Download Do Firms Choose Their Stock Liquidity? A Study of Innovative Firms

Do Firms Choose Their Stock Liquidity?
A Study of Innovative Firms and Their Stock Liquidity∗
Nishant Dass, Vikram Nanda, Chong (Steven) Xiao†
November 15, 2011
Abstract
In this paper, we ask whether firms can choose, or at least influence, their stock liquidity. We
study this by analyzing a sample of firms that, we hypothesize, will value stock liquidity more
than other firms – innovative firms that primarily hold intangible assets and must access capital
from the stock market. Given their reliance on equity markets, we find that innovative firms have
higher liquidity and that they take a variety of actions (e.g., split their stock or issue earnings
guidance, etc.) that help keep their stock more liquid. The need for liquidity is mitigated when
these firms have access to other sources of capital. Given that these firms rely more on equity
and less on debt, the role of monitoring the managers also rests on equity-holders instead of
banks or other creditors. Consistent with this prediction, we find that these firms have greater
institutional ownership, a higher likelihood of blockholders, and a more incentivized CEO compensation contract. The marginal impact on firm value (Tobin’s Q) of an exogenous increase in
liquidity (e.g., following decimalization of stock prices) is greater for innovative firms, especially
when CEOs have strong incentive contracts.
Keywords: Stock Liquidity, Innovative Firms
JEL Codes: G14, G30
∗
†
We appreciate the comments from seminar participants at the Georgia Institute of Technology.
College of Management, Georgia Institute of Technology, 800 West Peachtree St. NW, Atlanta, GA 30308.
Electronic copy available at: http://ssrn.com/abstract=1989589
Do Firms Choose Their Stock Liquidity?
A Study of Innovative Firms and Their Stock Liquidity
Abstract
In this paper, we ask whether firms can choose, or at least influence, their stock liquidity. We
study this by analyzing a sample of firms that, we hypothesize, will value stock liquidity more than
other firms – innovative firms that primarily hold intangible assets and must access capital from
the stock market. Given their reliance on equity markets, we find that innovative firms have higher
liquidity and that they take a variety of actions (e.g., split their stock or issue earnings guidance,
etc.) that help keep their stock more liquid. The need for liquidity is mitigated when these firms
have access to other sources of capital. Given that these firms rely more on equity and less on
debt, the role of monitoring the managers also rests on equity-holders instead of banks or other
creditors. Consistent with this prediction, we find that these firms have greater institutional ownership, a higher likelihood of blockholders, and a more incentivized CEO compensation contract.
The marginal impact on firm value (Tobin’s Q) of an exogenous increase in liquidity (e.g., following
decimalization of stock prices) is greater for innovative firms, especially when CEOs have strong
incentive contracts.
Keywords: Stock Liquidity, Innovative Firms
JEL Classification: G14, G30
Electronic copy available at: http://ssrn.com/abstract=1989589
1
Introduction
There is a vast literature in market microstructure that is devoted to the study of stock liquidity or
the lack thereof: illiquidity (see Easley and O’Hara (2003) for a survey). Broadly, stock illiquidity
is believed to reflect two types of costs – those due to adverse selection arising from the information
asymmetry between market participants and a non-information component that is attributed to
inventory/transactions costs. While the influence of liquidity on asset prices is far from resolved
(O’Hara, 2003), the liquidity of an asset is generally believed to be a desirable feature. Amihud
and Mendelson (1991), for instance, argue that “companies ... can benefit by undertaking steps to
increase the liquidity of their claims”. This notion is that companies can, at least partially, influence
the liquidity of their stock. Firms can, for instance, take actions that will lower the information
asymmetry in the market, as well as adopt policies e.g., stock-splits and stock offerings, that could
enhance trading volume and, thereby, price discovery. In this paper we adopt this perspective and
investigate whether and how firms attempt to enhance stock liquidity and the implications for firm
value. This is done in the context of firms that, we hypothesize, are more reliant on the stock
market for external financing and, hence, should value stock liquidity more than other firms.
We draw upon the existing literature on capital structure choice to identify one set of firms that
are shown to have lower leverage – specifically, the firms that produce unique or specialized products.
Titman and Wessels (1988) have argued that firms whose products are unique – proxied by firms
that are more innovative and have brand value – will have greater ripple effects of bankruptcy on
their customers, suppliers, and workers. As a result, these firms will have lower leverage ratios in
equilibrium. Further, assets that are essential in generating unique products typically are intangible
and/or have lower collateral value, and will thus result in lower firm leverage.1
We argue that these firms would then have to rely on equity markets for their capital needs,
and are therefore likely to take steps that maintain/enhance their stock liquidity. Innovative firms
are likely to produce more unique products and will therefore rely more on equity markets for their
capital needs. A second reason for innovative firms to rely on equity markets is to seek longer-term
financing and financing in which the managers would have more discretion. As a corollary, if these
firms do raise debt, it is more likely to be highly-rated public debt; and, if they use bank financing,
then it is likely to come with relatively fewer covenants. We classify firms as innovative either by
1
In our sample, firms that invest in R&D have a mean (median) leverage ratio of 16.8% (10.5%); this is significantly
smaller in comparison with the corresponding figures for non-R&D firms that have a 27.8% mean and 25.5% median
leverage ratio. These and other univariate tests are reported in Panels B-F of Table 1.
1
Electronic copy available at: http://ssrn.com/abstract=1989589
their investments in R&D or by the number of their patents/citations.2 Overall, we argue that these
innovative firms would take various steps to keep/make the firm more transparent and, thereby,
their stock more liquid.
We test these arguments in a sample of firms from the merged CRSP and Compustat data over
1990-2009. Using a variety of liquidity measures, we first investigate whether these types of firms
indeed have greater liquidity. We find strong empirical support for this prediction. Specifically, we
find that innovative firms tend to have lower stock illiquidity (measured a là Amihud, 2002), higher
stock turnover, lower bid-ask spread, and a lower probability of informed trading (as measured by
the PIN proposed in Easley et al., 2002). We also confirm our results by combining the various
attributes of innovation into an index using principal components (henceforth, the “innovation
index”). The results are not only statistically significant, but they are also economically meaningful
– e.g., a 10% increase in R&D is related with 7.4% lower illiquidity, 9.4% higher turnover, 10%
lower bid-ask spread, and 4.7% lower PIN. This is an important finding because we might expect
innovative firms, whose investments are likely to be informationally more opaque for the market, to
have a lower stock liquidity (Gopalan et al., 2011). However, what we find is that these firms have
higher stock liquidity. This finding suggests that the firms that are most at risk of being adversely
affected by illiquidity might be choosing policies intended to overcome these problems.
We argue that when the firm is less financially constrained and has access to other sources of
capital, it is less reliant on equity markets and, therefore, it may not need to manage its stock
liquidity as aggressively. Consistent with this, we find that the relationship between measures of
innovation and the stock liquidity is weaker when the firm is less financially constrained. Specifically,
we find that the negative relation between the innovation index and the above four measures of
illiquidity is significantly weaker when the firm has: either outstanding public debt, higher credit
ratings, the ability to extract more trade credit, or pays out dividends. Overall, this supports the
underlying premise that firms manage their stock liquidity when they are overly reliant on equity
markets for their capital needs.
In order to improve their stock liquidity, innovative firms can take steps to lower the information
asymmetry between insiders and the rest of the market. We take our cue from the existing finance
and accounting literatures that have shown the effects of firms’ actions on information asymmetry
around their stock. We show that innovative firms are much more likely to take deliberate actions
that are known to lower information asymmetry and correspondingly enhance their stock liquidity.
2
As a robustness check, we also examine firms on the basis of their advertising expenditures instead of innovation
activity to identify firms that produce unique products.
2
Here again, we characterize firms as being innovative by their investments in R&D, their number
of patents and citations of these patents, as well as an index combining principal components of
these three measures.
For instance, Coller and Yohn (1997) have shown that management is likely to provide earnings
guidance when there is greater information asymmetry about the firm, and that this information
asymmetry is reduced after the management’s guidance. We find that innovative firms are much
more likely to provide management guidance – e.g., a 1% increase in the number of patents is related
with a 2.5% increase in the frequency of earnings guidance from the firm’s management. Literature
on stock splits (e.g., Muscarella and Vetsuypens, 1996; Lin, Singh and Yu, 2009) has found support
for the hypothesis that these events lead to an increase in stock liquidity. Correspondingly, we find
that, conditional on stock prices, innovative firms are more likely to split their stock.
A variety of other various policies can also help innovative firms maintain their stock liquidity.
Specifically, these firms are more likely to make seasoned equity offerings and they are also more
likely to rely on the services of “more reputed” underwriters (defined later) for security issuance.
SEOs can help increase the investor base and, therefore, improve the stock liquidity (Merton, 1987;
Eckbo et al., 2000; and Butler et al., 2005). And, more reputed underwriters can play a key role in
increasing liquidity by helping access a wider investor base, providing price support, or playing the
role of a market maker, etc. (Amihud and Mendelson, 1988; Ellis, Michaely and O’Hara, 2000).
Finally, we find that actions taken by innovative firms may also make it more likely that stock
options on their stock are listed on exchanges (Mayhew and Mihov, 2004); this may be because
they generate enough trading interest in the stock.
We explicitly test whether these actions improve the firm’s liquidity. Given that firms take
these actions endogenously, we establish the causal effect of these actions in improving liquidity by
using an instrumental variable regression. We instrument the firms’ actions, such as managerial
guidance and the decisions to split the stock or make seasoned equity offerings with their respective
industry median or mean (we use means when the variable of interest is a dummy variable and the
median is zero). Using this methodology, we find evidence that these actions do reduce the stock’s
illiquidity.
Although innovative firms seem to rely on equity markets, there are certain characteristics of
the type of debt that these firms might prefer. We find that innovative firms are more likely
to issue public debt, have higher credit ratings, less likely to have covenants (and similarly, also
have fewer covenants in their loans). These results suggest a few things about the behavior of
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innovative firms: first, they go to capital markets, which can help lower the information asymmetry
in the market (Easterbrook, 1984); second, they maintain higher credit ratings, which eases raising
capital, especially because their assets are typically intangibile and cannot be collateralized easily
(Odders-White and Ready, 2006); and finally, given the long-term nature of their investments, they
prefer to raise capital such that there are fewer “interruptions” and more discretion.
But given the fewer covenants in their bank loans and the generic nature of covenants in public
debt (Chava, Kumar, and Warga, 2010), the role of monitoring must be taken by the equity markets.
To that effect, we find that innovative firms are more likely to have a larger institutional ownership
of their equity and also have more blockholders. Edmans and Manso (2011) have shown that these
equity holders are better at monitoring. Thus, our results suggest that the burden of monitoring
innovative firms lies on equity holders. Further evidence of this is found in the nature of executive
compensation contracts – we find that the equity-based compensation of CEOs in innovative firms
is larger. This result is also consistent with Holmstrom and Tirole (1993), who show that the
optimal contract should be more reliant on equity when the equity is more liquid.
Fang, Noe, and Tice (2009) show that stock liquidity is positively related with firm value. We
show that this is particularly true for innovative firms as they value liquidity much more than other
firms. We show this by testing the negative impact of an exogenous increase in stock illiquidity on
the firm’s Tobin’s Q. We find that this negative effect is significantly greater for more innovative
firms. To establish the causal effect of the change in illiquidity on the change in Tobin’s Q, we either
instrument the change in illiquidity with its industry median or analyze the change in illiquidity
due to an exogenous event. We consider three such events – the decimalization of stock prices in
April 2001, addition of the firm to the S&P 500 Index, and the introduction of options on the firm’s
stock. We show that the impact of this exogenous change in liquidity on firm value is significantly
greater for innovative firms.
Finally, we show that the negative impact of an increase in stock illiquidity is mainly concentrated in the sample of innovative firms especially when the manager’s compensation is more
equity-based. This is because when compensation contracts are loaded with incentives and the
stock is more liquid, then the manager’s actions can be monitored more easily. This is especially
true in innovative firms, where managers’ actions are hard to monitor. Overall, our results show
how the business and technological needs of firms can affect their financing decisions as well as the
various actions that can support such financing arrangements. This includes managers’ efforts to
enhance liquidity, while ensuring that incentive contracts and institutional holdings are all mutually
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reinforcing. In order to test for the robustness, we confirm that the main results continue to hold
when we use advertising expenses instead of innovation proxies to identify firms producing unique
products.
Our paper makes several contributions to the corporate finance literature. First, we provide
evidence on the firms’ ability to influence and improve their stock liquidity. Although it has been
argued in the literature that firms can and should improve their stock liquidity, the evidence has
been lacking so far. As a result, stock liquidity is seen to be determined exogenously. Our results
show that firms do care about the level of their liquidity and clearly take deliberate steps to improve
it, especially when maintaining a higher stock liquidity is crucial for them.
Second, our paper identifies many actions taken by firms that help with maintaining or improving stock liquidity. As such, our paper is related to many existing papers in the literature.
For example, our paper is related to the literature on the relation between information disclosure
and the stock liquidity as well as cost of capital (Diamond and Verrecchia, 1991). We show that
managers of innovative firms are more likely to provide earnings guidance, and thereby, reduce their
stock illiquidity. The literature on the liquidity effects of stock splits has been inconclusive as there
is evidence that stock splits lead to an increase in liquidity (Dennis and Strickland, 2003) which is
temporary (Lakonishok and Lev, 1987) or even decrease liquidity (Copeland, 1979). Our evidence
suggests that stock splits, when instrumented by the propensity of stock-splits in the industry, result
in a lower illiquidity for innovative firms. Kothare (1997) and Eckbo et al. (2000), among others,
have shown that SEOs improve stock liquidity, as reflected in narrower bid-ask spreads subsequent
to the public offering. We add to this literature and show that SEOs lower stock illiquidity, and in
addition, we show that innovative firms are more likely to do SEOs.
Third, our paper confirms the predictions of Holmström and Tirole (1993), and shows that
equity-based compensation contracts are most useful when the stock is more liquid. When stock
is more liquid, the efforts and actions of the manager are better reflected in stock prices, which
can improve monitoring. Further, incentive contracts become more powerful when the stock prices
reflect firm value and managerial actions more precisely.
The rest of the paper is structured as follows. We develop our empirical predictions in the next
section and describe the data in §3. §4 presents evidence on innovative firms having greater stock
liquidity and §5 shows the specific actions that these firms take in order to maintain or improve
their stock liquidity. In §6, we show the characteristics of debt issued by firms that have more
liquid stock and also show that the role of monitoring shifts to equity-holders. §7 shows that the
5
marginal value impact of an increase in liquidity is higher for innovative firms and §8 presents some
additional results. Concluding remarks are made in §9.
2
Hypotheses
We argue that firms take actions that can help them manipulate, if not choose, the level of their
stock liquidity. To test this hypothesis, we focus on a set of firms that most value stock liquidity.
Specifically, we argue that innovative firms produce unique products and have assets with lower
collateral values, which lowers their ability to raise debt. As a result, innovative firms must primarily
rely on equity markets for their capital needs. This implies that innovative firms would value stock
liquidity more than other firms that can access alternative sources of capital, such as debt, more
easily. This leads us to posit our first testable hypothesis:
H1: Innovative firms have greater stock liquidity but less so when they have access to alternative
sources of capital.
We build on the notion that firms can influence the level of their stock liquidity. Given the
reliance of innovative firms on the equity market for capital, we present our second hypothesis:
H2: Innovative firms will take deliberate actions that are known to improve stock liquidity.
Due to the strong preference of innovative firms for liquidity, we expect that a marginal improvement in liquidity would be more valuable for these firms. Therefore, our third testable hypothesis
is:
H3: The impact of a marginal increase in liquidity on value (Tobin’s Q) would be greater for
innovative firms.
