organization capital and analyst coverage asia … · capital. stein (1988) suggests a myopia story...
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Organization Capital and Analyst Coverage
Konan Chan Department of Finance, National Chengchi University
Re-Jin J. Guo Department of Finance, University of Illinois at Chicago
Yanzhi A. Wang Department of Finance, National Taiwan University
Hsiao-Lin Yang Department of Finance, National Chengchi University
Abstract
This study examines the effect of analyst coverage on firms’ investment in organization
capital. We argue that analyst coverage reduces information asymmetry, lowers the cost
of capital, and thus enhances firms’ investment in organization capital. By utilizing
exogenous reduction in the analyst coverage resulting from brokerage house mergers
and closures, we provide causal evidence of a significant decline in firms’ organization
capital investments subsequent to analyst coverage reduction. Further analysis indicates
a greater decline in organization capital investments for firms with higher costs of
capital. The decline in the post-event organization capital investments is accentuated in
firms with financial constraint and higher external equity dependence. Firm
productivity and operating performance deteriorate subsequently, especially for firms
with few organization capital investments. We also show that post-event compensations
decline, particularly for firms decreasing their organization capital investment.
Keywords: Organization Capital; Analyst Coverage; Cost of Capital
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1. Introduction
Organization capital has become increasingly important in improving corporate
performance (Corrado et al., 2009; Eisfeldt and Papanikolaou, 2013, 2014). 1
Organization capital is the accumulated intangible assets resulting from investments in
business process and management practices often embodied in unique corporate
designs and process.2 As organization capital integrates physical and human capital
effectively, it enhances a company’s production efficiency and competitive advantage
(Prescott and Vesscher, 1980; Lev and Radhakrishman, 2005; Eisfeldt and
Papanikolaou, 2013, 2014). 3 Thus, organization capital is called “the mother of
intangible assets” (Lev and Radhakrishnan (2015)). Organization capital is also
economically significant; the average selling, general and administrative (SG&A)
expense, a key component of organization capital investment, is about 3-5 times of the
average U.S. firms’ R&D expenditures during 1980 to 2016, with an average growth
rate of 640%.
Although recent papers have examined the effect of organization capital on firm
performance, stock returns, management quality, and corporate decisions,4 little is
1 Eisfeldt and Papanikolaou (2014) point out that the ratio of aggregate organization capital relative to physical capital is above unit between 1983 and 2012, with more firm investment in organization capital than in fixed investment in the past two decades. Corrado et al. (2009) show that firm-specific human and organization capital investments are the single largest category of business intangible, accounting for about 30 percent of all intangible assets in the U.S. 2 Corrado et al. (2009) examine intangible assets in three categories: computerized information, innovative property, and economic competencies. The category of economic competencies covers brand equity and firm-specific resource, including the costs of employer-provided worker training and management time devoted to enhancing the productivity of the firm. The authors estimate that the computerized information, scientific R&D, non-scientific R&D, brand equity, and firm-specific resources are approximately 14%, 25%, 24%, 7%, and 30% in the year of 2003. 3 Hasan and Cheung (2018) argue that organization as a resource base could effective integrate physical resources and management and assist firms to utilize valuable resources in the optimal way, achieve outperformance, and move to their prime life stage. Some other papers also document the importance of organization capital. 4 For example, recent research shows that organization capital has a positive effect on firm performance and stock returns (Lev and Radhakrishnan (2005), Lev, Radhakrishnan, and Zhang (2009), Li, Qiu, and Shen (2018)). Francis, Mani, and Wu (2015) find a positive relationship between organization capital and patent counts and citations. Additionally, Chan, Wang, and Yang (2015) suggest that firms with higher organization capital are more attractive and are likely to become an acquisition target.
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known about the determinants of a firm’s investment in organization capital. In this
paper, we investigate the potential role of research coverage provided by financial
analysts on firm’s organization capital, and provide a causal evidence of the effect of
external market financial intermediary on corporate organization capital investment.
We propose two competing explanations on the effect of analyst coverage. On one
hand, more financial analyst coverages could lead to more investments on organization
capital. Stein (1988) suggests a myopia story that managers tend to sacrifice long-term
benefits to boost current profits. Subsequent papers also find that firms could reduce
long-term investments, such as research and development (R&D), patents, and
marketing activities, in exchange of high current earnings (e.g., Bushee, 1998; Mizik,
2010; He and Tian, 2013). Organization capital also involves managerial myopia
because SG&A expenditures, a key component of organization capital investments,
reduce earnings. Managers may cut organization capital investments to lower expenses
and boost current earnings. Financial analysts, as an important financial market
intermediary, can serve as external monitors and pressure on managers, and therefore
the managerial myopia can be greatly mitigated (e.g., Brennan and Subrahmanyam,
1995; Hong, Lim and Stein, 2000; He and Tian, 2013). As a result, this managerial
myopia explanation predicts that as more (fewer) analysts engage in monitoring
managers, the firm would (not) avoid scarification of long-term investments and invest
more (less) on organization capital.
One the other hand, more financial analyst coverages could lead to less
organization capital. Organization capital investments, for instance, expenditures
regarding worker training, management compensation, new technology development
and business progress improvement, are costly, and the amount of organization capital
investment can be non-trivial. In addition, organization capital can be highly uncertain
and exhibit high degree of information asymmetry (e.g., Eisfeldt and Papanikolaou,
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2013). These features of organization capital suggest that firms are likely to rely on
equity issuance, and can be sensitive to varying costs of equity capital, an argument
similar to Brown, Fazzari and Petersen (2009) who suggest reliance on equity issuance
for R&D firms due to the uncertainty and information asymmetry features embedded
in R&D.
Financial analysts can also potentially reduce information asymmetry for the
investors in evaluating the benefits of organization capital and thus lower the cost of
capital for firms to make investments. In particular, analyst reports provide and evaluate
detailed information on a firm’s management, talents, new technology, and business
process improvement. Analysts’ collection and dissemination of organization capital
related information can reduce information asymmetry between managers and outside
investors, and mitigate the possibility that certain investors ignore or misvalue the
future benefits of organization capital. Consequently, this cost of capital explanation
predicts that as more (fewer) analysts engage in information collection and
dissemination on a firm, it can have a lower (higher) cost of capital, and can access
external market easier (more difficult) to finance its organization capital investments.
One important component of organization capital investment is talent recruiting
(Eisfeldt and Papanikolaou, 2013, 2014). If the managerial explanation is true, we
would expect to observe that firms pay more to top-executives because top-executives
would benefit themselves with more private benefits, especially when there is weak
external monitoring. Under the cost of capital explanation, we argue that the
compensation to top–talents (executives), mostly in employee stock options and
restricted stocks, can be particularly sensitive to fluctuation in costs of equity capital
(Chen, Truong and Veeraraghavan, 2015).5 With an exogenous stock to (increase) cost
5 One may suggest wage of total employees as an example of recruiting key talents. However, wage of employees is related to cost of goods sold, which is more closed to inputs of labor force but not firm organization capital.
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of equity capital, stock price depreciates and the intrinsic value of existing employee
stock options and share bonuses decline. Similarly, a higher cost of equity capital will
increase the costs of any new issues of employee stock options and share bonuses, and
discourage the firm to offer more compensations to top-managers. As a result, we
hypothesize that firm’s top executive compensation will decline subsequently upon the
exogenous shocks to analyst coverage. As talent recruiting costs comprise a key
component of organization capital investments, the organization capital after shocks are
expected to experience significant changes corresponding to two competing
explanations.
We examine the relation between analyst coverage and organization capital
investments of U.S. listed firms in the 1990-2014 period. We follow Eisfeldt and
Papanikolaou (2013) and accumulate SG&A expenses using the perpetual inventory
method to estimate organization capital of individual firms. The investment in
organization capital is then computed as the change in estimated organization capital.
We measure analyst coverage as the average of 12 monthly numbers of earnings
forecast estimates (He and Tian, 2013). The baseline ordinary least squared (OLS)
regression results indicate that the organization capital investment is positively
associated with analyst coverage, after controlling for important finance variables such
as firm size, R&D, profitability, Tobin’s Q, patents, and institutional ownership. Yet,
the OLS result cannot exclude the possibility that analysts tend to cover firms with
better organization capital investments. Arguably, firms with more intangible assets are
also more likely to attract analyst attention (e.g., Lang and Lundholm, 1996; Francis,
Hanna, and Philbrick, 1998; Bhushan, 1989; Bushman, Barth et al., 2001, Piotroski,
and Smith, 2005). Inevitably, there is an endogeneity concern about the relation
between analyst coverage and organization capital investment.
To alleviate the potential endogenous problem, we use quasi-natural experiments
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of brokerage house mergers and closures as our identification strategy and then perform
difference-in-differences regressions to establish the causal effects. We obtain
brokerage house mergers and closures information provided by Hong and Kacperczyk
(2010) and Kelly and Ljungqvist (2012) and collect a sample of firms that originally
covered by these broker analysts experience analyst coverage reduction after those
merger/closure events. The decline in analyst coverage due to brokerage house mergers
and closures could be treated as an exogenous shock to the affected (treated) firms.
Therefore, for each (treated) firm that is covered by the broker house that is merged or
closes, we use the propensity-score matching approach to select three control firms with
similar pre-event industry-adjusted analyst coverage, organization capital investment,
property, plant and equipment, and capital expenditures. For this difference-in-
differences analysis, our sample period starts from 1994 and ends in 2008. In measuring
organization capital investments over the two-year pre-event and two-year post-event
window, we require all treatment and control samples to have non-missing variables
for five years.
We provide consistent empirical results for our cost of capital hypothesis from our
difference-in-differences regression analysis. Both one-year and two-year changes in
organization capital investments of the treated firm are significantly lower than that of
the control firm in the post-event window when the treated firms suffer analyst coverage
reduction due to brokerage house mergers or closures. To the extent a firm’s
organization capital investment is stable over time, our results indicate an average of
2.8 to 3.3% decrease of the treated firms in their two-year organization capital
investments after the event compared with those of the control firms. Compared with
other organization capital determinants in the regression, this organization capital
reduction is economically and statistically significant. Furthermore, a placebo test of
1,000 random trials on the quasi-natural experiments further shows that our finding is
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not merely driven from chance.
