## Abstract

We study the real effects of market segmentation due to credit ratings by using a matched sample of firms just above and just below the investment-grade cutoff. These firms have similar observables, including average investment rates. However, flows into high-yield mutual funds have an economically significant effect on the issuance and investment of the speculative-grade firms relative to their matches, especially for firms likely to be financially constrained. The effect is associated with the discrete change in label from investment- to speculative-grade, not with changes in continuous measures of credit quality. We do not find similar effects at other rating boundaries.

Capital markets play a critical role in efficiently allocating capital across firms. Their ability to play this role, however, may be impeded by market segmentation. In this article, we study one of the most prominent divides in capital markets— the distinction drawn between investment- and speculative-grade firms. A large number of regulations, investment charters, and contracts reference this distinction, and recent research suggests that it can affect firms' capital structure and cost of capital (Kisgen 2006, KisgenStrahan:2010, EllulJotikasthiraLundblad:2011).

Market segmentation between investment- and speculative-grade firms may also have real effects. When investors withdraw capital from high-yield mutual funds, which are large buyers of speculative-grade bonds, arbitrage capital may not immediately offset this shock (Mitchell, Pedersen, and Pulvino 2007, DuffieStrulovici:2011). As a result, such high-yield fund flows may affect the supply and cost of capital that is available to speculative-grade firms. This in turn may cause some firms, particularly those unable to access other sources of financing, to cut their investment. This article presents 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, we construct a matched sample of firms just above (BBB−) and just below (BB+) the investment-grade cutoff. These firms have similar observable characteristics and the same average rates of investment, but flows into high-yield mutual funds only affect the supply and cost of capital for the speculative-grade (BB+) firms. As a consequence, high-yield mutual fund flows, which are largely driven by retail investors, result in the bond issuance and the investment of firms just below the cutoff diverging from the investment of their matches just above the cutoff. This effect is economically meaningful— a one-standard-deviation increase in high-yield fund flows increases the investment of BB+ firms relative to their BBB− matches by about 10% of their average rate of investment. The effect is stronger for firms that depend on external financing, are more likely to be financially constrained, and 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 these differences tend to average out over time.

Our matching methodology is designed to rule out alternative explanations that are based on firm investment opportunities. We match BB+ firms to BBB− firms on the basis of industry and firm characteristics, including size, leverage, Altman's $$z$$-score, $$Q$$, cash holdings, asset tangibility, profitability, and sales growth. Our identifying assumption is that firms close to the cutoff and with similar observable characteristics are also subject to similar shocks to profitability and investment opportunities. If this is the case, then by differencing the investment rates of matched BB+ and BBB− firms, we difference out any common shocks to investment opportunities that may be correlated with high-yield mutual fund flows. We can then interpret the differential effect of high-yield mutual fund flows on the investment of BB+ firms as evidence of recurring capital supply effects.

Despite our matching methodology, one may still worry that the investment opportunities of matched firms may not be exactly the same and that high-yield mutual fund flows may be responding to the differential investment opportunities of less creditworthy firms. This would be the case if, for instance, ratings were driven by unobservable characteristics known to rating agencies. Although we cannot completely rule out differences in unobservable characteristics, we address such concerns in two ways. First, we show that our results are robust to controlling for a variety of macroeconomic variables and are thus unlikely to be driven by differential sensitivities to the business cycle. Second, we conduct falsification tests at other rating boundaries: the investment-grade cutoff is the only one where the investment of firms below the cutoff is more sensitive to high-yield mutual fund flows than the investment of firms above the cutoff.

In summary, we find that shocks to the supply of capital of high-yield mutual funds result in the investment of firms just below the investment-grade cutoff diverging from the investment of similar firms just above the cutoff. Our work is related to the literature that studies the investment effects of shocks to the supply of bank capital 1 as well as the literature that studies the role of credit ratings in capital markets, in which Lemmon and Roberts (2010) is perhaps the most closely related article. Lemmon and Roberts (2010), one of several articles to study the period surrounding the savings and loan crisis, 2 argue that the collapse of the junk bond dealer Drexel Burnham Lambert constituted a capital supply shock that led speculative-grade firms to cut their acquisitions relative to unrated firms that were previously able to issue debt in private debt markets.

Our work makes three novel contributions to the literature. First, we study how recurring shocks to the capital of an important, largely retail-based investor class interact with market segmentation to affect real investment, while the existing literature mostly focuses on the effects of large, unexpected one-time changes in the institutional environment. 3 Our results suggest that distortions in real investment that are due to market segmentation are commonplace, and are not just isolated events that occur when the institutional environment undergoes dramatic changes. Second, our results emphasize how the investment effects of market segmentation vary with financial market conditions. The existing literature focuses on institutional changes and shows that changes that alter the set of creditors that firms can access are associated with changes in firm financing behavior. In contrast, our work shows that market segmentation has a particularly important impact on firm investment decisions when flows into high-yield mutual funds deviate from their long-run mean. Our empirical methodology, which adds to a growing body of literature that uses matching methods in finance (e.g., Villalonga 2004, MalmendierTate:2009, AlmeidaCampelloLaranjeiraWeisbenner:forthcoming, CampelloGrahamHarvey:2010), is important for making this point. While previous work has dealt with the simultaneity of capital supply and demand by seeking plausibly exogenous supply shocks, our approach seeks to effectively hold demand fixed observation by observation. Since market conditions are not exogenous, our methodology is necessary in order to isolate the time-varying effects of market segmentation on firm investment. Finally, by studying the effects of rating-based market segmentation on the financing and investment behavior of firms, we contribute to the current debate about the regulatory role of credit ratings in capital markets.

The remainder of the article 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 2. Section 3 describes our data and summarizes differences in firm characteristics across credit ratings. Section 4 reports our main results, and Section 5 concludes.

## 1. Institutional Background

We begin by briefly describing two institutional features of credit markets and ratings that motivate our empirical methodology. First, many regulations, as well as voluntary conventions, restrict the ability of certain investor classes to hold speculative-grade securities. Second, rating methodologies introduce noise and inertia in credit ratings. Once we review this institutional background, we describe our empirical methodology.

### 1.1 Regulations restrict holdings of speculative-grade securities

Many rules and regulations restrict the ability of certain investor classes to hold speculative-grade securities. Commercial banks have been prohibited from holding bonds that are rated BB+ and below since 1936. The Financial Institutions Reform, Recovery, and Enforcement Act of 1989 extended the ban on speculative-grade bond holdings to thrifts. 4 Most state insurance regulations follow the guidelines that were established by the National Association of Insurance Commissioners, which set higher risk charges for and a hard cap on holdings of speculative-grade bonds.5 In addition, the net capital rule for broker-dealers requires larger haircuts for speculative-grade securities (U.S. Securities and Exchange Commission 2003). 6

Conversely, high-yield mutual funds specify that a minimum share of their assets be invested in speculative-grade securities. Vanguard High-Yield Corporate Fund 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.” As of June 2011, Vanguard High-Yield Corporate Fund held 94% of its bond portfolio in speculative-grade and unrated bonds. The 80% minimum on holdings of speculative-grade bonds is typical—according to Morningstar, as of June 2011, high-yield mutual funds had on average 93% of their bond portfolios invested in speculative-grade and unrated bonds.

Finally, note that any speculative-grade bond purchases by investment-grade funds and investment-grade bond purchases by high-yield mutual funds will introduce noise in our measure of the supply of capital available to speculative-grade firms and thus bias us against finding any results.

### 1.2 The muddled origins of “investment-grade”

Given the large number of restrictions on investing in speculative-grade securities, one may worry that differences in firm characteristics and investment opportunities may be especially stark at the investment-grade cutoff. We examine differences in observable firm characteristics below, but the origins of the cutoff may also mitigate some of these concerns. When Moody's published the first credit ratings in 1909, it 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” (Harold 1938, Fons:2004). 7 Thus, in contrast to the modern distinction between BBB and BB bonds, Moody's originally thought of them as being of similar quality. 8

It was not until the 1930s that the modern distinction between speculative- and investment-grade bonds began to emerge. In 1931, the Comptroller of the Currency ruled that commercial banks could carry bonds rated BBB or higher at cost, but that they had to mark to market lower-rated and defaulted bonds. 9 In 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 initially concluded that the “regulation limits investments practically to those with an A rating” (Harold 1938). However, by 1938 Moody's had persuaded the regulators that bonds rated BBB were not “distinctly or predominantly speculative.” This history suggests that the investment-grade cutoff was not originally drawn to distinguish between firms with sharply different fundamentals; the cutoff could have been just as easily drawn at A versus BBB or BB versus B.

Over time, however, market institutions may have evolved around the cutoff to render its location more correlated with firm characteristics and investment opportunities. Below, we provide evidence that this is not the case and show that differences in observable firm characteristics at the investment-grade cutoff are similar to differences across other rating cutoffs.

### 1.3 Noise and inertia in credit ratings

Credit ratings do carry information about firms' credit quality and potentially their investment opportunities. 10 However, if ratings are subject to noise and inertia, we will be able to find pairs of firms that have similar characteristics but are on different sides of the cutoff. There are a number of reasons to believe that credit ratings are noisy, lagging measures of credit quality. First, rating methodologies emphasize stability. The agencies explicitly trade off rating accuracy versus stability and are reluctant to upgrade or downgrade firms if such changes might have to be reversed in the future (Cantor and Mann 2006). This is particularly true at the investment-grade 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 and followed by additional changes (Altman and Kao 1992). As a result, market-based measures, such as yield spreads, are more accurate than are credit ratings in forecasting defaults at short- and medium-term horizons (Cantor and Mann 2006).

