The impact of cash #ow volatility on debt and equity "nancing

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Journal of Financial Economics 54 (1999) 423}460
The impact of cash #ow volatility on
discretionary investment and the costs of
debt and equity "nancingq
Bernadette A. Minton!,*, Catherine Schrand"
!Fisher College of Business, The Ohio State University, Columbus, OH 43210-1144, USA
"The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
Received 30 April 1998; received in revised form 20 November 1998; accepted 12 August 1999
Abstract
We show that higher cash #ow volatility is associated with lower average levels of
investment in capital expenditures, R&D, and advertising. This association suggests that
"rms do not use external capital markets to fully cover cash #ow shortfalls but rather
permanently forgo investment. Cash #ow volatility also is associated with higher costs of
accessing external capital. Moreover, these higher costs, as measured by some proxies,
imply a greater sensitivity of investment to cash #ow volatility. Thus, cash #ow volatility
not only increases the likelihood that a "rm will need to access capital markets, it also
increases the costs of doing so. ( 1999 Elsevier Science S.A. All rights reserved.
JEL classixcation: G31
Keywords: Cash #ow volatility; Investment; Cost of equity "nancing; Cost of debt
"nancing
q
Previous versions of this paper were titled &Costs of Accounting Income versus Cash Flow
Volatility'. We thank Gordon Bodnar, John Core, Peter Easton, Chris GeH czy, Paul Gompers, Jarrad
Harford (the referee), Bob Holthausen, Steve Kaplan, Andrew Karolyi, Sara Moeller, Tim Opler,
Andre Perold, Tony Sanders, Bill Schwert (the editor), ReneH Stulz, Ralph Walkling, Franco Wong, and
workshop participants at Dartmouth, the Federal Reserve Bank of New York, Harvard, Michigan,
Minnesota, Ohio State, Purdue, Rochester, and Wharton for valuable comments, and Howard Yeh for
research assistance. Minton thanks the Dice Center for Financial Economics for "nancial support.
* Corresponding author. Tel.: #1-614-688-3125; fax: #1-614-292-2418.
E-mail address: minton.15@osu.edu (B.A. Minton)
0304-405X/99/$ - see front matter ( 1999 Elsevier Science S.A. All rights reserved.
PII: S 0 3 0 4 - 4 0 5 X ( 9 9 ) 0 0 0 4 2 - 2
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
1. Introduction
&As risk managers, we spend much of our time examining the factors that
cause cash #ows to #uctuate. This is important work, since low cash #ows may
throw budgets into disarray, distract managers from productive work, defer
capital expenditure or delay debt repayments. By avoiding these deadweight
losses, risk managers can rightly claim they add to shareholder value'. (See
Shimko, 1997.) Consistent with this claim that cash #ow volatility is costly, we
document that cash #ow volatility is associated both with lower investment and
with higher costs of accessing external capital.
Higher cash #ow volatility implies that a "rm is more likely to have periods of
internal cash #ow shortfalls. Our analysis indicates that "rms do not simply
react to these shortfalls by changing the timing of discretionary investment to
match cash #ow realizations. Rather, "rms forgo investment. Firms could
smooth internal cash #ow #uctuations using external capital markets. However,
Myers and Majluf (1984) show that external capital is more costly than internal
capital. Consequently, "rms that require more external capital relative to internal capital will have lower investment, all else equal, assuming "rms follow the
basic net present value (NPV) decision rule for capital budgeting.
A higher frequency of cash #ow shortfalls, however, is not the only reason that
volatility a!ects investment decisions. Cash #ow volatility also is positively
related to a "rm's cost of accessing external capital. Volatility can a!ect capital
costs because of capital market imperfections including information asymmetry
and contracting (e.g., debt covenants). For example, consider that analysts
are less likely to follow "rms with volatile cash #ows. Assuming that lower
analyst following implies greater information asymmetry and a higher cost
of accessing equity capital, "rms with higher cash #ow volatility will have
higher equity capital costs. Together, the two e!ects of cash #ow volatility imply
that reductions in cash #ow volatility through risk management activities can
reduce a "rm's expected &underinvestment' costs (Froot et al., 1993; Myers,
1977).
The basic "nding of the analysis is that cash #ow volatility is associated with
lower investment in average annual capital expenditures, research and development costs, and advertising expenses, even after industry-adjusting and controlling for the level of a "rm's average cash #ows and its growth opportunities. In
addition, "rms experiencing cash #ow shortfalls in a given year relative to their
peers or relative to their own historical experience have signi"cantly lower
discretionary investment in that year than "rms that are not experiencing
shortfalls. Sensitivity analyses indicate that the results are not driven by "rms in
"nancial distress or cross-sectional di!erences in investment opportunities.
Fazzari, Hubbard, and Petersen (FHP, 1988,1998), Hoshi et al. (1991), Kaplan
and Zingales (KZ, 1997), and Lamont (1997) "nd a negative contemporaneous
relation between annual investment levels and liquidity. These studies cannot
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
425
distinguish whether "rms with volatile cash #ows time their investment decisions to match internal cash #ow realizations or actually decrease their overall
level of investment. Our "ndings reveal a negative relation between volatility,
measured over a period, and the average level of investment measured over the
same period, suggesting that "rms that experience shortfalls ultimately forgo
investment. The magnitude of the forgone investment is large. Capital expenditures by "rms with high cash #ow volatility (in the highest quartile relative to
"rms in the same industry) are 19% below the mean level of capital expenditures
for the sample while capital expenditures by "rms with low cash #ow volatility
are 11% above the mean.
Three pieces of related evidence emerge from tests designed to further explain
our basic "nding. First, the negative relation between volatility remains after
controlling for a "rm's cost of accessing external capital. Second, there is a direct
relation between capital costs and investment levels. Moreover, "rms that we
claim have higher costs of accessing external capital (e.g., small "rms) have
a higher sensitivity of investment to volatility. Third, cash #ow volatility is
positively related to the costs of accessing external capital. Speci"cally, higher
cash #ow volatility is associated with worse S&P bond ratings, higher yields-tomaturity, lower analyst following, lower dividend payout ratios, higher bid}ask
spreads, and higher weighted average costs of capital. Taken together, the
evidence suggests that the basic "nding of an association between investment
and cash #ow volatility is not just a relation between investment and project risk
in disguise.
The results provide a benchmark for assessing the value of risk management
activities. However, the sensitivity of investment to volatility does not suggest
that "rms should necessarily reduce or eliminate cash #ow volatility. We
recognize that volatility is a choice variable and assume that managers make
rational decisions based on all available information. Our results provide an
additional source of information that managers can use to assess the bene"ts of
reducing cash #ow volatility. Firms must weigh these bene"ts against the costs,
which can vary across "rms and industries. Risk management costs are likely to
be low, for example, for "rms in the oil and gas, mining, and agriculture
industries where liquid, well-developed derivatives markets exist for a risk that
represents a signi"cant source of a "rm's cash #ow volatility. In contrast,
hedging costs are likely to be higher for "rms in which signi"cant cash #ow
volatility results from factors that are relatively uncorrelated with interest rates,
foreign exchange prices, or commodity prices. The cross-sectional variation in
these costs, relative to the potential bene"ts of reduced volatility, leads to
interesting cross-sectional implications about risk management decisions.
The positive association between a "rm's current cost of external capital and
its historical cash #ow volatility is a subtle but important distinction for risk
managers. One interpretation of this result is that debt and equityholders use
historical volatility to predict future cash #ow volatility when they set prices.
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
This interpretation implies that a "rm's cost of accessing capital will depend on
the expected persistence of cash #ow volatility into future periods. Hence,
cross-sectional di!erences in the persistence of the e!ects of risk management
activities will be associated with cross-sectional di!erences in the association
between volatility and the "rm's cost of accessing capital. In the extreme case,
risk management activities that have successfully reduced volatility, but which
are not expected to have a persistent e!ect on volatility in future periods, will not
necessarily reduce a "rm's current cost of accessing external markets. One
conjecture is that debt and equityholders do not view the use of short-term
"nancial derivatives to reduce volatility in the same way as the use of longerterm risk reduction activities, such as moving a plant overseas to reduce foreign
exchange price risk. Understanding how di!erent types of risk management
activities a!ect the costs that we document is an interesting avenue for future
research.
Although this paper provides the "rst direct evidence that cash #ow volatility
is related to lower investment, we are not the "rst to make this claim. Shapiro
and Titman (1986), Lessard (1990), Stulz (1990), and Froot et al. (1993) propose
a link between volatility and investment in the context of explaining hedging
activities that reduce cash #ow volatility. Consistent with these theories, Dolde
(1995), GeH czy et al. (1997), Mian (1996), Nance et al. (1993), and Tufano (1996)
"nd that "rms that have the greatest expected bene"ts from reducing volatility
are more active in risk management activities. These papers jointly test two
hypotheses: (1) volatility is costly for the reasons predicted by a particular theory
(or theories), and (2) "rms engage in a speci"c risk-management activity (such as
using derivatives) to reduce the volatility that creates the cost. Our direct
evidence of an association between volatility and discretionary investment
complements the "ndings of these indirect tests.
The paper proceeds as follows. Section 2 provides an outline of the various
predictions and tests. Section 3 describes the measure of cash #ow volatility and
the methodology for the analysis of the association between volatility and
investment. Section 4 reports the results of these tests. In Section 5, we examine
the relation between costs of accessing capital markets and investment. Section 6
presents the analyses of the relations between cash #ow volatility and these
costs. Section 7 provides concluding remarks.
2. Overview of the paper
This paper analyzes a large and representative sample of "rms over a sevenyear period. The primary advantage of this sample is that the evidence can be
generalized to a broad class of "rms and investment decisions. A disadvantage is
that the results are particularly susceptible to criticisms related to endogeneity
issues and omitted correlated variables, despite the use of industry-adjusted data
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
427
in the analysis. To mitigate these concerns, we perform three separate sets of
tests of the e!ects of cash #ow volatility on investment and the costs of accessing
capital, and we support these tests with numerous sensitivity analyses. While
there may be questions about the interpretation of the results from any of the
three individual tests, the results taken together support our conclusions. This
section provides a detailed outline of the approach.