We next examine whether analyst coverage affects the organization capital
investment through the cost of capital channel. We use a composite measure of implied
cost of capital calculated as the average of four different cost of capital estimates of
Claus and Thomas (2001), Gebhardt, Lee and Swaminathan (2001), Gode and
Mhanram (2003), and Easton (2004). Our empirical result shows that compared with
control firms, the treated firms are less likely to invest in organization capital in the
post-event window. This result holds true only for the treated firms with higher costs
of capital, suggesting that cost of capital is the channel of the causal effect of analyst
coverage on organization capital.
Moreover, we carry out additional tests to examine the cross-section effect of
analyst coverage on organization capital investment. We discuss two possible factors,
financial constraints and external equity issuance, to test the impact of analyst coverage
on organization capital investments. First, financial constraint gauges the wedge
between internal and external financing. The role of analyst coverage, which is
negatively related to cost of capital, will be more important when the firm is financially
constrained. Hence, we expect our result would be stronger for firms with financial
constraint. Second, whether or not firm operation will be subject to changes in costs of
capital is more relevant when the firm heavily relies on equity financing. Therefore, we
conjecture that the effect of analyst coverage will be stronger when the firm has more
equity financing. Furthermore, the cost of capital channel will predict that both effects
(financial constraints and external equity issuance) are accentuated in the subsample of
treated firms with high cost of capital. In support of our hypothesis, we empirically
show that the effect of analyst coverage on organization capital investment is significant
for more constrained firms and for firms with more equity financing, especially when
the cost of capital of the firm is high.
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We continue to evaluate the causal effect of analyst coverage on top-executive
compensation, a key component of organizational capital investments. Consistent with
our main hypothesis, we find that both one-year and two-year changes in top-executive
compensations of the treated firm are significantly lower than those of the control firm
in the post-event window, especially when the treated firm indeed decreases its
organization capital investment. The result once again supports the managerial myopia
explanation.
Finally, we investigate the real impact of organization capital investment decrease
resulting from the analyst coverage reduction. We examine the productivity and
operating performance of the treated firm in the post-event window of brokerage house
mergers or closures. Our difference-in-differences analysis indicates that treated firms
exhibit significantly lower total factor productivity and operating performance than do
control firms after brokerage house mergers or closures, especially among the treated
firms with lower organization capital investment.
This paper contributes to the literature in three ways. First, while organization
capital proves to be an important component of a firm’s intangible assets, there is
limited understanding of the determinant of investment in a firm’s organization capital.
We fill this gap and find that cost of capital (and thus analyst coverage) is one of
important factors in organization capital investments. Second, prior studies show that
greater analyst coverage decreases the cost of capital because of reduction in the level
of information asymmetry (Merton, 1987; Easley and O'Hara, 2004; Bowen et al., 2008;
Derrien and Kecskés, 2013). Third, in this paper, we further extend the effect of cost of
capital resulting from external financial intermediary to the investment decision on
intangibility. Earlier papers (He and Tian, 2013; Guo, Pérez-Castrillo and Toldrà-
Simats, 2018) study the impact of analyst coverage on firm innovation activity, yet they
do not distinguish the cost of capital hypothesis from monitoring hypothesis on the role
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of analyst coverage. Our paper provides new results, not only supporting the cost of
capital story (but not monitoring story) on the role of financial analysts, but also
evaluate analysts’ effects on investment in corporate intangibility.
The reminder of this paper proceeds as follows. Section 2 develops testable
hypotheses. Section 3 describes data and summary statistics. Section 4 provides the
baseline regression, difference-in-differences results, and additional tests. Section 5
concludes.
2. Organization capital, analyst coverage and hypothesis
Organization capital is conceptually the accumulated intangible assets associated
with investments in business process and management practices. These intangible
assets and know-how are usually embedded in unique corporate designs and process.
We use following anecdotal examples to show what kinds of organization capital could
be. First, the internet-based production installation and maintenance system of Cisco is
its unique know-how that helps Cisco saves $1.5 billion in late 1990s (Economist, June
26, 1999). Second, Amazon has its own so called item-to-item collaborative filtering
algorithm to recommend customers goods that they are potentially interested in, and
attracts more returning customers (Fortune, July 30, 2012). Third, Zappos.com that is
famous for its outstanding customer service in online apparel business, especially for
shoes. A related news is that Amazon acquired it and paid Zappos.com about 1.2 billion.
(Forbes, May 11, 2015).
Why do we care about organization capital? We highlight the importance of
organization capital by comparing the trend of organization capital and research and
development (R&D) in the U.S. We use comprehensive data from Compustat to
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observe the distribution of R&D and SG&A expenditures between 1980 and 2017.6
Figure 1 plots the average amount of R&D and SG&A expenditures. We find that the
SG&A has increased more rapidly and significantly than R&D over the past two
decades. We then follow the Fama-French five-industry classification to assign firms
into industries of consumer goods, manufacturing, high-tech, health products and others,
respectively. Figure 2 plots the average of R&D and SG&A expenditures for five
industries. We show that the there is no specific pattern of the growth of organization
capital across industries.
Organization capital is different from the traditional physical assets in terms of
accounting treatment, riskiness, tangibility and the fact that there is no mark-to-market
value of the organization capital. SG&A, a key component of firm organization capital,
is generally expensed and lowers the bottom-line in the income statement. A myopic
manager may cut organization capital investment and boost up current earnings.
Eisfeldt and Papanikolaou (2013) argue that organization capital is a risky capital and
shareholders require higher risk premium for firms with more organization capital.7
According to the literature, we have known that organization capital is the accumulation
of firm-specific knowledge within the company; outside investors cannot obtain
complete information. Firms’ cost of capital could increase due to information
asymmetry. In addition, uninformed investors are less willing to trade because of higher
potential loss from transacting with informed investors. Therefore, the behavior of
investing in organization capital may lead firms to have more restrictions to access
external financing market. Even with a positive NPV project, the firm may not be able
6 Lev and Radhakrishnan (2005) argue that selling, general, and administrative (SG&A) expenditures contains items that includes most of the expenditures to generate organization capital including labor costs such as wages, salaries, compensation, recruiting and employee training costs and IT expenditures. In this paper, we follow literatures and use SG&A to measure each company's organization capital. 7 They argue that key talent is one of components of organization capital, and key talents are more likely to leave the firm when outside option is more favorable.
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to undertake it due to the higher financing cost.
How would analyst coverage affect investments in organization capital? We
propose two competing hypotheses. Stein (1988) and following papers (e.g., Bushee,
1998; Mizik, 2010; He and Tian, 2013) suggests that myopic managers may sacrifice
long-term interests to boost current earnings, where R&D, patents and marketing
activities are possible long-term investments that could be in exchange of current
earnings. Investment of organization capital also reduces earnings and myopic
managers may cut organization capital. Past papers suggest that financial analysts can
serve as external monitors to managers, and analysts reduce the likelihood for a firm to
engage in managerial myopia (e.g., Brennan and Subrahmanyam, 1995; Hong, Lim and
Stein, 2000; He and Tian, 2013). Hence, this managerial myopia explanation predicts
that as more (fewer) analysts engage in monitoring managers, the firm would (not)
avoid scarification of long-term investments and invest more (less) on organization
capital.
The opposite hypothesis is the cost of capital explanation. Existing research
indicates that analyst reports could provide and integrate more detail information that
includes the collection, evaluation, and dissemination related to a firm’s future
performance. Through public disclosures (i.e., analyst report) that reduce information
asymmetry between managers and investors, and analyst report mitigates differences in
knowledge between the firm and the possibility that certain investors are not aware of
the firm (Merton, 1987). Accordingly, more analyst coverage increases analysts'
collective ability to uncover and disseminate information, as a result, enhances the
public information precision. Easley and O'Hara (2004) argue that attracting active
analysts following for a company and collective forecast of many analysts should be
much more accurate and thus can reduce the cost of capital. In addition, Bowen, Chen
and Cheng (2008) suggest that when analyst coverage is higher, the cost of raising
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equity capital is lower. They argue that a higher level of analyst coverage is a benefit
of improved financial reporting that could decrease information asymmetry and reduce
the cost of issuing equity. Derrien and Kecskés (2013) also show that a decrease in
analyst coverage will increase the cost of capital because of greater information
asymmetry. Therefore, we argue that analyst coverage, as a potential factor to decrease
the firm's cost of capital, could facilitate firms to invest more in organization capital.
As abovementioned, previous papers have shown that organization capital is
highly uncertain and with high degree of information asymmetry, the firm that desires
organization capital is concerned about its cost of capital for investment needs. Also,
research suggests that analyst coverage reduces information asymmetry in the capital
market and lowers the cost of capital for firms. Taking together, firms with more analyst
coverages may have lower costs of capital and can be easier to access external market
to finance investments in organization capital.
Therefore, we propose a causal effect of analyst coverage on organization capital,
and build up hypothesis #1 (hypothesis #1a and hypothesis #1b) below:
Hypothesis #1a: High (low) level of analyst coverage of a firm leads to less (more)
investment in organization capital if managerial myopia explanation is true.
Hypothesis #1b: High (low) level of analyst coverage of a firm leads to more (less)
investment in organization capital if cost of capital explanation is true.
3. Data and summary statistics
3.1. Sample selection
Our sample consists of all U.S. in the period of 1990-2014. We construct our data
from multiple sources. The organization capital and other accounting control variables
are collected and constructed from Compustat Industrial Annual files, market price
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information from CRSP, and analyst-related information from the Thomson Reuters
Institutional Brokers’ Estimate System (I/B/E/S), respectively. The patent data comes
from European Patent Office (EPO) Worldwide Patent Statistical Database and the
institutional ownership data is collected from Thomson’s CDA/Spectrum database
(from 13F). After excluding observations with missing records of organization capital
and analyst coverage, our final full sample contains 53,269 firm-year observations. We
describe the details of key variable construction in the following section.
