Download Mutual fund intermediation, equity issues, and the real

Mutual fund intermediation, equity issues, and the
real economy∗
Bill Yiqing Zu†
University of Melbourne
August 2014
Abstract
I investigate the real economic effect of mutual funds. As informed financiers, mutual
funds have superior ability to identify productive firms, and can allocate capital more
efficiently in equity issues. A simple model of mutual fund intermediation demonstrates a
positive relation between mutual fund investment in equity issues and subsequent output
growth. Empirically, at both the aggregate and industry level, I find that new equity
issues lead to higher real output, only when there is high mutual fund participation in
these issues. In addition, the participation-output sensitivity is stronger when fund flows
are low. At the firm-level, mutual fund investment in seasoned offerings positively predicts
subsequent firm productivity growth. Further tests suggest that the results are unlikely
to be driven by reverse causality explanations.
∗
I am greatly indebted to Neal Galpin, Bruce Grundy, Spencer Martin and Juan Sotes-Paladino for their
extensive help and support, and would also like to thank Antonio Gargano, Simon Gervais, Vincent Gregoire,
Patrick Kelly, Robert Korajczyk, Tong Yob Nam (discussant), Selim Topaloglu, and seminar participants at
the University of Melbourne Brown Bag and the 26th Australasian Finance and Banking Conference 2013 for
their valuable comments. I thank the Investment Company Institute (ICI) for providing the aggregate fund
flows dataset. All errors are my own.
†
Ph.D. Candidate, Department of Finance, University of Melbourne, Australia.
Email:
[email protected]
1
Introduction
The mutual fund industry has grown immensely over the past 20 years. As the largest group
of institutional investors, the industry’s total assets under management has grown from only
$134 billion in 1980 to about $12 trillion at the end of 2007, accounting for approximately 33%
of the total value of the U.S. stock market. The vast majority of academic studies on mutual
funds have focused on their secondary market activities, and in particular, their ability to
deliver superior returns above benchmarks. However, the social benefit of active management
does not come from the ability to earn excess returns – the average fund has to hold the
market portfolio. Instead, the real economic benefit of asset managers should be in the forms
of increased firm value and productivity growth (Greenwood and Scharfstein, 2013).
Despite their importance, the real economic effects of mutual funds have received much less
attention in the academic literature. In this paper, I investigate this issue by examining the
relation between mutual fund investment and real economic activities, through the channel of
primary market equity issues. As informed financiers, mutual funds have superior screening
abilities in the primary market (Gibson, Safieddine, and Sonti, 2004; Chemmanur, He, and
Hu, 2009). I argue that with this screening ability, mutual funds can allocate capital more
efficiently, and on average would finance the more productive firms in the economy. As a
result, mutual fund investment conveys information about the economy’s productivity, and
real output is higher when more equity issues are financed by mutual funds. In this regard,
mutual funds have a positive role in the real economy, in their capacity as informed financial
intermediaries.
The question of whether mutual funds have real effects is important to examine, but the
research in this area remains scarce. As the largest group of institutional investors, mutual
funds generate large trading volume and earn high fees from asset management. There is
much debate regarding their stock-selection or market-timing skills (see, for example, Elton
and Gruber (2013) for an extensive review). However, little is known about the real economic benefit of these activities. Mutual funds may exert influence on the real economy,
either through their primary market investment or through their secondary market activities.
Arguably, informed trading in the secondary market helps to incorporate information into
prices, thereby enhancing price efficiency and hence capital allocation efficiency (Fama, 1970;
2
Grossman and Stiglitz, 1980).1 However, this is a second-order effect, and the more direct
influence of mutual funds through their primary market investment as informed financiers
has received much less attention, especially regarding their real effect on firms and at the
aggregate level. This paper contributes to the literature by demonstrating the positive real
effects of mutual funds through this primary market channel.
I first present a simple one-period, two-sector model of mutual fund intermediation. In
the model, firms of differing productivities raise equity to finance their investment projects.
Individual investors cannot differentiate between firms’ productivities, nor do they know the
true productivity level. On the other hand, mutual funds have the ability to ex-ante differentiate between firms at a cost, and they also know the true productivity. Individuals choose to
allocate their wealth between mutual funds and direct investment in firms. More allocation
to mutual funds would entail better investment returns, but individuals would also incur the
convex information cost. In equilibrium, fund flows increase with individuals’ expected productivity, but the realization of output is determined by mutual funds’ investment (because
funds are assumed to have perfect information about productivity). Higher investment by
mutual funds is associated with higher subsequent real output, because mutual funds only
invest when they know that productivity is high. Furthermore, if individuals’ incorrectly perceive productivity to be low and thus allocate less fund flows, there would be an even higher
return to mutual funds’ investment, because funds are constrained to only the best projects.
The model therefore helps to establish a relation between real output, mutual fund investment
and fund flows that is empirically testable.
Empirically, I find evidence that is largely consistent with the model’s predictions. Using
U.S. data during the period from 1984 through 2011, I find that mutual fund participation2
in new equity issues (seasoned equity offerings and initial public offerings) positively predicts
subsequent issue-output sensitivity, after controlling for the total amount of issues and other
macroeconomic variables. When mutual funds are actively participating in new issues, the
issues are able to generate more real output, reflecting the higher productivity of the firms that
receive funding from mutual funds. Moreover, when fund flows are low, the fund participationgrowth relation is stronger. This is also consistent with the model’s prediction. When there
is low fund flows but high fund participation, it suggests that individuals and fund managers
1
2
Recently, Piacentino (2013) presents a theoretical model in this spirit.
Participation is measured as change in mutual fund holdings.
3
disagree about productivity. If fund managers have better information, their investment based
on the constrained capital would produce higher returns, generating the “flight-to-quality”
effect (Holmstrom and Tirole, 1997).
Since fund flows and equity issues are cyclical in nature, I test the model separately
on the overall growth in output as well as the cyclical component of output. The results
predominantly show up in the cyclical component of output, rather than the long-run trend.3
Further analysis at the industry-level confirm the aggregate results. Industries that receive
higher mutual fund participation in their equity issues have higher subsequent issue-output
sensitivity. In addition, the “flight-to-quality” effect is also observed at the industry level,
where high fund participation leads to higher cyclical growth especially when fund inflows are
lower. At the micro level, firms with highly participated issues by mutual funds exhibit higher
subsequent total factor productivity growth, after controlling for firm-specific characteristics.
This complements and offers further support for the aggregate and industry-level evidence,
and is consistent with the model assumption where mutual funds identify the most productive
firms to invest.
Taken together, the evidence is consistent with the hypothesis that mutual funds have
superior ability to identify productive equity-issuing firms, and have positive roles in the
economy by investing in these firms. Given these results, one may have concerns regarding
the potential reverse causality explanations. Specifically, one may argue that mutual funds
do not have superior skills. Instead, the results may be driven by the fact that mutual
funds simply invest more when they have more fund flows, and that more fund flows occur
when anticipated growth is high. This anticipation effect could induce a positive correlation
between fund participation and future productivity, even when mutual funds do not possess
superior skills. There are two observations that may alleviate this concern. First, if this story
is true, it would imply that fund flows and mutual fund participation are highly correlated.
This is not the case in the data. The contemporaneous correlation between aggregate fund
flows and mutual fund participation is only about 0.16. Thus, although fund flows contain
some information about future productivity, mutual fund participation contains additional
information independent of fund flows. Furthermore, in the regressions, I control for fund
flows to capture this passive effect.
3
The empirical tests here do not have implications for cross-sectional long-run growth effects, since there
is only one country studied.
4
Secondly, results show that when fund flows are low, higher participation leads to higher
subsequent growth. This is inconsistent with the reverse causality story above. If mutual funds
are simply “riding the wave”, lower fund flows would lead to lower participation, and future
growth would be lower because of low productivity. This would imply either a positive relation
or no relation between future growth and the interaction term of fund flows and participation.
Instead, the results show a strong negative relation, which suggests that mutual funds have
different information than individuals, and their investment contains superior information
about future productivity, especially during low fund flow times.
In order to further examine this alternative explanation, I separate the mutual fund participation into two groups – participation by active funds versus participation by passive funds.
If the positive relation between mutual fund participation and firm productivity is indeed due
to funds’ screening ability, one would expect the effect to predominantly come from active
funds rather than passive funds. Indeed, the results show that most of the effects are driven
by active funds’ participation, and passive funds’ participation have little predictive power
of future productivity. These results lend further support to the model’s hypotheses, and
suggest that the results are unlikely to be driven by reverse causality explanations.
The rest of the paper is structured as follows. I firstly review the related literature in
section 2. I then present the simple model of mutual fund intermediation in section 3, and
provide the empirically testable implications. Section 4 describes the data and variables used.
In section 5, I present and discuss the empirical results, along with some robustness tests and
discussions about alternative explanations. Section 6 concludes.
2
Related Literature
The role of mutual funds in the real economy can be largely categorized into primary market
investment and secondary market trading. In the primary market, a large body of empirical literature examines the role of financial institutions as active monitors, and find that
the stock market reacts positively towards pension fund activism announcements (Gillan and
Starks, 2000), but there is little evidence of long-run improvement in stock returns or operating performance (Smith, 1996; Del-Guercio and Hawkins, 1999). In contrast, Brav, Jiang,
Partnoy, and Thomas (2008) find an average abnormal return of 7% around the announcement of hedge fund activism activities, and they attribute the results as due to differences
5
in the governance and incentive structure of hedge funds versus pension funds. Although
the evidence on institutions’ monitoring capacity is mixed, there is strong evidence of their
screening ability. Several papers have shown, using different datasets, that institutions have
superior information and are able to identify above-average seasoned equity offering firms
with long-run abnormal returns (Gibson, Safieddine, and Sonti, 2004; Chemmanur, He, and
Hu, 2009; Demiralp, D’Mello, Schlingemann, and Subramaniam, 2011).