The "rst analysis examines the direct association between investment and
cash #ow volatility. We predict that a "rm's cash #ow volatility during a period
will be negatively associated with its average discretionary investment measured
over the same period. We test for this negative relation using annual crosssectional regressions of industry-adjusted capital expenditures, research and
development costs, and advertising expenses on industry-adjusted cash #ow
volatility. The methodology is described in Section 3 and the results are
presented in Section 4.1. One interpretation of the negative relation is that
volatility captures the likelihood that a "rm experiences a cash #ow shortfall.
Further evidence to support this interpretation is based on an examination of
"rm-level investment during periods of cash #ow shortfalls (Section 4.2).
Section 4 also includes robustness checks of the regression results to assess
whether the negative association between volatility and investment merely
represents the relation between investment and "rm characteristics that are
correlated with cash #ow volatility but omitted from the analysis. In particular,
prior research on "rms' investment decisions has found that investment is
positively related with cash #ow levels and suggests that the investment-cash
#ow sensitivities could di!er for "nancially constrained and healthy "rms.1
Section 4.1 includes an assessment of cross-sectional di!erences in investmentvoltatility sensitivities across cash #ow levels. Section 4.3 examines alternative
explanations for the results. Speci"cally, we address concerns about the causality of the relation between investment and volatility, the impact of "nancially
distressed "rms on the results, and variable speci"cation issues.
The remainder of the paper examines whether the source of the negative
relation between investment and volatility is a positive relation between volatility and the costs of external "nancing. In Section 5, we re-estimate the sensitivity
of investment to cash #ow volatility as in the "rst analysis and include a proxy
for a "rm's cost of accessing external capital and an interaction variable that is
the product of this proxy and cash #ow volatility. We predict that "rms with
higher costs of accessing capital will have lower investment, all else equal. The
coe$cient on the proxy measures the direct association between capital costs
1 FHP (1988) show that investment-cash #ow level sensitivities are greater for "rms with low
dividend payout ratios. The perspective of the FHP paper is that investment-cash #ow sensitivities
proxy for a "rm's degree of "nancing constraint. However, there is some debate about the
interpretation of the FHP results, with the debate focusing on the de"nition of "nancing constraints
(i.e., KZ, 1997; FHP, 1988,1998)
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
and investment, while the coe$cient on the interaction term indicates whether
the negative association between volatility and investment is a!ected by crosssectional di!erences in the costs of accessing external capital. We use nine
proxies for these costs: S&P bond ratings, yields-to-maturity on debt, stock
market betas, total equity price risk, weighted average costs of capital, analyst
following, dividend payout ratios, "rm size, and bid}ask spreads on common
stock.
In Section 6, we examine the associations between the proxies for the costs of
accessing external capital (except "rm size) and cash #ow volatility. We estimate
eight separate sets of annual cross-sectional regressions that measure the associations between cash #ow volatility and each of the proxies for capital costs.
Based on existing theory and empirical research, we predict that cash #ow
volatility is positively associated (in most cases) with costs of accessing external
capital. The regressions include controls for a "rm's level of cash #ows as well as
variables that have been identi"ed in prior research as determinants of the
proxies.
3. Methodology
Section 3.1 de"nes cash #ow and the methodology for measuring cash #ow
volatility. Section 3.2 de"nes the proxies for investment and the regression
equations used to estimate the association between investment and cash #ow
volatility.
3.1. Measures of cash yow and cash yow volatility
Operating cash #ow is computed quarterly for all non-"nancial "rms on
Compustat as sales (Compustat data item 2) less cost of goods sold (item 30) less
selling, general and administrative expenses (item 1) less the change in working
capital for the period. Working capital is current assets other than cash and
short-term investments less current liabilities and is calculated as the sum of the
non-missing amounts for accounts receivable (item 37), inventory (item 38), and
other current assets (item 39) less the sum of the non-missing amounts for
accounts payable (item 46), income taxes payable (item 47), and other current
liabilities (item 48). Quarterly selling, general and administrative expenses exclude one-quarter of annual research and development costs (item 46) and
advertising expenses (item 45) when those data items are available. Thus,
operating cash #ow represents the cash #ow available for discretionary investment.
Cash #ow volatility is de"ned as the coe$cient of variation in a "rm's
quarterly operating cash #ow over the six-year period preceding each of the
seven sample years from 1989 through 1995. Thus, for the sample year 1995, the
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
429
Table 1
Summary of "rms and industries in the sample
The sample includes all "rms in two-digit SIC industries in which there are at least ten "rms with
operating cash #ow data available. The number of sample "rms reported is the number with data
available for 1995. The "rms are on the Compustat quarterly data tapes from 1989 to 1994
Industry name
Two-digit
SIC code
Number of
sample "rms
Metal mining
Oil and gas extraction
Building construction * general contractors, operative builders
Non-building construction
Food and kindred products
Textile mill products
Apparel and other "nished products
Lumber and wood products, except furniture
Furniture and "xtures
Paper and allied products
Printing, publishing and allied
Chemicals and allied products
Petroleum re"ning and related industries
Rubber and miscellaneous plastic products
Stone, clay, glass, and concrete products
Primary metal industries
Fabricated metal, except machinery, transportation equipment
Machinery, except electrical
Electrical, electrical machinery, equipment, supplies
Transportation equipment
Measuring instruments; photographic goods; watches
Miscellaneous manufacturing industries
Water transportation
Communication
Electric, gas, sanitary services
Durable goods * wholesale
Non-durable goods * wholesale
General merchandise stores
Food stores
Apparel and accessory stores
Furniture, home furnishings stores
Eating and drinking places
Miscellaneous retail
Business services
Amusement, except motion pictures
Health services
Environmental services
10
13
15
16
20
22
23
24
25
26
27
28
29
30
32
33
34
35
36
37
38
39
44
48
49
50
51
53
54
56
57
58
59
73
79
80
87
20
64
14
11
47
24
22
14
15
30
36
105
33
30
16
48
49
125
106
43
63
22
10
19
23
41
33
22
16
16
11
24
32
57
13
19
14
1,287
430
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
coe$cient of variation is calculated using 24 quarters of data from the "rst "scal
quarter of 1989 to the fourth "scal quarter of 1994. A "rm is included in the
sample for a given year if it has at least 15 non-missing observations during the
24 quarters. The coe$cient of variation is the standard deviation of operating
cash #ow scaled by the absolute value of the mean over the same period. The
resulting metric is a unitless measure of variation that has been used by Albrecht
and Richardson (1990) and Michelson et al. (1995).
The coe$cient of variation of each "rm-year observation is adjusted relative
to the median for all sample "rms in the same two-digit SIC code for the same
sample year. This continuous measure of a "rm's industry-adjusted coe$cient of
variation in operating cash #ows is denoted CVCF. Industry-adjusted coe$cients of variation control for di!erences across industries in the quarterly
seasonality of cash #ows and in the nature of "rms' operations. Because of the
industry adjusting, we eliminate "rms in industries with less than ten "rms with
available data. We also delete seventy "rm-year observations representing
twenty-six "rms that are classi"ed as being in reorganization or liquidation
based on their Standard & Poor's (S&P) stock ratings.
The "nal annual samples consist of between 897 "rms (1989) and 1287 "rms
(1995) with available operating cash #ow data. Table 1 summarizes the number
of "rms by industry for the 1995 sample. The distributions of "rms in other
sample years are similar. The sample represents "rms in 37 separate two-digit
SIC codes and is consistent with the distribution of "rms on Compustat except
that our sample excludes "rms in the "nancial services industry.
3.2. Volatility and discretionary investment
The following model examines the relation between investment and volatility:
INVESTMENT"a #a CVCF# + a CONTROL #e .
(1)
0
1
i
i
1
i/2,3
INVESTMENT is one of three proxies for discretionary investment: capital
expenditures, R&D costs, or advertising expenses. Capital expenditures (Compustat data item 90), R&D costs (item 46), and advertising expenses (item 45) are
all scaled by the "rm's total assets at the beginning of the year. The extant
literature on the sensitivity of investment to cash #ow levels, including (FHP,
1988,1998) and KZ (1997), scales the only proxy for investment, capital expenditures, by beginning of period total "xed assets. In this paper, scaling by total
assets provides a consistent scaling variable across all three proxies for investment.2
2 The results are qualitatively similar if the investment variables are scaled by sales revenue.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
431
We compute average capital expenditures, R&D costs, and advertising expenses for the same rolling six-year periods over which we measure volatility.
Because the average investment variables are measured contemporaneously
with volatility, the results of the regression analyses indicate whether "rms with
higher volatility during a given period make lower average investments during
that same period. The three proxies for discretionary investment are adjusted
relative to the median for all sample "rms in the same two-digit SIC code for the
same sample year. Industry-adjusting the proxy variables for investment controls for variation across industries in capital intensity and growth during the
sample period.
The model includes two control variables (CONTROL) that measure growth.
FHP (1988) identify sales growth as a signi"cant determinant of capital expenditures. Sales growth is the average annual change in sales, scaled by beginning
of period sales, for the same rolling six-year periods as volatility. The second
proxy for growth opportunities is the average annual book-to-market ratio,
measured for the same rolling six-year periods as volatility.3 FHP and KZ also
include additional "rm and industry characteristics in the regression equations
that estimate the determinants of investment. However, it is the growth variables
that are consistently signi"cant across various studies. Like the dependent
variable and the coe$cient of variation, the control variables are industryadjusted.
Eq. (1) is estimated annually using ordinary least squares regressions for
each of the seven samples from 1989 to 1995. We present the means of the
annual coe$cient estimates. To test the hypothesis that the mean coe$cient
estimate is statistically di!erent from zero, we calculate and report a Z-statistic
(Z"tM /[p(t)/J(N!1)]) where tM and p(t) are the average and standard deviation
of the annual t-statistics, respectively, and N is the number of annual observations.4
We estimate two equations that control for the potential relation between
investment and cash #ow levels. Eq. (2) partitions the sample "rms based on the
level of industry-adjusted cash #ows:
INVESTMENT"b #b LO#b HI#b CVCF#b CVCF]LO
0
1
2
3
4
#b CVCF]HI# + b CONTROL #e .
5
i
i
2
i/6,7
(2)
3 Both FHP (1988) and KZ use variants of Tobin's Q as a proxy for growth opportunities. KZ
measure Tobin's Q as the ratio of the market value of assets to the book value of assets. FHP
measure Tobin's Q using replacement costs.
