02.002


https://doi.org/10.1111/j.1540-6261.2007.01228.x

https://doi.org/10.1111/j.1755-053X.2009.01026.x

19


Quantitative Analysis, 48(2), 427-458. Retrieved from

https://doi.org/10.1017/S0022109013000203

Georgakopoulos, N. (2017). The Logic of Securities Law. (1ª Edition). Cambridge University

Press.

Girão, L. F. A. P. (2016) Competição por informações, ciclo de vida e custo do capital no

Brasil. (Unpublished doctoral dissertation). UFPB, João Pessoa, Brazil. Retrieved from

http://repositorio.unb.br/handle/10482/21143

Grossman, S. J., & Stiglitz, J. E. (1980). On the Impossibility of Informationally Efficient

Markets. The American Economic Review, 70(3), 393-408. Retrieved from


Gul, F. A., Kim, J., & Qiu, A. A. (2010). Ownership concentration, foreign shareholding,

audit quality, and stock price synchronicity: evidence from China. Journal of Financial

Economics, 95(3), 425-442. Retrieved from https://doi.org/10.1016/j.jfineco.2009.11.005

Hayek, F. (1945). The use of knowledge in society. American Economic Review, 35, 519-530.

Retried from https://www.econlib.org/library/Essays/hykKnw.html

Hendershott, T., Livdan, D., & Schürhoff, N. (2015). Are institutions informed about news?



Hirschman, A. (1970). Exit, Voice and Loyalty: Responses to Decline in Firms,

Organizations, and States, Harvard University Press, Cambridge, MA.

Hofmeister, P. (2018). Executive Pay, Firm Performance and Shareholder Return. The Case

of Brazilian Public Firms. (Unpublished master dissertation). EAESP/FGV, São Paulo,

2018. Retrieved from http://hdl.handle.net/10438/20691

Holden, C., & Subrahmanyam, A. (1992). Long-lived private information and imperfect

competition. Journal of Finance, 47(1), 247-270. Retrieved from


Holmstrom, B., & Tirole, J. (1993). Market liquidity and performance monitoring. Journal of

Political Economy, 101(4), 678–709. Retrieved from


KPMG (2016). A Governança Corporativa e o Mercado de Capitais. Um panorama atual das

empresas abertas, com base nos seus Formulários de Referência. 2016/2017. (11ª Ed).

Retrieved from https://assets.kpmg.com/content/dam/kpmg/br/pdf/2016/12/br-estudo-

governanca-corporativa-2016-2017-11a-edicao-final.pdf.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315-1335.

Retrieved from https://www.jstor.org/stable/1913210

Leal, R. P. C., Silva, A. L. C., & Valadares, S. M. (2002). Estrutura de controle das

companhias brasileiras de capital aberto. Revista de Administração Contemporânea, 6(1),

7-18. Retrieved from http://dx.doi.org/10.1590/S1415-65552002000100002

Lesmond, D. A., Ogden, J. P., & Trzcinka, C. A. (1999). A New Estimate of Transaction

Costs. Review of Financial Studies, 12(5), 1113–1141. Retrieved from


Llorente, G., Michaely, R. Saar, G., & Wang. J. (2002). Dynamic Volume-Return Relation of

Individual Stocks. Review of Financial Studies, 15(4), 1005-1047. Retrieved from


Maffett, M. (2012). Financial reporting opacity and informed trading by international

institutional investors. Journal of Accounting and Economics, 54(2-3), 201-220.

