D O C U M E N T O D E T R A B A J O

Instituto de EconomíaTESIS d

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GÍSTER

I N S T I T U T O D E E C O N O M Í A

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Liquidity and Firm Investment:Evidence for Latin America

Francisco M. Muñoz.

2010

PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE I N S T I T U T O D E E C O N O M I A MAGISTER EN ECONOMIA

TESIS DE GRADO MAGISTER EN ECONOMIA

Muñoz Martínez, Francisco Manuel

Julio 2010

PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE I N S T I T U T O D E E C O N O M I A MAGISTER EN ECONOMIA

Liquidity and Firm Investment: Evidence for Latin America

Francisco Manuel Muñoz Martínez

Comisión

Jaime Casassus y Augusto Castillo

Santiago, julio 2011

Liquidity and Firm Investment:

Evidence for Latin America

⇤

Francisco Munoz Martınez†

October 25, 2011

Abstract

In a market where there are agents with heterogeneous beliefs and short sales constraints,

firms can take advantage of the overvaluation that occurs by issuing more shares to finance

new investment. This overvaluation generates a positive relationship between trading volume

and real investment by the firm. This relationship is studied in a panel of Latin American

firms, evidence being found that a higher turnover and a higher industry-adjusted turnover is

associated with greater firm investment. This e↵ect increases when the firm decides to issue

shares, thus giving support to the proposed mechanism. It is further noted that this e↵ect is

greater for firms with greater financial constraints and greater investment opportunities. This

is consistent with the fact that increased liquidity can encourage investment, as it improves the

conditions for external financing.

Resumen

En un mercado donde existen agentes con creencias heterogeneas y restricciones a las ventas

cortas, las firmas pueden aprovechar la sobrevaloracion que se produce emitiendo mas acciones

para financiar nueva inversion. Esta sobrevaloracion genera una relacion positiva entre volumen

transado e inversion real por parte de la firma. Dicha relacion es estudiada en un panel de firmas

de Latinoamerica, encontrando evidencia que mayor turnover y mayor turnover ajustado por

industria esta asociado a mayor inversion de las firmas. Este efecto aumenta cuando la firma

⇤I appreciate the comments of Jaime Casassus, Augusto Castillo, Mario Giarda, Daniel Munoz, Rodrigo Munoz,

David Ruiz and the seminar participants at the School of Management PUC and at the Annual Meeting 2011 of

the Chilean Economic Society (SECHI) . In particular I thank Borja Larrain and Jose Tessada for comments and

assistance in this work. Finally I thank the partial financial support from Grupo Security through Finance-UC.†Email: fmmunoz.ma@gmail.com

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decide emitir acciones, apoyando ası el mecanismo propuesto. Ademas, se observa que este

efecto es mayor para las firmas con mayores restricciones financieras y mayores oportunidades

de inversion. Esto es consistente con que una mayor liquidez puede incentivar la inversion, dado

que mejora las condiciones para el financiamiento externo.

Key Words: Firm Investment, liquidity, disagreement.

I Introduction

The study of liquidity in the stock market has attracted much attention in empirical and the-

oretical literature in recent years. Recently, there has been a growing interest in studying the

relationship that may exist between liquidity in the stock market and the real economy. At a

macroeconomic level, works such as Naes et al. (2011), Kaul and Kayacetin (2009), and Beber

et al. (2010) show evidence at the aggregate level and industry level, of a positive relationship

between liquidity and real variables such as GDP and investment. At the microeconomic level, the

relationship between liquidity and the decisions of firms has been studied in its di↵erent aspects

such as the issuing of shares, leverage and the performance of the firms. However, a study centered

on the relationship between investment and liquidity has not been done previously.

This paper seeks to provide evidence in this direction, studying the relationship between the

liquidity of the stock market and the investment decisions of firms. In a scenario where there are

agents with di↵erences of opinion and restrictions to short sales, firms seek to take advantage of the

overvaluation that occurs issuing more shares to finance investment. In this context, disagreement

will be reflected in an increased trading volume, thus creating a positive relationship between

trading volume and investment. This positive relationship is studied for a panel of firms listed

on stock exchanges in four Latin American countries (Argentina, Brazil, Chile, and Mexico), using

quarterly data from 1990-2010. I found a positive and significant relationship for di↵erent measures

of investment, at di↵erent horizons, using two measures of liquidity.

In order to explain this relationship, I present a theoretical model showing the relationship

between liquidity and investment, through issuance of shares by the firm. The proposed channel

is tested in the data, finding that the e↵ect of liquidity is greater for those firms that issue shares,

thus supporting the proposed channel.

Moreover, one can note that liquidity has a positive relationship with investment, because

the above facilitates the financing of investment. It should be noted that those firms that have

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greater financial constraints, should be more sensitive to liquidity. When estimating a regression

that incorporates the interaction between liquidity and a dummy that identifies whether the firm

has higher financial constraints, it appears that liquidity has a greater relation with investment

in firms with greater restrictions. In turn, this relationship should be stronger in firms that have

greater investment opportunities. It has been suggested in literature on the subject that firms that

have greater investment opportunities are more sensitive to market conditions in deciding their

investment (Zhang, 2007). Thus, liquidity should be a catalyst for the decision to invest. The

results show that the e↵ect of liquidity is higher in firms with greater investment opportunities,

which was tested including interaction between liquidity and a dummy that identifies whether the

firm has greater investment opportunities.

This work relates to three lines of research. First, it relates to the literature that studies the

relationship between stock market liquidity and the decisions of firms. On one hand, works such as

those of Butler et al. (2005) and Lipson and Mortal (2009) study the relationship between liquidity

and equity issuance decision, finding that firms with greater liquidity have lower issuance costs,

thus using more funding through the issue of shares. Thus, firms with higher liquidity tend to have

lower levels of leverage. On the other hand, Lesmond et al. (2008) find that firms that increase

their level of leverage increase the bid-ask spread (reduced liquidity). Similarly, Bharath et al.

(2008) show that firms that use a higher percentage of financing through debt, have lower liquidity

in the stock market.

Fang et al. (2009) focus on the relationship between liquidity and firm performance, finding

that firms with greater liquidity have a better performance measured as the market-to-book ratio of

assets. More related to this work, Gilchrist et al. (2005) find that greater variance in the predictions

of stock market analysts predicts greater actual investment and equity issuance. Another recent

work that has studied the firm-level investment in the United States is Polk and Sapienza (2009).

They find that there is more investment when the shares are overvalued, using discretionary accruals

as their proxy for mispricing.1 While there are studies that have linked the stock market with

firm investment, a study on the relationship between the liquidity in the stock market and firm

investment has not been conducted previously. This study provides evidence in that direction,

finding evidence of a positive relationship between liquidity and investment, which supports the

growing literature that finds a relationship between the liquidity of the stock market and the

decisions of firms.1These are defined as abnormal di↵erences between accounting profits of the business and cash flow.

3

Second, this research relates to the theoretical literature that links liquidity and investment.

The first is to review what has been studied with respect to liquidity and then focus on the rela-

tionship with the firm. As a survey, Hong and Stein (2006) show that models with “disagreement”

among investors are able to explain more clearly the relationship between trading volume and as-

set prices. 2 More recently, Banerjee and Kremer (2010) develop a model with “disagreement”

between investors, thus managing to explain various patterns of trading volume, as for example,

the positive autocorrelation in volume. Empirically, liquidity can be measured in many ways, the

most common being the traded volume, turnover, adjusted turnover, a measure of elasticity of the

liquidity provided by Amihud (2002), and a proxy bid-ask spread of Roll (1984) among others.

The study of liquidity in emerging markets has not garnered much attention. However, in this

area two works by Bekaert et al. (2007) and Lesmond (2005) stand out. The first finds predictability

from liquidity to asset returns, while the second makes a study of di↵erences in liquidity between

di↵erent emerging countries, finding that countries with weaker political and legal institutions have

a higher liquidity cost. Unlike these studies, this paper studies firm-level liquidity in emerging

markets.