4 An alternative test statistic is Z@"1/J¹ +N t /Jk /(k !2) where t is the t-statistic for year
t/1 i
i i
i
i and k is the degrees of freedom. Z@ assumes the annual parameter estimates are independent and is
i
likely overstated; Z corrects for the potential lack of independence.
!0.609
!0.616
0.073
136
0.016
0.012
0.030
129
Industry-adjusted CV of operating cash #ow
Mean
!1.713
!0.905
Median
!1.504
!0.901
Std Deviation
0.565
0.096
N
101
145
Industry-adjusted operating cash #ow
Mean
0.020
Median
0.013
Std Deviation
0.032
N
89
0.015
0.011
0.033
129
Decile 3
Decile 2
Decile 1
(LOW)
!0.000
!0.000
0.037
455
0.104
0.000
0.430
533
Deciles 4}7
Panel A: Firms ranked based on industry-adjusted coezcient of variation of operating cash yow
!0.018
!0.015
0.050
90
1.949
1.937
0.497
119
Decile 8
!0.037
!0.026
0.070
91
4.775
4.403
1.498
120
Decile 9
!0.053
!0.037
0.067
86
26.382
17.747
21.250
120
Decile 10
(HIGH)
Means, medians, and standard deviations of industry-adjusted coe$cients of variation (CV) and levels of operating cash #ow scaled by beginning of period
total assets. The coe$cient of variation equals the ratio of the standard deviation to the absolute value of the mean of operating cash #ow, calculated using
quarterly data from 1989 to "scal year end 1994. In Panel A, "rms are ranked on the basis of industry-adjusted coezcients of variation of operating cash
#ow within each two-digit SIC code. Decile 1, for example, contains all "rms with coe$cients of variation that are in the lowest decile in their respective
two-digit SIC code. Because each SIC code does not have a multiple of ten "rms, the number of observations varies across deciles. In Panel B, "rms are
ranked on the basis of industry-adjusted levels of operating cash #ow scaled by beginning of period total assets. A "rm is classi"ed as LO, MED, or HI if it is
in the lowest three, middle four, or highest three decile rankings in its industry, respectively. The results are reported for 1995. Results for other sample
years are similar
Table 2
Descriptive statistics of industry-adjusted coe$cients of variation and levels of operating cash #ow
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
!0.000
0.000
0.008
440
0.859
!0.300
5.483
431
Industry-adjusted CV of operating cash #ow
Mean
6.574
Median
1.375
Std Deviation
14.103
N
319
MED
(Deciles 4}7)
!0.073
!0.037
0.321
329
Industry-adjusted operating cash #ow
Mean
Median
Std Deviation
N
LO
(Deciles 1}3)
Panel B: Firms ranked based on industry-adjusted level of operating cash yow
0.368
!0.425
6.030
320
0.042
0.037
0.024
329
HI
Deciles (8}10)
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
433
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
For each annual estimation, LO (HI) is an indicator variable equal to one if the
"rm is in the lowest (highest) three deciles of the sample "rms when they are
ranked based on the industry-adjusted average annual level of operating cash
#ows scaled by beginning of period total assets. The inclusion of the cash #ow
level variables controls for the observed sensitivity of investments to cash #ow
levels documented by FHP (1988,1998), Cleary (1998), Hoshi et al. (1991),
KZ (1997), and Lamont (1997).5
Alternatively, Eq. (3) is an augmented version of Eq. (1) that includes a continuous measure of industry-adjusted annual operating cash #ows (in levels)
scaled by beginning of year total assets averaged over the same six-year period
as volatility (OPCF):
INVESTMENT"c #c OPCF#c OPCF2#c CVCF
0
1
2
3
#c CVCF]OPCF# + c CONTROL #e . (3)
4
i
i
3
i/5,6
OPCF2 controls for potential nonlinearities in the relation between investment and industry-adjusted average annual operating cash #ow. The interaction
variable (CVCF]OPCF) measures the impact of a "rm's cash #ow level on the
estimated sensitivity of investment to cash #ow volatility.
Table 2 provides descriptive evidence about the cash #ow volatility variable
(CVCF) used in Eqs. (1)}(3) and average annual cash #ow levels. In Panel A,
"rms are ranked into deciles (annually) based on industry-adjusted coe$cients
of variation in operating cash #ows. Each "rm in decile one, for example, has
a coe$cient of variation of operating cash #ow that is in the lowest ten percent
relative to other "rms in its industry.6 Statistics are reported for decile one (the
lowest volatility measure), decile two, decile three, the middle four deciles as
a group (deciles four through seven), decile eight, decile nine, and decile ten (the
highest volatility measure) for the sample year 1995. Results for other sample
years (not reported) are similar.7
Panel A of Table 2 illustrates that the increases in the coe$cients of variation
are non-linear across the deciles. This pattern emerges even though we remove
the top ten percent of decile 10 (top one percent of the sample "rms). The mean
5 We also estimate speci"cations that include a third control variable which is the industryadjusted log of "rm size where size is de"ned as the market value of a "rm's equity plus the book
value of its debt. The results are qualitatively similar to those reported.
6 Because the number of "rms in the two-digit SIC codes is not always a multiple of ten, each
decile contains a di!erent number of observations. As an example, if there are 32 "rms in an industry
for a sample year, SAS allocates the two extra observations to deciles three and six. If there are 34
"rms, SAS allocates the four extra observations to deciles two, four, six, and eight.
7 The number of observations per decile in Panels A and B are di!erent because cash #ow
volatility data can be missing for "rms that have annual cash #ow level data. We require that a "rm
has at least 15 quarters of non-missing data to calculate the volatility measure.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
435
results are driven by some extreme observations. The medians follow a similar,
although less dramatic, pattern. In the regression analyses, we set all coe$cients
of variation that are greater than 100 equal to 100, which approximately
represents the 99th percentile. In addition, in#uential observations in the annual
regressions are downweighted by the method of Welsch (1980).
Panel A also indicates that there is a negative association between CVCF and
mean levels of average annual operating cash #ow. Industry-adjusted cash #ow
levels range from 0.020 for "rms in the lowest decile ranked on CVCF to !0.053
for "rms in the highest decile. This pattern emerges despite the use of the
coe$cient of variation to measure volatility, which reduces the likelihood of
a mechanical relation between volatility and levels. This negative relation justi"es
the use of levels as control variables in Eqs. (2) and (3). The negative association
between volatility and levels is con"rmed in Panel B in which "rms are ranked
into deciles (annually) based on industry-adjusted cash #ow levels. For "rms that
have cash #ows that are in the lowest three deciles when compared to "rms in
their respective two-digit SIC code (LO), the average CVCF is 6.574, compared to
a CVCF of 0.368 for "rms in the highest three deciles (HI).
Panel B also indicates that the standard deviations of the coe$cients of
variation vary across "rms with di!erent cash #ow levels, which suggests that
Eq. (2) may be mis-speci"ed. The speci"cation of Eq. (2) as a pooled regression
with separate parameter estimates across groups is the most e$cient speci"cation only if the standard deviations of the independent variables are similar
across the groups (Greene, 1993, p. 236). In the case of dissimilar variances,
the appropriate technique is to estimate Eq. (1) separately for each group. The
results from the separately speci"ed equations are qualitatively similar to those
obtained from the pooled regression.
4. Results
Sections 4.1 and 4.2 present empirical results on the relation between cash
#ow volatility and investment. Section 4.3 provides robustness checks of the
analyses.
4.1. Regression analysis of investment and volatility
Table 3 reports the means of the annual coe$cient estimates from regression
Eqs. (1)}(3) using industry-adjusted average capital expenditures, R&D costs,
and advertising expenses as proxies for discretionary investment. We do not
present the coe$cient estimates on the control variables.8
8 In all regressions in Table 3, the estimated coe$cients on the control variables for growth are
signi"cant and of the predicted signs.
0.0025
(18.738)
0.0028
(9.791)
0.0065
(10.388)
0.0040
(8.597)
0.0106
(25.205)
0.0031
(2.708)
R&D costs
Advertising expenses
!0.0013
(!0.459)
!0.0071
(!21.002)
!0.0032
(!4.971)
0.0170
(8.582)
0.0091
(6.861)
0.0010
(2.810)
!0.0008
(!8.957)
0.0003
(0.501)
!0.0006
!(5.574)
!0.0008
(!3.121)
!0.0002
(!20.212)
!0.0003
(!4.843)
CVCF
!0.0007
(!0.831)
0.0008
(2.795)
0.0002
(2.361)
LO
HI
Intercept
LO
CVCF*
Intercept*
Capital expenditures
Dependent variable
Panel A: Regressions using indicator variables to control for operating cash yow levels
!0.0032
(!5.782)
!0.0041
(!4.481)
!0.0008
(!6.203)
HI
3.27}4.83
0.13}1.18%
3.03}10.22
0.19}6.70%
4.59}7.93
2.60}4.26%
Range of
Adj. R2
Means of annual regressions of industry-adjusted capital expenditures, research and development (R&D) costs, and advertising expense (proxy variables
for discretionary investment) on industry-adjusted operating cash #ow volatility (CVCF) and industry-adjusted sales growth and book-to-market ratios.