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, used by Moody's Leveraged Finance Group, is having separate groups analyze investment- and speculative-grade credits. This organizational structure could create conflicts of interest as the group who covers a particular firm would lose fee revenue if it up- or downgraded 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, however, issuers with a positive outlook default at the same rate as issuers rated one notch higher (Cantor and Hamilton 2005). Over the 1995–2005 period, BB+ firms with a positive outlook had a five-year default rate of only 0.95% compared with the 3.88% default rate of BBB− firms with a stable outlook (Cantor and Hamilton 2005). In fact, when reporting how accurately credit ratings forecast default, “Moody's traditionally adjusts an issuer's rating …1 notch downwards (upwards) for negative (positive) outlook” (Moody's 2011). Thus, 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, but their regulatory implications are quite different— until they are actually upgraded, BB+ firms with positive outlooks cannot access the investment-grade market.

## 2. Empirical Methodology

The previous section suggests two stylized facts that drive our empirical approach. First, regulations and investment charters restrict the ability of many investor groups to hold speculative-grade securities. Thus, shocks to the capital of investors who can hold speculative-grade bonds may have a significant effect on the ability of speculative-grade firms to raise capital and invest. In our empirical implementation, we focus on high-yield mutual funds, an investor class that holds about 20% of speculative-grade bonds and experiences recurring capital shocks that are due to fund flows.

Second, noise and inertia in credit ratings imply that there are BB+ firms that are similar to BBB− in terms of 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 on the basis of industry and firm characteristics and compare the investment sensitivities of the matched firms to high-yield mutual fund flows. 11

Our benchmark matching procedure uses industry, size, leverage, Altman's $$z$$-score, $$Q$$, cash holdings, and sales growth. These variables have the most explanatory power in regressions of BB+ versus BBB− rating on firm characteristics. Each quarter we take a firm that is rated BB+ and find a BBB− firm that is in the same Fama-French 48 industry classification and is the closest in terms of our matching variables. We measure closeness using the Mahalanobis distance, which measures the distance between firm characteristics and accounts for the variance of individual characteristics and the covariances between characteristics, as is standard in the literature. Although it would be possible to mechanically find the closest BBB− firm for every BB+ firm, we would like to ensure high-quality matches in which the BB+ firm and its BBB− match are very similar. To do so, we require the difference in each matching variable to be less than one standard deviation of that variable.

In addition to our benchmark matched sample, we also separately examine the subset of BB+ firms with positive rating outlooks. These firms have observable characteristics and default rates similar to BBB− firms, but they are still subject to the capital supply shocks that are associated with high-yield mutual fund flows.

Our approach is similar in spirit to the pseudo-experimental approaches used to estimate treatment effects in the program evaluation literature. Consider a firm, $$i$$, with true (continuous measure of) credit quality, $Si$ , and assigned credit rating, $Ri$ . Its investment can be written as a function of standard investment regression controls and flows into high-yield mutual funds:

(1)
\begin{align} In{v_{i,t}} = & {\alpha _i} + {\beta _Q} \cdot {Q_{i,t - 1}} + {\beta _{CF}} \cdot C{F_{i,t}} \\ & \, + {\beta _{Flows}}({R_i}) \cdot High - yield\,fund\,flow{s_{t - 1}} \\ & \, + Investment\,opportunitie{s_t}({S_i}) + {\varepsilon _{i,t}}. \\ \end{align}
We assume that unobservable common shocks to Investment opportunities$$(S_i)$$ vary continuously with the true credit quality, $Si$ , while the sensitivity of investment to high-yield fund flows, $βFlows(Ri)$ , depends on the assigned rating because of the previously described regulatory frictions. In particular, the institutional restrictions previously discussed suggest that $βFlows(BB+)>0$ and $βFlows(BBB−)=0$ .

Individual firms are too small for aggregate high-yield fund flows to be correlated with the firm-specific shocks, $εi,t$ . Fund flows are potentially correlated with the common shocks $Investmentopportunitiest$ , which would upward bias the coefficient $βFlows$ if we ran the simple regression from above. However, if our matching procedure is effective, we can find a BBB− firm, $$j$$, that has very similar underlying credit quality, $Sj$ , and hence is subject to the same common investment opportunities shocks, i.e., $Investmentopportunitiest(Si)=Investmentopportunitiest(Sj)$ . We can then difference the two equations to obtain

\begin{align} In{v_{i,t}} - In{v_{j,t}} = & ({\alpha _i} - {\alpha _j}) + ({\beta _{Flows}}(BB + ) \\ & - {\beta _{Flows}}(BBB - )) \cdot High - yield\,fund\,flow{s_{t - 1}} \\ & + {\beta _Q} \cdot ({Q_{i,t - 1}} - {Q_{j,t - 1}}) + {\beta _{CF}} \cdot (C{F_{i,t}} - C{F_{j,t}}) \\ & + ({\varepsilon _{i,t}} - {\varepsilon _{j,t}}) \\ \end{align}
or more compactly
(2)
\begin{align} \Delta In{v_{i,t}} = & \alpha + {\beta _{Flows}} \cdot High - yield\,fund\,flow{s_{t - 1}} + {\beta _Q} \cdot \Delta {Q_{i,t - 1}} \\ & \, + {\beta _{CF}} \cdot \Delta C{F_{i,t}} + {\eta _{i,t}}, \\ \end{align}
where $ΔX=XBB+−XBBB−$ 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 high-yield fund flows and investment opportunities. Finding a positive and statistically significant coefficient $βFlows$ is then evidence of a capital supply effect of fund flows on the investment of BB+ firms.

### 2.1 Limitations of the approach

Our empirical methodology is designed to compare pairs of firms that are subject to the same investment opportunities shocks. However, we can only verify that our matched firms are similar on observable characteristics. If matched firms differ on unobservable characteristics that are known to the credit rating agencies, then their investment opportunities may not be quite the same.

In the absence of a true experiment, we cannot completely rule out this concern. However, we attempt to address it in two ways. First, we show that our results are robust to controlling for a variety of macroeconomic variables and are thus unlikely to be driven by differential sensitivities to the business cycle. Second, we conduct falsification tests comparing the sensitivity of investment to high-yield mutual 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. If our results were driven by unobservable firm characteristics known to the credit rating agencies, then we would expect to find differential investment sensitivities around every cutoff.

Another limitation of our empirical approach is that firms could be selecting into different ratings on the basis of unobservable characteristics. The distribution of credit ratings shown in Figure 1 suggests that some selection might be taking place, as there are fewer firm-quarter observations that are rated BB+ than either BBB− or BB. Although the direction of any bias that is introduced by selection on unobservable characteristics is ambiguous, we believe that the most natural selection story would bias us against finding our results. The firms whose investment would be most affected by the volatility of high-yield 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 high-yield fund flows.

Figure 1

Distribution of Issuer Credit Ratings

This figure shows the distribution of S&P domestic long-term issuer credit rating for firms in the quarterly CRSP/Compustat merged data set, excluding financials and utilities. The sample period is 1986 Q1-2010 Q4.

Figure 1

Distribution of Issuer Credit Ratings

This figure shows the distribution of S&P domestic long-term issuer credit rating for firms in the quarterly CRSP/Compustat merged data set, excluding financials and utilities. The sample period is 1986 Q1-2010 Q4.

Management of an existing rating could also introduce bias into our results. A firm desiring to protect or increase its rating might cut investment in order to do so. If such behavior occurs at many rating cutoffs and drives our results, then our falsification tests around other rating cutoffs would fail. Of course, rating management could be most important around the investment-grade cutoff. For instance, BBB− firms that are in danger of being downgraded might cut investment in the hopes of maintaining their (valuable) investment-grade status. In contrast, BB+ firms are already speculative grade, so they have less to lose if downgraded and may be less likely to cut their investment in order to maintain their ratings. Note, however, that for such behavior to drive our results, downgrades would have to be more common when high-yield fund flows are high. In practice, downgrades tend to be countercyclical, while high-yield fund flows tend to be procyclical, so such a bias would work against finding our results.

To summarize, while our empirical methodology is designed to rule out alternative interpretations of our results, it has some limitations. Our macro-economic controls and our falsification tests, as well as our cross-sectional results, help alleviate concerns about unobservable characteristics and rating management but cannot completely eliminate them.

## 3. Data

In this section, we describe our sample construction and address three data-related 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 high-yield fund flows and show that flows are large relative to the capital and investment of speculative-grade firms.

### 3.1 Sample construction

Our sample covers domestic firms in the quarterly CRSP/Compustat merged data set, excluding financials and utilities, over the 1986 Q1-2010 Q4 period. The sample period is determined by the availability of Standard & Poor's (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 $$\frac{{CAP{X_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ the ratio of capital expenditures in quarter $$t$$ to net property, plant, and equipment (PPE) at the end of quarter $t−1$ . 12 Our regressions include standard controls: cash flow normalized by lagged capital, $$\frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ and $Qi,t−1$ . We also use size, leverage, Altman's $$z$$-score, cash holdings, and sales growth in our matching procedure. A list of variable definitions is in the Appendix, Table A1. To reduce the effect of outliers, we winsorize all variables at the first and ninety-ninth percentiles.