Retrieved from https://doi.org/10.1016/j.jacceco.2012.09.002

Martins, O. S., & Paulo, E. (2014). Information asymmetry in stock trading, economic and

financial characteristics and corporate governance in the Brazilian stock market. Revista

https://doi.org/10.1017/S0022109013000203

http://repositorio.unb.br/handle/10482/21143



https://www.econlib.org/library/Essays/hykKnw.html


http://hdl.handle.net/10438/20691



https://assets.kpmg.com/content/dam/kpmg/br/pdf/2016/12/br-estudo-governanca-corporativa-2016-2017-11a-edicao-final.pdf

https://assets.kpmg.com/content/dam/kpmg/br/pdf/2016/12/br-estudo-governanca-corporativa-2016-2017-11a-edicao-final.pdf


http://dx.doi.org/10.1590/S1415-65552002000100002



https://doi.org/10.1016/j.jacceco.2012.09.002

20


de Contabilidade & Finanças, 25(64), 33-45. Retrieved from

http://dx.doi.org/10.1590/S1519-70772014000100004

McCahery J., Sautner, Z., & Starks, L. (2016). Behind the scenes: the corporate governance

preferences of institutional investors. Journal of Finance, 71(6), 2905-2932. Retrieved

from https://doi.org/10.1111/jofi.12393

Morck, R., Yeung, B., & Yu, W. (2000). The information content of stock markets: why do

emerging markets have synchronous stock price movements? Journal of Financial

Economics, 58(1), 215-260, Retrieved from https://doi.org/10.1016/S0304-

405X(00)00071-4

Parrino, R., Sias, R., & Starks, L. (2003). Voting with their feet: institutional ownership

changes around forced CEO turnover. Journal of Financial Economics, 68(1), 3-46.

Retrieved from https://doi.org/10.1016/S0304-405X(02)00247-7.

Piotroski, J. D., & Roulstone, D. (2004). The influence of analysts, institutional investors, and

insiders on the incorporation of market, industry, and firm-specific information into stock

prices. Accounting Review, 79(4), 1119-1151. Retrieved from


Rogers, W. H. (1993). Regression standard errors in clustered samples. Stata Technical

Bulletin Reprints, 3(13), 88-94. Retrieved from

https://EconPapers.repec.org/RePEc:tsj:stbull:y:1994:v:3:i:13:sg17

Roll, R. (1988). “R2.” Journal of Finance, 43(3), 541-566. Retrieved from


Shleifer, A., & Vishny, R. Large shareholders and corporate control, Journal of Political

Economy, 94(3), 461–488, 1986. Retrieved https://www.jstor.org/stable/1833044

Subrahmanyam, A., & Titman, S. The Going-Public Decision and the Development of

Financial Markets, Journal of Finance, 54(3), 1045-1082, 1999. Retrieved from

https://doi.org/10.1111/0022-1082.00136

Tobin, J. (1982). On the Efficiency of the Financial System. Lloyd’s Banking Review, 153, 1–

15. Retrieved from: https://economicsociologydotorg.files.wordpress.com/2014/12/tobin-

on-the-efficiency-of-the-financial-system.pdf

Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data, (2ª Ed),

MIT Press.

Xu, N., Chan, K. C., Jiang, X., & Yi, Z. (2013). Do star analysts know more firm-specific

information? Evidence from China. Journal of Banking & Finance, 37(1), 89–102.

Retrieved from https://doi.org/10.1016/j.jbankfin.2012.08.014.

Yan, X., & Zhang, Z. (2009). Institutional Investors and Equity Returns: Are Short-Term

Institutions Better Informed? Review of Financial Studies, 22(2), 893–924. Retrieved

from https://doi.org/10.1093/revfin/hhl046

http://dx.doi.org/10.1590/S1519-70772014000100004


https://doi.org/10.1016/S0304-405X(00)00071-4

https://doi.org/10.1016/S0304-405X(00)00071-4

https://doi.org/10.1016/S0304-405X(02)00247-7


https://econpapers.repec.org/RePEc:tsj:stbull:y:1994:v:3:i:13:sg17



https://doi.org/10.1111/0022-1082.00136

https://economicsociologydotorg.files.wordpress.com/2014/12/tobin-on-the-efficiency-of-the-financial-system.pdf

https://economicsociologydotorg.files.wordpress.com/2014/12/tobin-on-the-efficiency-of-the-financial-system.pdf


https://doi.org/10.1093/revfin/hhl046

Documents

Corporate Governance Through Stock Trading in Brazil ... · voting rights (ordinary stocks, or ON), there is an ordinary stock concentration and a high level of non-voting shares