Operating cash #ow volatility is measured as the coe$cient of variation of a "rm's quarterly operating cash #ow over the six-year period preceding each of
the seven sample years from 1989 through 1995. The dependent variables and the control variables represent averages of annual amounts measured over
the same six-year period. LO and HI are indicator variables equal to one if a "rm is in the lowest or highest three decile rankings, respectively, based on the
level of its six-year average annual industry-adjusted operating cash #ows. Operating cash #ow is sales!cost of goods sold!selling, general and
administrative expenses (excluding R&D and advertising)!the change in working capital. For each equation, means of the seven annual least squares
values for each coe$cient (a6 ) are presented. Z-statistics to test the hypothesis that E(a6)"0 are shown in parentheses. In#uential observations in the
it
annual estimations are downweighted by the method of Welsch (1980). Coe$cient estimates on control variables included in the regressions
(industry-adjusted book-to-market ratio and industry-adjusted sales growth) are not presented
Table 3
Means of annual regressions of proxies for discretionary investment on cash #ow volatility
436
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
Intercept
0.0025
(20.547)
0.0042
(5.560)
0.0056
(11.468)
Dependent variable
Capital expenditures
R&D costs
Advertising expenses
0.2676
(25.329)
0.2050
(17.429)
0.0361
(5.955)
OPCF
1.3667
(5.657)
0.4727
(3.967)
!0.0780
(!2.088)
(OPCF)2
!0.0006
(!4.184)
!0.0007
(!4.484)
!0.0003
(!12.413)
CVCF
Panel B: Regressions using continuous variables to control for operating cash yow levels
!0.0059
(!2.749)
!0.0118
(!2.867)
!0.0033
(!2.235)
CVCF]OPCF
5.24}7.26%
4.15}11.68%
3.71}5.93%
Range of
Adj. R2
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
437
438
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
Taken together, the results in Table 3 indicate that discretionary investment
levels are sensitive to cash #ow volatility, and that the degree of the sensitivity is
a function of cash #ow levels. In Panel A, in the regressions that include an
intercept, the coe$cient of variation, and the control variables for growth,
higher industry-adjusted operating cash #ow volatility is associated with signi"cantly lower industry-adjusted capital expenditures, research and development
costs, and advertising expenses. However, the results from Eq. (2) indicate that
volatility is not an equally signi"cant determinant of investment across all levels
of cash #ows. For low cash #ow "rms, the negative relations between volatility
and capital expenditures (!0.0003) and R&D costs (!0.0008) are mitigated
(CVCF]LO"0.0002) or eliminated (CVCF]LO"0.0008), respectively. Advertising expenses are negatively associated with cash #ow volatility, but only
for "rms with high levels of cash #ows (CVCF]HI"!0.0032).
Although investment is not related to cash #ow volatility for low cash #ow
"rms, these "rms exhibit a lower absolute level of average industry-adjusted
capital expenditures and R&D costs than "rms with moderate or high cash #ow
levels. The sum of the intercept and the coe$cient estimate on the LO dummy
variable indicates that average annual capital expenditures as a percentage of
total assets for this group are 0.0004 below the industry median. Similarly, R&D
costs are 0.0031 below the industry median for low cash #ow "rms. Thus, the
average level of a "rm's investment over time is lower for low cash #ow "rms,
but cash #ow volatility does not have a signi"cant marginal e!ect on investment.9
The regression results presented in Panel B include the continuous measure of
cash #ow levels (Eq. (3)). These results also indicate that volatility is negatively
associated with investment, and that this relation varies across "rms as a function of cash #ow levels. As in Panel A, "rms with higher levels of cash #ows have
higher levels of investments, ceteris paribus. The interaction variable between
CVCF and OPCF has a negative and signi"cant association with each of the
three proxies for discretionary investment. Thus, the sensitivity of investment to
volatility is stronger as cash #ows increase. This result is consistent with the
evidence in Panel A that the impact of volatility is second order relative to the
e!ect of cash #ow levels for "rms with low cash #ows.
By adding cash #ow volatility, OPCF2, and CVOPCF]OPCF to the regression, we enhance the explanatory power of the basic investment-liquidity model
9 We also estimate Eqs. (1)}(3) using the sum of non-missing capital expenditures, R&D costs, and
advertising expenses as a measure of total discretionary investment, because "rms can potentially
substitute across these investments. The results (not reported) show a negative and signi"cant
relation between volatility and total investment in the analysis that excludes controls for a "rm's
operating cash #ow level. In the analysis that controls for cash #ow levels, there is a signi"cant,
negative relation between volatility and investment for moderate and high cash #ow "rms, consistent with the results for capital expenditures reported in Panel A of Table 3.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
439
in the existing investment literature that includes only the level of operating cash
#ow (OPCF) and the control variables for growth. The average incremental
R2 from adding these variables to the basic speci"cation (averaged across the
individual annual regressions) is 1.79, 2.48, and 2.52 for the regressions measuring the association between volatility and capital expenditures, R&D costs, and
advertising expenses, respectively.
4.2. Cash yow shortfalls and investment
As more direct evidence that shortfalls in cash #ows are associated with lower
investment, we examine the capital expenditures, R&D costs, and advertising
expenses of "rms that experience shortfalls. A "rm is de"ned to be in a shortfall
position for a sample year if it is in the lower quartile of the sample based on one
of four separate metrics:
1. average annual industry-adjusted operating cash #ows, or
2. average annual operating cash #ows that are not industry-adjusted, or
3. the di!erence between its annual operating cash #ows and its own historical
average annual operating cash #ows measured over the prior six-year period,
or
4. the di!erence between its annual industry-adjusted operating cash #ows and
its own historical average industry-adjusted annual operating cash #ows
measured over the prior six-year period.10
The "rst measure de"nes a shortfall relative to the "rm's industry peers and the
second measure de"nes a shortfall relative to all "rms in the sample. The last two
measures de"ne a "rm to be in a shortfall position relative to its own historical
cash #ow levels. As a benchmark against which to evaluate the investments of
these groups, we examine the investments of "rms in excess cash #ow positions
(the upper quartile in each analysis).11
The results in Table 4 indicate that "rms that experience shortfalls relative to
their industry peers or to the sample "rms have lower industry-adjusted levels of
discretionary investment than "rms with excess cash #ow levels, consistent with
10 As in Table 2, we delete all "rm-year observations with a CVCF above the 99th percentile. The
results are qualitatively the same if we include these observations.
11 The upper quartile is not an appropriate benchmark if "rms with &excess' cash #ow overinvest
(e.g., Lang et al., 1991). We also compare the shortfall "rms to "rms in the third quartile. The
di!erence between industry-adjusted capital expenditures of the two groups remains signi"cant
when "rms are sorted based on industry-adjusted cash #ows, but the signi"cance of the di!erence
between industry-adjusted R&D and advertising declines (p-values between 0.10 and 0.15). Comparing the shortfall "rms to the upper half of the sample, the di!erences between all three proxies for
investment are signi"cant.
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
the regression results in the previous section.12 The di!erences are signi"cant at
less than the 1% level in tests including observations from the full sample period.
In annual tests, these di!erences are signi"cant at conventional levels in all
sample years for all three proxies for investment. As Table 4 reports, industryadjusted capital expenditures are negative for "rms that experience cash #ow
shortfalls. Thus, these "rms spend less on capital expenditures than the median
"rm in their respective industries. However, for R&D and advertising, the
results indicate only that the shortfall "rms spend less than "rms in excess cash
#ow positions (i.e., the amounts are statistically lower but positive). Finally,
when we de"ne a shortfall relative to a "rm's own historical cash #ows, only
capital expenditures are signi"cantly di!erent from those of "rms with excess
cash #ows over the full sample period. In annual tests, this di!erence is signi"cant in four of the seven years.
4.3. Sensitivity analysis
The "rst sensitivity analysis examines whether "nancially distressed "rms
drive the results in Table 3. Because cash #ow levels and cash #ow volatility are
potentially correlated with a "rm's probability of "nancial distress, and "nancial
distress is potentially correlated with investment decisions, we perform two
analyses to examine the e!ects of "nancially distressed "rms on the results. First,
we include in regression Eq. (3) an industry-adjusted measure of leverage as
a proxy for "nancial distress. The leverage proxy equals the average annual
debt-to-equity ratio de"ned as the book value of long-term debt scaled by the
sum of the book values of long-term debt, common equity, and preferred stock.
The coe$cient on this variable is negative and signi"cant, consistent with the
prediction that more levered "rms invest less on average (Lang et al., 1996).
However, the signi"cance of the association between volatility and investment
holds.13
12 This evidence is not a necessary condition for a relation between cash #ow volatility and
average investment. Consider investment projects that require staged "nancing. A "rm with volatile
cash #ows may not invest in such projects even if the "rm has su$cient cash #ows to fund the "rst
stage if the "rm anticipates that it will be in a shortfall position when funding is required at later
stages. This scenario implies a negative relation between cash #ow volatility and average investment
in a cross-sectional analysis. However, this scenario will not imply a di!erence between investment
levels for "rms in shortfall and excess positions.
13 Annual correlation coe$cients between the industry-adjusted average leverage ratios and
industry-adjusted CVs are positive and less than 0.15. Annual correlation coe$cients between
industry-adjusted average leverage ratios and industry-adjusted sales growth are either insigni"cant
or less than 0.10 in absolute value. Annual correlation coe$cients between industry-adjusted
average leverage ratios and industry-adjusted average book-to-market ratios are negative and less
than 0.15 in absolute value.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
441
Table 4
Discretionary investment for "rms in a cash shortfall versus cash excess position
Capital expenditures, R&D costs, and advertising expense for "rms in a cash shortfall versus cash
excess position. A "rm is in a shortfall (excess) position and classi"ed as Short (Excess) if it is in the
lowest (highest) quartile based on its average annual industry-adjusted ratio of operating cash #ow
to beginning of period total assets (OPCF/TA), its average annual unadjusted OPCF/TA, the
di!erence between current year unadjusted OPCF/TA and the six-year average unadjusted
OPCF/TA, and the di!erence between current year industry-adjusted OPCF/TA and the six-year
average industry-adjusted OPCF/TA. Results for the discretionary investment variables represent
averages of the annual variable across the seven sample years from 1989 to 1995. The next to last
column reports t-statistics for the di!erences in the means of discretionary investment for the short
and excess quartiles. The last column reports the number of sample years in the annual analyses for
which the di!erence in the means is statistically signi"cant at better than the 10% signi"cance level
(d sig years)
Firm's cash position measured
Firm's cash position
Short
t-stat
d sig
years
Excess
Relative to industry peers (ranked on average annual industry-adjusted operating cash yow)
Industry-adjusted capital expenditures
!0.0004
0.0045
8.419
7
Industry-adjusted R&D costs
0.0018
0.0173
7.864
7
Industry-adjusted advertising expense
0.0053
0.0293
9.466
7
Relative to sample xrms (ranked on average annual operating
Capital expenditures
0.0159
R&D costs
0.0379
Advertising expense
0.0376
cash yow)
0.0217
0.0529
0.0649
9.135
6.461
9.692
7
7
7
Relative to its own historical average (ranked on annual operating cash yow less six-year average annual
operating cash yow)
Capital expenditures
0.0162
0.0192
3.729
4
R&D costs
0.0469
0.0465
!0.119
1
Advertising expense
0.0435
0.0448
0.377
1
Relative to its own historical industry-adjusted average (ranked on annual industry-adjusted operating
cash yow less six-year average annual industry-adjusted operating cash yow)
Industry-adjusted capital expenditures
0.0007
0.0038
4.358
4
Industry-adjusted R&D costs
0.0104
0.0079
!1.108
1
Industry-adjusted advertising expense
0.0046
0.0040
!0.244
0
Second, we identify and eliminate "nancially distressed "rms from the sample
and re-estimate the relation between volatility and investment (Eqs. (1) and (2)).