We take these hypotheses and other related predictions to data and test them in a large sample
of public firms. We describe our data sample next.
3
Data and Description of Variables
We draw our data from a variety of sources. We start with the accounting information of all
available firms in Compustat from 1990 to 2009. After matching these with stock price information
from CRSP, we are left with 12,863 firms and 94,142 firm-year observations. The main dependent
variable that we analyze is the firm’s stock liquidity and the independent variable of interest is the
firm’s innovation intensity. We describe these and other variables in detail below.
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3.1
Measures of Stock Liquidity
Although our intention is to measure the stock’s liquidity, the commonly used measures in the
literature in fact measure illiquidity. We follow the convention and adopt four different measures
of illiquidity in our analysis. The first measure is Amihud’s (2002) Illiquidity ratio. It is defined
as ln(AvgILLIQ × 108 ), where AvgILLIQ is an yearly average of illiquidity, which is measured as
the absolute return divided by dollar trading volume:
AvgILLIQi,t =
Daysi,t
X
|Ri,t,d |
1
,
Daysi,t
DolV oli,t,d
d=1
where Daysi,t is the number of valid observation days for stock i in fiscal year t, and Ri,t,d and
DolV oli,t,d are the daily return and daily dollar trading volume, respectively, of stock i on day d of
fiscal year t. This measure reflects the average stock price sensitivity to one dollar trading volume.
Higher AvgILLIQ is interpreted as lower stock liquidity.
The second measure is the yearly average of monthly trading turnover, which is calculated as:
T urnoveri,t =
12
1 X V oli,t,m
,
12
Shrouti,t,m
m=1
where V oli,t,m and Shrouti,t,m are the shares traded and number of shares outstanding of firm i in
month m of fiscal year t. In our analysis, we use Negative Turnover, which is simply the negative
of Turnover calculated above, and thus, measures the stock’s illiquidity instead of liquidity.
The third measure is the yearly average of daily bid-ask spread:
Bid − Ask Spreadi,t
Daysi,t
X
Aski,t,d − Bidi,t,d
1
=
Daysi,t
(Aski,t,d + Bidi,t,d )/2
d=1
where Daysi,t is the number of valid observation days for stock i in fiscal year t, and Aski,t,d and
Bidi,t,d are the closing ask and bid prices of the stock i on the day d of fiscal year t. Higher Bid-Ask
Spread is interpreted as lower stock liquidity.
The fourth measure is the Probability of Informed Trading (PIN ), which is proposed by Easley,
Kiefer, O’Hara, and Paperman (1996) as a proxy for informed trading. We directly obtain the PIN
measure for all NYSE and Amex common stocks over 1983-2001 from Søren Hvidkjær’s website.3
3.2
Identifying Innovative Firms
As described above, we focus on innovative firms in order to test our hypotheses regarding the firms’
influence on their stock liquidity. We use three main proxies for identifying firms as innovative and
3
http://sites.google.com/site/hvidkjaer/data.
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then further confirm the results with an additional (fourth) measure. The first firm characteristic
that we use to identify innovative firms is the expenditure on R&D. We define R&D as the ratio
of R&D expenses to lagged asset. Two other related measures of innovation are the number of
patents granted to the firm and the citations generated by these patents. Specifically, we define Log
Patents as the logarithm of one plus the number of patents divided by hundred and Log Citations
as the logarithm of one plus the number of citations divided by hundred. (We divide patents
and citations by hundred to obtain coefficients of reasonable magnitude.) We also construct an
“innovation index” using the principal components of these three variables; it is calculated as:
Innovation Indexi,t =
0.3366 × R&Di,t + 0.6660 × Log P atentsi,t + 0.6657 × Log Citationsi,t
100
Before constructing this Index, we winsorize the three individual components at the 1st and 99th
percentiles and standardized so that each component has zero mean and standard deviation as 1.
In addition to these measures of innovation, we also confirm our main results using Advertising as
an alternative characteristic to identify firms producing unique goods. It is defined as the ratio of
advertising expenses to lagged assets.
3.3
Other Dependent Variables
While we start with analyzing the stock liquidity innovative firms, we next characterize many
other features of these firms that help understand this relationship. For instance, we test whether
innovative firms take specific actions or have characteristics that help them improve/maintain their
stock liquidity. The dependent variables used in this analysis are described next. Guidance is the
logarithm of one plus the frequency of earnings guidance forecasts provided by the management
in the given fiscal year. Stock Splits is a binary variable that equals one if there is a stock split
in the given fiscal year; it equals zero otherwise. Listed Options is a binary variable that equals
one if the firm has options traded on its stock in the given fiscal year; it is zero otherwise. SEO
Dummy is a binary variable that equals one if the firm makes a seasoned equity offering (SEO) in
the given fiscal year, and is otherwise. Reputed Underwriter is a binary variable that equals one if
the firm hires a “reputable” underwriter for the SEO. We classify an underwriter as “reputable” if
its ranking is 8 or higher on the 0-to-9 scale in Jay Ritter’s IPO Underwriter Reputation Rankings
(1980 - 2009).4
Due to better informational transparency among market participants that comes with stock
liquidity, firms with greater stock liquidity will also have some characteristic features in their debt.
4
We obtain these from Jay Ritter’s website, http://bear.warrington.ufl.edu/ritter/ipodata.htm.
8
We test this using the following dependent variables. Public Debt Dummy is a binary variable that
is equal to one if the firm has a long-term S&P credit rating, and zero otherwise. Credit Rating
is an ordinal variable categorizing the firm’s long-term credit rating by S&P; firms without any
rating are grouped into the base category (denoted by 0) and the remaining firms are grouped
into six categories (ranging from 1 for CCC or below through 6 for AA and above). In analyzing
the bank loans taken out by firms, we define the variable Covenant Dummy that equals one if
the firm has at least one covenant in the loan borrowed in the given fiscal year, and it is zero
otherwise. Number of Covenants is the number of covenants in the bond issued in the fiscal year;
Equity-Based Compensation is the sum of options granted and restricted stock grant divided by
total compensation of CEO; Institutional Ownership is the number of shares held by institutional
investors divided by total number of shares outstanding; Blockholder Dummy is a binary variable
that is equal to 1 if there is at least one blockholder that holds 5% or more of the firm’s shares,
and 0 otherwise.
3.4
Firm Characteristics
We control for a number of firm characteristics that are known to be related to the stock liquidity.
Larger and older firms are likely to have greater liquidity; we control for size with Log Assets, which
is the natural logarithm of total assets, and for the Firm’s Age, which is the number of years since
the firm first appeared in CRSP Daily database. Firms that rely more heavily on debt and less on
equity will have lower liquidity; we control for the firm’s Leverage, which is defined as the sum of
long term debt and debt in current liabilities divided by total assets. Firms with more transparent
assets on the balance sheet will have more liquid stock; we proxy for this with Cash and Tangibility,
where the former is the ratio of cash and short term investments to lagged assets while the latter is
the ratio of net property, plant, and equipment to total assets. Firms on the NYSE stock exchange
tend to have greater stock liquidity; to that end, we include the NYSE Dummy, which is a binary
variable that equals 1 if the firm is listed on the NYSE, and 0 otherwise. We also control for the
firm’s growth opportunities with Tobin’s Q and operating peformance with ROA. The former is
the sum of total assets and the difference between market value and book value of common equity,
divided by total assets and the latter is the ratio of earnings before extraordinary items to lagged
assets. Finally, we control for Return Volatility, which is the standard deviation of daily stock
returns over the fiscal year.
We also employ some additional firm-specific control variables in tests for other dependent
variables; these are defined as follows. Stock Price is used as a control in the tests for stock-splits;
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it is defined as the firm’s closing stock price at fiscal year end. We analyze the innovative firms’
access to other sources of capital and argue that the need for greater stock liquidity would be lower
when the firm has access to other sources of capital. To that end, we use the following independent
variables. High Ratings Dummy is an indicator for the firm’s S&P credit ratings being higher
than or equal to A–. Access to trade credit is partly determined by market power, defined as the
price-to-cost margin of the firm. We use the Market Power Dummy, which is a binary variable
that equals 1 if the firm’s market power is higher than the sample median, and 0 otherwise. The
firm’s ability to pay dividends is a sign of less severe financial constraints; we control for this with
Dividend Dummy, which is a binary variable that equals 1 if the firm pays dividends to common
or prefered stockholders in the fiscal year, and is 0 otherwise.
Panel A of Table 1 presents the summary statistics for all the above variables; these are based on
the regression sample and, therefore, require that all the variables be non-missing simultaneously.
We winsorize all variables at the 1st and 99th percentiles.
4
4.1
Innovative Firms and Their Stock Liquidity
Evidence on the Stock Liquidity of Innovative Firms
We start by first documenting the results obtained from testing the main premise of this paper –
that, innovative firms will have greater stock liquidity because it is difficult for them to raise capital
in debt markets. Given the commonly used proxies for stock liquidity, we use measures of illiquidity
as dependent variables, and expect innovative firms to have lower illiquidity. The random-effects
model that we test can be represented as follows:
Stock Illiquidityit = α1 + β1 Innovativenessit + γ1 0F IRM + λi + φj + ψt + it .
(1)
Stock Illiquidity and Innovativeness are proxied by the variables described above in §3, and FIRM
refers to the firm-specific control variables. λi corresponds to firm i’s random-effects while φj and
ψt represent dummies for industry j and year t, respectively. Results obtained from estimating
equation (1) using the four different measures of Stock Illiquidity are presented in Table 2. Specifically, we use Amihud’s (2002) Illiquidity ratio, Negative Turnover, Bid-Ask Spread, and PIN as
the dependent variable in Panels A-D, respectively. In all four Panels of Table 2, we measure the
firm’s innovativeness with R&D, Log Patents, Log Citations, and the Innovation Index in columns
(1)–(4), respectively. The results are consistent with our predictions and show that innovative
firms have significantly lower stock illiquidity. Except when using PIN in Panel D, the estimated
10
coefficients on innovativeness are statistically significant and economically large. For instance, all
the coefficients on innovativeness across columns (1)–(4) in Panel A are significant at the 1% level.
These coefficients suggest that a 10 percentage points increase in R&D is related with a 7.4% lower
Illiquidity. We find similar results using the other dependent variables; for instance, a 10 percentage
points increase in R&D is related with a 9.4% (10% and 4.7%) standard deviations lower Negative
Turnover (Bid-Ask Spread and PIN, respectively). Therefore, overall, we find evidence of higher
stock liquidity of innovative firms. For brevity, we do not report the coefficients on the control
variables in Panels B-D.
4.2
When Innovative Firms Have Access to Other Sources of Capital
We argue that if the innovative firms are less reliant on stock markets for their capital needs, then
the need for greater stock liquidity would be mitigated. Similarly, if the firm is not financially
constrained, then the need to raise capital and consequently, the need for greater stock liquidity
would be diminished. We test these arguments using the following random-effects regression model:
Stock Illiquidityit = α2 + β2 (Innovativenessit ) × (Access to Other Capital)
+ β3 Innovativenessit + β4 (Access to Other Capital)
+ γ2 0F IRM + λi + φj + ψt + it .
(2)
We use the same four measures of stock illiquidity as above – Illiquidity, Negative Turnover, Bid-Ask
Spread, and PIN in columns (1)–(4), respectively, of each Panel in Table 3. For brevity, we only use
the Innovation Index as our measure of innovativeness although our results are robust to using the
individual components of this index. As per our prediction, although there is a negative relation
between innovativeness and illiquidity, this effect should be weaker when the firm has access to
other capital (i.e., while β3 is negative, β2 should be positive). In Panels A and B of Table 3, our
proxy for Access to Other Capital reflects the firm’s access to public debt markets. Specifically, we
use Public Debt Dummy and High Ratings Dummy in Panels A and B, respectively. Dass, Kale,
and Nanda (2011) have shown that firms with greater market power are able to extract more trade
credit from their partner firms along the supply chain. In that vein, we use the Market Power
Dummy as the proxy for Access to Other Capital in Panel C of Table 3. Finally, in Panel D, we
simply use the Dividend Dummy, which reflects whether the firm is financially constrained or not.
We interact it with the measure of innovativeness and, again, expect it to diminish the effect of
innovativeness. All these variables have been defined in §3 above. As before, FIRM, λi , φj , ψt ,
11
and it represent firm-specific control variables, firm i’s random-effects, dummy for industry j, and
dummy for year t, respectively.
The results in Table 3 confirm our predictions and show that the illiquidity of innovative firms is
lower, but less so when they have access to other sources of capital or when they are less financially
constrained. For instance, in Panel A, the estimates of β2 are positive and significant at least at
the 5% level, and β3 is significantly negative. In terms of the economic magnitude, we find that a
standard deviation increase in the Innovation Index is related with a 4% lower Illiquidity for firms
without access to public debt but only 2.9% lower Illiquidity for firms with access to public debt.
When measuring illiquidity with Negative Turnover and PIN, we find a positive association between
innovation and illiquidity for firms with access to public debt. Specifically, one standard deviation
increase in the Innovation Index is related with 4.6% higher Negative Turnover and 0.3% higher
PIN, respectively. We find similar results across Panels B–D. Specifically, firms that have a higher
credit rating, greater market power, and distribute dividends tend to have a weaker or positive
relationship between their innovativeness and stock illiquidity. Overall, the evidence presented in
Tables 2 and 3 support the hypothesis H1.
5
5.1
How Do Firms Influence Their Stock Liquidity?
Innovative Firms Take Deliberate Steps to Improve Their Stock Liquidity
So far, we have established a negative correlation between the innovativeness of firms and their stock
illiquidity. In this section, we argue that since innovative firms prefer a more liquid stock, they
would take deliberate steps to improve their stock liquidity. We test this hypothesis by identifying
actions that are known to improve liquidity, and then checking whether innovative firms are more
likely to take these actions. The empirical model that we test can be represented as follows:
Liquidity-improving Actionsi,t+1 = α3 + β5 Innovativenessit + γ3 0F IRM + λi + φj + ψt + i,t+1 . (3)
The first liquidity-improving action that we analyze is Guidance, which measures the frequency of
earnings forecast guidance provided by the management in the given fiscal year. Information asymmetry between market participants and a general lack of informational transparency is one reason
for greater stock illiquidity. Therefore, the firm can partially improve its liquidity by releasing more
information to the market. As such, innovative firms would be more likely to provide information
more frequently to the market. We find evidence in support of this prediction. Specifically, in Panel
A of Table 4, the coefficients on R&D in column (1), Log Patents in column (2), Log Citations in
12
column (3), and the Innovation Index in column (4) are statistically significant at the 1% level.
These results are also economically significant – e.g., 1% increase in the number of patents is related
with 2.5% increase in the frequency of earnings guidance.
The second liquidity-improving action that we analyze is Stock Splits. The level of stock price is
the most important determinant of a firm’s decision to split its stock; so, the effect of innovativeness
on stock splits must be conditional on stock price levels. Panel B of Table 4 presents the estimated
coefficients from the test based on the dependent variable Stock Splits. Our results show that,
conditional on stock prices, measures of innovativeness are positively related with the dummy
variable Stock Splits. Except column (1), where we proxy for innovativeness with a dummy variable
indicating investment in R&D, the effect of innovativeness is significantly positive at the 1% level.
For non-patenting firms, an 1 dollar increase in stock price is related with a 4% increase in the
likelihood of a stock split. In comparison, such increase in stock price for patenting firms is related
with a 4.4% increase in the likelihood of a stock split. In other words, conditional on stock prices,
the marginal effect of an increase in stock price on the likelihood of a stock split for patenting firms
is 10% higher than that for non-patenting firms.
