investment, cash flow, and corporate hedging

Journal of Corporate Finance 11 (2005) 628–644

www.elsevier.com/locate/econbase

Investment, cash flow, and corporate hedging

Sanjay Deshmukh a,*, Stephen C. Vogt b

aDepartment of Finance, DePaul University, 1 East Jackson Blvd., Chicago, IL 60604, USAbMesirow Financial, Chicago, IL 60610, USA

Received 24 September 2002; accepted 8 February 2005

Available online 27 July 2005

Abstract

We examine the underinvestment rationale for corporate hedging and test the hypothesis that if

firms hedge to reduce both their reliance on external funds and the volatility of internal cash flow,

then their investment spending should be less sensitive to prehedged cash flow. Our results are

consistent with this hypothesis and indicate that investment spending is less sensitive to cash flow for

hedgers than for nonhedgers. We also find that among hedgers, investment spending is less sensitive

to cash flow when the extent of hedging is higher. Our results are generally robust to five different

measures of cash flow.

D 2005 Elsevier B.V. All rights reserved.

JEL classification: G31

Keywords: Investment spending; Cash flow; Corporate hedging; Asymmetric information; Underinvestment

1. Introduction

In the presence of perfect capital markets, there would be no incentive for a firm to

hedge by using derivative instruments. This result obtains directly from the well-known

propositions of Miller and Modigliani (1958, 1961) on capital structure and dividend

policy. Thus, the rationale or motivation for hedging stems from the existence of market

imperfections.

0929-1199/$ -

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ress: [email protected] (S. Deshmukh).

S. Deshmukh, S.C. Vogt / Journal of Corporate Finance 11 (2005) 628–644 629

In this paper, we focus on one strand of the literature that argues that in the presence of

capital market imperfections, external funds are more costly than internal funds. We

examine the implications of the model in Froot et al. (1993). In contrast to most previous

studies, we test a single explanation rather than try to distinguish among the various

explanations. In addition, we use panel data that allows us to capture the time-series

impact of hedging on investment spending while controlling for the cross-sectional

variation in the data.

We examine the following testable hypothesis: If firms hedge to reduce both their

reliance on external funds and the volatility of internal cash flow, then their

investment spending should be less sensitive to internal cash flow in the presence of

hedging. Our results indicate that the sensitivity of investment spending to cash flow

for hedgers (derivative users) is substantially lower than that for nonhedgers

(nonderivative users).

We use five different measures of cash flow and find that our results are generally

robust to these different measures of cash flow. We also provide a more direct test of the

underinvestment hypothesis by examining the impact of the extent of hedging on the

sensitivity of investment to cash flow among hedgers. We show that, among hedgers,

investment spending is less sensitive to cash flow when the extent of hedging is higher.

Our overall results support the implications of the model in Froot et al. (1993). In sum,

firms in our sample appear to use derivative instruments to reduce the volatility of internal

cash flow and the attendant costs of underinvestment.

We organize the paper as follows. Section 2 is divided into two parts. The first part

provides a brief review of the literature on corporate hedging. The second part reviews the

model in Froot et al. (1993) to derive our testable hypotheses. Section 3 describes our data

and the variables we use. Section 4 discusses the empirical results. Section 5 concludes the

paper with a discussion of the findings.

2. Literature review and hypothesis development

2.1. Literature review

Mayers and Smith (1982) and Smith and Stulz (1985) argue that hedging can reduce the

expected tax liability of a firm in the presence of a convex tax schedule. The convexity in

the tax schedule results from progressive tax rates and from the presence of tax loss carry-

forwards and foreign tax credits.

Smith and Stulz (1985) also suggest that hedging can reduce the costs of financial

distress. If financial distress is costly, a firm may have an incentive to reduce its probability

by hedging and thus increase the expected value of the firm. By reducing the deadweight

costs of financial distress, hedging increases debt capacity and helps control the agency

problem associated with free cash flow. Hedging can also restrict the states in which the

firm could default on bond payments and thus help control the agency problem between

bondholders and stockholders.

Stulz (1984) and Smith and Stulz (1985) provide a rationale for hedging based on

managerial risk aversion. They argue that managers may have an incentive to lower their

S. Deshmukh, S.C. Vogt / Journal of Corporate Finance 11 (2005) 628–644630

exposure to firm-specific risk since much of their current and future wealth is tied to the

firm and are thus not well diversified.

Breeden and Viswanathan (1996) and DeMarzo and Duffie (1995) focus on asymmetric

information between managers and outside investors and on managers’ reputations. They

argue that outsiders cannot observe managerial quality and are thus unable to separate the

effect of managerial quality on performance from that of exogenous shocks. Therefore,

managers can engage in hedging to better convey their skills to the labor market.

Stulz (1990), Lessard (1990), and Froot et al. (1993) focus on investment policy to

provide a rationale for corporate hedging. Their models are based on the premise that

external funds are more costly than internal funds. When cash flow is low, the firm may

underinvest and consequently, investment will tend to fluctuate with internal funds. In this

setting, hedging can help a firm stabilize its internal cash flow and control the

underinvestment problem. All of these arguments focus on the benefits of hedging. An

exception is Tufano (1998), who argues that hedging may involve costs if it isolates

managers from the scrutiny of external capital markets.