3.2. Variable measurement
3.2.1 Measure of organization capital
We construct a variable of organization capital based on a firm’s selling, general,
and administrative (SG&A) expenses following the procedure described in Eisfeldt and
Papanikolaou (2013). The stock of organization capital (OC) is first calculated by using
perpetual inventory method
t
ttt cpi
ASGOCOC
&)1( 10 , (1)
where 0 is the depreciation rate of 15% (used by the Bureau of Economic Analysis
in its estimation of R&D capital in 2006) and cpi is the consumer price index. We
compute the initial organization capital ( 0OC ) as in equation (2):
0
10
&
g
ASGOC , (2)
where g is the average real growth rate of firm-level SG&A expenses (10% in our
sample). A firm’s record of SG&A expense is assigned a value of zero if the record is
missing in Compustat. The stock of organization capital calculated in equation (1) is
scaled by the firm’s total assets (TA). As the OC stock variable (as in equation (1)) is
an accumulated depreciated SG&A value, the current period OC stock variable at time
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t is a value incorporating information of prior periods. Our research question is to
investigate a firm’s incremental input of capital contributed to its OC stock variable at
t, which is measured as a flow variable, i.e. the investment in organization capital
(INVOC). The variable of INVOC1 and INVOC2 is a change in OC stock between two
periods in the following one- and two-year period compared with the current period,
respectively. We take the logarithm of organization capital investment to minimize the
potential problem of heteroscedasticity in the empirical analysis.8
)(1
1,1
t
t
t
tt TA
OC
TA
OCLnINVOC
(3)
)(2
2,2
t
t
t
tt TA
OC
TA
OCLnINVOC
(4)
3.2.2. Measure of analyst coverage
We use data from the summary file of I/B/E/S database to construct the variable
of analyst coverage. For each fiscal year of a firm, we take the average of the 12
monthly numbers of earnings forecast estimate to build analyst coverage (He and Tian
(2013)). We then take natural logarithm of the number of forecasts, LnCoverage, as the
major independent variable in this paper. For sample firms with no annual earnings
forecasts found in I/B/E/S in the specified window, the analyst coverage is set to be
zero.
3.2.3 Other control variables
We control for several firm and industry characteristics that may influence
organization capital and analyst coverage. LnSale is the logarithm of firm sales; RD is
8 Organization capital investments are changes in accumulated organization capitals. Because organization capital is the accumulated SG&A expenditures, there are very few firms yielding negative values of organization capital investments, which account for only fewer than 2% of our sample firms. For these firms with negative values, they are missing values after taking logarithm in our equations (3) and (4).
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research and development expenditure divided by book assets; ROA is operating
income before depreciation divided by book assets; PPE is property, plant, and
equipment divided by book assets; Leverage is long-term debt divided by total assets;
Capex is capital expenditure divided by total assets; TobinQ is equity plus book assets
minus book value of equity minus balance sheet deferred taxes divided by book assets;
Patent is the logarithm of the number of patents; HHI is Herfindahl-Hirschman index
by the sum of squared market shares of firms in a two-digit SIC industry code. IO is
institutional ownership calculated as the arithmetic mean of the four quarterly
institutional holdings reported through form 13F from Thomson’s CDA/Spectrum
database. All accounting variables obtained from Compustat and I/B/E/S are scaled by
firm’s total asset, and these accounting variables are adjusted by subtracting the 2-digit
SIC industry their medians. All variables are computed in fiscal year t. Variables are
with descriptions in the appendix.
We state economic intuition about effect of abovementioned variables. We include
firm sales (LnSale) and tangible assets (PPE) to proxy for firm growth and funds
available for investment, and expect that there is a positive relation with organization
capital investment. On the contrary, the higher level of capital expenditures proxies for
reduced funds available for doing investment. A negative relation between capital
expenditures (Capex) and organization capital investment is expected. Bushee (1998)
indicate that the large stockholdings institutional investors are significantly less likely
to cut investment decisions, and the institutional investors could play a monitor role
and then discipline managers make investment to maximize long-run value rather than
to meet short-term earnings goals. Firms with higher institutional ownership (IO) are
expected have increased organization capital investment. Existing studies show that
firms with a high level organization capital have higher operating performance.
Therefore, we expect that firms with lower profitable (ROA) and lower Tobin's Q
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(TobinQ) have a high level incentive to invest in organization capital in order to enhance
firm value. To exclude other intangible assets effect, we also control for RD and Patent
in the regression. In addition, we include Herfindahl-Hirschman index (HHI) to control
for industry competition effect.
3.3 Summary statistics
Table 1 presents the descriptive statistics for the variables in full sample analysis
during 1990-2014, where we winsorize all variables at the 1st and 99th percentiles to
alleviate the effect from outliers. On average, a firm in our sample has 0.0169
organization capital-to-assets ratio in year t+1 (0.0312 organization capital-to-assets
ratio in year t+2) and is followed by about 7 analyst coverages. Moreover, firms have
an averages of RD of 0.0413, ROA of 0.0985, PPE of 0.2569, leverage of 0.2063, Capex
of 0.0578, and Tobin's Q of 2.0241.
Insert Table 1
4. Empirical results
4.1 Why difference-in-differences analysis?
In this paper, we would like to examine whether the analyst coverage, through its
impact on the external information asymmetry and on a firm’s cost of capital, has
positive or negative effects on firm decision on organization capital investment.
Existing empirical evidence suggests that analyst coverage is negatively correlated with
information asymmetry. Derrien and Kecskés (2013) argue that a decrease in analyst
coverage will increase the cost of capital because of greater information asymmetry and
the affected companies will decrease their investment and financing activities as a way
to safeguard the firm’s reputation and achieve consistency with the analyst’s forecast
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earnings. In our study, we hypothesize that the analyst coverage, through its effect on a
firm’s cost of capital, can affect firm investment in organization capital up to two
subsequent years. In this part of our analysis, the dependent variables are INVOC1 and
INVOC2. We incorporate several control variables suggested in spirit of He and Tian
(2013), including LnSale, PPE, Capex, IO, Leverage, ROA, TobinQ, RD, Patent, and
HHI. ui and vt indicate firm and year fixed effects, respectively. Standard errors are
clustered by the firm level.
𝐼𝑁𝑉𝑂𝐶 𝛽 𝛽 𝐿𝑛𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 𝛽 𝐿𝑛𝑆𝑎𝑙𝑒 𝛽 𝑅𝐷
𝛽 𝑅𝑂𝐴 𝛽 𝑃𝑃𝐸 𝛽 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝛽 𝐶𝑎𝑝𝑒𝑥
𝛽 𝑇𝑜𝑏𝑖𝑛𝑄 𝛽 𝑃𝑎𝑡𝑒𝑛𝑡 𝛽 𝐻𝐻𝐼 𝛽 𝐼𝑂
𝑢 𝑣 𝜀 (5)
Early studies generally propose OLS regression analysis to examine the
relationship. In the appendix, the results indicate a positive relationship between analyst
coverage and investment in organization capital, with the coefficients of analyst
coverage highly significant at 1% level. The results are consistent with our hypothesis.
That is, as analysts can affect a firm’s level of information asymmetry and its cost of
capital, analyst coverage can impact a firm’s investment on organization capital.
Nevertheless, traditional OLS has serious endogeneity concern. Previous studies
show that analysts tend to cover firms with better information environment (Lang and
Lundholm, 1996; Francis, Hanna, and Philbrick, 1998; Bhushan, 1989; Bushman,
Piotroski, and Smith, 2005). Analysts may also strategically select firms with less
organization capital to provide coverage for, as organization capital-intensive firms
have higher systematic risk (Eisfeldt and Papanikolaou, 2013). That is, this type capital
has high-risk characteristic. However, Barth, Ron, and Maureen (2001) report that firms
with more intangible assets receive more analyst coverage. As it is hard to estimate the
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fair value on intangible assets, analysts may have an incentive to cover such firms.
Moreover, a significant correlation between organization capital investment measures
and analyst coverage cannot exclude the possibility that analysts actively select
diversified firms with high organization capital investment to provide coverage for.
Potential bias could also result from omitted variables, with unobservable firm
characteristics attracting analyst coverage and higher firm organization capital
investment at the same time. All these problems would make it difficult to conclude on
any causal relationship. Therefore, the significant correlation between analyst coverage
and organization capital in our OLS regression analysis could be plagued by problems
of endogeneity. In this section, we utilize quasi-natural experiments in order to address
the potential endogeneity issue.
4.2 Quasi-natural experiments
To overcome these obstacles, we employ a quasi-natural experimental design that
allows us to examine the reaction of firms to a plausibly exogenous decrease in
coverage caused by closures and mergers of brokerage houses (Hong and Kacperczyk,
2010; Kelly and Ljungqvist, 2012).The exogenous shocks in analyst coverage are
results of the brokerage house closures and mergers, which are ex ante uncorrelated
with firm investment and performance. Our empirical analysis is structured to examine
how the firm decision of investing in organization capital responds to such exogenous
shocks to the financial intermediary sector, where analysts potentially perform an
important role of narrowing the information asymmetry between firms and outside
investors. Such an experiment design enables us to identify a sample of treated firms,
which are subject to reduction of analyst coverage for firm-level performance or
investment related reason.
The first event in our natural experiment consists of brokerage house closures.
18
Kelly and Ljungqvist (2012) document that closures of brokerage houses are usually
motivated by business strategy considerations of themselves, and are not associated
with the heterogeneous characteristics of firms that they cover. The list of brokerage
house closures is collected from Kelly and Ljungqvist (2012) with events of brokerage
house closures in the period of 2000 and 2008.
The second event in our natural experiment consists of brokerage house mergers.
When two brokerage firms merge, the business integration and consolidation will
inevitably lead to high turnover of analysts (Hong and Kacperczyk, 2010; Wu and Zang,
2009). Similarly, loss in analyst coverage resulting from brokerage house mergers is
not directly associated with firm characteristics which analysts cover and create
exogenous variation in analyst coverage for us to examine its causal effect on firm’s
organization capital investment. Events of brokerage house mergers cover the period of
1994 and 2005.
4.3. Identification strategy
For brokerage house mergers, we use a procedure similar to that used in Hong and
Kacperczyk (2010) and Irani and Oesch (2013) and identify horizontal mergers in the
financial industries from the Securities Data Company (SDC) Mergers and Acquisitions
database in the period of 1994 to 2005. For closures, we start with collecting the I/B/E/S
identifiers of the brokerage house closures provided by Kelly and Ljungqvist (2012).