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 investment-grade 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. 13 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 the 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 closely corresponds 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 a position to issue senior secured bonds with recovery prospects that exceed 70%, the S&P long-term issuer credit rating is a good measure of the firms' ability to access the investment-grade market. 14 And to the extent that some firms with BB+ senior secured ratings are able to issue higher-rated securities, we will be less likely to find any effect of high-yield fund flows on the investment of speculative-grade firms.

### 3.3 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 means of firm characteristics by credit rating. As there are few AAA and AA+ firms, we combine these firms into one category. We do the same for firms that are rated CCC+ through CCC−.

Table 1

Means of firm characteristics by credit rating

AA + AA AA – A + A – BBB + BBB BBB – BB + BB BB – B + B – CCC
Assets 28778 20085 13705 13025 11536 9550 8670 6404 5864 4035 2779 2031 1466 2155 1969 1540
Book leverage 0.265 0.321 0.341 0.359 0.386 0.382 0.409 0.440 0.455 0.479 0.535 0.577 0.657 0.743 0.754 0.906
Market leverage 0.119 0.135 0.151 0.169 0.201 0.229 0.244 0.291 0.308 0.335 0.378 0.427 0.490 0.558 0.565 0.674
z-score 1.304 1.200 1.207 1.208 1.094 1.007 0.906 0.882 0.844 0.762 0.682 0.577 0.406 0.038 -0.175 -0.581
Interest coverage 27.161 17.035 15.030 14.217 11.540 10.051 9.082 7.128 7.142 6.166 4.929 4.261 2.130 0.904 -0.489 -0.887
Cash/Assets 0.091 0.079 0.068 0.069 0.070 0.065 0.063 0.061 0.069 0.069 0.075 0.081 0.094 0.119 0.151 0.108
PPE/Assets 0.374 0.399 0.348 0.345 0.347 0.368 0.389 0.365 0.341 0.372 0.354 0.351 0.341 0.364 0.352 0.405
$$Q$$ 2.461 2.411 2.193 2.078 1.939 1.766 1.745 1.557 1.563 1.515 1.493 1.455 1.404 1.414 1.478 1.415
Operating margin 0.244 0.209 0.184 0.191 0.183 0.183 0.195 0.159 0.166 0.172 0.169 0.178 0.145 0.110 0.028 0.030
ROA 0.103 0.093 0.080 0.079 0.068 0.059 0.055 0.045 0.042 0.037 0.031 0.018 -0.003 -0.043 -0.085 -0.137
CF/PPE 0.594 0.519 0.515 0.549 0.475 0.523 0.446 0.442 0.539 0.478 0.545 0.424 0.349 0.114 -0.183 -0.346
Capex/PPE 0.220 0.214 0.215 0.213 0.208 0.220 0.221 0.198 0.213 0.217 0.242 0.241 0.249 0.259 0.255 0.165
Sales growth 0.088 0.084 0.074 0.078 0.089 0.100 0.114 0.093 0.107 0.138 0.153 0.177 0.199 0.207 0.204 0.116

AA + AA AA – A + A – BBB + BBB BBB – BB + BB BB – B + B – CCC
Assets 28778 20085 13705 13025 11536 9550 8670 6404 5864 4035 2779 2031 1466 2155 1969 1540
Book leverage 0.265 0.321 0.341 0.359 0.386 0.382 0.409 0.440 0.455 0.479 0.535 0.577 0.657 0.743 0.754 0.906
Market leverage 0.119 0.135 0.151 0.169 0.201 0.229 0.244 0.291 0.308 0.335 0.378 0.427 0.490 0.558 0.565 0.674
z-score 1.304 1.200 1.207 1.208 1.094 1.007 0.906 0.882 0.844 0.762 0.682 0.577 0.406 0.038 -0.175 -0.581
Interest coverage 27.161 17.035 15.030 14.217 11.540 10.051 9.082 7.128 7.142 6.166 4.929 4.261 2.130 0.904 -0.489 -0.887
Cash/Assets 0.091 0.079 0.068 0.069 0.070 0.065 0.063 0.061 0.069 0.069 0.075 0.081 0.094 0.119 0.151 0.108
PPE/Assets 0.374 0.399 0.348 0.345 0.347 0.368 0.389 0.365 0.341 0.372 0.354 0.351 0.341 0.364 0.352 0.405
$$Q$$ 2.461 2.411 2.193 2.078 1.939 1.766 1.745 1.557 1.563 1.515 1.493 1.455 1.404 1.414 1.478 1.415
Operating margin 0.244 0.209 0.184 0.191 0.183 0.183 0.195 0.159 0.166 0.172 0.169 0.178 0.145 0.110 0.028 0.030
ROA 0.103 0.093 0.080 0.079 0.068 0.059 0.055 0.045 0.042 0.037 0.031 0.018 -0.003 -0.043 -0.085 -0.137
CF/PPE 0.594 0.519 0.515 0.549 0.475 0.523 0.446 0.442 0.539 0.478 0.545 0.424 0.349 0.114 -0.183 -0.346
Capex/PPE 0.220 0.214 0.215 0.213 0.208 0.220 0.221 0.198 0.213 0.217 0.242 0.241 0.249 0.259 0.255 0.165
Sales growth 0.088 0.084 0.074 0.078 0.089 0.100 0.114 0.093 0.107 0.138 0.153 0.177 0.199 0.207 0.204 0.116

This table reports the means of firm characteristics by credit rating for our sample of firms in the quarterly CRSP/Compustat merged data set, excluding financials and utilities. The sample period is 1986 Q1–2010 Q4. Variable definitions are in the Appendix, Table A1.

Lower-rated firms are generally smaller and more levered. In addition, they have lower values of Altman's $$z$$-score than do higher-rated firms. The ratio of net PPE 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 are lower-rated firms, whether one looks at operating margins, return of assets (ROA), or cash flow. Despite significant differences in $$Q$$ across credit ratings, and with the exception of CCC rated firms, which are likely to be in financial distress, firms appear to engage in similar levels of capital expenditures.

Importantly, the investment-grade cutoff does not stand out compared with other rating cutoffs. BB+ firms are on average about 30% smaller than are 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 9% higher than is the market leverage of BBB− firms, but there are only two other cutoffs with smaller percentage differences in market leverage. BB+ firms have somewhat higher operating margins but lower ROA and cash flow than do BBB− firms. The investment rate of both BB+ and BBB− firms is around 21.5%.

### 3.4 Flows are large relative to the investment of BB firms

The time series of aggregate flows into high-yield corporate bond mutual funds is from the Investment Company Institute, which is the national association of U.S. investment companies. At the end of 2010, the Investment Company Institute collected information on assets and flows from 8,545 mutual funds with $11.8 trillion in assets under management. In our data, assets under management of high-yield mutual funds start at$6 billion in 1986, grow to $168 billion by May 2007, fall to$104 billion in November 2008, and grow to \$219 billion by the end of 2010.

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 lags both between fund flows and bond issuance and between issuance and investment. 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−, $PPEt−1$ . 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 mutual fund flows relative to PPE and capital expenditures of firms rated BBB+ through BB−. Flows exhibit significant variation 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 of a one-standard-deviation increase in scaled flows on investment.

Figure 2

High-Yield Mutual Fund Flows relative to PPE and Capex of BBB and BB Firms

This figure shows the time series of flows into high-yield mutual funds. Monthly aggregate flows into high-yield mutual funds are from the Investment Company Institute. Cumulative high-yield fund flows calculated over four quarters are scaled by either total PPE or cumulative capital expenditures over four quarters of CRSP/Compustat firms rated BBB+ through BB−, excluding financials and utilities. The sample period is 1986 Q1-2010 Q4.

Figure 2

High-Yield Mutual Fund Flows relative to PPE and Capex of BBB and BB Firms

This figure shows the time series of flows into high-yield mutual funds. Monthly aggregate flows into high-yield mutual funds are from the Investment Company Institute. Cumulative high-yield fund flows calculated over four quarters are scaled by either total PPE or cumulative capital expenditures over four quarters of CRSP/Compustat firms rated BBB+ through BB−, excluding financials and utilities. The sample period is 1986 Q1-2010 Q4.

## 4. Results

### 4.1 Characteristics of matched BB+ and BBB− firms

Table 2 reports the characteristics of matched BB+ and BBB− firms. We match 1,056 out of 4,331 firm-quarter observations that are rated BB+ to 883 unique firm-quarter observations that are rated BBB−. We report the mean characteristics for each set of firms and the difference in means.

