Managing Financial Policy:
Evidence from the Financing of Major Investments
Erik Stafford *
Harvard University
Phone: 617-495-8064
Email: estafford@hbs.edu
June 2001
ABSTRACT:
How do managers set financial policy? This paper uses a sample of unconstrained firms
making major investments to examine intended financial policy decisions. The analysis
reveals that the financial policies of the sample firms can reasonably be characterized as
“pecking order” behavior as described by Donaldson (1961) and Myers (1984): (1) internal
funds are the dominant source; (2) equity issues are relatively unimportant; and (3) debt
issues are the residual financing variable. There is no evidence that dividend-paying firms
adjust dividend policy to accommodate the significant investment. Firms use the
investment event as an opportunity to increase their cash reserves, which is inconsistent
with a specific form of the pecking order theory of Myers and Majluf (1984). The
empirical results suggest (a) transaction costs appear to be an important determinant of
financial policies and (b) pecking order behavior does not necessarily provide strong
support for the pecking order theory.
I thank Gregor Andrade, Harry DeAngelo, Doug Diamond, J. B. Heaton, Peter Hecht, Jean Helwege, Robert
McDonald, Lisa Meulbroek, Todd, Pulvino, Raghu Rajan, Matthew Rothman, Jeremy Stein, Per Strömberg, Francis
Yared, and seminar participants at Columbia, Cornell, Duke, Harvard, NYU, University of Maryland, University of
Rochester, University of Chicago, USC, and the 1999 WFA Meetings for helpful comments and discussions. I am
especially grateful to Eugene Fama, Steven Kaplan, Mark Mitchell, and Luigi Zingales for their insightful advice and
guidance. Financial support from the Oscar Mayer Foundation and the Sanford Grossman Fellowship is greatly
appreciated.
How do managers set financial policy? In a world of perfect and complete capital
markets, capital structure and dividend policies are a matter of irrelevance [Modigliani and
Miller (1958, 1961)]. However, once the Modigliani-Miller assumptions are relaxed, financial
policy can become important. Imperfect information and transaction costs make it costly for
firms to frequently change their financial policies and therefore make financial slack valuable.
For example, if firms have target leverage ratios, fixed transaction costs act against the firm’s
desire to move towards the target. The firm establishes an optimal capital structure range, rather
than an optimal point, and readjusts when its leverage ratio falls outside this range [see Kane,
Marcus, and McDonald (1984) and Fischer, Heinkel, and Zechner (1989)]. Financial slack is
valuable because frequent security issues are not required and thus the firm avoids the fixed costs
associated with issuing securities. Myers and Majluf (1984) provide a richer model of financial
slack based on information asymmetries between managers and investors. In addition to the
fixed transaction costs associated with raising external funds, the firm also faces costs arising
from the manager being better informed about the value of the firm’s assets and the expected
profitability of its investment opportunities.
Financial slack is valuable because it allows
managers to avoid having to choose between issuing undervalued securities and forgoing positive
net present value investments.
This paper uses a sample of unconstrained firms making significant investments to
examine financial policy decisions. The goal of the paper is twofold: first to document the
intended financial policies of the sample firms and second to determine whether accepted
theories of financial policy can really explain the observed behavior. Special emphasis is placed
on financial slack, which is at the heart of most theories of financial policy.
Financially unconstrained firms making major investments comprise an interesting
sample from which to study intended financial policies in a setting where these policies are likely
to be important. The analysis exploits the fact that the right-hand side of the balance sheet must
accommodate a major change on the left-hand side, and that unconstrained firms are likely to
1
reveal their intended financial policies at this time. The sample avoids many of the problems
with measuring intended financial policies that arise because of transaction costs. At least some
transaction costs are required in most models of financial policy to explain why firms do not
continuously issue securities. Therefore, observed leverage ratios generally reflect the combined
effects of intended policy and deviations caused by random fluctuation in cash flows and
investment, which are not immediately offset. These costs are negligible for firms making major
investments, such that deviations from intended policies should be small following the event.