There have been several attempts to empirically test the predictions of the above

theories on hedging. Most studies use a dichotomous variable to model the decision to

hedge and focus on cross-sectional data. For instance, Nance et al. (1993) use survey data.

Mian (1996), Tufano (1996), Geczy et al. (1997), and Gay and Nam (1998) use data based

on actual derivatives use to distinguish among various explanations. The empirical

evidence with respect to the various explanations is mixed. One exception is Tufano

(1996), who finds support for managerial risk-aversion in the hedging practices of the

gold-mining industry. His is also the only study that takes a time-series approach.

Allayanis and Mozumdar (2000) examine the underinvestment hypothesis of hedging

(based on the model in Froot et al. (1993)) by examining the difference in the sensitivity of

investment to cash flow between hedgers and nonhedgers. They find evidence consistent

with the Froot et al. model, similar to our findings in this paper. However, our work differs

from theirs in several fundamental ways. For instance, Allayanis and Mozumdar (2000)

use a sample of firms based on the S&P 500 index and focus on currency hedgers. In

contrast, we focus on manufacturing firms, and our sample includes firms that engage in

interest-rate and/or currency hedging.

2.2. Underinvestment rationale and hypothesis development

Froot et al. (1993) provide a rationale for risk management based on the notion that

market imperfections cause external funds to be more costly than internal funds. They

model a firm that is faced with a two-period, investment-financing decision. The firm has

some liquid assets w in the first period and must choose its investment spending and

external financing requirements at this time. The first-period wealth w is assumed to be

random. In the second period, the output from the first-period investment is realized and

the outside investors are repaid. Froot et al. assume a concave production function and that

the firm prefers to finance the investment with internal funds before resorting to external

sources. They also assume a deadweight cost to external financing that is an increasing

function of the amount raised. Under these conditions, they show that there is

underinvestment relative to the optimal level under perfect capital markets. The


underinvestment results from the random nature of the first-period wealth w and the

existence of the deadweight costs of external finance.

The issue of hedging arises because w is random. The firm wants to maximize its

expected profits (or its net present value). The concavity of the production function leads

to a globally concave profit function whose value depends on w. Froot et al. assume that

the random fluctuations in w are completely hedgeable and that hedging has no impact on

the expected value of w. Therefore, the firm chooses its hedging policy at period zero to

maximize the profit function in the first period. Jensen’s inequality implies that the value

of the profit function under hedging will be greater than the expected value of the function

when the firm does not hedge and w is allowed to fluctuate.

Froot et al. argue that for hedging to be beneficial (in their model), the level of internal

wealth w must have a positive impact on the optimal level of investment chosen by the

firm. There is evidence that investment spending is sensitive to internal cash flow1.

The Froot et al. model implies that if a firm does not hedge, the variability in the cash

flow from assets in place can cause variability in both investment spending and/or external

funds raised. If the marginal cost of funds increases with the amount raised, a cash shortfall

can disrupt the optimal investment plan and adversely affect firm value. Therefore, firm

value can be enhanced if hedging activities can reduce the variability in internal cash flow.

The adverse impact of cash flow volatility on investment spending is empirically

documented in Minton and Schrand (1999). They find that cash flow volatility is

associated with both lower investment spending and higher costs of accessing capital

through external sources. They also find that firms do not appear to smooth their

investment spending to cash flow fluctuations (by using external funds) but seem to forgo

investment permanently.

The above discussion suggests that hedging can help a firm reduce its cash flow

volatility and the costs associated with underinvestment. Therefore, if firms hedge to

reduce both the cash flow volatility and their reliance on external funds, we should find

that the sensitivity of investment spending to cash flow is lower in the presence of

hedging.

We test this prediction by examining the sensitivity of investment to cash flow for a

sample of both hedgers and nonhedgers. If hedging lessens the impact of liquidity

constraints, we should observe a lower sensitivity of investment spending to prehedged

cash flow for hedgers relative to that for nonhedgers, other things equal. Further, we

expect that among hedgers, the sensitivity of investment spending to cash flow to vary

negatively with the extent of hedging.

3. Data and variables

We obtain our sample of hedgers/derivative users from the Database of Users of

Derivatives, published by Swaps Monitor Publications, New York. As of fiscal years

1 See Fazzari et al. (1988), Hoshi et al. (1991), Vogt (1994, 1997), Kaplan and Zingales (1997), Lamont (1997),

and Carpenter and Peterson (2000), among others.


ending in June 1990, SFAS 105 required public corporations to disclose the use of off-

balance sheet instruments in their financial statements. Therefore, as of June 1990, one can

determine whether a firm has a position in derivative instruments. The Swaps Monitor

database includes all publicly traded corporations and other entities that reported the use of

derivative instruments. Swaps Monitor compiles the information in the database from

public sources that include annual reports and filings with regulatory agencies.