Table 2

Characteristics of matched BB + and BBB – firms

Full Sample

Matched Sample

BB + with Positive Outlook

BBB – BB + Δ BBB – BB + Δ BBB – BB + Δ
Assets 5,864 4,035 – 1,829*** 4,072 3,889 – 183 5,159 5,006 – 153
Book leverage 0.455 0.479 0.024 0.437 0.453 0.017 0.444 0.425 -0.019
Market leverage 0.308 0.335 0.028** 0.321 0.327 0.005 0.289 0.268 -0.021
z-score 0.844 0.762 -0.082** 0.853 0.845 -0.008 0.811 0.774 -0.036
Interest coverage 7.142 6.166 -0.976 6.000 5.532 -0.468 6.501 6.066 -0.436
Cash/Assets 0.069 0.069 -0.000 0.043 0.045 0.002 0.041 0.043 0.001
PPE/Assets 0.341 0.372 0.031* 0.370 0.356 -0.013 0.408 0.368 -0.039
$$Q$$ 1.563 1.515 -0.048 1.351 1.372 0.020 1.473 1.554 0.081
Operating margin 0.166 0.172 0.005 0.169 0.166 -0.003 0.184 0.181 -0.003
ROA 0.042 0.037 -0.004 0.039 0.040 0.001 0.038 0.054 0.016*
CF/PPE 0.539 0.478 -0.060 0.484 0.480 -0.003 0.378 0.491 0.114
Capex/PPE 0.213 0.217 0.004 0.201 0.205 0.005 0.189 0.254 0.065**
Sales growth 0.107 0.138 0.031 0.113 0.128 0.015 0.143 0.167 0.023
Full Sample

Matched Sample

BB + with Positive Outlook

BBB – BB + Δ BBB – BB + Δ BBB – BB + Δ
Assets 5,864 4,035 – 1,829*** 4,072 3,889 – 183 5,159 5,006 – 153
Book leverage 0.455 0.479 0.024 0.437 0.453 0.017 0.444 0.425 -0.019
Market leverage 0.308 0.335 0.028** 0.321 0.327 0.005 0.289 0.268 -0.021
z-score 0.844 0.762 -0.082** 0.853 0.845 -0.008 0.811 0.774 -0.036
Interest coverage 7.142 6.166 -0.976 6.000 5.532 -0.468 6.501 6.066 -0.436
Cash/Assets 0.069 0.069 -0.000 0.043 0.045 0.002 0.041 0.043 0.001
PPE/Assets 0.341 0.372 0.031* 0.370 0.356 -0.013 0.408 0.368 -0.039
$$Q$$ 1.563 1.515 -0.048 1.351 1.372 0.020 1.473 1.554 0.081
Operating margin 0.166 0.172 0.005 0.169 0.166 -0.003 0.184 0.181 -0.003
ROA 0.042 0.037 -0.004 0.039 0.040 0.001 0.038 0.054 0.016*
CF/PPE 0.539 0.478 -0.060 0.484 0.480 -0.003 0.378 0.491 0.114
Capex/PPE 0.213 0.217 0.004 0.201 0.205 0.005 0.189 0.254 0.065**
Sales growth 0.107 0.138 0.031 0.113 0.128 0.015 0.143 0.167 0.023

This table reports the characteristics of matched BB + and BBB – firms. Each quarter, a given BB + firm is matched to a BBB – firm that is within the same Fama-French 48 industry classification and is closest in terms of log assets, market leverage, $$Q$$, cash-to-assets ratio, $$z$$-score, and sales growth. We measure distance using the Mahalanobis distance and require that the difference in each matching variable be smaller than one standard deviation of that variable. The full sample consists of 4,331 BB + and 5,897 BBB – firm-quarter observations. The matched sample consists of 1,056 BB + firm-quarter observations matched to 883 unique BBB – firm-quarter observations. The sample of BB + firms with positive outlooks consists of 143 BB + firm-quarter observations matched to 131 unique BBB – firm-quarter observations. The sample period is 1986 Q1–2010 Q4. Standard errors are adjusted for clustering by firm. *, **, and *** denote statistical significance at 10%, 5%, and 1%.

Our matching procedure successfully picks BB+ and BBB− firms that have similar size and leverage. Although, in the full sample, BB+ firms are on average $31.2%$ smaller than are BBB− firms, in the matched sample, the BB+ firms are only $4.5%$ smaller than their BBB− matches, and the difference is not statistically significant. Furthermore, none of the other differences in characteristics between BB+ firms and matched BBB− firms are statistically or economically signficant. Overall, our matching procedure selects a sample of BB+ and BBB− firms that are very similar along observable dimensions. 15

In addition to the full sample of BB+ firms, we consider the subsample of BB+ firms with positive outlooks. 16 These firms are larger, have lower leverage, and have higher profitability than do other BB+ firms. They are also more profitable and invest more than do their BBB− matches. BB+ firms with positive outlooks also have higher values of $$Q$$ and cash flow than do matched BBB− firms, though these differences are not statistically significant. Overall, these results are consistent with the notion that BB+ firms with positive outlooks have similar default rates as do BBB− firms. Yet, for regulatory purposes, these firms are still treated as speculative-grade, and as we will see shortly, their investment is still sensitive to flows into high-yield mutual funds.

### 4.2 Flows increase the investment of BB+ firms relative to BBB− firms

Table 3 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 $$Q$$ and cash flow:

(3)
\begin{align} \Delta \frac{{CAP{X_{i,t}}}}{{PP{E_{i,t - 1}}}} = & \alpha + {\beta _{Flows}} \cdot High - yield\,fund\,flow{s_{t - 1}} + {\beta _Q} \cdot \Delta {Q_{i,t - 1}} \\ & + {\beta _{CF}} \cdot \Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}} + {\varepsilon _{i,t}}, \\ \end{align}
where $ΔX=XBB+−XBBB−$ is the difference in firm characteristic $$X$$ between matched BB+ and BBB− firms. We use the procedure developed by Thompson (2010) to cluster the standard errors by both firm and quarter.

Table 3

Difference in the investment rates of matched firms and high-yield fund flows

(1) (2) (3) (4) (5)
$${\Delta }Q_{i,t-1}$$ 0.102*** 0.110*** 0.104*** 0.102*** 0.108*
(0.030) (0.033) (0.030) (0.030) (0.057)
$$\Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ 0.039*** 0.046*** 0.038*** 0.039*** 0.034
(0.012) (0.018) (0.012) (0.012) (0.022)
High-yield fund $$flows_{t-1}$$ 0.020** 0.021** 0.021** 0.022** 0.026
(0.009) (0.009) (0.009) (0.009) (0.022)
Investment-grade fund $$flows_{t-1}$$    -0.006
(0.008)
Constant 0.002 0.002 0.000 0.002 0.051*
(0.010) (0.011) (0.010) (0.010) (0.026)

$$N$$ 1056 888 982 1056 143
Adjusted $$R$$2 0.088 0.091 0.094 0.088 0.052

Notes  excluding 2008 crisis (2008–2010) excluding collapse of Drexel (1988–1992)  BB + firms with positive outlooks
(1) (2) (3) (4) (5)
$${\Delta }Q_{i,t-1}$$ 0.102*** 0.110*** 0.104*** 0.102*** 0.108*
(0.030) (0.033) (0.030) (0.030) (0.057)
$$\Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ 0.039*** 0.046*** 0.038*** 0.039*** 0.034
(0.012) (0.018) (0.012) (0.012) (0.022)
High-yield fund $$flows_{t-1}$$ 0.020** 0.021** 0.021** 0.022** 0.026
(0.009) (0.009) (0.009) (0.009) (0.022)
Investment-grade fund $$flows_{t-1}$$    -0.006
(0.008)
Constant 0.002 0.002 0.000 0.002 0.051*
(0.010) (0.011) (0.010) (0.010) (0.026)

$$N$$ 1056 888 982 1056 143
Adjusted $$R$$2 0.088 0.091 0.094 0.088 0.052

Notes  excluding 2008 crisis (2008–2010) excluding collapse of Drexel (1988–1992)  BB + firms with positive outlooks

This table reports the results of the regressions of the difference in the investment rates of matched BB + and BBB – firms on high-yield fund flows

$$\Delta \frac{{CAP{X_{i,t}}}}{{PP{E_{i,t - 1}}}} = \alpha + {\beta _{Flows}} \cdot High - yield\,fund\,flow{s_{t - 1}} + {\beta _Q} \cdot \Delta {Q_{i,t - 1}} + {\beta _{CF}} \cdot \Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}} + {\varepsilon _{i,t}},$$
where $${\Delta }X = X^{BB+} - X^{BBB-}$$ is the difference in firm characteristic $$X$$ between matched BB + and BBB – firms. The sample period is 1986 Q1-2010 Q4 unless otherwise noted. Cumulative high-yield mutual fund flows over the four quarters $$[t - 4, t - 1]$$ are scaled by the total 1 of all firms that are rated BBB + through BB – , $$1_{t-1}$$. The value of fund flows is standardized so that the coefficient on flows represents the effect of a one-standard-deviation change in fund flows. Standard errors are adjusted for clustering by both firm and quarter by using Thompson (2010). *, **, and *** denote statistical significance at 10%, 5%, and 1%.

Examining the results in column 1, the coefficient on flows is positive and statistically significant. A one-standard-deviation increase in high-yield flows increases the investment of BB+ firms relative to the investment of matched BBB− firms by 0.020, or about 10% of their mean investment rate. The constant term is close to zero and not statistically significant, which indicates that matched firms have on average similar investment rates.

In columns 2 and 3, we exclude the financial crisis period (2008–2010) and the period around the collapse of Drexel Burnham Lambert (1988–1992). The coefficient on flows is unchanged, showing that our results are not driven by a single episode. Instead, we are documenting the effects of recurring capital shocks to high-yield mutual funds on firm investment.

In column 5, we use the sample of BB+ firms with positive outlooks. The constant term is positive and statistically significant at 10%, which indicates that on average these firms invest more than do their BBB− matches. The sample size is less than one-seventh of the full sample, so with the exception of $$Q$$, which is now statistically significant at 10% instead of 1%, the other coefficients are no longer statistically significant. The point estimates, however, are remarkably similar, which suggests that the investment of BB+ firms with positive outlooks is similarly sensitive to high-yield fund flows.

Overall, Table 3 shows a statistically significant and economically meaningful effect of high-yield fund flows on the investment of BB+ firms relative to similar firms rated BBB−.