The empirical evidence of institutional monitoring and screening roles are perfectly consistent with the theory of financial intermediation. In order to overcome information problems
created by differently informed traders, one of the major services provided by financial intermediaries is to monitor, screen, and certify firm investment (Bhattacharya and Thakor,
1993). By having intermediaries that specialize in information acquisition and allowing them
to capture a return on the information (by holding the valuable assets), one can mitigate the
public good problem, and thus enable the funding of some positive net present value projects
which would otherwise be unfunded due to high information costs (Leland and Pyle, 1977;
Diamond, 1984).
Incorporating this idea, Holmstrom and Tirole (1997) examine the role of intermediaries in
the real economy. In their model, intermediaries perform the monitoring role and commit their
own capital as part of the monitoring process. In times of intermediary capital shocks, the
economy faces a reduction in the amount of valuable projects funded, and this creates a flightto-quality effect where intermediary capital is rationed to higher-quality firms. Schumpeterian
models of finance and growth also argue that financial intermediaries and markets help to
alleviate information problems and allocate capital more efficiently, thus promoting long-run
growth (Greenwood and Jovanovic, 1990; King and Levine, 1993b).
Empirical studies have found strong support for the causal relation between financial
development and economic growth. At the country level, most of the empirical tests examine
the banking sector for measures of financial development (King and Levine, 1993a; Beck and
Levine, 2002), and find that financial development leads to higher economic growth in crosscountry regressions. Levine (2005) provides an extensive survey of the empirical results along
this line of research. For firm-level evidence in the U.S., Chava and Purnanandam (2011) use
the Russian crisis in 1998 as an exogenous shock to the U.S. banking sector, and find that
bank-dependent firms experience larger valuation losses and reductions in real investment.
Lemmon and Roberts (2010) examine regulatory changes in the banking sector that adversely
6
affect below-investment-grade firms’ borrowing ability, and find that reductions in firms’ bank
borrowing lead to an almost one-to-one reduction in net investment. Ivashina and Scharfstein
(2010) and Duchin, Ozbas, and Sensoy (2010) provide evidence of reduced bank lending and
real investment after the financial crisis in 2008, suggesting that capital supply constraints
have real effects.
In addition to the banking sector, Bhattacharya and Thakor (1993) suggest that ratings
agencies also act as intermediaries in providing screening and certification services. In this
spirit, Faulkender and Petersen (2006) find that firms that have bond ratings have significantly
higher leverage. Similarly, Sufi (2009) find that the introduction of syndicate loan ratings leads
to an increase in firms’ subsequent asset growth, cash acquisitions, and investment. These
results suggest that ratings agencies provide valuable certification to the bond market to
reduce information problems.
Despite the large body of literature on the relation between credit intermediaries and the
real economy, limited attention has been paid to the real economic role of other financial
intermediaries such as mutual funds, albeit them being one of the largest group of equity
investors.4 Most of the studies on mutual funds have focused on their secondary market
effects. As mentioned previously, one of the main activities of mutual funds is to enhance
price efficiency. In this spirit, Piacentino (2013) presents a market microstructure model
where career-concerned delegated portfolio managers acquire more information and trade more
than individual investors, due to their additional incentives to signal through their trades.
This induces more private information being impounded into prices, thereby improving price
efficiency and capital allocation efficiency. This result is consistent with the empirical evidence
on institutional investors and price efficiency (Boehmer and Kelley, 2009), and suggest that
active portfolio managers may have positive externalities on real economic activities.
Although there is little research that links the primary equity market to mutual funds,
this is a relevant question to examine. From a theoretical perspective, equity market intermediaries are similar to banks in their capacity as monitors and alleviating the public-good
problem (Shleifer and Vishny, 1986). Moreover, if intermediation helps to reduce information
problems, one should expect them to be most useful in equity markets, where information
problems are more severe than debt markets. Although there is some empirical evidence of
4
Lewellen (2011) shows that financial institutions together hold around 68% of the U.S. equity market
value in 2007, and approximately half are held by mutual funds.
7
superior institutional monitoring and screening abilities through their investment returns, one
cannot conclude that this directly leads to improvements in economic growth, for the following
reasons.
First, there is little evidence of how institutional behavior affects firms’ real investment
decisions and productivity, which is central to the link between finance and growth. As
argued by Levine (2005), in order for financial development to influence growth, institutions
need to influence resource allocation decisions that foster productivity growth, rather than
simply promoting physical capital accumulation. Brav, Jiang, and Kim (2011) is one of the
first papers to examine these real effects, and they find significant increases in total factor
productivity of target firms within two years after hedge fund activism. The authors also
find that systematic risks of target firms decrease within three years post-activism event.
The paper offers important insights in demonstrating the role of institutions through the
productivity channel, in addition to the effect on stock returns and operating performance.
However, increases in the productivity of individual firms do not imply growth at the aggregate
level, since competition could result in a loss of output by other unproductive firms. Moreover,
studies on shareholder activism can only capture the monitoring aspect of institutions expost, but not the screening or certification effects ex-ante. To provide evidence for the latter,
one needs to examine the extent of institutional participation in primary markets, both in
aggregate and at a micro level.
Second, theoretical models such as Holmstrom and Tirole (1997), Chen (2001) and Bernanke
and Gertler (1989) suggest that in a general equilibrium setting, it is the capital shocks to institutions or productivity shocks that generate the macroeconomic dynamics and not just their
participation (although participation by institutions certainly improves output by facilitating
better investment). In Bernanke and Gertler (1989), productivity shocks reduce firms’ collaterals, and thus prevent them from raising capital for further investment, especially during
economic downturns when information asymmetry problems are more severe. In Holmstrom
and Tirole (1997), capital shocks to financial intermediaries hamper their ability to successfully
monitor projects, thus reducing the amount of real investment in the economy. In addition,
real business cycle models have also examined the impact of financial constraints/shocks on
firm investment and the real economy, showing that financial shocks or disintermediation can
have large effects in amplifying productivity shocks and generating business cycles (Jermann
and Quadrini, 2012). Therefore, when one looks at equity intermediation, capital constraint
8
of intermediaries is also important.
Empirically, a natural proxy for mutual funds’ capital constraints is fund flows. Large fund
outflows would reduce the amount of discretionary cash that funds have available to invest
in equity issues, and when they are unable to fund all the positive NPV investment, they are
forced to choose only the most profitable ones. On the other hand, large fund inflows may
force funds to purchase stocks even when trading opportunities are scarce (Khan, Kogan, and
Serafeim, 2012), and this could potentially reduce the efficiency of fund investment. Thus,
in order to examine the real effects, it is important to take into consideration funds’ capital
availability, both theoretically and empirically.
I now present a simple model that illustrates how mutual funds can enhance real activities
through their informational advantages, and derive some empirical implications.
3
A simple model
The model economy has one period, and consists of two production sectors, a representative
household, and a representative mutual fund. Each sector is described in detail below.
3.1
Production technology
There are two production sectors in equal mass, denoted as sector H and sector L. Firms in
each sector raise equity capital to fund their investment projects, which pay off at the end
of the period. The two sectors differ in their productivities. The L sector has productivity
AL , while the H sector has productivity AL + θ, with θ > 0. Both sectors have constantreturns-to-scale production technologies that take capital (which I denote as I) as the only
input:
YL = AL I,
YH = (AL + θ)I
(1)
Since these two sectors produce the consumption good, I refer to them as the “primary
market”. In addition to the primary market, there is also a secondary market that trades
shares in existing investment projects. One could think of the secondary market as an index
fund in the stock market. These existing projects will deliver some fixed level of output,
regardless of who is owning the shares. I denote the amount of capital stock in the secondary
market as S. I assume that the secondary market has an expected return that is equal to the
average of the two primary sectors, so that without information advantages, the risk neutral
9
investors are indifferent between investing in the primary market and secondary market. This
assumption implies that the secondary market return Rm is given by Rm = AL + θ/2, and
the existing projects will produce Rm S of output at period end. Further, I assume that the
average investment project is profitable, i.e. AL +
θ
2
> 1, therefore all investors are willing to
participate in either the primary market or the secondary market.
3.2
Mutual fund sector
There is one representative mutual fund in the economy that can invest on behalf of the
representative household. The mutual fund cannot take leverage, and thus can only invest up
to the amount of fund flows it receives, which I denote as F .
The mutual fund has access to a technology that can identify the more productive sector
H, and therefore allows them to only invest in that sector and avoid sector L. To obtain the
information, the mutual fund incurs cost, and I assume that the cost is convex in the amount of
investment, in the spirit of Berk and Green (2004). One can think of the convex cost structure
as giving the fund investor diminishing returns in their mutual fund investment. Specifically,
for an investment of f into the H sector, the mutual fund incurs 21 φf 2 of information cost,
where φ is a cost parameter. Ultimately this cost is borne by the investor. I also assume that
the mutual fund knows the true value of θ.
In addition to investing in the primary market, the mutual fund can also invest some fund
flows into the secondary market. I denote the primary market investment by mutual funds as
f , and thus the secondary market investment is given by F − f . Since there is no screening
in the secondary market, no information cost is incurred on these investments.5
I assume that there is no agency problem between the mutual fund and the household.
The fund will seek to maximize the return to households. Given their fund flows F , the
mutual fund solves the following problem:
max
0≤f ≤F
1
(AL + θ)f − φf 2 + (F − f )Rm
|
{z
}
|
{z 2 }
Primary market
where
Rm
(2)
Secondary market
θ
= AL +
2
5
I do not explicitly model the supply of the secondary market shares, and assume that there are liquidity
traders who supply shares to the mutual fund. The liquidity traders do not participate in the primary market,
and hence do not affect real output.
10
The fund flows F is determined endogenously by the household, which I describe below.
3.3
Households
There is one representative household that maximizes period-end consumption. The household
is risk-neutral, and has initial wealth W , which it can invest in the primary market to generate
future consumption.6
The household is uninformed, in the sense that it cannot differentiate between the two
production sectors, nor does the household know the true value of θ. Instead, the household
has its own information about θ, which I denote as θh , and all of the household’s investment
decisions are based on θh .7 Further, the household does not try to infer the true θ from mutual
funds (they firmly believe in their own θh ), therefore the household only expects mutual funds
to invest in the H sector, given the fund flows they provide.