To address how firms manage their financial policy when making major investments, I
examine various financial policy variables in event-time relative to an industry based comparison
group. This methodology provides a natural way to explore the dynamic aspects of financial
policies by examining how these policies accommodate major investments in the years before,
during, and after the investment. The event-time analysis reveals that the financial policies of the
sample firms can be reasonably characterized as “pecking order” behavior, much like those
making ordinary investments.
In particular, (1) internal funds are the dominant source of
financing, accounting for over 65% of investing activities in the event year, as compared to 86%
for comparable firms; (2) the relative importance of equity financing is unchanged for the firms
making major investments versus ordinary investments; and (3) debt issues are the residual
financing variable. Interestingly, dividend paying firms do not reduce or slow dividend growth
before or during the event, but instead are more likely to increase their dividends per share.
Dividend increases are also observed for firms that raise external financing.
Most accepted theories of financial policy have to some extent emerged from the survey
studies of Lintner (1956) and Donaldson (1961) that find managers are reluctant to reduce
dividends and prefer to finance investment with internal funds. As such, most theories can
explain the observed pecking order behavior in these data. In order to address whether the
observed pecking order behavior in financial policies is necessarily supportive of the pecking
order theory described by Myers (1984, 1991) and Myers and Majluf (1984), I develop a test
2
designed to distinguish between a specific form of the pecking order model and a generic
alternative. The pecking order theory described by Myers (1984, 1991) and Myers and Majluf
(1984) has costs of external finance that are convex in the amount raised, while a model of
financial policy based on fixed transaction costs is associated with concave costs of external
finance. Consequently, one prediction of the pecking order model is that firms try to minimize
external financing requirements by reducing cash reserves.
The data do not support this
prediction. In particular, firms appear to use the investment event as an opportunity to replenish
their cash reserves, finishing the investment period with high levels of cash relative to their
industry peers. This evidence, coupled with the finding that the event firms are more likely to
increase their dividends, suggests that firms do not minimize their financing requirements as the
pecking order theory predicts. The mere presence of pecking order behavior does not provide
strong support for the pecking order theory. Rather, the financing behavior of the sample firms is
more consistent with fixed transaction costs being the primary cost of external financing.
I. Data Description
The dataset for this paper consists of major corporate investments, defined as both
significant additions to the capital stock of the firm, and significant deviations from the firm’s
typical investment policy. The universe of firms for the purpose of this study are those covered
by Value Line from 1970 to 1996, excluding firms in financial industries. To the extent that
Value Line firms are unconstrained, these observations provide a unique opportunity to examine
the intended financial policies of these firms. There are 2,728 unique firms, representing 35,613
firm-years in the Value Line sample.1 All of the accounting data are from Compustat.
In order to compare annual investment expenditures across both firms and time, some
type of transformation is required. It is natural to scale investment by a measure of assets in
1
Andrade and Stafford (1999) develop the Value Line based industry classifications used in this paper.
3
place, such as book assets. However, book assets do not reflect current or historical advertising
and R&D expenditures. Therefore, I adjust book assets to reflect the accounting treatment of
advertising and R&D investment.2
I calculate annual investment intensities as investment
(advertising + CAPX + R&D) in year t divided by long-term book assets (book assets plus R&D
capital (RDC) plus advertising capital (ADC) minus cash & marketable securities minus working
capital (WC)) at the end of year t-1 (see Appendix A for details on variable definitions).