A larger investor base is related with greater stock liquidity, and the firm can widen its investor
base by making a seasoned equity offering (SEO). In Panel C, the dependent variable is SEO Dummy
and again, the independent variables of interest are the various measures of innovativeness. Across
columns (1)–(4), we find that coefficients on all four measures of innovativeness are positive and
mostly significant at the 1% level. The results are also economically meaningful – a 10% increase
in the number of patents is related with a 21% increase in the likelihood of an SEO.
The firm can also take some additional steps that can enhance the informational transparency
in the market. For instance, the firm can choose a more “reputed” underwriter for its equity
offerings. Reputed underwriters can certify issuer quality, will have access to a wider base of
potential investors, will be able to create broader interest in the equity offering, and are also known
to provide price support. As a result, innovative firms are more likely to use the services of a
reputed underwriter. The results are consistent with this prediction as the estimated coefficients
on all measures of innovativeness across columns (1)–(4) in Panel D are positive and significant at
the 1% level. The coefficient in column (1) suggests that a 10% increase in the number of patents
is related with a 58% increase in the likelihood of using a more reputed underwriter. The economic
effect of other innovativeness measures is similarly large.
Finally, in Panel E, we analyze whether innovative firms have options traded on their stocks.
13
Although the decision to list options is made by the exchange (Mayhew and Mihov, 2004), we
argue that the firm can still improve its information environment and generate trading interest in
the stock. This would ultimately make the stock more conducive to option listing. We test for
this by using Listed Options as a dependent variable. We find that, indeed, innovative firms are
more likely to have options traded on an exchange. The estimated coefficients on innovativeness
are positive and significant at the 1% level across all four columns in Panel E. Moreover, we find
that a 10% increase in the number of patents is related with a 110% increase in the likelihood of
options listed on an exchange.
Overall, the evidence presented in Panels A–E of Table 4 suggests that innovative firms, who
value stock liquidity more than others, do take deliberate actions to improve their stock liquidity.
5.2
The Effect of Innovative Firms’ Actions on Their Stock Illiquidity
Although we have shown that innovative firms take various steps that can improve the informational
environment and encourage trading in their stocks, in this section we directly test whether these
actions yield the desired result in terms of improved liquidity. However, the liquidity as well as the
propensity to take these actions, are both positively affected by the level of firm’s innovativeness.
Therefore, we pursue an instrumental variables methodology. With Illiquidity as the dependent
variable and using industry-level instruments for Guidance, Stock Splits, and SEO Dummy, we test
whether these specific actions are related with a lower stock illiquidity. We do not use Reputed
Underwriter because it is defined only within the much-smaller sample of SEOs. We also do not use
Listed Options because, as indicated above, these are not explicit actions taken by the firm. Rather,
these are the indirect results of the firm improving the information environment and generating
enough trading interest. The model that we estimate can be represented as:
Illiquidityi,t+1 = α4 + β6 (Instrumented Actionsit ) + β7 (Innovation Indexit )
+ γ4 0F IRM + λi + φj + ψt + i,t+1 .
(4)
The variables used in this regression are the same as those defined above, including the randomeffects as well as industry and year dummies. We instrument Guidance with its median value of
all the other firms in the corresponding Fama-French 48-industries. Since Stock Splits and SEO
Dummy are indicator variables, we are unable to use their median value in the industry as an
instrument; we instead rely on the respective mean values in the Fama-French 48-industries. All
four regressions reported in Table 5 are just-identified as we rely on a single instrument that is most
likely to be related with the corresponding firm-action but is unlikely to be related with the firm’s
14
stock illiquidity. As the first stage regression estimates in the bottom panel of Table 5 show, the
chosen instruments are strongly significant in predicting the firm’s actions. More importantly, we
find that these instrumented actions have a strong negative relation with the stock’s illiquidity (even
after controlling for the firm’s innovativeness and other characteristics). These results show that
the deliberate actions taken by innovative firms (illustrated in Table 4) do improve the firms’ stock
liquidity. These actions are useful in either improving the informational environment surrounding
the firm’s stock or widening the investor base; these eventually help enhance the stock liquidity,
which makes raising equity capital easier for innovative firms and also lowers their cost of capital.
Overall, the evidence presented in this section supports our hypothesis, H2.
6
Other Characteristics of Innovative Firms Seeking Greater Stock
Liquidity
6.1
Debt of Innovative Firms
So far, we have analyzed the stock liquidity of innovative firms, arguing that they prefer liquidity
because issuing debt is more difficult or costly due to the nature of their assets and investments.
In this section, we analyze how this need for stock liquidity interacts with the type of debt that
innovative firms raise. First, we argue that the attempts of innovative firms at mitigating the
information asymmetry in the stock market can also benefit them in the debt markets. Second,
the firm can also lower the information asymmetry by generating information in the public debt
markets. And third, due to their reliance on equity markets and the lower leverage ratio, innovative
firms will be received favorably by the creditors when they do issue debt. We test these arguments
using the following empirical model:
Debt Characteristicsi,t+1 = α5 + β8 Innovation Indexit + γ5 0F IRM + λi + φj + ψt + i,t+1 .
(5)
We present the results estimated from this model in Table 6. In Panel A, the dependent variable is
Public Debt Dummy; in this case, we also control for firm random effects (denoted by λi in equation
(5) above). Credit Rating, Covenant Dummy, and Number of Covenants are the dependent variable
in Panels B, C, and D, respectively, and as such, these samples do not constitute a panel of firms
across years. Due to this, we do not control for firm random effects; instead, we estimate equation
(5) for Covenant Dummy in Panel C as a Probit, and for the two ordinal variables in Panels B and
D as an Ordered Probit. We use R&D, Log Patents, Log Citations, and Innovation Index as our
measure of innovativeness in columns (1)–(4), respectively, of all the Panels in Table 6.
15
We find that although the coefficient on R&D is usually statistically insignificant from zero, the
other three estimated coefficients on innovativeness in columns (2)–(4) are usually significant at
the 1% level. These results suggest that innovative firms are: more likely to have a long-term S&P
credit rating, more likely to have a better rating conditional on having a long-term credit-rating,
less likely to have covenants in their loans, and also likely to have fewer covenants (if at all) in their
loans. These findings generally support the above predictions and the overall message in Table
6 is that innovative firms are better-quality borrowers either because they are subject to market
discipline and/or because they have lower leverage ratios. Finally, our findings are also consistent
with the notion that because of the nature of their investments, innovative firms prefer financial
contracts that are less limiting. This is not only reflected in their greater reliance on equity capital
but also in the fewer covenants that are included in their loan contracts.
6.2
Who Monitors the Innovative Firms?
Banks typically play an important role in monitoring borrowers. However, innovative firms have
lower leverage ratios and, as our evidence above shows, also tend to have fewer covenants in their
bank loans. If so, how are managers in these firms monitored? We argue that due to their reliance
on equity capital, the onus of monitoring lies with equity holders. Among all equity holders,
institutional investors, and particularly block holders, are better at monitoring firms. As such,
we argue that innovative firms are more likely to have blockholders and a greater institutional
ownership. These firms should also incentivize their managers with more equity-based compensation
contracts. This is optimal when the equity is more liquid as the effort of the manager can be better
reflect in stock prices (Holmström and Tirole, 1993). We test these claims with the following
regression model:
Equity Monitoringi,t+1 = α6 + β9 Innovation Indexit + γ6 0F IRM + λi + φj + ψt + i,t+1 .
(6)
We follow the same random-effects regression model as before, except our dependent variable is one
of the following: Institutional Ownership, Blockholder Dummy, or Equity-Based Compensation. The
results from the estimation are presented across Panels A–C of Table 7 and, as before, the measure
of innovativeness is R&D, Log Patents, Log Citations, or the Innovation Index in columns (1)-(4),
respectively. Our results strongly support the predictions – we find that innovativeness is positively
related with these equity-based measures and the estimated coefficients are mostly significant at the
1% level. With a larger institutional ownership and greater likelihood of blockholders, innovative
firms are monitored by the equity-holders. The CEO’s compensation contract is also more heavily
16
equity-based, thus relying on equity prices for monitoring the manager’s actions. This evidence is
consistent with the notion that innovative firms rely less on debt capital, and therefore, must be
monitored by equity-holders instead of creditors.
7
Marginal Impact of an Increase in Stock Liquidity
An important question that we have not addressed so far is whether these improvements in liquidity
ultimately help the innovative firms or not. In this section, we look for the impact of a change in
liquidity on the firms’ value to test whether the marginal impact on the value of innovative firms
is larger. We have argued in our hypothesis H3 that the positive impact of an increase in liquidity
(or, correspondingly, the negative impact of an increase in illiquidity) should be marginally greater
for innovative firms because their reliance on equity markets and the resulting greater need for
liquidity. However, both the firm’s value and its liquidity are influenced by its innovativeness. We
address this endogeneity in several different ways, starting with the following instrumental-variable
regression:
∆Tobin’s Qi;t,t+1 = ai + β9 Instrumented-∆Illiquidityi;t,t+1 + γ6 0F IRM + φj + ψt + i,t ,
(7)
where ai represents the firm fixed-effects while the rest of the vairables are same as those defined
earlier. The change in the firm’s stock illiquidity over the year t to t + 1 (∆Illiquidity) is instrumented by the (t, t + 1) change in the median illiquidity of all other firms in the same industry.
We estimate this just-identified IV regression separately for the sample of more and less innovative
firms. We categorize firms as more innovative if they make R&D investments, produce patents,
have citations on their patents, or have a positive Innovation Index. The firms that do not invest
in R&D, have no patents or citations, or have a negative Innovation Index are categorized as being
less innovative. The results from this analysis of the two subsamples are presented in Table 8, and
they clearly show that the negative impact of an increase in illiquidity is larger in the case of more
innovative firms.
Although the results in Table 8 support our prediction, we bolster these further by using a
variety of exogenous shocks to the firm’s stock illiquidity. The general model that we test can be
written as:
∆Tobin’s Qi;t−1,t+1 = α7 + β10 (∆Illiquidityi;t−1,t+1 ) × (Innovation Dummy)
+ β11 Innovation-Dummy + β12 ∆Illiquidityi;t−1,t+1
+ γ7 0F IRM + φj + i,t .
17
(8)
Instead of instrumenting the change in illiquidity, we now calculate this change from year 2000 to
2002, i.e., around the year prices were decimalized on stock exchanges. The dependent variable
(T obins0 Q) is also measured as a change over the years 2000-2002. Decimalization was an exogenous
event and resulted in a decrease in the illiquidity of all stocks, and this decrease was unrelated to
any change in the information asymmetry surrounding all the stocks. Therefore, equation (8) rules
out confounding effects and directly tests for the impact of a change in stock illiquidity on firm
value. We interact ∆Illiquidity with an Innovation Dummy that directly tests for the differential
impact on innovative firms’ value from a change in illiquidity. The Innovation Dummy equals
one if the firm invests in R&D, has patents, has patents that generate citations, or has a positive
Innovation Index ; we use these proxies in columns (1)–(4), respectively, of Table 9. We expect
β10 in equation (8) to be negative and we find that it is significantly negative at the 5% level in
columns (1) and (4). Even though the estimate coefficient is negative in columns (2) and (3), it
is not statistically significant. Therefore, we find some evidence that the marginal impact on firm
value due to a change in liquidity is greater for innovative firms.
In Table 10, we re-test the above model (8) with another exogenous shock that is known to
improve stock liquidity for reasons unrelated to information asymmetry – addition of a stock to the
S&P 500 Index. The sample in this test consists only of those stocks that are added to the S&P
500 Index at some point during our sample period. We calculate the change in stock illiquidity
and firm value over years (t − 1, t + 1) for all such firms; t denotes the year of their addition to
the Index. As in Table 9, we use R&D, patents, citations, and the InnovationIndex to define the
Innovation Dummy across the four columns, respectively, of Table 10, and expect the coefficient
β10 on the interaction term to be negative. We find that this coefficient is negative and mostly
significant at the 10% level.
Finally, we examine whether stock liquidity increases firm value more for innovative firms that
have higher equity-based compensation for the managers. We test this by repeating the regressions
of change in firm value on change in liquidity surrounding decimalization with a slight variation:
∆Tobin’s Qi;t−1,t+1 = α7 + β10 (∆Illiquidityi;t−1,t+1 ) × (Incentive Dummy)
+ β11 Incentive Dummy + β12 ∆Illiquidityi;t−1,t+1
+ γ7 0F IRM + φj + i,t .
(9)
where Incentive Dummy is a binary variable that equals one if the Equity-Based Compensation
is above median. We again perform the analysis on subsamples by based on whether they make
R&D investments, produce patents, have citations on their patents, or have a positive Innovation
18
Index. Table 11 presents the estimates of this test. We find that the negative impact of an increase
in stock illiquidity is mainly concentrated in the sample of innovative firms especially when the
manager’s compensation is more equity-based. The coefficient of the interaction term between
∆Illiquidityi;t−1,t+1 and Innovation Dummy is significantly negative for innovative firms but not for
the other firms. This is consistent with our prediction that innovative firms, whose assets are more
opaque and managers’ actions are harder to monitor, benefit more by designing the compensation
contract with more incentive. Such benefit is reflected in the greater value impact of an exogenous
increase in stock liquidity due to stock price decimalization.
The results in Tables 9 and 10 support our working hypothesis H3 and show that the marginal
impact of a change in stock liquidity on firm value is greater for innovative firms. Overall, this
lends further support to one of the main themes of our paper – that, innovative firms value stock
liquidity more than other firms.
8
Some Robustness Checks
In this section, we test for the robustness of the main results presented above by using an alternative
proxy for identifying firms that prefer stock liquidity. Titman and Wessels (1988) argue that firms
that invest more in advertising are more likely to produce unique products, and such firms, due
to the lack of collateralizable assets, are less able to sustain a high leverage ratio. We argue that,
therefore, firms that invest more in advertising are also likely to value greater stock liquidity.
Based on this general argument, we re-test some of the models presented above, but now using
Advertising instead of a measure of innovativeness. These results are put together in Table 12. We
define Advertising as the ratio of advertising expenses to lagged assets. In Panel A, we present
results from estimating the model (1), where we simply test for the relation between the level of
advertising and the stock illiquidity. As earlier, we measure the dependent variable using Amihud’s
(2002) Illiquidity, Negative Turnover, Bid-Ask Spread, and PIN in columns (1)–(4), respectively.
We find a strongly negative relation between the level of advertising and all the measures of the
firm’s stock illiquidity. This effect is statistically significant at the 1% level in column (1) and a 10
percentage points increase in Advertising is related with a 9.7% lower Illiquidity. The effects are
similarly large in the other three columns.
In Panel B of Table 12, we repeat the regression model (3) and ask whether firms that advertise
also take deliberate steps that can improve their stock liquidity. As before, we analyze a variety
of dependent variables: Guidance, Stock Splits, SEO Dummy, Reputed Underwriter, and Listed
19
Options in columns (1)–(5), respectively. The estimated coefficient on Advertising is positive and
statistically significant in columns (1), (2), and (5). These effects are also economically large; for
instance, a 10 percentage points increase in Advertising is related with 8.6% higher frequency of
earnings guidance from the management about future earnings . Thus, these results generally
support the notion that firms that prefer a greater stock liquidity can take a variety of steps to
improve it.
9
Conclusion
In this paper, we study the liquidity choice of firms. Although many of the firm’s actions are known
to influence stock liquidity, the literature has largely viewed stock liquidity as being determined
exogenously. We directly test for the firm’s deliberate influence on its stock liquidity by focusing
on a set of firms that are more likely to rely primarily on equity markets for their capital needs.