Several theoretical models have been developed to understand the relationship between liquidity

and investment. At a macroeconomic level, liquidity can have a relationship with real variables

such as GDP and investment through the channel called “flight to quality” (Longsta↵, 2004),

where changes in the portfolio of investors are generated by changes in expectations about the real

economy. Another possible explanation due to an investment channel, where liquid stock markets

facilitate investment in illiquid long-term projects (Levine, 1991).

At a microeconomic level, there are several explanations for the relationship between stock

market liquidity and the real economy. On the one hand, there is the model of Maug (1998)

focused on agency problems. This paper models the decision of monitoring investors, who trade

in order to profit from price appreciation, due to monitoring. Thus, more liquid markets tend to

support better management of the company. A model that takes into account the information

present in the stock prices is the one of Khanna and Sonti (2004), who show that liquidity can be

related positively to the performance of the company. This is because greater liquidity stimulates

the entry of informed investors, which makes the price more informative for the “stakeholders”,

thus improving the results of operations and relaxing financial constraints. Both models approach

2Brav and Heaton (2002) show that models where investors are rational structural uncertainty and have di↵erencesof opinion are equivalent to models where agents have a bias in their behavior.

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the relationship between liquidity and investment, but these do not predict a specific sign of such

a relationship.

On the other hand, there are models more related to asset mispricing, where I can highlight

Gilchrist et al. (2005) who focus on a mechanism based on share issuing. They develop a model

where the dispersion of beliefs and short-sale constraints can lead to stock market bubbles, these

being exploited by firms issuing new shares with an inflated price. Unlike the previous model,

Polk and Sapienza (2009) based on the model of Stein (1998), present a model that relates the

mispricing of stocks to investment, without the necessity of stock issuance. These authors show

that in a scenario where the stock price is overvalued and there are “stakeholders” who have short

time horizons, the wealth of these will depend on the overvaluation. Since the manager will decide

the optimal level of capital maximizing the wealth of “stakeholders”, price overvaluation will result

in a higher capital level.

Similar to Gilchrist et al. (2005), but focused on trade volume, I will present a model to observe

the relationship that may exist between trade volume and firm investment. In the model there are

two types of investors: a rational one, who knows the distribution of payment of the project and

an optimist, which always has a higher valuation than the rational one.. The latter can be thought

of as a positively overconfident investor, which gives more weight to his personal information than

to the market information. In this context, the firm must decide how many shares to issue in order

to finance its project, and since there is a di↵erence of opinion among the agents, the demand for

stocks will be positively a↵ected by these disagreements. Thus, the number of shares that the firm

decides to issue and the total investment will depend positively on this di↵erence of opinion among

investors. In turn, the trade volume will directly reflect the disagreement between the two agents.

Thus, the relationship between liquidity and investment will be positive. The main contribution of

this model is to propose a mechanism through which the trading volume and investment have an

explicit positive relationship.

It is important to note that this model does not predict a causality of trade volume to in-

vestment, but di↵erences of opinion are captured through the trade volume, generating a positive

relationship with investment. In econometric terms one can think that the trading volume is a

proxy for disagreement, which is the relevant variable that a↵ects investment.

Finally, this work is related in a lesser degree with the growing literature that studies the

relationship between the liquidity of the stock market and macroeconomic variables. Recent work

by Naes et al. (2011) shows evidence of a strong relationship between liquidity and real cycles,

5

finding a positive relationship of liquidity with the GDP growth and real investment. Similar works

such as Kaul and Kayacetin (2009) and Beber et al. (2010) show evidence in the same direction.

The former found that the cross-sectional average of individual stock order flows, can predict future

growth rates for industrial production and real GDP, while the latter found the information in the

in sector overflow is directly related to the release of macroeconomic fundamentals.

The results in this study are in line with this literature. The positive relationship between

liquidity and firm investment would be evidence that the aggregated results are, in part, generated

by those firms listed on the stock exchange, through the decisions of financing via share issues.

Before continuing with the rest of the investigation, I must recognize that these results are not

exempt from possible problems of endogeneity. The results do not seek to present a causal link

between liquidity and investment. I prefer to say that they are conditional correlations. However,

presenting these results is interesting for two reasons: first, being able to study a relationship that

had not been studied previously and is part of the recent literature that has been devoted to the

study of how financial market frictions a↵ect decisions of firms. Second, to be able to show evidence

concerning the sign and the mechanism of this relationship. In the literature on this theme there are

theoretical mechanisms that propose a positive relationship (Polk and Sapienza, 2009 and Gilchrist

et al., 2005) and others that propose an ambiguous relationship (Maug, 1998, Khana and Sonti,

2004). This paper presents a di↵erent mechanism that delivers a positive relationship, which is

corroborated by the data.

The rest of the research is organized as follows: Section II presents the theoretical model that

relates the trade volume to investment, thus motivating the hypothesis of this work. Section III

presents the econometric strategy and data, while Section IV shows the empirical results. Finally,

Section V is the conclusion.

II Model

The proposed model is developed in two periods, where in the first period the firm has to

decide how many shares to issue to finance a project. The firm has neither equity nor the ability

to obtain financing through debt, so that funding is only through share issue. The demand for

shares is determined by two groups of investors: a rational group and an optimistic group. In the

second period, they receive a random paid V for each unit invested. The interest rate will be set

to zero.The main di↵erence between this model and Gilchrist et al. (2005), is that they impose a

6

degree of overvaluation in the stock price, later showing a relationship between this valuation and

investment activities through the issue of shares. Thus, their model does not have an explicit link

between trading volume and investment.

Investors

Investors are divided into two groups: the first group is called “rational” (r). These are investors

who know the true distribution of payment of the project, V r ⇠ N(V �2). The second group consists

of “optimistic” agents (o), which have a positive bias ( �) over the distribution of “rational” agents.

Thus, their valuation is V o = V r + � with � > 0. This bias can be thought of as a di↵erence of

opinion between the two groups, as in the case of Banerjee and Kremer (2010). It is also similar to

the degree of market “sentiment” proposed by Baker and Stein (2004). The assumption that there

is an optimistic group can be interpreted in light of the result proposed by Miller (1977), where in

a scenario with short-sales constraints and a group of actors who overreacts, the price will reflect

the valuation of the group that overreacts in an optimistic way and that of the rational group.3This

is because pessimists will prefer to sell and due to short-sales constraints, they will be taken out of

the market.

In this case one could think of a group that may have a bias � with some normal distribution

centered at zero, allowing agents to be optimistic or pessimistic. When we include the assumption

of short-sale constraints, this normal distribution will be truncated from below, as very pessimistic

agents will be taken out of the market. Thus, the hope of this new truncated distribution will be

positive, and as a result the average demand will reflect the optimistic valuation of the “optimistic”

group. Thus, the positive bias of the optimistic group could be thought of as the hope of the trun-

cated distribution. The development of the model with the assumptions of short sales restrictions

and over-reaction would lead to the same conclusions.

Both agents have a utility function of the form U(W ) = � exp �W , where W i = (V i � P )xi

is wealth, with Xi the number of shares purchased by investor i and P the share price. Thus, the

3The assumption of short-sale constraints is justified by Bris (2007) who shows that in Argentine, Chile, and Brazilshort-selling is not performed in practice. In the case of Mexico, Jain et al. (2010) finds that naked short selling isprohibited.

7

problem of the investor i will be

maxX

iEi[� exp �W i] i = r, o

s.t W i = (V i � P )Xi

Using the assumption of normality in the distribution of V I obtain the following solution:4

Xr =V � P

��2(1)

Xo =V + � � P

��2(2)

XM =V � P

��2+

V + � � P

��2(3)

Equations (1) and (2) show the demands of each group. As is observed, the “optimistic” group

demand will always be higher than the “rational” group. Equation (3) represents the aggregate

market demand, assuming that both groups are of equal size.

Firm

The firm will decide how many shares to issue, which is analogous to how many units of project

to perform. If the firm is a price taker and this price is greater than the cost of investment , the firm

would decide to issue an infinite number of shares. However, its issuance capacity or scale of the

project will be limited by the capacity of the demand to absorb risk. Therefore, the firm observes a

downward sloping demand, behaving like a monopolist.5 The project technology is linear and the

cost of investment is set at one.