We identify our sample of hedgers/derivative users as of the end of fiscal year 1996

(data beyond 1996 are not available as Swaps Monitor stopped publishing its database of

derivative users). We derive our sample from the Interest Rate and Currency Edition of the

Swaps Monitor database and covers the period, 1992–1996. Therefore, our sample of

hedgers (or derivative users) includes firms that engage in interest-rate and/or currency

hedging. To examine the impact of hedging, we identify a control sample of nonhedgers

from a universe of firms that is not in the Swaps Monitor database, which (by default),

must comprise nonhedgers or nonderivatives users.

We choose our control (nonhedger) sample as follows. For each hedger firm, we choose

a control firm that has the same four-digit SIC code as the hedger and that closely matches

the hedger firm in terms of size as measured by the book value of assets. If no match is

found with the four-digit SIC code, we use the three-digit SIC code.

We match on firm size for two reasons. Larger firms may have lower asymmetric

information and face lower costs of external financing. Second, firm size provides

economies of scale in establishing risk-management or hedging programs. The industry

match accounts for the fact that risks inherent in some industries are more readily

hedgeable than are the risks in others, and may thus provide varying incentives to hedge.

There may also be differences (across industries) among the various cash flow measures

used. Third, firms’ derivatives use is likely to vary across industries. The industry-

matching criterion between hedgers and nonhedgers serves to address these problems.

We collect annual data from Compustat for the hedger and nonhedger samples over a

five-year period, 1992–1996. In contrast to previous studies, we use panel data to exploit

the time-series properties of the data and to provide for a more complete examination of

the impact of hedging on investment policy. We draw on the literature that examines the

impact of liquidity constraints on investment spending to derive our empirical

specification. As with many studies in this area, we focus on manufacturing firms (SIC

2000-3999) and estimate a fixed-effects model on the data.

Our dependent variable is the ratio of investment (Compustat item 128) to beginning-

of-year gross plant and equipment (item 7). The independent variables comprise a cash

flow variable and a proxy for investment or growth opportunities which we term as Q. We

construct Q as the ratio of market value of assets to book value of assets (item 6). We

calculate the market value of assets as the market value of equity (item 24 * item 25) plus

the book value of total liabilities. The book value of total liabilities equals the book value

of assets (item 6) less the book value of equity (item 60).

We acknowledge the difficulty of obtaining a clean measure of prehedged cash flow,

given the incomplete nature and lack of clarity and consistency in reporting. Our objective

is to derive cash flow measures that identify the level of prehedged cash flow available for

investment spending. Since financial statement data do not explicitly identify prehedged

cash flow, we estimate it from the available (financial statement) data. We draw on the


literature to derive several alternative measures of cash flow to identify and extract the

cash flow component that is relevant for our study. These alternative measures represent

variations on the cash flow measures used in the literature and provide a robustness check

for our study.

We begin with a measure of operating cash flow and make adjustments for other

relevant cash flows. The cash flow variable (Cash Flow) that we use in the regressions

refers to one of the following five cash flow measures:

CF Measure1 ¼ Operating Income ðOIÞ Before Depreciation item13ð ÞBeginning-of -year Gross Plant & Equipment item7ð Þ

CF Measure 2 ¼ Sales item12ð Þ � COGS item41ð Þ � SGA item189ð Þ � DNWC

Beginning-of -year Gross Plant & Equipment item7ð Þ

CF Measure 3 ¼ Income Before Extraordinary Items item18ð ÞþD & A item14ð Þ�DNWC


CF Measure 4 ¼ OI Before Depreciation item13ð Þ � Income Taxes Paid item317ð Þ � DNWC


CF Measure 5 ¼ OI Before Depreciation item13ð Þ � DNWC


where

Net Working Capital (NWC) equals Current Assets (item 4) minus Current Liabilities

(item 5)

COGS equals Cost of Goods Sold

DNWC equals Change in Net Working Capital (NWC)

SGA equals Selling, General, and Administrative Expenses

D & A equals Depreciation and Amortization

To gauge the extent of overlap between the five cash flow measures, we compute

pairwise correlation coefficients among them. The correlation coefficients suggest that

there is a significant overlap. For instance, the sample correlation coefficients between

Cash Flow Measure 1 and the other four cash flow measures vary between 0.71 and 0.86

and the pairwise correlations among the other cash flow measures (excluding Cash Flow

Measure 1) vary between 0.91 and 0.95. This significant overlap suggests that there must

be a strong component of prehedged cash flow present in all our measures.

The high correlation among all the cash flow measures also suggests that there must be

a significant economic overlap among them. This reasoning follows because we deflate all

the measures by the same number (item7) and the numerator (for a given measure)

represents some derivative of the numerator in the other measures.

We also construct another measure of cash flow that represents a slight variation on

Cash Flow Measure 4. In place of OI before Depreciation (item13), we use Sales (item12)

minus COGS (item41) minus SGA (item189). Technically, these two measures should

represent the same number. However, firms often include other sources of income when

they report operating income before depreciation. This inconsistency does not exist in our


sample because the (sample) pairwise correlation between Cash Flow Measure 4 and this

alternative measure is one. For robustness, we estimate our model using this alternative

measure. To complement and corroborate our findings, we also provide a more direct test

of the underinvestment hypothesis by examining the impact of the extent of hedging on the

sensitivity of investment spending to cash flow among hedgers.

