Job Market Paper
The Real Consequences of Market Segmentation∗
Sergey Chernenko
Adi Sunderam
Harvard University
Harvard University
schernenko@hbs.edu
sunderam@fas.harvard.edu
December 2, 2009
Abstract
We study the real effects of market segmentation due to credit ratings. We focus on a
matched sample of firms just above (BBB-) and just below (BB+) the investment-grade cutoff.
These firms are similar on observable characteristics, including average rates of investment.
However, flows into high-yield mutual funds only affect the cost of capital for the speculativegrade firms. We show that these cost of capital shocks have an economically significant effect
on issuance and investment, especially for firms that are smaller, do not pay dividends, cannot
substitute to bank loans, or are unlikely to access the asset-backed securities market. The
effect of fund flows is associated with the discrete change in label from investment-grade to
speculative-grade, not with changes in continuous measures of credit quality. We also conduct
falsification tests showing that the investment-grade cutoff is the only one where the investment
of firms just below the cutoff is more sensitive to fund flows than the investment of firms just
above the cutoff. Finally, we offer some suggestive evidence that during the recent credit boom
CDO flows increased the investment of BB+ firms relative to their BBB- matches.
JEL Classifications: G24, G31
Key Words: credit ratings, firm investment, mutual fund flows, market segmentation,
comovement
We thank Malcolm Baker, Bo Becker, Effi Benmelech, Dan Bergstresser, Lauren Cohen, Fritz Foley, Robin
Greenwood, Victoria Ivashina, David Scharfstein, Erik Stafford, Jeremy Stein and Harvard University finance lunch
seminar participants for helpful comments and suggestions, Doug Richardson at the Investment Company Institute
for providing mutual fund flow data, and Jerome Fons, Martin Fridson, Oleg Melentyev, and Michael Weilheimer
for discussing the high yield market with us. Appendix tables can be accessed at www.people.fas.harvard.edu/
~schernen/papers/credit_ratings_appendix.pdf
∗
1
Introduction
Capital markets play a critical role in the efficient allocation of capital across firms. Their ability
to play this role, however, may be impeded by market segmentation. In this paper, we study
one of the most prominent divides in capital markets—the distinction drawn between investmentand speculative-grade firms. A large number of regulations, investment charters, and contracts
reference this distinction, and recent research suggests that it can affect firms’ cost of capital (Ellul,
Jotikasthira, and Lundblad, 2009; Kisgen and Strahan, 2009).
Market segmentation between investment- and speculative-grade firms may also have real effects. When retail investors withdraw capital from high-yield mutual funds, which are large buyers of
speculative-grade bonds, arbitrage capital may not immediately offset this negative shock (Mitchell,
Pedersen, and Pulvino, 2007; Duffie and Strulovici, 2009). As a result, the supply of capital available to speculative-grade firms will be reduced. Unable to access other sources of financing, some
speculative-grade firms will be forced to cut their investment. This paper documents evidence of
this mechanism at work.
Simple comparisons between investment- and speculative-grade firms would be confounded by
differences in fundamentals between them. Instead, drawing on the econometric literature on treatment effects and regression discontinuity, we construct a matched sample of firms just above (BBB-)
and just below (BB+) the investment-grade cutoff. These firms are similar on observable characteristics, but high-yield fund flows only affect the cost of capital of speculative-grade firms. As a result,
although these firms have the same average rates of investment, fund flows cause the investment of
firms just below the cutoff to diverge from the investment of matched firms just above the cutoff.
This effect is economically meaningful—one standard deviation increase in fund flows increases the
investment of BB+ firms relative to their BBB- matches by about 7% of the average investment
rate. The effect is stronger for smaller, financially constrained firms that have limited ability to
substitute to either bank loans or the asset-backed securities market. Our results indicate that
market segmentation causes temporary differences in the investment of similar firms, but that these
differences tend to average out over time.
A natural objection to our interpretation of these results is that fund flows may be anticipating
1
investment opportunities. Our empirical methodology matches BB+ and BBB- firms based on
industry and firm characteristics such as size, leverage, Altman’s z-score, and Q. Our identifying
assumption is that firms close to the cutoff with similar observable characteristics are subject to
similar shocks to profitability and investment opportunities. If this assumption holds, then by
studying the differential sensitivity of investment to fund flows we difference out any common
shocks that may be correlated with fund flows. We can then interpret the differential effect of fund
flows on the investment of BB+ firms as evidence of a supply of capital effect.
Although our matching methodology differences out any common shocks, investment opportunities may still not be exactly the same, and fund flows may be picking up the differential investment
opportunities of less creditworthy firms. We address this concern in a number of ways. First,
drawing on the regression discontinuity literature, we control for the interaction of fund flows with
continuous measures of credit quality. Our results show that the greater sensitivity of BB+ firm
investment to fund flows is due to their speculative-grade label, not their size, leverage, or Altman’s
z-score. Second, we conduct falsification tests, examining the sensitivity of investment to fund flows
for firms around other rating cutoffs. We find that the investment-grade cutoff is the only one
where the investment of firms below the cutoff is more sensitive to fund flows than the investment
of firms above the cutoff. Thus, greater sensitivity to fund flows is not a general characteristic of
lower-rated firms.
Moreover, BB+ firms constitute only 10% of all speculative-grade firms, so fund flows are likely
to be driven by the performance and investment prospects of lower-rated firms, which are very
different on observable characteristics from BB+ firms. This suggests that the differential effect of
fund flows on BB+ firms relative to BBB- firms may be due to categorization. If investors emphasize
differences between investment- and speculative-grade firms and overlook differences within these
broad categories, they may treat two firms in the same category similarly, even if the firms have
very different performance and investment prospects (Barberis and Shleifer, 2003). Our evidence is
consistent with this idea. We find that even after controlling for the investment of matched BBBfirms, the investment of BB+ firms comoves with the investment of lower-rated speculative-grade
firms in unrelated industries.
Another objection to our identification strategy is that firms could be selecting into different
2
ratings based on unobservable characteristics. The distribution of credit ratings reported in Figure 1
suggests that some selection might be taking place, as there are fewer BB+ observations than either
BBB- or BB ones. The direction of any bias introduced by selection on unobservable characteristics
is ambiguous. While it could be the case that firms with lower sensitivity of investment to high-yield
fund flows choose to be investment grade, we think that the more natural selection story goes the
opposite way and suggests that our results should be treated as a lower bound on the potential
distortions in real behavior due to the investment-grade cutoff. Firms whose investment would be
most affected by the volatility of fund flows if they were rated BB+ have the strongest incentives
to alter their behavior to achieve a BBB- rating. Thus, firms that do carry a BB+ rating in our
data are likely to have a relatively low sensitivity of investment to fund flows.
In summary, we find that shocks to the supply of capital of high-yield mutual funds cause the
investment of firms just below the investment-grade cutoff to diverge from the investment of similar
firms just above the cutoff. Our approach also sheds light on the role of market segmentation
in the recent credit boom. By issuing AAA and AA rated securities backed by speculative-grade
bonds and loans, collateralized debt obligations (CDOs) allowed speculative-grade borrowers to
tap investment-grade sources of financing.1 We offer some suggestive evidence that CDO flows
increased the demand for speculative-grade securities and loans, allowing BB+ firms to increase
their investment relative to their BBB- matches.
Our work contributes to the literature studying the role of credit ratings in capital markets.2
Most papers in this literature study the information content of credit ratings and their effects on
assets prices and capital structure, while we focus on real investment. Lemmon and Roberts (2007)
Benmelech and Dlugosz (2009) study the structures and rating practices of CDOs. In their data, AAA and AA
rated tranches constitute 71% and 5% respectively of all CDO issuance. Average credit quality of the underlying
collateral, on the other hand, is B+, with only 0.2% rated investment grade.
2
Harold (1938) and Hickman (1958) are among the first to study the use of credit ratings and in particular
their ability to forecast defaults. The papers collected in Levich, Majnoni, and Reinhart (eds.) study the various
aspects of the role of credit ratings in the global financial markets. More recently, Faulkender and Petersen (2006)
find that firms with a credit rating that allows them to access public debt markets have 35% more debt than other
similar firms. Kisgen (2006) shows that firm financing decisions are affected by the discrete costs and benefits of
different credit ratings. Sufi (2009) shows how the introduction of syndicated bank loan ratings by Moody’s and
Standard & Poor’s in 1995 expanded the set of investors able to invest in syndicated loans and led to increased
debt issuance and investment by lower-rated borrowers. Kisgen and Strahan (2009) study the recent certification of
ratings from Dominion Bond Rating Service for regulatory purposes. They find that after certification bond yields
fell for firms for whom Dominion had a higher rating than the other certified rating agencies. Ellul, Jotikasthira, and
Lundblad (2009) find that regulatory constraints can force insurance companies to sell recently downgraded bonds,
generating downward price pressure and subsequent reversals.
1
3
are perhaps the most closely related to our work. They investigate the effect on firm investment
of the collapse of Drexel Burnham Lambert and of the concurrent regulatory changes. They use
a difference-in-differences approach to compare the investment of all speculative-grade firms with
unrated firms. We compare the financing and investment behavior of firms on both sides of the
investment-grade cutoff, and suggest that the effect of fund flows on the investment of BB+ firms
just below the cutoff is a spillover effect due to categorization. Furthermore, while the existing
literature focuses on isolated changes in the institutional environment, we study how recurring
shocks to the capital of an important investor class interact with market segmentation to affect real
investment.
In studying the capital supply effects of high-yield fund flows, our work also contributes to the
literature on market-driven corporate finance, reviewed recently by Baker (2009). Most of this
literature studies capital supply effects in equity markets.3 For most firms, however, external equity
is a relatively minor source of financing compared to retained earnings and debt (Mayer, 1988). It is
therefore important to understand the effects of market-driven corporate finance in credit markets.
The remainder of the paper is organized as follows. In the next section we review the institutional
background that motivates our empirical methodology, which we discuss in more detail in Section 3.
Section 4 describes our data and summarizes differences in firm characteristics across credit ratings.
Section 5 reports our main results. In Section 6 we discuss some suggestive evidence of the effect
of the recent CDO boom on the investment of speculative-grade firms. Section 7 concludes.
2
Institutional Background
We begin by briefly describing two institutional features of credit markets and credit ratings that
motivate our empirical methodology. First, many regulatory and voluntary conventions restrict the
ability of different investor groups to hold speculative-grade securities. Second, rating methodologies
introduce noise and inertia in credit ratings. Once we review this institutional background, we go
on to describe our empirical methodology.
3
For example, see Baker, Stein, and Wurgler (2003) and Polk and Sapienza (2009).
4
2.1
Regulations restrict holdings of speculative-grade securities
Many rules and regulations restrict the ability of different investor groups to hold speculative-grade
securities. Commercial banks have been prohibited from holding bonds rated BB+ and below since
1936.4 The Financial Institutions Reform, Recovery, and Enforcement Act of 1989 extended the ban
on commercial bank holdings of speculative-grade bonds to thrifts.5 Most state insurance regulations
follow the guidelines established by the National Association of Insurance Commissioners, which
sets both higher risk charges and a hard cap on holdings of speculative-grade bonds.6 In addition,
the net capital rule for broker-dealers requires larger haircuts for speculative-grade securities (U.S.
Securities and Exchange Commission, 2003).7
Although not subject to regulatory restrictions, most bond mutual funds specialize in either
investment- or speculative-grade bonds. Investment-grade funds typically limit their holdings of
speculative-grade bonds to 5-10% of fund assets. For instance, PIMCO Total Return Fund, the
largest corporate bond fund with $169 billion in assets, limits holdings of high-yield bonds to 10%
of fund assets (PIMCO Total Return Fund Prospectus). As of June 2009, PIMCO Total Return Fund invested 5% of its assets in speculative-grade and unrated securities. Vanguard Short-,
Intermediate-, and Long-Term Investment-Grade Funds ($46 billion in total assets) are more restrictive and invest exclusively in securities rated BBB- and above (Vanguard Bond Funds Prospectus).
Some investment-grade funds, such as Fidelity U.S. Bond Index Fund ($10 billion in assets), do not
have explicit limits on the credit quality of their portfolio holdings. Instead, they seek “to provide
investment results that correspond to the total return of the bonds in the Lehman Brothers U.S.
Aggregate Index” (Fidelity U.S. Bond Index Fund Prospectus). Since the index consists exclusively
of investment-grade securities, any speculative-grade holdings would expose such funds to signifi-
West (1973) argues that these regulations increased the spreads for speculative-grade borrowers relative to
investment-grade ones.
5
The act prohibited purchases of speculative-grade bonds and mandated existing holdings to be liquidated by
1994. As a result, thrifts’ share of the corporate bond market fell from around 7% in 1988 to 1% by 2008 (Table
L212, Corporate and Foreign Bonds, of the Flow of Funds Accounts of the United States).