We obtain our sample of treated firms from (1) the list of firms with duplicate coverage
provided by analysts at both the acquiring and target brokerages, (2) the list of firms
with coverage provided by analysts working at the closing brokerages, who have issued
earnings forecasts in the window of 365 calendar days prior to the merger and closure
transactions. The treated firms are subject to a potential increase in information
asymmetry, as the merged brokerage house eliminates duplicate coverage.
19
The control companies are the firms other than our treated firms. Correspondingly,
our event window starts 365 calendar days prior to and ends 365 calendar days
subsequent to the dates of merger transactions. To eliminate pre-treatment effect, we
match each treatment firm with three control firm on Analyst Coverage, INVOCt-1, PPE,
and Capex in the year prior to the events of brokerage house mergers and closures9. We
use a propensity-score (PS) matching procedure to select the control sample and require
a within 0.002 caliper of propensity score to ensure the similarity of characteristics
between the treatment and control samples.10
We further require non-missing control variables of treatment and control sample
firms during a five-year window (from year -2 to year +2). For our difference-in-
differences analysis, we have 1,425 pairs of treatment-control observations in the
sample period from 1994 to 2008. We adjust all the accounting variables are by
industry-median value based on the 2-digit SIC code.
Table 2 presents and compares the mean values of characteristic variables for both
the treatment and control samples in the pre-event year. Our results indicate that there
is no significant difference in many of firm characteristics we examine. As a result, our
identification strategy enables us to make causal inference on the effect of analyst
coverage on organization capital investment without the concerns of pre-treatment
effects.
Insert Table 2
4.4. Difference-in-differences regression: average treatment effect
9 For the accounting information, we record the financial statement data from the last fiscal year that ended before the merger to construct variables in the pre-event window and data from the first complete fiscal year-end after the merger to construct variables in the post-event window. 10 Our result remain unchanged if we select 0.005 or 0.01 caliper of propensity score.
20
To empirically carry out our identification strategy detailed above, we adopt a
difference-in-differences (DiD) approach in testing the change in firm’s investment in
organization capital subsequent to an exogenous reduction in analyst coverage. Using
the DiD methodology makes it possible to compare the change in our variable of
interest, INVOC, observed in difference between the treated and non-treated (control)
samples before and after the shock. In our main analysis, the treated sample consists of
firms which are covered by both analysts working at the acquiring/target brokerage
houses prior to the mergers or firms covered by analysts working at the closing
brokerages, while the control sample consists of the PS-matched firms. A direct
comparison of INVOC before and after the shocks could result in falsified conclusion,
as a potential trend could affect organization capital investment of all firms over time.
The DiD approach mitigates this potential problem. Specifically, we implement our
DiD analysis in the following regression models:
𝐼𝑁𝑉𝑂𝐶 𝛽 𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝛽 𝐴𝑓𝑡𝑒𝑟 𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑓𝑡𝑒𝑟
𝛽 𝐿𝑛𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 𝛽 𝐿𝑛𝑆𝑎𝑙𝑒 𝛽 𝑅𝐷
𝛽 𝑅𝑂𝐴 𝛽 𝑃𝑃𝐸 𝛽 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝛽 𝐶𝑎𝑝𝑒𝑥
𝛽 𝑇𝑜𝑏𝑖𝑛𝑄 𝛽 𝑃𝑎𝑡𝑒𝑛𝑡 𝛽 𝐻𝐻𝐼 𝛽 𝐼𝑂
𝑢 𝑣 𝜀 (6)
We conduct tests on Hypothesis #1 and present our results on the average treatment
effect from the DiD analysis on the organization capital investment in Table 3. The
estimated DiD effect is indeed negative and significant, confirming that there is a
significant effect of reduced analyst coverage on organization capital investment of our
treated firms.
The remaining columns of Table 3 present impact of reduced coverage on the
dependent variable of INVOC. The dependent variable is INVOC1 in model (1) and (2),
and INVOC2 in model (3) and (4). Columns (1)+(3) display the estimated results with
21
the year-fixed effect, while columns (2)+(4) display those with the year-/firm-fixed
effects. Across all four different specifications, our DiD treatment effect is highly
significant at 5% level and of similar magnitude. Our results are consistent with the
Hypothesis #1a that the investment in organization capital deteriorates upon increased
information asymmetry resulting from the reduced analyst coverage. Such effect is not
only statistically significant, but also economically meaningful. For example, the
coefficient estimate on the Treatment*After in column (4) is -0.0399, indicating that a
drop in coverage in our treatment sample results in a decrease of 4% organization
capital which economically significant is higher than ROA, PPE, Leverage, Capex,
Patent, and HHI.
Insert Table 3
4.5 Placebo test
We conduct a placebo test to examine whether our finding is merely driven by
chance, an issue in part related to the data snooping bias. We randomly select non-
treated firms, pretend that their analyst coverage decreases due to brokerage house
merger or closure, and conduct difference-in-differences analysis. For specifically, we
firstly replace each treated firm with another randomly selected firm from the pool of
non-treated firms and term it as a pseudo treated firm. We estimate the coefficient of a
dummy Treatment×After based on equation (5) by the sample of pseudo treated firms
and their matched firms. We retain coefficient estimates of Treatment×After, and then
repeat the above procedure for 1000 times. Because these pseudo treated firms are
randomly assigned, we expect no effect for those pseudo treated firms.
Figure 3 reports the placebo test for the distribution of coefficients on the
22
interaction term in difference-in-differences regression in model (2) and model (4) of
Table 3. The definition of INVOC1 and INVOC2 is a change in organization capital
stock between two periods in the following one- and two-year period compared with
the current period, respectively. Given that coefficients of Treatment×After in model (2)
and model (4) of Table 3 are -0.013 and -0.0278, the coefficients of Treatment×After
from 1,000 random placebos cluster around -0.001 and -0.002 in the settings of model
(2) and model (4). Only less than 25 cases out of 1,000 trials yield similar impacts of
our Table 3. Therefore, our finding in supportive of the positive effect of analyst
coverage on organization capital investment is not merely driven by chance.
4.6. DiD regression: channel through cost of capital
We investigate the channel by which the external research coverage provided by
analysts can affect the internal managerial decision in investing organization capital.
We hypothesize that analysts, as important information intermediaries, can facilitate
investors’ analysis and access of corporate information and reduce information
asymmetry. Firms, with a mitigated problem of information asymmetry with outside
investors, can therefore benefit with a reduced cost of capital (COC) from a high level
of analyst coverage. In order to test the above argument, we conduct further DiD
analysis on subsamples sorted by COC estimates. We utilize an average of the following
four cost of capital estimates by Claus and Thomas (2001), Gebhardt, Lee and
Swaminathan (2001), Gode and Mhanram (2003), and Easton (2004).
We create two subsamples of "High cost of capital" /"Low cost of capital",
consisting of firms with the COC higher/lower than sample median (of COC level).
Table 4 presents the DID regression results on subsamples sorted by COC triggered by
the reduced analyst coverage. The dependent variable is INVOC1 in model (1)/(3) and
23
INVOC2 in model (2)/(4). Results of Table 4 indicate that our treatment effect
concentrates on subsamples of "High cost of capital", indicating that firms decrease
their investment in organization capital after an exogenous reduction of analyst
coverage, and this decrease in organization capital investment only occurs when a firm
maintains a higher level of cost of capital.
Insert Table 4
4.7. DID regression: effects of financial constraints, and external equity issuance
For firms depending on the access to external equity capital to finance their
investment projects, their decision making can be highly dependent on their costs of
capital raising from the market. A firm may have to forgo its investment projects once
an increase in COC turns the project NPV from positive to negative. Chang, Dasgupta
and Hilary (2006) argue that there is a negative relationship between the number of
analysts providing research coverage and the extent of information asymmetry faced
by the firm. They document that firms with more analyst coverage are more likely to
issue equity and their equity issuance decisions are less dependent on market conditions,
consistent with that firms with higher analyst coverage have better access to and are
less constrained by external financing. Furthermore, Derrien and Kecskés (2013) show
that firms experience an increase in COC upon a negative shock to analyst coverage. In
our study, we expect that managers will decrease the organization capital investment
in the post-event window. In addition, such an effect will be more pronounced among
financially constrained firms.
We employ the HP index to measure financial constraint. Companies with a higher
financial constraint index are more likely to experience tighter financial conditions. HP
index is constructed by Hadlock and Pierce (2010). They show that smaller firms are
24
more likely to be constrained, and firm age is also particularly useful in predicting
financial constraints. We follow below equation and based on size, size-squared, and
firm age to detect financially constrained firms.
HP =－0.737 Size + 0.043 Size2 – 0.040 Age (7)
where Size equals the log of inflation-adjusted total assets (in 2004 dollars), and Age is
the number of years the firm is listed with non-missing stock prices on Compustat. In
calculating the index, we winsorize Size and Age at 1% tails on the low end, and
winsorize Size at the $4.5 billion and Age at 37 years on the high end. We modify our
DiD regression specification as the following to incorporate the potential effects of
financial constraints.
𝐼𝑁𝑉𝑂𝐶 𝛽 𝛽 𝐴𝑓𝑡𝑒𝑟 𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑓𝑡𝑒𝑟
𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑓𝑡𝑒𝑟 𝐻𝑃𝐼𝑛𝑑𝑒𝑥 𝛽 𝐴𝑓𝑡𝑒𝑟 𝐻𝑖𝑔ℎ 𝐻𝑃
𝛽 𝐻𝑖𝑔ℎ 𝐻𝑃 𝛽 𝐿𝑛𝑆𝑎𝑙𝑒 𝛽 𝑅𝐷 𝛽 𝑅𝑂𝐴 𝛽 𝑃𝑃𝐸
𝛽 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝛽 𝐶𝑎𝑝𝑒𝑥
𝛽 𝑇𝑜𝑏𝑖𝑛𝑄 𝛽 𝑃𝑎𝑡𝑒𝑛𝑡 𝛽 𝐻𝐻𝐼 𝛽 𝐼𝑂
𝑢 𝑣 𝜀 (8)
Table 5 presents results from the estimation of the difference-in-differences
regressions on organization capital investment cost on the interactive effect of financial
constraint, where High HP is an indicator variable, which is equal to one if the
company's constraint index (HP) is higher than top tercile group.11 We conduct the
DID regression on subsamples with "High cost of capital" and "Low cost of capital".