4. Empirical results

We restrict our sample of hedgers to those firms for which we can identify a nonhedger

(control) firm whose book value of assets is within 20% of that of the hedger firm. In our

final sample of 312 hedger firms, about 57% (177 firms) of the control firms have the

same four-digit SIC code as the corresponding hedgers. The remaining 43% have the same

three-digit SIC code.

In all of the empirical analyses, we focus on those firm-year observations for which (the

relevant) cash flow measure is positive. As Kaplan and Zingales (1997) argue, negative

cash flow might be a sign of financial distress, in which case the firm’s creditors could

force the firm to use its cash flow to repay debt rather than spend it for investment

purposes. In these cases, the sensitivity of investment to cash flow is likely to be biased

downward. In Panel A of Table 1, we provide some relevant statistics on the time-series

nature of the hedger sample. For brevity, we report average values (by year) for the

median, mean, and the number of observations across the five cash flow measures.

In Panel B of Table 1, we provide more summary statistics on key variables for both the

hedger sample and the control sample of nonhedgers. The results suggest that the samples

are closely matched, as indicated by the median values for Investment Spending, Q, Book

Value of Assets, and the five cash flow measures. Again, for brevity, the values for the

median, mean, and the number of observations for each variable (except the five cash flow

measures) represent the average of the values of the median, mean, and the number of

observations across the five cash flow measures. The number of observations is not the

same across the samples (and variables), since we include all firm-year observations for

which data are available. Since our sample of hedgers engage in interest-rate or currency

hedging or both, we also include summary statistics on long-term debt and foreign income.

We report the relevant summary statistics on the long-term debt ratio and the ratio of

foreign pretax income to total pretax income for both the hedgers and the nonhedgers. We

calculate the long-term debt ratio as the ratio of long-term debt (item9) to the book value

of total assets, and the total pretax income equals the sum of domestic pretax income

(item272) and foreign pretax income (item273). Even though the median (and mean)

values for both the long-term debt ratio and the ratio of foreign income to total income are

lower for nonhedgers, the two samples seem to be reasonably closely matched. In addition,

the nonhedgers appear to have exposure to both interest rates and exchange rates, but

choose not to hedge. Therefore, the control sample of nonhedgers is quite closely matched,

in terms of key attributes, to the hedger sample and provides an interesting backdrop to

investigate the impact of hedging (or lack thereof) on investment policy.

In Table 2, we present results from the estimation of a fixed-effects regression model of

Investment Spending on Cash Flow and Q. Panel A contains the results for the hedger

Table 1

Summary statistics

Panel A: Time-series nature of the hedger sample

Year Number of

observations

Median book value of

assets ($ millions)

Mean book value of

assets ($ millions)

1992 233 190.17 699.48

1993 246 188.79 657.25

1994 253 217.02 696.07

1995 261 271.22 748.17

1996 275 295.13 766.95

Panel B: Summary statistics on hedgers and nonhedgers

Variable Hedgers Nonhedgers

Median Mean N Median Mean N

Investment spending 0.1094 0.1656 1177 0.1155 0.2322 947

Q 1.4690 1.7603 1168 1.5479 1.9576 885

Book value of assets ($ millions) 231.29 717.38 1268 218.40 660.56 1097

Cash flow measure 1 0.3170 0.6806 1289 0.3561 0.6655 1065





Long-term debt ratio 0.1678 0.2109 1268 0.1332 0.1870 1091

Foreign income to total income 0.1854 0.2977 767 0.1248 0.2773 404

The sample consists of 312 hedger firms and 312 nonhedger firms. We base our summary statistics on annual data

over the period, 1992–1996, and calculate them by using all available firm-year observations on each variable.

Panel A presents the time-series nature of the hedger sample and reports average values (by year) across all the

cash flow measures. Panel B presents summary statistics on key variables for both the hedger sample and the

nonhedger (control) sample. All item numbers (below) refer to annual data items from Compustat. We calculate

Investment Spending as the ratio of investment (item 128) to beginning-of-year gross plant and equipment (item

7). Q is the ratio of market value of assets to book value of assets (item 6). The market value of assets is the

market value of equity (item 24 * item 25) plus the book value of total liabilities. The book value of total

liabilities equals assets (item 6) minus book value of equity (item 60). Cash Flow Measure 1 equals operating

income before depreciation (item 13) deflated by the beginning-of-year gross plant and equipment (item 7). Cash

Flow Measure 2 equals [sales (item 12) minus COGS (item 41) minus SGA (item 189) minus change in net

working capital] deflated by the beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 3

equals [income before extraordinary items (item 18) plus depreciation and amortization (item 14) minus change in

net working capital] deflated by the beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 4

equals [operating income before depreciation (item 13) minus income taxes paid (item 317) minus change in net


equals [operating income before depreciation (item 13) minus change in net working capital] deflated by the

beginning-of-year gross plant and equipment (item 7). We calculate net working capital as current assets (item 4)

minus current liabilities (item 5). Long-term debt ratio equals the ratio of long-term debt (item 9) to book value of

assets (item 6). We calculate Foreign Income to Total Income as the ratio of foreign pretax income (item 273) to

the sum of domestic pretax income (item 272) and foreign pretax income (item 273). N refers to the number of

observations. In Panel B, the median, mean, and the number of observations (N) for each variable (except the five

cash flow measures)represent average values of the median, mean and N, for that variable, across the five cash

flow measures.