The household can choose to invest through the mutual fund sector by giving F to the
mutual fund, or invest directly in the primary market. The household perceives that any
investment through the mutual fund is guaranteed to provide an expected return of AL + θh ,
less the information cost. On the other hand, investment made directly in the primary market
will earn an expected return of AL + θh /2.
Given their initial wealth W , the household solves the following problem:
max
0≤F ≤W
E(RM F (F )) + D × E(RD )
(3)
s.t. W = F + D
where E(RM F (F )) represents the expected return from mutual fund investment, given the
fund flows, and E(RD ) is the expected return from direct investment. Since the household
only expects the mutual fund to invest in the H sector, with the convex cost structure and
6
I assume that the household cannot invest in the secondary market. By assumption, the primary market
and the secondary market delivers the same expected return to the household. These assumptions are to
simplify the model, and do not change the model implications.
7
Here I do not explicitly model the household’s information acquisition problem, nor do I specify why the
household has different information from the mutual fund. If households and mutual funds have the same
information, households would be able to allocate the perfect amount of capital to funds, and fund flows would
not have an impact on funds’ real effects.
11
with E(RD ) = AL + θh /2, the above problem becomes:
max
0≤F ≤W
s.t.
1 2
θh
(AL + θ )F − φF + D AL +
2
2
h
(4)
W =F +D
where D denotes the direct investment, and F denotes the household’s fund flows.
3.4
Equilibrium analysis and model implications
I now solve for the mutual fund and the household’s optimization problems. The household’s
problem in equation (4) along with the budget constraint imply the first order conditions:
F∗ =
θh
2φ
D∗ = W −
(5)
θh
2φ
(6)
For the fund manager, the optimization problem in equation (2) implies:
f=
θ
2φ
However, since the mutual fund cannot take leverage, the fund’s investment in primary
market is bounded by the amount of fund flow it receives, F , which in turn is determined
by the household’s perception of investment opportunities, θh . Therefore, the actual level of
mutual fund investment in the primary market is given by:
∗
f = min
θ
θh
,
2φ 2φ
(7)
A simple comparison between (7) and (5) shows that if the household has perfect information about investment opportunities, that is, if θh = θ, then the household will provide the
fund flows that match with the optimal investment strategy of the mutual fund, i.e. f = F ∗ .
However, if the household has different (and incorrect) information about investment opportunities, then the supply of funds to the mutual fund sector may not coincide with the fund
manager’s investment strategy. Specifically, if the household perceives productivity to be low,
and allocates too little capital to the mutual fund relative to what the mutual fund desires,
this may carry negative consequences for the aggregate economy, since some of the capital
12
would be unnecessarily invested in the unproductive sector.
The model shows that the extent to which the fund manager invests in the primary market
will provide some information about investment opportunities, as long as the mutual fund has
superior information relative to households. To examine the aggregate effect of mutual funds’
information advantages, I define aggregate output in the economy by:
1 2
θ
Y = (AL + θ)f − φf + (W − F ) AL +
+ Rm S
2
2
(8)
where the first two terms represent output attributed to mutual fund investment less information costs, the third term represents output produced by direct investment by the household,
and the last term represents output from the existing projects (the secondary market).
As noted above, mutual fund investment f contains some information about investment
opportunities θ. However, the extent to which this is true may depend on the size of θh
versus θ. In particular, (7) shows that if θf ≤ θ, mutual fund investment f would reflect
the household’s perceived productivity, but if θf > θ, f would reflect the true productivity.
This suggests that aggregate output may take two possible forms in terms of the observables,
which I analyze separately.
Case 1: θh ≤ θ and F = f . In this case, one can substitute F = f into equation (8) and
obtain
1
Y = (AL + θ)f − φf 2 + W
2
θ
1 2
= f − φf + W AL +
2
2
θ
θ
AL +
− f AL +
+ Rm S
2
2
θ
+ Rm S
2
(9)
Aggregate output is increasing in the linear term of mutual fund investment f , and is
decreasing in the square term of fund investment f 2 , due to the convex information cost.
Further, since output is determined by the actual productivity θ and not the household’s
perceived θh , fund flows do not play a part in determining output here.
Case 2: θh > θ and F > f . In this case, mutual fund investment f is determined by
the fund’s information about true productivity θ. Therefore, one can rewrite the aggregate
13
output in equation (8) in terms of the observable f , given that θ = 2φf from equation (7):
1 2
2φf
+ Rm S
Y = (AL + 2φf )f − φf + (W − F ) AL +
2
2
3
= (AL + W φ)f + φf 2 − φF f + W AL − F AL + Rm S
2
(10)
Consistent with the first case, equation (10) shows that mutual fund investment in primary
issues conveys information about the true productivity θ. In particular, aggregate output is
positively related to the linear term of f . This is due to fund’s information advantage and its
ability to invest in the more productive sector, so that its investment decision reveals future
productivity and hence output. This is the key prediction of the model. As shown above,
this result does not depend on the relative size of θh and θ, i.e. the extent to which the
household overestimates or underestimates productivity. Another interesting prediction is in
the third term in equation (10), where the interaction term between fund flows F and fund
primary investment f has a negative effect on aggregate output. As fund flow F decreases,
the sensitivity of aggregate output to mutual fund investment f becomes stronger.
Note that equations (10) and (9) provide similar predictions about the relation between
mutual fund investment and output, albeit different in magnitude. In addition, equation (10)
provides additional predictions on the interaction effect between fund flows and mutual fund
investment. Hence, in the empirical tests I will adopt (10) as the testing equation.8
Although the variables f, Rm , and F in equation (10) are all empirically observable, one
problem with estimating the equation is that the variables are in levels. Therefore I scale
equation (10) by initial wealth W to obtain a relation with stationary variables:
Y
f
3 f2
φF f
F
S
= AL + (AL + W φ)
+ φ
−
−
AL +
Rm
W
W
2 W
W
W
W
(11)
Equation (11) can then be tested using empirical counterparts of f /W, F/W , and Rm .
This relation should hold at both the aggregate and industry level, since in the model, the
sectors could either be within an industry, or different industries within the whole economy.
I summarize these predictions in the hypotheses below:
8
One may argue that the two equations do not give the same predictions, because the coefficient on the
square term of f is positive in (9), while it is negative in (10). This difference is due to the functional form
adopted for the convex information cost, and is a caveat of the model. Intuitively, the square term of f should
load negatively on aggregate output due to the assumption of convex costs.
14
Hypothesis 1: At the aggregate level, mutual fund investment in the primary market (f )
is positively related to subsequent output growth. Furthermore, the interaction between
fund flows and mutual fund investment negatively predicts future growth.
Hypothesis 2: At the industry level, mutual fund investment in the primary market positively predicts subsequent industry output. The interaction between fund flows and
mutual fund investment negatively predicts industry output.
An important assumption of the model is that mutual funds have superior screening skills.
If this is indeed the case, the equity issuing firms that receive more mutual fund investment
should have higher productivity growth in subsequent periods. This is a testable assumption
which will also be explored.
4
Data and methodology
In this section I describe the data and variable constructions, along with the empirical specification that maps the model to the data.
4.1
Macroeconomic and equity issuance data
I use real GDP and real industry value-added to measure aggregate and industry output,
respectively. I obtain quarterly real GDP and annual real industry value-added from the
U.S. Bureau of Economic Analysis (BEA), and calculate growth rates as the log-differences
of levels.
In addition to overall output effects, I also examine the effect of mutual fund investment
(and the lack of it) on the cyclical fluctuations in output. Although the model does not
differentiate between the long-run trend and the cyclical component, there is substantial
motivation for this differentiation from an empirical perspective. Both equity issues and
aggregate fund flows are highly cyclical in nature (Covas and Den Haan, 2011; Jank, 2012),
and in addition, Peress (2014) shows that information aggregation in the stock market has
an impact on the transitory component of growth. Therefore it is worthwhile to examine the
long-run trend and the cyclical component separately. I follow Covas and Den Haan (2011)
and use the Hodrick-Prescott filter (Hodrick and Prescott, 1997) with a smoothing coefficient
15
of 1600 for quarterly data and 6.25 for annual data to extract the cyclical component of
output.
Data on aggregate fund flows for domestic equity funds in the U.S. is obtained from
the Investment Company Institute (ICI). ICI collects monthly aggregate data on fund sales,
redemptions, and exchanges, as well as total net asset values (TNA).9 I follow Warther (1995)
and Jank (2012), and calculate quarterly net fund flows as new sales minus redemptions plus
exchanges-in minus exchanges-out.10 I aggregate monthly flows to the quarterly/annual level
in order to match up with the frequency of macroeconomic variables. The sample period
spans from 1984Q1 to 2011Q4.
Equity issuance data is obtained from the Securities Data Corporation’s (SDC) Global
New Issues database, which provides information on issuer profile, issuer SIC code, issue
price, issue size, issue date, filing date, among other details, of all seasoned equity offerings
and initial public offerings issued on the NYSE, AMEX, and NASDAQ. I exclude units,
ADRs, closed-end funds issues, and exclude financial firms (SIC codes 6000-6999) and utilities
(SIC codes 4900-4949) from the sample. The firms’ accounting information and stock market
information are obtained from COMPUSTAT and CRSP, respectively. I use the firms’ NAICS
(North American Industry Classification System) codes from COMPUSTAT to match with
the industry value-added data. If the NAICS code is missing from COMPUSTAT, I use the
NAICS-SIC matching table provided by the U.S. Census.11 The final sample yields a total of
15,038 firm-issue observations.
Other variables used include the aggregate stock market returns Rm , which is the valueweighted market return of the CRSP universe. For industry-level tests, I construct industrylevel value-weighted returns. To measure wealth available for investment W at the aggregate
level, I use the total assets of non-financial business sectors (BusAssets, obtained from the
Federal Reserve) as a proxy, while at the industry level, I use the market value of equity for
the industry. I also control for total credit issuance in the economy (Credit), using quarterly/annual total credit borrowings in non-financial business sectors, obtained from the Fed9
According to ICI, the database covers about 98% of assets in the mutual fund industry. The advantage of
using ICI data for aggregate flows over other mutual fund databases such as the CRSP Survivorship-bias-free
database is that ICI reports actual flows, whereas fund-level databases do not have such information, and thus
fund flows would have to be estimated.