6
Risk charges for A, BBB, BB, and B rated bonds are 0.4%, 1.3%, 4.6%, and 10% respectively. The portfolio
share of all non-investment grade bonds is capped at 20%. As a result of these restrictions, insurance companies’
share of all speculative-grade bonds is only 8.5%, one-fourth of their 34% share of all investment-grade bonds (Ellul,
Jotikasthira, and Lundblad, 2009).
7
Haircuts for investment-grade nonconvertible debt securities paying a fixed interest rate vary between 2% and
9% depending on maturity. Haircuts for speculative-grade bonds are generally 15%.
4
5
cant tracking error and would be thus quite costly. Not surprisingly, as of June 2009, Fidelity U.S.
Bond Index Fund invested only 0.5% of its assets in speculative-grade bonds.
Conversely, high-yield funds specify a minimum share of their assets to be invested in speculativegrade securities. Vanguard High-Yield Corporate Fund ($11 billion in assets) must invest “at least
80% of its assets in corporate bonds that are rated below Baa by Moody’s Investor Service, Inc.
(Moody’s); have an equivalent rating by any other independent bond-rating agency” (Vanguard
High-Yield Corporate Fund Prospectus). As of June 2009, Vanguard High-Yield Corporate Fund
held 91% of its bond portfolio in speculative-grade and unrated bonds. The 80% minimum on
holdings of speculative-grade bonds is typical—as of June 2009, high-yield mutual funds on average
had 87% of their bond portfolios invested in speculative-grade bonds.
2.2
The muddled origins of “investment grade”
Given the large number of restrictions on investing in speculative-grade securities, one may worry
that the specific location of the investment-grade cutoff between BBB- and BB+ is endogenous. In
particular, differences in firm characteristics and investment opportunities may be especially stark
across this ratings transition. We examine differences in observable firm characteristics below, but
the origins of the distinction between investment and speculative grades may also mitigate some of
these concerns.8
When Moody’s published the first credit ratings in 1909, it did not use the terms “investment
grade” and “speculative grade.” Instead the agency used the term “grade” to refer to three groups
of credit ratings: AAA, AA, and A bonds constituted the “first-grade,” BBB and BB bonds the
“second-grade,” B and lower rated bonds “low grade.” Thus in contrast to the modern distinction
between BBB and BB bonds, Moody’s originally thought of them as being of similar quality.9
It was not until 1931 that the modern distinction between speculative- and investment-grade
bonds began to emerge. On September 11, 1931, the Comptroller of the Currency ruled that comSee Fons (2004) and Harold (1938) for more on the history of credit ratings and the distinction between investment and speculative grades.
9
As Poor’s Publishing, Standard Statistics, and Fitch Publishing companies entered the credit ratings market
in 1916, 1922, and 1924, they generally followed similar characterizations. Appendix Table A1 reports the rating
symbols used by the principal rating agencies in the early 1930s. All agencies described BB as either “Good” or
“Fair,” and none referred to it as speculative (Harold, 1938).
8
6
mercial banks could carry bonds rated BBB or higher at cost, but that they had to mark to market
lower rated and defaulted bonds.10 On February 15, 1936, the Comptroller and the Federal Reserve
went further and completely prohibited commercial banks from purchasing “ ‘investment securities’
in which the investment characteristics are distinctly or predominantly speculative” (Harold, 1938).
The ruling caused significant confusion regarding the precise definition of “speculative” securities. American Banker concluded that the “regulation limits investments practically to those with
an A rating” (Harold, 1938). Faced with an outcry from the banking community, the Comptroller
backtracked, and by 1938 Moody’s persuaded the regulators that bonds rated BBB are not “distinctly or predominantly speculative.” Subsequent regulations continued to use the same BBB vs.
BB cutoff, but its origins suggests that the cutoff could have just as easily been drawn at A vs.
BBB or BB vs. B.
This history suggests that the investment-grade cutoff was not originally drawn to distinguish
between firms with sharply different fundamentals. Over time, however, market institutions may
have evolved around the cutoff to make its location more correlated with firm characteristics and
investment opportunities. Below we provide evidence that this is not the case, showing that differences in observable firm characteristics at the investment-grade cutoff are similar to differences
across other rating transitions.
2.3
Noise and inertia in credit ratings
Credit ratings do carry information about firms’ credit quality and potentially about their investment opportunities.11 However, if ratings are subject to noise and inertia, we will be able to find
pairs of firms that are similar on observable characteristics but are on different sides of the cutoff.
There is a number of reasons to believe that credit ratings are noisy, lagging measures of credit
quality. First, ratings methodologies explicitly aim to provide “stability.” The agencies trade off
Although the ruling applied only to national banks, many state banking departments followed the Comptroller’s
lead and introduced similar restrictions for state chartered banks.
11
There is still considerable debate, however, as to whether credit ratings contain any information not already
available to investors. Market-based measures of credit quality tend to be better predictors of default than credit
ratings, at least at short- and medium-term horizons (Moody’s, 2006). Kliger and Sarig (2000) and Tang (2009) argue
that Moody’s refinement of its rating system revealed new information about rated firms. Jorion, Liu, and Shi (2005)
find greater informational effects of credit rating changes after Regulation Fair Disclosure prohibited companies from
selectively disclosing nonpublic information, but excluded rating analysts from the new regulations.
10
7
rating accuracy versus stability and are reluctant to upgrade or downgrade firms if such changes
might have to be reversed in the future (Moody’s, 2006). This is particularly true at the investmentgrade cutoff, as the agencies are aware that their decisions affect the ability of market participants
to hold certain bonds. Moreover, even when the agencies do adjust credit ratings, the adjustment
is likely to be only partial, followed by additional changes (Altman and Kao, 1992). As a result,
market-based measures such as yield spreads are more accurate than credit ratings in forecasting
defaults at short- and medium-term horizons (Moody’s, 2006).12
In addition to the inertia in ratings generated by the explicit goal of stability, credit rating agencies’ organizational structures may create incentives for analysts to be conservative in upgrading
or downgrading firms. One such organizational practice, exemplified by Moody’s Leverage Finance
Group, is having separate groups analyzing investment- and speculative-grade credits. This organizational structure could create conflicts of interest as the group covering a particular firm would
lose fee revenue if it downgraded or upgraded the client across the investment-grade cutoff.
Rating outlooks introduce a further wedge between the information and regulatory content
of credit ratings. These outlooks “assess the potential for an issuer rating change” but are not
“necessarily a precursor for a rating change” (Standard & Poor’s, 2008). On average, issuers with
a positive outlook default at the same rate as issuers rated one notch higher (Moody’s, 2005).13
Thus, although the information content of a BB+ rating with a positive outlook is the same as,
if not better than, that of an unconditional BBB- rating, their regulatory implications are quite
different—until they are actually upgraded, BB+ firms with positive outlooks cannot access the
investment-grade market.
Appendix Table A3 provides some suggestive evidence of credit ratings inertia in our data. Panel A compares
the characteristics of BB+ firms in the two years before they are upgraded with the characteristics of all BBB- firms.
Other than being smaller, BB+ firms that are subsequently upgraded perform much better than an average BBBfirm. They have higher valuations, lower market leverage, higher profitability and cash holdings, and invest more
than BBB- firms. Conversely, Panel B shows that BBB- firms that are downgraded in the next two years perform
significantly worse than the average BB+ firm. They have significantly higher leverage, lower values of Altman’s
z-score and interest coverage, much worse profitability and they invest less than BB+ firms.
13
In fact, over the 1995-2005 period, BB+ firms with a positive outlook had a 5-year default rate of only 0.95%,
significantly lower than the 3.88% default rate of BBB- firms with stable outlook (Moody’s, 2005).
12
8
3
Empirical Methodology
The previous section suggests two stylized facts that drive our empirical approach. First, regulatory
and voluntary conventions restrict the ability of many investor groups to hold speculative-grade
securities. Thus, if there are limits to arbitrage, arbitrageurs might be unable to fully offset liquidity
or noise shocks suffered by traditional speculative-grade investor groups. In particular, since highyield mutual funds hold about 20% of speculative-grade bonds, flows into high-yield mutual funds
can have a significant effect on the investment of speculative-grade firms. Second, noise and inertia
in credit ratings imply that there are BB+ firms that are similar to BBB- in terms of observable
firm characteristics and investment opportunities. That is, there are firms rated BB+ that “should
be” BBB- and vice versa.
Our empirical strategy is to match BB+ and BBB- firms based on industry and firm characteristics such as size, leverage, Altman’s z-score, and Q, and to compare the investment sensitivities of
the matched firms to high-yield fund flows. Our benchmark matching procedure uses industry and
size.14 Within each industry-quarter we match each firm rated BB+ with a firm rated BBB- that
is of similar size. We consider firms to be of similar size if the ratio of their book assets is within
[0.5, 2]. Starting with the Fama-French 48 industries, we match firms using 38, 30, and 17 industry
definitions until we either have a successful match or are unable to match a given BB+ firm.
In addition to our benchmark matched sample, we examine two subsets of firms closer to the
investment-grade cutoff. The first subset consists of less levered BB+ firms. Excluding the top
25% of BB+ firms by market leverage results in a matched sample of BB+ and BBB- firms with
virtually no differences in observable characteristics. The second subset consists of BB+ firms with
positive outlooks. These firms have observable characteristics and default rates similar to BBBfirms, but their cost of capital is still subject to shocks associated with high-yield fund flows.
Our approach is similar in spirit to the pseudo-experimental approaches like regression discontinuity and differences-in-differences. Consider a regression of the investment of firms that have a true,
unobservable rating R on flows into high-yield mutual funds and standard investment regression
14
We obtain similar results when matching on leverage, z-score, or Q in addition to size.
9
controls:
R
R
R
R
R
Invi,t
= αR + βQ · QR
i,t−1 + βCF · CFi,t + βF lows · F lowst−1 + InvOpportunitiest + εi,t
where common shocks to investment opportunities InvOpportunitiesR
t are unobservable and where
we assume the coefficients on Q and cash flow to be the same across ratings.15 Individual firms are
too small for aggregate fund flows to be correlated with the firm-specific shocks εR
i,t . Fund flows are
potentially correlated with the common shocks InvOpportunitiesR
t , which would bias the coefficient
βFRlows upward. However, if our matching procedure is effective in identifying BB+ and BBB- firms
that are subject to the same common investment opportunities shocks (BB+ firms that “should
be” BBB- and vice versa), we can difference the two equations to obtain
BBB−
BB+
BBB−
Invi,t
− Invj,t
= (αBB+ − αBBB− ) + (βFBB+
lows − βF lows ) · F lowst−1
BBB−
BB+
BBB−
+βQ · (QBB+
− CFj,t
) + (εBB+
− εBBB−
)
i,t−1 − Qj,t−1 ) + βCF · (CFi,t
i,t
j,t
or more compactly
∆Invi,t = α + βF lows · F lowst−1 + βQ · ∆Qi,t−1 + βCF · ∆CFi,t + ηi,t
where ∆X = X BB+ − X BBB− is the difference in firm characteristic X between matched BB+
and BBB- firms. By differencing the investment of matched firms, we thus remove any correlation
between fund flows and investment opportunities. Finding a positive and statistically significant
coefficient βF lows is then evidence of a capital supply effect of fund flows on the investment of BB+
firms.16
BB+
BBB−
BB+
BBB−
In untabulated results we show that the assumption that βQ
= βQ
and βCF
= βCF
cannot be
rejected in our data.
16
Note that the reasons we expect high-yield fund flows to affect the investment of speculative-grade firms do not
extend to investment-grade fund flows. Investment-grade funds account for a much smaller share of the investmentgrade corporate bond market than the share of high-yield mutual funds in the speculative-grade bond market.
Furthermore, investment-grade firms have access to more alternative sources of financing. Therefore we would not
expect investment-grade flows to have any effect on the difference in the investment rates of BB+ and BBB- firms,
and including investment-grade flows would only make the statistical inference more difficult. Untabulated results
indicate that when investment- and speculative-grade fund flows are in fact included together, the coefficient on
investment-grade fund flows is small and not statistically significant. The coefficient on speculative-grade fund flows
retains its magnitude; its statistical signficance is somewhat lower due to the correlation between investment- and
15
10
Our matching procedure selects firms that have similar observable characteristics but are on
different sides of the investment-grade cutoff, and documents the differential sensitivity of the
investment of BB+ firms to fund flows. An alternative econometric approach to implement our
empirical strategy is propensity score weighting, which is a panel regression that uses the full
sample of all BB+ and BBB- firms but gives more weight to firms close to the cutoff (Imbens and
Wooldridge, 2009; Hirano, Imbens, and Ridder, 2003). This approach delivers similar results, which
are reported in Appendix Table A8.
