the impact of cash ow volatility on debt and equity...

qPrevious versions of this paper were titled &Costs of Accounting Income versus Cash FlowVolatility'. We thank Gordon Bodnar, John Core, Peter Easton, Chris GeH czy, Paul Gompers, JarradHarford (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, andworkshop 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 forresearch 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: [email protected] (B.A. Minton)

Journal of Financial Economics 54 (1999) 423}460

The impact of cash #ow volatility ondiscretionary 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 ofinvestment 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 ratherpermanently forgo investment. Cash #ow volatility also is associated with higher costs ofaccessing external capital. Moreover, these higher costs, as measured by some proxies,imply a greater sensitivity of investment to cash #ow volatility. Thus, cash #ow volatilitynot only increases the likelihood that a "rm will need to access capital markets, it alsoincreases 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

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

1. Introduction

&As risk managers, we spend much of our time examining the factors thatcause cash #ows to #uctuate. This is important work, since low cash #ows maythrow budgets into disarray, distract managers from productive work, defercapital expenditure or delay debt repayments. By avoiding these deadweightlosses, risk managers can rightly claim they add to shareholder value'. (SeeShimko, 1997.) Consistent with this claim that cash #ow volatility is costly, wedocument that cash #ow volatility is associated both with lower investment andwith higher costs of accessing external capital.

Higher cash #ow volatility implies that a "rm is more likely to have periods ofinternal cash #ow shortfalls. Our analysis indicates that "rms do not simplyreact to these shortfalls by changing the timing of discretionary investment tomatch cash #ow realizations. Rather, "rms forgo investment. Firms couldsmooth internal cash #ow #uctuations using external capital markets. However,Myers and Majluf (1984) show that external capital is more costly than internalcapital. Consequently, "rms that require more external capital relative to inter-nal capital will have lower investment, all else equal, assuming "rms follow thebasic net present value (NPV) decision rule for capital budgeting.

A higher frequency of cash #ow shortfalls, however, is not the only reason thatvolatility a!ects investment decisions. Cash #ow volatility also is positivelyrelated to a "rm's cost of accessing external capital. Volatility can a!ect capitalcosts because of capital market imperfections including information asymmetryand contracting (e.g., debt covenants). For example, consider that analystsare less likely to follow "rms with volatile cash #ows. Assuming that loweranalyst following implies greater information asymmetry and a higher costof accessing equity capital, "rms with higher cash #ow volatility will havehigher equity capital costs. Together, the two e!ects of cash #ow volatility implythat reductions in cash #ow volatility through risk management activities canreduce 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 withlower investment in average annual capital expenditures, research and develop-ment costs, and advertising expenses, even after industry-adjusting and control-ling for the level of a "rm's average cash #ows and its growth opportunities. Inaddition, "rms experiencing cash #ow shortfalls in a given year relative to theirpeers or relative to their own historical experience have signi"cantly lowerdiscretionary investment in that year than "rms that are not experiencingshortfalls. 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), Kaplanand Zingales (KZ, 1997), and Lamont (1997) "nd a negative contemporaneousrelation between annual investment levels and liquidity. These studies cannot

424 B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460

distinguish whether "rms with volatile cash #ows time their investment deci-sions to match internal cash #ow realizations or actually decrease their overalllevel of investment. Our "ndings reveal a negative relation between volatility,measured over a period, and the average level of investment measured over thesame period, suggesting that "rms that experience shortfalls ultimately forgoinvestment. The magnitude of the forgone investment is large. Capital expendi-tures 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 expendituresfor the sample while capital expenditures by "rms with low cash #ow volatilityare 11% above the mean.

Three pieces of related evidence emerge from tests designed to further explainour basic "nding. First, the negative relation between volatility remains aftercontrolling for a "rm's cost of accessing external capital. Second, there is a directrelation between capital costs and investment levels. Moreover, "rms that weclaim have higher costs of accessing external capital (e.g., small "rms) havea higher sensitivity of investment to volatility. Third, cash #ow volatility ispositively related to the costs of accessing external capital. Speci"cally, highercash #ow volatility is associated with worse S&P bond ratings, higher yields-to-maturity, lower analyst following, lower dividend payout ratios, higher bid}askspreads, and higher weighted average costs of capital. Taken together, theevidence suggests that the basic "nding of an association between investmentand cash #ow volatility is not just a relation between investment and project riskin disguise.

The results provide a benchmark for assessing the value of risk managementactivities. However, the sensitivity of investment to volatility does not suggestthat "rms should necessarily reduce or eliminate cash #ow volatility. Werecognize that volatility is a choice variable and assume that managers makerational decisions based on all available information. Our results provide anadditional source of information that managers can use to assess the bene"ts ofreducing cash #ow volatility. Firms must weigh these bene"ts against the costs,which can vary across "rms and industries. Risk management costs are likely tobe low, for example, for "rms in the oil and gas, mining, and agricultureindustries where liquid, well-developed derivatives markets exist for a risk thatrepresents 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 #owvolatility results from factors that are relatively uncorrelated with interest rates,foreign exchange prices, or commodity prices. The cross-sectional variation inthese costs, relative to the potential bene"ts of reduced volatility, leads tointeresting cross-sectional implications about risk management decisions.

The positive association between a "rm's current cost of external capital andits historical cash #ow volatility is a subtle but important distinction for riskmanagers. One interpretation of this result is that debt and equityholders usehistorical volatility to predict future cash #ow volatility when they set prices.

B.A. Minton, C. Schrand / Journal of Financial Economics 54 (1999) 423}460 425

This interpretation implies that a "rm's cost of accessing capital will depend onthe expected persistence of cash #ow volatility into future periods. Hence,cross-sectional di!erences in the persistence of the e!ects of risk managementactivities will be associated with cross-sectional di!erences in the associationbetween volatility and the "rm's cost of accessing capital. In the extreme case,risk management activities that have successfully reduced volatility, but whichare not expected to have a persistent e!ect on volatility in future periods, will notnecessarily reduce a "rm's current cost of accessing external markets. Oneconjecture 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 longer-term risk reduction activities, such as moving a plant overseas to reduce foreignexchange price risk. Understanding how di!erent types of risk managementactivities a!ect the costs that we document is an interesting avenue for futureresearch.

Although this paper provides the "rst direct evidence that cash #ow volatilityis related to lower investment, we are not the "rst to make this claim. Shapiroand Titman (1986), Lessard (1990), Stulz (1990), and Froot et al. (1993) proposea link between volatility and investment in the context of explaining hedgingactivities 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 volatilityare more active in risk management activities. These papers jointly test twohypotheses: (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 asusing derivatives) to reduce the volatility that creates the cost. Our directevidence of an association between volatility and discretionary investmentcomplements the "ndings of these indirect tests.

The paper proceeds as follows. Section 2 provides an outline of the variouspredictions and tests. Section 3 describes the measure of cash #ow volatility andthe methodology for the analysis of the association between volatility andinvestment. Section 4 reports the results of these tests. In Section 5, we examinethe relation between costs of accessing capital markets and investment. Section 6presents the analyses of the relations between cash #ow volatility and thesecosts. Section 7 provides concluding remarks.

2. Overview of the paper

This paper analyzes a large and representative sample of "rms over a seven-year period. The primary advantage of this sample is that the evidence can begeneralized to a broad class of "rms and investment decisions. A disadvantage isthat the results are particularly susceptible to criticisms related to endogeneityissues and omitted correlated variables, despite the use of industry-adjusted data


1FHP (1988) show that investment-cash #ow level sensitivities are greater for "rms with lowdividend payout ratios. The perspective of the FHP paper is that investment-cash #ow sensitivitiesproxy for a "rm's degree of "nancing constraint. However, there is some debate about theinterpretation of the FHP results, with the debate focusing on the de"nition of "nancing constraints(i.e., KZ, 1997; FHP, 1988,1998)

in the analysis. To mitigate these concerns, we perform three separate sets oftests of the e!ects of cash #ow volatility on investment and the costs of accessingcapital, and we support these tests with numerous sensitivity analyses. Whilethere may be questions about the interpretation of the results from any of thethree individual tests, the results taken together support our conclusions. Thissection provides a detailed outline of the approach.

The "rst analysis examines the direct association between investment andcash #ow volatility. We predict that a "rm's cash #ow volatility during a periodwill be negatively associated with its average discretionary investment measuredover the same period. We test for this negative relation using annual cross-sectional regressions of industry-adjusted capital expenditures, research anddevelopment costs, and advertising expenses on industry-adjusted cash #owvolatility. The methodology is described in Section 3 and the results arepresented in Section 4.1. One interpretation of the negative relation is thatvolatility 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 assesswhether the negative association between volatility and investment merelyrepresents the relation between investment and "rm characteristics that arecorrelated with cash #ow volatility but omitted from the analysis. In particular,prior research on "rms' investment decisions has found that investment ispositively related with cash #ow levels and suggests that the investment-cash#ow sensitivities could di!er for "nancially constrained and healthy "rms.1Section 4.1 includes an assessment of cross-sectional di!erences in investment-voltatility sensitivities across cash #ow levels. Section 4.3 examines alternativeexplanations for the results. Speci"cally, we address concerns about the causal-ity of the relation between investment and volatility, the impact of "nanciallydistressed "rms on the results, and variable speci"cation issues.