Table 2

Sensitivity of investment spending to cash flow

Variable Cash flow measure 1 Cash flow measure 2 Cash flow measure 3 Cash flow measure 4 Cash flow measure 5

Panel A: Hedgers

Cash flow 0.1718*** (31.66) 0.0485*** (3.64) 0.0690*** (4.08) 0.0210 (1.19) 0.0530*** (3.62)

Qt� 1 0.0264*** (4.19) 0.0327*** (5.00) 0.0314*** (4.74) 0.0509*** (6.42) 0.0435*** (6.06)

Constant 0.0386*** (3.23) 0.0810*** (6.48) 0.0843*** (6.86) 0.0655*** (4.37) 0.0621*** (4.52)

F 507.24*** 21.41*** 21.75*** 21.88*** 27.72***

Number of observations 1154 1069 982 1029 1086

R2 (Overall) 0.5835 0.2385 0.2195 0.1829 0.2296

Panel B: Nonhedgers

Cash flow 0.2066*** (15.42) 0.6253*** (50.30) 0.2925*** (12.11) 0.1325*** (5.32) 0.6215*** (47.77)

Qt� 1 0.0524*** (6.97) �0.0145 (�1.54) 0.0351*** (4.91) 0.0386*** (5.94) �0.0156 (�1.58)

Constant �0.0186 (�1.18) �0.0461** (�2.42) 0.0351** (2.33) 0.0711*** (4.86) �0.0406** (�2.03)

F 197.37*** 1288.50*** 100.85*** 36.56*** 1161.07***


R2 (Overall) 0.5144 0.7036 0.3722 0.2713 0.6772

This table presents results from the estimation of a fixed-effects regression model of Investment Spending on beginning-of-year Q (Qt� 1) and Cash Flow. We estimate the

model separately for the hedger and nonhedger (control) samples. We provide the results in Panels A and B, respectively. We estimate the model for five different measures

of cash flow and on annual data over the period, 1992–1996. The samples consist of 312 hedger firms and 312 nonhedger firms. All item numbers (below) refer to annual

data items from Compustat. We calculate Investment Spending as the ratio of investment (item 128) to beginning-of-year gross plant and equipment (item 7). Q is the ratio

of market value of assets to book value of assets (item 6). The market value of assets is the market value of equity (item 24 * item 25) plus the book value of total

liabilities. The book value of total liabilities equals assets (item 6) minus book value of equity (item 60). Cash Flow Measure 1 equals operating income before

depreciation (item 13) deflated by the beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 2 equals [sales (item 12) minus COGS (item 41) minus

SGA (item 189) minus change in net working capital] deflated by the beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 3 equals [income before

extraordinary items (item 18) plus depreciation and amortization (item 14) minus change in net working capital] deflated by the beginning-of-year gross plant and

equipment (item 7). Cash Flow Measure 4 equals [operating income before depreciation (item 13) minus income taxes paid (item 317) minus change in net working

capital] deflated by the beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 5 equals [operating income before depreciation (item 13) minus change

in net working capital] deflated by the beginning-of-year gross plant and equipment (item 7). We calculate net working capital as current assets (item 4) minus current

liabilities (item 5). Each of the columns (columns 2 through 6) in the table presents results from the estimation of a fixed-effects model using that cash flow measure. The t-

statistics are in parentheses after the coefficients. ***, ** and * represent significance at the 1%, 5%, and the 10% levels, respectively.

S.Desh

mukh,S.C.Vogt/JournalofCorporate

Finance

11(2005)628–644

636


sample. The coefficients on both Q (in all cases) and Cash Flow (in four of five cases) are

positive and statistically significant at the 1% level. The signs of the coefficients are

consistent with earlier studies. The results for the nonhedger (control) sample in Panel B

indicate that the coefficients on Cash Flow are positive and significant at the 1% level

across all five measures of cash flow. The coefficient on Q is positive and significant at the

1% level in three of the five cases. In addition, the coefficient on Cash Flow for the

nonhedger sample appears to be much higher than that for the hedger sample across the

different measures of cash flow. These results suggest that the investment spending for

nonhedgers appears to be more sensitive to cash flow. The results are consistent with our

hypothesis.

To test whether the difference in the sensitivity of investment spending to cash flow

is significant across hedgers and nonhedgers, we estimate a fixed-effects model on the

combined data. To test for the significance of a differential effect, we construct a

dummy variable bHedgeQ that equals one if the firm is a hedger, and zero otherwise.

We interact this dummy variable with Cash Flow, which results in an interactive

dummy variable that equals Cash Flow for hedgers, and zero otherwise. We include this

interactive dummy variable as an independent variable along with Cash Flow and Q

and estimate a fixed-effects model with Investment Spending as the dependent variable.

The coefficient on the interactive dummy variable measures the magnitude of the

differential impact (of cash flow on investment spending) between hedgers and

nonhedgers. The results in Table 3 indicate that the coefficient on the interactive

dummy variable (Cash Flow * Hedge) is negative and significant at the 1% level across

all five measures of cash flow. This result suggests that, other things equal, the

investment spending of hedgers is less sensitive to cash flow than for nonhedgers. The

coefficient on Cash Flow is positive and significant at the 1% level across all the cash

flow measures, and the coefficient on Q is positive and significant at the 1% level in

four of the five cases.