Both matching and propensity score weighting assume that BB+ and BBB- firms are subject to
the same investment opportunities shocks. One might still be concerned that investment opportunities are not quite the same and that flows are picking up the differential investment opportunities
of less creditworthy firms. We address this concern in a number of ways. First, drawing on the regression discontinuity design literature, we include the interactions of firm characteristics with fund
flows to show that it is the discontinuous change in credit rating and not changes in continuous
measures of credit quality that determines the sensitivity of firm investment to fund flows. Controlling for up to five powers of firms characteristics interacted with flows, there is still a discontinuous
change in the sensitivity of investment to fund flows as firms are rated BBB- versus BB+.
Second, the basic version of the differential investment opportunities story predicts that investment of lower-rated firms around any rating cutoff is more sensitive to fund flows than investment
of higher-rated firms. For example, it predicts that the investment of B+ firms is more sensitive to
fund flows than the investment of BB- firms. This is not what we find in falsification tests comparing the sensitivity of investment to fund flows around other rating cutoffs—the investment-grade
cutoff is the only one where the investment of firms below the cutoff is more sensitive to fund flows
than the investment of firms above the cutoff. Therefore, one requires a more complicated story
according to which the cutoff endogenously arose at the place of dramatic change in the nature of
investment opportunities. Comparing the characteristics of matched firms suggests that this is not
the case, as do the historical origins of the investment-grade cutoff.
speculative-grade fund flows.
11
4
Data
In this section we describe our sample construction and variable definitions and address three datarelated issues before turning to our results. First, we discuss which of a firm’s potentially numerous
credit ratings determines whether it can access the investment-grade market. Second, we examine
differences in firm characteristics across credit ratings to show that there is no abrupt change in firm
characteristics around the investment-grade cutoff. Third, we explain how we measure fund flows,
and show that flows are large relative to the capital and investment of speculative-grade firms.
4.1
Sample construction
Our sample is the quarterly CRSP/Compustat merged data set covering the 1986Q1-2007Q4 period.
The sample period is determined by the availability of S&P domestic long-term issuer credit ratings
in Compustat starting in December 1985. Some of our specifications use rating outlooks and bank
loan ratings from the S&P RatingsDirect and Ratings IQuery databases.
We measure investment as
CAP Xi,t
,
P P Ei,t−1
the ratio of capital expenditures in quarter t to net property,
plant, and equipment at the end of quarter t − 1.17 Our regressions include standard controls: cash
flow normalized by lagged capital,
CFi,t
,
P P Ei,t−1
and Qi,t−1 . Cash flow is income before extraordinary
items plus depreciation.18 Q is the market value of equity from CRSP plus assets minus the book
value of stockholder equity, all divided by assets. We also control for size, leverage, Altman’s zscore, and interest coverage. Market leverage is book debt divided by the sum of book debt and
market value of equity from CRSP. Altman’s z-score is 1.2 · W Ci,t /Assetsi,t + 1.4 · REi,t /Assetsi,t +
3.3 · EBITi,t /Assetsi,t + Salesi,t /Assetsi,t , where W Ci,t is working capital, i.e., current assets minus
current liabilities, and REi,t is retained earnings (Altman, 1968). We use the modified version of
Altman’s z-score, which excludes leverage, because we control for leverage directly. Interest coverage
is the ratio of EBIT to interest expense, calculated using four-quarter moving averages of EBIT
Because our proposed mechanism links fund flows and investment through debt issuance, changes in assets could
offset the effect of flows on investment if we scaled capital expenditures by assets instead of PPE. Nevertheless, we
obtain similar but weaker results when we do so. Appendix Table A4 reports the results of our investment regressions
when investment is alternatively scaled by assets, assets net of cash, or PPE. We also try scaling by assets net of
cash to mitigate the mechanical effect of debt issuance on the measured rate of investment. The results confirm
our intuition—the effect of fund flows on investment is strongest when investment is scaled by PPE, weakest when
investment is scaled by assets, and in between when investment is scaled by assets net of cash.
18
To make our results more comparable with papers using annual data, we annualize investment and cash flow.
17
12
and interest expense. To reduce the effect of outliers we winsorize all variables at the first and
ninety-ninth percentiles.
4.2
Measuring access to the investment-grade market
While regulations distinguish between investment and speculative grades at the security level, investment activity occurs at the firm level. We therefore need a firm-level measure of access to the
investment-grade market. The senior secured credit rating is typically the highest rating a firm can
achieve on an individual security and is therefore the right measure of access to the investmentgrade market. A firm with a BB+ senior secured rating has no way to access the investment-grade
market during periods of low or negative flows into high-yield mutual funds.19 In comparison, a
firm with a BB+ senior unsecured rating that has unencumbered collateral may still be able to
access investment-grade market by issuing senior secured debt.
We use S&P long-term issuer credit rating, which is a “current opinion of an issuer’s overall
creditworthiness, apart from its ability to repay individual obligations” and corresponds closely to
the senior secured rating (Standard & Poor’s, 2008). S&P may “notch up”—rate individual issues
above the issuer credit rating—when it “can confidently project recovery prospects exceeding 70%”
(Standard & Poor’s, 2008). Since few firms are in position to issue senior secured bonds with
recovery prospects exceeding 70%, S&P long-term issuer credit rating is a good measure of firm’s
ability to access the investment-grade market.20 And to the extent that some firms with BB+ senior
secured rating are able to issue higher rated securities, we will be less likely to find any effect of
fund flows on the investment of speculative-grade firms.
4.3
No break in firm characteristics at the investment-grade cutoff
Our identification strategy and falsification tests require that differences in firm characteristics at
the investment-grade cutoff be similar to differences across other rating cutoffs. Table 1 reports the
The two primary exceptions are obtaining a guarantee from another entity and issuing asset-backed securities.
During the 1982-2008 period, average recovery rates for senior secured bonds were 52.3% when measured by
post-default trading prices and 63.6% when measured by ultimate recoveries (Moody’s, 2009). In untabulated results
we estimate that less than 15% of all non-convertible bond issues by non-financial firms are notched up. When
weighted by total proceeds, a bit more than 10% are notched up. The vast majority of noteched up issues are assetand mortgage-backed bonds.
19
20
13
means of firm characteristics by credit rating.21 As there are few AAA and AA+ firms, we combine
these firms into one category. We do the same for firms rated CCC+ through CCC-.
Lower-rated firms are generally smaller and more levered. In addition, they have lower values
Altman’s z-score and interest coverage than higher-rated firms. The ratio of net property, plant, and
equipment to assets is relatively constant across credit ratings. Q varies from 2.5 for the most highly
rated firms to 1.4 for CCC rated firms. Higher-rated firms are more profitable than lower rated
firms, whether one looks at operating margins, ROA, or cash flow. Despite significant differences
in Q across credit ratings, firms appear to engage in similar levels of capital expenditures.
Importantly, the investment-grade cutoff does not stand out compared to other rating cutoffs.
BB+ firms are on average 27.8% smaller than BBB- firms, but there are similar differences in size
around other lower rated cutoffs, and our empirical methodology matches on size to produce a
sample of comparably sized firms. The market leverage of BB+ firms is 11.6% larger than the
market leverage of BBB- firms, but there are only three other cutoffs with smaller percentage
differences in market leverage. BB+ firms have higher operating margins but lower ROA and cash
flow than BBB- firms. The investment rate of both BB+ and BBB- firms is around 22%.
X̄1 −X̄0
We also examine normalized differences in firm characteristics ∆X = √
, where Sg2 is the
2
2
S0 +S1
sample variance of firm characteristic X in group g. Imbens and Wooldridge (2009) advocate
using the normalized difference as a scale-free measure of the difference in distributions. They
suggest that researchers should check that all normalized differences are below 0.25 because linear
regression methods can be sensitive to the specification otherwise. At the investment-grade cutoff,
all normalized differences are comfortably below the 0.25 threshold.22
4.4
Flows are large relative to the investment of BB rated firms
The time series of aggregate flows into high-yield corporate bond mutual funds are from the Investment Company Institute, the national association of U.S. investment companies. At the end
of 2008, the Investment Company Institute collected information on assets and flows from 8,889
mutual funds with $9,601 billion in assets under management.
21
22
Appendix Table A2 reports differences in firm characteristics across adjacent credit ratings.
Panel B of Appendix Table A2 reports normalized differences across adjacent credit ratings.
14
The appropriate measure of flows should capture their magnitude relative to the capital of firms
close to the investment-grade cutoff, and also account for the time lag between fund flows and bond
issuance on one hand and issuance and investment on the other hand. To accomplish these goals,
we calculate cumulative flows over the four quarters [t − 4, t − 1] and scale flows by the total PPE
of firms rated BBB+ through BB-, P P Et−1 . Our results are robust to calculating flows over other
windows and using alternative scalings, in particular scaling flows by total net assets (TNA) of
high-yield mutual funds. Figure 2 shows the time-series of high-yield fund flows relative to PPE
and capital expenditures of firms rated BBB+ through BB-. Flows vary significantly over time and
are large relative to the investment of these firms. In our regressions, we standardize flows so that
the coefficients can be interpreted as the effect on investment of a one standard deviation increase
in scaled flows.
5
Results
5.1
Characteristics of matched BB+ and BBB- firms
Table 3 reports the characteristics of matched BB+ and BBB- firms. We match 2,818 out of 3,519
firm-quarter observations rated BB+ to 1,992 unique firm-quarter observations rated BBB-. We
report both the difference in means and the normalized difference.
Our matching procedure successfully picks BB+ and BBB- firms that have similar size. Although
in the full sample, BB+ firms are on average 27.8% smaller than BBB- firms, in the matched sample,
BB+ firms are actually slightly larger than matched BBB- firms. The difference in size, however,
is not statistically significant.
BB+ firms are still more levered than matched BBB- firms. The differences in book and market
leverage are around 0.05. BB+ firms also have lower Altman’s z-score and interest coverage than
matched BBB- firms. However, the normalized differences for these measures of leverage are all
below the 0.25 cutoff suggested by Imbens and Wooldridge (2009). With the exception of ROA,
none of the other differences are statistically or economically significant. Overall, our matching
procedure selects a sample of BB+ and BBB- firms that are similar along most dimensions. In our
15
regressions, we are careful to control for the remaining differences in observable characteristics such
as leverage.
In addition to the full sample of BB+ firms, we consider two subsamples of firms that are
closer to the investment-grade cutoff. The first subsample excludes the top 25% of BB+ firms
by market leverage. The second subsample consists of BB+ firms with positive outlooks.23 The
differences in leverage are now greatly reduced. In the subsample that excludes the top 25% of
BB+ firms by market leverage, the only remaining statistically significant difference is in terms of
the z-score.24 And in the subsample that consists of BB+ firms with positive outlooks, the only
statistically significant difference is in terms of the ROA, with speculative-grade firms being in fact
more profitable than investment-grade ones. BB+ firms with positive outlooks also have higher
values of Q and cash flow than matched BBB- firms. Overall, the two subsamples of BB+ firms
select firms that are even closer to the investment-grade cutoff than the ones in the full sample and
result in an even better match.
5.2
Flows increase the investment of BB+ firms relative to BBB- firms
Table 4 reports the results of our baseline regressions. We regress the difference in the investment
rates of matched BB+ and BBB- firms on high-yield mutual fund flows and differences in firm
characteristics
∆
CAP Xi,t
CFi,t
= α + βF lows · F lowst−1 + βQ · ∆Qi,t−1 + βCF · ∆
+ εi,t
P P Ei,t−1
P P Ei,t−1
where ∆X = X BB+ − X BBB− is the difference in firm characteristic X between matched BB+ and
BBB- firms. We use Thompson (2006) to adjust the standard errors for clustering by both firm and
quarter.
Examining the results in column 1, the coefficient on flows is positive and statistically significant.
One standard deviation increase in flows increases the investment of BB+ firms relative to the
In the subsample that excludes the top 25% of BB+ firms by market leverage, we match 2,130 BB+ observations
to 1,603 unique BBB- firm-quarter observations. In the subsample of BB+ firms with positive outlook, we match
253 BB+ observations to 247 unique BBB- first-quarter observations.
24
Of the four components of z-score, the only statistically significant difference is in retained earnings. There are
no statistically significance differences in profitability, sales, or working capital.
23
16
investment of matched BBB- firms by 0.014, or about 7% of the mean investment rate. The
constant term is close to zero and not statistically significant, indicating that on average matched
firms have similar investment rates.
Controlling for size, market leverage, z-score, and interest coverage in column 2 does not affect
the magnitude or the statistical significance of the coefficient on fund flows. The number of observations drops by almost a quarter, primarily due to missing values of z-score. Because neither
z-score nor interest coverage have significant explanatory power, we omit them from the regressions
in column 3.