The remainder of the paper examines whether the source of the negativerelation between investment and volatility is a positive relation between volatil-ity and the costs of external "nancing. In Section 5, we re-estimate the sensitivityof investment to cash #ow volatility as in the "rst analysis and include a proxyfor a "rm's cost of accessing external capital and an interaction variable that isthe product of this proxy and cash #ow volatility. We predict that "rms withhigher costs of accessing capital will have lower investment, all else equal. Thecoe$cient on the proxy measures the direct association between capital costs


and investment, while the coe$cient on the interaction term indicates whetherthe negative association between volatility and investment is a!ected by cross-sectional di!erences in the costs of accessing external capital. We use nineproxies for these costs: S&P bond ratings, yields-to-maturity on debt, stockmarket betas, total equity price risk, weighted average costs of capital, analystfollowing, dividend payout ratios, "rm size, and bid}ask spreads on commonstock.

In Section 6, we examine the associations between the proxies for the costs ofaccessing external capital (except "rm size) and cash #ow volatility. We estimateeight separate sets of annual cross-sectional regressions that measure the associ-ations between cash #ow volatility and each of the proxies for capital costs.Based on existing theory and empirical research, we predict that cash #owvolatility is positively associated (in most cases) with costs of accessing externalcapital. The regressions include controls for a "rm's level of cash #ows as well asvariables that have been identi"ed in prior research as determinants of theproxies.

3. Methodology

Section 3.1 de"nes cash #ow and the methodology for measuring cash #owvolatility. Section 3.2 de"nes the proxies for investment and the regressionequations used to estimate the association between investment and cash #owvolatility.

3.1. Measures of cash yow and cash yow volatility

Operating cash #ow is computed quarterly for all non-"nancial "rms onCompustat as sales (Compustat data item 2) less cost of goods sold (item 30) lessselling, general and administrative expenses (item 1) less the change in workingcapital for the period. Working capital is current assets other than cash andshort-term investments less current liabilities and is calculated as the sum of thenon-missing amounts for accounts receivable (item 37), inventory (item 38), andother current assets (item 39) less the sum of the non-missing amounts foraccounts payable (item 46), income taxes payable (item 47), and other currentliabilities (item 48). Quarterly selling, general and administrative expenses ex-clude one-quarter of annual research and development costs (item 46) andadvertising expenses (item 45) when those data items are available. Thus,operating cash #ow represents the cash #ow available for discretionary invest-ment.

Cash #ow volatility is de"ned as the coe$cient of variation in a "rm'squarterly operating cash #ow over the six-year period preceding each of theseven sample years from 1989 through 1995. Thus, for the sample year 1995, the


Table 1Summary 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 withoperating cash #ow data available. The number of sample "rms reported is the number with dataavailable for 1995. The "rms are on the Compustat quarterly data tapes from 1989 to 1994

Industry name Two-digitSIC code

Number ofsample "rms

Metal mining 10 20Oil and gas extraction 13 64Building construction* general contractors, operative builders 15 14Non-building construction 16 11Food and kindred products 20 47Textile mill products 22 24Apparel and other "nished products 23 22Lumber and wood products, except furniture 24 14Furniture and "xtures 25 15Paper and allied products 26 30Printing, publishing and allied 27 36Chemicals and allied products 28 105Petroleum re"ning and related industries 29 33Rubber and miscellaneous plastic products 30 30Stone, clay, glass, and concrete products 32 16Primary metal industries 33 48Fabricated metal, except machinery, transportation equipment 34 49Machinery, except electrical 35 125Electrical, electrical machinery, equipment, supplies 36 106Transportation equipment 37 43Measuring instruments; photographic goods; watches 38 63Miscellaneous manufacturing industries 39 22Water transportation 44 10Communication 48 19Electric, gas, sanitary services 49 23Durable goods * wholesale 50 41Non-durable goods * wholesale 51 33General merchandise stores 53 22Food stores 54 16Apparel and accessory stores 56 16Furniture, home furnishings stores 57 11Eating and drinking places 58 24Miscellaneous retail 59 32Business services 73 57Amusement, except motion pictures 79 13Health services 80 19Environmental services 87 14

1,287


2The results are qualitatively similar if the investment variables are scaled by sales revenue.

coe$cient of variation is calculated using 24 quarters of data from the "rst "scalquarter of 1989 to the fourth "scal quarter of 1994. A "rm is included in thesample for a given year if it has at least 15 non-missing observations during the24 quarters. The coe$cient of variation is the standard deviation of operatingcash #ow scaled by the absolute value of the mean over the same period. Theresulting metric is a unitless measure of variation that has been used by Albrechtand Richardson (1990) and Michelson et al. (1995).

The coe$cient of variation of each "rm-year observation is adjusted relativeto the median for all sample "rms in the same two-digit SIC code for the samesample year. This continuous measure of a "rm's industry-adjusted coe$cient ofvariation in operating cash #ows is denoted CVCF. Industry-adjusted coe$-cients of variation control for di!erences across industries in the quarterlyseasonality of cash #ows and in the nature of "rms' operations. Because of theindustry adjusting, we eliminate "rms in industries with less than ten "rms withavailable data. We also delete seventy "rm-year observations representingtwenty-six "rms that are classi"ed as being in reorganization or liquidationbased 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 numberof "rms by industry for the 1995 sample. The distributions of "rms in othersample years are similar. The sample represents "rms in 37 separate two-digitSIC codes and is consistent with the distribution of "rms on Compustat exceptthat 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"a0#a

1CVCF# +

i/2,3

aiCONTROL

i#e

1. (1)

INVESTMENT is one of three proxies for discretionary investment: capitalexpenditures, R&D costs, or advertising expenses. Capital expenditures (Com-pustat data item 90), R&D costs (item 46), and advertising expenses (item 45) areall scaled by the "rm's total assets at the beginning of the year. The extantliterature on the sensitivity of investment to cash #ow levels, including (FHP,1988,1998) and KZ (1997), scales the only proxy for investment, capital expendi-tures, by beginning of period total "xed assets. In this paper, scaling by totalassets provides a consistent scaling variable across all three proxies for invest-ment.2


3Both FHP (1988) and KZ use variants of Tobin's Q as a proxy for growth opportunities. KZmeasure Tobin's Q as the ratio of the market value of assets to the book value of assets. FHPmeasure Tobin's Q using replacement costs.

4An alternative test statistic is Z@"1/J¹ +Nt/1

ti/Jk

i/(k

i!2) where t

iis the t-statistic for year

i and kiis the degrees of freedom. Z@ assumes the annual parameter estimates are independent and is

likely overstated; Z corrects for the potential lack of independence.

We compute average capital expenditures, R&D costs, and advertising ex-penses for the same rolling six-year periods over which we measure volatility.Because the average investment variables are measured contemporaneouslywith volatility, the results of the regression analyses indicate whether "rms withhigher volatility during a given period make lower average investments duringthat same period. The three proxies for discretionary investment are adjustedrelative to the median for all sample "rms in the same two-digit SIC code for thesame sample year. Industry-adjusting the proxy variables for investment con-trols for variation across industries in capital intensity and growth during thesample period.

The model includes two control variables (CONTROL) that measure growth.FHP (1988) identify sales growth as a signi"cant determinant of capital expendi-tures. Sales growth is the average annual change in sales, scaled by beginningof period sales, for the same rolling six-year periods as volatility. The secondproxy 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 alsoinclude additional "rm and industry characteristics in the regression equationsthat estimate the determinants of investment. However, it is the growth variablesthat are consistently signi"cant across various studies. Like the dependentvariable and the coe$cient of variation, the control variables are industry-adjusted.

Eq. (1) is estimated annually using ordinary least squares regressions foreach of the seven samples from 1989 to 1995. We present the means of theannual coe$cient estimates. To test the hypothesis that the mean coe$cientestimate 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 deviationof the annual t-statistics, respectively, and N is the number of annual observa-tions.4

We estimate two equations that control for the potential relation betweeninvestment and cash #ow levels. Eq. (2) partitions the sample "rms based on thelevel of industry-adjusted cash #ows:

INVESTMENT"b0#b

1LO#b

2HI#b

3CVCF#b

4CVCF]LO

#b5CVCF]HI# +

i/6,7

biCONTROL

i#e

2. (2)


Tab

le2

Des

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stat

istics

ofin

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ry-a

dju

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coe$

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tsofva

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Mea

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120

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0.03

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(Dec

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(Dec

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)

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0N

319

431

320


5We also estimate speci"cations that include a third control variable which is the industry-adjusted log of "rm size where size is de"ned as the market value of a "rm's equity plus the bookvalue of its debt. The results are qualitatively similar to those reported.

6Because the number of "rms in the two-digit SIC codes is not always a multiple of ten, eachdecile contains a di!erent number of observations. As an example, if there are 32 "rms in an industryfor 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.

7The number of observations per decile in Panels A and B are di!erent because cash #owvolatility data can be missing for "rms that have annual cash #ow level data. We require that a "rmhas at least 15 quarters of non-missing data to calculate the volatility measure.