In period 1 the firm will solve the following problem,

maxS

(P � 1)S

s.t P = V +�

2� ��2S

2

where the restriction corresponds to equality between aggregate demand and supply of shares (equa-

tion (3)). By solving the optimization problem, I obtain the equilibrium of issuance, price, and

investment.4For the derivation of this result see Appendix.5This form of issuance by the firm is similar to the model proposed by Baker et al. (2003).

8

Proposition 1. The price, issuance and investment in equilibrium are given by:

P ⇤ =(V + 1)

2+

�

4(4)

S⇤ =V � 1 + 1

2�

��2(5)

I⇤ =

✓(V + 1)

2+

�

4

◆ V � 1 + 1

2�

��2

!(6)

See Appendix for solution.

The price in the equation (4) is similar to the price of a monopolist, Note that this increases at

the level of optimism of the agents. This result is similar to the model of Baker and Stein (2004),

who show as a result that price increases at the level of market “sentiment”. The issuance, as shown

in equation (5) will also increase at the level of optimism. This result is similar to that obtained

by Ljungqvist et al. (2006) where they model share issuance in “Hot Markets”, which would be

similar to the issuance in markets where there is an optimistic group. Finally, equation (6) shows

firm investment, which is defined as the number of shares issued at the equilibrium price. As both

components increase at the level of optimism, so will the investment. This first result is central

to this investigation, as it is observed that the level of firm’s investment depends positively on the

di↵erences of opinion among investors.

As the main objective of this work is to see the relationship between trading volume and firm

investment, the definition of volume should be given. Generally, in those models where the net

supply of assets is equal to zero, the trading volume is defined as V ol = |Xr| = | � Xo|. This

definition is not correct when the net supply of assets is positive, as in this case. However, we can

define volume through an excess demand function, keeping the intuition of the previous definition.

Thus, I define volume as V ol = |Xo � S⇤/2| = | �Xr + S⇤/2|. The idea behind this definition is

that it measures the reallocation of shares by di↵erences of opinion among the agents. In the event

that both groups of agents have the same beliefs, shares are divided in equal parts, being in this

case zero the proposed measure. Applying this definition, I obtain:

Proposition 2. The volume is given by:

V ol =1

4��2|�| (7)

9

See Appendix for solution.

Equation (7) shows that trading volume will increase at the level of disagreement between OoptimisticO

agents and “rational” agents. This result is similar to that found by Banerjee and Kremer (2010)

and Scheinkeman and Xiong (2003), which show that volume depends on the degree of disagree-

ment between the agents. Using Propositions 1 and 2, the relationship between trading volume

and investment can be obtained

Proposition 3. Greater trading volume is related to greater investment.

Proposition 3 is the one to be tested empirically for firms listed on stock exchanges in Latin

America. It is important to note that volume is not the cause of greater investment. It is disagree-

ment among investors in the stock market what leads to a better scenario for firms to issue shares,

a result similar to Derrien (2005) and Ljungqvist et al. (2006), and so increase investment. Since

I do not know the disagreement among investors, trading volume may be a good proxy, as shown

in Proposition 2.

If I go to a context in which both agents are rational, the trade volume would be zero, since

� = 0.6 Thus, a relationship between trading volume and investment should not be observed.

III Empirical Strategy

The hypothesis presented in the previous section will be tested in a panel of firms listed on

stock exchanges in Argentina, Brazil, Chile and Mexico for the period 1990-2010, using quarterly

frequency data. The main equation to be estimated is:

Investmentict+j

= ↵i

+ ↵ct

+ �Liquidityict

+ ✓Xict

+ ✏ict

(8)

where i stands firm, c country, t quarter and j the number of advances. The investment will be

defined as growth in total assets, growth in property, plant and equipment (PPE), and growth in

inventories. Long di↵erences are used (1, 2 and 3 years) since the investment is not carried out

immediately.

There are conceptual di↵erences between these three measures of investment. Generally, the

6This result was originally proposed by Milgrom and Stockey (1982), who show that a model with rational agentsshould not generate trading volume.

10

studies for U.S. firms use capital expenditure as the measure of investment. However, in the case

of Latin America, this variable is not reported by firms in their financial statements. Thus, results

will be presented with these three measures in order to obtain robust results to the definition of

investment. The first is defined as the growth in total assets, which roughly captures investment.

The problem is that it includes investments at di↵erent horizons, and also includes changes in

accounts that are not necessarily handled by the firm, such as receivable accounts. The second

measure is closer to the capital expenditures measure used in the U.S., since it is defined as the

growth in fixed assets (property, plant and equipment). This should reflect investment decisions by

the firm with a long-term horizon. Finally, the change in inventories reflects short-term investments

which are decided by the firm. Thus, the latter two measures would be more correct in the case of

this study, because di↵erences of opinion a↵ect the investment decisions made by firms.

Liquidity will be measured in di↵erent ways, as will be explained later. The other controls are

the standard regressors in the literature of firm investment.7 Thus, I include leverage as measured by

the total liabilities over total assets, Tobin’s Q ratio defined as market-to-book assets,8 which reflects

firm investment opportunities and cash flow,9 that represents part of the financial constraints that

the firm might present. Fixed e↵ects at the firm level (↵i

) and country-quarter fixed e↵ects (↵ct

).

The first seek to capture firm-specific characteristics that do not vary over time, while the second

capture cycle e↵ects inherent to each country.

As shown in equation (8), the central hypothesis is that the parameter � is positive and

significant, reflecting that increased liquidity (greater disagreement among investors) is related to

increased investment by the firm. An insignificant � parameter would lead us to a context of rational

agents where there are no di↵erences of opinion among investors, in the absence of a relationship

between investment and the trade volume.

However, a second step is to analyze whether the relationship makes itself present through

the channel proposed by the model. This would be that the relationship between investment and

liquidity occurs more sharply for companies that issue shares. To test this hypothesis, the base

specification adds an interaction between a dummy that identifies if the firm issued shares and

the measure of liquidity. A positive and significant parameter would reflect the relevance of the

proposed channel. Otherwise, what would happen is that there are channels other than than

issuance that may generate this relationship.

7See Almeida and Campello (2007) and Polk and Sapienza (2009).8It is defined as (Stock Market Capitalizationt + Total Debtt)/Total Assetst.9Defined as (Depreciationt + EBITt)/ Total Assetst.

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If there is a positive relationship between liquidity and investment, it is because liquidity

facilitates financing of investment. By separating firms according to financial constraints, it should

be observed that those firms more financially constrained are more sensitive to liquidity. Following

Almeida and Campello (2007), companies were separated into large and small according to their

total assets, with the object of capturing financial constraints.10 This separation is also supported

by Beck et al. (2008) who show evidence that there is a di↵erence in funding between large and

small companies, finding that small firms tend to have less external financing. In this way, liquidity

could relax these di↵erences for the small firms thus encouraging further investment. This will

be tested by adding to the base specification a dummy representing whether the firm is large or

not, and making it interact with the liquidity variable. A significant and negative coe�cient would

represent that for those firms with greater financial constraints, liquidity is more relevant.

Finally, if liquidity encourages more investment, this e↵ect should be more pronounced in those

firms that have greater investment opportunities. It is argued that firms with greater investment

opportunities (“growth”) would have greater ease in doing “market timing” at the time of making

their investment, while those with lower investment opportunities (“value”) tend to be more stable

in their investment (Zhang, 2007). Thus, in a context where there is greater liquidity (larger

di↵erences of opinion) “growth” firms would take advantage of this scenario, investing more.11

To test this, I include in the base regression a dummy indicating whether the firm is “value” or

“growth”, interacted with the measure of liquidity. If the coe�cient is significant and negative, it

would be evidence that the e↵ect of liquidity is greater for “growth” firms than for “value” firms.

Data

The firm level data was obtained from software Economatica, excluding financial industries.12

For the case in which a firm has more than one series of shares, I took the most traded series. The

data was winsorized at 2 % top and bottom in order to eliminate possible problems of outliers.