10
Exchanges-ins and exchanges-outs are transfers into (out of) equity funds from/to other fund groups. These
differ from sales/redemptions in that there is no cashflow between the funds and the investor. Nevertheless,
they represent funds’ reduced ability to invest in equity.
11
http://www.census.gov/eos/www/naics/concordances/2002_NAICS_to_1987_SIC.xls
16
eral Reserve. This is an important control variable, as the amount of credit issuance influence
firms’ ability to invest, in the same way as equity issues. In addition, I use quarterly/annual
consensus GDP growth forecasts as a control variable. The one period ahead forecasts are
obtained from the Survey of Professional Forecasters, compiled by the Federal Reserve of
Philadelphia. For firm-level tests, control variables include firm size (defined as market value
of assets) and prior returns (defined as the cumulative stock returns from months t − 12 to
t − 1 prior to the month of the issue). I deflate all aggregate variables that are in levels by the
GDP deflator, and deflate all industry variables in levels by the industry value-added price
index.
4.2
Mutual fund investment
Due to data limitations, it is very difficult to obtain information on allocations to institutions
in new equity issues (Chemmanur, He, and Hu, 2009). Therefore I follow the approach of
Gibson, Safieddine, and Sonti (2004) and Demiralp, D’Mello, Schlingemann, and Subramaniam (2011), and use the change in mutual fund holdings as a proxy to gauge the extent of
fund investment in these issues.
I calculate a measure of mutual fund participation for each new issue (SEO or IPO). I
obtain information on mutual fund holdings at the quarter-end prior to and the quarterend immediately after each issue, from the Thomson Reuters Mutual Fund Holdings (s12)
database. I define mutual fund participation for issue i at time t to be the increase in the
number of shares held by all funds over the issuing quarter, scaled by the number of shares in
the issue. Average participation is then defined as the mean participation rate over all issues
during the quarter/year, and represents the proportion of all new issues that is contributed
by mutual funds. Since this measure is based on holdings and is noisy, I create a dummy
variable P ARTt that equals to one for quarters where the average participation is higher than
the sample median, and zero otherwise (for the industry tests, the dummy equals one when
the average participation is higher than the cross-sectional median across all industries for
each year). The variable f in the regression can be obtained by multiplying P ARTt with total
equity issues Issuet (total issues multiplied by the proportion coming from mutual funds).
Table 1 shows the aggregate level summary statistics. Average quarterly GDP growth
rates are about 0.68%, while market returns are about 2.8% per quarter. Total proceeds
from primary issues are similar to net fund flows as a ratio of total assets of non-financial
17
business sectors, however fund flows display larger variation than equity issues. The average
participation rate in primary issues by mutual funds is about 24%, which is approximately
in line with the average holdings of the stock market by mutual funds. This participation
rate also exhibits a fair amount of variation, suggesting that mutual funds actively move in
and out of primary issues, rather than passively investing in the issues. However, this average
participation rate is very noisy since the measure is constructed from change in holdings
(which contains trading activities in the secondary market), therefore I use the dummy variable
P ART for high/low participation in the regressions.12
4.3
Model specification
In order to specify the regression in terms of stationary variables, I use the total assets of
non-financial business sectors as a proxy for W , and scale all aggregate variables in equation
(11) by it.13 For industry tests, I scale the variables by the lagged industry market value
of equity (for all firms in that industry in CRSP). For firm-level tests, I scale by the lagged
market value of assets (the market value of equity plus the book value of total liabilities).
The full specification for the growth rate regression and cyclical component regression is
as follows (the only difference being the dependent variable):
Ln(Cycl)t
1
2
=α+
β1 × ft−1 + β2 × ft−1
+ β3 × F lowt−1
BusAssetst−2
BusAssetst−2
!
1
+ β4 × (F lowt−1 ft−1 ) +
γ1 × Issuet−1 + γ2 × (F lowt−1 P ARTt−1 )
BusAssetst−2
!
Grtht or
+ γ3 × (F lowt−1 Issuet−1 )
+ γ4 × P ARTt−1 + γ5 × Rm,t + γ6 × Rm,t−1
+ δ × Controlst−1 + εt
(12)
where
ft−1 ≡ Issuet−1 × P ARTt−1
, and the controls include credit issuance
Creditt−1
BusAssetst−2 ,
consensus forecast of GDP growth
F orecastt−1 , total assets of non-financial business sectors BusAssetst−2 (to control for the
scale effect), and four lags of the dependent variable to account for autocorrelation in output
12
Using the actual participation rate yields similar estimates.
For robustness I also use total market capitalization (of CRSP stocks) as an alternative proxy, and all the
results are similar.
13
18
growth or the cyclical component of output.
The first four variables along with Rm,t in equation (12) correspond to those in equation
(11) implied by the model. The main coefficients of interest are β1 (positive) and β4 (negative).
The second set of variables with coefficients γ1 through γ3 are included in the regression to
control for the base effects arising from the interaction terms in the main variables.
The empirical specification above corresponds to the model in specifying a lead-lag relation
between equity issues / mutual fund investment and subsequent output growth. The only
period t independent variable is the stock market return Rm , and this is because in the model,
the output from new issues Y and existing projects Rm are realized at the same time. I scale
the model by BusAssetst−2 , because this corresponds to the beginning-of-period assets when
agents make their issue/investment decisions, which is consistent with the model. Empirically,
this choice does not affect the results, and all the results remain consistent when the model
is scaled by BusAssetst−1 .
5
Empirical results
5.1
Aggregate results
To test the model at the aggregate level, I perform OLS regressions on equation (12) using
various specifications. All regressions include four lags of the dependent variable, and I use
Newey-West standard errors with four lags to calculate the t-statistics. Table 2 reports the
predictive regression results for GDP growth.14
Panel 1 shows that fund flows are strongly correlated with subsequent economic growth.
The predictive power of fund flows subsumes the predictive power of market returns in panel
2, which is consistent with Jank (2012). This result also suggests that although individual
investors may not know the true productivity, they do have some noisy signals and are able to
predict growth to some extent.15 Panel 3 shows that current period market returns is weakly
and positively related to output. Panel 4 shows that equity issues by themselves do not have
predictive power for growth. Indeed, models of finance and growth (Levine, 2005) suggest
14
I scale the regression by BusAssetst−2 as in equation (12), however for brevity I leave it out in tabulating
the tables.
15
Note that although equation (12) shows that fund flows should negatively predict output, the empirical
findings of a positive prediction does not invalidate the model. In the model, the household’s information θh
is not correlated with the true productivity θ, and the negative prediction in the model is due to the budget
constraint D = W − F . In reality, when households have some information about the true productivity, their
fund flows would positively predict output. This is the story in Jank (2012).
19
that the accumulation of physical capital does not contribute strongly to growth, but rather
it is the advances in productivity that drive growth.
Panels 5 and 6 show the results for two specifications of the main model, with panel 6 being
the full regression in equation (12). The main variable of interest is f here. Although not
statistically significant in any of the regressions, the coefficients on f are all positive, and have
opposite signs than Issue with similar magnitudes. This provides some suggestive evidence
in support of the model, albeit weak. The empirical specification of f as Issue × P ART
gives an additional interpretation. Since P ART is a dummy variable, the coefficient on Issue
itself measures the impact of new equity issues on subsequent growth when mutual funds
have low participation in these issues, and the coefficient on f is the effect of new issues on
subsequent growth when mutual funds participate more in the issues. The opposite signs
on these coefficients suggest that when mutual funds do not participate in the issues, the
issues do not generate growth (or are even associated with lower subsequent growth16 ), but
when mutual funds participate the issues are more productive. This is consistent with the
model and the notion that mutual funds are informed. However, given the weak statistical
significance (and lack of significance on the other variables), the results on aggregate growth
is not conclusive.
Next I examine whether mutual fund intermediation affects the cyclical component of
growth. Erel, Julio, Kim, and Weisbach (2012) and Covas and Den Haan (2011) have shown
that equity issues vary greatly over the business cycle. Further, Peress (2014) theoretically
show that informed trading in the stock market can enhance capital allocation efficiency and
thus promote transitory TFP growth, even in the absence of technological growth. In this
sense, mutual fund intermediation may also affect the cyclical component of output.
Table 3 shows the predictive regression results for the log cyclical component of real GDP.
The variables are as defined in table 2. As opposed to the aggregate growth results, fund flows
do not predict cyclical growth unconditionally (panels 1 and 2). On the other hand, equity
issues predict lower subsequent cyclical growth unconditionally in panel 4, and this result is
similar to the aggregate growth results.
In the main regressions in panels 5 and 6, the key variable f is significantly positive.
Consistent with the model, more equity issues invested by mutual funds predict higher output.
16
It may seem strange that increasing equity issues lowers subsequent growth. However this may be consistent with market-timing by firms to issue new securities when prices are high. If high prices are followed by
slowing growth, one may observe high issues followed by lower growth.
20
The empirical specification of Issue×P ART can also be interpreted in a similar fashion. When
mutual funds participate less in equity issues (P ART = 0), new issues are associated with
lower subsequent growth than when mutual funds participate more in those issues, holding
the level of issues constant. The result are consistent with the hypothesis that mutual funds
are able to identify the productive issues and invest when their private signals are good.
This gives them a positive role in the economy. Note that while mutual fund participation
P ART negatively predicts growth unconditionally, the magnitude is small compared to the
interaction term with Issue (-0.0003 versus 0.5377).
Other significant predictors of cyclical growth include the consensus professional forecast
and fund flows (conditional on the other variables). In addition, the interaction variable
F low × P ART negatively predicts subsequent cyclical growth. This variable is included as
a base for F low × f . Although the variable in the model F low × f is insignificant in the
regression, the negative coefficient on F low × P ART suggests that when fund flows are low,
higher mutual fund participation can generate higher subsequent output, over and above
the high fund participation effect from P ART . Although the model does not predict an
interaction effect between F low and P ART , the regression result suggests that mutual funds
may be particularly effective when they are capital constrained.