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 areranked based on the industry-adjusted average annual level of operating cash#ows scaled by beginning of period total assets. The inclusion of the cash #owlevel variables controls for the observed sensitivity of investments to cash #owlevels 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 con-tinuous measure of industry-adjusted annual operating cash #ows (in levels)scaled by beginning of year total assets averaged over the same six-year periodas volatility (OPCF):

INVESTMENT"c0#c

1OPCF#c

2OPCF2#c

3CVCF

#c4

CVCF]OPCF# +i/5,6

ciCONTROL

i#e

3. (3)

OPCF2 controls for potential nonlinearities in the relation between invest-ment and industry-adjusted average annual operating cash #ow. The interactionvariable (CVCF]OPCF) measures the impact of a "rm's cash #ow level on theestimated 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$cientsof variation in operating cash #ows. Each "rm in decile one, for example, hasa coe$cient of variation of operating cash #ow that is in the lowest ten percentrelative to other "rms in its industry.6 Statistics are reported for decile one (thelowest volatility measure), decile two, decile three, the middle four deciles asa group (deciles four through seven), decile eight, decile nine, and decile ten (thehighest volatility measure) for the sample year 1995. Results for other sampleyears (not reported) are similar.7

Panel A of Table 2 illustrates that the increases in the coe$cients of variationare non-linear across the deciles. This pattern emerges even though we removethe top ten percent of decile 10 (top one percent of the sample "rms). The mean


8 In all regressions in Table 3, the estimated coe$cients on the control variables for growth aresigni"cant and of the predicted signs.

results are driven by some extreme observations. The medians follow a similar,although less dramatic, pattern. In the regression analyses, we set all coe$cientsof variation that are greater than 100 equal to 100, which approximatelyrepresents the 99th percentile. In addition, in#uential observations in the annualregressions are downweighted by the method of Welsch (1980).

Panel A also indicates that there is a negative association between CVCF andmean levels of average annual operating cash #ow. Industry-adjusted cash #owlevels range from 0.020 for "rms in the lowest decile ranked on CVCF to !0.053for "rms in the highest decile. This pattern emerges despite the use of thecoe$cient of variation to measure volatility, which reduces the likelihood ofa mechanical relation between volatility and levels. This negative relation justi"esthe use of levels as control variables in Eqs. (2) and (3). The negative associationbetween volatility and levels is con"rmed in Panel B in which "rms are rankedinto deciles (annually) based on industry-adjusted cash #ow levels. For "rms thathave cash #ows that are in the lowest three deciles when compared to "rms intheir respective two-digit SIC code (LO), the average CVCF is 6.574, compared toa CVCF of 0.368 for "rms in the highest three deciles (HI).

Panel B also indicates that the standard deviations of the coe$cients ofvariation vary across "rms with di!erent cash #ow levels, which suggests thatEq. (2) may be mis-speci"ed. The speci"cation of Eq. (2) as a pooled regressionwith separate parameter estimates across groups is the most e$cient speci"ca-tion only if the standard deviations of the independent variables are similaracross the groups (Greene, 1993, p. 236). In the case of dissimilar variances,the appropriate technique is to estimate Eq. (1) separately for each group. Theresults from the separately speci"ed equations are qualitatively similar to thoseobtained 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 theanalyses.

4.1. Regression analysis of investment and volatility

Table 3 reports the means of the annual coe$cient estimates from regressionEqs. (1)}(3) using industry-adjusted average capital expenditures, R&D costs,and advertising expenses as proxies for discretionary investment. We do notpresent the coe$cient estimates on the control variables.8


Tab

le3

Mea

nsof

annual

regr

essions

ofpro

xies

for

discr

etio

nary

inve

stm

enton

cash#ow

vola

tilit

y

Mea

nsofa

nnual

regr

essionsofi

ndust

ry-a

djust

edca

pita

lexp

endi

ture

s,re

sear

chan

ddev

elop

men

t(R

&D

)cost

s,an

dad

vert

isin

gex

pen

se(p

roxy

variab

les

fordiscr

etio

nar

yin

vest

men

t)on

indu

stry

-adju

sted

oper

atin

gca

sh#ow

vola

tilit

y(C

VC

F)a

ndin

dust

ry-a

dju

sted

sale

sgr

ow

than

dbo

ok-

to-m

arke

tra

tios

.O

per

atin

gca

sh#ow

vola

tilit

yis

mea

sure

das

theco

e$ci

entofv

aria

tion

ofa"rm's

quar

terly

ope

rating

cash#ow

ove

rth

esix-

year

period

pre

cedi

ng

each

ofth

ese

ven

sam

ple

year

sfrom

1989

thro

ugh

1995

.The

depe

nden

tva

riab

lesan

dth

eco

ntr

olv

aria

bles

repr

esen

tav

erag

esofa

nnual

amou

ntsm

easu

red

over

thesa

mesix-

year

per

iod.

LO

and

HIar

ein

dic

atorva

riab

leseq

ual

toon

eif

a"rm

isin

thelo

wes

torhig

hes

tthre

edec

ilera

nkin

gs,r

espec

tive

ly,b

ased

on

the

leve

lof

its

six-

year

aver

age

annua

lin

dust

ry-a

djust

edop

erat

ing

cash#ow

s.O

pera

ting

cash#ow

issa

les!

cost

of

goods

sold!

selli

ng,ge

nera

lan

dad

min

istr

ativ

eex

pense

s(e

xclu

ding

R&

Dan

dad

vert

isin

g)!

the

chan

gein

work

ing

capital

.For

each

equat

ion,

mea

nsofth

ese

ven

annua

lle

astsq

uare

sva

lues

for

each

coe$

cien

t(a6

it)ar

epre

sent

ed.Z

-sta

tist

ics

tote

stth

ehyp

othes

isth

atE(a6

)"0

are

show

nin

pare

nth

eses

.In#ue

ntial

obse

rvat

ions

inth

ean

nual

estim

atio

ns

are

dow

nw

eigh

ted

byth

em

etho

dof

Wel

sch

(198

0).

Coe$

cien

tes

tim

ates

on

contr

ol

variab

les

incl

uded

inth

ere

gres

sions

(indu

stry

-adju

sted

book

-to-m

arket

ratio

and

indus

try-

adju

sted

sale

sgr

ow

th)ar

enot

pre

sente

d

Pan

elA

:R

egre

ssio

nsus

ing

indi

cato

rva

riab

les

toco

ntro

lfo

rop

erat

ing

cashy

owle

vels

Dep

ende

ntva

riab

leIn

terc

ept*

CV

CF*

Ran

geof

Adj

.R

2In

terc

ept

LO

HI

CV

CF

LO

HI

Cap

ital

expen

diture

s0.

0025

!0.

0002

2.60}4.

26%

(18.

738)

(!20

.212

)

0.00

28!

0.00

320.

0010

!0.

0003

0.00

02!

0.00

084.

59}7.

93(9

.791

)(!

4.97

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.810

)(!

4.84

3)(2

.361

)(!

6.20

3)

R&

Dco

sts

0.00

65!

0.00

060.

19}6.

70%

(10.

388)

!(5

.574

)

0.00

40!

0.00

710.

0091

!0.

0008

0.00

08!

0.00

413.

03}10

.22

(8.5

97)

(!21

.002

)(6

.861

)(!

3.12

1)(2

.795

)(!

4.48

1)

Adve

rtisin

gex

pense

s0.

0106

!0.

0008

0.13}1.

18%

(25.

205)

(!8.

957)

0.00

31!

0.00

130.

0170

0.00

03!

0.00

07!

0.00

323.

27}4.

83(2

.708

)(!

0.45

9)(8

.582

)(0

.501

)(!

0.83

1)(!

5.78

2)


Pan

elB

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ssio

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cont

inuo

usva

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les

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ende

ntva

riab

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OP

CF

(OPC

F)2

CV

CF

CV

CF]

OP

CF

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2

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ital

expen

ditu

res

0.00

250.

0361

!0.

0780

!0.

0003

!0.

0033

3.71}5.

93%

(20.

547)

(5.9

55)

(!2.

088)

(!12

.413

)(!

2.23

5)

R&

Dco

sts

0.00

420.

2050

0.47

27!

0.00

07!

0.01

184.

15}11

.68%

(5.5

60)

(17.

429)

(3.9

67)

(!4.

484)

(!2.

867)

Adve

rtisin

gex

pense

s0.

0056

0.26

761.

3667

!0.

0006

!0.

0059

5.24}7.

26%

(11.

468)

(25.

329)

(5.6

57)

(!4.

184)

(!2.

749)


9We also estimate Eqs. (1)}(3) using the sum of non-missing capital expenditures, R&D costs, andadvertising expenses as a measure of total discretionary investment, because "rms can potentiallysubstitute across these investments. The results (not reported) show a negative and signi"cantrelation between volatility and total investment in the analysis that excludes controls for a "rm'soperating 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, consis-tent with the results for capital expenditures reported in Panel A of Table 3.