Since there is no consensus on a measure of "nancial distress, we identify
distressed "rms using seven di!erent metrics that are proposed by existing
studies. A "rm-year observation is considered distressed if it has: (1) speculative
grade debt (S&P bond ratings of BB and worse); (2) a negative earnings-price
ratio; (3) negative average annual asset growth calculated over the six-year
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
period preceding the sample year; (4) average total assets in the lowest quartile
of total assets; (5) a debt-equity ratio in the sample year in the highest quartile;
(6) an average dividend payout ratio less than 10%, or (7) an average interest
coverage ratio in the lowest quartile. These metrics are based on results in Barth,
Beaver, and Landsman (BBL, 1997), FHP (1988), and KZ (1997). The results for
each of the seven reduced samples (not presented) are qualitatively similar to
those reported in Table 3. Thus, "nancially distressed "rms do not appear to
drive the results.
The second sensitivity analysis examines the causality of the relation between
cash #ow volatility and discretionary investment. Our interpretation of the
results in Table 3 is that cash #ow volatility, on average, leads to lower
investment. However, an alternative explanation is that di!erent levels of investment (the dependent variable) produce di!erent volatilities due to the nature of
the investments. To some degree, and with a lag, expenditure choices and patterns
may determine the nature of the cash #ow stream, both in levels and volatilities.
This concern is partially mitigated by the analysis in Section 5 that shows that
investment-volatility sensitivities are related to the costs of accessing capital. Such
a relation would not be expected if investment levels determine volatility.
While we cannot provide conclusive evidence that higher volatility leads to
lower investment rather than lower investment leading to higher volatility, we
present additional results that are more consistent with our interpretation of the
results. First, cash #ow volatility is not highly correlated with our proxies for
growth. We would expect a signi"cant and positive correlation if investment
determines cash #ow volatility. Over the seven-year sample period, the correlation coe$cients between industry-adjusted operating cash #ow volatility and
average annual sales growth and book-to-market ratios (industry-adjusted),
respectively, are 0.05 and 0.02.
Second, industry-adjusted cash #ow volatility is signi"cantly and positively
related to the industry-adjusted volatilities of the three proxy variables for
discretionary investment across all levels of cash #ows. We would expect this
positive relation if cash #ow volatility leads to lower investment. However, if
di!erent levels of investment (the dependent variable) produce di!erent volatilities, we expect no association between the volatility of investment and cash #ow
volatility.14
Third, we "nd that earnings volatility is not related to average investment
levels and that the inclusion of earnings volatility in Eqs. (2) and (3) does not
change the negative relation between cash #ow volatility and investment. This
result is consistent with our interpretation that greater cash #ow volatility
14 There are production functions under which more volatile expenditures might produce more
volatile cash #ows. In this case, investment volatility would lag cash #ow volatility. Because we
observe a contemporaneous relation, this concern is somewhat mitigated although not eliminated.
We thank the referee for o!ering this alternative interpretation.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
443
implies more frequent periods of cash #ow shortfalls, and that these shortfalls
are related to lower investment. If the causality works in the other direction and
investment decisions a!ect volatility, it is unclear why investment decisions
would a!ect only cash #ow volatility and not earnings volatility. In sum, these
three pieces of evidence are consistent with our interpretation that cash #ow
volatility is associated with lower average investment because it measures the
incidence of cash #ow shortfalls.
The third sensitivity analysis shows that the results are insensitive to our
method of industry adjusting. An alternative to industry adjusting the CV is to
"rst industry-adjust the quarterly cash #ows and then compute the CV of this
measure. The results using this alternative measure of volatility to control for
quarterly seasonality in operating cash #ows are qualitatively similar to those
presented in Table 3.
The fourth sensitivity analysis indicates that the results are not in#uenced by
cross-sectional variation in growth opportunities. This evidence supplements
the controls for growth provided by sales growth and book-to-market ratios
and industry-adjusting. Volatility remains a signi"cant negative determinant of
investment (in Eq. (3)) for both the upper and lower deciles of "rms partitioned
based on book-to-market ratios as an indicator of growth.
The "nal sensitivity analysis indicates that the results are robust to alternative
de"nitions for &volatility' in cash #ows. The coe$cient of variation attempts to
control for a mechanical relation between volatility and levels by scaling the
standard deviation of cash #ows by the absolute value of the mean. An alternative methodology is to scale cash #ows, e.g., by total assets, and to compute the
standard deviation of the ratio. We present the results using the coe$cient of
variation because the scaling choice (assets in this example) will induce the
results if asset levels are correlated with investment decisions. However, we also
perform the analyses in Table 3 using the standard deviation of cash-return on
assets, cash-return on the book value of equity, and cash-return on the market
value of equity as the measure of cash #ow volatility. The results are qualitatively similar.
5. Investment, volatility, and the costs of accessing external capital
This section investigates whether "rms' investment decisions are directly
related to the costs of accessing capital markets and whether these costs a!ect
the sensitivity of investment to cash #ow volatility. This analysis also demonstrates whether cash #ow volatility remains a signi"cant determinant of investment after controlling for a "rm's cost of accessing capital. This cost, in part,
captures a "rm's average project risk. Therefore, the analysis provides evidence
about whether project risk is a correlated omitted variable that explains our
basic "nding of a negative relation between investment and volatility.
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
5.1. Methodology
The regression model is an augmented version of Eq. (2) that includes a proxy
for the industry-adjusted cost of accessing capital (CAPCOST), an interaction
variable equal to the product of CAPCOST and the industry-adjusted coe$cient of variation of operating cash #ow (CVCF), and controls for cash #ow
levels:
INVESTMENT"d #d CVCF#d CAPCOST
1
2
3
#d (CVCF]CAPCOST)
4
# + MD [d #d CVCF#d CAPCOST
i 1i
2i
3i
i/LO,HI
#d (CVCF]CAPCOST)]N#e .
(4)
4i
4
D (D ) is an indicator variable equal to one if the observation has cash #ows
LO HI
in the lowest (highest) three deciles based on its industry-adjusted average
annual level of operating cash #ows scaled by beginning of period total assets.
CAPCOST is included in the regression to directly measure the relation
between the costs of external capital and investment, but it also serves as a proxy
for project risk. The interaction variable (CVCF]CAPCOST) measures
whether cross-sectional variation in the costs of accessing capital markets
mitigates (or exacerbates) the impact of volatility on investment levels. Nine
separate variables are used as proxies for a "rm's costs of accessing debt and
equity markets. Table 5 outlines the calculation of each variable.
Five of the nine variables are related to a "rm's risk-adjusted cost of capital:
(1) S&P bond rating (SPBOND), (2) yield-to-maturity (YTM), (3) systematic risk
(BETA), (4) total equity price risk (pRET), and (5) weighted average cost of
capital (WACC). We predict that "rms with a higher risk-adjusted cost of capital
on an industry-adjusted basis will have lower industry-adjusted levels of investment, ceteris paribus.
SPBOND and YTM are proxies for a "rm's cost of accessing debt capital.
Calomiris et al. (1995) and Ogden (1987) suggest that "rms with worse (higher)
S&P bond ratings have higher debt "nancing costs.15 WACC is a combination
of a "rm's YTM and the annual average of its daily equity return (RET) from
15 Using S&P bond ratings as a proxy for the cost of accessing debt capital in an OLS regression
assumes that the yield spreads between ratings categories are equivalent. However, Simkins (1998)
reports that the average spreads for 1991}1995 vary. For example, the average spreads between
A and BBB "rms is 42 basis points, between BBB and BB "rms is 110 basis points, and between BB
and B "rms is 183 basis points. Section 5.2 provides a sensitivity analysis of this variable as a proxy
for the cost of debt.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
445
Table 5
De"nitions of variables that proxy for the cost of accessing external capital markets
Variable
Variable name
De"nition
Cost of debt capital
S&P bond rating
SPBOND
The average S&P rating from Compustat (data item
280) and S&P Bond Guides.
Yield-to-maturity
YTM
Weighted-average YTM on long-term debt (excluding convertible debt), calculated using data from S&P
Bond Guides.
Cost of equity capital
Systematic risk
BETA
The annual beta of common stock obtained from
CRSP stock "les.
Total equity price risk
pRET
The annual standard deviation of daily market returns obtained from CRSP stock "les.
Other costs of accessing capital markets
Weighted average cost
WACC
of capital
The after-tax YTM times the book value of long-term
debt scaled by SIZE plus the return on equity from
CRSP times the market value of equity scaled by
SIZE.
Analyst following
ANALYST
The maximum number of analysts making a forecast
of earnings during the sample calendar year from
I/B/E/S.
Dividend payout
ratio
DIV
The ratio of total cash dividends paid during the "scal
year (Compustat data item 21) to the sum of net cash
#ows and total cash dividends paid during the year.
Bid}ask spread
BASPRD
Annual average of the daily di!erence between the
bid and ask prices scaled by the daily closing price.
Total "rm
capitalization
SIZE
The market value of equity plus the book value of debt
plus preferred stock (Compustat data item 130). The
market value of equity is share price times the number
of common shares outstanding (Compustat data item
199]data item 25). The book value of debt equals
long-term debt plus the current portion of long-term
debt (Compustat data item 9#data item 34).
CRSP. The after-tax YTM is weighted by the debt-to-equity ratio (where the
denominator is the market value of total common equity plus the book value of
debt) and RET is weighted by one minus the debt-to-equity ratio.
BETA and pRET measure a "rm's cost of accessing equity capital. In
a Sharpe-Lintner world, cross-sectional variation in "rms' costs of equity is the
direct result of cross-sectional variation in "rms' betas. Thus, if the assumptions
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B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
underlying the Sharpe-Lintner CAPM hold, the higher a "rm's stock beta, the
higher is its cost of raising equity capital. If these assumptions do not hold, then
other systematic factors not captured by beta or unsystematic risk can a!ect the
cost of equity. In this case, the higher the total risk of a "rm's stock (systematic
risk plus unsystematic risk), the higher is its risk-adjusted cost of raising equity
capital.