#### 4.2.1 Results are robust to alternative matching procedures.

In untabulated results, we show that our findings are robust to alternative matching procedures. We examine a variety of different procedures, varying the procedure along three primary dimensions. First, we experiment with different metrics to measure the distance between firm characteristics, using the Euclidean distance metric and the propensity score instead of the Mahalanobis metric utilized in the baseline. Next, we try different sets of matching variables, including log assets, book and market leverage, $$Q$$, cash holdings, tangibility, $$z$$-score, sales growth, and ROA. Finally, we examine different match quality restrictions, and require the characteristics of matched firms to be within half, rather than a full, standard deviation of each other. The results remain quantitatively, qualitatively, and statistically similar for all variations of our baseline matching procedure.

### 4.3 Results are robust to controlling for macro variables

Our results so far indicate that the investment of BB+ firms is more sensitive to flows into high-yield 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 high-yield mutual fund flows is evidence of the real effects of capital supply shocks in the presence of market segmentation. Our matching procedure is designed to ensure that the identifying assumption holds so that our interpretation is valid.

However, there still may be concerns that the investment opportunities of matched firms are not quite the same, which would invalidate our interpretation of the results. A natural alternative is that the investment opportunities of lower-rated firms are more sensitive to the business cycle and that high-yield fund flows are picking up this greater sensitivity.

We address this possibility by directly controlling for a number of macro- economic variables. 17 The variables we control for are the level of the VIX, the term spread, the Baa−Aaa credit spread, the aggregate stock market return, and GDP growth. We measure these variables as of quarter $t−1$ , but our results are robust to using average values over the four quarters $[t−4,t−1]$ or to using contemporaneous values.

Table 4 presents the results. The first column shows the basic results in this specification without controlling for any macro variables. The next five columns individually control for each macro variable. None of the macro variables is significant, and the coefficients on flows are significant and of similar magnitudes to our previous results. The final column simultaneously controls for all of our macro variables. Again, the coefficient on flows is unaffected. If anything, the results are slightly stronger when controlling for all macro variables. Thus, it seems unlikely that the differential sensitivity of BB+ investment to fund flows is driven by macroeconomic factors.

Table 4

Controlling for macroeconomic variables

(1) (2) (3) (4) (5) (6) (7)
$${\Delta }Q_{i, t-1}$$ 0.102*** 0.102*** 0.101*** 0.102*** 0.102*** 0.102*** 0.100***
(0.030) (0.030) (0.029) (0.030) (0.030) (0.030) (0.030)
$$\Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ 0.039*** 0.039*** 0.038*** 0.039*** 0.039*** 0.039*** 0.039***
(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
High-yield fund $$flows_{t-1}$$ 0.020** 0.020** 0.023*** 0.020** 0.021** 0.020** 0.024***
(0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009)
$$VIX_{t-1}$$  0.003     0.000
(0.007)     (0.011)
Term $$spread_{t-1}$$   -0.013    -0.016
(0.009)    (0.010)
Credit $$spread_{t-1}$$    0.000   0.007
(0.008)   (0.010)
Stock market $$return_{t-1}$$     -0.003  -0.005
(0.005)  (0.006)
GDP $$growth_{t-1}$$      0.001 0.004
(0.008) (0.007)
Constant 0.002 0.002 0.002 0.002 0.002 0.002 0.002
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)

$$N$$ 1056 1052 1056 1056 1056 1056 1052
Adjusted $$R$$2 0.088 0.087 0.091 0.087 0.087 0.087 0.090
(1) (2) (3) (4) (5) (6) (7)
$${\Delta }Q_{i, t-1}$$ 0.102*** 0.102*** 0.101*** 0.102*** 0.102*** 0.102*** 0.100***
(0.030) (0.030) (0.029) (0.030) (0.030) (0.030) (0.030)
$$\Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ 0.039*** 0.039*** 0.038*** 0.039*** 0.039*** 0.039*** 0.039***
(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
High-yield fund $$flows_{t-1}$$ 0.020** 0.020** 0.023*** 0.020** 0.021** 0.020** 0.024***
(0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009)
$$VIX_{t-1}$$  0.003     0.000
(0.007)     (0.011)
Term $$spread_{t-1}$$   -0.013    -0.016
(0.009)    (0.010)
Credit $$spread_{t-1}$$    0.000   0.007
(0.008)   (0.010)
Stock market $$return_{t-1}$$     -0.003  -0.005
(0.005)  (0.006)
GDP $$growth_{t-1}$$      0.001 0.004
(0.008) (0.007)
Constant 0.002 0.002 0.002 0.002 0.002 0.002 0.002
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)

$$N$$ 1056 1052 1056 1056 1056 1056 1052
Adjusted $$R$$2 0.088 0.087 0.091 0.087 0.087 0.087 0.090

This table reports the results of the regressions of the difference in the investment rates of matched BB + and BBB – firms on high-yield fund flows and macroeconomic variables

$$\Delta \frac{{CAP{X_{i,t}}}}{{PP{E_{i,t - 1}}}} = \alpha + {\beta _{Flows}} \cdot High - yield{\mkern 1mu} fund{\mkern 1mu} flow{s_{t - 1}} + {\beta _Q} \cdot \Delta {Q_{i,t - 1}} + {\beta _{CF}} \cdot \Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}} + {\varepsilon _{i,t}},$$
where $${\Delta }X = X^{BB+} - X^{BBB-}$$ is the difference in firm characteristic $$X$$ between matched BB + and BBB – firms. Macroeconomic variables are defined in the Appendix, Table A1, and are standardized so that the coefficients represent the effect of a one-standard-deviation change in the explanatory variables. The sample period is 1986 Q1–2010 Q4. Standard errors are adjusted for clustering by both firm and quarter by using Thompson (2010). *, **, and *** denote statistical significance at 10%, 5%, and 1%.

### 4.4. No differential sensitivity to flows around other cutoffs

Next, we conduct falsification tests by using matched firm pairs that are around other rating cutoffs. If the BB+ firms in our sample differed from their BBB− matches along unobservable firm characteristics known to the rating agencies, we would expect firms to differ along those unobservable characteristics around every other rating cutoff as well. Thus, if our results were driven by such unobservable firm characteristics, we would expect to find differential investment sensitivities driven by the same unobservable characteristics around every cutoff. To test this hypothesis, for each credit rating cutoff from A through B, we match firms just below the cutoff with firms just above the cutoff that are in the same industry and have similar size, market leverage, $$z$$-score, $$Q$$, cash holdings, and sales growth. 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. 18

Each column of Table 5 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. The results show that the investment-grade cutoff is the only one where there is a differential sensitivity of investment to high-yield mutual fund flows. This suggests that our results are not driven by differences in matched firm characteristics that are unobservable to us but are known to the credit rating agencies. 19 Of course, we cannot completely rule out the possibility that special information known to the rating agencies is correlated with investment opportunities only at the investment-grade cutoff. However, our results on 1) BB+ firms with positive outlook, whose information content is basically the same as of the BBB− rating; 2) the insensitivity of the difference in the investment rates to macroeconomic variables; and 3) higher sensitivity of firms without access to other financing sources, which we discuss next, help alleviate such concerns.

Table 5

Placebo regressions

A – BBB + BBB BBB – BB + BB BB – B +
$${\Delta }Q_{i, t-1}$$ 0.031** 0.073*** 0.052*** 0.016 0.061*** 0.102*** 0.082*** 0.113*** 0.095*** 0.143***
(0.015) (0.021) (0.019) (0.019) (0.017) (0.030) (0.021) (0.022) (0.021) (0.020)
$$\Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ 0.035* 0.005 0.038* 0.067*** 0.055*** 0.039*** 0.060*** 0.056*** 0.050*** 0.020
(0.021) (0.041) (0.022) (0.021) (0.021) (0.012) (0.014) (0.012) (0.010) (0.013)
High-yield fund $$flows_{t-1}$$ 0.003 – 0.002 – 0.010 0.004 – 0.004 0.020** – 0.004 – 0.003 0.006 – 0.001
(0.006) (0.008) (0.007) (0.005) (0.006) (0.009) (0.009) (0.008) (0.009) (0.010)
Constant 0.011* 0.019** – 0.008 – 0.017** 0.004 0.002 0.025** – 0.000 – 0.001 0.021**
(0.007) (0.009) (0.010) (0.007) (0.007) (0.010) (0.010) (0.009) (0.009) (0.010)

$$N$$ 664 572 746 1177 1159 1056 1219 2341 2758 1536
Adjusted $$R$$2 0.027 0.046 0.030 0.039 0.070 0.088 0.069 0.059 0.053 0.054
A – BBB + BBB BBB – BB + BB BB – B +
$${\Delta }Q_{i, t-1}$$ 0.031** 0.073*** 0.052*** 0.016 0.061*** 0.102*** 0.082*** 0.113*** 0.095*** 0.143***
(0.015) (0.021) (0.019) (0.019) (0.017) (0.030) (0.021) (0.022) (0.021) (0.020)
$$\Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ 0.035* 0.005 0.038* 0.067*** 0.055*** 0.039*** 0.060*** 0.056*** 0.050*** 0.020
(0.021) (0.041) (0.022) (0.021) (0.021) (0.012) (0.014) (0.012) (0.010) (0.013)
High-yield fund $$flows_{t-1}$$ 0.003 – 0.002 – 0.010 0.004 – 0.004 0.020** – 0.004 – 0.003 0.006 – 0.001
(0.006) (0.008) (0.007) (0.005) (0.006) (0.009) (0.009) (0.008) (0.009) (0.010)
Constant 0.011* 0.019** – 0.008 – 0.017** 0.004 0.002 0.025** – 0.000 – 0.001 0.021**
(0.007) (0.009) (0.010) (0.007) (0.007) (0.010) (0.010) (0.009) (0.009) (0.010)

$$N$$ 664 572 746 1177 1159 1056 1219 2341 2758 1536
Adjusted $$R$$2 0.027 0.046 0.030 0.039 0.070 0.088 0.069 0.059 0.053 0.054