Investment Intensityi ,t =
Advertising i ,t + CAPX i ,t + R&Di ,t
Assetsi ,t −1 + ADCi ,t −1 + RDCi ,t −1 − Cashi ,t −1 − WCi ,t −1
(1)
All annual investment intensities greater than 0.25 (approximately the 90th percentile
across all firm-years) are considered “large” investments.3 In order to identify “non-routine”
investments, annual percentage deviations from each firm’s mean investment intensity are
calculated for all firms that have at least four annual observations. All annual percentage
deviations greater than 50% are considered “non-routine” investments. Significant investment
events are defined as the intersection of the “large” and “non-routine” investments. The final
sample satisfies the following additional criteria:
1. Must have been in Value Line for at least two years before the event to avoid sample
selection bias. Firms entering the Value Line sample are likely to have recently experienced
unusually high growth, and may not be directly comparable to the other firms, which are
generally more established.
2. Must not be part of a consecutive series of investment events that is longer than three years.
It is not clear whether or not management originally planned these to be unusually long-lived
investment events.
2
Based on previous research on capitalizing and amortizing intangible assets (see Ayanian (1983), Hall (1993), and
Lev and Sougiannis (1996)) advertising and R&D expenditures are assumed to have useful lives of two and six
years, respectively. All expenditures are assumed to occur in the middle of the fiscal year, and that they are subject
to straight-line depreciation. Missing advertising and R&D values surrounded by valid observations are set to the
average of the surrounding values, while missing values at the beginning or end are set to the value implied by
growth at the median annual growth rate of all firms. Finally, when there are insufficient data to calculate the
advertising or R&D capital stock, but either the current or the prior expenditure is available, the available
expenditure is capitalized using the median capitalization factor.
3
Annual investment intensities greater than 1.0 are dropped from the sample (20 observations).
4
3. Firms with multiple investment events must have at least four years between them. This is to
allow for meaningful analysis of pre- and post-event financial policy.
4. The firm does not make an acquisition or merger in the year of the significant investment. In
particular, if the firm makes an acquisition greater than 5% of beginning of period long-term
book assets, or CRSP identifies the firm as an acquirer, the observation is excluded. I
exclude mergers because they may be largely financial transactions rather than investment
events (see Shleifer and Vishny (2001)).
The events are not meant to be anomalous in economic terms, only in their potential to
reveal intended financial policies. For example, Nucor, a “mini-mill” steel producer (steel
industry), completed two significant investment events over the sample period, the first
investment event lasting one year and the second lasting two years. In January 1981, Nucor
announced plans to build a steel joist and grinding ball plant in Brigham City, Utah, spending
over $100M on capital expenditures for the year. Construction on the plant began in the spring
of 1981, and steel production at the plant began by the end of the same year. By 1987, Nucor had
experienced great success in the steel industry and again looked to expand its capacity. In April
1987, Nucor announced a joint venture with a large Japanese steel maker (Yamato Kogyo Co.) to
build a $175M mill in Arkansas. Nucor owned 51% of the joint venture, which began production
in the second half of 1988. In 1987, Nucor also began construction on a $250M flat-rolled steel
mill in Indiana, becoming the first mini-mill to enter this segment of the steel market. The
Indiana mill increased the firm’s steel capacity by 40%, and became operational in the middle of
1989.
The final sample includes 794 significant investment event-years by 538 different firms.
Table 1 displays the number of events by year and by industry, as well as the number of Value
Line firms that otherwise meet the sample criteria, and Appendix B describes a few more of these
events.
5
II. Methodology for Analyzing Event-Time Financial Policy
To address how firms manage financial policy when making significant investments, I
examine various financial policy variables in event-time relative to an industry based comparison
group. There is a dynamic aspect to financial policy that is generally difficult to capture unless
some type of event-time analysis is performed. The event-time analysis provides a natural way to
explore how financial policy accommodates major investments in the years before, during, and
after the event.
In order for the output to be useful, a meaningful comparison group and adjustment
procedure are required to determine the level of, or change in, the variables expected in the
absence of a major investment. There are two important considerations in determining the
“abnormal” financial policy variables: (1) mean reversion in accounting variables; and (2) crosssectional dependence of abnormal variables. First, a wide variety of accounting variables exhibit
mean reversion, which may induce spurious correlations if event firms tend to have either “high”
or “low” levels of the variable before the event. Second, major corporate actions are not random
events, and thus may not represent independent observations. The very nature of an event sample
is that all of the sample firms have chosen to participate in the event, while other firms have
chosen not to.