In columns 4-6, we estimate the same regressions as in columns 1-3 but using the subsample of
less levered BB+ firms. Although statistical significance is somewhat weaker due to smaller sample
sizes, the economic magnitudes remain the same. In columns 7-9, we use the sample of BB+ firms
with positive outlooks. The sample size is about one tenth of the full sample, and the coefficient
on flows is no longer statistically significant. The point estimates, however, are remarkably similar.
Overall, our results in Table 4 indicate a statistically significant and economically meaningful
effect of fund flows on the investment of BB+ firms relative to similar firms rated BBB-.
5.3
Results are robust to alternative matching procedures
Table 5 shows that our results are robust to alternative matching procedures. Our benchmark
matching procedure successively matches BB+ and BBB- firms within Fama-French 48, 38, 30, and
17 industries until we either have a successful match or are unable to match a given BB+ firm.
Columns 2-4 show that we get even stronger results when we stop the match process at 30, 38, or
48 industries. In particular when, in column 4, we match inside 48 industries only, the coefficient
on fund flows is 0.018, more than a quarter larger than the coefficient of 0.014 in our benchmark
specification.
In columns 5 and 6, we continue matching inside the 48 industries, but adjust the size constraints.
Specifically, in column 5 we relax the size constraints to require the ratio of book assets to be within
[1/3, 3]. The coefficient on fund flows is 0.016, somewhat smaller than in column 4, but larger than
in the benchmark specification of column 1. In column 6 we tighten the size constraints to require
17
the ratio of BB+ to matched BBB- firm assets to be within [0.5, 1.5]. The effect of fund flows on
investment is stronger for this sample.
In columns 7-9, we match firms on assets and one of three other firm characteristics: leverage,
z-score, and Q.25 When matching on assets and leverage or assets and z-score, the coefficient on
fund flows is very similar to the benchmark specification. Matching on assets and Q results in a
coefficient on fund flows of 0.011 that is significant at better than 10%.
5.4
Results are robust to alternative calculations of flows
Our results are also robust to alternative constructions of fund flows. In Panel A of Table 6, we
calculate fund flows over different windows. In the first four columns, we calculate flows over the
four quarters [t − 3 − lag, t − lag]. In the last four columns, we calculate flows over the two quarters
[t − 1 − lag, t − lag]. Our results are robust to measuring flows over two quarters. Consistent
with there being a lag between flows and bond issuance and a similar lag between issuance and
investment, results are actually stronger when using longer lags.
Nor do the results depend on how we scale fund flows. Panel B of Table 6 shows that we get
similar results when scaling flows by a) total PPE of all speculative-grade firms, b) total assets of
firms rated BBB+ through BB-, or c) total net assets of high-yield mutual funds.
5.5
Rating, not size or leverage, determines the sensitivity to flows
Our results hold for a subsample of less levered BB+ firms for which there are virtually no differences
in observable characteristics between BB+ and BBB- firms. To further dispel any concern that
our results might be driven by the investment of smaller and more levered firms, we control for
interactions of firm characteristics with fund flows. Table 7 presents the results. Column 1 is
identical to column 2 in Table 4, controlling for Q, cash flow, size, leverage, z-score and interest
coverage, but not their interactions with fund flows. As we interact firm characteristics with fund
flows in column 2, the direct effect of flows is not affected. For compactness, we do not report the
We use Fama-French 48, 38, 30, and 17 industries to look for the closest match according to the normalized
Euclidean distance. We require the ratio of assets to be within [0.5, 2] and the absolute differences in leverage, z-score,
and Q to be less than 0.2, 0.6, and 0.9. These values correspond to roughly one standard deviation of each one of
these three variables.
25
18
coefficients on the interactions of firm characteristics with fund flows. Only the interaction of size
with flows is consistently negative and statistically significant. Later on we examine more closely
whether the investment of small speculative-grade firms is more sensitive to fund flows.
In columns 3-6 we control for up to five powers of firm characteristics and their interactions
with fund flows. The direct effect of flows is not affected and actually gets stronger. In particular,
controlling for five powers of firm characteristics and their interactions with flows increases the
coefficient on flows from 0.016 in column 1 to 0.023 in column 6.
5.6
There is no differential sensitivity to flows around other cutoffs
Our results so far indicate that the investment of BB+ firms is more sensitive to flows into highyield mutual funds than the investment of matched BBB- firms. Our identifying assumption is
that firms close to the investment-grade cutoff are subject to similar investment opportunities
shocks. If this assumption holds, the differential sensitivity of investment to fund flows is evidence
of the real effects of capital supply shocks in the presence of market segmentation. Our empirical
approach of matching on industry and size and controlling for other firm characteristics, and their
interactions with fund flows, is designed to ensure that the identifying assumption holds so that our
interpretation is valid.
There may still be concerns, however, that investment opportunities are not quite the same, and
that fund flows are driven by the differential investment opportunities of lower-rated firms. If this
were the case, we would expect investment of lower-rated firms around any rating cutoff to be more
sensitive to fund flows than investment of higher-rated firms. To test this hypothesis, we conduct
falsification tests using matched firm pairs around other rating cutoffs. For each credit rating cutoff
from A through B, we match firms just below the cutoff with firms just above the cutoff that are
of similar size and in the same industry.26 For example, we match firms rated A with firms rated
A+. As there are few firms rated above A or below B, we do not report the results for cutoffs above
A and below B.27 To focus on firms closer to each cutoff and get a better match on both size and
As before, we successively match firms within Fama-French 48, 38, 30, and 17 industries until we either have
a successful match or are unable to match a given BB+ firm. We consider firms to be of similar size if the ratio of
their book assets is within [0.5, 2].
27
We find similar results, i.e., that investment of lower rated firms is not more sensitive to fund flows than
26
19
leverage, we exclude the top 25% of most levered firms below the cutoff.
Each column of Table 8 reports the results of our placebo regressions for firms with the credit
rating specified by column heading and matched to firms rated one notch higher. Thus, the first
column compares the investment of A firms with the investment of matched A+ firms. Our regressions control for size, leverage, z-score and interest coverage, as well as the interactions of firm
characteristics with fund flows.
Other than the BB+ vs. BBB- cutoff, the only other cutoff for which there is a statistically
significant effect of fund flows on investment is BBB+ vs. A-. The effect goes the other way—
investment of BBB+ firms is less sensitive to fund flows than investment of matched A- firms.
Appendix Table A6 indicates that our match of BBB+ and A- firms is far from perfect. There are
still large differences in Q and interest coverage, which may explain the results we find.
Our results indicate that the investment-grade cutoff is the only one where the investment of
firms below the cutoff is more sensitive to fund flows than the investment of firms above the cutoff.
Importantly, the lack of statistically significant results around other cutoffs is not due to smaller
sample sizes and weaker power. Other than the A- vs. A cutoff, all other cutoffs have more
observations than our sample of BB+ and BBB- firms.
5.7
Comovement of investment within credit grade
Our placebo regressions suggest that our results are not caused by fund flows picking up the differential investment opportunities of lower-rated firms. Instead, flows into high-yield mutual funds
are likely driven by the performance and investment opportunities of BB-, B+, and B firms, which
together account for 61% of the total number of speculative-grade firms. In comparison, the share
of BB+ firms is only 11.6%. Thus, the effect of fund flows on the investment of BB+ firms may be
a spillover effect due to categorization. If investors emphasize differences between broad categories,
such as investment- versus speculative-grade firms, and overlook differences within these broad categories, they may treat two firms in the same category similarly, even if the firms have very different
performance and investment prospects.28 Categorization may thus cause firms at the boundary to
investment of higher rated firms, for these other cutoffs.
28
For example, the difference in 5-year default rates between investment- and speculative-grade firms is 14%, but
the difference in default rates between the lowest (CCC through C) and higher rated (BB+) speculative-grade firms
20
behave much more like dissimilar firms in the rest of the category and less so like fundamentally
similar firms just across the boundary.
Table 9 provides evidence consistent with this idea by examining whether the investment of
BB+ firms comoves with the investment of unrelated speculative-grade firms further away from
the cutoff. We define unrelated firms as firms outside firm i’s industry rated A through BBB+ for
investment grade and BB- through B for speculative grade. These rating restrictions on unrelated
speculative-grade firms are intended to ensure that they are in fact unrelated and that they do
not face immediate financial distress. To accomplish the first objective we omit firms rated BB to
create a buffer between BB+ and unrelated speculative-grade firms. As a result, the BB+ firms
are quite different from the rest of the category. For example, while average 5-year default rates
for BBB- and BB+ firms are 3.74% and 5.41%, average 5-year default rates for BB-, B+, and B
firms are 12.32%, 17.65%, and 23.84%. To accomplish the second objective we omit firms rated
B- and below. These firms are likely to face immediate financial distress. In particular, B- firms
have very high leverage, negative profitability in terms of ROA and operating cash flow, and even
negative interest coverage. We impose symmetric restrictions for unrelated investment-grade firms
by omitting one notch (BBB) and using the next three notches: BBB+, A-, and A.
The columns of Table 9 alternatively use Fama-French 48, 38, 30, and 17 industries to define
unrelated firms. For each industry definition, we regress the difference in the investment rates
of matched firms on either (i) unrelated speculative-grade investment and unrelated investmentgrade investment, or (ii) unrelated speculative-grade investment alone. We use both specifications
because the two investment series are highly correlated. The results in Table 9 show that unrelated
speculative-grade investment drives out unrelated investment-grade investment, but the presence of
unrelated investment-grade investment as a regressor increases the standard errors. When we regress
the difference in the investment rates of matched firms on unrelated speculative-grade investment
alone, the coefficient is unchanged and is significant at the 5% or 10% level depending on industry
definition.29 The economic magnitude is relatively large. If investment of firms rated BB- through B
is 39%, almost three times as much.
29
In comparison, when we regress the difference in the investment rates of matched firms on unrelated investmentgrade investment alone, we get similar coefficients but only in the case of Fama-French 17 industries is the coefficient
significant at 10%.
21
that are outside firm i Fama-French 48 industry increases by one standard deviation (about 0.040),
investment of firm i increases by about 0.013 relative to the investment of matched BBB- firm.
5.8
Higher sensitivity of firms without access to other financing sources
Are certain types of firms are more sensitive to fund flows? In principle financially constrained firms
with limited access to other sources of financing should be more sensitive to fund flows. Small firms
and firms that do not pay dividends are more likely to be financially constrained (Baker, Stein,
and Wurgler, 2003; Fazzari, Hubbard, and Petersen, 1988). On the other hand, the investment of
firms that can borrow from banks or have access to the asset-backed securities market should be
less sensitive to fund flows.
Table 10 tests this idea by estimating our investment regressions for four different sample splits.
In columns 1 and 2, we split BB+ firms by size. For smaller firms, the coefficient on flows is
positive and statistically significant. For larger firms, the coefficient is positive but not statistically
significant. Although we cannot reject that the two coefficients are the same, it is encouraging that
the effect of fund flows is stronger in the sample of smaller BB+ firms. Note that our methodology is
somewhat different than the typical cross-sectional analysis. We are keeping matched pairs together,
but splitting the sample of matched pairs based on BB+ firm characteristics. Thus our results imply
that small BB+ firms are more sensitive to flows relative to their small BBB- matches than large
BB+ firms are relative to their large BBB- matches.30
In columns 3 and 4, we split BB+ firms by whether they pay dividends. For dividend paying
firms, there is no differential sensitivity of investment to fund flows between BB+ and BBB- firms.
The investment of BB+ firms that do not pay dividends, on the other hand, is strongly sensitive
to fund flows. The coefficient on flows for these firms is 0.027, more than five times as large as for
dividend payers.
Next, we measure firms’ ability to borrow from banks by whether they have a bank loan rating.
Such firms should find it easier to substitute to the syndicated loan market when fund flows are low.
This is what we find in columns 5 and 6. The investment of firms with a loan rating is not sensitive
Match quality is similar across the different sample splits, and we are careful to include the interactions of firm
characteristics with fund flows to make sure that our cross-sectional results are not diven by differences in match
quality.
30
22
to fund flows, but the investment of firms without a loan rating is very sensitive to fund flows. One
standard deviation increase in fund flows increases the investment of these firms by 0.034, or about
15% of their mean level of investment.