This table reports the results of the placebo regressions of the difference in the investment rates of matched firms around other credit rating cutoffs on high-yield fund flows

$$\Delta \frac{{CAP{X_{i,t}}}}{{PP{E_{i,t - 1}}}} = \alpha + {\beta _{Flows}} \cdot High - yield fund flow{s_{t - 1}} + {\beta _Q} \cdot \Delta {Q_{i,t - 1}} + {\beta _{CF}} \cdot \Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}} + {\varepsilon _{i,t}},$$
where $ΔX=XR−XR^$ is the difference in firm characteristic $$X$$ between firms rated $$R$$ and matched firms rated one notch higher, $R^$ . In each column, the sample consists of firms that are 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 + . Firms are matched using the Mahalanobis distance on log assets, market leverage, $$Q$$, cash-to-assets, $$z$$-score, and sales growth within the Fama-French 48 industries classification. We require the difference in each matching variable to be less than one standard deviation of that variable. The sample period is 1986 Q1–2010 Q4. Cumulative high-yield mutual fund flows over the four quarters $$[t - 4, t - 1]$$ are scaled by the total 1 of all firms that are rated BBB + through BB – , $$1_{t-1}$$. The value of fund flows is standardized so that the coefficient on flows represents the effect of a one-standard-deviation change in fund flows. Standard errors are adjusted for clustering by both firm and quarter by using Thompson (2010). *, **, and *** denote statistical significance at 10%, 5%, and 1%.

Taken together, the last two sections suggest that our results are not driven by differences in investment opportunities between firms within a matched pair. While our analysis focuses on differencing out common shocks to firm demand for capital, the drivers of capital supply (i.e., fund flows) may also alleviate concerns about differential investment opportunities for two reasons. First, high-yield fund flows are dominated by retail investors. According to the Investment Company Institute, retail funds made up 97.5% of all high-yield mutual fund assets in 1996 (the furthest the data go back). Institutional asset share grows slowly over time and reaches 25% at the end of our sample period. Frazzini and Lamont (2008) use retail mutual fund flows as a measure of investor sentiment and show that fund flows predict low subsequent returns, which suggests that retail investors do not have precise information about firm investment opportunities. 20 Second, BB+ firms constitute only 11% of all speculative-grade firms. Thus, fund flows are likely to be driven by the investment prospects of, and possibly investor sentiment for, lower-rated firms, which are very different from BB+ firms on observable characteristics. 21

### 4.5 Higher sensitivity of firms without access to other financing sources

Are certain types of firms more sensitive to high-yield fund flows? In principle, financially constrained firms with limited access to other sources of financing should be more sensitive to flows into high-yield mutual funds. We consider several proxies for financial constraints that have been put forward by the literature: firms that do not pay dividends (Fazzari, Hubbard, and Petersen 1988, Baker, Stein, and Wurgler 2003), firms with low cash flow from operations and high dependence on external financing (Rajan and Zingales 1998), and firms with a high cash flow sensitivity of cash (Almeida, Campello, and Weisbach 2004). 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 6 tests these ideas by estimating our investment regressions for six different sample splits. In columns 1 and 2, 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 high-yield fund flows. The coefficient on flows for these firms is 0.045, which is more than twice the coefficient in our benchmark regression. 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 on the basis of BB+ firm characteristics.

Table 6

Cross-sectional splits

Dividend Payer

Low Cash Flow

Top 20 External Dependence Industry

Bank Loan Rating

Top 5 ABS Industry

High Cash Flow Sensitivity of Cash

Yes No Yes No Yes No Yes No Yes No No Yes
$${\Delta }Q_{i, t-1}$$ 0.055 0.143*** 0.152*** 0.051 0.161*** -0.001 0.115*** 0.140** 0.068** 0.122*** 0.049 0.115***
(0.038) (0.040) (0.045) (0.035) (0.038) (0.036) (0.039) (0.062) (0.031) (0.038) (0.034) (0.039)
$$\Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ 0.040*** 0.036* -0.004 0.058*** 0.045* 0.040*** 0.048* -0.036 0.028*** 0.042*** 0.021 0.065***
(0.012) (0.019) (0.018) (0.012) (0.027) (0.009) (0.029) (0.061) (0.009) (0.015) (0.014) (0.009)
High-yield fund $$flows_{t-1}$$ 0.002 0.045*** 0.029** 0.009 0.028 0.012 0.014 0.035* -0.009 0.031*** 0.004 0.027**
(0.009) (0.015) (0.013) (0.010) (0.018) (0.007) (0.011) (0.020) (0.011) (0.011) (0.008) (0.012)

Constant -0.017 0.029* -0.008 0.005 -0.014 0.021** 0.001 -0.000 -0.014 0.010 -0.016 0.003
(0.011) (0.017) (0.012) (0.015) (0.017) (0.010) (0.015) (0.020) (0.015) (0.013) (0.012) (0.015)

$$N$$ 632 418 528 528 486 570 541 180 284 772 513 524
Adjusted $$R$$2 0.053 0.145 0.096 0.112 0.130 0.063 0.077 0.102 0.095 0.099 0.026 0.140
Dividend Payer

Low Cash Flow

Top 20 External Dependence Industry

Bank Loan Rating

Top 5 ABS Industry

High Cash Flow Sensitivity of Cash

Yes No Yes No Yes No Yes No Yes No No Yes
$${\Delta }Q_{i, t-1}$$ 0.055 0.143*** 0.152*** 0.051 0.161*** -0.001 0.115*** 0.140** 0.068** 0.122*** 0.049 0.115***
(0.038) (0.040) (0.045) (0.035) (0.038) (0.036) (0.039) (0.062) (0.031) (0.038) (0.034) (0.039)
$$\Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}}$$ 0.040*** 0.036* -0.004 0.058*** 0.045* 0.040*** 0.048* -0.036 0.028*** 0.042*** 0.021 0.065***
(0.012) (0.019) (0.018) (0.012) (0.027) (0.009) (0.029) (0.061) (0.009) (0.015) (0.014) (0.009)
High-yield fund $$flows_{t-1}$$ 0.002 0.045*** 0.029** 0.009 0.028 0.012 0.014 0.035* -0.009 0.031*** 0.004 0.027**
(0.009) (0.015) (0.013) (0.010) (0.018) (0.007) (0.011) (0.020) (0.011) (0.011) (0.008) (0.012)

Constant -0.017 0.029* -0.008 0.005 -0.014 0.021** 0.001 -0.000 -0.014 0.010 -0.016 0.003
(0.011) (0.017) (0.012) (0.015) (0.017) (0.010) (0.015) (0.020) (0.015) (0.013) (0.012) (0.015)

$$N$$ 632 418 528 528 486 570 541 180 284 772 513 524
Adjusted $$R$$2 0.053 0.145 0.096 0.112 0.130 0.063 0.077 0.102 0.095 0.099 0.026 0.140

This table reports the results of the regressions of the difference in the investment rates of matched BB + and BBB – firms on high-yield fund flows,

$$\Delta \frac{{CAP{X_{i,t}}}}{{PP{E_{i,t - 1}}}} = \alpha + {\beta _{Flows}} \cdot High - yield fund flow{s_{t - 1}} + {\beta _Q} \cdot \Delta {Q_{i,t - 1}} + {\beta _{CF}} \cdot \Delta \frac{{C{F_{i,t}}}}{{PP{E_{i,t - 1}}}} + {\varepsilon _{i,t}},$$
estimated separately for subsamples of BB + firms split by dividends, cash flows, external dependence, having a bank loan rating, access to the asset-backed securities market, and cash flow sensitivity of cash. $${\Delta }X = X^{BB+} - X^{BBB-}$$ is the difference in firm characteristic $$X$$ between matched BB + and BBB – firms. Dividend payers are firms with positive cash dividends. Low cash flow firms are the ones below the median of the cash-to-assets ratio for BB + firms. Rajan and Zingales’s (1998) measure of external dependence is calculated using the annual Compustat data for the 1970–1985 period. Bank loan rating indicates the existence of a bank loan rating. The top five industries by the share of asset- and mortage-backed securities issuance in total industry-level bond issuance are electrical equipment, personal services, automobiles and trucks, machinery, and retail. The sample period is 1986 Q1–2010 Q4 in all regressions, except for the bank loan rating split, in which the sample period is 1986 Q1–2005 Q2. Standard errors are adjusted for clustering by both firm and quarter by using Thompson (2010). *, **, and *** denote statistical significance at 10%, 5%, and 1%.

We next split BB+ firms by their cash flow from operations in columns 3 and 4. We find that the investment of BB+ firms with low cash flow is sensitive to high-yield fund flows, while the investment of BB+ firms with high cash flow is not. 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 low cash flow BB+ firms.

In columns 5 and 6, we split BB+ firms by their Rajan and Zingales (1998) measure of external dependence, which is meant to capture at the industry level the share of capital expenditures that is financed externally versus using internal cash flow. 22 BB+ firms in the top twenty industries by external dependence exhibit a higher sensitivity to high-yield fund flows than do firms outside the top twenty, though the point estimates are not statistically significant. Still, this cross-sectional split is a useful complement to the others, since a firm's industry cannot reveal anything about its credit quality relative to a matched firm in the same industry.