This may lead to positive cross-correlations in abnormal financial policy
variables, leading to overstated test-statistics if not accounted for.
A. Calculating Abnormal Financial Policy Variables
The comparison group consists of all firms in the same industry, with similar levels of the
variable in the year prior. Specifically, for each variable examined, three groupings of the Value
Line sample firms are formed (independent of industry) based on whether the level of the firm’s
financial policy variable falls below the 33rd percentile, between the 33rd and 67th percentiles, or
above the 67th percentile of the distribution of the variable across all firm-years in the year before
the event. These groupings are then intersected with the industry classifications to form the
6
comparison groups. This method of grouping assumes that firms in the same industry are subject
to similar shocks that may affect the cross-sectional variation in the variable, but which are
unrelated to the actual investment event. Moreover, by matching on the prior-year level of the
variable, spurious correlations related to mean reversion in accounting variables are less likely.
This comparison group definition is similar in spirit to the matching procedure advocated by
Barber and Lyon (1996), which controls for both the pre-event level of the variable and the
industry of the sample firms.
Once the comparison group is identified, an abnormal financial policy variable is
calculated for each sample firm by subtracting the mean of the variable associated with the
comparison group, as in equation (2). This adjustment should roughly account for the typical
behavior of firms in the comparison group, and thus provide a meaningful benchmark against
which to evaluate the sample firms’ financial policy variables.
AbnX i ,t = X i ,t − mean( X j ,t )
(2)
where i indexes all event firms and j indexes all firms in the comparison group for firm i.
Finally, the mean and standard error are calculated using a variant of the “time series of
cross-sections approach” developed by Fama and MacBeth (1973). In this approach, the time
series mean of annual cross-sectional means of the individual abnormal financial policy variables
is calculated, and a t-statistic is calculated using the time series standard error. In other words,
each year in calendar-time, I calculate the mean abnormal financial policy variable for all firms
completing a significant investment that year, AbnX t . The mean and standard deviation of the
time
series
of
annual
means
( T ⋅ mean( AbnX t ) / std ( AbnX t ) ).
is
then
used
to
calculate
the
t-statistic,
This time series of cross-sections approach produces
standard errors that are robust to cross-sectional dependence because the variation in the annual
means includes the effects of cross-correlated abnormal financial policy variables.
B. Simulation Evidence
One concern with any type of adjustment is that the properties of the resulting statistic
7
may not be known, particularly when the underlying data are not well behaved. This problem is
common to most corporate event-time studies, and Barber and Lyon (1996) document how
sensitive results can be to different methodologies. To further complicate the matter, there are
several differences between the financial policy variables in this study and the operating
performance variables examined by Barber and Lyon, which may be problematic. The most
severe is that many financial policy variables are bounded below at zero, and often have a large
mass at zero, whereas profitability measures, such as return on assets, are close to normally
distributed. Moreover, the sample size of 50 firms that Barber and Lyon examine is many times
smaller than the significant investment sample in this study. In order to determine whether the
adjustment procedure described above is reasonable for the variables of interest in this study, I
perform simulations to test the size of the test statistics. The details of the simulation procedure
and the results are in Appendix C.
The simulation results on the specification of the test statistics are presented in Table C.1.
In particular, the fraction of random samples that reject the null hypothesis are reported. For
virtually all of the variables studied, the adjustment procedure produces well-specified
t-statistics. The only exceptions are that the test statistics for the cash-to-sales, dividend-toincome, and long-term debt-to-sales ratios tend to reject too often when negative. Overall, the
abnormal financial policy methodology appears to produce well-specified t-statistics, which
should be robust to cross-correlated observations.