Results in High cost of capital indicate that the drop in analyst coverage causes
reduction in organization capital for financial constraints samples. The intersection
terms of High HP with Treatment*After is negative and significant. On the contrary,
11 We do not use median to cut the sample because only firms with very high HP score are financially constrained (e.g., Almeida, Campello and Weisbach, 2004; Chen and Wang, 2012). Thus, we do not use the median to split the sample into constrained and unconstrained firms.
25
there are no significant relationship between organization capital and decreasing in
analyst coverage event for unconstrained samples.
Insert Table 5
We predict that the company relies on external financing will decrease their
organization capital investment after analyst coverage decreasing. Table 6 shows that
the impact of analyst coverage reduction on organization capital investment based on
partition of cost of capital. High Equity is an indicator variable which is equal to one
for firms with an above quartile number of equity issuance, and zero otherwise. We
obtain the number of equity issuance from Compustat. We indeed find that the effect
of analyst coverage reduction on organization capital investment is more pronounced
among firms with higher equity issuance.
𝐼𝑁𝑉𝑂𝐶 𝛽 𝛽 𝐴𝑓𝑡𝑒𝑟 𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑓𝑡𝑒𝑟
𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑓𝑡𝑒𝑟 𝐻𝑖𝑔ℎ 𝐸𝑞𝑢𝑖𝑡𝑦 𝛽 𝐴𝑓𝑡𝑒𝑟 𝐻𝑖𝑔ℎ 𝐸𝑞𝑢𝑖𝑡𝑦
𝛽 𝐻𝑖𝑔ℎ 𝐸𝑞𝑢𝑖𝑡𝑦 𝛽 𝐿𝑛𝑆𝑎𝑙𝑒 𝛽 𝑅𝐷
𝛽 𝑅𝑂𝐴 𝛽 𝑃𝑃𝐸 𝛽 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝛽 𝐶𝑎𝑝𝑒𝑥
𝛽 𝑇𝑜𝑏𝑖𝑛𝑄 𝛽 𝑃𝑎𝑡𝑒𝑛𝑡 𝛽 𝐻𝐻𝐼 𝛽 𝐼𝑂
𝑢 𝑣 𝜀 (9)
Insert Table 6
4.8. DID regression: compensation of top executives
Eisfeldt and Papanikolaou (2013, 2014) suggest that compensation to key-talents
is a major component of firm’s organization capital. When analyst coverage reduces
due to brokerage house merger or closure, the consequent high cost of capital impedes
investment in key-talents, and firm may offer talents worse compensation package. This
is true because the intrinsic value of existing employee stock options and share bonuses
are lower when cost of capital is high. Moreover, new issues of employee stock options
26
and share bonuses are costly as the cost of equity capital is high, deterring the intention
for the firm to offer more compensations to top-managers. Therefore, the firm with
higher cost of equity capital offers lower compensations to top-executives, and
accordingly has lower organization capital investment.
We perform DiD analysis for compensation to top-executives. We employ
changes in total compensation (TDC) as the dependent variable, where TDC is obtained
from the S&P ExecuCompstat database.
𝑇𝐷𝐶 𝛽 𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝛽 𝐴𝑓𝑡𝑒𝑟 𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑓𝑡𝑒𝑟
𝛽 𝐿𝑛𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 𝛽 𝐿𝑛𝑆𝑎𝑙𝑒 𝛽 𝑅𝐷
𝛽 𝑅𝑂𝐴 𝛽 𝑃𝑃𝐸 𝛽 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝛽 𝐶𝑎𝑝𝑒𝑥
𝛽 𝑇𝑜𝑏𝑖𝑛𝑄 𝛽 𝑃𝑎𝑡𝑒𝑛𝑡 𝛽 𝐻𝐻𝐼 𝛽 𝐼𝑂
𝑢 𝑣 𝜀 (10)
Table 7 report the DiD analysis result. We use one-year changes in top-executive
compensations in model (1) and model (2), and two-year changes in compensations in
model (3) and model (4). We further partition our sample into post-event low and high
organization capital investment subgroups by the median in order to examine whether
decreases in compensation are related to organization capital investments. We find that
firms tend to cut compensation to top executives after analyst coverage decreases due
to brokerage house merger or closure, especially when the firms do decrease their
investments in organization capital. This result is consistent with our cost of capital
hypothesis.
Insert Table 7
Table 7 also answers the question whether our finding is driven by an alternative
monitoring hypothesis. Monitoring hypothesis suggests that analysts serve as external
monitors to manager and to prevent agency problem (e.g., Doukas, Kim, and Pantzalis,
2005; Yu, 2008; Irani and Oesch, 2013). He and Tian (2013) and Guo, Pérez-Castrillo
27
and Toldrà-Simats (2018) also study the impact of analyst coverage on firm innovation
activity, and examine the information hypothesis against pressure hypothesis.
Particularly, their information hypothesis does not exactly distinguish effects of cost of
capital and monitoring. In our paper, we could use test of top-executive compensation
to tease out cost of capital story and monitoring explanation. Since top-executive
compensation belongs to private benefit, the reduction in coverage should lead to less
monitoring, and accordingly manager compensation tend to increase if the monitoring
explanation is true (Chen, Harford, and Lin, 2015). By contrast, as we have mentioned,
manager compensation should decrease if analyst coverage decreases under cost of
capital explanation. Our Table 7 shows that reduction of analyst coverage resulting
from brokerage house merge or closure reduces executive compensations. Therefore,
our result supports the cost of capital story but not the monitoring explanation.
4.9. DID regression: total factor productivity and operating performance
In this section, we examine the real effect of analyst coverage and how the effect
is related to the organization capital. We use Compustat data to compute TFP for each
firm. Cobb-Douglas production function is assumed in this paper (Keller and Yeaple
(2009) and Kedia and Philippon (2009)). We follow below equation and estimate TFP
in the same three-digit SIC industry.
iLiKii LaKaYTFP lnln)ln()ln( (11)
Yi is sales, Ki is property, plant, and equipment, Li is number of employees. We expect
that firms with lower organization capital are more likely to decrease productive
efficiency. We use the median value of changes in organization capital to define High
OC and Low OC investments. We then estimate each firm's TFP at year t+2 after the
event year. Table 8 reports the effect of organization capital investment on total factor
28
productivity. We find that drop in analyst coverage and decreases in organization capital
investment results in decline total factor productivity, supporting the traditional notion
that investment in organization capital improves firm productivity.
Insert Table 8
Moreover, we examine the operating performance of firms, and present results in
Table 9. The dependent variable is return on assets (ROA) of one year and two years
after the event. Similar to total factor productivity, we find that decreases in analyst
coverage and results in deterioration of firm operating performance, especially when
organization capital investment of firm also decreases. This result is consistent with our
test for firm productivity and supports the real effect of analyst coverage on firm
performance through organization capital.
Insert Table 9
5. Conclusion
Organization capital has become an important role in improving corporate
performance, and it serves as a key component of firm intangibility. In this paper we
focus on the effect of analyst coverage on firm’s organization capital investment and
suggest a positive causal effect of analyst coverage.
We propose two competing explanations on the effect of analyst coverage. On one
hand, we hypothesize that managers tend to sacrifice long-term interests to boost
current profits, known as the managerial myopia explanation. Organization capital is
also a long-term investment, and organization investments include SG&A expenditures
that reduce earnings. Therefore, managers may reduce organization capital investments
to boost current earnings. Financial analysts are accordingly able to avoid the
29
managerial myopia and lead to more organization capital investments.
On the other hand, we hypothesize that investment in organization capital is costly
and heavily relies on external financing. Moreover, the intangibility of the firm
organization capital causes information asymmetry, which is highly related to cost of
equity capital. By the same token, analyst coverage reduces information asymmetry
between insiders and outside market participants, and lowers the cost of capital for
investment needs. Therefore, a firm with more analyst coverages could have a lower
cost of capital, and accordingly is easier to access external market to finance its
investment need in accumulating organization capital.
We collect U.S. listed firms between 1990 and 2014 to explore our major
hypothesis. We followed Eisfeldt and Papanikolaou (2013) and accumulate SG&A
expenses based on the perpetual inventory method to estimate organization capital of
individual firms, and then compute the change in estimated organization capital as the
investment in organization capital. The baseline model based on ordinary least squared
regressions shows that the organization capital investment is positively related to the
level of analyst coverage.
We use quasi-natural experiments of brokerage house merger and closure as our
identification strategy and then perform difference-in-differences regressions to
establish the causal effect, a way in spirit of Hong Kacperczyk (2010) and Kelly and
Ljungqvist (2012). The difference-in-differences regression results are consistent with
our main argument. Both one-year and two-year changes in organization capital
investment of the treated firm are significantly lower than that of the control firm after
the treated firm suffers analyst coverage decreases that are resulted from the brokerage
house merger or closure. This result holds true especially when the firms have high
costs of capital, suggesting that cost of capital is the channel of the causal effect of
analyst coverage on organization capital. All these results support the cost of capital
30
explanation.
Furthermore, we carry out two additional tests to examine when the effect of
analyst coverage on organization capital investment is more profound. We find that the
effect of analyst coverage on organization capital investment is more profound when
the firm is more constrained, and with more equity financing, especially when the cost
of capital of the firm is high.
We examine the top-executives compensation of firms and their analyst coverage.
We find that firms decrease compensation to top-executives after analyst coverage
decreases due to brokerage house merger or closure. The implication of the result is
that compensation to managers, a key component in organization capital, decreases with
analyst coverage.
Finally, we investigate the real impact of analyst coverage reduction by examining
the productivity of the treated firm. By using difference-in-differences regression,
treated firms exhibit lower total factor productivity and operating performance than do
control firms after brokerage house merger or closure, especially for the treated firms
with lower organization capital investment.