This practice is quite common for firm-level data. The sample contains about 7,000 observations

and 450 firms. This panel is highly unbalanced, as the firms financial information is not reported

in complete form for all the periods. Table 1 presents the descriptive statistics of the variables of

interest.10These were divided for each quarter and each country between those who were above the total assets median

(large) and those below (small).11Firms were separated in each country and each quarter according to the book-to-market of equity. Firms with a

book-to-market above the median are considered “value”and the firms that are below are considered “growth”.12These are the “Fondos” and “Finanzas y Seguros” industries in Economatica.

12

The main measure of liquidity will be turnover.13 This measure is created using daily data of

the quantity of shares traded and the total number of shares of the firm. As in Lesmond (2005),

I eliminate days when trading volume is greater than the total number of shares of the firm. The

measure is defined as:

PQ

t=1 Traded Sharest

DQ

⇤ Total Shares(9)

where DQ

is the number of days that were transactions in the quarter.

The second measure of liquidity is the turnover adjusted by industry. This is defined as the

turnover of the firm divided by the turnover of the industry.14 The latter is created as the average

turnover of the firms belonging to that industry in each quarter.15 Thus, I will be measuring

whether the investment increases in those firms where di↵erences of opinion are greater in respect

to the average di↵erences of opinion in its industry.

IV Results

Before reviewing the empirical results, I present Figure 1 with the aim of showing that the

proposed relationship between liquidity and investment is positive. This figure shows the average

investment (growth in fixed assets, PPE) at one year term, for percentiles 0-33, 33-66 and 66-100

of liquidity (turnover). It can be clearly seen that investment is increasing in the liquidity of the

stock market, supporting the central hypothesis of this research.

Turning to the econometric analysis, Table 2 presents the results of the proposed specification

in equation (8), using turnover as the measure of firm liquidity. The coe�cient of interest, � , is

significant and positive for the three definitions of investment at 1, 2 and 3 years. This shows that

firms which have a higher turnover (higher disagreement according to the proposed model) incur

in higher investment. This variable is economically significant. For example, an increase in one

standard deviation of turnover leads to an increase in investment in fixed assets of 1.3 % in one

year, which is enough if we consider that the unconditional media of this investment is 6 % (see

Table 1). These orders of magnitude are maintained for other investments and di↵erent horizons.

At first the hypothesis here stated, appears to be fulfilled, showing that the prediction of a model

13This is used as a measure of trading volume, since the latter is not scaled, thus presenting a high correlation withfirm size.

14This measure is similar to adjusted turnover proposed by Sadka and Shcherbina (2007) , except that they scaleby the turnover of all market.

15It would be the turnover of an “Equally-Weighted” portfolio at industry level.

13

of rational agents would not be met in this case.

The rest of the controls, as in the case of leverage appears to be significant and negative. This

would be evidence in favor of the over-investment channel. Firms with higher leverage level will

require a greater cash flow to pay interest and capital, thereby reducing its capital to invest in new

projects. The coe�cient of this variable is similar in magnitude and significance to the study by

Aivazian et al. (2005) for the relationship between leverage and investment in the case of firms in

Canada.

Tobin’s Q which reflects investment opportunities, turns out to be positive and significant.

Thus, firms with greater investment opportunities have increased investment. This result is fairly

standard in literature on the subject. The coe�cients are similar in significance and magnitude to

those obtained by Polk and Sapienza (2009) for U.S. firms.

Finally, cash flow is significant and positive. The idea behind this result is that the positive

relationship reflects the financial constraints that face the firm. If a company is restricted for foreign

credit, cash flow will allow you to perform a greater investment (Fazzari et al., 1988). The results

for this variable are similar to those found by Almeida and Campello (2007) both in significance

and magnitude. In the case of inventories, the e↵ect is negative and not significant. Studies like

Benito (2005) and Carpenter (1998) are inconclusive about the sign and the significance of this

variable.

Table 3 presents the results for the measure of industry-adjusted turnover. One can see that the

results hold for the case of the variable of interest, showing that firms which have a disagreement

higher than the industry average have higher investment. The economic significance is maintained

for this variable. The results for the rest of the controls are maintained.

The above results suggest a positive relationship between stock market liquidity and firm-level

investment, as seen at the macro level in the work of Naes et al. (2011), Kaul and Kayacetin (2009)

and Beber et al. (2010). However, as was mentioned above, it is important to study whether this

result is obtained through the issuance of shares, testing in this manner if the channel proposed by

the model is the one that generates such a relationship. Table 4 shows the results where I add the

interaction between a dummy that identifies if the firm issued shares and the measure of liquidity.

This table is constructed using as measures of investment the PPE and Inventories and both

measures of liquidity. It can be seen that liquidity has an impact on investment that is amplified

in cases where there was an issue of shares. This evidence is consistent with the channel proposed

by the model. Liquidity will be more relevant for firms that issued shares, since the di↵erences of

14

opinion make issuance more favorable, thereby increasing investment. The coe�cients shows that

the importance of liquidity in the case of issuance, on average, is more than double that in the case

without issue.

It is important to recognize that this result does not hold when I use the change in total

assets as a measure of investment. One possible explanation, as mentioned above, is that total

assets include investments at di↵erent horizons, making the e↵ect of liquidity be the average of

di↵erent types of investment. Moreover, the change in total assets includes several items that are

not handled by the manager. For such items liquidity should have no e↵ect. Another alternative is

that this is evidence that other mechanisms are also generating this relationship between liquidity

and investment. With the results obtained I cannot rule out the mechanism proposed by Polk and

Sapienza (2009), where the mispricing of assets directly a↵ects the investment without any share

issuance. However, it is clear that the mechanisms proposed by Maug (1998) and Khanna and Sonti

(2004) are not reflected in the data. This is because both studies suggest that higher liquidity leads

to more e�cient investment decisions by firms, which does not imply greater investment, as was

found in the results presented.

Tables 5 and 6 show evidence respecting the di↵erential e↵ect for firms with greater financing

constraints. The results are consistent with the intuition presented in the previous section. In those

firms with larger financial constraints (small ones), liquidity is shown to have a closer relationship

with investment than with firms with lower financial restrictions (large), supporting the evidence

found by Beck et al. (2008). This is reflected in the negative and significant coe�cient on the

interaction “Large X Liquidity”. It is important to note that the e↵ect on large firms (represented

by the sum of both coe�cients that incorporate liquidity) is not significant in the majority of the

cases. This result is observed for the definitions of PPE and Inventories investment and for both

measures of liquidity. These results are maintained if I separate firms by size of stock market cap-

italization, that is to say that low capitalization firms have a higher relationship between liquidity

and investment.

Finally, Tables 7 and 8 show whether the e↵ect of liquidity on investment is greater for firms

that have greater investment opportunities (“growth”). It can be seen that the e↵ect is greater

for “growth” firms, thus supporting the idea that these firms would be more sensitive to the

characteristics of the market when making their investment, as stated by Zhang (2007). This result

is true for both measures of liquidity.

However, it is necessary to recognize certain problems in the estimates. The first is to discuss

15

that previous regressions were performed with other measures of liquidity commonly used in the

literature on the subject such as Roll (1984) and Amihud (2002), but the results are not maintained.

One possible explanation is that in the case of Roll (1984) a daily autocovariance must be calculated,

in order to obtain the quarterly measure of liquidity. Since in these countries shares are not traded

every day, the covariance was calculated with very little data, making it unrepresentative. In the

case of Amihud (2002) the expected signs were obtained, but the results were not significant. While

these measures had been studied in emerging countries, they were calculated at the aggregate level,

so in these countries the use of firm-level data is something little explored so far.