We estimate the model in Table 3 with another measure of cash flow that equals [Sales

(item12) minus COGS (item41) minus SGA (item189) minus change in net working

capital minus income taxes paid (item317)] deflated by the beginning-of-year gross plant

and equipment. The results, not reported, are significant and qualitatively the same as

those for the other measures of cash flow. We also allow for a differential impact of Q on

the investment spending of hedgers and nonhedgers. We do so by including an interactive

dummy variable in the model in Table 3 that equals Q for hedgers, and zero otherwise. The

results are robust to this variation in the specification.

We test for collinearity in our data by computing the variance inflation factors for the

independent variables. The mean variance inflation factor (across all the explanatory

variables) varies between 1.42 and 2.06 across the five models estimated with the (five)

different cash flow measures2. The low value for the variance inflation factors indicates

that collinearity is not a problem in our data.

2 Collinearity is likely to be a problem if the largest variance inflation factor (VIF) is greater than ten and the

mean of the VIFs (across all independent variables) is substantially larger than one. For further details, see

Chatterjee et al. (2000).

Table 3

Sensitivity of investment spending to cash flow: hegders vs. nonhedgers


Cash flow 0.2127*** (19.26) 0.6205*** (61.45) 0.2935*** (13.87) 0.1297*** (4.94) 0.6155*** (56.97)

Cash flow * Hedge �0.0406*** (�3.16) �0.5663*** (�29.17) �0.2251*** (�7.99) �0.1078*** (�3.45) �0.5554*** (�26.76)

Qt� 1 0.0417*** (8.58) 0.0076 (1.35) 0.0335*** (6.98) 0.0441*** (8.62) 0.0121** (2.02)

Constant 0.0062 (0.65) 0.0349*** (3.15) 0.0625*** (6.64) 0.0706*** (6.79) 0.0270** (2.27)

F 416.64*** 1288.89*** 97.76*** 36.93*** 1112.84***


R2 (Overall) 0.5510 0.5534 0.2957 0.2188 0.5295

This table presents results from the estimation of a fixed-effects regression model of Investment Spending on beginning-of-year Q (Qt� 1), Cash Flow, and Cash Flow

interacted with a dummy variable Hedge. We estimate the model on the combined hedger and nonhedger samples and on annual data over the period, 1992–1996. The

sample consists of 312 hedger firms and 312 nonhedger firms. All item numbers (below) refer to annual data items from Compustat. We calculate Investment Spending as

the ratio of investment (item 128) to beginning-of-year gross plant and equipment (item 7). Q is the ratio of market value of assets to book value of assets (item 6). The

market value of assets is the market value of equity (item 24 * item 25) plus the book value of total liabilities. The book value of total liabilities equals assets (item 6)

minus book value of equity (item 60). Cash Flow Measure 1 equals operating income before depreciation (item 13) deflated by the beginning-of-year gross plant and

equipment (item 7). Cash Flow Measure 2 equals [sales (item 12) minus COGS (item 41) minus SGA (item 189) minus change in net working capital] deflated by the

beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 3 equals [income before extraordinary items (item 18) plus depreciation and amortization (item

14) minus change in net working capital] deflated by the beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 4 equals [operating income before

depreciation (item 13) minus income taxes paid (item 317) minus change in net working capital] deflated by the beginning-of-year gross plant and equipment (item 7).

Cash Flow Measure 5 equals [operating income before depreciation (item 13) minus change in net working capital] deflated by the beginning-of-year gross plant and

equipment (item 7). We calculate net working capital as current assets (item 4) minus current liabilities (item 5). Hedge is a dummy variable that equals one if the firm is a

hedger and zero otherwise. Each of the columns (columns 2 through 6) in the table presents results from the estimation of a fixed-effects model using that cash flow

measure. The t-statistics are in parentheses after the coefficients. ***, ** and * represent significance at the 1%, 5%, and the 10% levels, respectively.

S.Desh


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We also estimate a random-effects model on the combined data. The qualitative nature

of the results, not reported, is the same as in Table 3. We perform a Hausman specification

test to gauge the appropriateness of the fixed-effects model relative to the random-effects

model. The random-effects model assumes that the individual firm effects are uncorrelated

with the explanatory variables. Under this null hypothesis, both the fixed-effects and the

random-effects estimator provide consistent estimates but the fixed-effects estimator is

inefficient. Under the alternative, the fixed-effects estimator yields consistent estimates,

but the random effects estimator does not. For four of the five cash flow measures, the

specification test rejects the null hypothesis (at the 1% level) that the individual firm

effects are uncorrelated with the explanatory variables. Thus, the test favors a fixed-effects

specification.