Overall the aggregate-level evidence is largely in support of the model, especially in explaining the cyclical component of output. The results suggest that mutual funds may indeed
have superior ability to identify productive equity issues, and hence have a positive role in the
real economy through efficient capital allocation. However, the aggregate results have weak
statistical power due to the small sample. This problem can be partially alleviated by extending the tests to the industry-level, and exploiting both the time-series and cross-sectional
variations.
5.2
Industry-level results
In this section, I present the industry-level results. Specifically, I test whether the relation
in equation (12) is able to explain cross-industry variations in value-added growth and the
cyclical component of value-added.
I run OLS panel regressions, using annual industry value-added growth and the log cyclical
component of value-added as the dependent variables. For the independent variables, only
the variables F low, Credit and F orecast are aggregate-level variables, and all other variables
21
are defined at the industry level. Issuei,t is the new equity issues in industry i in year t, and
P ARTi,t is the mutual fund participation dummy for industry i in year t, and equals to one
if industry i has higher mutual fund participation in new issues relative to the cross-sectional
median in year t, zero otherwise. As in the aggregate tests, fi,t is defined as Issuei,t ×
P ARTi,t . The market return Ri,t is defined as the industry’s value-weighted return. To scale
the regression, I replace BusAssetst−2 in equation (12) with the industry market value of
equity IndM Vt−2 .
Table 4 reports the pooled panel regression results. All regressions include four lags of
the dependent variable, and standard errors are clustered at the industry level. I also include
industry fixed-effects to capture unobservable sources of value-added specific to each industry.
For exposition purposes, I only present the results for the full model here.
Panel 1 in table 4 is the predictive regression results for value-added growth. The control
variables credit issuance, consensus forecast, lagged industry market capitalization and lagged
market return are significant predictors of value-added growth. The main variables of interest,
f is positive and weakly significant (t-stat 1.74). These results are similar to the aggregate
evidence in suggesting that mutual fund intermediation has some impact on the overall growth
of industry value-added, albeit weak.
Panel 2 presents the predictive regression results for the log cyclical component of valueadded. In line with the aggregate evidence, the main variables of interest have strong effects
on the cyclical component of output. Unconditionally, new equity issues are associated with
lower subsequent output (coefficient -1.1039), while the variable f is significantly positive, and
the magnitude is larger than the coefficient on Issue, suggesting that the equity issues invested
by mutual funds lead to higher cyclical output. When mutual funds do not participate in the
new issues (P ART = 0), the issues are not productive, and when there is high mutual fund
participation (P ART = 1), the issues are associated with much higher subsequent output,
and the net effect is positive (1.7154 − 1.1039 − 0.0014 = 0.6101).
The interaction term F low × P ART is significantly negative, which is also consistent
with the aggregate evidence. In times of low fund flows, higher mutual fund participation
leads to even higher cyclical output, which suggests that funds are particularly effective at
identifying good projects in low fund flow times (which are often associated with bad economic
conditions). In addition, the squared term of f is significantly negative, which is consistent
with the convex information cost in the model.
22
In panels 3 and 4, I include time fixed effects in addition to industry fixed effects, in
order to account for potential cross-sectional correlations among industry growth rates due
to common macroeconomic shocks, which cannot be captured by within-industry clustered
standard errors. The aggregate independent variables F lowt , Creditt and F orecastt are
dropped due to collinearity with the time dummies. Overall, the results are still very similar
to those reported in panels 1 and 2, both in coefficient estimates and significance levels, with
Issuei,t negatively predicting cyclical output, and f positively predicting cyclical output.
5.2.1
Robustness: dynamic panel regressions
Although the industry results presented above support the model’s main hypotheses, there is
one econometric problem in these panel regressions. The unobservable industry fixed effects
in the panel regressions are captured by the error terms, and due to the lagged dependent
variables in the specification, this creates a positive correlation between the dependent variables and the error term, leading to biased coefficients. One way to address this problem is
to apply the dynamic panel regression technique proposed by Arellano and Bond (1991) as a
Generalized Method of Moments (GMM) estimator. This estimator takes first-difference of
the regression equation to eliminate the fixed-effect, and then uses lagged values of the dependent variable in levels as instruments for the first-differences. Asymptotically, this approach
produces consistent and efficient estimates even when the industry fixed effect is correlated
with the lagged dependent variable.
However, as pointed out by Beck, Levine, and Loayza (2000), there are several shortcomings with the first-difference estimator, the main one being that in finite samples, the difference
estimator has a large bias and poor precision. To address this problem, Arellano and Bover
(1995) propose a system GMM estimator that estimates the difference equation jointly with
the regression equation in levels. In addition to having better precision in finite samples, the
system estimator maintains the cross-industry variation by including the regression in levels,
and also improves the strength of the instruments (Blundell and Bond, 1998).
The consistency of the dynamic panel GMM estimators requires two critical assumptions,
one being that the error term does not exhibit serial correlation, and second being that the
instruments are valid. Arellano and Bond (1991) propose two tests for these assumptions. The
serial correlation test examines the first- and second-order serial correlation in the error term,
under the null hypothesis of no serial correlation (with a normal distribution). The Hansen’s
23
J-test of overidentifying restrictions tests the overall validity of the instruments, and has a χ2
distribution with J − K degrees of freedom, where J is the number of instruments and K is
the number of regressors. Under the null hypothesis, the instruments are valid and exogenous,
and failure to reject the null hypothesis lends support to the model.
At this point, it is important to note that although the GMM estimators produce consistent
and efficient estimates asymptotically, one should be careful with their performances in finite
samples, due to the potential over-fitting problem when instrument numbers are large. In
particular, when the lagged dependent variable is persistent, lagged levels of these variables
may be weak instruments for the difference equations (Blundell and Bond, 1998). Thus,
the advantage of using the GMM estimators to achieve asymptotic consistency needs to be
balanced with the potential biases in finite samples and the potential poor performance of
using internal instruments. Therefore, as Beck and Levine (2004) point out, it is important to
apply different estimators (GMM and OLS), and to use these results as complements to draw
inferences. With this in mind, I now proceed to the regression results. I only report results
for the cyclical component of industry value-added, as the previous results suggest that the
effect of mutual fund intermediation is primarily on the cyclical component of growth.
Table 5 reports the GMM results for the main regression in equation (12), using lags six and
further of the dependent variable Ln(Cycl)t /BusAssetst−2 as instruments for the four lags of
dependent variable for the difference equation, and lags five and further as instruments in the
levels equation. All other independent variables are treated as predetermined variables, and
are instrumented with the original variable and all lags. Panels 1 and 2 apply the one-step
estimator with robust standard errors, while panels 3 and 4 apply the two-step estimator.
Panels 2 and 4 also include time fixed-effects to capture cross-sectional correlation across
industries.
Overall, results are very similar across the four regressions, and are similar to those reported in table 4. In times of low fund participation, new equity issues lead to lower cyclical
growth, but become productive when they are actively invested in by mutual funds. In
addition, f 2 is significantly negative in all specifications, suggesting that mutual fund intermediation may have diminishing returns. The interaction term F low × P ART is negative
at the 10% level in all specifications. Also note that all the coefficient estimates are similar
in magnitude and significance across the four regressions, suggesting that the estimator used
does not affect the results.
24
The autocorrelation tests fail to reject the null hypothesis of no first and second order
autocorrelation for the two-step estimators, and there is only weak evidence of first-order
autocorrelation for the one-step estimators. The Hansen test fails to reject the null hypothesis
of valid instruments in all four specifications. However, it is important to note that in the case
of large instrument count in finite samples, the Hansen test could be weakened to generate
implausibly good p-values (Roodman, 2009), and should be interpreted with caution.
5.3
Firm-level productivity
In this section, I test the key assumption of the model, which is that mutual funds have
superior information relative to uninformed investors and can invest only in the productive
firms. A direct test of this assumption is to examine the relation between mutual fund
investment and the firms’ future productivity growth. If mutual funds indeed have superior
information, their investment in firms should convey information about the firms’ future
performance and productivity.
In order to examine productivity, I restrict the sample to SEO-issuing firms only, due
to data requirements for variables prior to the issue, which are unavailable for IPO firms. I
employ several measures of productivity. First, I follow King and Levine (1993a) and assume
a Cobb-Douglas production function of the form Y = AK α Lβ , where Y is output or revenue
(COMPUSTAT item REV), K is capital (property, plant and equipment, net of depreciation,
COMPUSTAT item PPENT), and L is labor (number of employees, COMPUSTAT item
EMP). A denotes total factor productivity (TFP). Taking logarithms and differencing over
five years yields GY = α(GK) + β(GL) + GA, where GY is the growth rate of output, GK
is the growth rate of capital, GL is the growth rate of labor, and GA is the growth rate of
total factor productivity, which captures the growth rate of all other inputs than capital and
labor that contributes to output growth. I denote this growth rate as KLT F Pi,t+5 for firm i
over the five years post-SEO. I follow King and Levine (1993a) and use α = 0.3 and β = 0.7
in the estimation.
The shortcoming of KLT F P is that capital and labor shares are exogenously specified
rather than estimated. As Olley and Pakes (1996) demonstrate, such estimates obtained from
log-linear Cobb-Douglas production functions may suffer from simultaneity and selection biases. Simultaneity arises because productivity is known to the profit-maximizing firms when
they choose their input levels. Since productivity is observed by the firms but not the econo25
metrician, failing to account for increases in inputs due to increased productivity will result in
biased OLS estimates. On the other hand, selection biases result from the correlation between
productivity shocks and exit decisions. Firms with larger capital stocks are more likely to stay
in the market despite a negative productivity shock than firms with smaller capital stocks,
because more capital stocks can be expected to produce greater future profits. Therefore, the
exit decision and capital stock for a given productivity shock needs to be controlled for in estimating α. Olley and Pakes (1996) propose a three-step semi-parametric estimator to address
these problems. They use investment as a proxy for unobserved time-varying productivity
shocks, and thus controlling for simultaneity problems. The selection biases are addressed by
estimating survival probabilities. The resulting estimator gives consistent estimates of α and
β, from which estimates of time-varying firm-level productivity can be obtained17 .