C. Investment Activity in Event-Time
To demonstrate the effectiveness of the event-time approach, abnormal investment
activity results are displayed in Table 2. The sample consists of firms making significant
investments, and as such, it is important to examine how the investment activity of these firms
evolves in event-time.
Beginning of period long-term book assets scales all the various
components of investment activity.
This table confirms that these are indeed significant
investments, as the event firms on average have investment intensities over 14 percentage points
8
higher than comparable firms, during the event year (t-statistic = 21.7).4 This is more than
double the average aggregate investment intensity for Value Line firms from 1972-1996, which
is 12.7%.
On average, it appears that there is an investment run-up the year before the event,
culminating in year t with the significant investment. The investment event is immediately
followed by a sharp decline in investment activity in years t+1 and t+2. This same general
pattern persists when sales, rather then long-term assets, scale investment activity. Finally, most
of the abnormal investment comes from CAPX. Advertising expenditures are marginally higher
during the event year and R&D expenditures are significantly larger.
III. Financial Policy Surrounding Significant Investments
The estimates presented in this section provide insights into how financial policies adjust
to accommodate the significant investment in year t, and capture the average financial position of
firms in the years surrounding the event.
Emphasis is on cash flows, dividends, external
financing, and the resulting capital structures. The reported coefficients are averages of adjusted
financial policy variables for firms making significant investments in year t.
A. Cash Flows
Firms tend to finance the majority of their investment with retained earnings, and it is
therefore useful to first examine the cash flows of the sample firms. Table 3 presents several
different abnormal cash flow measures for firms making significant investments. In order to
facilitate event-time comparisons, all cash flow measures are scaled by sales rather than total
book assets, as sales should be less sensitive to the investment event.5 Thus, the variables are
4
Equivalently, the average investment intensity for the event firms is 14.2 percentage points higher than what is
expected, given the firm’s industry and the amount invested in the year prior.
5
The investment event is essentially tantamount to a significant increase in total book assets. However, there is no
such mechanical relation between the investment event and sales.
9
actually cash flow margins, which are typically interpreted as measures of profitability. The
particular cash flow variables examined are EBITDA, operating cash flows, pre-tax cash flows,
and after-tax cash flows.6
Firms that make significant investments have slightly higher cash flows than comparable
firms do in the years before the investment, consistently peaking in year t-1. For example, in the
three years prior to the investment event, average abnormal operating margins range from 0.0054
to 0.0095 (corresponding t-statistics are 1.34 and 2.34). The aggregate average cash flow margin
for all Value Line firms is 0.13 (results not reported), implying that firms making major
investments have cash flow margins roughly 4% to 7% higher than non-event firms prior to the
event.
During the event year, and following the investment, cash flow margins are
indistinguishable from those of the comparable firms. The average abnormal operating margins
during the event and in the two years after the event are -0.003, 0.0003, and 0.003 (t-statistics
equal -0.44, 0.06, and 0.48, respectively). The results from examining changes in the cash flowto-sales ratio are consistent with the level results.
The three-years before the event are
characterized by marginally positive abnormal increases in cash flow margins, and then changes
are generally flat following the event.
Cash flow margins and return on assets are often used as measures of operating
performance. The assumption is that if the profitability of a firm is at least as high as that of
comparable firms, then the firm is performing efficiently. However, the denominators in each of
these measures are also related to performance. For example, examining the cash flow margin
for a firm experiencing a large decrease in sales, with no change in profitability, may disguise the
fact that the firm is enduring serious operating problems. To ensure that the previous cash flow
inferences are consistent with strong operating performance, sales growth is also examined
6
Variable definitions are reported in Appendix A.
10
(results not reported). The average abnormal sales growth of the event firms is invariably nonnegative and reliably positive in the year before the event and during the event year. This
suggests that the “operating performance” of the sample firms is indeed slightly higher than that
of comparable firms in the years before the significant investment, and certainly no worse than
average subsequently.