Next, we measure firms' ability to borrow from banks by whether they have a bank loan rating. 23 Such firms should find it easier to substitute to bank loans or the syndicated loan market when high-yield fund flows are low. This is what we find in columns 7 and 8. The investment of firms with a loan rating is not sensitive to fund flows, but the investment of firms without a loan rating is.

In columns 9 and 10, 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. The top five industries by ABS issuance (electrical equipment, personal services, automobiles and trucks, machinery, and retail) are the only ones in which ABS makes up more than 10% of total issuance, and therefore we label firms as having access to the asset-backed market if they are in the top five.

The results in columns 9 and 10 indicate that the investment of firms in the top five ABS industries is not sensitive to high-yield mutual fund flows. In fact, the coefficient on fund flows is negative but not statistically significant. Only the investment of firms in industries that are not significant ABS issuers responds to fund flows.

Finally, in columns 11 and 12, we split firms by their cash flow sensitivity of cash. Because we estimate cash flow sensitivity of cash for each firm, we do not have a sufficient amount of nonoverlapping data and are forced to use the same 1986 Q1–2010 Q4 sample period. As a result, our estimated cash flow sensitivities of cash, and hence our sample splits, could be subject to a forward-looking bias and should be interpreted with caution. Nevertheless, the results show that the investment of firms with a low cash flow sensitivity of cash, which should not be financially constrained, does not respond to high-yield mutual fund flows. In contrast, the investment of firms with a high sensitivity, which are likely to be financially constrained, strongly responds to fund flows.

Taken together, our results in Table 6 indicate that the investment of firms with limited ability to substitute away from the high-yield market responds strongly to flows into high-yield mutual funds, while the investment of other firms is generally not affected.

### 4.6 BB+ bond issuance is more sensitive to flows than is BBB− bond issuance

So far we have explored the connection between high-yield fund flows and firm investment without documenting a particular mechanism. In this section, we document the effect of fund flows on bond issuance.24 We continue to use the same empirical methodology, regressing the difference in the bond issuance of matched BB+ and BBB− firms on high-yield mutual fund flows.

Table 7 presents the results. The first column shows that BB+ issuance is more sensitive to high-yield fund flows than BBB− issuance. A one-standard-deviation increase in flows increases issuance (as a fraction of assets) by 0.30% relative to the mean issuance rate of 0.56%. Our power is somewhat limited since there are only forty-six BB+ issuance events and sixty-one BBB− issuance events in our data. However, the coefficient on flows is still statistically significant. The second and third columns show that the results are robust to the exclusion of the financial crisis period (2008–2010) and the period around the collapse of Drexel Burnham Lambert (1988–1992). Finally, column 4 shows that the results are robust to controlling for macro variables.

Table 7

Bond issuance by matched firms and high-yield fund flows

(1) (2) (3) (4)
High-yield fund $$flows_{t-1}$$ 0.003** 0.003** 0.003** 0.004***
(0.001) (0.001) (0.001) (0.001)
$$VIX_{t-1}$$    -0.002
(0.002)
Term $$spread_{t-1}$$    -0.000
(0.002)
Credit $$spread_{t-1}$$    -0.002
(0.002)
Stock market $$return_{t-1}$$    -0.001
(0.001)
GDP $$growth_{t-1}$$    -0.001
(0.001)
Constant -0.001 -0.001 -0.001 -0.001
(0.001) (0.001) (0.001) (0.001)

$$N$$ 1056 888 982 1052
Adjusted $$R$$2 0.004 0.005 0.003 0.006

Notes  excluding 2008 crisis (2008–2010) excluding collapse of Drexel (1988–1992)
(1) (2) (3) (4)
High-yield fund $$flows_{t-1}$$ 0.003** 0.003** 0.003** 0.004***
(0.001) (0.001) (0.001) (0.001)
$$VIX_{t-1}$$    -0.002
(0.002)
Term $$spread_{t-1}$$    -0.000
(0.002)
Credit $$spread_{t-1}$$    -0.002
(0.002)
Stock market $$return_{t-1}$$    -0.001
(0.001)
GDP $$growth_{t-1}$$    -0.001
(0.001)
Constant -0.001 -0.001 -0.001 -0.001
(0.001) (0.001) (0.001) (0.001)

$$N$$ 1056 888 982 1052
Adjusted $$R$$2 0.004 0.005 0.003 0.006

Notes  excluding 2008 crisis (2008–2010) excluding collapse of Drexel (1988–1992)

This table reports the results of the regressions of the difference in bond issuance by matched BB + and BBB – firms on high-yield fund flows:

$$\Delta Issuanc{e_{i,t}} = \alpha + {\beta _{Flows}} \cdot High - yield\,fund\,flow{s_{t - 1}} + {\varepsilon _{i,t}}.$$
Issuance of nonconvertible, not asset- or mortgage-backed bonds from SDC is scaled by lagged assets. Cumulative high-yield mutual fund flows over the four quarters $$[t - 4$$, $$t - 1]$$ are scaled by the total assets of all firms that are rated BBB + through BB – , Assetst–1. The value of fund flows is standardized so that the coefficient on flows represents the effect of a one-standard-deviation change in fund flows. The sample period is 1986 Q1–2010 Q4, except for columns 2 and 3. In column 2, the sample period excludes the 2008–2010 period around the 2008 financial crisis. In column 3, the sample period excludes the 1988–1992 period around the collapse of Drexel Burnham Lambert. Standard errors are adjusted for clustering by both firm and quarter by using Thompson (2010). *, **, and *** denote statistical significance at 10%, 5%, and 1%.

Thus, it appears that bond issuance does play an important role in connecting high-yield mutual fund flows to the investment of BB+ firms. 25

## 5. 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 significant consequences for firm investment.

Our article makes three contributions. First, we show that BB+ firms and their BBB− matches have on average similar investment rates, which suggests that the average allocation of capital is efficient across segments. Second, we find that flows into high-yield mutual funds increase the investment of BB+ firms relative to their BBB− matches. Thus, the interaction of market segmentation and financial market conditions exposes firms to nonfundamental variation in the availability and cost of capital, which in turn leads to excess volatility in their investment. This is particularly true for firms that do not have access to other sources of financing. Third, we show that flows also increase BB+ bond issuance, suggesting that the availability of capital is an important driver of investment.

The distortions induced by fund flows are economically meaningful but not excessively large. However, our estimates are likely to be a lower bound because the firms that face the largest costs of a BB+ rating are likely to alter their behavior in order to obtain a BBB− rating. By highlighting the distortionary effects of rules and regulations tied to credit ratings on firm investment, our work contributes to the policy debate about the role of credit ratings. In the aftermath of the financial crisis, it is particularly important to understand interactions between financial frictions and the real economy. Distortions like the ones we document may be particularly important in economic downturns, when they can amplify shocks to the real economy.

However, our results should not be interpreted as suggesting that credit ratings are not valuable or that the division of the corporate bond market into two grades is not efficient in a broader sense. Credit ratings carry information and may help investors economize on information production costs and manage agency problems between investors and fund managers. What we want to emphasize is that sharp divides can have significant, recurring, and time-varying effects on real investment that should be weighed carefully against any potential benefits.

For their guidance and very helpful discussions, we are especially grateful to our advisers Fritz Foley, Robin Greenwood, David Scharfstein, and Jeremy Stein. We thank Malcolm Baker, Bo Becker, Efraim Benmelech, Dan Bergstresser, Alexander Butler, John Campbell, Lauren Cohen, Andrew Ellul, Sam Hanson, Victoria Ivashina, Andrew Karolyi, Darren Kisgen, Erik Stafford, Laura Starks (the editor), an anonymous referee, and the seminar participants at Arizona State University, the Bank of Canada, Cornell University, the Federal Reserve Bank of Boston, the Federal Reserve Bank of New York, Harvard University, the Finance Down Under Conference, Imperial College London, the London School of Economics, the NBER Credit Rating Agencies session, The Ohio State University, Rice University, the Trans-Atlantic Doctoral Conference, the University of Illinois at Urbana-Champaign, the WFA 2010 Annual Meetings, and Yale University for helpful comments and suggestions; Doug Richardson at the Investment Company Institute for providing mutual fund flows data; and Jerome Fons, Martin Fridson, Oleg Melentyev, and Michael Weilheimer for discussing with us the high-yield market.