Taken together, the results in Table 3 indicate that discretionary investmentlevels are sensitive to cash #ow volatility, and that the degree of the sensitivity isa function of cash #ow levels. In Panel A, in the regressions that include anintercept, 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 developmentcosts, and advertising expenses. However, the results from Eq. (2) indicate thatvolatility is not an equally signi"cant determinant of investment across all levelsof cash #ows. For low cash #ow "rms, the negative relations between volatilityand capital expenditures (!0.0003) and R&D costs (!0.0008) are mitigated(CVCF]LO"0.0002) or eliminated (CVCF]LO"0.0008), respectively. Ad-vertising expenses are negatively associated with cash #ow volatility, but onlyfor "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-adjustedcapital expenditures and R&D costs than "rms with moderate or high cash #owlevels. The sum of the intercept and the coe$cient estimate on the LO dummyvariable indicates that average annual capital expenditures as a percentage oftotal assets for this group are 0.0004 below the industry median. Similarly, R&Dcosts are 0.0031 below the industry median for low cash #ow "rms. Thus, theaverage 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 invest-ment.9

The regression results presented in Panel B include the continuous measure ofcash #ow levels (Eq. (3)). These results also indicate that volatility is negativelyassociated with investment, and that this relation varies across "rms as a func-tion of cash #ow levels. As in Panel A, "rms with higher levels of cash #ows havehigher levels of investments, ceteris paribus. The interaction variable betweenCVCF and OPCF has a negative and signi"cant association with each of thethree proxies for discretionary investment. Thus, the sensitivity of investment tovolatility is stronger as cash #ows increase. This result is consistent with theevidence in Panel A that the impact of volatility is second order relative to thee!ect of cash #ow levels for "rms with low cash #ows.

By adding cash #ow volatility, OPCF2, and CVOPCF]OPCF to the regres-sion, we enhance the explanatory power of the basic investment-liquidity model


10As in Table 2, we delete all "rm-year observations with a CVCF above the 99th percentile. Theresults are qualitatively the same if we include these observations.

11The 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. Thedi!erence between industry-adjusted capital expenditures of the two groups remains signi"cantwhen "rms are sorted based on industry-adjusted cash #ows, but the signi"cance of the di!erencebetween industry-adjusted R&D and advertising declines (p-values between 0.10 and 0.15). Compar-ing the shortfall "rms to the upper half of the sample, the di!erences between all three proxies forinvestment are signi"cant.

in the existing investment literature that includes only the level of operating cash#ow (OPCF) and the control variables for growth. The average incrementalR2 from adding these variables to the basic speci"cation (averaged across theindividual annual regressions) is 1.79, 2.48, and 2.52 for the regressions measur-ing the association between volatility and capital expenditures, R&D costs, andadvertising expenses, respectively.

4.2. Cash yow shortfalls and investment

As more direct evidence that shortfalls in cash #ows are associated with lowerinvestment, we examine the capital expenditures, R&D costs, and advertisingexpenses of "rms that experience shortfalls. A "rm is de"ned to be in a shortfallposition for a sample year if it is in the lower quartile of the sample based on oneof four separate metrics:

1. average annual industry-adjusted operating cash #ows, or2. average annual operating cash #ows that are not industry-adjusted, or3. 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 andits own historical average industry-adjusted annual operating cash #owsmeasured over the prior six-year period.10

The "rst measure de"nes a shortfall relative to the "rm's industry peers and thesecond measure de"nes a shortfall relative to all "rms in the sample. The last twomeasures de"ne a "rm to be in a shortfall position relative to its own historicalcash #ow levels. As a benchmark against which to evaluate the investments ofthese 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 totheir industry peers or to the sample "rms have lower industry-adjusted levels ofdiscretionary investment than "rms with excess cash #ow levels, consistent with


12This evidence is not a necessary condition for a relation between cash #ow volatility andaverage investment. Consider investment projects that require staged "nancing. A "rm with volatilecash #ows may not invest in such projects even if the "rm has su$cient cash #ows to fund the "rststage if the "rm anticipates that it will be in a shortfall position when funding is required at laterstages. This scenario implies a negative relation between cash #ow volatility and average investmentin a cross-sectional analysis. However, this scenario will not imply a di!erence between investmentlevels for "rms in shortfall and excess positions.

13Annual correlation coe$cients between the industry-adjusted average leverage ratios andindustry-adjusted CVs are positive and less than 0.15. Annual correlation coe$cients betweenindustry-adjusted average leverage ratios and industry-adjusted sales growth are either insigni"cantor less than 0.10 in absolute value. Annual correlation coe$cients between industry-adjustedaverage leverage ratios and industry-adjusted average book-to-market ratios are negative and lessthan 0.15 in absolute value.

the regression results in the previous section.12 The di!erences are signi"cant atless 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 allsample years for all three proxies for investment. As Table 4 reports, industry-adjusted capital expenditures are negative for "rms that experience cash #owshortfalls. Thus, these "rms spend less on capital expenditures than the median"rm in their respective industries. However, for R&D and advertising, theresults 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, onlycapital expenditures are signi"cantly di!erent from those of "rms with excesscash #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 "rmsdrive the results in Table 3. Because cash #ow levels and cash #ow volatility arepotentially correlated with a "rm's probability of "nancial distress, and "nancialdistress is potentially correlated with investment decisions, we perform twoanalyses 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 asa proxy for "nancial distress. The leverage proxy equals the average annualdebt-to-equity ratio de"ned as the book value of long-term debt scaled by thesum 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 theprediction that more levered "rms invest less on average (Lang et al., 1996).However, the signi"cance of the association between volatility and investmentholds.13


Table 4Discretionary 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 cashexcess position. A "rm is in a shortfall (excess) position and classi"ed as Short (Excess) if it is in thelowest (highest) quartile based on its average annual industry-adjusted ratio of operating cash #owto beginning of period total assets (OPCF/TA), its average annual unadjusted OPCF/TA, thedi!erence between current year unadjusted OPCF/TA and the six-year average unadjustedOPCF/TA, and the di!erence between current year industry-adjusted OPCF/TA and the six-yearaverage industry-adjusted OPCF/TA. Results for the discretionary investment variables representaverages of the annual variable across the seven sample years from 1989 to 1995. The next to lastcolumn reports t-statistics for the di!erences in the means of discretionary investment for the shortand excess quartiles. The last column reports the number of sample years in the annual analyses forwhich 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 t-stat d sigyears

Short Excess

Relative to industry peers (ranked on average annual industry-adjusted operating cash yow)Industry-adjusted capital expenditures !0.0004 0.0045 8.419 7Industry-adjusted R&D costs 0.0018 0.0173 7.864 7Industry-adjusted advertising expense 0.0053 0.0293 9.466 7

Relative to sample xrms (ranked on average annual operating cash yow)Capital expenditures 0.0159 0.0217 9.135 7R&D costs 0.0379 0.0529 6.461 7Advertising expense 0.0376 0.0649 9.692 7

Relative to its own historical average (ranked on annual operating cash yow less six-year average annualoperating cash yow)

Capital expenditures 0.0162 0.0192 3.729 4R&D costs 0.0469 0.0465 !0.119 1Advertising expense 0.0435 0.0448 0.377 1

Relative to its own historical industry-adjusted average (ranked on annual industry-adjusted operatingcash yow less six-year average annual industry-adjusted operating cash yow)

Industry-adjusted capital expenditures 0.0007 0.0038 4.358 4Industry-adjusted R&D costs 0.0104 0.0079 !1.108 1Industry-adjusted advertising expense 0.0046 0.0040 !0.244 0

Second, we identify and eliminate "nancially distressed "rms from the sampleand re-estimate the relation between volatility and investment (Eqs. (1) and (2)).Since there is no consensus on a measure of "nancial distress, we identifydistressed "rms using seven di!erent metrics that are proposed by existingstudies. A "rm-year observation is considered distressed if it has: (1) speculativegrade debt (S&P bond ratings of BB and worse); (2) a negative earnings-priceratio; (3) negative average annual asset growth calculated over the six-year


14There are production functions under which more volatile expenditures might produce morevolatile cash #ows. In this case, investment volatility would lag cash #ow volatility. Because weobserve a contemporaneous relation, this concern is somewhat mitigated although not eliminated.We thank the referee for o!ering this alternative interpretation.

period preceding the sample year; (4) average total assets in the lowest quartileof 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 interestcoverage 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 foreach of the seven reduced samples (not presented) are qualitatively similar tothose reported in Table 3. Thus, "nancially distressed "rms do not appear todrive the results.

The second sensitivity analysis examines the causality of the relation betweencash #ow volatility and discretionary investment. Our interpretation of theresults in Table 3 is that cash #ow volatility, on average, leads to lowerinvestment. However, an alternative explanation is that di!erent levels of invest-ment (the dependent variable) produce di!erent volatilities due to the nature ofthe investments. To some degree, and with a lag, expenditure choices and patternsmay 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 thatinvestment-volatility sensitivities are related to the costs of accessing capital. Sucha relation would not be expected if investment levels determine volatility.

While we cannot provide conclusive evidence that higher volatility leads tolower investment rather than lower investment leading to higher volatility, wepresent additional results that are more consistent with our interpretation of theresults. First, cash #ow volatility is not highly correlated with our proxies forgrowth. We would expect a signi"cant and positive correlation if investmentdetermines cash #ow volatility. Over the seven-year sample period, the correla-tion coe$cients between industry-adjusted operating cash #ow volatility andaverage 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 positivelyrelated to the industry-adjusted volatilities of the three proxy variables fordiscretionary investment across all levels of cash #ows. We would expect thispositive relation if cash #ow volatility leads to lower investment. However, ifdi!erent levels of investment (the dependent variable) produce di!erent volatil-ities, we expect no association between the volatility of investment and cash #owvolatility.14

Third, we "nd that earnings volatility is not related to average investmentlevels and that the inclusion of earnings volatility in Eqs. (2) and (3) does notchange the negative relation between cash #ow volatility and investment. Thisresult is consistent with our interpretation that greater cash #ow volatility


implies more frequent periods of cash #ow shortfalls, and that these shortfallsare related to lower investment. If the causality works in the other direction andinvestment decisions a!ect volatility, it is unclear why investment decisionswould a!ect only cash #ow volatility and not earnings volatility. In sum, thesethree pieces of evidence are consistent with our interpretation that cash #owvolatility is associated with lower average investment because it measures theincidence of cash #ow shortfalls.