B. Dividend Policy
Do managers reduce dividends or slow dividend growth to accommodate major
investments? While there is a large literature devoted to dividend policy, there is little empirical
evidence on the interaction between dividend policy and significant investments [see the review
by Allen and Michaely (1995)]. Firms making major investments provide a unique sample for
studying this aspect of financial policy, as the investment event exerts strong pressure on the firm
to alter its dividend policy. A dividend reduction may allow the firm to avoid the direct
transaction costs of issuing securities, as well as the potential costs associated with asymmetric
information. On the other hand, dividends may serve an important signaling role, such that a
dividend cut is not actually a feasible source of financing.
Table 4 reports the fraction of Value Line and event firms satisfying various dividend
policy criteria and test statistics denoting whether the fractions are significantly different across
the two samples. There are several interesting observations. First, dividend increasesdefined
as a 25% increase in dividends per share, adjusting for stock splits and stock dividendsare
significantly more likely for event firms in the years before, and the year of, the investment event.
In particular, during any given year, 11.6% of the Value Line firms increase their dividends per
share at least 25%, whereas in both the year before the event and the year of the event, roughly
20% of the firms making major investments increase their dividends by this amount. At the
same time, dividend decreases are significantly less likely for event firms in the years before and
during the investment event. Firms making significant investments are only half as likely to
reduce their dividends per share by at least 25% as other Value Line firms in the two years before
11
the event, and during the event year.7 Moreover, these general findings hold for the subset of
event firms that raise external financing in year t.8
This suggests that dividends do not
accommodate investment in the sense of providing a source of funds, but rather dividend
payments exert additional pressure on an already tight sources and uses constraint.
Second, it appears that firms making major investments are significantly less likely to pay
dividends in the years before the event than other Value Line firms. However, by the time the
event has occurred, the payment frequency of event firms is indistinguishable from that of the
rest of the Value Line sample. Consistent with this evidence, the event firms are nearly twice as
likely to initiate dividends in the years before the event, and just as likely in the years afterwards.
Dividend omissions by the event firms are not reliably different from other Value Line firms,
except during the event year and again two years after the investment. During the event year,
firms are less likely to omit dividends, whereas, two years after the investment, firms are twice as
likely to omit dividends. There is some hint that dividend policy may accommodate investment
at longer horizons, much the way it does for small, rapidly growing firms. In other words, firms
that expect to invest more than their cash flows allow are less likely to pay dividends at all.
Overall, it appears that once dividends are paid, which describes the vast majority of the
sample, firms are extremely reluctant to reduce payouts, or even to slow their growth unless they
hit bad times (also see Fama and French (2001)). However, dividend policy may accommodate
investment for the event firms much the same way it does for small, growth firmsboth are less
likely to pay dividends at all.
7
These general findings (event firms are significantly more likely to increase dividends in year t, and marginally less
likely to reduce dividends in years t-1 and t) are also true for the group of firms that make substantial dividend
payments in year t-3, defined as those above the 75th percentile of dividends-to-sales. The notion being that firms
that actually pay substantial dividends will have a greater source of funding from a dividend cut, than firms with low
dividend payout, and thus may be more likely to use this financing option.
8
Firms raising external financing are identified as those with debt issues plus equity issues in year t larger than 5% of
beginning of period long-term book assets. The results are virtually identical if these firms are instead identified as
those with debt issues plus equity issues greater than 25% of investment in year t.
12
C. External Financing
On average, external financing accounts for 32.9% of investing activity for firms making
significant investments, while external funds account for only 14% of investing activities for
firms making ordinary investments (see Stafford (1999)). Even for this sample, where external
financing is more likely to be necessary, internal funds are by far the dominant source of
financing. Moreover, the relative importance of debt and equity financing for firms making
significant investments is similar to that for other firms. Debt issues account for 89.2% of
external financing for ordinary investments and 93.8% for significant investments, on average.