Finally, in columns 7 and 8 we split firms by whether they have access to the asset-backed
securities market. Issuing asset- or mortgage-backed securities through a bankruptcy remote trust
can allow firms to tap investment-grade sources of financing. Since certain assets are much easier
to securitize than others, access to the asset-backed securities market depends on the nature of
firm assets. For instance, credit cards, car loans, and certain types of machinery and equipment
are easier to borrow against in the asset-backed securities market. We therefore measure firms’
ability to substitute to the asset-backed market by whether they are in an industry with significant
issuance of asset- and mortgage-backed securities. Appendix Table A7 reports for each Fama-French
48 industry the share of asset- and mortgage-backed securities in total nonconvertible bond issuance
over our sample period by non-financial firms. Excluding the Other industry, the top five industries
by ABS issuance are Electrical Equipment, Personal Services, Automobiles and Trucks, Machinery,
and Retail.31 With the exception of Construction, asset- and mortgage-backed securities account
for less than 10% of the total nonconvertible debt issuance for firms outside the top five industries
by ABS issuance. This suggests limited reliance on and ability to issue asset-backed securities by
firms outside the top five industries. We therefore say that firms have access to the asset-backed
market if they are in the top five industry by asset-backed issuance.32
The results in columns 7 and 8 indicate that the investment of firms in the top five ABS industries
is not sensitive to fund flows. In fact the coefficient on fund flows is negative but not statistically
significant. It is the investment of firms in other industries that responds to fund flows.
Taken together, our results in Table 10 indicate that the investment of firms with limited ability
to substitute away from the high-yield market strongly responds to flows into high-yield mutual
funds.
Two firms—General Environmental Management and various ARG Funding trusts of AutoNation—account for
almost all asset- and mortage-backed issuance of the Other industry. Our results are robust to defining the top five
industries by ABS issuance to include Other and exclude Retail.
32
We get qualitatively similar though weaker results when we look at firms in the top ten industries.
31
23
5.9
BB issuance is more sensitive to flows than BBB issuance
So far we have explored the connection between fund flows and firm investment without discussing
the intermediate step—debt issuance. Our hypothesis is that flows into high-yield mutual funds
increase the supply of capital invested in high-yield bonds. This allows speculative-grade firms
to increase their bond issuance, using the proceeds to finance increases in investment. We now
document the effect of fund flows on issuance of both BB and BBB rated bonds.
Since few firms issue new bonds in any given quarter, firm-level regressions of quarterly bond
issuance on fund flows would be quite noisy. Therefore, we look at the aggregate issuance of BB
and BBB rated bonds. Figure 3 plots the issuance of BB rated nonconvertible bonds by nonfinancial firms and flows into high-yield mutual funds. The two series track each other closely, with
issuance lagging flows by a few quarters. Table 11 formalizes this evidence by estimating univariate
regressions of quarterly bond issuance on lagged fund flows. To account for serial correlation due
to overlapping observations, we report Newey-West standard errors allowing for four lags. The
coefficient of 0.45 in the regression of BB bond issuance on fund flows suggests that as fund flows
increase by $1, BB bond issuance increases by $0.45. More importantly, the coefficient in the
regression of BB bond issuance is almost twice as large as the coefficient in the regression of BBB
bond issuance and the R2 is three times as large.
6
Market Segmentation and the Credit Boom
What role did market segmentation play in the recent credit boom? Popular accounts suggest that
the process of securitization turned subprime mortgages and other low credit quality assets into
investment-grade securities, which mutual funds, insurance companies, and other ratings-reliant
investors could buy. Thus, securitization essentially channeled capital from a market segment that
typically invested in safe assets into the hands of subprime borrowers. This lowered the cost of
capital for subprime borrowers, fueling growth in both house prices and housing construction.
A similar mechanism was likely at work in the financing of speculative-grade firms. Collateralized
debt obligations (CDOs) grew exponentially during the 2002-2007 period, and were a large source of
demand for speculative-grade bonds and loans. By issuing AAA and AA rated securities backed by
24
speculative-grade bonds and loans, CDOs allowed speculative-grade borrowers to tap traditionally
investment-grade sources of financing such as insurance companies and pension funds. By 2007,
these institutional investors accounted for almost two-thirds of the leveraged loan market through
their holdings of CDOs, and their demand led to a decrease in institutional loan spreads (Ivashina
and Sun, 2008). Demand from institutional investors may have increased the overall supply of
term loans to speculative-grade firms, allowing the latter to increase their capital expenditures and
working capital (Nini, 2008).
In Figure 4 and Table 12 we present evidence suggesting that CDOs played an important role
in financing speculative-grade investment. Figure 4 plots high-yield mutual fund flows, CDO flows,
and the difference in the investment rates of matched BB+ and BBB- firms.33 CDO flows and
investment of BB+ firms relative to their BBB- matches both rise dramatically during this period,
while high-yield mutual fund flows do not and are close to zero.
Table 12 formalizes this evidence by regressing the difference in the investment rates of matched
BB+ and BBB- firms on both high-yield fund flows and CDO flows. Although the coefficients
on both fund flows and CDO flows are positive, only CDO flows have a statistically significant
coefficient during this period. Given the short sample period, we suggest caution in interpreting
these results. Nevertheless, our evidence is consistent with the idea that market segmentation
and categorization played an important role in the recent credit boom. CDO flows were mostly
driven by the demand for AAA-rated securities created through tranching of speculative-grade loans
(Benmelech and Dlugosz, 2009), with little attention paid to the investment opportunities of the
underlying firms.
7
Conclusion
The sharp distinction drawn between investment- and speculative-grade firms is one of the most
salient features of credit markets. These terms are more than convenient labels—we show that this
segmentation has real consequences for firm investment.
Our paper makes three contributions. First, we show that on average BB+ and BBB- firms in
33
CDO flows are the total liabilities of dollar-denominated cash flow CDOs rated by S&P in a given quarter.
25
our matched sample have similar characteristics and rates of investment, suggesting that the average
allocation of capital is efficient across segments. Second, we find that shocks to the supply of capital
of high-yield mutual funds cause the investment of BB+ firms just below the investment-grade cutoff
to diverge from the investment of BBB- firms just above the cutoff. Thus, market segmentation
exposes firms to non-fundamental variation in their costs of capital, which in turn leads to excess
volatility in their investment. Third, we suggest that segmentation facilitates categorization by
investors. Fund flows induce excess comovement of the investment of BB+ firms with the investment
of dissimilar, lower-rated firms in unrelated industries.
The magnitudes of the distortions induced by fund flows are economically meaningful but not
excessively large. Our results, however, are likely to be a lower bound on the potential distortions
in real behavior because firms facing the largest costs of speculative-grade ratings are likely to alter
their behavior in order to obtain a BBB- rating.
Our work adds to a broader understanding of the structure and organization of capital markets.
Our results should not be interpreted as suggesting that credit ratings are not valuable, or that a
divide into grades is not efficient in a broader sense. Credit ratings do carry information, and may
help investors economize on the costs of information production. What we want to emphasize is
that sharp divides can have significant effects on real investment that should be weighed carefully
against any potential benefits.
26
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29
Figure 1
Distribution of Issuer Credit Ratings
Distribution of S&P domestic long-term issuer credit ratings of non-financial firms in the quarterly
CRSP/Compustat merged data set. The sample period is 1986Q1-2007Q4.
8000
6000
4000
2000
0
AAA
AA
A+
ABBB BB+
BBB
CCC+ CCC- CC
AA+ AAA
BBB+ BBB- BB
B+
BCCC CC+
C
30
Figure 2
High-Yield Fund Flows Relative to PPE and Capex of BBB and BB Firms
0.06
0.60
0.04
0.40
0.02
0.20
0.00
0.00
-0.02
-0.20
1986q1 1989q1 1992q1 1995q1 1998q1 2001q1 2004q1 2007q1
Flows/PPE
Flows/Capex
31
Flows/Capex
Flows/PPE
Cumulative high-yield fund flows calculated over four quarters are scaled by either total PPE or
cumulative capital expenditures over four quarters of all non-financial firms rated BBB+ through BB-.
The sample period is 1986Q1-2007Q4.
Figure 3
Speculative-Grade Bond Issuance and Fund Flows
Issuance of BB+, BB, and BB- rated nonconvertible bonds by all non-financial domestic corporations in SDC. Bond issuance is scaled by total assets of all non-financial firms in Compustat rated BBB+
through BB-. Four-quarter moving average of bond issuance is plotted. Flows into high-yield mutual
funds are calculated over four quarters and scaled by total assets of all non-financial firms in Compustat
rated BBB+ through BB-.
0.03
0.03
0.02
0.01
0.01
0.01
0.00
0.01
-0.01
0.00
1986q1 1989q1 1992q1 1995q1 1998q1 2001q1 2004q1 2007q1
Bond Issuance
Fund Flows
32
Fund Flows
Issuance
0.02
Figure 4
High-Yield Fund Flows, CDO Flows, and Firm Investment
High-yield mutual fund flows are calculated over a four-quarter moving window and scaled by total PPE of firms rated BBB+ through BB-. CDO flows are total liabilities of dollar-denominated cash
flow CDOs rated by S&P in a given quarter. CDO flows are calculated over two quarters and scaled
by total PPE of firms rated BBB+ through BB-. Investment is the four-quarter moving average of the
difference in the investment rates of BB+ and matched BBB- firms.
0.05
0.00
0.05
-0.05
-0.10
2002q1
0.00
2003q1
2004q1
Investment
2005q1
Fund Flows
33
2006q1
2007q1
CDO Flows
2008q1
CDO Flows
Investment, Fund Flows
0.10
Table 1
Means of Firm Characteristics by Credit Rating
The sample of non-financial firms in the quarterly CRSP/Compustat merged data set.
The sample period is 1986Q1-2007Q4. Book
leverage is book debt divided by the sum of book debt and stockholder equity. Market leverage is book debt divided by the sum of book debt
and market value of equity from CRSP. Altman’s Z-score is 1.2·W Ci,t /Assetsi,t +1.4·REi,t /Assetsi,t +3.3·EBITi,t /Assetsi,t +Salesi,t /Assetsi,t ,
where W Ci,t is working capital, and REi,t is retained earnings. Interest coverage is the ratio of EBIT to interest expense, calculated using
four-quarter moving averages of EBIT and interest expense. Q is the market value of equity from CRSP plus assets minus the book value of
stockhoder equity, all divided by assets. Operating margin is operating income before depreciation divided by sales. ROA is income before
extraordinary items divided by assets. Cash flow is income before extraordinary items plus depreciation. Investment, cash flow, and ROA are
annualized.
34
Investment Grade
AA+
AA
AAA+
A
AAssets
25306 18007 12077 11505 9908
8330
Book leverage
0.269
0.320
0.338
0.370 0.383 0.385
Market leverage
0.118
0.135
0.151
0.174 0.201 0.234
Z-score
1.293
1.193
1.183
1.176 1.083 0.983
Interest coverage 24.446 16.038 14.537 12.926 10.862 8.692
Cash/Assets
0.087
0.077
0.065
0.060 0.067 0.059
PPE/Assets
0.391
0.409
0.365
0.366 0.356 0.391
Q
2.477
2.411
2.201
2.076 1.939 1.778
Operating margin 0.243
0.210
0.182
0.190 0.179 0.186
ROA
0.102
0.093
0.079
0.078 0.066 0.057
CF/PPE
0.555
0.490
0.445
0.455 0.444 0.443
Capex/PPE
0.220
0.214
0.215
0.212 0.211 0.216
BBB+
7639
0.413
0.248
0.890
8.371
0.058
0.409
1.751
0.195
0.054
0.404
0.222
BBB
5993
0.440
0.302
0.851
6.198
0.053
0.388
1.520
0.159
0.043
0.378
0.200
BBB5253
0.459
0.313
0.813
6.723
0.063
0.350
1.575
0.165
0.042
0.502
0.221
BB+
3793
0.498
0.350
0.734
5.373
0.065
0.381
1.513
0.169
0.037
0.448
0.219
BB
2362
0.544
0.387
0.687
4.641
0.069
0.360
1.491
0.160
0.031
0.479
0.242
Speculative Grade
BBB+
B
1743
1243
1860
0.586 0.651 0.741
0.433 0.491 0.551
0.578 0.433 0.071
3.892 2.124 0.843
0.080 0.094 0.125
0.353 0.344 0.350
1.471 1.409 1.452
0.173 0.140 0.094
0.019 −0.002 −0.043
0.399 0.323 0.102
0.243 0.256 0.277
B1421
0.735
0.546
−0.167
−0.664
0.163
0.342
1.555
0.011
−0.092
−0.195
0.282
CCC
1205
0.875
0.664
−0.462
−0.796
0.106
0.403
1.415
0.032
−0.124
−0.278
0.175
Table 2
Default Rates by Credit Rating
Panel A reports 1-, 5-, and 10-year default rates by credit rating. Each column of Panel B reports the default rate for firms with the
specified credit rating relative to the default rate for firms rated one notch higher. For example, column BB+ reports the ratio of BB+ and
BBB- default rates. CCC includes firms rated CCC+ through C. Data from the Standard & Poor’s 2007 Annual Global Corporate Default
Study And Rating Transitions. The sample period is 1981-2007.