31
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35
Appendix A. Variable definition Variables Definition and description OC We calculate the stock of organization capital using the perpetual inventory method.
t
tiitit cpi
ASGOCOC ,
10
&)1( ,
where SG&A is selling, general, and administrative expenses, 0 is the depreciation
rate of 15%, which is used by the Bureau of Economic Analysis in its estimation of R&D capital in 2006, and tcpi is the consumer price index. We compute the initial
organization capital (0OC ) as
0
10
&
g
ASGOC ,
where g is the average real growth rate of fm-level SG&A expense, which equals 10% in our sample. OC is scaled by total assets and measured at year t.
Coverage Coverage is the average of 12 monthly numbers of earnings forecasts over the fiscal year t.
LnCoverage LnCoverage is the logarithm of the average of 12 monthly numbers of earnings forecasts over the fiscal year t.
LnSale LnSale is the logarithm of sales measured at the end of fiscal year t. RD Research and development expenditure divided by the book value of total assets
measured at the end of fiscal year t, and is set to zero if missing. ROA Return on assets ratio defined as operating income before depreciation divided by
the book value of total assets, measured at the end of fiscal year t. PPE PPE is the ratio of property, plant, and equipment divided by the book value of total
assets measured at the end of fiscal year t. Leverage Leverage is defined as the book value of debt divided by the book value of total
assets measured at the end of fiscal year t. Capex Capital expenditure scaled by the book value of total assets measured at the end of
fiscal year t. TobinQ TobinQ is the market-to-book ratio during the fiscal year t, calculated as the market
value of equity plus the book value of assets minus the book value of equity minus balance sheet deferred taxes (set to zero if missing) divided by the book value of assets.
HHI HHI is Herfindahl-Hirschman index, defined as the sum of squared market shares of firms in a two-digit SIC industry.
Patent Patent is the logarithm of the number of patents in year t. IO IO is the institutional ownership over the fiscal year t, calculated as the average of
four quarterly institutional holdings reported through form 13F. Cost of Capital The definition of cost of capital is based on a composite measure that is the average
of the following four individual cost of capital estimates: Claus and Thomas (2001), Gebhardt, lee and Swaminathan (2001), Gode and Mhanram (2003), and Easton (2004). Detailed descriptions of individual cost of capital estimates as follow. (1) Claus and Thomas (2001)
T
TT
ii
ii
rgr
gBrROEE
r
BrROEEBP
)1()(
)1(
)1( 00
400
1 0
10000
where 0P is the market equity; r is the implied cost of capital; B is the book
equity; g is set to the current risk-free rate minus 3%; T=5.
(2) Gebhardt, lee and Swaminathan (2001)
1
00
1001
1 0
10000
11
TTT
T
ii
ii
rr
BrROEE
r
BrROEEBP
where 0P is the market equity; r is the implied cost of capital; B is the book
equity; T=12.
36
(3) Gode and Mhanram (2003)
)1()2
( 4
45
1
12
0
12
0
EPS
EPSEPS
EPS
EPSEPS
P
EPSAAr
0
112
1
P
dpsA
where 0P is the market equity; r is the implied cost of capital; %3 fr ;
forecasted dividend per share: ii EPSkdps , where k is estimated using the
current dividend payout ratio and equals [dividends paid/earnings]
(4) Easton (2004)
0
120 P
EPSEPSr
where 0P is the market equity; r is the implied cost of capital; iEPS :
forecasted earnings
37
Appendix B. Baseline regressions of organization capital on analyst coverage This table presents regressions of organization capital on analyst coverage. Dependent variables are the changes in organization capital investment, namely, organization capital at year t+1 minus organization capital at year t (INVOC1) and organization capital at year t+2 minus organization capital at year t (INVOC2). LnCoverage is the logarithm of the average of 12 monthly numbers of earnings forecasts over the fiscal year t. Other variables are described in the Appendix A. Accounting variables are measured at year t and adjusted by each firm’s total assets. All regressions include the year-fixed effect and firm-fixed effect. The t-statistics in parentheses are based on standard errors clustered at the firm level. Model Dependent variable
(1) INVOC1
(2) INVOC2
LnCoverage 0.0301*** 0.0572*** (10.23) (11.06) LnSale 0.0346*** 0.0557*** (12.39) (11.48) RD -0.5151*** -1.0720*** (-9.42) (-11.10) ROA -0.3601*** -0.4325*** (-17.01) (-12.86) PPE -0.1051*** -0.1709*** (-6.74) (-6.28) Leverage 0.1413*** 0.2139*** (12.64) (10.90) Capex 0.0146 0.0859** (0.59) (2.30) TobinQ -0.0329*** -0.0344*** (-20.31) (-15.74) Patent -0.0049** -0.0022 (-2.16) (-0.58) HHI -0.0073 0.0102 (-0.20) (0.16) IO 0.0569*** 0.1059*** (7.83) (8.37) Intercept -0.0283*** -0.0566*** (-3.99) (-4.97) Year FE Yes Yes Firm FE Yes Yes Clustered by firm Yes Yes Observations 58,121 51,146 Adjusted R2 0.107 0.107
38
Table 1.
Summary Statistics This table presents the summary statistics of the variables in full sample during 1990-2014. The variables of interest are changes in organization capital investment, namely, organization capital at year t+1 minus organization capital at year t (INVOC1) and organization capital at year t+2 minus organization capital at year t (INVOC2). All variables are winsorized at 1% and 99% percentiles.
Variable Obs. 10th
percentile Mean Median
Standard deviation
90th percentile
INVOC1 58,121 -0.1458 0.0169 0.0000 0.2260 0.1649
INVOC2 51,146 -0.2279 0.0312 -0.0015 0.3347 0.2657
Coverage 58,121 1.0000 6.6871 4.3333 6.4697 16.0833
LnCoverage 58,121 0.6931 1.7414 1.6740 0.7583 2.8381 LnSale 58,121 3.6072 6.0005 5.9112 2.0063 8.6799 RD 58,121 0.0000 0.0413 0.0000 0.0753 0.1333 ROA 58,121 -0.0258 0.0985 0.1171 0.1500 0.2376 PPE 58,121 0.0250 0.2569 0.1859 0.2294 0.6235 Leverage 58,121 0.0000 0.2063 0.1670 0.1997 0.4798 Capex 58,121 0.0061 0.0578 0.0385 0.0626 0.1305 TobinQ 58,121 0.9388 2.0241 1.4972 1.5329 3.7218 Patent 58,121 0.0000 0.4824 0.0000 1.0530 2.0575 HHI 58,121 0.0255 0.0655 0.0437 0.0622 0.1155 IO 58,121 0.1420 0.5408 0.5507 0.2875 0.9155
39
Table 2.
Summary Statistics: Treatment and Control samples The table presents the summary statistics of variables for both the treatment and control samples in the pre-event window. The accounting data are collected from the Compustat Industrial Annual data files. Market price information is retrieved from CRSP. Analyst-related information is gathered from Thomson Reuters Institutional Brokers’ Estimate System (I/B/E/S). We use the propensity-score matching approach to select control firms upon matching with pre-event industry-adjusted analyst coverage, organization capital investment, PPE, and capital expenditure. All accounting variables are adjusted by the industry-median value (SIC 2-digit code). Appendix A describes the detailed definition of each variable. Variable Treatment Control Difference T-statistics LnCoverage 0.6528 0.6246 0.0282 1.31 INVOCt-1 -0.0178 -0.0244 0.0066 1.25 LnSale 1.2267 0.9707 0.2560 4.61 RD 0.0058 0.0030 0.0028 1.68 ROA 0.0297 0.0245 0.0053 1.42 PPE 0.0280 0.0411 -0.0131 2.52 Leverage 0.0420 0.0426 -0.0006 0.09 Capex 0.0124 0.0211 -0.0087 -4.80 TobinQ 0.5699 0.5290 0.0409 0.77 Patent 0.7982 0.7017 0.0965 1.99 HHI 0.0653 0.0649 0.0004 0.15 Observations 1,425 1,425
40
Table 3.
Change in organization capital investment: difference-in-differences regression This table shows the difference-in-differences regression results on change in organization capital. The dependent variables are INVOC1 and INVOC2. Treatment is an indicator variable which is equal to one for the treatment sample (firms covered by broker mergers/closures), and zero otherwise (control firms). After is equal to one for the broker post-mergers/post-closures time period, and zero otherwise. The intersection term of Treatment*After captures the difference-in-differences effect. We include the year-fixed effect and firm-fixed effect in regressions. The t-statistics in parentheses are based on standard errors clustered at the firm level. All accounting variables are adjusted by the industry-median value (SIC 2-digit code). ***, ** and * denote significance at 1%, 5% and 10% levels (two tailed), respectively.
INVOC1 INVOC2 Model (1) (2) (3) (4) Treatment 0.0127** 0.0058 0.0208* 0.0196
(2.25) (0.67) (1.74) (1.04) After 0.0301*** 0.0193*** 0.0617*** 0.0399*** (5.13) (3.36) (4.62) (3.15) Treatment*After -0.0162** -0.0130** -0.0330** -0.0278**
(-2.38) (-1.97) (-2.38) (-2.12) LnSale 0.0018 0.0514*** 0.0019 0.0935***
(1.28) (8.79) (0.72) (8.71) RD -0.2562*** -0.9842*** -0.5866*** -1.9590***
(-3.58) (-7.75) (-3.90) (-6.12) ROA -0.1551*** -0.3147*** -0.3015*** -0.5251***
(-4.41) (-5.73) (-4.25) (-5.54) PPE -0.0260* -0.1068*** -0.0466 -0.1560*
(-1.85) (-2.68) (-1.50) (-1.69) Leverage 0.0518*** 0.1411*** 0.0908*** 0.1944***
(4.35) (5.69) (3.92) (3.96) Capex 0.0513 0.0155 0.0983 0.1616
(1.24) (0.27) (0.97) (1.35) TobinQ -0.0173*** -0.0275*** -0.0161*** -0.0197***
(-5.93) (-6.49) (-3.92) (-3.56) Patent -0.0011 -0.0021 -0.0029 -0.0005
(-0.74) (-0.43) (-1.01) (-0.05) HHI 0.1380*** 0.0975 0.1548*** -0.1212
(4.37) (1.11) (2.83) (-0.59) IO 0.0048 0.0190 0.0229 0.0788** (0.55) (0.95) (1.23) (2.09) Intercept -0.0300* -0.0600*** -0.0617** -0.1418***
(-1.90) (-2.72) (-2.38) (-3.40) Year FE Yes Yes Yes Yes Firm FE No Yes No Yes Clustered by firm Yes Yes Yes Yes Observations 11,400 11,400 5,700 5,700 Adjusted R2 0.079 0.139 0.082 0.171
41
Table 4.