We predict that four additional proxies are associated with a "rm's cost of
accessing capital because of capital market imperfections: (1) analyst following
(ANALYST), (2) bid}ask spreads (BASPRD), (3) "rm size, and (4) dividend
payout ratios (DIV). These proxies are related to the costs of accessing capital
because they measure information asymmetry or the demand for liquidity, both
of which can lead to cross-sectional variation in the costs of accessing equity.
Lang and Lundholm (1996) and Brennan and Hughes (1991) show that
analyst following is negatively associated with information asymmetry. Amihud
and Mendelson (1988) "nd that bid}ask spreads are positively related to information asymmetry. As summarized in Botosan (1997), information asymmetry can have a positive association with a "rm's cost of equity for two reasons.
First, greater information reduces transaction costs which creates greater
demand for a "rm's securities. The greater demand increases liquidity and
&liquidity-enhancing policies can increase the value of the "rm by reducing its
cost of capital'. (See Amihud and Mendelson, 1988.) Second, greater information
reduces estimation risk about the value of a "rm's equity. Lower estimation risk
will reduce the cost of equity if estimation risk is non-diversi"able. In summary,
greater analyst following and lower bid}ask spreads represent a lower cost of
accessing external capital.
Firm size (the natural logarithm of SIZE) is also a proxy for information
asymmetry. Atiase (1985), Brennan and Hughes (1991), and Collins et al. (1987)
suggest that large "rms have less information asymmetry than small "rms.
Consistent with this lower information asymmetry, Ritter (1987) "nds that large
"rms have lower costs of issuing securities. Thus, we predict that large "rms
have lower costs of accessing capital markets.
Dividend payout ratios (DIV) measure the liquidity of an investment in
a "rm's stock. Asquith and Mullins (1983), Aharony and Swary (1980), Lang and
Litzenberger (1989), and Hepworth (1953) indicate that capital markets value
dividends because of liquidity constraints when equityholders are unable to
borrow and lend freely, or because dividends provide a credible signal of
management's private information. Because liquidity is associated with a lower
cost of accessing capital markets, we predict that high dividend payout "rms
have lower costs of accessing capital.16 The dividend payout ratio is de"ned as
16 Firms can also provide liquidity with stock repurchases or special dividends. These strategies
bias against observing a relation between dividend payout ratios as a measure of the cost of
accessing capital and investment.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
447
the ratio of total dividends paid during the year to net cash #ows (before
dividends) during the year because we focus on cash #ow volatility. The results
are qualitatively unchanged using the traditional de"nition of a dividend payout
ratio, dividends per share divided by earnings per share.
5.2. Results
Results are reported in Table 6. Coe$cient estimates on the control variables
and intercepts are not reported. Three main results emerge from this analysis.
First, even after controlling for the relation between CAPCOST and investment,
a negative and signi"cant association between volatility and investment remains
across all regression equations (except when yield-to-maturity is used as a proxy
for the cost of accessing external capital). Therefore, the documented association
between investment and volatility does not simply re#ect an omitted correlated
variable related to a "rm's cost of external "nancing such as project risk. Rather,
the results in Table 6 are consistent with the notion that volatility measures the
incidence of internal cash #ow shortfalls.
Second, higher costs of accessing external capital are associated with lower
investment, on average. As Table 6 reports, lower capital expenditures are
signi"cantly associated with worse S&P bond ratings (higher numerical code),17
higher total equity price risk, higher bid}ask spreads, higher weighted average
costs of capital (marginally signi"cant), and lower information asymmetry as
measured by analyst following and "rm size. As before, cash #ow levels appear
to have a "rst-order e!ect on investment. The bene"ts of "rm size in terms of
higher investment are increasing in the level of a "rm's cash #ows. Similarly, the
negative relation between investment and bid}ask spreads is eliminated for "rms
with high cash #ows.
Two results are contrary to our predictions. Lower investment is associated
with lower systematic risk and higher dividend payout ratios. We expect the
opposite relations assuming that beta is positively associated with the cost of
accessing external capital and that the dividend payout ratio is negatively
associated with these costs. One possible explanation for the negative association between dividends and investment is that "rms consider dividend
payments to be non-discretionary. Alternatively, Smith and Watts (1992)
suggest that dividend paying "rms are more mature and have fewer investment
17 The analysis includes only "rms with S&P bond ratings. Gilchrist and Himmelberg (1998) and
Calomiris et al. (1995) contend that rated "rms have a lower cost of accessing external capital
relative to unrated "rms. To control for this potential selection bias, following Gilchrist and
Himmelberg (1998) we estimate Eq. (4) using as the proxy for CAPCOST an indicator variable that
is equal to one if the "rm has an S&P bond rating and equal to zero otherwise. The results are
similar except that the coe$cient on the interaction term between CAPCOST and CVCF is
insigni"cant.
Predicted
Sign
!
!
!
!
!
#
#
!
#
Proxy for
CAPCOST
SPBOND
YTM
WACC
BETA
p(RET)
ANALYST
DIV
BASPRD
SIZE
!0.0003
(!3.294)
0.0001
(0.062)
!0.0006
(!1.680)
0.0024
(5.278)
!0.1030
(!5.823)
0.0001
(2.751)
!0.0009
(!2.677)
!0.0366
(!2.363)
0.0004
(3.186)
CAPCOST
!0.0001
(!0.639)
!0.0001
(!0.867)
!0.0003
(!0.456)
!0.0022
(!1.646)
!0.0285
(!1.254)
0.0001
(1.160)
0.0006
(1.742)
!0.0280
(!0.211)
0.0003
(1.567)
!0.0002
(!0.920)
!0.0007
(!0.826)
!0.0011
(!1.191)
0.0041
(3.639)
0.0388
(1.693)
0.0001
(1.333)
!0.0015
(!2.985)
0.0341
(2.470)
0.0006
(2.696)
CAPCOST]
LO
HI
!0.0003
(!2.517)
!0.0002
(!0.445)
!0.0004
(!1.840)
!0.0005
(!8.040)
!0.0006
(!4.198)
!0.0004
(!6.967)
!0.0003
(!4.164)
!0.0007
(!6.497)
!0.0002
(!2.934)
CVCF
0.0002
(1.104)
0.0001
(0.122)
0.0001
(0.796)
0.0005
(4.835)
0.0005
(2.522)
0.0002
(3.279)
0.0002
(2.319)
0.0006
(4.350)
0.0001
(1.103)
!0.0010
(!4.303)
!0.0012
(!1.808)
!0.0018
(!2.226)
!0.0006
(!4.117)
!0.0013
(!4.651)
!0.0008
(!7.444)
!0.0008
(!6.108)
!0.0003
(!1.599)
!0.0009
(!6.316)
CVCF]
LO
HI
0.0001
(1.822)
0.0004
(0.472)
0.0007
(0.418)
0.0002
(0.422)
!0.0120
(!1.248)
0.00001
(1.255)
0.0000
(0.382)
!0.0085
(!1.175)
0.0001
(2.746)
CVCF]
CAPCOST
!0.0001
(!2.150)
!0.0007
(!1.248)
!0.0006
(!0.428)
!0.0004
(!0.708)
0.0062
(0.507)
!0.0000
(!0.305)
0.0000
(0.090)
0.0121
(1.069)
!0.0001
(!3.588)
0.0000
(0.117)
!0.0005
(0.469)
!0.0002
(0.936)
!0.0018
(!2.246)
!0.0254
(!1.580)
!0.0000
(!1.250)
0.0001
(!0.037)
0.0201
(0.284)
!0.0003
(!4.565)
CVCF]CAPCOST]
LO
HI
5.85}8.83
!1.21}7.89
4.57}8.42
5.88}10.53
6.04}14.39
6.21}15.41
!1.00}22.10
3.42}10.46
4.49}11.07%
Range of Adj. R2
Means of annual regressions of average annual industry-adjusted capital expenditures on industry-adjusted operating cash #ow volatility (CVCF), proxies for the average
annual costs of accessing external capital (CAPCOST), and an interaction variable equal to the product of cash #ow volatility and a proxy for the average annual cost of
accessing external capital (CVCF]CAPCOST). The proxies for the cost of accessing external capital are S&P bond ratings (SPBOND), yield-to-maturity (YTM), weighted
average cost of capital (WACC), equity beta (BETA), standard deviation of returns (pRET), analyst following (ANALYST), dividend payout ratios (DIV), bid}ask spread
(BASPRD), and the natural logarithm of "rm size (SIZE). All of the proxies are industry-adjusted. Detailed de"nitions are in Table 5. The regressions also include controls for
industry-adjusted growth as measured by industry-adjusted average annual sales growth and book-to-market ratios. LO and HI are indicator variables equal to one if a "rm is
in the lowest or highest three decile rankings, respectively, based on the level of its industry-adjusted operating cash #ows. Operating cash #ow equals sales!cost of goods
sold!selling, general and administrative expenses (excluding R&D and advertising)!the change in working capital. Operating cash #ow volatility is measured as the
coe$cient of variation of a "rm's quarterly operating cash #ow over the six-year period preceding each of the seven sample years from 1989 through 1995. Averages of all other
variables (including the dependent variable) are calculated over the same period for which cash #ow volatility is measured. For each equation, the mean of the seven annual least
squares values of the coe$cient on the interaction variable (a6 is presented. Z-statistics to test the hypothesis that E(a6)"0 are shown in parentheses. In#uential observations in
it
the annual estimations are downweighted by the method of Welsch (1980). Coe$cient estimates on intercepts and control variables included in the regressions (industryadjusted book-to-market ratio and industry-adjusted sales growth) are not presented.
Table 6
Means of annual regressions of capital expenditures on cash #ow volatility, proxies for the costs of accessing external capital, and interaction variables between volatilities and
proxies for the costs of accessing external capital
448
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
449
opportunities. This explanation is also consistent with the positive association
between systematic risk and investment if beta re#ects information about
growth that is not captured by book-to-market ratios or the other controls for
growth.