The second problem is that although the theoretical model shows a relationship between

disagreement and trading volume, I must recognize that there may be other variables that generate

changes in the turnover. The important thing is to see if this a↵ects the relationship found between

liquidity and investment. In first place, supporting the use of turnover as a proxy for di↵erences

of opinion, is the work of Sadka and Shcherbina (2007), who show that firms where disagreement

among analysts about future earnings of companies is higher also have a high turnover, existing

thus a positive correlation between turnover and disagreement among analysts. This result is also

found by Thakor and Whited (2011) and Diether et al. (2002), empirically validating turnover as

a proxy of di↵erences of opinion.16 However, one might think that variables related to the business

cycle are generating changes in the turnover. In order to avoid this problem quarter-country fixed

e↵ects were incorporated, which would be removing any macroeconomic factor that could a↵ect all

the firms in each country the same. Thus, liquidity would give us net information of the e↵ect of

the cycle.

An alternative are the studies that have used turnover as a proxy for investment horizons and

information on prices. In the case of investment horizons, the work of Polk and Sapienza (2009)

and Dong et al. (2007) use market turnover as proxy for investment horizons. They find that when

there is a greater number of short-term investors (high turnover), the e↵ect of asset’s mispricing

over investment is higher. However, the underlying reason of why turnover represents investors with

short term horizons, seems to stem from overconfidence among investors. As shown in Cremers

(2010) as in Odean (1999), Barber and Odean (2000) and Grinblatt and Keloharju (2009), investors

who trade most frequently are those who are overconfident. Thus, this interpretation would be in

line with this work, because turnover captures the overconfidence among investors, which is reflected

16Other works have that used turnover as a proxy for di↵erences of opinion are Berkman et al. (2009), Hong andStein (2007), Diether et al. (2002), among others.

16

in that they trade more.

With regard to information on prices, the empirical evidence is inconclusive about the rela-

tionship between turnover and information. On the one hand, Hou et al. (2006) found a positive

correlation between turnover and information, using the R2 from a regression of returns as its

measure of information, while Ferreira et al. (2011) and Gordon et al. (2009) find a negative

relationship, when they estimate using PIN as its measure of information.17 With respect to the

relationship between investment and information, Chen et al . (2007) found that in the presence of

more informative prices the e↵ect of asset price is higher on investment. However, the direct rela-

tionship between information and investment is not clear. Theoretically, it is suggested that more

information on prices should lead to more e�cient investment (Khan and Sonti, 2004), which does

not imply that investment should be higher or lower. Thus, if turnover only captures information

in prices, its relation to investment is not conclusive, since it is not clear whether this means more

information and, even more so , neither is it clear if more information means more investment.

Another possible problem is that turnover is calculated over total shares issued and not over

the shares that are available to be traded (Free-Float). For this reason, the denominator of the

turnover may not be the correct one. Donelli et al. (2010), shows that the ownership structure in

Chile is quite stable over time. If this holds for all the other countries in the sample I would not

have problems in the estimation; there would only be a change of magnitude in the coe�cients.

In general, in the regressions of investments at the level of the firms, the problem generated

by the inclusion of Tobin’s Q as a regressor has been recognized. The problem is that this measure

appears to have a high degree of measurement error, if the aim is to capture investment opportuni-

ties. The e↵ect of this measurement error is inconsistency in estimated parameters using methods

such as fixed e↵ects. In the literature two methods have been proposed to solve this problem. The

first proposed by Erikson and Whited (2000, 2002) (hereafter EW) delivers consistent parameters,

by estimating with GMM using third and fourth moments for identification. The second method

proposed by Biorn (2000) (hereafter VI) proposes to instrument Tobin’s Q with lags of it, as long

as there is a serial correlation of this variable.

Recently, Almeida et al. (2011) by means of a Monte Carlo exercise pitted one estimator against

the other, finding that the EW estimator is biased in the presence of fixed e↵ects, heteroskedasticity

and small samples, while using VI delivers unbiased parameters. In this paper I use the VI method

in order to analyze whether the results are robust when considering the problem of measurement

17This measure represents the probability that an informed agent traded on the market.

17

error. The instruments used were Tobin’s Q with two or three lags. The autocorrelation of this

variable is high, so they would be valid instruments. This strategy was conducted to estimate all

the tables previously discussed, finding that the results are maintained or improved, so the results

remain robust when controlling for measurement error in Tobin’s Q.18

As a robustness check, I performed three exercises: an estimate for di↵erent time periods,

an estimate taking one country out of the sample and an estimate taking one industry out of the

sample. In the first case, I took out observations until the first six years of the sample and then the

last six. Overall, the results hold, showing that they are not dependent on the time of the sample.

In the second case, when Argentina, Brazil and Chile are taken out of the sample, the results remain

very similar, while when Mexico is taken out, the overall results are similar but some coe�cients

of interest lose significance. It seems that Mexico is a country where the hypotheses stated are

fulfilled most. Finally, the results remained the same when the exercise to do with industry was

carried out.

V Conclusions

In a model where there are 2 types of investors a rational and an optimistic one, a positive

relationship between firm investment and trading volume for firms that are financed through the

issue of shares can be observed. This prediction was tested for a panel of firms in Latin America,

finding that stock market liquidity relates positively to investment, and that this e↵ect is even

greater for firms that issue shares, thus supporting the mechanism proposed by the model.

In addition, I found evidence that firms with greater funding constraints, have a greater e↵ect of

liquidity on investment. Which supports the hypothesis of a positive relationship between liquidity

and investment, because liquidity enables external financing.

On the other hand, there is an asymmetric e↵ect between “value” and “growth” firms, being the

“growth” ones more sensitive to liquidity. Thus, firms with greater growth opportunities (“growth”)

would carry out investment strategies more related to the state of the market. This is consistent

with the idea that more liquidity creates a better scenario for external financing.

18It was also estimated using only a lag of two periods and the results are similar.

18

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22

Apendix

Solution for Asset Demands

Investor i problem:

maxX

iEi[� exp �W i] s.t W i = (V i � P )Xi (10)

As V is normally distributed, using the characteristic function of the normal distribution I obtain:

maxX

i� exp(�Ei[V i]Xi � �PXi +

�2�2(Xi)2

2) (11)

FOC �Ei[V i]� �P + �2�2Xi = 0 (12)

Solving Xi, I obtain assets demands

Xr =V � P

��2(13)

Xo =V + � � P

��2(14)

Proof Proposition 1

Firms problem:

maxS

(P � 1)S s.a P = V +�

2� ��2S

2(15)

Di↵erentiating with respect to the number of shares issued by the firm.

CPO : V +1

2� � 1� ��2S = 0 (16)

Solving for S I obtain:

S⇤ =V � 1 + 1

2�

��2(17)

Substituting this result into the demand function for shares gives the equilibrium price.

P ⇤ =(V + 1)

2+

�

4(18)

23

The investment is calculated by multiplying the equilibrium price with the number of shares issued

by the firm.

I⇤ = P ⇤ ⇤ S⇤ (19)

=

V � 1 + 1

2�

��2

!✓(V + 1)

2+

�

4

◆(20)

Proof Proposition 2

First derive the equilibrium demands of both types of investors, these are

Xr =V � 1� �/2

2��2(21)

Xo =V � 1 + 3�/2

2��2(22)

Then I calculate

V ol =

����Xo � S

2

���� (23)

V ol =

�����V � 1 + 3�/2

2��2� 1

2

V � 1 + 1

2�

��2

!����� (24)

V ol =1

4��2|�| (25)

Proof Proposition 3

As was presented in Proposition 1 and 2, investment and trading volume are monotone increas-

ing in the level of disagreement among investors (�). Thus, greater trading volume will be related

to increased investment.

24

Figure 1: Firm investment splited by firm turnover percentiles

The sample consists of quarterly data from 1990-2010 for firms in Argentina, Brazil, Chile andMexico. All variables were winsorized at 2 % in each tail. Investment is defined as one-year growthin fixed assets (Property, Plant and Equipment). Investment was separated according to percentilesof turnover, defined as Number of Shares Traded/Shares Outstanding of each firm.