The third sensitivity analysis shows that the results are insensitive to ourmethod 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 thismeasure. The results using this alternative measure of volatility to control forquarterly seasonality in operating cash #ows are qualitatively similar to thosepresented in Table 3.

The fourth sensitivity analysis indicates that the results are not in#uenced bycross-sectional variation in growth opportunities. This evidence supplementsthe controls for growth provided by sales growth and book-to-market ratiosand industry-adjusting. Volatility remains a signi"cant negative determinant ofinvestment (in Eq. (3)) for both the upper and lower deciles of "rms partitionedbased on book-to-market ratios as an indicator of growth.

The "nal sensitivity analysis indicates that the results are robust to alternativede"nitions for &volatility' in cash #ows. The coe$cient of variation attempts tocontrol for a mechanical relation between volatility and levels by scaling thestandard deviation of cash #ows by the absolute value of the mean. An alterna-tive methodology is to scale cash #ows, e.g., by total assets, and to compute thestandard deviation of the ratio. We present the results using the coe$cient ofvariation because the scaling choice (assets in this example) will induce theresults if asset levels are correlated with investment decisions. However, we alsoperform the analyses in Table 3 using the standard deviation of cash-return onassets, cash-return on the book value of equity, and cash-return on the marketvalue of equity as the measure of cash #ow volatility. The results are qualitat-ively similar.

5. Investment, volatility, and the costs of accessing external capital

This section investigates whether "rms' investment decisions are directlyrelated to the costs of accessing capital markets and whether these costs a!ectthe sensitivity of investment to cash #ow volatility. This analysis also demon-strates whether cash #ow volatility remains a signi"cant determinant of invest-ment 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 evidenceabout whether project risk is a correlated omitted variable that explains ourbasic "nding of a negative relation between investment and volatility.


15Using S&P bond ratings as a proxy for the cost of accessing debt capital in an OLS regressionassumes 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 betweenA and BBB "rms is 42 basis points, between BBB and BB "rms is 110 basis points, and between BBand B "rms is 183 basis points. Section 5.2 provides a sensitivity analysis of this variable as a proxyfor the cost of debt.

5.1. Methodology

The regression model is an augmented version of Eq. (2) that includes a proxyfor the industry-adjusted cost of accessing capital (CAPCOST), an interactionvariable equal to the product of CAPCOST and the industry-adjusted coe$c-ient of variation of operating cash #ow (CVCF), and controls for cash #owlevels:

INVESTMENT"d1#d

2CVCF#d

3CAPCOST

#d4

(CVCF]CAPCOST)

# +i/LO,HI

MDi[d

1i#d

2iCVCF#d

3iCAPCOST

#d4i

(CVCF]CAPCOST)]N#e4. (4)

DLO

(DHI

) is an indicator variable equal to one if the observation has cash #owsin the lowest (highest) three deciles based on its industry-adjusted averageannual level of operating cash #ows scaled by beginning of period total assets.

CAPCOST is included in the regression to directly measure the relationbetween the costs of external capital and investment, but it also serves as a proxyfor project risk. The interaction variable (CVCF]CAPCOST) measureswhether cross-sectional variation in the costs of accessing capital marketsmitigates (or exacerbates) the impact of volatility on investment levels. Nineseparate variables are used as proxies for a "rm's costs of accessing debt andequity 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 ofcapital (WACC). We predict that "rms with a higher risk-adjusted cost of capitalon an industry-adjusted basis will have lower industry-adjusted levels of invest-ment, 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 combinationof a "rm's YTM and the annual average of its daily equity return (RET) from


Table 5De"nitions of variables that proxy for the cost of accessing external capital markets

Variable Variable name De"nition

Cost of debt capitalS&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 (exclud-ing convertible debt), calculated using data from S&PBond Guides.

Cost of equity capitalSystematic 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 re-turns obtained from CRSP stock "les.

Other costs of accessing capital marketsWeighted average costof capital

WACC The after-tax YTM times the book value of long-termdebt scaled by SIZE plus the return on equity fromCRSP times the market value of equity scaled bySIZE.

Analyst following ANALYST The maximum number of analysts making a forecastof earnings during the sample calendar year fromI/B/E/S.

Dividend payoutratio

DIV The ratio of total cash dividends paid during the "scalyear (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 thebid and ask prices scaled by the daily closing price.

Total "rmcapitalization

SIZE The market value of equity plus the book value of debtplus preferred stock (Compustat data item 130). Themarket value of equity is share price times the numberof common shares outstanding (Compustat data item199]data item 25). The book value of debt equalslong-term debt plus the current portion of long-termdebt (Compustat data item 9#data item 34).

CRSP. The after-tax YTM is weighted by the debt-to-equity ratio (where thedenominator is the market value of total common equity plus the book value ofdebt) and RET is weighted by one minus the debt-to-equity ratio.

BETA and pRET measure a "rm's cost of accessing equity capital. Ina Sharpe-Lintner world, cross-sectional variation in "rms' costs of equity is thedirect result of cross-sectional variation in "rms' betas. Thus, if the assumptions


16Firms can also provide liquidity with stock repurchases or special dividends. These strategiesbias against observing a relation between dividend payout ratios as a measure of the cost ofaccessing capital and investment.

underlying the Sharpe-Lintner CAPM hold, the higher a "rm's stock beta, thehigher is its cost of raising equity capital. If these assumptions do not hold, thenother systematic factors not captured by beta or unsystematic risk can a!ect thecost of equity. In this case, the higher the total risk of a "rm's stock (systematicrisk plus unsystematic risk), the higher is its risk-adjusted cost of raising equitycapital.

We predict that four additional proxies are associated with a "rm's cost ofaccessing capital because of capital market imperfections: (1) analyst following(ANALYST), (2) bid}ask spreads (BASPRD), (3) "rm size, and (4) dividendpayout ratios (DIV). These proxies are related to the costs of accessing capitalbecause they measure information asymmetry or the demand for liquidity, bothof which can lead to cross-sectional variation in the costs of accessing equity.

Lang and Lundholm (1996) and Brennan and Hughes (1991) show thatanalyst following is negatively associated with information asymmetry. Amihudand Mendelson (1988) "nd that bid}ask spreads are positively related to in-formation asymmetry. As summarized in Botosan (1997), information asym-metry can have a positive association with a "rm's cost of equity for two reasons.First, greater information reduces transaction costs which creates greaterdemand for a "rm's securities. The greater demand increases liquidity and&liquidity-enhancing policies can increase the value of the "rm by reducing itscost of capital'. (See Amihud and Mendelson, 1988.) Second, greater informationreduces estimation risk about the value of a "rm's equity. Lower estimation riskwill 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 ofaccessing external capital.

Firm size (the natural logarithm of SIZE) is also a proxy for informationasymmetry. 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 "rmshave lower costs of accessing capital markets.

Dividend payout ratios (DIV) measure the liquidity of an investment ina "rm's stock. Asquith and Mullins (1983), Aharony and Swary (1980), Lang andLitzenberger (1989), and Hepworth (1953) indicate that capital markets valuedividends because of liquidity constraints when equityholders are unable toborrow and lend freely, or because dividends provide a credible signal ofmanagement's private information. Because liquidity is associated with a lowercost of accessing capital markets, we predict that high dividend payout "rmshave lower costs of accessing capital.16 The dividend payout ratio is de"ned as


17The analysis includes only "rms with S&P bond ratings. Gilchrist and Himmelberg (1998) andCalomiris et al. (1995) contend that rated "rms have a lower cost of accessing external capitalrelative to unrated "rms. To control for this potential selection bias, following Gilchrist andHimmelberg (1998) we estimate Eq. (4) using as the proxy for CAPCOST an indicator variable thatis equal to one if the "rm has an S&P bond rating and equal to zero otherwise. The results aresimilar except that the coe$cient on the interaction term between CAPCOST and CVCF isinsigni"cant.

the ratio of total dividends paid during the year to net cash #ows (beforedividends) during the year because we focus on cash #ow volatility. The resultsare qualitatively unchanged using the traditional de"nition of a dividend payoutratio, dividends per share divided by earnings per share.

5.2. Results

Results are reported in Table 6. Coe$cient estimates on the control variablesand 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 remainsacross all regression equations (except when yield-to-maturity is used as a proxyfor the cost of accessing external capital). Therefore, the documented associationbetween investment and volatility does not simply re#ect an omitted correlatedvariable 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 theincidence of internal cash #ow shortfalls.