Applying the estimator to the production function Y = AK α Lβ , I obtain estimates of
α = 0.1934 and β = 0.6397. From these I calculate annual total factor productivity in
the same way as King and Levine (1993a), and calculate productivity growth for five years
post-SEO issue for each firm-issue. I denote this growth rate as OP T F Pi,t+5 .
Complementary to the TFP measures above, I also employ two additional measures, revenue growth and five-year average return on assets (ROA). Revenue growth (RevGrthi,t+5 )
is calculated as the five-year growth-rate in revenue for firm i post-SEO issue. Average ROA
is defined as operating income before depreciation (COMPUSTAT item OIBDP) divided by
beginning-of-period book value of assets (COMPUSTAT item AT), and taking five-year averages post-SEO issue. The advantage of these two measures is that they do not rely on
structural assumptions about production functions. However, this is also a drawback at the
same time, because it is unclear how these measures map to TFP in a theoretical framework.
Since the test here is on the effect of mutual fund investment on firm-level productivity
growth, the full regression implied by the model does not apply here. Instead, I run OLS
regressions of the various productivity measures on equity issue, fund participation and other
control variables. If mutual funds indeed have superior information, their investment should
lead to higher productivity growth. Therefore, I only include a subset of the variables in
equation (12) that are consistent with this prediction. For control variables, I include firm
size (LnSizet , defined as log of market value of total assets in the year before the SEO issue),
17
For detailed algorithms on the estimation method, see Yasar, Raciborski, and Poi (2008) and Keller and
Yeaple (2009) Appendix A.
26
prior 12 month stock returns (P riorrett ), aggregate fund flows during the quarter of issue
scaled by total assets of non-financial business sectors (F lowt ), and aggregate credit issuance
during the quarter of the issue scaled by total assets of non-financial business sectors (Creditt ).
I define firm-level equity issue EqIssuei,t as issue proceeds scaled by market value of assets
prior to the issue. The mutual fund participation dummy P ARTi,t is equal to one if fund
participation for issue i is above the median across all issues during the same year (to capture
cross-sectional variation in participation). In all regressions, I include industry and year fixed
effects, and cluster the standard errors at the industry level.
Table 6 reports the regression results. Panels 1 and 2 report results on TFP measures, and
panels 3 and 4 report results on operating performance measures. For both TFP measures,
equity issue in low participation times have little impact on TFP growth, but when fund
participation is high, more issues lead to significant higher subsequent TFP growth, given by
the coefficient on EqIssue × HighP art. With the exception of F lowt , most of the coefficients
are similar in magnitudes between the two TFP measures.
For the operating performance measures, results are a little mixed. Equity issues in low
participation times lead to higher subsequent revenue growth, and the effect is even stronger
during high participation times. However for ROA this is not the case. High equity issues lead
to no changes in subsequent ROA (the coefficient is negative but statistically insignificant),
and in high participation times the effect is marginally negative. However, high participation
unconditionally leads to higher subsequent ROA, which is in contrary to the results on TFP
measures and revenue growth, and the magnitude on HighP art is smaller than the magnitude
on EqIssue × HighP art, suggesting that the overall effect of high participation on ROA is
still negative. Hence, ROA may be capturing information other than TFP, and may not be
a good measure for firm-level productivity. Moreover, Brav, Jiang, and Kim (2011) find that
hedge fund activism has a much larger effect on plant and firm-level productivity (TFP) than
on operating margins. This is similar to the findings here, in that operating performance
measures and TFP measures are not substitutable.
One other result worth noting is that the coefficient on F low × HighP art is insignificant across all four measures of productivity. This is in contrary to both the aggregate
and industry-level results, and suggests that aggregate fund flows have little impact on the
firm-level participation-growth relation, albeit being significant at the macro level.
Overall, these firm-level results suggest that mutual fund participation is positively related
27
to subsequent firm productivity growth, measured by TFP. This confirms the assumption
that mutual funds can identify productive firms and invest in those firms only, and provides
complementary evidence to the aggregate and industry-level results.
5.4
Alternative explanations and robustness
Although the results above are in support of a positive role of mutual funds in the economy, one
cannot fully reject alternative explanations of the findings. Specifically, one may argue that the
association between mutual fund investment and subsequent growth is not driven by mutual
funds’ active investment, but is instead driven by fund flows. When investors perceive future
productivity to be high (and rightly so), they would invest more into mutual funds. Given
their mandates, fund managers are then forced to invest some of the additional fund flows into
the primary market, driving up their participation and hence f . This would naturally create
a positive relation between mutual fund investment in new issues and subsequent growth,
even when fund managers do not possess superior information. In fact, it is the individual
investors that are doing the work, rather than mutual funds. This is the explanation offered
by Jank (2012) in explaining the predictive power of fund flows on GDP growth.
In order to alleviate this reverse causality concern, I make two observations. First, if this
alternative story is true, there should be high correlations between fund flows F and mutual
fund participation in equity issues P ART , at both the aggregate and industry level. However,
the data shows that at the aggregate level, the contemporaneous correlation between fund
flows and participation is only 0.16 (p-value = 0.08). At the industry level, this correlation
is even lower, at only 0.002 (insignificant with a p-value of 0.94). This suggests that on
average, mutual funds do not passively invest upon receiving fund flows, but instead make
active decisions to move in and out from the primary market over time. Furthermore, the
passive effect of fund flows is already partially taken into account by including fund flows as
an independent variable in the regressions.
Second, the reverse causality story implies that when fund flows are high, mutual funds
would participate more in equity issues, and future output would increase (although not due
to mutual fund investment). This implies a positive relation between the interaction term
F × P ART and subsequent output, or at least no relation. Instead, the regression results at
both the aggregate and industry level show a strong negative relation. This result cannot be
explained by the reverse causality story, where individuals and mutual funds invest more at
28
the same time.
To further examine this alternative explanation, I separate mutual fund investment into
participation by active funds and passive (index) funds, and examine their effects on output.
If the reverse causality explanation is true, there should be no distinction between active and
passive funds, since all mutual funds are passive in their primary investment. However, in
the information story, the effect should be predominantly coming from active funds, while
investment by passive funds should have little real effects.
I use the fund names in the s12 fund holdings database to identify passive funds. I follow
Schwarz (2012), and classify funds with names containing “index”, “indx”, or “idx” as index
or passive funds. All other funds are considered as active. I perform regressions at both the
aggregate and industry level, and include participation by active (ActP ART ) and passive
funds (P assP ART ) separately in the regression. In addition, I interact Issue with both the
active and passive participation, to obtain investment by active funds (fact ) and passive funds
(fpass ). The other variables f 2 and F low × f are constructed using only the active funds’
participation.
Table 7 presents the results. Since the previous results suggest that mutual funds mainly
affect the cyclical component of output, I only present the cyclical results here. Panel 1 shows
the aggregate regression results. When the overall mutual fund investment is separated into
active and passive, neither has a significant relation with subsequent output. Nevertheless,
the coefficients on fact and fpass have opposite signs, and fact is positively related to subsequent output, which is in line with the previous results. On the other hand, fpass has a
negative sign. However since none of the coefficients are significant, one cannot infer much
from this regression. The lack of significance may be due to the weakened statistical power
from separating participation into two groups, since the aggregate regression only has 107
observations.
Panel 2 presents the industry-level results. New issues invested by active funds (fact )
positively predicts subsequent growth, while the coefficient on issues invested by passive funds
(fpass ) is insignificant. This is consistent with the information explanation, where active
funds have superior information, but is inconsistent with the passive investment explanation.
In addition, F low × fact is significantly negative, and this is consistent with the model’s
prediction. When fund flows are lower, more equity investment by active funds faciliates
higher subsequent growth. In table 4, this coefficient is insignificant, and the results here
29
suggest that this may be due to the weakened power when including passive funds in the
sample.
Although the tests here have weak statistical power due to the split sample, they offer
some evidence in support of the information story, and suggests that the main results are
unlikely to be due to the reverse causality story.
6
Conclusion
This paper investigates the role of mutual funds in the real economy, by examining the impact
of mutual fund participation in primary equity issues on economic growth. Prior literature on
mutual funds have largely focused on their secondary market activities, with little attention to
their intermediation role in the primary market. However, consistent with intermediation theory, mutual funds should have informational advantages, and thus be able to exert screening
and monitoring efforts more effectively to reduce information problems and facilitate efficient
allocation of capital. Although there is evidence that institutional shareholders exert monitoring and screening effort to add value, the role of these participants at the macroeconomic
level is still unclear. This is one of the first papers to examine this issue, and results suggest
that intermediaries play an important role in equity markets in reducing information problems
and facilitating quality investment.
Consistent with a simple model of mutual fund intermediation, I find that high mutual
fund investment in equity issues lead to significantly higher subsequent economic growth, at
both the aggregate and industry level. Moreover, the participation-growth effect is stronger
when fund flows are low and funds are constrained. These effects predominantly appear the
cyclical component of output, and there is little effect on long-run trend growth. At the
firm-level, high mutual fund investment in SEOs also leads to significantly higher subsequent
productivity growth (measured by TFP). These results are largely consistent with the notion that intermediaries’ advantage in screening may facilitate efficient capital allocation and
enhance output.
Future research in this area still needs to address potential alternative explanations. Although some suggested evidence has been offered here, by examining separately the active
versus passive funds, the results are somewhat weakened by a small sample size. In addition,
the model takes equity issues as exogenous, and does not consider the strategic decision of
30
firms in issuing new securities. These features could be incorporated in future research.
The findings in this paper have additional implications for future research in the economic growth literature. Levine (2005) suggests that financial intermediaries and financial
development can foster growth, and a large body of empirical literature has found support
in cross-country evidence. However, the literature has largely ignored the role of intermediation outside of private credit and the banking sector, and uses measures such as the size and
liquidity of the stock market as proxies for stock market development. Results in this paper
suggests that these may not be accurate proxies, because they ignore the difference in levels
of stock market intermediation across markets.
31
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Table 1: Summary Statistics
Reported are summary statistics of variables. Grtht is the quarterly log-differences of real GDP
levels (in 2009 dollars). Ln(Cycl)t /BusAssetst−2 is the log of the cyclical component of real GDP
from the HP-filter, scaled by two-quarter lagged total assets of the non-financial business sectors.