We borrow from the literature on capital structure choice and argue that firms producing unique
products cannot keep as high leverage ratios as other firms. This may either be due to the strategic
externalities of their capital structure choice or simply due to the scarcity of collateralizable assets.
The existing literature has shown that innovative firms (say, investing in R&D) and firms that
have brand value (by investing in advertising) are likely candidates for firms that must maintain
lower leverage ratios. Their heavy reliance on equity markets requires that they keep their stock
liquid, and as such, they take actions that help improve their stock liquidity – either by reducing
the information asymmetry surrounding the stock or by (indirectly) lowering the trading costs.
We find strong empirical evidence for these arguments in a large sample of public firms over
1990-2009. We find that innovative firms have significantly lower stock illiquidity, have higher
turnovers, lower bid-ask spreads, and a lower probability of informed trading (“PIN”). This is an
important finding because firms with informationally opaque assets are generally expected to have
lower stock liquidity. These effects are weaker if the firm is less financially constrained and is
able to access capital from other sources. Innovative firms are more likely to take deliberate steps
that are known to improve stock liquidity, such as the management providing guidance on future
earnings, announcing stock splits, making seasoned equity offerings (SEOs), choosing more reputed
underwriters, and generating trading interest in the stock such that options are more likely to be
listed on their shares.
If the innovative firms do issue debt, it is more highly rated public debt; this is consistent with
innovative firms returning to public capital markets, which helps with improving informational
20
transparency. Their private debt (i.e., bank loans) is less likely to have covenants; this reflects their
lower leverage ratios and lower informational asymmetry. Given their reliance on equity markets,
the role of monitoring the management rests with equity-holders. As such, we find that innovative
firms have higher institutional ownership, higher likelihood of block holders, and more incentivized
CEO compensation contract. The preference of innovative firms for greater liquidity is reflected
in a bigger impact on firm value due to an exogenous change in stock liquidity (say, following
decimalization of stock prices on exchanges).
Overall, we find strong evidence of firms being able to influence stock liquidity by taking deliberate steps to dispel information asymmetry. This is especially true of firms that are most vulnerable
to and most affected by informational asymmetries.
21
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23
Appendix: Variable Definitions
Primary Dependent Variables
• Illiquidity is defined as ln(AvgILLIQ × 108 ), where AvgILLIQ is an yearly average of illiquidity measured
PDaysi,t |Ri,t,d |
1
as the absolute return divided by dollar trading volume:AvgILLIQi,t = Days
where
d=1
DolV oli,t,d
i,t
Daysi,t is the number of valid observation days for stock i in fiscal year t, and Ri,t,d and DolV oli,t,d are the
return and dollar trading volume of stock i on day d in the fiscal year t.
P12
V oli,t,m
• Negative Turnover = −T urnoveri,t = −1
m=1 Shrouti,t,m where V oli,t,m and Shrouti,t,m are the trading
12
volume in shares and number of shares outstanding for firm i in month m of fiscal year t. (We use “negative”
turnover so that it measures illiquidity like the other dependent variables defined here.)
PDaysi,t Aski,t,d −Bidi,t,d
1
• Bid − Ask Spreadi,t = Days
where Daysi,t is the number of observations for
d=1
(Aski,t,d +Bidi,t,d )/2
i,t
stock i in fiscal year t, and Aski,t,d and Bidi,t,d are the closing ask and bid prices of the stock i on day d of
year t.
• PIN is the probability of informed trading proposed by Easley, Kiefer, O’Hara, and Paperman (1996). We
obtain this variable directly from Søren Hvidkjær’s website: http://sites.google.com/site/hvidkjaer/
data.
Measures of Innovativeness
• R&D is the ratio of the firm’s R&D expenditure to lagged assets.
• Log Patents is ln(1 + number of patents/100).
• Log Citations is ln(1 + number of citations/100).
atents+0.6657×Log Citations
• Innovation Index = 0.3366×R&D+0.6660×Log P100
where each of the index components
has first been winsorized at 1% and 99% level and standardized.
Other Dependent Variables
• Guidance is the logarithm of one plus the frequency of earnings guidance forecasts provided by the management
in the fiscal year.
• Stock Splits is a binary variable that is equal to one if there is a stock split in the fiscal year, and it is zero
otherwise.
• Listed Options is a binary variable that is equal to one if the firm has listed options available in the given fiscal
year, and it is zero otherwise.
• SEO Dummy is a binary variable that is equal to one if the firm does a seasoned equity offering (SEO) in the
given fiscal year, and it is zero otherwise.
• Reputed Underwriter is a binary variable that is equal to one if the firm hires a “reputable” underwriter for
the SEO. Reputable underwriters are those that rank equal to or higher than eight in Prof. Jay Ritter’s IPO
Underwriter Reputation Rankings (1980-2009).
• Public Debt Dummy is a binary variable that is equal to one if the firm has a long-term S&P credit rating,
and it is zero otherwise.
• Credit Rating is an ordinal variable measuring the firm’s long-term credit rating by S&P. It is equal to 1 if the
firm is rated CCC+ or below; 2 if it is rated between B- to B; 3 if it is rated between BB- to BB+; 4 if the
rating i between BBB- to BBB+; 5 if the rating is between A- to A+; and 6 if the rating is AA- or higher.
• Covenant Dummy is a binary variable that is equal to one if there is a covenant in the loan borrowed by the
firm in the given fiscal year, and it is zero otherwise. These data are from Dealscan.
• Number of Covenants counts the number of covenants in the bank loan issued in the given fiscal year. If there
are multiple loans borrowed in the year, then we take an average of the number of covenants across all the
loans weighted by the loan amount.
• Equity-Based Compensation is the sum of options and restricted stock granted to the CEO, divided by the
CEO’s total compensation.
• Institutional Ownership is the number of shares held by all the institutional investors listed in 13F, calculated
as a ratio of the total number of the firm’s shares outstanding.
• Blockholder Dummy is a binary variable that is equal to one if there is at least one blockholder that has a
minimum of 5% equity ownership in the firm, and it is zero otherwise.
24
Firm Characteristics
• Log Assets is the natural logarithm of total assets.
• Leverage is the sum of long term debt and debt in current liabilities divided by total assets.
• Cash is the cash and short term investments to lagged asset ratio.
• Tobin’s Q is the sum of total assets and the difference between market value and book value of total common
equity, divided by total assets.
• ROA is equal to earnings before extraordinary items to lagged asset ratio.
• Tangibility is the total value of property, plant and equipment, divided by total assets.
• Firm’s Age is the age of the firm in years.
• Return Volatility is the standard deviation of daily stock returns in the fiscal year.
• Stock Price is the firm’s fiscal year end closing price.
• Market Power is defined as sale minus cost of goods sold and selling, general and administrative expense,
divided by sale.
• Market Power Dummy is a binary variable that is equal to 1 if the Market Power of the firm is higher than
the sample median and 0 otherwise.
• High Ratings Dummy is a binary variable that is equal to 1 if the firm has S&P credit rating equal to or higher
than A- and 0 otherwise.
• NYSE Dummy is a binary variable that is equal to 1 if the firm is listed in the New York Stock Exchange and
0 otherwise.
• Dividend Dummy is a binary variable that is equal to 1 if the firm pays dividend to common or prefered
stockholders in the fiscal year and 0 otherwise.
• Free Cash Flow is the sum of net cash flow from operating activities and net cash flow from investing activities,
divided by total assets.
CEO Characteristics
• CEO age is the age of CEO in years.
• CEO Tenure measured in months for the CEO in the fiscal year.
• CEO Ownership is the CEO’s stock ownership of the firm.
Loan Characteristics
• Log Loan Amount is the natural logarithm of loan amount borrowed in the fiscal year. If there are more than
one loan borrowed in the year, the variable would be the sum of all the loans.
• Log Loan Maturity is the time to maturity of loan borrowed in the fiscal year. If there are more than one loan
borrowed in the year, the variable would be an average of all the loans weighted by loan amount.
• Syndicate Dummy is a binary variable that is equal to 1 if at least one loan borrowed in the fiscal year is a
syndicated loan.
Other Independent Variables
• Advertising is the advertising expense to lagged asset ratio.
25
Table 1, Panel A: Summary Statistics. This panel presents summary statistics of the main variables used in
our analyses.
Units
N
Mean
Median
Std. Dev.
94,129
94,142
90,707
19,299
2.403
–1.282
0.036
0.199
2.395
–0.795
0.020
0.186
3.438
1.467
0.045
0.080
fraction
logarithm
logarithm
94,142
94,142
94,142
94,142
94,142
0.047
0.003
0.006
0.008
0.009
0.000
0.000
0.000
–0.004
0.000
0.085
0.008
0.016
0.022
0.020
logarithm
0/1
0/1
0/1
0/1
0/1
79,781
94,142
69,701
94,142
5,463
94,142
94,142
16,047
16,047
18,133
94,142
91,456
0.306
0.066
0.406
0.058
0.717
0.233
0.811
0.533
1.145
0.381
0.362
0.235
0.000
0.000
0.000
0.000
1.000
0.000
0.000
1.000
1.000
0.383
0.000
0.000
0.575
0.249
0.491
0.234
0.451
0.423
1.631
0.499
1.302
0.296
0.481
0.424
$
fraction
0/1
0/1
0/1
fraction
94,142
94,142
94,142
94,142
94,142
94,142
94,142
94,142
93,839
76,064
94,142
94,142
94,142
93,234
5.364
0.229
0.211
2.031
-0.038
0.278
13.606
0.042
17.036
-0.043
0.063
0.304
0.400
-0.054
5.183
0.186
0.094
1.398
0.028
0.198
9.000
0.036
10.750
0.095
0
0
0
-0.005
2.249
0.222
0.290
1.884
0.250
0.247
13.952
0.026
18.034
0.791
0.244
0.460
0.490
0.225
logarithm
year
month
%
logarithm
logarithm
0/1
94,142
23,854
24,275
13,660
16,047
14,851
16,047
1.153
55.523
79.803
0.044
4.847
3.597
0.699
1.099
55.000
52.000
0.011
5.011
3.760
1.000
.989
7.523
89.424
0.076
1.732
0.717
0.459
Primary Dependent Variables:
Illiquidity
Negative Turnover
Bid-Ask Spread
PIN
Measures of Innovativeness:
R&D
Log Patents
Log Citations
Innovation Index
Advertising
Other Dependent Variables
Guidance
Stock Splits
Listed Options
SEO Dummy
Reputed Underwriter
Public Debt Dummy
Credit Rating
Covenant Dummy
Number of Covenants
Equity-Based Compensation
Institutional Ownership
Blockholder Dummy
Firm-specific Control Variables:
Log Assets
Leverage
Cash
Tobin’s Q
ROA
Tangibility
Firm’s Age
Return Volatility
Stock Price
Market Power
High Rating Dummy
NYSE Dummy
Dividend Dummy
Free Cash Flow
Other Independent Variables:
Log Number of Analysts
CEO Age
CEO Tenure
CEO Ownership
Log Loan Amount
Log Loan Maturity
Syndicate Dummy
0/1
fraction
fraction
0/1
logarithm
fraction
fraction
fraction
fraction
year
26
In panel B-F, we present univariate tests of Leverage, Illiquidity, Negative Turnover, Bid-Ask Spread, and PIN between
subsamples that have non-positive and positive value of R&D, Log Patents, Log Citations, and Innovation Index. In
column 2 and 3, we report mean in the first row, median in parentheses, and number of observations in brackets.
Panel B: Univariate test of Leverage
Dummy=0 Dummy=1 Mean (Difference)
By R&D Dummy
By Patents Dummy
By Citations Dummy
By Innovation Index Dummy
0.278
(0.255)
[52,658]
0.239
(0.197)
[76,524]
0.237
(0.194)
[78,750]
0.267
(0.241)
[62,354]
0.168
(0.105)
[41,484]
0.187
(0.148)
[17,618]
0.189
(0.153)
[15,392]
0.155
(0.082)
[31,788]
T statistics
Wilcoxon Z
0.110
77.83***
79.66***
0.052
27.98***
24.92***
0.048
24.66***
20.62***
0.112
75.83***
80.85***
T statistics
Wilcoxon Z
0.367
16.27***
15.25***
1.800
64.00***
61.82***
1.696
56.92***
55.13***
0.751
31.86***
30.79***
T statistics
Wilcoxon Z
0.468
49.21***
57.68***
0.314
25.67***
35.02***
0.278
21.57***
28.74***
0.469
46.92***
59.82***
Panel C: Univariate test of Illiquidity
Dummy=0 Dummy=1 Mean (Difference)
By R&D Dummy
By Patents Dummy
By Citations Dummy
By Innovation Index Dummy
2.564
(2.540)
[52,647]
2.740
(2.817)
[76,512]
2.680
(2.739)
[78,738]
2.656
(2.706)
[62,342]
2.198
(2.239)
[41,482]
0.940
(0.813)
[17,617]
0.984
(0.847)
[15,391]
1.905
(1.911)
[31,787]
Panel D: Univariate test of Negative Turnover
Dummy=0 Dummy=1 Mean (Difference)
By R&D Dummy
By Patents Dummy
By Citations Dummy
By Innovation Index Dummy
-1.076
(-0.659)
[52,658]
-1.223
(-0.749)
[76,524]
-1.237
(-0.763)
[78,750]
-1.124
(-0.677)
[62,354]
-1.544
(-0.998)
[41,484]
-1.537
(-0.992)
[17,618]
-1.515
(-0.956)
[15,392]
-1.593
(-1.049)
[31,788]
27
Panel E: Univariate test of Bid-Ask Spread
Dummy=0 Dummy=1 Mean (Difference)
By R&D Dummy
0.039
(0.021)
[50,464]
0.039
(0.022)
[73,746]
0.038
(0.021)
[75,966]
0.039
(0.021)
[59,873]
By Patents Dummy
By Citations Dummy
By Innovation Index Dummy
0.032
(0.018)
[40,243]
0.023
(0.013)
[16,961]
0.025
(0.015)
[14,741]
0.030
(0.017)
[30,834]
T statistics
Wilcoxon Z
0.007
23.31***
19.26***
0.016
40.63***
39.56***
0.013
31.97***
24.70***
0.009
28.90***
21.43***
T statistics
Wilcoxon Z
0.008
6.20***
6.01***
0.036
27.85***
28.93***
0.037
28.07***
29.22***
0.028
22.47***
22.86***
Panel F: Univariate test of PIN
Dummy=0 Dummy=1 Mean (Difference)
By R&D Dummy
By Patents Dummy
By Citations Dummy
By Innovation Index Dummy
0.202
(0.188)
[12,547]
0.209
(0.196)
[14,310]
0.208
(0.196)
[14,442]
0.208
(0.194)
[13,467]
0.194
(0.182)
[6,752]
0.172
(0.159)
[4,989]
0.172
(0.159)
[4,857]
0.180
(0.168)
[5,832]
*** p<0.01, ** p<0.05, * p<0.1
28
Table 2: Stock Liquidity of Innovative Firms. In this table, we show that innovative firms tend to have lower
stock liquidity. We present estimates from regressions with firm random-effects, where the dependent variable is a
measure of the firm’s stock illiquidity and the independent variable of interest is a measure of innovation. We start
with the dependent variable Illiquidity in Panel A and then alternatively use Negative Turnover, Bid-Ask Spread,
and PIN in Panels B, C, and D, respectively. All these dependent variables are measured in year t + 1 while the
independent variables are measured in year t. The following firm characteristics are also included in the regressions:
Log Assets, Leverage, Cash, Tobin’s Q, NYSE Dummy, ROA, Tangibility, Firm’s Age, and Return Volatility. All the
variables are defined in the Appendix. Year and industry dummies are also included.