Second, higher costs of accessing external capital are associated with lowerinvestment, on average. As Table 6 reports, lower capital expenditures aresigni"cantly associated with worse S&P bond ratings (higher numerical code),17higher total equity price risk, higher bid}ask spreads, higher weighted averagecosts of capital (marginally signi"cant), and lower information asymmetry asmeasured by analyst following and "rm size. As before, cash #ow levels appearto have a "rst-order e!ect on investment. The bene"ts of "rm size in terms ofhigher investment are increasing in the level of a "rm's cash #ows. Similarly, thenegative relation between investment and bid}ask spreads is eliminated for "rmswith high cash #ows.

Two results are contrary to our predictions. Lower investment is associatedwith lower systematic risk and higher dividend payout ratios. We expect theopposite relations assuming that beta is positively associated with the cost ofaccessing external capital and that the dividend payout ratio is negativelyassociated with these costs. One possible explanation for the negative associ-ation between dividends and investment is that "rms consider dividendpayments to be non-discretionary. Alternatively, Smith and Watts (1992)suggest that dividend paying "rms are more mature and have fewer investment


Tab

le6

Mea

nsofa

nnua

lreg

ress

ionsofc

apital

expen

ditu

reson

cash#ow

vola

tilit

y,pr

oxie

sfo

rth

eco

stsofa

cces

sing

exte

rnal

capital

,and

inte

ract

ion

variab

lesbet

wee

nvo

latilit

iesan

dpr

oxie

sfo

rth

eco

sts

ofac

cess

ing

exte

rnal

capi

tal

Mea

ns

ofan

nua

lre

gres

sion

sof

aver

age

annua

lin

dus

try-

adju

sted

capi

talex

pen

ditu

res

onin

dust

ry-a

djust

edop

erat

ing

cash#ow

vola

tilit

y(C

VC

F),

proxi

esfo

rth

eav

erag

ean

nua

lco

sts

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cess

ing

exte

rnal

capi

tal(C

APC

OST

),an

dan

inte

ract

ion

variab

leeq

ualto

the

pro

duct

ofca

sh#ow

vola

tilit

yan

da

proxy

for

the

aver

age

annua

lco

stof

acce

ssin

gex

tern

alca

pital

(CV

CF

]C

AP

CO

ST).

The

pro

xies

for

the

cost

ofac

cess

ing

exte

rnal

capital

are

S&P

bond

rating

s(S

PB

ON

D),

yiel

d-to

-mat

urity

(YT

M),

wei

ghte

dav

erag

eco

stofca

pital

(WA

CC

),eq

uity

beta

(BE

TA

),st

andar

ddev

iation

ofre

turn

s(p

RE

T),

anal

yst

follo

win

g(A

NA

LY

ST),

divi

den

dpa

yout

ratios

(DIV

),bid}as

ksp

read

(BA

SPR

D),

and

the

nat

ura

lloga

rith

mof"rm

size

(SIZ

E).

All

oft

hepr

oxie

sar

ein

dust

ry-a

dju

sted

.Det

aile

dde"nitio

nsar

ein

Tab

le5.

The

regr

ession

sal

soin

clude

cont

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for

indus

try-

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grow

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mea

sure

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-adj

ust

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nual

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ow

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k-to

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ket

ratios

.LO

and

HIar

ein

dic

atorva

riab

leseq

ual

toone

ifa"rm

isin

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low

estor

hig

hest

thre

edec

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nkin

gs,r

espec

tive

ly,b

ased

onth

ele

velof

its

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cash#ow

s.O

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atin

gca

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equa

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les!

cost

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ods

sold!

selli

ng,

gene

ral

and

adm

inistr

ativ

eex

pen

ses

(exc

ludin

gR

&D

and

adve

rtisin

g)!

the

chan

gein

work

ing

capi

tal.

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atin

gca

sh#ow

vola

tilit

yis

mea

sure

das

the

coe$

cien

tofv

aria

tion

ofa"rm's

qua

rter

lyop

erat

ing

cash#ow

ove

rth

esix-

year

per

iod

prec

edin

gea

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the

seve

nsa

mpl

eye

arsfrom

1989

thro

ugh

1995

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rage

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allo

ther

variab

les(in

cludi

ngth

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ende

ntva

riab

le)a

reca

lcul

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over

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hic

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vola

tilit

yis

mea

sure

d.F

orea

cheq

uation

,them

ean

oft

hese

ven

annu

alle

ast

squa

resva

lues

oft

heco

e$ci

enton

thein

tera

ctio

nva

riab

le(a6

itis

pre

sent

ed.Z

-sta

tist

icsto

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the

hyp

othe

sisth

atE(a6

)"0

are

show

nin

par

enth

eses

.In#

uent

ialo

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vationsin

the

annua

les

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atio

ns

are

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ted

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the

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elsc

h(1

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on

inte

rcep

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lva

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les

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-to-

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xyfo

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4.49}11

.07%

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294)

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639)

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517)

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303)

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.103

)(!

6.31

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.746

)(!

3.58

8)(!

4.56

5)


opportunities. This explanation is also consistent with the positive associationbetween systematic risk and investment if beta re#ects information aboutgrowth that is not captured by book-to-market ratios or the other controls forgrowth.

Third, signi"cant coe$cients on some of the interaction variables(CVCF]CAPCOST) indicate that the negative association between operatingcash #ow volatility and capital expenditures is mitigated for "rms with lowercosts of accessing external capital markets. Large capitalization "rms which weclaim have a lower cost of accessing external capital have a less negative andmarginally signi"cant sensitivity of investment to cash #ow volatility, on aver-age. However, large "rms with either extremely low or extremely high cash #owsdo 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 negativeimpact of cash #ow volatility on investment for "rms with high cash #ows.Conditional on the negative relation between investment and the costs ofaccessing debt capital as measured by S&P bond ratings, lower costs (lowernumerical ratings) do not mitigate the impact of volatility on investment.

Assuming that "rms with higher cash #ow volatility are more likely to haveinsu$cient internal capital in some periods and require external capital to fundinvestment, the negative association between volatility and investment is consis-tent with the Myers and Majluf (1984) pecking order hypothesis. As furtherevidence on this issue, we also estimate the relation between investment andvolatility separately for "rms with low or high average annual cash balances.This speci"cation is based on Myers' (1984) evidence that internal cash can actas a substitute for external "nancing. A "rm is a low-cash "rm (high-cash "rm) ifit is in the lowest three (highest three) deciles of "rms ranked on the basis of itsindustry-adjusted annual cash balances (Compustat data item 1) averaged overthe same six-year period over which volatility is measured. The results show thatthe association between volatility and investment is more negative for the lowcash "rms. These "rms are more likely to require external capital to fundinvestment because they lack su$cient internal cash bu!ers.

6. Volatility and the cost of accessing capital markets

This section presents evidence that the negative association between invest-ment and volatility is consistent with the basic NPV investment rule by showingthat volatility is directly related to the costs of accessing external capital. Unlikethe tests of the association between volatility and investment, however, thedependent variables in each of the separate regressions of CAPCOST onvolatility are measured at the end of the period over which volatility is mea-sured. For example, volatility measured over the six-year period 1988}1994 ismatched with the "rm's S&P bond rating for 1995. In contrast, in the tests of


18Sloan (1996) "nds that earnings levels are a better predictor of future cash #ow levels than arehistorical cash #ow levels. The conditions under which earnings volatility is a better predictor offuture cash #ow volatility is an open question.

19Trueman and Titman (1988) make a similar prediction. They demonstrate that the incentives tosmooth income and the costs of volatility are related to industry classi"cation because the probabil-ity and costs of bankruptcy vary across industries.

discretionary investment, average investment and volatility are measured overcontemporaneous six-year periods.

The dependent variable is measured di!erently because our predictions aboutwhy volatility a!ects investment di!er from our predictions about why it a!ectsthe proxies for the cost of accessing capital. The contemporaneous measurementof volatility and investment in the discretionary investment tests re#ects theprediction that higher cash #ow volatility over a period, and consequently morelikely realizations of cash shortfalls, is associated with lower investment duringthat same period. In the tests of the association between volatility and the costsof "nancing, the prediction is that historical volatility is relevant because debtand equityholders use historical volatility to predict future volatility. In thiscase, a 1995 bond rating, for example, re#ects the creditor's assessment of futurevolatility as of 1995, and historical data is one factor that creditors can use tomake this assessment.

This di!erence in the analysis calls into question whether cash #ow volatilityis the measure that investors use to assess the risk of future cash #ows. Debt andequityholders alternatively could use earnings volatility to assess future cash#ow volatility.18 Consequently, one could argue that earnings volatility is anomitted variable in the analyses in this section. As a robustness check of theresults, we estimate all of the regressions outlined in this section including notonly cash #ow volatility and cash #ow levels but also earnings volatility andearnings levels. When the earnings variables a!ect the results, we discuss thee!ects.

6.1. Volatility and the costs of accessing debt and equity

We predict positive associations between cash #ow volatility and the twoproxies for a "rm's cost of accessing debt capital, which are S&P bond ratingsand yields-to-maturity. With interim payments, volatility increases a "rm'sprobability 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 servicerequirements. Higher cash #ow volatility increases the probability that the "rm'scash #ow realization in any given payment period will not cover its debt servicerequirements.19


20When the measure of cash #ows is cash #ows after investment, the measure of earnings isoperating income, which is after depreciation.