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### APPENDIX

Table A1

Variable definitions

Variable Definition
ABS share The share of asset- and mortage-backed bonds in total nonconvertible bond issuance by Fama-French 48 industry. Nonconvertible bond issuance by domestic publicly traded firms is from SDC. Shelf registrations and initiations of medium-term note programs are excluded.
Book leverage Book debt divided by the sum of book debt and stockholder equity
Cash flow Income before extraordinary items plus depreciation. Cash flow is annualized.
Cash flow sensitivity of cash Cash flow sensitivity of cash is the coefficient on cash flow in the regression of the change in cash (scaled by assets) on cash flow, Q, and log assets (Almeida, Campello, and Weisbach 2004). For each firm, we estimate the cash flow sensitivity of cash by using quarterly data over the 1986 Q1-2010 Q4 sample period and requiring at least ten observations.
Credit spread Difference in yields between Moody’s Baa- and Aaa-rated industrial bonds. Average of the end-of-month values (from the Federal Reserve Statistical Release H.15 “Selected Interest Rates”) during quarter t
External dependence Rajan and Zingales (1998) measure of external dependence, calculated at the Fama-French 48 industries level. Using the annual Compustat data set that covers the 1970–1985 period, we first calculate for each firm total capital expenditures minus total cash flows from operations during this period, all scaled by total capital expenditures. We then take the industry median as the industry measure of external dependence.
Flowst–1 Monthly aggregate flows into high-yield mutual funds are from the Investment Company Institute. Cumulative flows over the four quarters [t – 4, t – 1] are scaled by the total PPE of firms rated BBB + through BB – , PPEt–1. The value of fund flows is standardized so that the coefficient on flows represents the effect of a one standard deviation change in high-yield fund flows.
GDP growth Percentage change during quarter t in the seasonally adjusted real GDP (from the Bureau of Economic Analysis)
Interest coverage The ratio of EBIT to interest expense, calculated using four-quarter moving averages of EBIT and interest expense
Investment Capital expenditures scaled by lagged PPE, $CAPXi,tPPEi,t−1$ . Investment is annualized.
Market leverage Book debt divided by the sum of book debt and market value of equity from CRSP
Operating margin Operating income before depreciation divided by sales
Q Market value of equity from CRSP plus assets minus the book value of stockholder equity, all divided by assets
ROA Income before extraordinary items divided by assets. ROA is annualized.
Sales growth Percentage change in sales over the last four quarters
Stock market return Value-weighted return on all NYSE, AMEX, and NASDAQ stocks minus the one-month Treasury bill rate. Average of monthly values (from Kenneth French’s Web site) during quarter t
Term spread Difference in yields between ten-year constant-maturity Treasuries and three-month Treasury bills. Average of the end-of-month values (from the Federal Reserve Statistical Release H.15 “Selected Interest Rates”) during quarter t
VIX Chicago Board Options Exchange Volatility Index. Average of the end-of-month values (from Thomson’s Datastream) during quarter t
z-score 1.2·WCi, t/Assetsi, t + 1.4·REi, t/Assetsi, t + 3.3·EBITi, t/Assetsi, t + Salesi, t/Assetsi, t, where WCi, t is working capital and REi, t is retained earnings. We exclude leverage from the calculation because we directly use leverage as one of the matching variables.
Variable Definition
ABS share The share of asset- and mortage-backed bonds in total nonconvertible bond issuance by Fama-French 48 industry. Nonconvertible bond issuance by domestic publicly traded firms is from SDC. Shelf registrations and initiations of medium-term note programs are excluded.
Book leverage Book debt divided by the sum of book debt and stockholder equity
Cash flow Income before extraordinary items plus depreciation. Cash flow is annualized.
Cash flow sensitivity of cash Cash flow sensitivity of cash is the coefficient on cash flow in the regression of the change in cash (scaled by assets) on cash flow, Q, and log assets (Almeida, Campello, and Weisbach 2004). For each firm, we estimate the cash flow sensitivity of cash by using quarterly data over the 1986 Q1-2010 Q4 sample period and requiring at least ten observations.
Credit spread Difference in yields between Moody’s Baa- and Aaa-rated industrial bonds. Average of the end-of-month values (from the Federal Reserve Statistical Release H.15 “Selected Interest Rates”) during quarter t
External dependence Rajan and Zingales (1998) measure of external dependence, calculated at the Fama-French 48 industries level. Using the annual Compustat data set that covers the 1970–1985 period, we first calculate for each firm total capital expenditures minus total cash flows from operations during this period, all scaled by total capital expenditures. We then take the industry median as the industry measure of external dependence.
Flowst–1 Monthly aggregate flows into high-yield mutual funds are from the Investment Company Institute. Cumulative flows over the four quarters [t – 4, t – 1] are scaled by the total PPE of firms rated BBB + through BB – , PPEt–1. The value of fund flows is standardized so that the coefficient on flows represents the effect of a one standard deviation change in high-yield fund flows.
GDP growth Percentage change during quarter t in the seasonally adjusted real GDP (from the Bureau of Economic Analysis)
Interest coverage The ratio of EBIT to interest expense, calculated using four-quarter moving averages of EBIT and interest expense
Investment Capital expenditures scaled by lagged PPE, $CAPXi,tPPEi,t−1$ . Investment is annualized.
Market leverage Book debt divided by the sum of book debt and market value of equity from CRSP
Operating margin Operating income before depreciation divided by sales
Q Market value of equity from CRSP plus assets minus the book value of stockholder equity, all divided by assets
ROA Income before extraordinary items divided by assets. ROA is annualized.
Sales growth Percentage change in sales over the last four quarters
Stock market return Value-weighted return on all NYSE, AMEX, and NASDAQ stocks minus the one-month Treasury bill rate. Average of monthly values (from Kenneth French’s Web site) during quarter t
Term spread Difference in yields between ten-year constant-maturity Treasuries and three-month Treasury bills. Average of the end-of-month values (from the Federal Reserve Statistical Release H.15 “Selected Interest Rates”) during quarter t
VIX Chicago Board Options Exchange Volatility Index. Average of the end-of-month values (from Thomson’s Datastream) during quarter t
z-score 1.2·WCi, t/Assetsi, t + 1.4·REi, t/Assetsi, t + 3.3·EBITi, t/Assetsi, t + Salesi, t/Assetsi, t, where WCi, t is working capital and REi, t is retained earnings. We exclude leverage from the calculation because we directly use leverage as one of the matching variables.
2
4
The act prohibited purchases of speculative-grade bonds and mandated that existing holdings be liquidated by 1994. As a result, thrifts' share of the corporate bond market fell from around 7% in 1988 to less than 1% by 2010 (Flow of Funds Accounts of the United States, Table L212, Corporate and Foreign Bonds).
5
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 noninvestment-grade bonds is capped at 20%. As a result of these restrictions, insurance companies' share of all speculative-grade bonds is only 8.5%, which is one-fourth of their 34% share of all investment-grade bonds (Ellul, Jotikasthira, and Lundblad 2011).
6
Haircuts for investment-grade nonconvertible debt securities that pay a fixed interest rate vary between 2% and 9%, depending on maturity. Haircuts for speculative-grade bonds are generally 15%.
7
For consistency of exposition, we use Standard & Poor's rating scale throughout the article.
8
As Poor's Publishing, Standard Statistics, and Fitch Publishing entered the credit rating market in 1916, 1922, and 1924, respectively, they generally followed similar characterizations. All agencies described BB as either “Good” or “Fair,” and none referred to it as speculative (Harold 1938).
9
Although the ruling applied only to national banks, many state banking regulators followed the Comptroller's lead and introduced similar restrictions for state-chartered banks.
10
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 are credit ratings, at least at short- and medium-term horizons (Cantor and Mann 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 the Regulation Fair Disclosure prohibited companies from selectively disclosing nonpublic information but excluded rating analysts from the new regulation.
11
For a review of matching estimators, see Imbens (2004) and Abadie and Imbens (2006).
12
To make our results more comparable with articles that use annual data, we annualize investment and cash flow.
13
The two primary exceptions are obtaining a guarantee from another entity and issuing asset-backed securities.
14
In untabulated results we estimate that approximately 10% of all nonconvertible bond issues (weighted by proceeds) by nonfinancial firms are notched up. The vast majority of notched-up issues are asset- and mortgage-backed bonds.
15
In untabulated results, we do not find any differences in corporate governance, as measured by the G Index (Gompers, Ishii, and Metrick 2003) and its components, between the matched BB+ and BBB− firms.
16
In this subsample, we match 143 BB+ observations to 131 unique BBB− firm-quarter observations.
17
Our results are also robust to the exclusion of the three recessions in our sample period.
18
We find similar results for these other cutoffs, i.e., that the investment of lower-rated firms is not more sensitive to fund flows than is the investment of higher-rated firms.
19
Note that the lack of statistically significant results around other cutoffs does not appear to be due to smaller sample sizes and weaker power.
20
Lee, Shleifer, and Thaler (1991) and Baker and Wurgler (2007) use the closed-end fund discount as a measure of investor sentiment. In untabulated results, we find that high-yield mutual fund flows are strongly negatively correlated with the discount on closed-end high-yield funds, which suggests that fund flows might be driven by investor sentiment.
21
For example, while average five-year default rates for BBB− and BB+ firms are 3.93% and 5.89%, respectively, average five-year default rates for BB−, B+, and B firms, which together account for more than 60% of the total number of speculative-grade firms, are 12.41%, 18.28%, and 26.03% (Standard & Poor's 2010), respectively.
22
To calculate industry external dependence, we use nonoverlapping data from the 1970–1985 period in order to prevent the realizations of fund flows during our sample period from affecting our measures of external dependence. We get similar results, however, when using the same sample period to measure external dependence and estimate our investment regressions.
23
Unfortunately, our data on bank loan ratings are limited to the 1986 Q1–2005 Q2 period.
24
There is a growing body of literature that documents the effects of ratings on firm financing decisions. Faulkender and Petersen (2006) find that firms with a credit rating that allows them to access public debt markets have 35% more debt than do other similar firms. Kisgen (2006) argues that firm financing decisions are affected by the discrete costs and benefits of different credit ratings. Sufi (2009) studies how the introduction of syndicated bank loan ratings by Moody's and S&P 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.
25
In unreported regressions, we estimate our investment regressions on the small sample of forty-six BB+ firm-quarter observations with positive issuance activity. The coefficient on flows within this sample is three times the coefficient in the full sample and has a p-value of 12%. This evidence is consistent with the link between firm investment and high-yield fund flows being strongest for firms that do issue.