35
1 year
5 years
10 years
1 year
5 years
10 years
AAA
AA+
AA
AA-
0.00
0.28
0.67
0.00
0.20
0.35
0.00
0.18
0.72
0.02
0.45
1.08
0.71
0.52
0.90
2.06
2.50
1.50
Investment Grade
A+
A
ABBB+ BBB BBB- BB+
BB
Panel A: Default Rates
0.05
0.07
0.06
0.15
0.23
0.31
0.52
0.81
0.61
0.60
0.73
1.74
1.95
3.74
5.41
8.38
1.46
1.73
2.12
3.71
4.44
6.91 10.21 14.62
Panel B: Ratios of Default Rates
2.50
1.40
0.86
2.50
1.53
1.35
1.68
1.56
1.36
0.98
1.22
2.38
1.12
1.92
1.45
1.55
1.35
1.18
1.23
1.75
1.20
1.56
1.48
1.43
Speculative Grade
BBB+
B
B-
CCC
1.44
12.32
21.03
2.53
17.65
26.11
6.27
23.84
30.43
9.06
29.44
35.73
25.59
44.50
49.76
1.78
1.47
1.44
1.76
1.43
1.24
2.48
1.35
1.17
1.44
1.23
1.17
2.82
1.51
1.39
Table 3
Characteristics of Matched BB+ and BBB- Firms
This table reports differences in the characteristics of matched BB+ and BBB- firms across three
alternative matched samples. The first one includes all BB+ firms that are matched to a BBB- firm.
The second one excludes the top 25% of BB+ firms by market leverage. The third one consists of BB+
firms with positive outlooks. Each quarter a given firm rated BB+ is matched to a similar size firm in
its industry that is rated BBB-. Firms are considered to be of similar size if the ratio of their assets is
within [0.5, 2]. Starting with the Fama-French 48 industries, we match firms using 38, 30, and 17 industry
definitions until we either have a successful match or are unable to match a given BB+ firm. In the full
sample, 2, 818 out of 3, 519 firm-quarter observations rated BB+ are matched to 1, 968 unique firm-quarter
observations rated BBB-. In the subsample of BB+ firms that excludes the top 25% by market leverage,
2, 130 out of 2, 645 firm-quarter observations rated BB+ are matched to 1, 603 unique firm-quarter
observations rated BBB-. In the subsample of BB+ firms with positive outlook, 253 out of firm-quarter
observations rated BB+ are matched to 247 unique firm-quarter observations rated BBB-. The sample
period is 1986Q1-2007Q4. The table reports both the difference in means ∆X = X̄BB+ − X̄BBB− and the
X̄
−X̄BBB−
normalized difference ∆X = �BB+
, where Sg2 is the sample variance of X for group g. Normalized
2
2
SBB+ +SBBB−
difference is a scale-free measure of the difference in distributions (Imbens and Wooldridge (2009)).
Book leverage is book debt divided by the sum of book debt and stockholder equity. Market leverage is
book debt divided by the sum of book debt and market value of equity from CRSP. Altman’s Z-score is
1.2 · W Ci,t /Assetsi,t + 1.4 · REi,t /Assetsi,t + 3.3 · EBITi,t /Assetsi,t + Salesi,t /Assetsi,t , where W Ci,t is
working capital, and REi,t is retained earnings. Interest coverage is the ratio of EBIT to interest expense,
calculated using four-quarter moving averages of EBIT and interest expense. Q is the market value of
equity from CRSP plus assets minus the book value of stockhoder equity, all divided by assets. Operating
margin is operating income before depreciation divided by sales. ROA is income before extraordinary
items divided by assets. Cash flow is income before extraordinary items plus depreciation. Investment
and cash flow are annualized. Standard errors are adjusted for clustering by firm. *, **, and *** denote
statistical significance at 10%, 5%, and 1%.
Assets
Book leverage
Market leverage
Z-score
Interest coverage
Cash/Assets
PPE/Assets
Q
Operating margin
ROA
CF/PPE
Capex/PPE
All BB+ firms
Normalized
Difference
Difference
244
0.036
0.050∗∗
0.147
0.053∗∗∗
0.205
−0.111∗∗
−0.151
−1.348∗
−0.096
−0.004
−0.038
0.022
0.065
−0.065
−0.066
−0.001
−0.006
−0.007∗∗
−0.070
−0.044
−0.041
−0.006
−0.025
Excluding top 25% of
BB+ firms by market leverage
Normalized
Difference
Difference
275
0.047
0.015
0.042
−0.006
−0.025
−0.098∗
−0.130
−0.875
−0.058
0.001
0.005
0.012
0.034
0.014
0.013
0.001
0.003
0.000
0.003
0.010
0.009
0.006
0.025
36
BB+ with positive outlook
Normalized
Difference
Difference
148
0.018
0.055
0.136
−0.002
−0.011
−0.010
−0.013
−1.114
−0.077
−0.019
−0.143
0.029
0.080
0.135
0.116
0.002
0.007
0.017∗
0.181
0.148
0.110
0.030
0.112
Table 4
Difference in the Investment Rates of Matched Firms and Fund Flows
Regressions of the difference in the investment rates of matched BB+ and BBB- firms on high-yield fund
flows and differences in firm characteristics
∆
�
CAP Xi,t
= α + βF lows · F lowst−1 +
βX · ∆Xi,t−1 + εi,t
P P Ei,t−1
X
where ∆X = X BB+ − X BBB− is the difference in firm characteristic X between matched BB+ and BBBfirms. Results for the full sample of matched BB+ and BBB- firms are in columns 1-3. Results for the
subsample of BB+ firms that excludes the top 25% of BB+ firms by market leverage are in columns 4-6.
Results for the subsample of BB+ firms with positive outlooks are in columns 7-9. The sample period is
1986Q1-2007Q4. Cumulative high-yield mutual fund flows over the four quarters [t − 4, t − 1] are scaled
by the total PPE of all firms rated BBB+ through BB-, P P Et−1 . The value of fund flows is standardized
so that the coefficient on flows represents the effect of one standard deviation change in fund flows. Q is
the market value of equity from CRSP plus assets minus the book value of stockhoder equity, all divided
by assets. Cash flow is income before extraordinary items plus depreciation. Investment and cash flow are
annualized. Market leverage is book debt divided by the sum of book debt and market value of equity
from CRSP. Altman’s Z-score is 1.2 · W Ci,t /Assetsi,t + 1.4 · REi,t /Assetsi,t + 3.3 · EBITi,t /Assetsi,t +
Salesi,t /Assetsi,t , where W Ci,t is working capital, and REi,t is retained earnings. Interest coverage is the
ratio of EBIT to interest expense, calculated using four-quarter moving averages of EBIT and interest
expense. Standard errors are adjusted for clustering by both firm and quarter using Thompson (2006). *,
**, and *** denote statistical significance at 10%, 5%, and 1%.
Excluding top 25% of
All BB+ firms
BB+ firms by market leverage
(1)
(2)
(3)
(4)
(5)
(6)
∆Qi,t−1
0.044∗∗∗ 0.022
0.027∗∗∗ 0.045∗∗∗ 0.023
0.026∗∗
(0.010) (0.014) (0.010) (0.010) (0.015)
(0.011)
∆CFi,t /PPEi,t−1
0.074∗∗∗ 0.058∗∗∗ 0.071∗∗∗ 0.066∗∗∗ 0.056∗∗∗
0.064∗∗∗
(0.014) (0.013) (0.014) (0.014) (0.013)
(0.014)
Flowst−1
0.014∗∗ 0.016∗∗ 0.014∗∗ 0.017∗∗ 0.017∗∗
0.016∗∗
(0.006) (0.007) (0.006) (0.008) (0.009)
(0.008)
∆Log(Assetsi,t−1 )
−0.050∗∗−0.043∗∗
−0.048∗
−0.041∗
(0.023) (0.021)
(0.027)
(0.024)
∆Leveragei,t−1
−0.175∗∗∗−0.146∗∗∗
−0.177∗∗∗
−0.191∗∗∗
(0.045) (0.038)
(0.065)
(0.048)
∆Z-scorei,t−1
0.011
0.021
(0.014)
(0.016)
∆Coveragei,t−1
0.001
0.001
(0.001)
(0.001)
Constant
−0.001
0.013
0.007
0.005
0.015
0.006
(0.009) (0.010) (0.009) (0.010) (0.011)
(0.010)
N
2818
2147
2818
2130
1628
2130
Adjusted R2
0.138
0.144
0.153
0.122
0.130
0.140
37
BB+ with positive outlook
(7)
(8)
(9)
0.026
0.001
0.004
(0.023) (0.032)
(0.023)
0.043∗ −0.017
0.042∗
(0.024) (0.033)
(0.025)
0.012
0.013
0.019
(0.027) (0.031)
(0.027)
−0.104
−0.074
(0.077)
(0.058)
−0.400∗∗ −0.331∗∗∗
(0.186)
(0.116)
−0.034
(0.047)
0.003
(0.002)
0.020
0.065∗∗
0.026
(0.024) (0.033)
(0.023)
253
172
253
0.048
0.093
0.094
Table 5
Robustness to Alternative Matching Procedures
This table shows the robustness of our results to alternative matching procedures. Column 1 uses
our benchmark matched sample that starts with Fama-French 48 industries and matches firms within
38, 30, and 17 industry definitions until we either have a successful match or are unable to match a
given BB+ firm. In columns 2-4 we stop the match process at 30, 38, and 48 industries. In columns
5 and 6 we match within the 48 industries and require the asset ratio of matched firms to be within
[1/3, 3] and [0.5, 1.5]. In columns 7-9 we match within 48, 38, 30, and 17 industries. In column 7, we
match on assets and market leverage, and require the asset ratio to be within [0.5, 2] and the absolute
difference in leverage to be less than 0.2. In column 8, we match on assets and z-score, and require the
asset ratio to be within [0.5, 2] and the absolute difference in z-score to be less than 0.6. In column 9,
we match on assets and Q, and require the asset ratio to be within [0.5, 2] and the absolute difference
in Q to be less than 0.9. The sample period is 1986Q1-2007Q4. Cumulative high-yield mutual fund
flows over the four quarters [t − 4, t − 1] are scaled by the total PPE of all firms rated BBB+ through
BB-, P P Et−1 . The value of fund flows is standardized so that the coefficient on flows represents the
effect of one standard deviation change in fund flows.
Q is the market value of equity from CRSP
plus assets minus the book value of stockhoder equity, all divided by assets. Cash flow is income before
extraordinary items plus depreciation. Investment and cash flow are annualized. Market leverage is
book debt divided by the sum of book debt and market value of equity from CRSP. Altman’s Z-score
is 1.2 · W Ci,t /Assetsi,t + 1.4 · REi,t /Assetsi,t + 3.3 · EBITi,t /Assetsi,t + Salesi,t /Assetsi,t , where W Ci,t
is working capital, and REi,t is retained earnings. Interest coverage is the ratio of EBIT to interest
expense, calculated using four-quarter moving averages of EBIT and interest expense. Standard errors are
adjusted for clustering by both firm and quarter using Thompson (2006). *, **, and *** denote statistical
significance at 10%, 5%, and 1%.