Change in organization capital investment: difference-in-differences regression
on subsamples sorted by cost of capital This table reports the effect of analyst coverage on organization capital sorted by cost of capital. We utilize an average of the following four cost of capital estimates by Claus and Thomas (2001), Gebhardt, Lee and Swaminathan (2001), Gode and Mhanram (2003), and Easton (2004). We divide the sample into two groups based on cost of capital in the year after the event window. The sample firm is classified as the "High cost of capital" group if its cost of capital is higher than median value, and as the "Low cost of capital" group otherwise. Treatment is an indicator variable which is equal to one for the treatment sample (firms covered by broker mergers/closures), and zero otherwise (control firms). After is equal to one for the broker post-mergers/post-closures time period, and zero otherwise. The intersection term of Treatment*After captures the difference-in-differences effect. We include the year-fixed effect and firm-fixed effect in regressions. The t-statistics in parentheses are based on standard errors clustered at the firm level. All accounting variables are adjusted by the industry-median value (SIC 2-digit code). ***, ** and * denote significance at 1%, 5% and 10% levels (two tailed), respectively.
High cost of capital Low cost of capital Model Dependent variable
(1) INVOC1
(2) INVOC2
(3) INVOC1
(4) INVOC2
Treatment 0.0129 0.0407 -0.0151 -0.0028 (0.95) (1.39) (-0.70) (-0.06)
After 0.0388*** 0.0756*** -0.0144 -0.0352 (4.70) (3.92) (-1.50) (-1.51) Treatment*After -0.0225** -0.0429** 0.0108 0.0171 (-2.37) (-2.35) (1.13) (0.84) LnSale 0.0429*** 0.0959*** 0.0599*** 0.0965***
(5.62) (5.93) (5.94) (6.05) RD -1.0322*** -2.1148*** -0.9312*** -1.6879***
(-4.82) (-4.26) (-4.66) (-4.25) ROA -0.3630*** -0.5444*** -0.3468*** -0.6324***
(-4.46) (-4.80) (-3.97) (-3.71) PPE -0.1349*** -0.1026 -0.1831*** -0.2846**
(-2.59) (-0.65) (-2.71) (-2.11) Leverage 0.1764*** 0.2256*** 0.0957*** 0.2017**
(4.74) (3.11) (2.61) (2.53) Capex -0.0044 0.1145 -0.0471 0.1524
(-0.06) (0.70) (-0.53) (1.05) TobinQ -0.0279*** -0.0243*** -0.0242*** -0.0192**
(-3.99) (-2.75) (-4.79) (-2.43) Patent -0.0012 0.0014 -0.0008 0.0082
(-0.22) (0.11) (-0.10) (0.57) HHI 0.1255 -0.1695 0.1553 0.0061 (0.96) (-0.51) (1.13) (0.02) IO 0.0181 0.0595 0.0785** 0.1164* (0.62) (1.05) (2.48) (1.80) Intercept -0.0778** -0.1513** -0.0768* -0.1772*
(-2.46) (-2.53) (-1.92) (-1.88) Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Clustered by firm Yes Yes Yes Yes Observations 6,132 3,066 4,184 2,092 Adjusted R2 0.164 0.208 0.121 0.150
42
Table 5.
Difference-in-differences regression: effect of financial constraints This table presents the effect of analyst coverage on organization capital based on partition of financial constraint index. We first divide the sample into two groups based on the cost of capital in the year after the event window. The sample firm is classified as the "High cost of capital" group if its cost of capital is higher than median value, and as the "Low cost of capital" group otherwise. Next, we use the HP index to measure financial constraint. High HP is an indicator variable, which is equal to one if the HP index is in the top tercile of the sample. Treatment is an indicator variable which is equal to one for the treatment sample (firms covered by broker mergers/closures), and zero otherwise (control firms). After is equal to one for the broker post-mergers/post-closures time period, and zero otherwise. The intersection term of Treatment*After*High HP captures the difference-in-difference-in-differences effect. We include the year-fixed effect and firm-fixed effect in regressions. The t-statistics in parentheses are based on standard errors clustered at the firm level. All accounting variables are adjusted by the industry-median value (SIC 2-digit code). ***, ** and * denote significance at 1%, 5% and 10% levels (two tailed), respectively. High cost of capital Low cost of capital Model Dependent variable
(1) INVOC1
(2) INVOC2
(3) INVOC1
(4) INVOC2
After 0.0248*** 0.0438** -0.0086 -0.0305 (3.11) (2.20) (-0.82) (-1.19) Treatment*After -0.0062 -0.0086 -0.0003 0.0037 (-0.77) (-0.53) (-0.03) (0.18) Treatment*After*High HP -0.0388* -0.0755** 0.0261 0.0359 (-1.94) (-1.98) (1.33) (0.88) After*High HP 0.0380** 0.0852** -0.0195 -0.0191 (2.18) (2.53) (-1.16) (-0.54) High HP -0.0052 -0.0124 0.0251 0.0271 (-0.43) (-0.45) (1.36) (0.75) LnSale 0.0404*** 0.0882*** 0.0602*** 0.0965***
(5.13) (5.47) (5.94) (6.03) RD -1.0277*** -2.0826*** -0.9376*** -1.6967***
(-4.80) (-4.24) (-4.69) (-4.28) ROA -0.3590*** -0.5303*** -0.3514*** -0.6356***
(-4.39) (-4.64) (-3.99) (-3.69) PPE -0.1468*** -0.1418 -0.1786*** -0.2770**
(-2.91) (-0.96) (-2.67) (-2.06) Leverage 0.1743*** 0.2241*** 0.0933** 0.1965**
(4.73) (3.10) (2.54) (2.46) Capex 0.0009 0.1254 -0.0588 0.1278
(0.01) (0.78) (-0.66) (0.89) TobinQ -0.0273*** -0.0242*** -0.0246*** -0.0196**
(-3.94) (-2.76) (-4.88) (-2.49) Patent -0.0014 0.0002 -0.0004 0.0089
(-0.25) (0.02) (-0.05) (0.62) HHI 0.1364 -0.1259 0.1540 0.0060
(1.05) (-0.40) (1.13) (0.02) IO 0.0144 0.0473 0.0767** 0.1112* (0.50) (0.82) (2.44) (1.73) Intercept -0.0632** -0.1103* -0.1036*** -0.1987**
(-2.03) (-1.71) (-2.82) (-2.35) Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Clustered by firm Yes Yes Yes Yes Observations 6,132 3,066 4,184 2,092 Adjusted R2 0.167 0.215 0.121 0.151
43
Table 6.
Difference-in-differences regression: effect of external equity dependence This table reports the effect of analyst coverage on organization capital based on partition of external financing situation. We first divide the sample into two groups based on cost of capital in the year after the event window. The sample firm is classified as the "High cost of capital" group if its cost of capital is higher than median value, and as the "Low cost of capital" group otherwise. We obtain the number of equity issuance from Compustat before the event year. High Equity is an indicator variable that equals to one if equity issuance is in the top tercile of the sample, and zero otherwise. Treatment is an indicator variable which is equal to one for the treatment sample (firms covered by broker mergers/closures), and zero otherwise (control firms). After is equal to one for the broker post-mergers/post-closures time period, and zero otherwise. The intersection term of Treatment*After*High Equity captures the difference-in-difference-in-differences effect. We include the year-fixed effect and firm-fixed effect in regressions. The t-statistics in parentheses are based on standard errors clustered at the firm level. All accounting variables are adjusted by industry-median value (SIC 2-digit code). ***, ** and * denote significance at 1%, 5% and 10% levels (two tailed), respectively.
High cost of capital Low cost of capital Model Dependent variable
(1) INVOC1
(2) INVOC2
(3) INVOC1
(4) INVOC2
After 0.0199** 0.0366* -0.0075 -0.0358 (2.34) (1.79) (-0.77) (-1.56) Treatment*After -0.0075 -0.0119 0.0027 0.0116 (-0.80) (-0.69) (0.29) (0.62) Treatment*After*High Equity -0.0344* -0.0649* 0.0252 0.0262 (-1.81) (-1.75) (1.12) (0.54) After* High Equity 0.0475*** 0.1026*** -0.0203 -0.0008 (2.64) (2.74) (-1.02) (-0.02) High Equity -0.0004 -0.0110 0.0096 0.0065 (-0.04) (-0.45) (0.98) (0.31) LnSale 0.0434*** 0.0929*** 0.0598*** 0.0941***
(5.63) (5.78) (5.82) (5.69) RD -0.9667*** -1.9708*** -0.9078*** -1.7189***
(-4.50) (-3.95) (-4.51) (-4.34) ROA -0.3869*** -0.5802*** -0.3407*** -0.6703***
(-4.66) (-4.99) (-3.84) (-3.98) PPE -0.1372*** -0.1068 -0.1843*** -0.2832**
(-2.62) (-0.69) (-2.70) (-2.06) Leverage 0.1803*** 0.2374*** 0.0946** 0.1896**
(4.75) (3.23) (2.57) (2.42) Capex -0.0203 0.0546 -0.0715 0.1619
(-0.28) (0.33) (-0.78) (1.09) TobinQ -0.0277*** -0.0244** -0.0248*** -0.0188**
(-3.65) (-2.49) (-4.68) (-2.31) Patent -0.0011 -0.0020 0.0004 0.0102
(-0.20) (-0.16) (0.05) (0.68) HHI 0.1616 -0.0890 0.1442 0.0199 (1.24) (-0.29) (1.04) (0.07) IO 0.0140 0.0451 0.0806*** 0.1113* (0.48) (0.76) (2.60) (1.75) Intercept -0.0632* -0.1096* -0.0922*** -0.1815**
(-1.96) (-1.66) (-2.64) (-2.35) Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Clustered by firm Yes Yes Yes Yes Observations 5,872 2,936 4,112 2,056 Adjusted R2 0.169 0.210 0.121 0.153
44
Table 7.