25

Table 1: Summary Statistics

This table shows descriptive statistics of the variables of interest. The sample consistsof quarterly data from 1990-2010 for firms in Argentina, Brazil, Chile and Mexico. Allvariables were winsorized at 2 % in each tail. The dependent variables are growthin total assets, growth in fixed assets (Property, Plant and Equipment) and growthin Inventories, where 4I

t+j

= (It+j

� It

)/It

. The variables measuring liquidity areTurnover constructed as Number of Shares Traded/Shares Outstanding and FirmTurn./Ind. constructed as Number of Shares Traded/Shares Outstanding of each firmover the industry average. The control variable group includes Leverage defined asTotal Liabilities over Total Assets, Tobin’s Q defined as (Stock Market Capitaliza-tion + Total Liabilities)/Total Assets and Cash = (EBIT + Depreciation)/Total Assets.

Variable Mean Standard Dev. Min. Max. ObservationsInvestment

4t+4I1/K 0.06 0.32 -0.57 2.10 9222

4t+8I1/K 0.12 0.58 -0.86 3.94 8018

4t+12I1/K 0.15 0.72 -0.96 5.14 6823

4t+4I2/K 0.06 0.23 -0.50 1.37 9116

4t+8I2/K 0.12 0.40 -0.67 2.61 7932

4t+12I2/K 0.18 0.56 -0.75 3.58 6733

4t+4I3/K 0.15 1.89 -1.00 56.94 7300

4t+8I3/K 0.31 3.05 -1.00 81.19 6412

4t+12I3/K 0.37 2.91 -1.00 94.67 5572

Liquidity

Turnover 0.0017 0.0034 0 0.03 10394Firm Turn./Ind. 1.02 1.19 0 5.99 10394

Controls

Leverage 0.46 0.19 0.02 0.97 10394Tobin’s Q 1.25 0.74 0.36 6.86 10394Cash 1.10 3.72 -7.44 41.49 10394

26

Tab

le2:

Usingfirm

turnover

asliqu

iditymeasure

This

table

show

span

elestimationincludingfirm

fixede↵

ects

andcountry-quarterfixede↵

ects,withrobust

stan

darderrors

that

correct

forheteroscedasticity

andserial

correlationat

thefirm

level.

Thesample

consistsof

quarterlydatafrom

1990

-201

0forfirm

sin

Argentina,

Brazil,Chilean

dMexico.

Allvariab

leswerewinsorizedat

2%

ineach

tail.Thedep

endentvariab

lesaregrow

thin

totalassets,grow

thin

fixed

assets

(Property,Plant

and

Equ

ipment)

and

grow

thin

Inventories,

where4I t+j

=(I

t+j

�I t)/I t.

Con

trolsincludeTurnover

constructed

asNumber

ofShares

Traded

/Shares

Outstanding,

Leverag

edefi

ned

asTotal

Liabilitiesover

Total

Assets,

Tob

in’s

Qdefi

ned

as(Stock

MarketCap

italization+

Total

Liabilities)/T

otal

Assetsan

dCash=

(EBIT

+Dep

reciation)/Total

Assets.

TotalAssets

PPE

Inventories

Variables

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

Turnover

6.08

1**

12.831

***

19.249

**3.69

2*6.25

2*9.71

1*8.71

1***

12.470

*16

.818

*(2.392

)(4.508

)(7.545

)(2.209

)(3.663

)(5.820

)(3.223

)(6.537

)(9.989

)Leverag

e-0.297

***

-0.680

***

-0.757

***

-0.300

***

-0.522

***

-0.549

***

-0.350

***

-0.576

***

-0.694

**(0.061

)(0.127

)(0.201

)(0.051

)(0.093

)(0.147

)(0.101

)(0.192

)(0.338

)Tob

in’s

Q0.12

7***

0.20

9***

0.27

5***

0.10

9***

0.18

2***

0.25

5***

0.10

9***

0.19

0***

0.25

0**

(0.020

)(0.041

)(0.070

)(0.019

)(0.037

)(0.074

)(0.026

)(0.068

)(0.119

)Cash

0.04

0***

0.10

3***

0.18

3***

0.00

50.01

3**

0.03

5**

-0.016

-0.017

-0.013

(0.011

)(0.028

)(0.030

)(0.003

)(0.007

)(0.016

)(0.012

)(0.014

)(0.021

)

Observations

7,30

46,38

05,45

87,28

26,36

55,44

36,96

56,13

55,33

7R

20.20

30.23

10.25

50.22

60.23

90.22

20.09

70.10

80.11

3Number

ofFirms

422

385

326

423

386

326

395

370

336

***p<0.01

,**

p<0.05

,*p<0.1

27

Tab

le3:

Usingfirm

turnover

relative

toindustry

turnover

asliqu

iditymeasure

This

table

show

span

elestimationincludingfirm

fixede↵

ects

andcountry-quarterfixede↵

ects,withrobust

stan

darderrors

that

correctfor

heteroscedasticity

andserial

correlationat

thefirm

level.Thesampleconsistsof

quarterlydatafrom

1990

-201

0forfirm

sin

Argentina,

Brazil,

Chilean

dMexico.

Allvariab

leswerewinsorizedat

2%

ineach

tail.Thedep

endentvariab

lesaregrow

thin

totalassets,grow

thin

fixedassets

(Property,Plant

andEqu

ipment)

andgrow

thin

Inventories,where4I t+j

=(I

t+j

�I t)/I t.Con

trolsincludeTurnover

Firm/Ind.constructed

asNumber

ofShares

Traded

/Shares

Outstandingof

each

firm

over

theindustry

averag

e,leverage

isdefi

ned

asTotal

Liabilitiesover

Total

Assets,Tob

in’sQ

defi

ned

as(Stock

MarketCap

italization+

Total

Liabilities)/T

otal

Assetsan

dCash=

(EBIT

+Dep

reciation)/Total

Assets.

TotalAssets

PPE

Inventories

Variables

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

Firm

Turn./Ind.

0.01

1**

0.02

3***

0.03

9**

0.01

1**

0.01

5**

0.02

0*0.01

8**

0.02

2*0.02

4(0.005

)(0.009

)(0.016

)(0.004

)(0.008

)(0.012

)(0.007

)(0.013

)(0.021

)Leverag

e-0.298

***

-0.683

***

-0.765

***

-0.301

***

-0.524

***

-0.554

***

-0.354

***

-0.579

***

-0.698

**(0.061

)(0.128

)(0.202

)(0.051

)(0.093

)(0.146

)(0.101

)(0.191

)(0.335

)Tob

in’s

Q0.12

8***

0.20

9***

0.27

2***

0.11

0***

0.18

2***

0.25

4***

0.11

0***

0.18

9***

0.24

7**

(0.021

)(0.042

)(0.071

)(0.019

)(0.037

)(0.074

)(0.026

)(0.069

)(0.119

)Cash

0.04

0***

0.10

3***

0.18

2***

0.00

50.01

3*0.03

5**

-0.017

-0.017

-0.013

(0.011

)(0.028

)(0.030

)(0.003

)(0.007

)(0.016

)(0.013

)(0.014

)(0.021

)

Observations

7,30

46,38

05,45

87,28

26,36

55,44

36,96

56,13

55,33

7R

20.20

20.22

90.25

20.22

70.23

90.22

10.09

70.10

70.11

1Number

ofFirms

422

385

326

423

386

326

395

370

336

***p<0.01

,**

p<0.05

,*p<0.1

28

Tab

le4:

Interactionwithshareissuan

ce

Thistableshow

span

elestimationincludingfirm

fixede↵

ects

andcountry-quarterfixede↵

ects,withrobust

stan

darderrors

that

correctforheteroscedasticity

andserial

correlation

atthefirm

level.

Thesample

consistsof

quarterlydatafrom

1990

-201

0forfirm

sin

Argentina,

Brazil,Chilean

dMexico.