∆Qi,t−1
∆CFi,t /PPEi,t−1
Flowst−1
∆Log(Assetsi,t−1 )
∆Leveragei,t−1
Constant
N
Adjusted R2
Match variables
Fama-French
industry definitions
(1)
(2)
(3)
(4)
(5)
(6)
0.027∗∗∗ 0.026∗∗ 0.026∗∗ 0.032∗∗∗ 0.034∗∗∗ 0.028∗∗
(0.010) (0.010) (0.011) (0.012) (0.011) (0.014)
0.071∗∗∗ 0.082∗∗∗ 0.070∗∗∗ 0.070∗∗∗ 0.065∗∗∗ 0.074∗∗∗
(0.014) (0.015) (0.013) (0.014) (0.013) (0.016)
0.014∗∗ 0.015∗∗ 0.017∗∗ 0.018∗∗ 0.016∗∗ 0.019∗∗
(0.006) (0.007) (0.007) (0.008) (0.007) (0.009)
−0.043∗∗ −0.045∗∗ −0.049∗∗ −0.054∗∗ −0.030∗ −0.055∗
(0.021) (0.022) (0.022) (0.024) (0.016) (0.031)
−0.146∗∗∗ −0.147∗∗∗ −0.163∗∗∗ −0.164∗∗∗ −0.150∗∗∗ −0.179∗∗∗
(0.038) (0.041) (0.038) (0.043) (0.040) (0.046)
0.007
0.008
0.008
0.010
0.013
0.007
(0.009) (0.010) (0.010) (0.011) (0.010) (0.011)
2818
2437
2253
1890
2317
1605
0.153
0.169
0.149
0.154
0.137
0.162
Assets
Assets
Assets
Assets
Assets
Assets
48, 38
30, 17
48, 38
30
48, 38
48
38
48
48
(7)
(8)
(9)
0.027∗∗
0.024∗∗ 0.047∗∗
(0.011)
(0.010) (0.022)
0.065∗∗∗
0.049∗∗∗ 0.074∗∗∗
(0.012)
(0.012) (0.013)
0.014∗∗
0.015∗∗ 0.011∗
(0.007)
(0.007) (0.007)
−0.026
−0.037∗∗ −0.044∗∗∗
(0.018)
(0.019) (0.016)
−0.242∗∗∗ −0.168∗∗∗ −0.148∗∗∗
(0.069)
(0.045) (0.046)
0.005
0.007
0.013
(0.009)
(0.012) (0.010)
2108
1917
2318
0.117
0.109
0.137
Assets
Assets
Assets
Leverage
Z-score
Q
48, 38
48, 38
30, 17
30, 17
30, 17
Table 6
Robustness to Alternative Calculations of Fund Flows
This table shows the robustness of our results to alternative calculations of fund flows. In Panel
A, we use alternative lags and windows for calculating fund flows. In columns 1-4, fund flows are
calculated over the four quarters [t − 3 − lag, t − lag]. In columns 5-8, fund flows are calculated over the
two quarters [t − 1 − lag, t − lag]. In Panel B, we calculate flows over the four quarters [t − 4, t − 1] and
use alternative scalings. In the first column, fund flows are scaled by the total PPE of BBB+ through
BB- firms. In the second column, fund flows are scaled by the total PPE of speculative-grade firms, BB+
through CCC-. In the third column, fund flows are scaled by fund total net assets, TNA. In the fourth
column, fund flows are scaled by the total assets of BBB+ through BB- firms. The value of fund flows is
standardized so that the coefficient on flows represents the effect of one standard deviation change in fund
flows. Q is the market value of equity from CRSP plus assets minus the book value of stockhoder equity,
all divided by assets. Cash flow is income before extraordinary items plus depreciation. Investment and
cash flow are annualized. Market leverage is book debt divided by the sum of book debt and market value
of equity from CRSP. The sample period is 1986Q1-2007Q4. Standard errors are adjusted for clustering
by both firm and quarter using Thompson (2006). *, **, and *** denote statistical significance at 10%,
5%, and 1%.
∆Qi,t−1
∆CFi,t /PPEi,t−1
Flowst−1
∆Log(Assetsi,t−1 )
∆Leveragei,t−1
Constant
N
Adjusted R2
∆Qi,t−1
∆CFi,t /PPEi,t−1
Flowst−1
∆Log(Assetsi,t−1 )
∆Leveragei,t−1
Constant
N
Adjusted R2
Panel A: Alternative Lags of Fund Flows
4 quarters of flows
2 quarters of flows
Lag 1
Lag 2
Lag 3
Lag 4
Lag 1
Lag 2
Lag 3
Lag 4
0.027∗∗∗
0.026∗∗∗
0.026∗∗∗
0.027∗∗∗
0.027∗∗∗
0.026∗∗∗
0.026∗∗∗ 0.026∗∗∗
(0.010)
(0.010)
(0.010)
(0.010)
(0.010)
(0.010)
(0.010)
(0.010)
0.071∗∗∗
0.071∗∗∗
0.072∗∗∗
0.072∗∗∗
0.071∗∗∗
0.071∗∗∗
0.072∗∗∗ 0.072∗∗∗
(0.014)
(0.014)
(0.014)
(0.015)
(0.014)
(0.014)
(0.014)
(0.014)
0.014∗∗
0.015∗∗∗
0.015∗∗∗
0.014∗∗
0.012∗
0.015∗∗∗
0.015∗∗∗
0.013∗∗
(0.006)
(0.005)
(0.005)
(0.005)
(0.007)
(0.005)
(0.005)
(0.005)
−0.043∗∗ −0.043∗∗ −0.044∗∗ −0.043∗∗ −0.044∗∗ −0.044∗∗ −0.044∗∗ −0.043∗∗
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
−0.146∗∗∗ −0.148∗∗∗ −0.148∗∗∗ −0.147∗∗∗ −0.144∗∗∗ −0.147∗∗∗ −0.147∗∗∗ −0.147∗∗∗
(0.038)
(0.038)
(0.038)
(0.038)
(0.038)
(0.038)
(0.038)
(0.038)
0.007
0.007
0.007
0.007
0.007
0.007
0.007
0.007
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
2818
2818
2818
2818
2818
2818
2818
2818
0.153
0.153
0.153
0.153
0.152
0.153
0.153
0.152
Panel B: Alternative Scaling of Fund Flows
PPE of BBB+
PPE of BB+
Assets of BBB+
through BBthrough CCCTNA
through BB0.027∗∗∗
0.027∗∗∗
0.027∗∗∗
0.027∗∗∗
(0.010)
(0.010)
(0.010)
(0.010)
0.071∗∗∗
0.071∗∗∗
0.071∗∗∗
0.071∗∗∗
(0.014)
(0.014)
(0.014)
(0.015)
0.014∗∗
0.014∗∗
0.014∗∗
0.016∗∗∗
(0.006)
(0.007)
(0.005)
(0.006)
−0.043∗∗
−0.043∗∗
−0.043∗∗
−0.043∗∗
(0.021)
(0.021)
(0.021)
(0.021)
−0.146∗∗∗
−0.146∗∗∗
−0.145∗∗∗
−0.146∗∗∗
(0.038)
(0.038)
(0.038)
(0.038)
0.007
0.007
0.007
0.007
(0.009)
(0.009)
(0.009)
(0.009)
2818
2818
2818
2818
0.153
0.153
0.153
0.154
39
Table 7
Controlling for Interactions of Firm Characteristics with Fund Flows
Regressions of the difference in the investment rates of matched BB+ and BBB- firms on high-yield fund
flows and differences in the characteristics of matched firms interacted with fund flows
∆
�
CAP Xi,t
= α + βF lows · F lowst−1 +
(βX · ∆Xi,t−1 + βX·F lows · ∆Xi,t−1 · F lowst−1 ) + εi,t
P P Ei,t−1
X
where ∆X = X BB+ − X BBB− is the difference in firm characteristic X between matched BB+ and BBBfirms. Coefficients on the interactions of firm characteristics with fund flows are not reported. The sample
period is 1986Q1-2007Q4. Cumulative high-yield mutual fund flows over the four quarters [t − 4, t − 1]
are scaled by the total PPE of all firms rated BBB+ through BB-, P P Et−1 . The value of fund flows is
standardized so that the coefficient on flows represents the effect of one standard deviation change in fund
flows. Q is the market value of equity from CRSP plus assets minus the book value of stockhoder equity,
all divided by assets. Cash flow is income before extraordinary items plus depreciation. Investment and
cash flow are annualized. Market leverage is book debt divided by the sum of book debt and market value
of equity from CRSP. Standard errors are adjusted for clustering by both firm and quarter using Thompson
(2006). *, **, and *** denote statistical significance at 10%, 5%, and 1%.
∆Qi,t−1
∆CFi,t /PPEi,t−1
Flowst−1
∆Log(Assetsi,t−1 )
∆Leveragei,t−1
∆Z-scorei,t−1
∆Coveragei,t−1
Constant
N
Adjusted R2
Interactions with flows
Powers of characteristics
(1)
0.022
(0.014)
0.058∗∗∗
(0.013)
0.016∗∗
(0.007)
−0.050∗∗
(0.023)
−0.175∗∗∗
(0.045)
0.011
(0.014)
0.001
(0.001)
0.013
(0.010)
2147
0.144
No
1
(2)
0.022∗
(0.013)
0.058∗∗∗
(0.013)
0.017∗∗
(0.007)
−0.045∗∗
(0.022)
−0.168∗∗∗
(0.046)
0.011
(0.014)
0.001
(0.001)
0.014
(0.010)
2147
0.152
Yes
1
40
(3)
0.019
(0.012)
0.064∗∗∗
(0.014)
0.019∗∗
(0.009)
−0.043∗∗
(0.022)
−0.196∗∗∗
(0.054)
0.008
(0.014)
0.001∗
(0.001)
0.019∗
(0.010)
2147
0.166
Yes
1-2
(4)
0.017
(0.015)
0.077∗∗∗
(0.015)
0.021∗∗
(0.009)
−0.035
(0.026)
−0.184∗∗
(0.074)
−0.009
(0.018)
0.003∗∗∗
(0.001)
0.018∗
(0.011)
2147
0.170
Yes
1-3
(5)
0.018
(0.015)
0.078∗∗∗
(0.014)
0.022∗∗
(0.010)
0.002
(0.037)
−0.180∗∗
(0.075)
−0.009
(0.019)
0.002∗∗
(0.001)
0.011
(0.013)
2147
0.172
Yes
1-4
(6)
0.025
(0.016)
0.091∗∗∗
(0.019)
0.023∗∗
(0.010)
0.005
(0.047)
−0.189∗∗
(0.090)
−0.021
(0.021)
0.002
(0.001)
0.009
(0.013)
2147
0.171
Yes
1-5
Table 8
Placebo Regressions
Placebo regressions of the difference in the investment rates of matched firms around other credit
rating cutoffs on fund flows and differences in the characteristics of matched firms interacted with fund
flows
∆
�
CAP Xi,t
= α + βF lows · F lowst−1 +
(βX · ∆Xi,t−1 + βX·F lows · ∆Xi,t−1 · F lowst−1 ) + εi,t
P P Ei,t−1
X
where ∆X = X R − X R̂ is the difference in firm characteristic X between firms rated R and matched firms
rated one notch higher, R̂. In each column the sample consists of firms specified by the column name and
matched firms rated one notch higher. For example, the sample in the first column is firms rated A and
matched firms rated A+. Around each rating cutoff, top 25% of the most levered firms below the cutoff
are excluded. Characteristics of matched firms are reported in Appendix Table A6. The interactions of
firm characteristics with fund flows are included but not reported. The sample period is 1986Q1-2007Q4.
Cumulative high-yield mutual fund flows over the four quarters [t − 4, t − 1] are scaled by the total PPE of
all firms rated BBB+ through BB-, P P Et−1 . The value of fund flows is standardized so that the coefficient
on flows represents the effect of one standard deviation change in fund flows. Q is the market value of
equity from CRSP plus assets minus the book value of stockhoder equity, all divided by assets. Cash flow
is income before extraordinary items plus depreciation. Investment and cash flow are annualized. Market
leverage is book debt divided by the sum of book debt and market value of equity from CRSP. Altman’s
Z-score is 1.2 · W Ci,t /Assetsi,t + 1.4 · REi,t /Assetsi,t + 3.3 · EBITi,t /Assetsi,t + Salesi,t /Assetsi,t , where
W Ci,t is working capital, and REi,t is retained earnings. Interest coverage is the ratio of EBIT to interest
expense, calculated using four-quarter moving averages of EBIT and interest expense. Standard errors are
adjusted for clustering by both firm and quarter using Thompson (2006). *, **, and *** denote statistical
significance at 10%, 5%, and 1%.