Panel A: Dependent Variable is Illiquidity
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
-1.785***
[-11.39]
Log Patents
-8.232***
[-7.67]
Log Citations
-3.726***
[-7.66]
Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
-1.173***
[-110.99]
1.500***
[29.98]
-0.515***
[-18.40]
-0.429***
[-75.43]
-0.794***
[-18.02]
-1.142***
[-29.74]
0.102
[1.37]
-0.002
[-1.31]
10.204***
[24.97]
8.379***
[88.24]
-1.162***
[-109.33]
1.516***
[30.34]
-0.584***
[-21.53]
-0.436***
[-76.89]
-0.784***
[-17.83]
-1.049***
[-28.12]
0.082
[1.11]
-0.002
[-0.93]
10.358***
[25.32]
8.275***
[86.00]
-1.162***
[-109.73]
1.513***
[30.25]
-0.582***
[-21.49]
-0.436***
[-76.79]
-0.784***
[-17.84]
-1.049***
[-28.12]
0.084
[1.14]
-0.002
[-1.01]
10.373***
[25.38]
8.282***
[86.40]
-4.138***
[-10.23]
-1.160***
[-109.59]
1.507***
[30.16]
-0.574***
[-21.17]
-0.435***
[-76.51]
-0.783***
[-17.84]
-1.067***
[-28.60]
0.089
[1.21]
-0.002
[-0.90]
10.377***
[25.39]
8.236***
[85.60]
93,319
76.3%
Yes
Yes
Yes
93,319
76.2%
Yes
Yes
Yes
93,319
76.2%
Yes
Yes
Yes
93,319
76.3%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
29
Panel B: Dependent Variable is Negative Turnover
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
-1.201***
[-7.85]
Log Patents
-0.678
[-0.58]
Log Citations
-1.018**
[-1.98]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
-0.282***
[-36.25]
0.039
[0.98]
-0.419***
[-14.93]
-0.170***
[-33.46]
0.295***
[10.71]
-0.407***
[-11.25]
-0.021
[-0.40]
0.010***
[10.85]
-12.559***
[-33.08]
0.971***
[14.91]
-0.279***
[-35.90]
0.057
[1.44]
-0.464***
[-16.88]
-0.175***
[-34.49]
0.300***
[10.88]
-0.337***
[-9.75]
-0.030
[-0.58]
0.010***
[11.03]
-12.502***
[-32.94]
0.952***
[14.50]
-0.278***
[-35.75]
0.055
[1.39]
-0.464***
[-16.89]
-0.175***
[-34.47]
0.301***
[10.91]
-0.338***
[-9.78]
-0.029
[-0.57]
0.010***
[11.13]
-12.491***
[-32.94]
0.940***
[14.34]
-1.294***
[-3.01]
-0.277***
[-35.63]
0.052
[1.32]
-0.461***
[-16.76]
-0.174***
[-34.38]
0.301***
[10.93]
-0.345***
[-9.92]
-0.028
[-0.55]
0.010***
[11.21]
-12.487***
[-32.92]
0.922***
[14.01]
93,470
24.1%
Yes
Yes
Yes
93,470
23.7%
Yes
Yes
Yes
93,470
23.8%
Yes
Yes
Yes
93,470
23.8%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
30
Panel C: Dependent Variable is Bid-Ask Spread
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
-0.037***
[-10.63]
Log Patents
-0.085***
[-4.49]
Log Citations
-0.093***
[-10.70]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
-0.009***
[-40.03]
0.024***
[18.68]
-0.009***
[-15.69]
-0.004***
[-35.72]
-0.007***
[-9.90]
-0.016***
[-17.63]
-0.004**
[-2.10]
0.000***
[9.16]
0.463***
[36.37]
0.057***
[25.14]
-0.009***
[-38.87]
0.024***
[18.96]
-0.011***
[-18.69]
-0.004***
[-37.61]
-0.007***
[-9.64]
-0.014***
[-15.95]
-0.004**
[-2.32]
0.000***
[9.38]
0.465***
[36.49]
0.056***
[24.21]
-0.009***
[-38.73]
0.024***
[18.82]
-0.011***
[-18.72]
-0.004***
[-37.42]
-0.007***
[-9.55]
-0.014***
[-16.06]
-0.004**
[-2.26]
0.000***
[9.50]
0.466***
[36.60]
0.055***
[23.89]
-0.077***
[-10.63]
-0.009***
[-38.71]
0.024***
[18.80]
-0.010***
[-18.39]
-0.004***
[-37.18]
-0.007***
[-9.56]
-0.014***
[-16.37]
-0.004**
[-2.24]
0.000***
[9.54]
0.466***
[36.58]
0.055***
[23.63]
92,787
51.2%
Yes
Yes
Yes
92,787
50.9%
Yes
Yes
Yes
92,787
51.0%
Yes
Yes
Yes
92,787
51.0%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
31
Panel D: Dependent Variable is PIN
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
-0.093***
[-5.25]
Log Patents
0.020
[0.25]
Log Citations
-0.019
[-0.52]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
-0.026***
[-45.82]
0.029***
[8.09]
-0.002
[-0.46]
-0.011***
[-18.25]
-0.022***
[-11.25]
-0.021***
[-4.46]
-0.001
[-0.28]
-0.000
[-1.14]
-0.300***
[-5.51]
0.421***
[52.85]
-0.026***
[-43.60]
0.031***
[8.44]
-0.004
[-1.14]
-0.011***
[-19.61]
-0.022***
[-11.16]
-0.016***
[-3.52]
-0.001
[-0.29]
-0.000
[-1.10]
-0.298***
[-5.48]
0.422***
[51.54]
-0.026***
[-44.18]
0.031***
[8.43]
-0.004
[-1.15]
-0.011***
[-19.61]
-0.022***
[-11.12]
-0.016***
[-3.53]
-0.001
[-0.30]
-0.000
[-0.99]
-0.298***
[-5.47]
0.421***
[51.79]
-0.026
[-0.89]
-0.026***
[-43.66]
0.031***
[8.39]
-0.004
[-1.14]
-0.011***
[-19.52]
-0.022***
[-11.11]
-0.016***
[-3.56]
-0.001
[-0.30]
-0.000
[-0.92]
-0.298***
[-5.47]
0.420***
[51.42]
17,888
51.0%
Yes
Yes
Yes
17,888
50.8%
Yes
Yes
Yes
17,888
50.9%
Yes
Yes
Yes
17,888
50.9%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
32
Table 3: Stock Liquidity of Innovative Firms Conditional on Access to Public Debt, Market Power and
Dividend Policy. In this table, we show that relationship between innovation and the stock liquidity is weaker when
the firm has alternative capital access or pays dividend. We present estimates from regressions with firm randomeffects, where the dependent variables are measures of the firm’s stock illiquidity and the independent variables of
interest a measure of innovation and its interaction with Public Debt Dummy (Panel A), High Ratings Dummy (Panel
B), Market Power Dummy (Panel C), and Dividend Dummy (Panel D). The following firm characteristics are also
included in the regressions: Log Assets, Leverage, Cash, Tobin’s Q, NYSE Dummy, ROA, Tangibility, Firm’s Age and
Return Volatility. All the variables are defined in the Appendix. Year and industry dummies are also included.
Panel A:
INDEPENDENT VARIABLES
Innovation Index * Public Debt Dummy
Innovation Index
Public Debt Dummy
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
Illiquidity
(1)
Negative Turnover
(2)
Bid-Ask Spread
(3)
PIN
(4)
1.519**
[2.26]
-4.649***
[-9.84]
-0.162***
[-5.17]
-1.147***
[-104.03]
1.535***
[30.61]
-0.578***
[-21.31]
-0.434***
[-76.28]
-0.765***
[-17.48]
-1.075***
[-28.76]
0.079
[1.07]
-0.001
[-0.68]
10.481***
[25.64]
8.164***
[83.54]
6.423***
[8.67]
-3.445***
[-6.83]
-0.139***
[-5.40]
-0.268***
[-32.85]
0.068*
[1.71]
-0.457***
[-16.63]
-0.173***
[-34.07]
0.311***
[11.24]
-0.358***
[-10.29]
-0.031
[-0.60]
0.010***
[10.79]
-12.401***
[-32.79]
0.890***
[13.19]
0.029**
[2.52]
-0.088***
[-10.07]
0.006***
[10.95]
-0.010***
[-39.11]
0.023***
[17.66]
-0.010***
[-18.00]
-0.004***
[-37.34]
-0.008***
[-10.73]
-0.014***
[-16.13]
-0.004**
[-1.99]
0.000***
[8.75]
0.463***
[36.32]
0.058***
[24.57]
0.119***
[2.90]
-0.090**
[-2.24]
-0.006***
[-3.78]
-0.025***
[-38.36]
0.032***
[8.71]
-0.004
[-1.13]
-0.011***
[-19.40]
-0.022***
[-11.10]
-0.017***
[-3.69]
-0.001
[-0.25]
-0.000
[-0.91]
-0.294***
[-5.41]
0.417***
[50.36]
93,319
76.4%
Yes
Yes
Yes
93,470
24.1%
Yes
Yes
Yes
92,787
51.2%
Yes
Yes
Yes
17,888
51.1%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
33
Panel B:
INDEPENDENT VARIABLES
Innovation Index * High Rating Dummy
Innovation Index
High Rating Dummy
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
Illiquidity
(1)
Negative Turnover
(2)
Bid-Ask Spread
(3)
PIN
(4)
3.303***
[4.06]
-4.632***
[-10.91]
0.001
[0.03]
-1.161***
[-109.31]
1.510***
[30.18]
-0.571***
[-21.07]
-0.435***
[-76.49]
-0.785***
[-17.89]
-1.069***
[-28.66]
0.087
[1.18]
-0.002
[-1.07]
10.383***
[25.41]
8.248***
[85.30]
6.621***
[8.38]
-2.367***
[-5.25]
0.247***
[6.92]
-0.285***
[-36.62]
0.068*
[1.71]
-0.451***
[-16.42]
-0.175***
[-34.63]
0.288***
[10.54]
-0.344***
[-9.91]
-0.032
[-0.61]
0.009***
[9.78]
-12.494***
[-32.99]
0.993***
[15.01]
0.016
[1.02]
-0.080***
[-10.43]
0.003***
[4.26]
-0.009***
[-38.81]
0.024***
[18.90]
-0.010***
[-18.30]
-0.004***
[-37.28]
-0.007***
[-9.75]
-0.014***
[-16.34]
-0.004**
[-2.25]
0.000***
[9.29]
0.467***
[36.67]
0.055***
[23.78]
0.110***
[2.80]
-0.047
[-1.47]
-0.006***
[-3.47]
-0.025***
[-41.93]
0.030***
[8.29]
-0.004
[-1.17]
-0.011***
[-19.36]
-0.022***
[-11.09]
-0.016***
[-3.63]
-0.001
[-0.25]
-0.000
[-0.77]
-0.298***
[-5.48]
0.419***
[51.00]
93,319
76.3%
Yes
Yes
Yes
93,470
24.7%
Yes
Yes
Yes
92,787
51.2%
Yes
Yes
Yes
17,888
50.9%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
34
Panel C:
INDEPENDENT VARIABLES
Innovation Index * Market Power Dummy
Innovation Index
Market Power Dummy
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
Illiquidity
(1)
Negative Turnover
(2)
Bid-Ask Spread
(3)
PIN
(4)
6.027***
[11.32]
-6.816***
[-12.65]
-0.533***
[-27.44]
-1.174***
[-99.79]
1.679***
[30.31]
-0.511***
[-16.30]
-0.440***
[-67.19]
-0.712***
[-14.92]
-0.835***
[-19.42]
0.288***
[3.51]
-0.001
[-0.42]
9.040***
[20.75]
8.452***
[58.06]
1.171**
[2.03]
-1.281**
[-2.43]
-0.177***
[-10.41]
-0.285***
[-31.27]
0.107**
[2.39]
-0.510***
[-15.35]
-0.189***
[-31.90]
0.340***
[10.60]
-0.214***
[-5.22]
0.026
[0.43]
0.010***
[10.32]
-12.595***
[-31.23]
1.060***
[9.34]
0.133***
[13.02]
-0.145***
[-13.89]
-0.004***
[-9.82]
-0.009***
[-34.21]
0.027***
[19.03]
-0.010***
[-15.03]
-0.004***
[-32.22]
-0.007***
[-8.55]
-0.013***
[-12.75]
-0.002
[-1.06]
0.000***
[8.67]
0.450***
[32.01]
0.059***
[16.26]
0.106***
[2.67]
-0.073*
[-1.69]
-0.010***
[-5.74]
-0.027***
[-39.56]
0.031***
[8.14]
-0.000
[-0.06]
-0.011***
[-18.20]
-0.021***
[-9.50]
-0.010*
[-1.79]
0.010**
[2.18]
0.000
[0.20]
-0.316***
[-5.68]
0.415***
[26.89]
75,355
77.3%
Yes
Yes
Yes
75,463
24.9%
Yes
Yes
Yes
74,927
51.1%
Yes
Yes
Yes
14,141
50.7%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
35
Panel D:
INDEPENDENT VARIABLES
Innovation Index * Dividend Dummy
Innovation Index
Dividend Dummy
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
Illiquidity
(1)
Negative Turnover
(2)
Bid-Ask Spread
(3)
PIN
(4)
2.583***
[4.25]
-5.237***
[-10.88]
0.026
[1.17]
-1.162***
[-110.10]
1.511***
[30.22]
-0.570***
[-21.04]
-0.435***
[-76.33]
-0.793***
[-18.00]
-1.064***
[-28.51]
0.085
[1.16]
-0.002
[-1.16]
10.418***
[25.47]
8.240***
[85.25]
5.710***
[8.65]
-3.680***
[-6.82]
0.037**
[2.10]
-0.279***
[-35.76]
0.059
[1.49]
-0.452***
[-16.44]
-0.174***
[-34.27]
0.283***
[10.33]
-0.340***
[-9.76]
-0.035
[-0.67]
0.009***
[10.03]
-12.433***
[-32.83]
0.940***
[14.30]
0.030***
[2.60]
-0.090***
[-10.11]
-0.001*
[-1.87]
-0.009***
[-38.89]
0.024***
[18.77]
-0.010***
[-18.40]
-0.004***
[-37.11]
-0.007***
[-9.34]
-0.014***
[-16.42]
-0.004**
[-2.24]
0.000***
[9.47]
0.466***
[36.50]
0.055***
[23.64]
0.078
[1.64]
-0.079*
[-1.75]
-0.002
[-1.29]
-0.026***
[-43.04]
0.030***
[8.29]
-0.004
[-1.12]
-0.011***
[-19.46]
-0.022***
[-11.08]
-0.016***
[-3.61]
-0.001
[-0.26]
-0.000
[-0.84]
-0.302***
[-5.54]
0.421***
[51.49]
93,319
76.3%
Yes
Yes
Yes
93,470
24.5%
Yes
Yes
Yes
92,787
51.0%
Yes
Yes
Yes
17,888
50.9%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
36
Table 4: Actions of Innovative Firms to Improve Stock Liquidity. In this table, we show that innovative
firms are taking actions to improve stock liquidity. Panel A presents regressions with firm random-effects where the
dependent variable is Guidance, Panel B,C,E present probit regressions with firm random-effects where the dependent
variables are Stock Splits, SEO Dummy, and Listed Options respectively. Panel D presents probit regression where
the dependent variable is Reputed Underwriter. All these dependent variables are measured in year t+1 while the
independent variables are measured in year t. The independent variables of interest include four different proxies
for innovation: R&D, Log Patents, Log Citations, and Innovation Index. The following firm characteristics are also
included in the regressions: Log Assets, Leverage, Cash, Tobin’s Q, NYSE Dummy, ROA, Tangibility, Firm’s Age and
Return Volatility. All the variables are defined in the Appendix. Year and industry dummies are also included.