Total executive compensation: difference-in-differences regression on subsamples
sorted by changes in organization capital investment This table reports the effect of analyst coverage on executive compensation sorted by changes in organization capital investment. We divide the sample into two groups based on changes in organization capital investment after the event year. The sample firm is classified as the "Low OC investment" group if its change in organization capital investment is lower than median value, and as the "High OC investment" group otherwise. The dependent variables are changes in total compensation (TDC), namely, TDCt+1-TDCt in Models (1) and (3) and TDCt+2-TDCt in Models (2) and (4). Treatment is an indicator variable which is equal to one for the treatment sample (firms covered by broker mergers/closures), and zero otherwise (control firms). After is equal to one for the broker post-mergers/post-closures time period, and zero otherwise. The intersection term of Treatment*After captures the difference-in-differences effect. We include the year-fixed effect and firm-fixed effect. The t-statistics in the parentheses are based on standard errors clustered at the firm level. ***, ** and * denote significance at 1%, 5% and 10% levels (two tailed), respectively. Low OC investment High OC investment Model (1) (2) (3) (4) Treatment 0.2038 0.5841* 0.5560 0.2355 (0.93) (1.65) (1.61) (0.44) After 0.3062** 0.5878** -0.0296 0.0067 (2.15) (2.51) (-0.14) (0.02) Treatment*After -0.4559** -0.8620** -0.0736 0.1602 (-2.18) (-2.46) (-0.30) (0.39) LnSale -0.6425*** -1.0372*** -1.1524*** -2.1292***
(-3.73) (-4.15) (-3.48) (-3.13) RD 7.6379** 9.4011** 1.1137 2.4242
(2.10) (1.98) (0.13) (0.26) ROA -0.8130 -1.2601 -1.4468 -1.3306
(-0.32) (-0.56) (-0.56) (-0.39) PPE 0.9138 0.8372 3.0920** 2.2974
(0.41) (0.38) (2.10) (1.09) Leverage 0.9210 0.6875 1.1208 0.9756
(0.92) (0.53) (1.33) (0.87) Capex -6.4711 -8.6216 -5.3978** -3.0201
(-1.63) (-1.20) (-2.41) (-1.15) TobinQ 0.0566 -0.4093** 0.1051 -0.3184
(0.48) (-2.47) (0.51) (-1.32) Patent -0.0605 0.0646 -0.3932 -0.9321*
(-0.57) (0.50) (-1.21) (-1.78) HHI 3.3356 1.2189 -3.9851 -9.6647 (1.09) (0.26) (-0.78) (-1.57) IO 0.3281 -0.1838 0.8011 0.6863 (0.48) (-0.24) (1.25) (0.62) Intercept 0.8999 1.7075** 0.8271 3.1910***
(1.20) (2.28) (1.06) (2.60) Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Clustered by firm Yes Yes Yes Yes Observations 4,007 3,953 3,771 3,721 Adjusted R2 0.019 0.066 0.037 0.073
45
Table 8.
Total factor productivity: difference-in-differences regression on subsamples
sorted by changes in organization capital investment
This table reports the effect of analyst coverage on total factor productivity sorted by changes in organization capital investment. We divide the sample into two groups based on changes in organization capital investment after the event year. The sample firm is classified as the "Low OC investment" group if its change in organization capital investment is lower than median value, and as the "High OC investment" group otherwise. The dependent variables are the total factor productivity (TFP) in the one year or two years after the event, namely, TFPt+1 in Models (1) and (3) and TFPt+2 in Models (2) and (4). Treatment is an indicator variable which is equal to one for the treatment sample (firms covered by broker mergers/closures), and zero otherwise (control firms). After is equal to one for the broker post-mergers/post-closures time period, and zero otherwise. The intersection term of Treatment*After captures the difference-in-differences effect. We include year-fixed effect and firm-fixed effect. The t-statistics in the parentheses are based on standard errors clustered at the firm level. All accounting variables are adjusted by the industry-median value (SIC 2-digit code). ***, ** and * denote significance at 1%, 5% and 10% levels (two tailed), respectively.
Low OC investment High OC investment Model (1) (2) (3) (4) Treatment 0.0675* 0.1016** 0.0157 0.0675
(1.66) (2.43) (0.30) (1.07) After 0.0412** 0.0421** -0.0267 -0.0006 (2.37) (2.47) (-1.24) (-0.03) Treatment*After -0.0346* -0.0546** 0.0015 -0.0179
(-1.66) (-2.57) (0.06) (-0.69) RD 0.6943* -0.0315 0.1304 0.2613
(1.77) (-0.09) (0.40) (0.83) ROA 0.7390*** 0.0304 0.9165*** 0.2819*
(4.57) (0.26) (5.56) (1.85) PPE -0.5755*** 0.0478 -0.9006*** -0.4401**
(-3.10) (0.22) (-6.04) (-2.14) Leverage 0.3261*** 0.3784*** 0.2597*** 0.1822**
(3.28) (3.82) (3.31) (2.19) TobinQ 0.0355*** 0.0198*** 0.0219** 0.0028
(4.76) (2.74) (2.48) (0.24) Patent 0.0009 0.0049 0.0050 -0.0100
(0.06) (0.35) (0.34) (-0.64) HHI 0.0502 -0.5471* 0.2209 -0.2227
(0.14) (-1.68) (0.69) (-0.60) IO 0.0330 -0.0651 -0.0853 -0.1267* (0.51) (-1.13) (-1.16) (-1.72) Intercept 0.1029 0.2427*** 0.0456 0.1848**
(1.25) (3.11) (0.57) (2.17) Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Clustered by firm Yes Yes Yes Yes Observations 5,228 5,169 4,908 4,842 Adjusted R2 0.111 0.050 0.099 0.029
46
Table 9.
Return on assets: difference-in-differences regression on subsamples sorted by
changes in organization capital investment
This table reports the effect of analyst coverage decreasing on return on assets sorted by changes in organization capital investment. We divide the sample into two groups based on changes in organization capital investment after the event year. The sample firm is classified as the "Low OC investment" group if its change in organization capital investment is lower than median value, and as the "High OC investment" group otherwise. The dependent variables are returns on assets (ROA) in the one year and two years after the event, namely, ROAt+1 in Model (1) and (3) and ROAt+2 in Model (2) and (4). Treatment is an indicator variable which is equal to one for the treatment sample (firms covered by broker mergers/closures), and zero otherwise (control firms). After is equal to one for the broker post-mergers/post-closures time period, and zero otherwise. The intersection term of Treatment*After captures the difference-in-differences effect. We include the year-fixed effect and firm-fixed effect. The t-statistics in the parentheses are based on standard errors clustered at the firm level. All accounting variables are adjusted by the industry-median value (SIC 2-digit code). ***, ** and * denote significance at 1%, 5% and 10% levels (two tailed), respectively.
Low OC investment High OC investment Model (1) (2) (3) (4) Treatment 0.0193** 0.0097 0.0007 -0.0113
(2.32) (0.98) (0.07) (-1.19) After 0.0084** 0.0050 -0.0103** -0.0101** (1.98) (1.40) (-2.26) (-2.13) Treatment*After -0.0089** -0.0102** -0.0034 0.0008
(-2.00) (-2.34) (-0.69) (0.16) RD -0.0105 0.0984 -0.1343 -0.0000
(-0.13) (1.09) (-1.64) (-0.00) PPE -0.0315 0.0217 -0.0197 0.0061
(-0.90) (0.70) (-0.69) (0.20) Leverage 0.0121 0.0402** -0.0060 0.0078
(0.63) (2.34) (-0.31) (0.47) TobinQ 0.0177*** 0.0062*** 0.0157*** 0.0037*
(9.20) (2.75) (7.86) (1.96) Patent -0.0071** -0.0030 -0.0054 -0.0079**
(-2.03) (-0.99) (-1.33) (-2.34) HHI -0.0637 -0.0685 -0.1055 -0.0666
(-0.61) (-0.79) (-1.12) (-0.63) IO -0.0027 -0.0178 -0.0131 -0.0431*** (-0.21) (-1.31) (-0.98) (-3.15) Intercept 0.0025 0.0141 0.0437* 0.0672***
(0.15) (0.84) (1.95) (2.79) Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Clustered by firm Yes Yes Yes Yes Observations 5,755 5,688 5,629 5,524 Adjusted R2 0.099 0.040 0.113 0.051
47
Figure 1. R&D and SG&A distribution (aggregate) in millions
Figure 2. R&D and SG&A distribution (industry level)
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48
Panel A: INVOC1
Panel B: INVOC2
Figure 3.
Distribution of coefficients on the interaction variable in model (2) and model (4) of Table 3:
A placebo test
This figure reports a placebo test for the distribution of coefficients on the interaction term in difference-in-differences regression in model (2) and model (4) of Table 3. We replace the treatment firm with another firm randomly selected from the non-treatment sample as the pseudo brokerage house closures and mergers events. Then, we use a propensity-score matching procedure to select the control sample. Treatment is equal to one for a pseudo brokerage house closures and mergers event, and zero otherwise, and After is equal to one for the years after the pseudo event year, and zero otherwise. We re-run the difference-in-differences regression in model (2) and (4) of Table 3. We record the coefficient on the interaction variable, Treatment×After. We repeat this procedure 1,000 times and hence obtain 1,000 estimated coefficients on the interaction variable. Figure 3 presents the distribution of these coefficients.
0
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<-0
.017
-0.0
17 ~
-0.
015
-0.0
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-0.
013
-0.0
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-0.
011
-0.0
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-0.
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-0.
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005
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003
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001
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0.0
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.034
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-0.
030
-0.0
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026
-0.0
26 ~
-0.
022
-0.0
22 ~
-0.
018
-0.0
18 ~
-0.
014
-0.0
14 ~
-0.
010
-0.0
10 ~
-0.
006
-0.0
06 ~
-0.
002
-0.0
02 ~
0.0
02
0.00
2 ~
0.00
6
0.00
6 ~
0.01
0
0.01
0 ~
0.01
4
0.01
4 ~
0.01
8
0.01
8 ~
0.02
2
0.02
2 ~
0.02
6
0.02
6 ~
0.03
0
0.03
0 ~
0.03
4
>=
0.03
4