A
ABBB+
BBB
BBBBB+
BB
BBB+
B
0.001
0.007
0.028∗∗∗ 0.026∗∗ 0.043∗∗∗ 0.023
0.021
0.051∗∗∗ 0.042∗∗∗ 0.064∗∗∗
(0.007) (0.011) (0.006) (0.011) (0.009) (0.014) (0.013) (0.012) (0.011) (0.012)
∆CFi,t /PPEi,t−1
0.061∗∗∗ 0.037∗∗∗ 0.055∗∗∗ 0.041∗∗ 0.071∗∗∗ 0.056∗∗∗ 0.046∗∗∗ 0.042∗∗∗ 0.034∗∗∗ 0.024∗∗∗
(0.017) (0.010) (0.015) (0.016) (0.011) (0.013) (0.010) (0.010) (0.007) (0.008)
Flowst−1
0.003
0.008 −0.020∗∗∗ 0.005
0.001
0.019∗∗ −0.005 −0.005
0.005
0.008
(0.007) (0.007) (0.008) (0.005) (0.005) (0.009) (0.009) (0.008) (0.008) (0.011)
∆Log(Assetsi,t−1 ) −0.006 −0.024 −0.012 −0.043∗∗ −0.028 −0.042∗ −0.012 −0.064∗∗∗−0.075∗∗∗−0.037
(0.018) (0.024) (0.022) (0.020) (0.019) (0.025) (0.022) (0.020) (0.021) (0.030)
∆Leveragei,t−1
−0.296∗∗∗−0.243∗∗ −0.026 −0.165∗∗∗−0.039 −0.170∗∗ −0.225∗∗∗−0.156∗∗∗−0.255∗∗∗−0.219∗∗∗
(0.079) (0.118) (0.073) (0.049) (0.043) (0.066) (0.052) (0.038) (0.035) (0.045)
∆Z-scorei,t−1
−0.045∗∗∗−0.009
0.005
0.029
0.018
0.022
0.027∗ 0.025∗ 0.016
0.047∗∗∗
(0.015) (0.018) (0.017) (0.018) (0.014) (0.017) (0.015) (0.013) (0.012) (0.015)
∆Coveragei,t−1
0.001∗∗∗−0.000
0.000 −0.001
0.000
0.001
0.001
0.001∗∗ 0.001∗ −0.000
(0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Constant
−0.002 −0.003
0.023∗∗∗ 0.000
0.008
0.015
0.028∗∗∗ 0.007
0.028∗∗∗ 0.044∗∗∗
(0.008) (0.011) (0.009) (0.008) (0.007) (0.011) (0.010) (0.008) (0.009) (0.013)
N
1777
1510
1643
2625
2480
1628
2472
4156
4093
1966
Adjusted R2
0.154
0.083
0.137
0.111
0.146
0.138
0.151
0.176
0.167
0.154
∆Qi,t−1
41
Table 9
Comovement of Investment within Credit Grade
Regressions of the difference in the investment rates of matched BB+ and BBB- firms on the investment rates of unrelated investment- and speculative-grade firms and differences in the characteristics
of matched firms
∆
�
CAP Xi,t
= α + βSGrade · Speculative-Gradei,t + βIGrade · Investment-Gradei,t +
βX · ∆Xi,t−1 + εi,t
P P Ei,t−1
X
where ∆X = X BB+ − X BBB− is the difference in firm characteristic X between matched BB+ and BBBfirms. Average investment of unrelated firms is calculated using firms outside firm i industry, using FamaFrench 48, 38, 30, and 17 industry definitions. Average investment of unrelated investment-grade firms is
calculated using firms rated A through BBB+. Average investment of unrelated speculative-grade firms is
calculated using firms rated BB- through B. The sample period is 1986Q1-2007Q4. Q is the market value
of equity from CRSP plus assets minus the book value of stockhoder equity, all divided by assets. Cash flow
is income before extraordinary items plus depreciation. Investment and cash flow are annualized. Market
leverage is book debt divided by the sum of book debt and market value of equity from CRSP. Standard
errors are adjusted for clustering by both firm and quarter using Thompson (2006). *, **, and *** denote
statistical significance at 10%, 5%, and 1%.
∆Qi,t−1
∆CFi,t /PPEi,t−1
Unrelated Investment Gradei,t
Unrelated Speculative Gradei,t
∆Log(Assetsi,t−1 )
∆Leveragei,t−1
Constant
N
Adjusted R2
Fama-French 48
Fama-French 38
Fama-French 30
Fama-French 17
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.027∗∗∗ 0.027∗∗∗ 0.027∗∗∗ 0.027∗∗∗ 0.027∗∗∗ 0.027∗∗∗ 0.028∗∗∗ 0.028∗∗∗
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
0.072∗∗∗ 0.072∗∗∗ 0.072∗∗∗ 0.072∗∗∗ 0.072∗∗∗ 0.072∗∗∗ 0.072∗∗∗ 0.072∗∗∗
(0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.014) (0.014)
−0.089
−0.051
−0.054
0.039
(0.265)
(0.258)
(0.262)
(0.265)
0.382∗
0.335∗∗ 0.325
0.298∗
0.346∗
0.318∗∗ 0.306
0.326∗∗
(0.211) (0.161) (0.213) (0.164) (0.207) (0.158) (0.209) (0.160)
−0.044∗∗ −0.044∗∗ −0.044∗∗ −0.044∗∗ −0.044∗∗ −0.044∗∗ −0.043∗∗ −0.043∗∗
(0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021)
−0.144∗∗∗ −0.144∗∗∗ −0.144∗∗∗ −0.144∗∗∗ −0.145∗∗∗ −0.145∗∗∗ −0.144∗∗∗−0.144∗∗∗
(0.038) (0.038) (0.038) (0.038) (0.038) (0.038) (0.038) (0.038)
−0.069 −0.076∗ −0.063 −0.067∗ −0.067 −0.072∗ −0.076∗ −0.072∗
(0.045) (0.039) (0.045) (0.040) (0.044) (0.039) (0.044) (0.039)
2818
2818
2818
2818
2818
2818
2818
2818
0.153
0.153
0.152
0.152
0.152
0.153
0.153
0.153
42
Table 10
Cross-Sectional Splits
Regressions of the difference in the investment rates of matched BB+ and BBB- firms on high-yield fund
flows and differences in the characteristics of matched firms interacted with fund flows
∆
�
CAP Xi,t
= α + βF lows · F lowst−1 +
(βX · ∆Xi,t−1 + βX·F lows · ∆Xi,t−1 · F lowst−1 ) + εi,t
P P Ei,t−1
X
are estimated separately for subsamples of BB+ firms split by firm size, dividends, having a bank loan
rating, and access to the asset-backed securities market. ∆X = X BB+ − X BBB− is the difference in
firm characteristic X between matched BB+ and BBB- firms. Small firms are the ones below the median
of assets for BB+ firms. Dividend payers are observations with positive cash dividends. Bank loan
rating indicates the presence of a bank loan rating. Top 5 industries by the share of asset- and mortagebacked securities issuance in total industry-level non-convertible bond issuance are Electrical Equipment,
Personal Services, Automobiles and Trucks, Machinery, and Retail. Appendix Table A7 reports for each
Fama-French 48 industry the share of asset- and mortgage-backed issuance in total nonconvertible bond
issuance by publicly traded non-financial domestic corporations. The sample period is 1986Q1-2007Q4 in
all regressions except for the bank loan rating split, where the sample period is 1986Q1-2005Q2. Q is the
market value of equity from CRSP plus assets minus the book value of stockhoder equity, all divided by
assets. Cash flow is income before extraordinary items plus depreciation. Investment and cash flow are
annualized. Market leverage is book debt divided by the sum of book debt and market value of equity
from CRSP. Standard errors are adjusted for clustering by both firm and quarter using Thompson (2006).
*, **, and *** denote statistical significance at 10%, 5%, and 1%.
∆Qi,t−1
∆CFi,t /PPEi,t−1
Flowst−1
∆Log(Assetsi,t−1 )
∆Leveragei,t−1
Constant
N
Adjusted R2
Small Size
Dividend Payer
Yes
No
Yes
No
0.024
0.032∗∗∗ 0.036∗∗∗ 0.015
(0.015) (0.012) (0.012)
(0.013)
0.086∗∗∗ 0.058∗∗∗ 0.102∗∗∗ 0.046∗∗∗
(0.019) (0.019) (0.018)
(0.018)
0.017∗∗ 0.013
0.005
0.027∗∗
(0.009) (0.010) (0.006)
(0.012)
−0.035 −0.051∗ −0.027
−0.061∗
(0.026) (0.030) (0.021)
(0.032)
−0.096 −0.193∗∗∗ −0.108∗∗ −0.177∗∗∗
(0.060) (0.039) (0.048)
(0.052)
0.001
0.009 −0.004
0.019
(0.013) (0.012) (0.009)
(0.015)
1409
1409
1576
1234
0.164
0.159
0.217
0.122
43
Bank Loan Rating
Yes
No
0.010
0.066∗∗∗
(0.011)
(0.017)
0.088∗∗∗
0.054∗∗∗
(0.019)
(0.018)
0.008
0.034∗∗∗
(0.008)
(0.013)
−0.046∗
−0.039
(0.025)
(0.048)
−0.153∗∗∗ −0.183∗∗
(0.048)
(0.077)
0.008
0.017
(0.012)
(0.022)
1773
584
0.171
0.204
Top 5 ABS Industry
Yes
No
0.018
0.028∗∗
(0.012)
(0.012)
0.063∗∗∗
0.073∗∗∗
(0.020)
(0.016)
−0.006
0.018∗∗
(0.011)
(0.008)
−0.087∗∗∗ −0.033
(0.030)
(0.022)
−0.255∗∗∗ −0.108∗∗
(0.076)
(0.047)
0.023
0.003
(0.015)
(0.011)
583
2235
0.220
0.154
Table 11
Aggregate BB and BBB Bond Issuance and High-Yield Fund Flows
Regressions of quarterly issuance of nonconvertible bonds by non-financial domestic corporations in
SDC on high-yield fund flows. BB bond issuance includes BB+, BB, and BB- rated bonds. BBB bond
issuance includes BBB+, BBB, and BBB- rated bonds. Bond issuance is scaled by total assets of all
non-financial firms in Compustat rated BBB+ through BB-. Fund flows are calculated over the four
quarters [t − 4, t − 1] and scaled by the total assets of firms rated BBB+ through BB-. The sample
period is 1986Q1-2007Q4. Newey-West standard errors allowing for four lags are reported. *, **, and ***
denote statistical significance at 10%, 5%, and 1%.
Flowst−1
Constant
N
Adjusted R2
BB
0.450∗∗∗
(0.126)
0.007∗∗∗
(0.001)
88
0.299
BBB
0.271∗∗∗
(0.100)
0.010∗∗∗
(0.001)
88
0.103
44
BB - BBB
0.179∗∗∗
(0.067)
−0.003∗∗∗
(0.001)
88
0.054
Table 12
CDO Flows and Investment of Matched BB+ and BBB- Firms
Regressions of the difference in the investment rates of matched BB+ BBB- firms on CDO flows
and high-yield fund flows
∆
�
CAP Xi,t
= α + βF undF lows · FundFlowst−1 + βCDOF lows · CDO Flowst−1 +
βX · ∆Xi,t−1 + εi,t
P P Ei,t−1
X
where ∆X = X BB+ − X BBB− is the difference in firm characteristic X between matched BB+ and BBBfirms. Cumulative high-yield mutual fund flows over the four quarters [t − 4, t − 1] are scaled by the total
PPE of all firms rated BBB+ through BB-, P P Et−1 . The value of fund flows is standardized so that the
coefficient on flows represents the effect of one standard deviation change in fund flows. CDO flows are the
total liabilities of dollar-denominated cash flow CDOs rated by S&P in a given quarter. Cumulative CDO
flows are calculated over 1, 2, and 4 quarters. CDO flows are scaled by the total PPE of all firms rated
BBB+ through BB-, P P Et−1 . The value of CDO flows is standardized so that the coefficient represents
the effect of one standard deviation change in CDO flows. The sample period is 2002Q1-2007Q4. Q is
the market value of equity from CRSP plus assets minus the book value of stockhoder equity, all divided
by assets. Cash flow is income before extraordinary items plus depreciation. Investment and cash flow are
annualized. Market leverage is book debt divided by the sum of book debt and market value of equity
from CRSP. Standard errors are adjusted for clustering by both firm and quarter using Thompson (2006).
*, **, and *** denote statistical significance at 10%, 5%, and 1%.
CDO flows calculated over
2 quarters
(3)
(4)
0.045∗∗∗
0.032∗∗
(0.013)
(0.013)
0.034
0.032
(0.024)
(0.023)
0.012
0.012
(0.008)
(0.008)
0.016∗
0.014∗
(0.009)
(0.008)
0.003
(0.033)
−0.158∗∗∗
(0.055)
−0.025∗∗
−0.021∗
(0.011)
(0.011)
1072
1072
0.084
0.097
1 quarter
∆Qi,t−1
∆CFi,t /PPEi,t−1
Fund Flowst−1
CDO Flowst−1
(1)
0.046∗∗∗
(0.012)
0.036
(0.024)
0.012
(0.008)
0.016∗
(0.009)
∆Log(Assetsi,t−1 )
∆Leveragei,t−1
Constant
N
Adjusted R2
−0.026∗∗
(0.011)
1122
0.089
(2)
0.032∗∗
(0.013)
0.034
(0.024)
0.012
(0.008)
0.014∗
(0.008)
0.004
(0.032)
−0.165∗∗∗
(0.057)
−0.021∗
(0.011)
1122
0.103
45
4 quarters
(5)
(6)
0.041∗∗∗
0.026∗
(0.013)
(0.014)
0.029
0.026
(0.024)
(0.023)
0.012
0.012
(0.008)
(0.008)
0.014
0.013
(0.010)
(0.009)
0.005
(0.034)
−0.173∗∗∗
(0.056)
−0.024∗∗
−0.021∗
(0.012)
(0.012)
981
981
0.070
0.086