Third, signi"cant coe$cients on some of the interaction variables
(CVCF]CAPCOST) indicate that the negative association between operating
cash #ow volatility and capital expenditures is mitigated for "rms with lower
costs of accessing external capital markets. Large capitalization "rms which we
claim have a lower cost of accessing external capital have a less negative and
marginally signi"cant sensitivity of investment to cash #ow volatility, on average. However, large "rms with either extremely low or extremely high cash #ows
do not bene"t from their size. Similarly, lower costs of accessing equity capital,
as measured by either beta or total equity price risk, mitigate the negative
impact of cash #ow volatility on investment for "rms with high cash #ows.
Conditional on the negative relation between investment and the costs of
accessing debt capital as measured by S&P bond ratings, lower costs (lower
numerical ratings) do not mitigate the impact of volatility on investment.
Assuming that "rms with higher cash #ow volatility are more likely to have
insu$cient internal capital in some periods and require external capital to fund
investment, the negative association between volatility and investment is consistent with the Myers and Majluf (1984) pecking order hypothesis. As further
evidence on this issue, we also estimate the relation between investment and
volatility separately for "rms with low or high average annual cash balances.
This speci"cation is based on Myers' (1984) evidence that internal cash can act
as a substitute for external "nancing. A "rm is a low-cash "rm (high-cash "rm) if
it is in the lowest three (highest three) deciles of "rms ranked on the basis of its
industry-adjusted annual cash balances (Compustat data item 1) averaged over
the same six-year period over which volatility is measured. The results show that
the association between volatility and investment is more negative for the low
cash "rms. These "rms are more likely to require external capital to fund
investment because they lack su$cient internal cash bu!ers.
6. Volatility and the cost of accessing capital markets
This section presents evidence that the negative association between investment and volatility is consistent with the basic NPV investment rule by showing
that volatility is directly related to the costs of accessing external capital. Unlike
the tests of the association between volatility and investment, however, the
dependent variables in each of the separate regressions of CAPCOST on
volatility are measured at the end of the period over which volatility is measured. For example, volatility measured over the six-year period 1988}1994 is
matched with the "rm's S&P bond rating for 1995. In contrast, in the tests of
450
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
discretionary investment, average investment and volatility are measured over
contemporaneous six-year periods.
The dependent variable is measured di!erently because our predictions about
why volatility a!ects investment di!er from our predictions about why it a!ects
the proxies for the cost of accessing capital. The contemporaneous measurement
of volatility and investment in the discretionary investment tests re#ects the
prediction that higher cash #ow volatility over a period, and consequently more
likely realizations of cash shortfalls, is associated with lower investment during
that same period. In the tests of the association between volatility and the costs
of "nancing, the prediction is that historical volatility is relevant because debt
and equityholders use historical volatility to predict future volatility. In this
case, a 1995 bond rating, for example, re#ects the creditor's assessment of future
volatility as of 1995, and historical data is one factor that creditors can use to
make this assessment.
This di!erence in the analysis calls into question whether cash #ow volatility
is the measure that investors use to assess the risk of future cash #ows. Debt and
equityholders alternatively could use earnings volatility to assess future cash
#ow volatility.18 Consequently, one could argue that earnings volatility is an
omitted variable in the analyses in this section. As a robustness check of the
results, we estimate all of the regressions outlined in this section including not
only cash #ow volatility and cash #ow levels but also earnings volatility and
earnings levels. When the earnings variables a!ect the results, we discuss the
e!ects.
6.1. Volatility and the costs of accessing debt and equity
We predict positive associations between cash #ow volatility and the two
proxies for a "rm's cost of accessing debt capital, which are S&P bond ratings
and yields-to-maturity. With interim payments, volatility increases a "rm's
probability of default, other things equal. For a "rm to avoid technical default,
cash #ows in every period must be su$cient to cover the "rm's debt service
requirements. Higher cash #ow volatility increases the probability that the "rm's
cash #ow realization in any given payment period will not cover its debt service
requirements.19
18 Sloan (1996) "nds that earnings levels are a better predictor of future cash #ow levels than are
historical cash #ow levels. The conditions under which earnings volatility is a better predictor of
future cash #ow volatility is an open question.
19 Trueman and Titman (1988) make a similar prediction. They demonstrate that the incentives to
smooth income and the costs of volatility are related to industry classi"cation because the probability and costs of bankruptcy vary across industries.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
451
Because debtholders have a claim only on cash #ows after the results of all
"rm decisions including investment decisions, the cash #ows that are relevant in
debt valuation are cash #ows after investment (CFAI).20 Quarterly CFAI equals
operating cash #ow, as de"ned in Section 3.1, less net capital expenditures,
research and development costs (Compustat item 46 divided by four), and
advertising expenses (item 45 divided by four). Net capital expenditures equal
gross capital expenditures (item 90) less capitalized interest (item 147 divided by
four) less the after-tax proceeds from sales of PPE (item 83 times one minus the
tax rate). In all calculations, the tax rate (TR) is equal to 46% before 1987, 38%
in 1987, and 34% after 1987. The correlation between industry-adjusted average
operating cash #ow and cash #ow after investment is 97.5%.
Although we are not aware of any direct empirical evidence on the association
between cash #ow volatility and the cost of debt, indirect evidence is consistent
with a positive association between earnings volatility and the cost of debt.
Collins et al. (1981) and Lys (1984), for example, "nd negative returns at
announcements of accounting rule changes that are predicted to increase earnings volatility and indicate that the magnitude of the reaction is positively
related to a "rm's debt constraints. In cross-sectional studies, Bartov (1993) and
Imho! and Thomas (1988) show that "rms adjust their real activities to avoid
volatility, and that the extent of these adjustments varies with "rms' debt
constraints. These studies suggest that managers have incentives to smooth
earnings because smoother earnings reduce debt-related costs.
We predict positive associations between volatility and systematic risk
(BETA) and total equity price risk (pRET), which represent two of our proxies
for the costs of accessing equity capital. As discussed by Beaver et al. (1970),
estimations of these associations test the joint hypothesis that cash #ow volatility is correlated with a price-relevant risk and that the market impounds this
information in security prices.
We do not make a directional prediction about the association between
volatility and analyst following as a proxy for a "rm's cost of accessing equity.
The ultimate product of an analyst is a report that makes a stock buy or sell
recommendation, but one element of the report is the "rm's earnings forecast. If
analysts value forecast accuracy and it is more di$cult to predict earnings for
high-volatility "rms, then volatility can negatively a!ect the analyst following
decision. Beidleman (1973), Brennan and Hughes (1991), and articles in the
popular press suggest that analysts are less likely to follow stocks of "rms with
more volatile earnings because it makes their job of estimating &normal' earnings
more di$cult. In addition, Schipper (1991) notes that &readers of analyst reports
may use forecast accuracy as a quantitative measure of the quality of the overall
20 When the measure of cash #ows is cash #ows after investment, the measure of earnings is
operating income, which is after depreciation.
452
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
report; this e!ect will create a preference for accuracy2'. These arguments
suggest a negative relation between analyst following and volatility assuming
a positive correlation between cash #ow volatility and earnings volatility.
However, Barth et al. (1998) argue that analysts add the greatest value, and
thus potentially reap the highest compensation, when information asymmetry is
greatest. In this case, assuming a positive association between cash #ow volatility and information asymmetry, analysts would prefer to follow high-volatility
"rms.
We predict that dividend payout ratios are negatively associated with cash
#ow volatility. Aharony and Swary (1980) show that negative stock price
reactions to dividend decreases are larger in magnitude than positive reactions
to dividend increases. This evidence suggests that equityholders value stable
dividends. If dividend stability is a priority, "rms with higher cash #ow volatility
are forced to maintain lower dividends to avoid the costs associated with cutting
a dividend.
We predict a positive association between volatility and bid}ask spreads. This
prediction is based on an assumption that historical cash #ow volatility is
associated with greater uncertainty about future cash #ows. Amihud and
Mendelson (1988) show that greater uncertainty is associated with higher
bid}ask spreads.
Because equityholders have a claim only on residual cash #ows after
debtholders are paid, we examine whether systematic risk, total equity price risk,
analyst following, bid}ask spreads, and dividend payout ratios are associated
with net cash #ows, after both investment and interest charges.21 Quarterly net
cash #ows (NETCF) are measured as cash #ow after investment (CFAI) less
after-tax interest expense (Compustat item 22 times one minus the tax rate) plus
after-tax capitalized interest (item 147 divided by four times one minus the tax
rate). The correlation between industry-adjusted average operating cash #ow
and net cash #ow is 88.0%.
In summary, we predict a positive association between volatility and S&P bond
ratings, yields-to-maturity, stock market betas, total equity price risk, and bid}ask
spreads. The "rst four predictions also imply a positive association between
volatility and a "rm's weighted average cost of capital (WACC). We predict
a negative association between volatility and dividend payout ratios. No prediction is made about the association between volatility and analyst following.
6.2. Results
We estimate variations of Eqs. (1) and (2) to examine whether volatility is
associated with the proxies for the cost of accessing external capital. Each
21 When the measure of cash #ows is net cash #ows, the measure of earnings is net income.
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
453
regression equation includes control variables that prior research has identi"ed
as determinants of the dependent variable. The control variables are di!erent in
each equation. Because the control variables are not the focus of our analysis,
they are not described in detail. Table 7 summarizes the control variables, the
predicted signs, and the source that justi"es the use of the variable as a control.22
As in the investment regressions, we compute the means of the seven annual
ordinary least squares regression coe$cients for 1989}1995 for each dependent
variable. Coe$cient estimates for the control variables are not presented. The
results for these variables are consistent with the predictions from the literature
cited in Table 7.
Table 8 reports the results. In the regressions that exclude the controls for
a "rm's cash #ow level, the mean coe$cient estimates on the volatility of cash
#ow are statistically signi"cant and of the predicted sign in all regressions except
when BETA is the dependent variable. However, once we control for the level of
a "rm's cash #ows, the impact of volatility changes. The discussion focuses on
the regressions that control for the level of a "rm's cash #ows.
As Table 8 reports, the volatility of cash #ow after investment is associated
with worse S&P bond ratings (higher numerical codes) and higher yields-tomaturity.23 These results are consistent with the prediction that higher volatility
increases the likelihood that a "rm will not be able to meet its debt payments, all
else equal.24 Similarly, the volatility of cash #ow after investment is positively
related to a "rm's WACC.