F lowt /BusAssetst−1 is the net cash flow into domestic equity funds during the quarter, scaled by
beginning-of-quarter total assets of the non-financial business sectors. Creditt /BusAssetst−1 is the
total borrowing in non-financial business sectors during the quarter, scaled by beginning total assets of
the non-financial business sectors. Rm,t is the quarterly value-weighted return of the CRSP universe.
Issuet /BusAssetst−1 is the sum of proceeds from all SEOs and IPOs during the quarter, scaled by
beginning total assets of the non-financial business sectors. AvgP artt is the average participation
rate by all mutual funds in the SEOs and IPOs, measured by the change of holdings over the quarter
scaled by issue proceeds. F orecastt is the one-quarter ahead real GDP growth rate forecast by the
Survey of Professional Forecasters.
Variable
N
Mean
Std. Dev.
Grtht
111
0.006841
0.006198
Ln(Cycl)t /BusAssetst−2
110
0.000045
0.000589
Flowt / BusAssetst−1
111
0.000938
0.001669
Creditt / BusAssetst−1
111
0.072867
0.024595
Rm,t
112
0.027925
0.088196
Issuet / BusAssetst−1
111
0.000821
0.000546
AvgPartt
112
0.237586
0.230758
Forecastt
112
0.006496
0.002589
Table 2: GDP growth rates regressions
Reported are predictive OLS regression results for GDP growth rates. The dependent variable is
log-differences of real GDP. Rm,t is the value-weighted market return of the CRSP universe of stocks.
Creditt is the total borrowing in non-financial business sectors during the quarter, scaled by beginning
total assets of the non-financial business sectors. F lowt is the net cash flow into domestic equity funds,
scaled by beginning total assets of the non-financial business sectors. Issuet is the sum of proceeds
from all SEOs and IPOs during the quarter, scaled by beginning total assets of the non-financial
business sectors. P ARTt is a dummy variable that equals one if the quarter has higher average
mutual fund participation in new issues than the median over the sample period, zero otherwise. ft
is defined as Issuet × P ARTt . F orecastt is the one-quarter ahead real GDP growth rate forecast by
the Survey of Professional Forecasters. BusAssetst is the total assets of the non-financial business
sectors. All regression specifications include four lags of the dependent variable (unreported for
brevity). t-statistics are reported in parentheses and are calculated using Newey-West standard errors
with four lags. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Dependent Variable: Grtht
Flowt
Creditt−1
Forecastt−1
BusAssetst−2
(1)
(2)
(3)
(4)
(5)
(6)
0.9811***
0.7850**
0.9227***
0.9471**
1.1157**
2.8991
(3.09)
(2.38)
(2.67)
(2.05)
(2.27)
(1.11)
0.0214
0.0155
0.0119
0.0119
0.0108
0.0090
(0.90)
(0.70)
(0.52)
(0.52)
(0.47)
(0.39)
0.1716**
0.1779**
0.2057**
0.2057**
0.2044**
0.2121**
(2.19)
(2.36)
(2.53)
(2.52)
(2.48)
(2.59)
-0.0000**
-0.0000**
-0.0000*
-0.0000*
-0.0000
-0.0000
(-2.07)
(-2.10)
(-1.95)
(-1.93)
(-1.20)
(-1.07)
0.0093
0.0094*
0.0094*
0.0104
0.0142**
(1.49)
(1.73)
(1.67)
(1.60)
(2.45)
0.0172*
0.0172*
0.0165*
0.0161
(1.77)
(1.73)
(1.72)
(1.59)
-0.0893
-4.3261
-8.4938
(-0.06)
(-1.17)
(-1.49)
-0.0035*
-0.0042*
(-1.71)
(-1.73)
2.5840
6.6818
(1.31)
(1.41)
Rm,t−1
Rm,t
Issuet−1
PARTt−1
ft−1
f2t−1
-0.0044
(-1.03)
Flowt−1 × ft−1
0.0749
(0.71)
Flowt−1 × PARTt−1
-2.1787
(-0.95)
Flowt−1 ×Issuet−1
-0.0000
(-0.26)
Intercept
N
Adj. R
2
0.0039
0.0039
0.0024
0.0024
0.0066*
0.0077
(1.56)
(1.55)
(0.89)
(0.84)
(1.67)
(1.63)
107
107
107
107
107
107
32.29%
33.16%
38.59%
37.94%
38.44%
38.23%
Table 3: Cyclical component of GDP regressions
Reported are predictive OLS regression results for the cyclical component of GDP. The dependent
variable is the log of the cyclical component of real GDP, extracted using the HP-filter, scaled by
two-quarter lagged total assets of the non-financial business sectors. Rm,t is the value-weighted market
return of the CRSP universe of stocks. Creditt is the total borrowing in non-financial business sectors
during the quarter, scaled by beginning total assets of the non-financial business sectors. F lowt is the
net cash flow into domestic equity funds, scaled by beginning total assets of the non-financial business
sectors. Issuet is the sum of proceeds from all SEOs and IPOs during the quarter, scaled by beginning
total assets of the non-financial business sectors. P ARTt is a dummy variable that equals one if the
quarter has higher average mutual fund participation in new issues than the median over the sample
period, zero otherwise. ft is defined as Issuet × P ARTt . F orecastt is the one-quarter ahead real
GDP growth rate forecast by the Survey of Professional Forecasters. BusAssetst is the total assets
of the non-financial business sectors. All regression specifications include four lags of the dependent
variable (unreported for brevity). t-statistics are reported in parentheses and are calculated using
Newey-West standard errors with four lags. *, **, and *** denote statistical significance at the 10%,
5%, and 1% levels, respectively.
Dependent Variable: Ln(Cycl)t /BusAssetst−2
Flowt−1
Creditt−1
Forecastt−1
BusAssetst−2
(1)
(2)
(3)
(4)
(5)
(6)
0.0202
0.0121
0.0129
0.0231
0.0379*
0.2999**
(1.17)
(0.66)
(0.68)
(1.08)
(1.76)
(2.53)
0.0018
0.0016
0.0015
0.0015
0.0017
0.0019*
(1.63)
(1.51)
(1.33)
(1.31)
(1.59)
(1.85)
0.0056*
0.0059*
0.0061*
0.0061*
0.0061*
0.0068**
(1.68)
(1.89)
(1.85)
(1.89)
(1.87)
(2.27)
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
(0.37)
(0.41)
(0.49)
(0.62)
(1.59)
(1.47)
0.0004
0.0004
0.0004
0.0005
0.0005*
(1.34)
(1.36)
(1.37)
(1.35)
(1.89)
0.0001
0.0001
0.0001
0.0000
(0.34)
(0.30)
(0.14)
(0.09)
-0.0379
-0.4296**
-0.8798***
(-0.47)
(-2.10)
(-2.64)
-0.0003***
-0.0004***
(-2.74)
(-2.96)
0.2267*
0.5377**
(1.99)
(2.47)
Rm,t−1
Rm,t
Issuet−1
PARTt−1
ft−1
f2t−1
-0.0002
(-1.26)
Flowt−1 × ft−1
0.0058
(1.30)
Flowt−1 × PARTt−1
-0.2025**
(-2.39)
Flowt−1 ×Issuet−1
-0.0000
(-0.83)
Intercept
N
Adj. R
2
-0.0003*
-0.0003*
-0.0003**
-0.0003*
-0.0000
0.0001
(-1.97)
(-1.98)
(-1.99)
(-1.81)
(-0.11)
(0.51)
106
106
106
106
106
106
82.27%
82.41%
82.27%
82.13%
82.88%
83.71%
Table 4: Industry Panel Regressions
Reported are predictive OLS regressions for annual industry value-added growth and the cyclical
component. Grthi,t+1 is the log-differences in real industry value-added for industry i in year t.
Ln(Cycli,t )/IndM Vt−2 is the log cyclical component of industry value-added, scaled by two-period
lagged industry market value of equity in CRSP. Ri,t is the value-weighted industry return of all
CRSP stocks within industry i in year t. Creditt , F lowt and F orecastt are as defined in Table 2 and
3. Issuei,t is the sum of proceeds from all SEOs and IPOs during the quarter in industry i, scaled
by beginning industry market value of equity. P ARTi,t is a dummy variable that equals one if the
industry has higher average mutual fund participation in new issues than the median industry in year
t, zero otherwise. fi,t is defined as Issuei,t × P ARTi,t . IndM Vi,t is the industry market value of
equity in CRSP. All regressions include four lags of the dependent variable (unreported for brevity).
t-statistics are reported in parentheses, and standard errors are clustered at the industry level. The
first two panels include industry fixed effects, and the last two panels include both industry and year
fixed effects. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
(1)
(2)
(3)
(4)
Dependent variable
Grthi,t
Ln(Cycli,t )/IndM Vt−2
Grthi,t
Ln(Cycli,t )/IndM Vt−2
Flowt−1
1.1770
0.0026
(1.13)
(0.07)
0.1641**
0.0047*
(2.38)
(1.95)
1.4222**
-0.0004
(2.53)
(-0.01)
-0.0000*
0.0000*
0.0000
0.0000
(-1.74)
(1.83)
(0.41)
(1.11)
0.0322**
0.0056*
0.0264*
0.0060*
(2.20)
(1.93)
(1.68)
(1.81)
0.0165
0.0008
0.0106
0.0006
(1.23)
(1.60)
(0.64)
(0.85)
1.2864
-1.1039***
1.3089
-1.1028***
(1.17)
(-3.98)
(1.27)
(-3.95)
-0.0120
-0.0014**
0.0005
-0.0016**
(-1.07)
(-2.15)
(0.04)
(-2.42)
1.3525*
1.7154***
0.9890
1.7351***
(1.74)
(3.14)
(1.25)
(3.16)
0.4318
-2.7251**
1.6898
-2.8640**
(0.03)
(-2.61)
(0.13)
(-2.55)
0.1107
0.0160
0.1198
0.0149
(0.38)
(0.84)
(0.40)
(0.80)
0.5053
-0.1588**
-0.3580
-0.1457*
(0.40)
(-2.14)
(-0.27)
(-1.71)
-0.0000*
0.0000
-0.0000
0.0000
(-1.83)
(1.44)
(-1.63)
(1.38)
-0.0405
-0.0018**
0.0903***
0.0003
(-1.25)
(-2.02)
(4.82)
(0.22)
1101
1043
1101
1043
9.56%
82.49%
11.83%
82.77%
Industry FE
Y
Y
Y
Y
Time FE
N
N
Y
Y
Creditt−1
Forecastt−1
IndMVi,t−2
Ri,t−1
Ri,t
Issuei,t−1
PARTi,t−1
fi,t−1
f2i,t−1
Flowt−1 × fi,t−1
Flowt−1 × PARTi,t−1
Flowt−1 × Issuei,t−1
Intercept
N
Adj. R
2
Table 5: Industry Dynamic Panel Regressions
Reported are dynamic panel GMM regressions for cyclical component of annual industry value-added,
using the Arellano and Bover (1995) system GMM estimator. Ln(Cycli,t )/IndM Vt−2 is the log
cyclical component of industry value-added, scaled by two-period lagged industry market value of
equity in CRSP. Independent variables are defined as in Table 4. All regressions include four lags of
the dependent variable (unreported for brevity), instrumented with lags 6 and further. t-statistics are
reported in parentheses, and standard errors are robust for both the one-step and two-step estimators.