Panel A: Dependent Variable is Guidance
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
0.069*
[1.80]
Log Patents
2.479***
[5.41]
Log Citations
0.591***
[3.12]
Innovation Index
Log Number of Analysts
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
0.140***
[34.65]
0.022***
[9.20]
-0.020
[-1.59]
-0.028***
[-3.52]
0.014***
[12.27]
0.048***
[4.64]
0.163***
[18.84]
-0.141***
[-8.91]
0.001
[1.57]
0.118
[1.34]
-0.413***
[-19.85]
0.138***
[34.23]
0.020***
[8.40]
-0.018
[-1.50]
-0.025***
[-3.17]
0.014***
[12.40]
0.047***
[4.59]
0.161***
[19.30]
-0.141***
[-8.89]
0.000
[1.07]
0.091
[1.03]
-0.389***
[-18.58]
0.139***
[34.50]
0.021***
[8.75]
-0.019
[-1.56]
-0.025***
[-3.21]
0.014***
[12.48]
0.047***
[4.60]
0.160***
[19.18]
-0.141***
[-8.92]
0.000
[1.37]
0.103
[1.17]
-0.401***
[-19.10]
0.724***
[4.70]
0.138***
[34.15]
0.021***
[8.62]
-0.017
[-1.42]
-0.027***
[-3.47]
0.014***
[12.16]
0.047***
[4.62]
0.164***
[19.57]
-0.141***
[-8.90]
0.000
[1.23]
0.097
[1.11]
-0.392***
[-18.62]
84,298
27.7%
Yes
Yes
Yes
84,298
27.9%
Yes
Yes
Yes
84,298
27.8%
Yes
Yes
Yes
84,298
27.8%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
37
Panel B: Dependent Variable is Stock Splits
INDEPENDENT VARIABLES
Stock Price * R&D Dummy
(1)
(2)
(3)
0.001
[1.51]
Stock Price * Patents Dummy
0.004***
[4.60]
Stock Price * Citations Dummy
0.005***
[5.77]
Stock Price * Innovation Index Dummy
Stock Price
R&D Dummy
0.038***
[46.50]
-0.081***
[-2.64]
Patents Dummy
0.037***
[48.93]
0.037***
[49.20]
-0.182***
[-5.03]
Innovation Index Dummy
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
Pseudo R-squared
Firm Random-effects
Industry Dummies
Year Dummies
0.004***
[5.08]
0.037***
[47.24]
-0.145***
[-4.19]
Citations Dummy
Log Assets
(4)
-0.176***
[-22.37]
0.038
[0.82]
-0.091***
[-2.69]
-0.029***
[-5.75]
0.071***
[2.67]
-0.091**
[-2.41]
0.038
[0.72]
-0.006***
[-7.15]
4.979***
[12.07]
6.044
[0.01]
-0.175***
[-22.12]
0.043
[0.94]
-0.090***
[-2.64]
-0.030***
[-5.84]
0.071***
[2.66]
-0.082**
[-2.18]
0.049
[0.93]
-0.006***
[-7.46]
4.855***
[11.75]
6.020
[0.01]
-0.175***
[-22.11]
0.043
[0.92]
-0.090***
[-2.65]
-0.030***
[-5.90]
0.071***
[2.67]
-0.080**
[-2.15]
0.050
[0.94]
-0.006***
[-7.61]
4.847***
[11.75]
6.026
[0.01]
-0.160***
[-5.36]
-0.176***
[-22.35]
0.025
[0.55]
-0.089***
[-2.62]
-0.030***
[-5.88]
0.075***
[2.81]
-0.115***
[-3.02]
0.037
[0.70]
-0.006***
[-7.40]
4.830***
[11.71]
6.110
[0.01]
89,580
1.7%
Yes
Yes
Yes
89,580
1.7%
Yes
Yes
Yes
89,580
1.7%
Yes
Yes
Yes
89,580
1.7%
Yes
Yes
Yes
t-statistics are in brackets
*** p<0.01, ** p<0.05, * p<0.1
38
Panel C: Dependent Variable is SEO Dummy
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
1.227***
[7.82]
Log Patents
2.185
[1.57]
Log Citations
1.725***
[2.68]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
Pseudo R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
0.027***
[3.99]
0.596***
[13.22]
0.127***
[3.69]
0.071***
[15.12]
0.221***
[8.40]
0.111***
[2.64]
0.067
[1.21]
-0.014***
[-16.76]
-4.505***
[-8.75]
-2.219***
[-25.97]
0.025***
[3.62]
0.566***
[12.62]
0.188***
[5.63]
0.077***
[16.67]
0.211***
[8.06]
0.005
[0.13]
0.058
[1.06]
-0.015***
[-16.99]
-4.552***
[-8.88]
-2.194***
[-25.33]
0.023***
[3.44]
0.570***
[12.70]
0.187***
[5.59]
0.077***
[16.56]
0.211***
[8.06]
0.006
[0.16]
0.058
[1.05]
-0.015***
[-17.11]
-4.580***
[-8.93]
-2.180***
[-25.24]
1.883***
[3.83]
0.022***
[3.16]
0.575***
[12.81]
0.180***
[5.35]
0.076***
[16.28]
0.212***
[8.11]
0.019
[0.49]
0.060
[1.08]
-0.015***
[-17.24]
-4.591***
[-8.94]
-2.153***
[-24.81]
93,651
1.3%
Yes
Yes
Yes
93,651
1.3%
Yes
Yes
Yes
93,651
1.3%
Yes
Yes
Yes
93,651
1.3%
Yes
Yes
Yes
t-statistics in brackets
*** p<0.01, ** p<0.05, * p<0.1
39
Panel D: Dependent Variable is Reputed Underwriter
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
1.533***
[3.69]
Log Patents
15.940***
[3.39]
Log Citations
6.326***
[3.25]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
Pseudo R-squared
Industry Dummies
Year Dummies
(4)
0.491***
[15.97]
-0.186
[-1.37]
0.343***
[3.64]
0.084***
[5.59]
0.373***
[5.41]
0.188
[1.60]
0.019
[0.13]
-0.012***
[-4.52]
-3.919**
[-2.48]
-3.431***
[-11.79]
0.476***
[15.54]
-0.211
[-1.54]
0.392***
[4.15]
0.089***
[5.93]
0.369***
[5.37]
0.065
[0.57]
-0.019
[-0.13]
-0.012***
[-4.72]
-3.984**
[-2.53]
-3.267***
[-11.21]
0.479***
[15.64]
-0.200
[-1.47]
0.394***
[4.19]
0.089***
[5.89]
0.366***
[5.33]
0.053
[0.47]
-0.026
[-0.18]
-0.012***
[-4.66]
-4.121***
[-2.61]
-3.294***
[-11.31]
6.325***
[4.11]
0.476***
[15.57]
-0.192
[-1.41]
0.371***
[3.94]
0.086***
[5.70]
0.374***
[5.45]
0.107
[0.94]
-0.015
[-0.10]
-0.012***
[-4.71]
-4.073***
[-2.58]
-3.242***
[-11.13]
4,147
27.4%
Yes
Yes
4,147
27.4%
Yes
Yes
4,147
27.4%
Yes
Yes
4,147
27.5%
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
40
Panel E: Dependent Variable is Listed Options
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
3.001***
[9.43]
Log Patents
26.791***
[10.44]
Log Citations
10.784***
[9.80]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
Pseudo R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
1.602***
[67.45]
-1.639***
[-17.85]
0.255***
[4.37]
0.374***
[38.81]
1.054***
[11.33]
0.462***
[6.53]
-0.011
[-0.08]
-0.018***
[-7.48]
1.112
[1.27]
3.411
[0.01]
1.561***
[65.91]
-1.644***
[-17.97]
0.388***
[6.82]
0.386***
[40.44]
1.010***
[10.96]
0.325***
[4.75]
0.033
[0.24]
-0.019***
[-7.99]
0.944
[1.08]
3.837
[0.01]
1.563***
[66.20]
-1.630***
[-17.82]
0.387***
[6.81]
0.386***
[40.36]
1.016***
[11.03]
0.321***
[4.69]
0.028
[0.21]
-0.018***
[-7.74]
0.795
[0.91]
3.853
[0.01]
11.404***
[12.41]
1.559***
[65.94]
-1.617***
[-17.70]
0.362***
[6.37]
0.382***
[39.96]
1.005***
[10.96]
0.370***
[5.40]
0.022
[0.16]
-0.018***
[-7.90]
0.866
[0.99]
3.832
[0.01]
71,621
42.1%
Yes
Yes
Yes
71,621
41.7%
Yes
Yes
Yes
71,621
41.7%
Yes
Yes
Yes
71,621
41.5%
Yes
Yes
Yes
t-statistics in brackets
*** p<0.01, ** p<0.05, * p<0.1
41
Table 5: Actions of Innovative Firms and the Impact on Stock Liquidity. In this table, we show that the
actions innovative firms take do improve stock liquidity. We present estimates from IV regressions with firm randomeffects where the dependent variable is Illiquidity measured in year t+1. The independent variables of interest include
Guidance, Stock Splits, and SEO Dummy instrumented by Industry Guidance, Industry Stock Splits, and Industry
SEO Dummy, respectively. We control for firm innovation as well as the following firm characteristics.
Dependent Variable is Illiquidity
INDEPENDENT VARIABLES
Guidance
(1)
(2)
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
Firm Random-effects
Industry Dummies
Year Dummies
-2.926***
[-6.16]
-1.111***
[-96.16]
1.321***
[27.45]
-0.199***
[-4.71]
-0.301***
[-23.58]
-0.694***
[-9.12]
-0.598***
[-9.91]
0.161**
[2.43]
-0.004**
[-2.15]
15.793***
[23.05]
7.379***
[39.52]
79,087
Yes
Yes
Yes
93,319
Yes
Yes
Yes
93,319
Yes
Yes
Yes
SEO Dummy
0.245***
[23.76]
Industry Stock Splits
0.392***
[13.84]
Industry SEO Dummy
Firm-specific Control Variables
R-squared
79,087
Yes
Yes
Yes
-4.360***
[-12.61]
-1.133***
[-86.34]
1.495***
[42.61]
-0.629***
[-25.65]
-0.433***
[-131.53]
-0.779***
[-21.73]
-1.009***
[-38.74]
0.029
[0.58]
-0.004***
[-3.75]
8.768***
[30.85]
8.333***
[95.54]
-4.830***
[-9.43]
-3.589***
[-8.39]
-1.113***
[-105.33]
1.401***
[33.81]
0.487***
[4.39]
-0.393***
[-71.53]
-0.623***
[-10.01]
-1.036***
[-32.15]
0.222***
[3.71]
-0.010***
[-6.33]
9.559***
[29.08]
8.201***
[62.90]
FIRST-STAGE INSTRUMENTAL VARIABLES
INDEPENDENT VARIABLES Guidance Stock Splits
Industry Guidance
-0.695***
[-3.99]
-4.214***
[-5.70]
-1.843*
[-1.69]
-3.162***
[-6.62]
-1.042***
[-40.04]
1.280***
[23.61]
0.070
[0.35]
-0.309***
[-23.37]
-0.673***
[-8.17]
-0.688***
[-9.92]
0.107
[1.32]
-0.011***
[-4.01]
13.164***
[13.01]
7.549***
[33.11]
-5.192***
[-10.49]
SEO Dummy
Log Assets
(4)
-0.537***
[-4.32]
Stock Splits
Innovation Index
(3)
Yes
76.7%
Yes
66.7%
t-statistics are in brackets
*** p<0.01, ** p<0.05, * p<0.1
42
0.374***
[13.32]
Yes
76.3%
Yes
69.8%
Table 6: Debt financing of Innovative Firms. In this table, we show that innovative firms are more likely to
issue public debt, have higher credit ratings, less likely to have covenants, and fewer covenants in their loans. Panel
A presents probit regressions with firm random-effects where the dependent variable is Public Debt Dummy. Panel
C presents probit regressions where the dependent variable is Covenant Dummy. Panel B and D present ordered
probit regressions where the dependent variable are Credit Rating and Number of covenants, respectively. All these
dependent variables are measured in year t+1. The independent variables of interest include four different proxies for
innovation: R&D, Log Patents, Log Citations, and Innovation Index. The following firm characteristics measured in
year t are also included in the regressions: Log Assets, Leverage, Cash, Tobin’s Q, NYSE Dummy, ROA, Tangibility,
Firm’s Age and Return Volatility. In the regressions of covenant we also control for loan characteristics: Log Loan
Amount, Log Loan Maturity and Syndicate Dummy measured in year t+1. All the variables are defined in the
Appendix. Year and industry dummies are also included.