Table 5 reports the event-time frequency of debt and equity issues for firms making
significant investments and for the full Value Line sample. Again, it is clear that debt issues are
the dominant source of external financing. Over 56% of the event firms issue debt worth over
5% of beginning of period long-term book assets in the year of the event, compared to 25% of all
Value Line firms.
This is also dramatically larger than the 11.7% of event firms issuing
equivalent amounts of equity in the event year.
The vast majority of security issues occur during the actual event year. Nonetheless, debt
issues also appear to be significantly more likely in the year after the event, suggesting that there
is some follow-up financing that is required. In particular, 25% of all Value Line firms issue
debt over 5% of beginning of period long-term book assets in any given year, whereas, over 32%
of the firms making significant investments issue an equivalent amount of debt in year t+1
(t-statistic of difference is 2.17). The event-time frequency of equity issues is not as dramatic, as
they are more evenly spaced over years t-1 through t+1, peaking in the year before the event.
Table 6 presents abnormal external financing variables, which largely confirm the results
presented so far. Net debt issues are by far the primary source of external funding for firms
making significant investments, with an abnormal measure more than 7 times larger than that for
net equity issues. In the years before the event, net debt issues are somewhat lower for event
firms than for comparable firms. Virtually all debt financing occurs in the year of the event. In
13
addition, most of the net debt financing comes from long-term debt issues, as long-term debt
reductions for event firms are indistinguishable from those made by comparable firms.
Moreover, short-term debt issues are not particularly important, although they are marginally
higher than expected in the year of the event.
Again, equity issues are more evenly spaced over years t-1 through t+1.
Equity
repurchases by firms making significant investments are not reliably different from comparable
firms, which is interesting in light of the dividend policy evidence. In other words, not only are
firms reluctant to reduce dividends in the years before and during major investments, but equity
repurchase programs appear to continue unaltered as well. In the year after the investment event,
the average abnormal equity repurchase is significantly lower for the sample firms than for the
comparable firms. This is sufficient to make abnormal net equity issues in year t+1 marginally
positive, despite the average amount raised from equity issues being similar for event firms and
comparable firms.
The financing of significant investments appears to be quite similar to that for ordinary
investment, with internal funds being the dominant source, equity issues remaining relatively
unimportant, and debt being the residual variable. Although equity issues are significantly more
likely for event firms than for comparable firms, debt financing overwhelms both the amount of
equity financing and the frequency of equity issues.
D. Leverage
In the three years before the event, long-term debt to total book assets is significantly
lower for event firms than for comparable firms, with average abnormal long-term debt ratios
ranging from -0.014 to -0.02 (corresponding t-statistics are -3.71 and -4.74, see Table 7). As
indicated in the previous table, most of the financing during the event year is provided by debt
issues, resulting in a large increase in leverage. At the end of the event year, the average
abnormal long-term debt ratio is 2.41 percentage points higher than expected, with a t-statistic of
5.62. The average aggregate long-term debt ratio for the Value Line firms is 0.223 (see Stafford
14
(1999)), implying an average increase in long-term debt ratios for firms making significant
investments over 10%. In the years following the event, long-term debt tends towards the
average of the comparison firms, and is in fact indistinguishable in years t+1 and t+2. Examining
total liabilities produces similar results, as short-term and long-term debt ratios behave similarly,
although by year t+2, abnormal total liabilities are significantly negative.
On the whole, firms making significant investments have significantly lower levels of
debt than comparable firms prior to the event, but similar to the comparison group afterwards.
As such, common equity as a fraction of total assets behaves just the oppositehigher than
average prior to the event, over correction the year of the event, and then drifting upwards
afterwards.
E. Pecking Order Behavior in Financial Policy
The event-time analysis suggests that the pecking order descriptions of Donaldson (1961)
and Myers (1984, 1991) can reasonably characterize the financial policies of firms that make
significant investments.