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 indebt valuation are cash #ows after investment (CFAI).20 Quarterly CFAI equalsoperating cash #ow, as de"ned in Section 3.1, less net capital expenditures,research and development costs (Compustat item 46 divided by four), andadvertising expenses (item 45 divided by four). Net capital expenditures equalgross capital expenditures (item 90) less capitalized interest (item 147 divided byfour) less the after-tax proceeds from sales of PPE (item 83 times one minus thetax 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 averageoperating cash #ow and cash #ow after investment is 97.5%.

Although we are not aware of any direct empirical evidence on the associationbetween cash #ow volatility and the cost of debt, indirect evidence is consistentwith a positive association between earnings volatility and the cost of debt.Collins et al. (1981) and Lys (1984), for example, "nd negative returns atannouncements of accounting rule changes that are predicted to increase earn-ings volatility and indicate that the magnitude of the reaction is positivelyrelated to a "rm's debt constraints. In cross-sectional studies, Bartov (1993) andImho! and Thomas (1988) show that "rms adjust their real activities to avoidvolatility, and that the extent of these adjustments varies with "rms' debtconstraints. These studies suggest that managers have incentives to smoothearnings 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 proxiesfor the costs of accessing equity capital. As discussed by Beaver et al. (1970),estimations of these associations test the joint hypothesis that cash #ow volatil-ity is correlated with a price-relevant risk and that the market impounds thisinformation in security prices.

We do not make a directional prediction about the association betweenvolatility 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 sellrecommendation, but one element of the report is the "rm's earnings forecast. Ifanalysts value forecast accuracy and it is more di$cult to predict earnings forhigh-volatility "rms, then volatility can negatively a!ect the analyst followingdecision. Beidleman (1973), Brennan and Hughes (1991), and articles in thepopular press suggest that analysts are less likely to follow stocks of "rms withmore volatile earnings because it makes their job of estimating &normal' earningsmore di$cult. In addition, Schipper (1991) notes that &readers of analyst reportsmay use forecast accuracy as a quantitative measure of the quality of the overall


21When the measure of cash #ows is net cash #ows, the measure of earnings is net income.

report; this e!ect will create a preference for accuracy2'. These argumentssuggest a negative relation between analyst following and volatility assuminga positive correlation between cash #ow volatility and earnings volatility.

However, Barth et al. (1998) argue that analysts add the greatest value, andthus potentially reap the highest compensation, when information asymmetry isgreatest. In this case, assuming a positive association between cash #ow volatil-ity 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 pricereactions to dividend decreases are larger in magnitude than positive reactionsto dividend increases. This evidence suggests that equityholders value stabledividends. If dividend stability is a priority, "rms with higher cash #ow volatilityare forced to maintain lower dividends to avoid the costs associated with cuttinga dividend.

We predict a positive association between volatility and bid}ask spreads. Thisprediction is based on an assumption that historical cash #ow volatility isassociated with greater uncertainty about future cash #ows. Amihud andMendelson (1988) show that greater uncertainty is associated with higherbid}ask spreads.

Because equityholders have a claim only on residual cash #ows afterdebtholders are paid, we examine whether systematic risk, total equity price risk,analyst following, bid}ask spreads, and dividend payout ratios are associatedwith net cash #ows, after both investment and interest charges.21 Quarterly netcash #ows (NETCF) are measured as cash #ow after investment (CFAI) lessafter-tax interest expense (Compustat item 22 times one minus the tax rate) plusafter-tax capitalized interest (item 147 divided by four times one minus the taxrate). The correlation between industry-adjusted average operating cash #owand net cash #ow is 88.0%.

In summary, we predict a positive association between volatility and S&P bondratings, yields-to-maturity, stock market betas, total equity price risk, and bid}askspreads. The "rst four predictions also imply a positive association betweenvolatility and a "rm's weighted average cost of capital (WACC). We predicta negative association between volatility and dividend payout ratios. No predic-tion 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 isassociated with the proxies for the cost of accessing external capital. Each


22The control variables in the WACC regression are the same as the control variables in the YTMregression.

23As a robustness check of this result, we exclude "rms with S&P bond ratings below investmentgrade and re-estimate the relation between bond ratings and volatility. The results are qualitativelysimilar to those presented in Table 8.

24Because of the high correlation between bond ratings and volatility, we orthogonalize the S&Pbond rating variable with respect to the volatility of cash #ow after investment before including it asa control variable in the yield-to-maturity and WACC regressions.

25Total equity price risk is statistically and positively related to earnings volatility for high-earnings "rms when earnings volatility is included along with cash #ow volatility in the regression.

26We orthogonalize (pRET) with respect to net cash #ow volatility before using it as a controlvariable in the bid}ask spread regression. When bid}ask spread is regressed only on volatility, weobserve a positive relation. However, when pRET (unorthogonalized) is added to the regressionalong with trading volume and "rm size, there is a negative and signi"cant relation betweenBASPRD and volatility.

regression equation includes control variables that prior research has identi"edas determinants of the dependent variable. The control variables are di!erent ineach equation. Because the control variables are not the focus of our analysis,they are not described in detail. Table 7 summarizes the control variables, thepredicted 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 annualordinary least squares regression coe$cients for 1989}1995 for each dependentvariable. Coe$cient estimates for the control variables are not presented. Theresults for these variables are consistent with the predictions from the literaturecited in Table 7.

Table 8 reports the results. In the regressions that exclude the controls fora "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 exceptwhen BETA is the dependent variable. However, once we control for the level ofa "rm's cash #ows, the impact of volatility changes. The discussion focuses onthe regressions that control for the level of a "rm's cash #ows.

As Table 8 reports, the volatility of cash #ow after investment is associatedwith worse S&P bond ratings (higher numerical codes) and higher yields-to-maturity.23 These results are consistent with the prediction that higher volatilityincreases the likelihood that a "rm will not be able to meet its debt payments, allelse equal.24 Similarly, the volatility of cash #ow after investment is positivelyrelated to a "rm's WACC.

Net cash #ow volatility is not signi"cantly associated with stock market betasor total equity price risk once we control for the level of a "rm's net cash #ows.25However, net cash #ow volatility is signi"cantly related to the proxies for thecosts of accessing equity capital that result because of market imperfections.Speci"cally, volatility has a signi"cant positive association with bid}askspreads.26 This positive association is consistent with the joint claim that


Tab

le7

Sum

mar

yof

cont

rolva

riab

les

Sum

mar

yof

contr

olva

riab

les

that

are

used

inth

ere

gres

sions

that

estim

ate

the

rela

tion

betw

een

vola

tilit

yan

dth

eco

sts

ofac

cess

ing

exte

rnal

capital

mar

kets

.The

pro

xies

are

S&

Pbo

nd

ratings

,yie

ld-to-m

atur

ity,

equi

tybe

ta,s

tand

ard

devi

atio

nofr

eturn

s,div

iden

dpay

out

ratios

,anal

ystfo

llow

ing,

and

bid}as

ksp

read

s.The

table

list

sth

eci

tation(s)f

orth

epre

dict

edco

e$ci

entes

tim

ate

asw

ella

sth

epre

dict

edsign

oft

he

coe$

cien

tes

tim

ate

(inpa

rent

hese

s)fo

rth

eco

ntr

olva

riab

le.T

he

sour

cesar

e:A

B:A

lford

and

Boa

tsm

an(1

995)

;AM

:Am

ihud

and

Men

del

son

(198

9);B

anz:

Ban

z(1

981)

;BK

S:B

eave

r,K

ettler

,an

dSc

hole

s(1

970)

;Bhus

han:

Bhus

han

(198

9);B

H:B

renna

nan

dH

ughes

(199

1);C

N:C

heu

ngan

dN

g(1

992)

;EY

R:E

der

ingt

on,

Yaw

itz,

and

Rob

erts

(198

7);

Ham

ada:

Ham

ada

(197

2);H

L:H

owean

dLin

(199

2);K

U:K

apla

nan

dU

rwitz

(197

9);M

P:M

enya

han

dP

audy

al(1

996)

;OB:O'B

rien

and

Bhu

shan

(199

0);

Ogd

en:O

gden

(198

7),a

ndSW

:Sm

ith

and

Wat

ts(1

992)

Con

trolva

riab

les

Pro

xies

for

cost

sofac

cess

ing

debt

mar

ket

sPro

xies

for

cost

sofac

cess

ing

equity

mar

kets

S&P

bon

dra

ting

Yie

ld-to-

mat

urity

Bet

aSta

nda

rdde

viat

ion

ofre

turn

s

Div

iden

dpo

licy

Ana

lyst

follow

ing

Bid}as

ksp

read

OB

,BH

,Firm

size

Ogd

en(!

)O

gden

(!)

Ban

z(!

)A

B(!

)SW

(#)

Bhush

an(#

)S&

Pbon

dra

ting

EY

R(#

)Lev

erag

eK

U,O

gden

(#)

EY

R(#

)H

amad

a,BK

S(#

)SW

(#)

Bet

aK

U(#

)Bid}as

ksp

read

CN

(!)

(#)!

Tra

ding

volu

me

CN

(#)

HL

,MP

(!)

Div

iden

dpay

out

ratio

BK

S(!

)BK

S(!

)H

L(!

)Tota

leq

uity

risk

HL

,MP

,A

M(#

)A

bnorm

alre

turn

sO

B,B

H(#

)G

row

th"

BK

S(#

)SW

(!)

OB

(#)

Sha

reP

rice

CN

(!)