We investigate if the exclusion of observations with negative cash flow induces any

bias in our data. Among observations with negative cash flow for the combined sample of

hedgers and nonhedgers, there are many extreme outliers across all the cash flow

measures. For our tests, we truncate our sample at the first percentile. Despite this data

filter, we retain a substantial fraction of the observations with negative cash flow (along

with observations with positive cash flow) for our robustness tests. Table 4 summarizes the

data on observations with both negative cash flow and positive cash flow for the combined

sample of hedgers and nonhedgers. As mentioned earlier, all the results presented in the

tables are based on observations with positive cash flow. Therefore, the number of positive

observations in Table 4 (for each cash flow measure) is exactly the same as that underlying

the regressions in Table 3.

Table 4

Distribution of observations with negative and positive cash flow

Cash flow measure Total number of

negative observations

Number of negative

observations retained

Number of positive

observations

Cash flow measure 1 179 157 2022





This table presents the distribution of observations with negative and positive cash flow for the combined sample

of hedgers and nonhedgers. The sample consists of 312 hedger firms and 312 nonhedger firms. The distribution

(of observations) is presented for each of the five cash flow measures and is based on annual data over the period,

1992–1996. All item numbers (below) refer to annual data items from Compustat. Cash Flow Measure 1 equals

operating income before depreciation (item 13) deflated by the beginning-of-year gross plant and equipment (item

7). Cash Flow Measure 2 equals [sales (item 12) minus COGS (item 41) minus SGA (item 189) minus change in


equals [income before extraordinary items (item 18) plus depreciation and amortization (item 14) minus change in


equals [operating income before depreciation (item 13) minus income taxes paid (item 317) minus change in net


equals [operating income before depreciation (item 13) minus change in net working capital] deflated by the

beginning-of-year gross plant and equipment (item 7). We calculate net working capital as current assets (item 4)

minus current liabilities (item 5).


Next, we estimate the model in Table 3 for each of the five cash flow measures on

our expanded, combined sample of hedgers and nonhedgers. The combined sample

now contains observations with negative cash flow. The results, not reported for

brevity, suggest that the coefficient on the interactive dummy variable (Cash Flow *

Hedge) is negative and significant at the 1% level in three of the five cases, at the 5%

level in one case, and not significant in the fifth case. Thus, our results are consistent

with those in Table 3 and remain robust to the inclusion of observations with negative

cash flow.

To further test the underinvestment hypothesis of corporate hedging, we examine the

impact of the extent of hedging on the investment-cash flow relation. If firms hedge to

reduce both the cash flow volatility and their reliance on external funds, then the

sensitivity of investment spending to cash flow, all else equal, should be lower when the

extent of hedging is higher. We define a firm’s extent of hedging as the ratio of the total

notional value of interest-rate and currency derivatives to the book value of assets. We

obtain the data on notional values from the Swaps Monitor database.

For this test, we focus on the sample of hedgers with available data on the notional

values of their derivatives. Detailed summary statistics on the extent of hedging variable

show that it is clustered for a sizable fraction of the sample. Therefore, since it does not

provide sufficient variation, we do not use the extent of hedging as a continuous variable.

Instead, we create a dummy variable (called Extentdum) whose value equals one if the

extent of hedging is above its sample median value, and zero otherwise. The dummy

variable allows us to separate the sample of hedgers into two groups based on whether the

extent of hedging is bhighQ (dummy equals one) or blowQ (dummy equals zero). We then

interact this dummy variable with Cash Flow to create an interactive dummy variable

whose value equals Cash Flow if the extent of hedging is above its sample median value,

and zero otherwise.

We include the above interactive dummy variable as an independent variable along with

Cash Flow and Q and estimate a fixed-effects model with Investment Spending as the

dependent variable. The coefficient on the interactive dummy variable measures the

magnitude of the difference in the sensitivity of investment spending to cash flow between

firms with a bhighQ extent of hedging and those with a blowQ extent of hedging. The resultsfrom Table 5 indicate that the coefficients on both Cash Flow and Q are positive and

significant at the 1% level across all the cash flow measures. The results also indicate that

the coefficient on the interactive dummy variable (Cash Flow * Extentdum) is negative

and statistically significant at the 1% level in one case, at the 5% level in three cases, and

at the 10% level in the fifth case. This result suggests that, other things equal, the

sensitivity of investment spending to cash flow is lower when the extent of hedging is

higher. This new finding strengthens our previous results and supports the notion that

firms appear to hedge to reduce their cash flow volatility and their reliance on external

funds.

We note that the lower sensitivity of investment spending to cash flow for hedgers,

relative to nonhedgers, suggests that our sample of hedgers (or derivative users) is

hedging, and not speculating. This finding is consistent with Guay (1999), who

empirically examines the role of derivatives usage in firms that initiate the use of these

instruments. He observes that firm risk, measured in a variety of ways, decreases following

Table 5

Effect of the extent of hedging on the sensitivity of investment spending to cash flow


Cash flow 0.2915*** (10.88) 0.0851*** (5.07) 0.1022*** (4.16) 0.0856*** (4.36) 0.0856*** (5.14)

Cash flow * Extentdum �0.0336*** (�2.87) �0.0217** (�2.03) �0.0410** (�2.03) �0.0248* (�1.74) �0.0226** (�2.13)

Qt� 1 0.0211*** (2.77) 0.0362*** (6.05) 0.0416*** (6.31) 0.0360*** (5.73) 0.0359*** (6.02)