Panel A: Dependent Variable is Public Debt Dummy
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
0.469
[1.03]
Log Patents
7.147***
[2.96]
Log Citations
5.703***
[5.08]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
Pseudo R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
1.144***
[58.20]
2.158***
[26.18]
-0.097
[-1.27]
0.074***
[5.79]
0.688***
[10.07]
0.045
[0.42]
-0.220*
[-1.81]
0.015***
[8.36]
-6.033***
[-6.24]
-28.737
[-0.09]
1.137***
[57.59]
2.161***
[26.26]
-0.081
[-1.08]
0.075***
[6.01]
0.679***
[9.97]
0.029
[0.27]
-0.221*
[-1.82]
0.015***
[8.08]
-6.176***
[-6.38]
-28.561
[-0.09]
1.135***
[57.61]
2.171***
[26.36]
-0.086
[-1.15]
0.074***
[5.86]
0.677***
[9.92]
0.035
[0.34]
-0.226*
[-1.86]
0.015***
[8.01]
-6.294***
[-6.49]
-9.889***
[-19.24]
3.869***
[4.30]
1.136***
[57.57]
2.170***
[26.34]
-0.093
[-1.24]
0.073***
[5.81]
0.680***
[9.97]
0.047
[0.45]
-0.226*
[-1.85]
0.015***
[8.01]
-6.238***
[-6.44]
-28.525
[-0.09]
93,651
37.4%
Yes
Yes
Yes
93,651
37.3%
Yes
Yes
Yes
93,651
37.3%
Yes
Yes
Yes
93,651
37.3%
Yes
Yes
Yes
t-statistics are in brackets
*** p<0.01, ** p<0.05, * p<0.1
43
Panel B: Dependent Variable is Credit Rating
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
-0.540
[-0.94]
Log Patents
5.870***
[2.73]
Log Citations
2.653**
[2.48]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Observations
Pseudo R-squared
Industry Dummies
Year Dummies
(4)
0.478***
[24.35]
-1.943***
[-15.42]
-0.596***
[-5.98]
0.246***
[13.10]
0.135**
[2.54]
2.149***
[13.27]
0.369***
[3.23]
0.005***
[4.22]
-42.808***
[-27.15]
0.470***
[23.59]
-1.915***
[-15.20]
-0.618***
[-6.39]
0.236***
[13.06]
0.132**
[2.48]
2.196***
[13.84]
0.383***
[3.37]
0.004***
[3.60]
-42.978***
[-27.10]
0.471***
[23.71]
-1.917***
[-15.23]
-0.621***
[-6.41]
0.236***
[13.06]
0.133**
[2.50]
2.197***
[13.85]
0.381***
[3.35]
0.004***
[3.75]
-42.962***
[-27.09]
2.061**
[2.50]
0.471***
[23.63]
-1.913***
[-15.19]
-0.626***
[-6.43]
0.235***
[12.98]
0.133**
[2.50]
2.204***
[13.88]
0.383***
[3.36]
0.004***
[3.67]
-42.972***
[-27.09]
20,763
33.3%
Yes
Yes
20,763
33.3%
Yes
Yes
20,763
33.3%
Yes
Yes
20,763
33.3%
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
44
Panel C: Dependent Variable is Covenant Dummy
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
0.138
[0.43]
Log Patents
-11.800***
[-5.61]
Log Citations
-4.754***
[-4.76]
Innovation Index
Log Loan Amount
Log Loan Maturity
Syndicate Dummy
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
Pseudo R-squared
Industry Dummies
Year Dummies
(4)
0.206***
[9.95]
0.155***
[7.48]
0.104***
[2.60]
-0.296***
[-15.13]
-0.265***
[-3.39]
-0.143*
[-1.78]
-0.036***
[-2.78]
0.111***
[3.01]
0.721***
[7.28]
0.093
[1.04]
-0.005***
[-3.99]
-1.362
[-1.45]
0.604
[1.24]
0.210***
[10.01]
0.152***
[7.30]
0.099**
[2.46]
-0.277***
[-13.93]
-0.310***
[-3.97]
-0.137*
[-1.73]
-0.028**
[-2.28]
0.113***
[3.07]
0.690***
[7.29]
0.074
[0.82]
-0.004***
[-3.09]
-1.124
[-1.19]
0.332
[0.69]
0.208***
[9.94]
0.152***
[7.34]
0.101**
[2.52]
-0.282***
[-14.26]
-0.303***
[-3.88]
-0.134*
[-1.69]
-0.028**
[-2.31]
0.113***
[3.05]
0.689***
[7.25]
0.080
[0.89]
-0.004***
[-3.42]
-1.225
[-1.30]
0.403
[0.83]
-3.957***
[-5.16]
0.208***
[9.98]
0.152***
[7.31]
0.099**
[2.46]
-0.278***
[-14.07]
-0.314***
[-4.01]
-0.122
[-1.54]
-0.025**
[-2.06]
0.112***
[3.02]
0.665***
[6.98]
0.076
[0.85]
-0.004***
[-3.28]
-1.164
[-1.24]
0.348
[0.72]
14,794
23.7%
Yes
Yes
14,794
24.0%
Yes
Yes
14,794
23.9%
Yes
Yes
14,794
23.9%
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
45
Panel D: Dependent Variable is Number of Covenants
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
-0.565**
[-2.02]
Log Patents
-10.844***
[-6.39]
Log Citations
-4.542***
[-5.58]
Innovation Index
Log Loan Amount
Log Loan Maturity
Syndicate Dummy
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Observations
Pseudo R-squared
Industry Dummies
Year Dummies
(4)
0.100***
[5.65]
0.226***
[12.77]
0.203***
[6.04]
-0.262***
[-15.94]
0.041
[0.61]
-0.085
[-1.20]
-0.053***
[-5.11]
0.057*
[1.86]
0.716***
[7.69]
-0.117*
[-1.65]
-0.005***
[-5.69]
-2.238***
[-2.72]
0.104***
[5.69]
0.225***
[12.59]
0.203***
[6.01]
-0.247***
[-14.64]
0.021
[0.32]
-0.107
[-1.57]
-0.053***
[-5.37]
0.061**
[2.01]
0.743***
[8.43]
-0.129*
[-1.82]
-0.004***
[-4.65]
-2.101**
[-2.55]
0.103***
[5.63]
0.225***
[12.62]
0.204***
[6.06]
-0.251***
[-14.93]
0.025
[0.37]
-0.106
[-1.55]
-0.053***
[-5.37]
0.061**
[2.00]
0.742***
[8.38]
-0.125*
[-1.77]
-0.005***
[-4.98]
-2.161***
[-2.62]
-3.908***
[-6.26]
0.103***
[5.67]
0.224***
[12.58]
0.202***
[6.00]
-0.248***
[-14.72]
0.014
[0.21]
-0.094
[-1.37]
-0.050***
[-5.07]
0.060*
[1.96]
0.717***
[8.11]
-0.129*
[-1.82]
-0.004***
[-4.80]
-2.109**
[-2.56]
14,794
8.8%
Yes
Yes
14,794
8.9%
Yes
Yes
14,794
8.9%
Yes
Yes
14,794
8.9%
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
46
Table 7: Institutional Ownership, Blockholders and Incentive contract of Innovative Firms. In this
table, we show that the role of monitoring for innovative firms rests on equity-holders. Panel A and C present
regressions with firm random-effects, where the dependent variables are Institutional Ownership and Equity-Based
Compensation respectively. Panel B presents probit regressions with firm random-effects, where the dependent
variables is Blockholder Dummy. All these dependent variables are measured in year t+1. The independent variables
of interest include four different proxies for innovation: R&D, Log Patents, Log Citations, and Innovation Index. The
following firm characteristics measured in year t are also included in the regressions: Log Assets, Leverage, Cash,
Tobin’s Q, NYSE Dummy, ROA, Tangibility, Firm’s Age and Return Volatility. In the regressions of equity-based
compensation we also control for CEO characteristics: CEO Age, CEO Tenure and CEO Ownership measured in
year t+1. All the variables are defined in the Appendix. Year and industry dummies are also included.
Panel A: Dependent Variable is Institutional Ownership
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
0.006
[0.51]
Log Patents
0.902***
[8.93]
Log Citations
0.119***
[2.69]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
0.064***
[90.41]
-0.086***
[-25.41]
0.020***
[8.53]
0.012***
[35.46]
0.038***
[8.08]
0.052***
[18.89]
-0.012**
[-2.47]
0.000
[0.70]
-0.978***
[-35.11]
0.029***
[2.89]
0.063***
[89.14]
-0.085***
[-25.24]
0.020***
[9.00]
0.012***
[35.70]
0.038***
[8.01]
0.053***
[19.57]
-0.013***
[-2.60]
0.000
[0.33]
-0.985***
[-35.34]
0.038***
[3.72]
0.064***
[89.93]
-0.086***
[-25.32]
0.020***
[8.84]
0.012***
[35.79]
0.038***
[8.11]
0.052***
[19.41]
-0.012**
[-2.53]
0.000
[0.68]
-0.982***
[-35.20]
0.032***
[3.13]
0.217***
[5.96]
0.063***
[89.67]
-0.085***
[-25.19]
0.020***
[8.68]
0.012***
[35.47]
0.038***
[8.08]
0.053***
[19.75]
-0.013***
[-2.63]
0.000
[0.55]
-0.983***
[-35.26]
0.036***
[3.50]
93,651
37.7%
Yes
Yes
Yes
93,651
38.0%
Yes
Yes
Yes
93,651
37.8%
Yes
Yes
Yes
93,651
37.9%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
47
Panel B: Dependent Variable is Blockholder Dummy
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
1.054***
[4.63]
Log Patents
11.972***
[6.17]
Log Citations
5.137***
[5.84]
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
Pseudo R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
0.212***
[17.86]
-0.307***
[-4.69]
0.199***
[4.51]
0.014**
[2.23]
-0.027
[-0.46]
0.335***
[6.64]
-0.213**
[-2.27]
0.001
[0.86]
-11.035***
[-19.99]
-0.075
[-0.54]
0.198***
[16.56]
-0.312***
[-4.79]
0.247***
[5.71]
0.017***
[2.74]
-0.033
[-0.56]
0.284***
[5.84]
-0.205**
[-2.18]
0.001
[0.31]
-11.124***
[-20.15]
0.051
[0.36]
0.200***
[16.76]
-0.309***
[-4.73]
0.245***
[5.68]
0.017***
[2.66]
-0.031
[-0.53]
0.286***
[5.87]
-0.207**
[-2.21]
0.001
[0.47]
-11.130***
[-20.16]
0.030
[0.21]
4.951***
[7.07]
0.198***
[16.62]
-0.300***
[-4.60]
0.234***
[5.41]
0.015**
[2.37]
-0.031
[-0.53]
0.307***
[6.28]
-0.208**
[-2.22]
0.001
[0.34]
-11.142***
[-20.19]
0.075
[0.53]
93,651
30.0%
Yes
Yes
Yes
93,651
29.9%
Yes
Yes
Yes
93,651
29.9%
Yes
Yes
Yes
93,651
29.8%
Yes
Yes
Yes
t-statistics are in brackets
*** p<0.01, ** p<0.05, * p<0.1
48
Panel C: Dependent Variable is Equity-Based Compensation
INDEPENDENT VARIABLES
R&D
(1)
(2)
(3)
0.308***
[3.17]
Log Patents
2.181***
[3.94]
Log Citations
0.810***
[3.33]
Innovation Index
CEO Age
CEO Tenure
CEO Ownership
Free Cash Flow
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
(4)
-0.004***
[-5.84]
-0.000***
[-3.82]
-0.586***
[-7.84]
-0.045*
[-1.77]
0.041***
[8.66]
-0.056**
[-2.18]
0.039*
[1.90]
0.013***
[4.01]
-0.004
[-0.34]
0.122***
[3.77]
-0.079**
[-2.47]
-0.001***
[-3.46]
0.103
[0.28]
0.220***
[2.84]
-0.004***
[-5.86]
-0.000***
[-3.92]
-0.590***
[-7.91]
-0.054**
[-2.08]
0.037***
[7.80]
-0.058**
[-2.28]
0.053***
[2.74]
0.014***
[4.52]
-0.007
[-0.55]
0.108***
[3.46]
-0.079**
[-2.45]
-0.001***
[-3.80]
0.143
[0.39]
0.250***
[3.26]
-0.004***
[-5.84]
-0.000***
[-3.88]
-0.593***
[-7.94]
-0.053**
[-2.08]
0.038***
[8.03]
-0.058**
[-2.28]
0.053***
[2.71]
0.014***
[4.50]
-0.007
[-0.56]
0.106***
[3.36]
-0.080**
[-2.47]
-0.001***
[-3.65]
0.138
[0.38]
0.246***
[3.20]
0.819***
[4.26]
-0.004***
[-5.84]
-0.000***
[-3.91]
-0.587***
[-7.85]
-0.052**
[-2.05]
0.037***
[7.86]
-0.056**
[-2.21]
0.050**
[2.57]
0.014***
[4.39]
-0.005
[-0.45]
0.112***
[3.57]
-0.079**
[-2.45]
-0.001***
[-3.80]
0.127
[0.35]
0.254***
[3.32]
7,947
19.2%
Yes
Yes
Yes
7,947
19.3%
Yes
Yes
Yes
7,947
19.3%
Yes
Yes
Yes
7,947
19.4%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
49
50
Observations
Kleibergen-Paap rk Wald F statistic
Firm Fixed-effects
Industry Dummies
Year Dummies
Intercept
Return Volatility
Firm’s Age
Tangibility
ROA
NYSE Dummy
Tobin’s Q
Cash
Leverage
43,729
566.94
Yes
Yes
Yes
-0.274***
[-8.03]
0.006
[0.21]
0.000
[0.08]
-0.947***
[-3.94]
-0.152***
[-12.80]
-0.069***
[-11.65]
0.056***
[2.73]
-0.119***
[-6.16]
-0.188***
[-25.96]
∆ Illiquidityt,t+1
Log Assets
(1)
R&D=0
INDEPENDENT VARIABLES
34,617
649.19
Yes
Yes
Yes
-0.283***
[-13.76]
-0.043
[-0.95]
0.001*
[1.65]
-2.148***
[-7.20]
-0.206***
[-15.54]
-0.112***
[-15.66]
0.057**
[2.42]
-0.098***
[-6.50]
-0.176***
[-47.76]
(2)
R&D>0
62,455
1222.02
Yes
Yes
Yes
-0.285***
[-14.29]
0.014
[0.59]
-0.000
[-0.36]
-1.247***
[-6.61]
-0.156***
[-18.53]
-0.086***
[-17.79]
0.066***
[3.83]
-0.107***
[-8.13]
-0.180***
[-46.27]
(3)
Patents=0
15,189
306.27
Yes
Yes
Yes
-0.231***
[-7.46]
-0.054
[-0.80]
0.001*
[1.82]
-1.222***
[-2.70]
-0.205***
[-10.53]
-0.096***
[-8.16]
0.030
[0.90]
-0.098***
[-4.31]
-0.169***
[-29.80]
(4)
Patents>0
Dependent Variable is ∆ T obin0 sQt,t+1
64,469
1241.76
Yes
Yes
Yes
-0.282***
[-14.60]
0.010
[0.42]
-0.000
[-0.44]
-1.217***
[-6.56]
-0.156***
[-18.76]
-0.086***
[-17.89]
0.067***
[4.00]
-0.106***
[-8.32]
-0.180***
[-47.54]
(5)
Citations=0
13,228
280.58
Yes
Yes
Yes
-0.238***
[-7.36]
-0.045
[-0.58]
0.001
[1.55]
-1.375***
[-2.73]
-0.205***
[-10.11]
-0.108***
[-8.04]
0.036
[0.91]
-0.103***
[-4.16]
-0.170***
[-28.61]
(6)
Citations>0
51,152
701.39
Yes
Yes
Yes
-0.273***
[-9.15]
-0.001
[-0.03]
0.000
[0.66]
-1.006***
[-4.59]
-0.145***
[-13.62]
-0.074***
[-13.93]
0.056***
[3.05]
-0.112***
[-6.58]
-0.185***
[-30.92]
(7)
Index<0
26,600
546.78
Yes
Yes
Yes
-0.276***
[-12.67]
-0.044
[-0.80]
0.001
[1.19]
-2.299***
[-6.54]
-0.214***
[-14.12]
-0.113***
[-13.20]
0.062**
[2.16]
-0.095***
[-5.70]
-0.176***
[-45.10]
(8)
Index>0
Table 8: Firm Value and Stock Liquidity of Innovative Firms. In this table, we show that stock liquidity is positively related with firm value and such
relationship is stronger for innovative firms. We present the IV regressions with firm fixed-effects, where the dependent variable is ∆ Tobin’s Q from year t to
t+1 and the independent variable of interest is ∆ Illiquidity from year t to t+1 instrumented by change in the median Illiquidity of the other firms in the same
industry from year t to t+1. We present estimates for subsamples that have non-positive and positive value of R&D, Log Patents, Log Citations, and Innovation
Index in year t. The following firm characteristics measured in year t are also included in the regressions: Log Assets, Leverage, Cash, Tobin’s Q, NYSE Dummy,
ROA, Tangibility, Firm’s Age and Return Volatility. All the variables are defined in the Appendix. Year dummies are also included.
51
0.319***
[23.81]
Yes
26.9%
0.406***
[25.48]
Yes
37.6%
0.406***
[34.96]
Yes
33.7%
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
Firm-specific Control Variables
R-squared
∆ Industry Illiquidityt,t+1
FIRST-STAGE INSTRUMENTAL VARIABLES:
0.374***
[17.50]
Yes
34.9%
0.405***
[35.24]
Yes
33.7%
0.381***
[16.75]
Yes
36.4%
Dependent variable: ∆ Illiquidityt,t+1
0.326***
[26.48]
Yes
29.6%
0.430***
[23.38]
Yes
38.6%
Table 9: Firm Value and Stock Liquidity of Innovative Firms Following Decimalization. In this table,
we show that an exogenous increase in stock liquidity due to stock price decimalization positively impact firm value
and such impact is greater for innovative firms. We present regressions where the dependent variable is ∆ Tobin’s Q
from year t-1 to t+1 surrounding the year of decimalization and the independent variable of interest is ∆ Illiqudity
from year t-1 to t+1 surrounding the year of decimalization and its interaction with four measures of innovativeness:
R&D Dummy, Patents Dummy, Citations Dummy, Innovation Index Dummy measured in year t-1. The following
firm characteristics measured in year t-1 are also included in the regressions: Log Assets, Leverage, Cash, Tobin’s Q,
NYSE Dummy, ROA, Tangibility, Firm’s Age and Return Volatility. All the variables are defined in the Appendix.