HL

,MP

(!)

!Man

ypap

ers

prop

ose

that

anal

yst

follo

win

gis

rela

ted

toin

form

atio

nas

ymm

etry

.W

eus

ebid}as

ksp

read

asa

mea

sure

ofin

form

atio

nas

ymm

etry

."F

orbe

tasan

ddiv

iden

dpa

youtra

tios,

we

use

mar

ket

-to-b

ook

ratiosan

dse

par

atel

ysa

lesgr

ow

thas

pro

xies

forgr

owth

.Foran

alys

tfo

llow

ing,

cons

iste

ntw

ith

them

ethod

ology

ofO'B

rien

and

Bhu

shan

(199

0),g

row

this

the

net

entr

yof"

rmsin

toth

esa

mple"rm's

indust

ryove

rth

e"ve

-yea

rper

iod

prior

toth

esa

mpl

eye

ar.


volatility is related to information asymmetry and that information asymmetryis related to higher spreads. In addition, volatility has a marginally signi"cantnegative association with analyst following as a proxy for information asym-metry. These negative associations are consistent with the joint claim thatanalysts are more likely to make erroneous stock buy/sell recommendationswhen volatility is high and that analysts attempt to reduce this likelihood by notfollowing "rms with volatile cash #ows. Including earnings volatility in theregression with controls for cash #ow and earnings levels, we "nd that only cash#ow volatility a!ects analyst following even though earnings are the morefrequently 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 withdividend payout ratios. The observation that dividend payout ratios are nega-tively associated with earnings volatility is consistent with Smith and Warner's(1979) observation that dividend restrictions in bond covenants are frequentlybased on accounting earnings realizations.

Finally, as in Table 3, the level of a "rm's net cash #ows has a "rst-order e!ecton the costs of accessing external capital as measured by some proxies. Inparticular, the intercepts indicate that low cash #ow "rms have worse S&P bondratings, higher equity betas, and higher equity price risk than "rms with mediancash #ows. High cash #ow "rms have better S&P bond ratings and lowerdividend payout ratios.

7. Summary and conclusions

This paper provides direct evidence that cash #ow volatility is associated withlower average levels of investment in capital expenditures, research and develop-ment costs, and advertising expenses. Cash #ow volatility remains a signi"cantnegative determinant of investment even after controlling for the costs ofaccessing external capital. Moreover, cash #ow volatility increases these costs.In particular, cash #ow volatility is related to worse S&P bond ratings, higheryields-to-maturity, higher weighted average costs of capital, higher bid}askspreads, lower analyst following, and lower dividend payout ratios. The resultsrelated to the role of capital costs in the investment decision and the importanceof cash #ow volatility in the presence of these costs imply that the sensitivity ofinvestment to volatility does not result because volatility is a proxy for projectrisk. Rather, cash #ow volatility is related to investment because it increases thelikelihood that a "rm will need to access capital markets and it also increases thecosts 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


Tab

le8

Mea

ns

ofan

nual

regr

essions

ofpro

xies

for

the

cost

ofac

cess

ing

capi

talm

arket

son

cash#ow

vola

tility

Mea

nsof

annu

alre

gres

sionsofp

roxi

esfo

rth

eco

stsofa

cces

sing

capital

mar

ket

son

cash#ow

vola

tility

.Pro

xies

forth

ese

cost

sar

ede"ned

inTab

le5.

LO

and

HIar

ein

dica

torva

riab

leseq

ualt

oon

eif

a"rm

isin

thelo

wes

torhig

hes

tth

ree

deci

lera

nkin

gs,r

espec

tive

ly,b

ased

on

thele

velo

fits

indust

ry-a

dju

sted

cash#ow

.Cas

h#ow

afte

rin

vest

men

t(C

FA

I)is

oper

atin

gca

sh#ow

!ne

tca

pita

lexp

enditur

esin

clud

ing

R&

Dan

dad

vert

isin

g.N

etca

sh#ow

(NET

CF

)is

oper

atin

gca

sh#ow!

net

capital

expen

ditu

res(inc

ludi

ngR

&D

and

adve

rtisin

g)!

afte

rtax

inte

rest

char

ges.

Cas

h#ow

vola

tilit

iesre

pre

sent

the

coe$

cien

tof

variat

ion

(CV

)in

cash#ow

estim

ated

over

the

24quar

ters

inth

esix

year

sprior

toth

eye

aroft

heca

lcul

atio

noft

he

dep

enden

tva

riab

le.C

ash#ow

san

dot

her

cont

rolv

aria

bles

arem

easu

red

inth

esa

meye

aras

thedep

enden

tva

riab

le.A

llva

riab

lesar

ein

dus

try-

adju

sted

.For

each

equa

tion

,mea

nsof

these

ven

annu

alle

astsq

uar

esva

lues

for

each

coe$

cien

t(a6

it)ar

epr

esen

ted.

Z-s

tatist

ics

tote

stth

ehyp

oth

esis

that

E(a6

)"0

are

show

nin

par

enth

eses

.In#ue

ntial

obse

rvat

ions

inth

ean

nual

estim

atio

nsar

edow

nwei

ghte

dby

the

met

hod

ofW

elsc

h(1

980)

.C

oe$

cien

tes

tim

ates

on

contr

olva

riab

les

incl

uded

inth

ere

gres

sions

(sum

mar

ized

inTab

le7)

are

notpre

sente

d.

Dep

enden

tva

riab

leIn

terc

ept

Inte

rcep

t]In

terc

ept]

CV

CV

]C

V]

Ran

geof

LO

HI

LO

HI

Adj.

R2

Cos

tsof

acce

ssin

gde

btm

arke

ts:

Cas

hy

owis

dex

ned

asca

shy

owaf

ter

inve

stm

ent

(CF

AI)

S&P

bond

rating

0.15

070.

0303

57.3

1}62

.01%

(0.9

97)

(6.2

72)

0.01

040.

2429

0.24

940.

0293

0.00

980.

0173

57.0

2}61

.87

(0.0

17)

(2.0

45)

(1.9

15)

(2.8

05)

(0.5

36)

(0.3

54)

Yie

ld-to-m

atur

ity

0.46

750.

0192

29.6

2}51

.53%

(4.6

05)

(4.3

90)

0.48

010.

1555

!0.

0424

0.02

06!

0.01

25!

0.00

3128

.89}

51.2

8(3

.676

)(1

.007

)(!

0.76

6)(2

.313

)(!

0.22

4)(!

0.14

0)

Cos

tsof

acce

ssin

gde

btan

deq

uity

mar

kets

:C

ashy

owis

dex

ned

asca

shy

owaf

ter

inve

stm

ent

(CF

AI)

WA

CC

!0.

0046

0.00

5560

.59}

85.6

3%(!

0.18

7)(2

.517

)

!0.

0094

0.03

900.

0178

0.00

930.

0062

!0.

0078

62.1

1}85

.78

(!0.

271)

(0.4

04)

(0.3

42)

(1.8

40)

(0.0

83)

(!1.

384)


Cos

tsof

acce

ssin

geq

uity

mar

kets

:C

ashy

owis

dex

ned

asne

tca

shy

ow(N

ET

CF

)

Stock

mar

ket

beta

0.25

320.

0019

4.78}16

.01%

(14.

024)

(1.2

51)

0.27

430.

1241

!0.

0912

0.00

08!

0.00

090.

0042

3.95}20

.08

(12.

754)

(3.3

58)

(!5.

661)

(0.2

84)

(!0.

644)

(0.7

87)

Stan

dard

dev

iation

of

stock

retu

rns

0.00

290.

0000

277

.63}

88.5

4%(2

.576

)(2

.050

)

0.00

260.

0012

!0.

0002

!0.

0000

!0.

0000

20.

0001

78.0

9}88

.39

(2.6

85)

(2.1

09)

(!0.

934)

(!0.

065)

(!0.

587)

(1.5

58)

Ana

lyst

follow

ing

1.23

47!

0.02

3166

.98}

70.6

8%(3

.914

)(!

4.49

1)

1.41

04!

0.27

50!

0.39

25!

0.02

34!

0.02

420.

0047

66.4

6}70

.64

(4.6

06)

(!1.

035)

(!1.

518)

(!1.

621)

(!1.

084)

(0.3

61)

Div

iden

dpol

icy

0.15

25!

0.00

1215

.22}

23.4

6%(1

5.72

4)(!

2.96

3)

0.22

82!

0.14

55!

0.14

49!

0.00

14!

0.00

140.

0006

21.6

4}28

.32

(17.

154)

(!8.

129)

(!14

.411

)(!

2.43

9)(!

0.63

9)(0

.999

)

Bid}as

ksp

read

!0.

0052

0.00

0276

.53}

86.9

0%(!

5.63

2)(3

.001

)

!0.

0048

!0.

0008

!0.

0008

0.00

020.

0000

!0.

0001

77.5

8}86

.73

(!6.

433)

(!1.

217)

(!1.

345)

(3.5

84)

(0.6

63)

(!1.

253)


investment. We do not claim that these results imply that "rms should reduce oreliminate volatility. Rather, the e!ects of volatility represent one factor thata "rm should consider in its risk management decisions. Firms must decide howto trade-o! the expected negative impact of volatility on investment levelsagainst the other e!ects of managing volatility.

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