Allvariab

leswerewinsorizedat

2%

ineach

tail.The

dep

endentvariab

lesaregrow

thin

totalassets,grow

thin

fixedassets

(Property,Plant

andEqu

ipment)

andgrow

thin

Inventories,where4I t+j

=(I

t+j

�I t)/I t.Con

trolsinclude

Turnover

constructed

asNumber

ofShares

Traded

/Shares

Outstanding,

IssueX

Turnover

istheinteractionbetweenadummythat

identifies

ifthefirm

issued

andTurnover,

Firm

Turn./Ind.constructed

asNumber

ofShares

Traded

/Shares

Outstandingof

each

firm

over

theindustry

averag

e,IssueX

Firm

Turn./Ind.is

theinteractionbetweena

dummythat

identifies

ifthefirm

issued

andFirm

Turn./Ind.,Leverag

eis

defi

ned

asTotal

Liabilitiesover

Total

Assets,

Tob

in’s

Qdefi

ned

as(Stock

MarketCap

italization+

Total

Liabilities)/T

otal

Assetsan

dCash=

(EBIT

+Dep

reciation)/Total

Assets.

PPE

Inventories

PPE

Inventories

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

Turnover

3.49

95.74

08.70

57.60

2**

11.397

*15

.884

*(2.133

)(3.510

)(5.561

)(3.115

)(6.193

)(9.473

)IssueX

Turnover

1.51

94.34

49.49

9**

9.06

5**

9.47

88.60

5(2.627

)(3.700

)(4.669

)(4.022

)(6.441

)(9.129

)Firm

Turn./Ind.

0.01

0**

0.01

3*0.01

80.01

6**

0.01

90.02

2(0.004

)(0.007

)(0.012

)(0.007

)(0.013

)(0.020

)IssueX

Firm

Turn./Ind.

0.00

90.01

8**

0.02

2*0.02

1**

0.03

3**

0.01

7(0.006

)(0.008

)(0.012

)(0.010

)(0.015

)(0.023

)Leverag

e-0.300

***

-0.521

***

-0.549

***

-0.349

***

-0.576

***

-0.694

**-0.300

***

-0.522

***

-0.553

***

-0.352

***

-0.577

***

-0.698

**(0.050

)(0.093

)(0.147

)(0.101

)(0.191

)(0.338

)(0.050

)(0.093

)(0.146

)(0.101

)(0.191

)(0.335

)Tob

in’s

Q0.10

9***

0.18

0***

0.25

2***

0.10

6***

0.18

6***

0.24

6**

0.10

8***

0.17

9***

0.25

1***

0.10

6***

0.18

4***

0.24

5**

(0.018

)(0.036

)(0.073

)(0.026

)(0.068

)(0.118

)(0.018

)(0.036

)(0.073

)(0.026

)(0.068

)(0.118

)Cash

0.00

50.01

3**

0.03

6**

-0.016

-0.016

-0.012

0.00

50.01

3**

0.03

5**

-0.016

-0.016

-0.013

(0.003

)(0.007

)(0.016

)(0.012

)(0.014

)(0.021

)(0.003

)(0.007

)(0.016

)(0.013

)(0.015

)(0.021

)

Observations

7,28

26,36

55,44

36,96

56,13

55,33

77,28

26,36

55,44

36,96

56,13

55,33

7R

20.22

60.24

00.22

30.09

80.10

90.11

30.22

80.24

00.22

20.09

70.10

80.11

1Number

ofFirms

423

386

326

395

370

336

423

386

326

395

370

336

***p<0.01

,**

p<0.05

,*p<0.1

29

Tab

le5:

Interactionwithfinan

cial

constraints,usingturnover

This

table

show

span

elestimationincludingfirm

fixede↵

ects

andcountry-quarterfixede↵

ects,withrobust

stan

darderrors

that

correctfor

heteroscedasticity

andserial

correlationat

thefirm

level.Thesampleconsistsof

quarterlydatafrom

1990

-201

0forfirm

sin

Argentina,

Brazil,

Chilean

dMexico.

Allvariab

leswerewinsorizedat

2%

ineach

tail.Thedep

endentvariab

lesaregrow

thin

totalassets,grow

thin

fixed

assets

(Property,Plant

andEqu

ipment)

andgrow

thin

Inventories,

where4I t+j

=(I

t+j

�I t)/I t.Con

trolsincludeTurnover

constructed

asNumber

ofShares

Traded

/Shares

Outstanding,

Large

XTurnover

istheinteractionbetweenadummythat

identifies

whether

thefirm

islarger

byassets

andturnover,Leverag

edefi

ned

asTotal

Liabilitiesover

Total

Assets,

Tob

in’s

Qdefi

ned

as(Stock

MarketCap

italization+

Total

Liabilities)/T

otal

Assetsan

dCash=

(EBIT

+Dep

reciation)/Total

Assets.

TotalAssets

PPE

Inventories

Variables

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

Turnover

7.09

0***

12.995

***

20.351

***

5.66

5**

10.744

***

17.381

***

11.584

***

20.079

***

27.018

**(2.562

)(4.275

)(6.459

)(2.632

)(4.078

)(6.137

)(3.415

)(7.059

)(11.23

2)Large

XTurnover

-3.285

-0.538

-3.599

-6.417

**-14.74

5***

-24.93

5***

-9.193

**-24.68

2***

-32.82

3***

(3.685

)(8.556

)(11.32

2)(2.686

)(4.593

)(6.818

)(3.819

)(8.410

)(12.32

2)Leverag

e-0.294

***

-0.679

***

-0.755

***

-0.295

***

-0.510

***

-0.530

***

-0.341

***

-0.553

***

-0.665

*(0.061

)(0.128

)(0.202

)(0.050

)(0.092

)(0.144

)(0.102

)(0.193

)(0.340

)Tob

in’s

Q0.12

6***

0.20

9***

0.27

4***

0.10

8***

0.17

8***

0.24

6***

0.10

7***

0.18

2***

0.23

9**

(0.020

)(0.041

)(0.070

)(0.018

)(0.035

)(0.071

)(0.026

)(0.066

)(0.114

)Cash

0.04

0***

0.10

3***

0.18

3***

0.00

50.01

4**

0.03

5**

-0.016

-0.015

-0.012

(0.011

)(0.028

)(0.030

)(0.003

)(0.006

)(0.016

)(0.013

)(0.015

)(0.021

)

Observations

7,30

46,38

05,45

87,28

26,36

55,44

36,96

56,13

55,33

7R

20.20

40.23

10.25

50.22

80.24

40.23

10.09

80.11

10.11

6Number

ofFirms

422

385

326

423

386

326

395

370

336

***p<0.01

,**

p<0.05

,*p<0.1

30

Tab

le6:

Interactionwithfinan

cial

constraints,usingfirm

turnover

relative

toindustry

turnover

This

table

show

span

elestimationincludingfirm

fixede↵

ects

andcountry-quarterfixede↵

ects,withrobust

stan

darderrors

that

correct

forheteroscedasticity

andserial

correlationat

thefirm

level.Thesample

consistsof

quarterlydatafrom

1990

-201

0forfirm

sin

Argentina,

Brazil,Chilean

dMexico.

Allvariab

leswerewinsorizedat

2%

ineach

tail.Thedep

endentvariab

lesaregrow

thin

totalassets,grow

thin

fixedassets

(Property,Plant

andEqu

ipment)

andgrow

thin

Inventories,where4I t+j

=(I

t+j

�I t)/I t.Con

trolsincludeTurnover

Firm/Ind.

defi

ned

asNumber

ofShares

Traded

/Shares

Outstandingof

each

firm

over

theindustry

averag

e,Large

XFirm

Turn./Ind.istheinteraction

betweenadummythat

identifies

whether

thefirm

islarger

byassets

andFirm

Turn./Ind.,Leverag

edefi

ned

astotalassets

Total

liab

ilities,

Tob

in’s

Qdefi

ned

as(Stock

MarketCap

italization+

Total

Liabilities)/T

otal

Assetsan

dCash=

(EBIT

+Dep

reciation)/Total

Assets.

TotalAssets

PPE

Inventories

Variables

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

Firm

Turn./Ind.

0.01

1**

0.01

7*0.03

2*0.01

6***

0.02

4**

0.03

7**

0.02

3***

0.04

2**

0.04

4(0.006

)(0.010

)(0.017

)(0.006

)(0.010

)(0.017

)(0.009

)(0.019

)(0.030

)Large

XFirm

Turn./Ind.