Industry dummies are also included.
Dependent Variable is ∆ T obin0 sQt−1,t+1
INDEPENDENT VARIABLES
∆ Illiqudityt−1,t+1 * Innovativeness
Innovativeness
∆ Illiqudityt−1,t+1
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Industry Dummies
R&D
Patents
Citations
Index
-0.019**
[-2.30]
0.057***
[2.99]
-0.070***
[-12.69]
-0.033***
[-7.21]
0.010
[0.27]
-0.247***
[-8.15]
-0.142***
[-25.58]
0.009
[0.58]
-0.393***
[-9.58]
-0.146***
[-3.98]
0.002***
[4.16]
0.332
[0.85]
0.420***
[4.25]
-0.006
[-0.55]
0.034**
[2.00]
-0.077***
[-15.94]
-0.034***
[-7.21]
0.005
[0.13]
-0.248***
[-8.23]
-0.141***
[-25.57]
0.007
[0.44]
-0.394***
[-9.62]
-0.146***
[-3.97]
0.002***
[4.21]
0.351
[0.90]
0.433***
[4.11]
-0.005
[-0.50]
0.036**
[2.06]
-0.078***
[-16.01]
-0.034***
[-7.23]
0.005
[0.13]
-0.248***
[-8.23]
-0.141***
[-25.57]
0.007
[0.44]
-0.394***
[-9.62]
-0.146***
[-3.98]
0.002***
[4.19]
0.343
[0.88]
0.434***
[4.12]
-0.017**
[-1.99]
0.044**
[2.51]
-0.072***
[-13.74]
-0.033***
[-7.20]
0.006
[0.17]
-0.247***
[-8.12]
-0.142***
[-25.43]
0.008
[0.52]
-0.395***
[-9.61]
-0.145***
[-3.94]
0.002***
[4.07]
0.336
[0.86]
0.439***
[4.23]
4,161
50.2%
Yes
4,161
50.0%
Yes
4,161
50.0%
Yes
4,161
50.1%
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
52
Table 10: Firm Value and Stock Liquidity of Innovative Firms Following S&P addition. In this table,
we show that an increase in stock liquidity due to the S&P addition impacts firm value and such impact is greater
for innovative firms. We present regressions where the dependent variable is ∆ Tobin’s Q from year t-1 to t+1
surrounding the year of S&P addition (option listing) and the independent variable of interest is ∆ Illiqudity from
year t-1 to t+1 surrounding the year of S&P addition (option listing) and its interaction with four measures of
innovativeness: R&D Dummy, Patents Dummy, Citations Dummy, Innovation Index Dummy measured in year t-1.
The following firm characteristics measured in year t-1 are also included in the regressions: Log Assets, Leverage,
Cash, Tobin’s Q, NYSE Dummy, ROA, Tangibility, Firm’s Age and Return Volatility. All the variables are defined
in the Appendix. Industry dummies are also included.
S&P Addition: Dependent Variable is ∆ T obin0 sQt−1,t+1
INDEPENDENT VARIABLES
∆ Illiqudityt−1,t+1 * Innovativeness
Innovativeness
∆ Illiqudityt−1,t+1
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Industry Dummies
R&D
Patents
Citations
Index
-0.119*
[-1.72]
0.013
[0.13]
-0.129***
[-3.07]
0.036
[0.93]
-0.312*
[-1.76]
-0.143
[-1.16]
-0.112***
[-6.92]
-0.156**
[-2.20]
0.116
[0.34]
-0.080
[-0.42]
-0.000
[-0.26]
-8.518***
[-2.90]
-0.017
[-0.05]
-0.122*
[-1.77]
0.093
[1.19]
-0.150***
[-3.63]
0.026
[0.70]
-0.407**
[-2.34]
-0.151
[-1.23]
-0.113***
[-7.10]
-0.140*
[-1.97]
0.048
[0.15]
-0.092
[-0.49]
-0.001
[-0.30]
-8.789***
[-2.96]
0.229
[0.61]
-0.142**
[-2.15]
0.117
[1.38]
-0.146***
[-3.71]
0.024
[0.65]
-0.406**
[-2.38]
-0.147
[-1.20]
-0.113***
[-7.09]
-0.135*
[-1.91]
0.014
[0.04]
-0.094
[-0.50]
-0.001
[-0.37]
-8.826***
[-2.97]
-0.066
[-0.18]
-0.079
[-1.12]
0.117
[1.53]
-0.150***
[-3.55]
0.027
[0.73]
-0.328*
[-1.87]
-0.141
[-1.13]
-0.114***
[-7.19]
-0.152**
[-2.19]
0.104
[0.30]
-0.074
[-0.40]
-0.000
[-0.22]
-9.208***
[-3.15]
0.202
[0.53]
262
65.1%
Yes
262
66.0%
Yes
262
66.7%
Yes
262
65.3%
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
53
54
Observations
R-squared
Industry Dummies
Intercept
Return Volatility
Firm’s Age
Tangibility
ROA
NYSE Dummy
Tobin’s Q
Cash
Leverage
Log Assets
Incentive Dummy
∆ Illiqudityt−1,t+1 * Incentive Dummy
∆ Illiqudityt−1,t+1
INDEPENDENT VARIABLES
(1)
R&D=0
-0.080***
[-5.57]
-0.019
[-0.85]
-0.024
[-0.85]
-0.031***
[-3.11]
0.014
[0.20]
-0.048
[-0.40]
-0.135***
[-7.86]
0.009
[0.32]
-0.482**
[-2.02]
-0.128*
[-1.84]
0.002***
[2.75]
-0.330
[-0.36]
0.244**
[2.12]
712
49.6%
Yes
(2)
R&D>0
-0.067***
[-3.14]
-0.060**
[-2.49]
-0.098***
[-3.10]
0.009
[0.66]
-0.022
[-0.24]
-0.129**
[-2.23]
-0.127***
[-14.88]
-0.039
[-0.89]
-0.478***
[-5.24]
-0.244**
[-2.01]
0.001
[1.28]
-2.040
[-1.24]
0.661***
[5.74]
582
70.8%
Yes
(3)
Patents=0
-0.082***
[-6.09]
-0.022
[-1.15]
-0.042
[-1.53]
-0.031***
[-3.00]
0.060
[0.87]
-0.112
[-1.40]
-0.138***
[-10.26]
0.006
[0.23]
-0.525***
[-4.66]
-0.157**
[-2.16]
0.002**
[2.55]
-0.507
[-0.52]
0.066
[0.72]
803
57.4%
Yes
(4)
Patents>0
-0.070***
[-2.68]
-0.079***
[-2.62]
-0.085**
[-2.42]
0.003
[0.20]
-0.126
[-1.20]
-0.119
[-1.56]
-0.121***
[-13.25]
-0.041
[-0.91]
-0.422***
[-2.86]
-0.167
[-1.43]
0.002*
[1.96]
-2.463
[-1.39]
-0.001
[-0.01]
491
72.9%
Yes
Dependent Variable is ∆ T obin0 sQt−1,t+1
(5)
Citations=0
-0.081***
[-6.10]
-0.024
[-1.31]
-0.045*
[-1.70]
-0.032***
[-3.16]
0.054
[0.79]
-0.110
[-1.39]
-0.139***
[-10.30]
0.010
[0.38]
-0.531***
[-4.83]
-0.156**
[-2.19]
0.002***
[2.60]
-0.546
[-0.57]
0.149
[1.36]
818
57.7%
Yes
(6)
Citations>0
-0.071***
[-2.71]
-0.082***
[-2.65]
-0.087**
[-2.45]
0.006
[0.41]
-0.120
[-1.12]
-0.120
[-1.56]
-0.120***
[-13.13]
-0.038
[-0.82]
-0.419***
[-2.80]
-0.201*
[-1.68]
0.001
[1.63]
-2.446
[-1.34]
0.146
[0.89]
476
72.9%
Yes
(7)
Index<0
-0.089***
[-5.93]
-0.022
[-0.92]
-0.032
[-1.05]
-0.029***
[-2.74]
0.076
[1.09]
-0.012
[-0.10]
-0.143***
[-8.00]
-0.004
[-0.16]
-0.472
[-1.62]
-0.106
[-1.46]
0.002***
[2.88]
0.089
[0.10]
0.241***
[2.79]
718
51.3%
Yes
(8)
Index>0
-0.055**
[-2.58]
-0.068***
[-2.78]
-0.098***
[-3.02]
0.004
[0.32]
-0.158*
[-1.66]
-0.125**
[-2.07]
-0.124***
[-14.74]
-0.015
[-0.36]
-0.495***
[-5.45]
-0.247**
[-2.04]
0.001
[1.59]
-2.532
[-1.55]
0.183
[1.23]
576
70.6%
Yes
Table 11: CEO Incentive Contract, Liquidity, and Firm Value of Innovative Firms. In this table, we show that the positive impact of liquidity on
firm value is concentrated in the sample of innovative firms with more equity-based compensation for the managers. We present regressions where the dependent
variable is ∆ Tobin’s Q from year t-1 to t+1 surrounding the year of decimalization and the independent variable of interest is ∆ Illiqudity from year t-1 to
t+1 surrounding the year of decimalization and its interaction with Incentive Dummy. We present estimates for subsamples that have non-positive and positive
value of R&D, Log Patents, Log Citations, and Innovation Index in year t-1. The following firm characteristics measured in year t-1 are also included in the
regressions: Log Assets, Leverage, Cash, Tobin’s Q, NYSE Dummy, ROA, Tangibility, Firm’s Age and Return Volatility. All the variables are defined in the
Appendix. Industry dummies are also included.
55
t-statistics using robust, firm-clustered standard errors are in brackets; *** p<0.01, ** p<0.05, * p<0.1
Table 12: Advertising as Alternative Variable. In this table, we present regressions using Advertising as
independent variable. Panel A presents regressions with firm random-effects, where is the dependent variables are
the measures of stock illiquidity. Panel B presents regressions where the dependent variables are the measures of
actions presented in Table 4. The methodology is the same as previous regressions on innovation. The following
firm characteristics are also included in the regressions: Log Assets, Leverage, Cash, Tobin’s Q, NYSE Dummy, ROA,
Tangibility, Firm’s Age and Return Volatility. All the variables are defined in the Appendix.
Panel A:
INDEPENDENT VARIABLES
Advertising
Innovation Index
Log Assets
Leverage
Cash
Tobin’s Q
NYSE Dummy
ROA
Tangibility
Firm’s Age
Return Volatility
Intercept
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
Illiquidity
(1)
Negative Turnover
(2)
Bid-Ask Spread
(3)
PIN
(4)
-2.321***
[-4.77]
-4.139***
[-10.24]
-1.160***
[-109.77]
1.504***
[30.15]
-0.570***
[-21.00]
-0.434***
[-76.60]
-0.782***
[-17.85]
-1.072***
[-28.72]
0.089
[1.20]
-0.002
[-0.88]
10.368***
[25.38]
8.243***
[85.77]
-2.408***
[-5.37]
-1.284***
[-2.98]
-0.276***
[-35.72]
0.049
[1.23]
-0.458***
[-16.64]
-0.174***
[-34.42]
0.302***
[11.00]
-0.349***
[-10.05]
-0.031
[-0.59]
0.010***
[11.28]
-12.501***
[-32.96]
0.931***
[14.14]
-0.026**
[-2.42]
-0.077***
[-10.62]
-0.009***
[-38.73]
0.024***
[18.78]
-0.010***
[-18.23]
-0.004***
[-37.16]
-0.007***
[-9.55]
-0.014***
[-16.43]
-0.004**
[-2.24]
0.000***
[9.56]
0.466***
[36.57]
0.055***
[23.67]
-0.112***
[-3.49]
-0.022
[-0.77]
-0.026***
[-43.75]
0.030***
[8.38]
-0.004
[-1.14]
-0.011***
[-19.53]
-0.022***
[-11.07]
-0.016***
[-3.56]
-0.002
[-0.44]
-0.000
[-0.80]
-0.298***
[-5.47]
0.421***
[51.38]
93,319
76.3%
Yes
Yes
Yes
93,470
24.0%
Yes
Yes
Yes
92,787
51.0%
Yes
Yes
Yes
17,888
51.0%
Yes
Yes
Yes
t-statistics using robust, firm-clustered standard errors are in brackets
*** p<0.01, ** p<0.05, * p<0.1
56
57
(2)
0.807**
[2.11]
0.233
[0.59]
0.047***
[8.60]
-0.280***
[-6.94]
0.007
[0.24]
0.087***
[23.03]
0.123***
[5.74]
0.276***
[8.14]
0.069
[1.52]
-0.001
[-0.90]
3.302***
[8.78]
4.939
[0.01]
89,870
00.4%
Yes
Yes
Yes
(1)
0.859***
[5.32]
1.116***
[7.11]
0.064***
[25.78]
-0.057***
[-4.52]
-0.027***
[-3.38]
0.019***
[16.19]
0.054***
[4.89]
0.163***
[18.99]
-0.131***
[-7.90]
0.000
[0.03]
-0.144
[-1.58]
-0.519***
[-23.70]
84,298
22.1%
Yes
Yes
Yes
INDEPENDENT VARIABLES
Advertising
t-statistics are in brackets
*** p<0.01, ** p<0.05, * p<0.1
Observations
R-squared
Firm Random-effects
Industry Dummies
Year Dummies
Intercept
Return Volatility
Firm’s Age
Tangibility
ROA
NYSE Dummy
Tobin’s Q
Cash
Leverage
Log Assets
Innovation Index
Stock Splits
Guidance
93,651
37.3%
Yes
Yes
Yes
2.228**
[2.21]
3.847***
[4.27]
1.136***
[57.57]
2.174***
[26.37]
-0.095
[-1.27]
0.072***
[5.72]
0.678***
[9.94]
0.050
[0.48]
-0.223*
[-1.83]
0.015***
[7.95]
-6.248***
[-6.45]
-9.258***
[-42.68]
Public Debt
Dummy
(3)
Panel B:
20,763
33.3%
No
Yes
Yes
-0.675
[-0.75]
2.089**
[2.53]
0.470***
[23.62]
-1.913***
[-15.19]
-0.624***
[-6.42]
0.236***
[12.91]
0.134**
[2.52]
2.209***
[13.85]
0.380***
[3.33]
0.004***
[3.71]
-42.956***
[-27.10]
(4)
Credit Rating
93,651
01.3%
Yes
Yes
Yes
-0.717
[-1.40]
1.894***
[3.85]
0.022***
[3.16]
0.574***
[12.78]
0.180***
[5.38]
0.076***
[16.32]
0.212***
[8.12]
0.020
[0.50]
0.056
[1.02]
-0.015***
[-17.22]
-4.579***
[-8.93]
-2.151***
[-24.79]
(5)
SEO Dummy
4,147
27.5%
No
Yes
Yes
0.958
[0.68]
6.341***
[4.34]
0.476***
[21.32]
-0.192
[-1.55]
0.370***
[3.97]
0.086***
[6.23]
0.374***
[5.88]
0.107
[0.98]
-0.008
[-0.05]
-0.012***
[-5.25]
-4.126**
[-2.58]
-3.243***
[-13.30]
Reputed Underwriter
(6)
Op-
71,621
41.4%
Yes
Yes
Yes
5.700***
[5.66]
11.425***
[12.44]
1.554***
[65.91]
-1.613***
[-17.67]
0.358***
[6.30]
0.380***
[39.81]
0.997***
[10.92]
0.378***
[5.52]
0.028
[0.21]
-0.019***
[-7.96]
0.928
[1.06]
3.767
[0.01]
Listed
tions
(7)