Constant �0.0120 (�0.75) 0.0502*** (4.59) 0.0497*** (4.17) 0.0568*** (4.92) 0.0504** (4.62)

F 46.14*** 25.42*** 22.08*** 20.05*** 25.47***


R2 (Overall) 0.1824 0.1955 0.2112 0.2083 0.1938

This table presents results from the estimation of a fixed-effects regression model of Investment Spending on beginning-of-year Q (Qt� 1), Cash Flow, and Cash Flow

interacted with a dummy variable Extentdum. We estimate the model for the hedger sample and on annual data over the period, 1992–1996. The sample consists of 312

hedger firms. All item numbers (below) refer to annual data items from Compustat. We calculate Investment Spending as the ratio of investment (item 128) to beginning-

of-year gross plant and equipment (item 7). Q is the ratio of market value of assets to book value of assets (item 6). The market value of assets is the market value of equity

(item 24 * item 25) plus the book value of total liabilities. The book value of total liabilities equals assets (item 6) minus book value of equity(item 60). Cash Flow

Measure 1 equals operating income before depreciation (item 13) deflated by the beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 2 equals [sales

(item 12) minus COGS (item 41) minus SGA (item 189) minus change in net working capital] deflated by the beginning-of-year gross plant and equipment (item 7). Cash

Flow Measure 3 equals [income before extraordinary items (item 18) plus depreciation and amortization (item 14) minus change in net working capital] deflated by the

beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 4 equals [operating income before depreciation (item 13) minus income taxes paid (item 317)

minus change in net working capital] deflated by the beginning-of-year gross plant and equipment (item 7). Cash Flow Measure 5 equals [operating income before

depreciation (item 13) minus change in net working capital] deflated by the beginning-of-year gross plant and equipment (item 7). We calculate net working capital as

current assets (item 4) minus current liabilities (item 5). Extentdum is a dummy variable that equals one if the extent of hedging is above its sample median value and zero

otherwise. The extent of hedging equals the ratio of the total notional value of interest-rate and currency derivatives to book value of assets (item 6). Each of the columns

(columns 2 through 6) in the table presents results from the estimation of a fixed-effects model using that cash flow measure. The t-statistics are in parentheses after the

coefficients. *** , ** and * represent significance at the 1%, 5%, and the 10% levels, respectively.

S.Desh


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11(2005)628–644

641


derivatives use. His results suggest that, in sum, firms appear to be using derivatives to

hedge risk and not to increase it.

5. Summary and conclusions

In this paper, we test the underinvestment hypothesis of corporate hedging. In contrast

to previous studies, we focus on a single explanation.

Froot et al. (1993) argue that if firms hedge to reduce their reliance on external sources

for funds, their cash flow will be less volatile and the investment more stable. This

reasoning suggests that in the presence of hedging, the firm’s investment will be less

sensitive to prehedged cash flow.

We test the hypothesis that the sensitivity of investment spending to cash flow is lower

for hedgers than for nonhedgers. Our results are consistent with this hypothesis in that the

investment spending of hedgers is less sensitive to prehedged cash flow than is that of

nonhedgers.

To further test the underinvestment hypothesis, we examine the impact of the extent of

hedging on the investment-cash flow relation. We find that among hedgers, the sensitivity

of investment spending to cash flow is lower when the extent of hedging is higher.

Our overall results are generally robust to five different measures of cash flow. These

new findings contribute to our understanding of the hedging behavior of firms. In sum, our

results are consistent with an underinvestment rationale for corporate hedging and suggest

that firms hedge to reduce both their cash flow volatility and their dependence on external

funds.

Acknowledgements

We thank Ali Fatemi, Anand Goel, Keith Howe, and Carl Luft for their comments and

Irina Krop for research assistance. We would also like to thank David Scharfstein and

Sheridan Titman for some useful suggestions. We are also grateful to Editor Jeffry Netter

and an anonymous referee for greatly improving the paper. Sanjay Deshmukh thanks the

University Research Council at DePaul University for providing a research grant for this

paper. All remaining errors are our own.

Appendix A. Data and Computational Procedures

We obtain our sample of hedgers/derivative users from the Database of Users of

Derivatives, published by Swaps Monitor Publications, New York. We identify our sample

of hedgers as of the end of fiscal year 1996. Our final sample of 312 hedger firms covers

the period, 1992–96 and includes firms that engage in interest-rate and/or currency

hedging. For each hedger firm, we choose a control firm that is a nonhedger and has the

same four-digit SIC code (or three-digit SIC code) as the hedger, and which closely

matches the hedger firm in terms of size as measured by the book value of assets. We


derive the control sample of nonhedgers (or nonderivative users) from a universe of firms

that is not in the Swaps Monitor database. We collect annual data from Compustat and the

Swaps Monitor database over a five-year period, 1992–1996. We use panel data to exploit

the time-series properties of the data and to provide for a more complete examination of

the impact of hedging on investment policy. We focus on manufacturing firms (SIC 2000-

3999) and estimate a fixed-effects model on the data. We perform a Hausman specification

test to gauge the appropriateness of the fixed effects model relative to the random effects

model. The specification test favors a fixed-effects specification.

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