Panels 1 and 3 include industry fixed effects, and Panels 2 and 4 include both industry and year fixed
effects. The Wald test is for joint significance of all independent variables. The autocorrelation test
tests for first and second order autocorrelation in the residuals according to Arellano and Bond (1991).
The Hansen’s J-test tests for validity of instruments. *, **, and *** denote statistical significance at
the 10%, 5%, and 1% levels, respectively.
Depdendent Variable: Ln(Cycli,t )/IndM Vt−2
One-step
(1)
Flowt−1
Creditt−1
Forecastt−1
IndMVi,t−2
Ri,t−1
Ri,t
Issuei,t−1
PARTi,t−1
fi,t−1
f2i,t−1
Flowt−1 × fi,t−1
Flowt−1 ×PARTi,t−1
Flowt−1 × Issuei,t−1
Intercept
N
Time FE
Wald test (p-value)
Two-step
(2)
(3)
0.0597
0.0614
(1.37)
(1.35)
0.0073**
0.0064
(2.03)
(1.51)
0.0015
0.0064
(0.04)
(0.15)
(4)
0.0000**
0.0000***
0.0000*
0.0000**
(2.53)
(2.65)
(1.96)
(2.23)
0.0046*
0.0049
0.0044
0.0035
(1.74)
(1.60)
(1.62)
(1.43)
0.0009*
0.0007
0.0010*
0.0007
(1.70)
(1.02)
(1.79)
(0.78)
-0.3784**
-0.3428**
-0.3645*
-0.3300**
(-2.11)
(-2.00)
(-1.95)
(-2.22)
-0.0018***
-0.0020***
-0.0017***
-0.0018***
(-2.83)
(-2.87)
(-2.81)
(-3.15)
1.8439***
1.8452***
1.8568***
1.9603***
(3.41)
(3.46)
(3.43)
(3.53)
-3.3307***
-3.4829***
-3.2353***
-3.5798**
(-3.34)
(-3.25)
(-3.03)
(-2.56)
0.0300
0.0266
0.0272
0.0392*
(1.59)
(1.44)
(1.24)
(1.96)
-0.1487*
-0.1439*
-0.1471*
-0.1493**
(-1.94)
(-1.71)
(-1.81)
(-1.96)
-0.0000
-0.0000
-0.0000
-0.0000
(-0.82)
(-1.06)
(-0.89)
(-1.15)
-0.0032***
-0.0002
-0.0031***
-0.0003
(-3.03)
(-0.13)
(-2.83)
(-0.33)
1043
1043
1043
1043
N
Y
N
Y
0.000
0.000
0.000
0.000
Autocorrelation test
1st order
0.075
0.066
0.175
0.183
(p-value)
2nd order
0.768
0.744
0.790
0.827
Chi-sq
42.02
23.99
42.02
30.25
p-value
1.000
1.000
1.000
1.000
Hansen test
Table 6: Firm-level Productivity Regressions: SEO firms
Reported are OLS regression results for firm-level productivity measures on equity issues and mutual
fund participation, for SEO issuing firms. KLTFPi,t+5 is the five-year growth rate in productivity,
with the productivity measured using the method in King and Levine (1993a). OPTFPi,t+5 is the
five-year growth rate in productivity post-SEO, with the productivity measured using the method in
Olley and Pakes (1996). RevGrthi,t+5 is the five-year revenue growth rate post-SEO. AvgROAi,t+5
is the average return on assets over the five years post SEO issuance. LnSizei,t is the natural log of
market value of assets ending the year before the issue. Priorreti,t is the cumulative stock returns
from month t-12 to t-1. EqIssuei,t is issue proceeds scaled by market value of assets prior to the issue.
PARTi,t is a dummy equal to 1 if fund participation is above the median across all stocks during the
issuing year. Other variables are defined as in Table 4. All regressions include industry and year fixed
effects. t-statistics are reported in parenthesis, and standard errors are clustered by industry. *, **,
and *** denote statistical significance at the 10%, 5%, and 1%, respectively.
Dependent Variable
LnSizei,t
Priorreti,t
EqIssuei,t
PARTi,t
EqIssuei,t × PARTi,t
Flowt
CreditIssuet
Flowt × PARTi,t
Intercept
Adj. R2
(1)
(2)
(3)
(4)
KLTFPi,t+5
OPTFPi,t+5
RevGrthi,t+5
AvgROAi,t+5
-0.0002
-0.0009
-0.0024
0.0004
(-0.40)
(-1.13)
(-1.58)
(1.26)
-0.0157**
-0.0042
0.0174
0.0013
(-2.05)
(-0.40)
(1.42)
(0.87)
0.0364
0.1380
0.3448***
-0.0641
(0.48)
(1.22)
(2.63)
(-1.59)
-0.1066***
-0.1001***
-0.0411
0.0271**
(-3.04)
(-2.82)
(-1.05)
(2.14)
0.2543**
0.2537**
0.3434***
-0.0532*
(2.29)
(2.37)
(2.81)
(-1.69)
0.9737
-21.5279*
-30.3687*
-10.4809***
(0.08)
(-1.78)
(-1.79)
(-2.94)
1.2556
-0.8136
-3.2203**
0.1015
(1.40)
(-0.66)
(-2.42)
(0.26)
14.8408
10.0109
5.3618
3.4190
(1.27)
(0.87)
(0.31)
(1.08)
0.5328***
0.8996***
1.4001***
0.1451***
(4.52)
(6.29)
(8.87)
(3.93)
0.057
0.080
0.071
0.294
Table 7: Robustness: Active versus passive funds
Reported are OLS regression results for active versus passive funds. Panel (1) shows the aggregate
results, the dependent variable being the log of the cyclical component of real GDP, extracted using the
HP-filter, scaled by two-quarter lagged total assets of the non-financial business sectors. ActP ARTt
(P assP ARTt ) is a dummy variable that equals one if the average participation in new equity issues
by all active (passive) funds are above the sample median, zero otherwise. fact,t is defined as Issuet ×
ActP ARTt . fpass,t is defined as Issuet × P assP ARTt . The other variables are defined as in Table
3. Panel (2) shows the industry level results, with the dependent variable being the log cyclical
component of industry value-added, scaled by two-period lagged industry market value of equity in
CRSP. ActP ARTi,t (P assP ARTi,t ) is a dummy variable that equals one if the average participation
in new equity issues in industry i by all active (passive) funds are above the cross-sectional sample
median, zero otherwise. fact,i,t is defined as Issuei,t × ActP ARTi,t , and fpass,i,t is defined as Issuei,t ×
P assP ARTi,t . The other variables are defined as in Table 4. All regressions include four lags of
the dependent variable. t-statistics are reported in parenthesis, and standard errors are clustered
by industry in Panel (2). *, **, and *** denote statistical significance at the 10%, 5%, and 1%,
respectively.
Panel (1): Aggregate
Panel (2): Industry
Ln(Cycl)t /BusAssetst−2
Ln(Cycli,t )/IndM Vt−2
F lowt−1
0.0694
F lowt−1
(1.24)
Creditt−1
0.0015
(-1.38)
Creditt−1
(1.45)
F orecastt−1
0.0073**
-0.0000
F orecastt−1
0.0004
IndM Vi,t−2
-0.0001
Ri,t−1
0.0054*
(1.88)
Ri,t
0.0010*
(-0.23)
Issuet−1
-0.1159
(1.97)
Issuei,t−1
(-0.38)
ActP ARTt−1
-0.0000
0.0001
ActP ARTi,t−1
0.0504
P assP ARTi,t−1
-0.0736
fact,i,t−1
-0.0002
fpass,i,t−1
0.0046
-0.1351
F lowt−1 × fact,i,t−1
-0.0000
F lowt−1 × ActP ARTi,t−1
-0.0002
F lowt−1 × Issuei,t−1
Adj. R
82.64%
0.0000**
(2.33)
Intercept
(-1.11)
2
0.0642
(0.54)
(-0.20)
Intercept
-0.0114*
(-2.00)
(-1.45)
F lowt−1 × Issuet−1
-1.5779
(-1.35)
(0.71)
F lowt−1 × ActP ARTt−1
0.2299
(0.42)
2
fact,i,t−1
(-0.32)
F lowt−1 × fact,t−1
2.6880***
(2.76)
(-0.74)
2
fact,t−1
-0.0006*
(-1.80)
(0.17)
fpass,t−1
-0.0026**
(-2.52)
(0.62)
fact,t−1
-1.9571***
(-3.66)
(-0.01)
P assP ARTt−1
0.0000*
(1.75)
(1.35)
Rm,t
0.0135
(0.42)
(-0.14)
Rm,t−1
0.0001
(0.15)
(2.40)
BusAssetst−2
-0.1794
0.0003
(0.48)
Adj. R
2
83.06%