Net cash #ow volatility is not signi"cantly associated with stock market betas
or total equity price risk once we control for the level of a "rm's net cash #ows.25
However, net cash #ow volatility is signi"cantly related to the proxies for the
costs of accessing equity capital that result because of market imperfections.
Speci"cally, volatility has a signi"cant positive association with bid}ask
spreads.26 This positive association is consistent with the joint claim that
22 The control variables in the WACC regression are the same as the control variables in the YTM
regression.
23 As a robustness check of this result, we exclude "rms with S&P bond ratings below investment
grade and re-estimate the relation between bond ratings and volatility. The results are qualitatively
similar to those presented in Table 8.
24 Because of the high correlation between bond ratings and volatility, we orthogonalize the S&P
bond rating variable with respect to the volatility of cash #ow after investment before including it as
a control variable in the yield-to-maturity and WACC regressions.
25 Total equity price risk is statistically and positively related to earnings volatility for highearnings "rms when earnings volatility is included along with cash #ow volatility in the regression.
26 We orthogonalize (pRET) with respect to net cash #ow volatility before using it as a control
variable in the bid}ask spread regression. When bid}ask spread is regressed only on volatility, we
observe a positive relation. However, when pRET (unorthogonalized) is added to the regression
along with trading volume and "rm size, there is a negative and signi"cant relation between
BASPRD and volatility.
KU, Ogden
(#)
KU (#)
Ogden (!)
Ogden (!)
EYR (#)
EYR (#)
BKS (#)
BKS (!)
Hamada,
BKS (#)
Banz (!)
CN (!)
CN (!)
CN (#)
BKS (!)
AB (!)
Standard
deviation of
returns
SW (!)
SW (#)
SW (#)
Dividend
policy
Beta
S&P bond
rating
Yield-tomaturity
Proxies for costs of accessing equity markets
Proxies for costs of accessing
debt markets
OB, BH (#)
OB (#)
(#)!
OB, BH,
Bhushan (#)
Analyst
following
HL, MP (!)
HL, MP (!)
HL (!)
HL, MP,
AM (#)
Bid}ask spread
!Many papers propose that analyst following is related to information asymmetry. We use bid}ask spread as a measure of information asymmetry.
"For betas and dividend payout ratios, we use market-to-book ratios and separately sales growth as proxies for growth. For analyst following, consistent
with the methodology of O'Brien and Bhushan (1990), growth is the net entry of "rms into the sample "rm's industry over the "ve-year period prior to the
sample year.
Abnormal returns
Growth"
Share Price
Beta
Bid}ask spread
Trading volume
Dividend payout ratio
Total equity risk
Firm size
S&P bond rating
Leverage
Control variables
Summary of control variables that are used in the regressions that estimate the relation between volatility and the costs of accessing external capital
markets. The proxies are S&P bond ratings, yield-to-maturity, equity beta, standard deviation of returns, dividend payout ratios, analyst following, and
bid}ask spreads. The table lists the citation(s) for the predicted coe$cient estimate as well as the predicted sign of the coe$cient estimate (in parentheses)
for the control variable. The sources are: AB: Alford and Boatsman (1995); AM: Amihud and Mendelson (1989); Banz: Banz (1981); BKS: Beaver, Kettler,
and Scholes (1970); Bhushan: Bhushan (1989); BH: Brennan and Hughes (1991); CN: Cheung and Ng (1992); EYR: Ederington, Yawitz, and Roberts (1987);
Hamada: Hamada (1972); HL: Howe and Lin (1992); KU: Kaplan and Urwitz (1979); MP: Menyah and Paudyal (1996); OB: O'Brien and Bhushan (1990);
Ogden: Ogden (1987), and SW: Smith and Watts (1992)
Table 7
Summary of control variables
454
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
455
volatility is related to information asymmetry and that information asymmetry
is related to higher spreads. In addition, volatility has a marginally signi"cant
negative association with analyst following as a proxy for information asymmetry. These negative associations are consistent with the joint claim that
analysts are more likely to make erroneous stock buy/sell recommendations
when volatility is high and that analysts attempt to reduce this likelihood by not
following "rms with volatile cash #ows. Including earnings volatility in the
regression with controls for cash #ow and earnings levels, we "nd that only cash
#ow volatility a!ects analyst following even though earnings are the more
frequently forecasted number.
Net cash #ow volatility also is negatively related to dividend payout ratios.
However, including controls for earnings volatility and the levels of earnings,
both cash #ow volatility and earnings volatility are negatively associated with
dividend payout ratios. The observation that dividend payout ratios are negatively associated with earnings volatility is consistent with Smith and Warner's
(1979) observation that dividend restrictions in bond covenants are frequently
based on accounting earnings realizations.
Finally, as in Table 3, the level of a "rm's net cash #ows has a "rst-order e!ect
on the costs of accessing external capital as measured by some proxies. In
particular, the intercepts indicate that low cash #ow "rms have worse S&P bond
ratings, higher equity betas, and higher equity price risk than "rms with median
cash #ows. High cash #ow "rms have better S&P bond ratings and lower
dividend payout ratios.
7. Summary and conclusions
This paper provides direct evidence that cash #ow volatility is associated with
lower average levels of investment in capital expenditures, research and development costs, and advertising expenses. Cash #ow volatility remains a signi"cant
negative determinant of investment even after controlling for the costs of
accessing external capital. Moreover, cash #ow volatility increases these costs.
In particular, cash #ow volatility is related to worse S&P bond ratings, higher
yields-to-maturity, higher weighted average costs of capital, higher bid}ask
spreads, lower analyst following, and lower dividend payout ratios. The results
related to the role of capital costs in the investment decision and the importance
of cash #ow volatility in the presence of these costs imply that the sensitivity of
investment to volatility does not result because volatility is a proxy for project
risk. Rather, cash #ow volatility is related to investment because it increases the
likelihood that a "rm will need to access capital markets and it also increases the
costs of doing so.
Taken together, the results suggest that "rms do not completely smooth cash
#ow volatility through time to maintain investment levels, but rather forgo some
Intercept
Intercept]
LO
Intercept]
HI
CV
0.0206
(2.313)
!0.0424
(!0.766)
0.4801
(3.676)
0.1555
(1.007)
0.0192
(4.390)
0.4675
(4.605)
0.0293
(2.805)
0.2494
(1.915)
0.0104
(0.017)
0.2429
(2.045)
0.0303
(6.272)
0.1507
(0.997)
WACC
!0.0094
(!0.271)
!0.0046
(!0.187)
0.0390
(0.404)
0.0178
(0.342)
0.0093
(1.840)
0.0055
(2.517)
Costs of accessing debt and equity markets: Cash yow is dexned as cash yow after investment (CFAI)
Yield-to-maturity
S&P bond rating
Costs of accessing debt markets: Cash yow is dexned as cash yow after investment (CFAI)
Dependent variable
0.0062
(0.083)
!0.0125
(!0.224)
0.0098
(0.536)
CV]
LO
!0.0078
(!1.384)
!0.0031
(!0.140)
0.0173
(0.354)
CV]
HI
62.11}85.78
60.59}85.63%
28.89}51.28
29.62}51.53%
57.02}61.87
57.31}62.01%
Range of
Adj. R2
Means of annual regressions of proxies for the costs of accessing capital markets on cash #ow volatility. Proxies for these costs are de"ned in Table 5. LO
and HI are indicator variables equal to one if a "rm is in the lowest or highest three decile rankings, respectively, based on the level of its industry-adjusted
cash #ow. Cash #ow after investment (CFAI) is operating cash #ow!net capital expenditures including R&D and advertising. Net cash #ow (NETCF) is
operating cash #ow!net capital expenditures (including R&D and advertising)!aftertax interest charges. Cash #ow volatilities represent the coe$cient
of variation (CV) in cash #ow estimated over the 24 quarters in the six years prior to the year of the calculation of the dependent variable. Cash #ows and
other control variables are measured in the same year as the dependent variable. All variables are industry-adjusted. For each equation, means of the seven
annual least squares values for each coe$cient (a6 ) are presented. Z-statistics to test the hypothesis that E(a6)"0 are shown in parentheses. In#uential
it
observations in the annual estimations are downweighted by the method of Welsch (1980). Coe$cient estimates on control variables included in the
regressions (summarized in Table 7) are not presented.
Table 8
Means of annual regressions of proxies for the cost of accessing capital markets on cash #ow volatility
456
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
Bid}ask spread
Dividend policy
Analyst following
Standard deviation of
stock returns
Stock market beta
0.0000
(0.663)
!0.0001
(!1.253)
77.58}86.73
!0.0008
(!1.345)
0.0002
(3.584)
!0.0008
(!1.217)
!0.0048
(!6.433)
21.64}28.32
15.22}23.46%
66.46}70.64
66.98}70.68%
78.09}88.39
77.63}88.54%
3.95}20.08
76.53}86.90%
0.0006
(0.999)
0.0047
(0.361)
0.0001
(1.558)
0.0042
(0.787)
0.0002
(3.001)
!0.0014
(!0.639)
!0.0242
(!1.084)
!0.00002
(!0.587)
!0.0009
(!0.644)
4.78}16.01%
!0.0052
(!5.632)
!0.0014
(!2.439)
!0.1449
(!14.411)
0.2282
(17.154)
!0.1455
(!8.129)
!0.0012
(!2.963)
0.1525
(15.724)
!0.0234
(!1.621)
!0.3925
(!1.518)
1.4104
(4.606)
!0.2750
(!1.035)
!0.0231
(!4.491)
1.2347
(3.914)
!0.0000
(!0.065)
!0.0002
(!0.934)
0.0026
(2.685)
0.0012
(2.109)
0.00002
(2.050)
0.0029
(2.576)
0.0008
(0.284)
!0.0912
(!5.661)
0.2743
(12.754)
0.1241
(3.358)
0.0019
(1.251)
0.2532
(14.024)
Costs of accessing equity markets: Cash yow is dexned as net cash yow (NETCF)
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
457
458
B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460
investment. We do not claim that these results imply that "rms should reduce or
eliminate volatility. Rather, the e!ects of volatility represent one factor that
a "rm should consider in its risk management decisions. Firms must decide how
to trade-o! the expected negative impact of volatility on investment levels
against the other e!ects of managing volatility.
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