-0.000

0.01

40.01

5-0.012

*-0.022

*-0.041

*-0.011

-0.046

*-0.048

(0.007

)(0.016

)(0.025

)(0.007

)(0.013

)(0.022

)(0.012

)(0.024

)(0.040

)Leverag

e-0.298

***

-0.685

***

-0.767

***

-0.299

***

-0.520

***

-0.548

***

-0.351

***

-0.567

***

-0.689

**(0.061

)(0.128

)(0.202

)(0.050

)(0.092

)(0.144

)(0.102

)(0.192

)(0.335

)Tob

in’s

Q0.12

8***

0.21

0***

0.27

4***

0.10

8***

0.17

9***

0.24

7***

0.10

8***

0.18

3***

0.24

1**

(0.020

)(0.042

)(0.071

)(0.018

)(0.036

)(0.071

)(0.026

)(0.067

)(0.115

)Cash

0.04

0***

0.10

3***

0.18

2***

0.00

50.01

3**

0.03

6**

-0.016

-0.016

-0.013

(0.011

)(0.028

)(0.030

)(0.003

)(0.007

)(0.016

)(0.013

)(0.015

)(0.022

)

Observations

7,30

46,38

05,45

87,28

26,36

55,44

36,96

56,13

55,33

7R

20.20

20.22

90.25

20.22

80.24

10.22

40.09

70.10

90.11

2Number

ofFirms

422

385

326

423

386

326

395

370

336

***p<0.01

,**

p<0.05

,*p<0.1

31

Tab

le7:

Interactionwithgrow

thop

portunities,

usingfirm

turnover

This

table

show

span

elestimationincludingfirm

fixede↵

ects

andcountry-quarterfixede↵

ects,withrobust

stan

darderrors

that

correct

forheteroscedasticity

andserial

correlationat

thefirm

level.

Thesample

consistsof

quarterlydatafrom

1990

-201

0forfirm

sin

Argentina,

Brazil,

Chilean

dMexico.

All

variab

leswerewinsorized

at2

%in

each

tail.

Thedep

endentvariab

lesaregrow

thin

totalassets,

grow

thin

fixed

assets

(Property,Plant

and

Equ

ipment)

and

grow

thin

Inventories,

where4I t+j

=(I

t+j

�I t)/I t.

Con

trolsinclude

Turnover

constructed

asNumber

ofShares

Traded

/Shares

Outstanding,

High

B/M

XTurnover

istheinteraction

between

adummy

that

identifies

whether

thefirm

has

ahigh

boo

k-to-M

arketof

assets

and

Turnover,Leverag

eis

defi

ned

asTotal

Liabilitiesover

Total

Assets,Tob

in’sQ

defi

ned

as(Stock

MarketCap

italization+

Total

Liabilities)/T

otal

Assetsan

dCash=

(EBIT

+Dep

reciation)/Total

Assets.

TotalAssets

PPE

Inventories

Variables

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

Turnover

12.237

***

19.651

***

27.787

***

6.80

9**

9.94

5**

13.449

10.445

**16

.926

*19

.532

(3.422

)(5.768

)(10.62

7)(3.048

)(4.949

)(8.318

)(4.290

)(8.778

)(14.22

5)HighB/M

XTurnover

-12.36

2***

-13.78

1***

-17.10

5*-6.267

**-7.469

*-7.500

-3.492

-8.990

-5.480

(3.555

)(5.013

)(8.828

)(2.603

)(4.179

)(7.027

)(3.805

)(7.497

)(11.56

4)Leverag

e-0.297

***

-0.679

***

-0.751

***

-0.300

***

-0.521

***

-0.546

***

-0.350

***

-0.574

***

-0.691

**(0.060

)(0.125

)(0.199

)(0.051

)(0.093

)(0.148

)(0.101

)(0.191

)(0.339

)Tob

in’s

Q0.11

1***

0.19

1***

0.25

2***

0.10

1***

0.17

2***

0.24

5***

0.10

5***

0.17

7***

0.24

2**

(0.018

)(0.040

)(0.070

)(0.017

)(0.035

)(0.071

)(0.025

)(0.066

)(0.118

)Cash

0.03

9***

0.10

2***

0.18

2***

0.00

40.01

2*0.03

5**

-0.017

-0.017

-0.014

(0.011

)(0.028

)(0.030

)(0.003

)(0.007

)(0.016

)(0.013

)(0.014

)(0.021

)

Observations

7,30

46,38

05,45

87,28

26,36

55,44

36,96

56,13

55,33

7R

20.20

80.23

40.25

80.22

90.24

10.22

30.09

70.10

90.11

3Number

ofFirms

422

385

326

423

386

326

395

370

336

***p<0.01

,**

p<0.05

,*p<0.1

32

Tab

le8:

Interactionwithgrow

thop

portunities,

usingfirm

turnover

relative

toindustry

turnover

This

table

show

span

elestimationincludingfirm

fixede↵

ects

andcountry-quarterfixede↵

ects,withrobust

stan

darderrors

that

correct

forheteroscedasticity

andserial

correlationat

thefirm

level.

Thesample

consistsof

quarterlydatafrom

1990

-201

0forfirm

sin

Argentina,

Brazil,Chilean

dMexico.

Allvariab

leswerewinsorizedat

2%

ineach

tail.Thedep

endentvariab

lesaregrow

thin

totalassets,grow

thin

fixed

assets

(Property,Plant

and

Equ

ipment)

and

grow

thin

Inventorys,where4I t+j

=(I

t+j

�I t)/I t.

Con

trolsincludeTurnover

Firm/Industry

Number

builtas

Shares

Traded

/Shares

Outstandingof

each

sign

ature

ontheindustry

averag

e,HighB/M

XFirm

Turn./Ind.

which

istheinteraction

between

adummythat

identifies

whether

thefirm

has

ahigh

boo

k-to-M

arketof

assets

and

Firm

Turn./Ind.,

Leverag

eis

defi

ned

asTotal

Liabilitiesover

Total

Assets,

Tob

in’s

Qdefi

ned

as(Stock

MarketCap

italization+

Total

Liabilities)/A

ssets

totalan

dCash=

(EBIT

+Dep

reciation)/Total

Assets

TotalAssets

PPE

Inventories

Variables

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

4t+4I/K

4t+8I/K

4t+12I/K

Firm

Turn./Ind.

0.02

7***

0.04

3***

0.05

6**

0.02

2***

0.03

1***

0.03

7**

0.02

7***

0.03

9**

0.03

7(0.007

)(0.013

)(0.022

)(0.006

)(0.010

)(0.016

)(0.010

)(0.018

)(0.028

)HighB/M

XFirm

Turn./Ind.

-0.031

***

-0.040

***

-0.035

*-0.023

***

-0.031

***

-0.034

**-0.017

*-0.033

*-0.027

(0.008

)(0.014

)(0.020

)(0.006

)(0.010

)(0.014

)(0.010

)(0.020

)(0.027

)Leverag

e-0.304

***

-0.691

***

-0.771

***

-0.306

***

-0.530

***

-0.560

***

-0.355

***

-0.582

***

-0.699

**(0.060

)(0.126

)(0.201

)(0.050

)(0.092

)(0.145

)(0.101

)(0.189

)(0.334

)Tob

in’s

Q0.10

8***

0.18

4***

0.25

0***

0.09

5***

0.16

3***

0.23

2***

0.09

8***

0.16

7**

0.23

0*(0.020

)(0.041

)(0.070

)(0.017

)(0.035

)(0.073

)(0.025

)(0.067

)(0.120

)Cash

0.03

9***

0.10

2***

0.18

1***

0.00

40.01

2*0.03

4**

-0.017

-0.018

-0.015

(0.011

)(0.028

)(0.030

)(0.003

)(0.006

)(0.016

)(0.013

)(0.015

)(0.022

)

Observations

7,30

46,38

05,45

87,28

26,36

55,44

36,96

56,13

55,33

7R

20.20

70.23

20.25

30.23

30.24

40.22

40.09

70.10

80.11

1Number

ofFirms

422

385

326

423

386

326

395

370

336

***p<0.01

,**

p<0.05

,*p<0.1

33