Internal funds are the dominant source of financing, the relative
importance of equity issues is the same for firms making significant investments as for firms
making ordinary investments, and debt issues are the residual financing variable. Interestingly,
dividend-paying firms do not reduce dividends or slow dividend growth in the year of the
investment event or in the prior years, which is consistent with the finding of Fama and French
(2001) that dividends do not accommodate ordinary investment.
V. Does the Pecking Order Theory Really Explain Pecking Order Behavior?
A. Implications of the Pecking Order Theory for Firms Making Major Investments
Myers and Majluf (1984) and Myers (1984, 1991) develop Donaldson’s (1961)
observations on financing decisions into the pecking order theory. The model assumes that
dividends are “sticky” in the sense of Lintner (1956), and that they are only gradually adjusted to
accommodate growth opportunities.
Unpredictable shocks to profitability and growth
15
opportunities create situations where internally generated funds are insufficient (or more than
sufficient) to finance investment. When there is an internal fund deficit, and external financing is
necessary, firms issue the safest securities first. Safety refers to value being insensitive to the
value of the firm’s assets, such that information asymmetry has less impact on pricing. Riskless
debt is the most preferred security, followed by risky debt, hybrid securities such as convertible
debt, and equity only as a last resort. The pecking order theory of financial policy usually takes
investment and dividend decisions as given as well, leaving the manager to concentrate on
financing decisions [see Shyam-Sunder and Myers (1999)]. In addition to the transaction costs
associated with raising external funds, the firm also faces costs arising from the manager being
better informed about the value of the firm’s assets and the expected profitability of its
investment opportunities.9
The pecking order theory implies that financial slack is valuable because it reduces the
possibility that the firm will bypass positive NPV projects or risk issuing underpriced securities.
This intuition is extended by DeMarzo and Duffie (1997) who develop a model where a firm has
private information about the value of its underlying assets, and as a result, faces a downward
sloping demand function when selling claims on these assets. In essence, the firm faces a
“lemons” problem when issuing securities because investors suspect that the amount issued is
greater when the private information implies that the securities are overvalued. Consequently,
when the firm seeks to raise a large amount of external funds, investor’s willingness to pay is
reduced. Similar reasoning is used by Froot, Scharfstein, and Stein (1993) and Froot and Stein
(1998) to justify their assumption of convex costs of external financing (convex in the amount
raised). They argue that increasing marginal costs of external financing are consistent with either
asymmetric information or agency costs. Increasing marginal costs of external financing appear
9
The traditional pecking order theory of Myers and Majluf (1984) actually ignores transaction costs.
16
to describe how information asymmetry leads firms to prefer internal financing, which is at the
heart of the pecking order theory.
Testing theories of financial policy is difficult because the theories have evolved to
describe important empirical aspects of typical financial policies. As such, most empirical
findings are consistent with most theories. For example, to the extent that major investments are
somewhat anticipated and that cash flows and profitability are correlated with investment
opportunities, it is likely that firms making significant investments will have low leverage before
the event. Thus, most models predict that debt issues will cover the deficit. This suggests that
distinguishing between models of financial policy by examining the type of financing or whether
the firm increases its leverage will be difficult. However, it may be possible to distinguish
between theories by comparing each model’s prediction about the amount of external funds
raised, and resulting cash balances.
The key to testing the pecking order theory is the shape of the cost of external financing
function. With convex costs of external financing, the pecking order predicts that firms requiring
external funds will first exhaust internal funds, including excess cash balances, and then issue
debt to cover the deficit.
The marginal costs of financing increase as additional external
financing is required, creating an incentive for the firm to minimize the amount of external funds
raised. On the other hand, a model with concave costs of external finance predicts that firms
making significant investments requiring external financing will use this as an opportunity to
readjust their capital structures, including their cash balances.
The actual amount of external financing raised, and thus, what is done with excess cash,
is at the heart of most theories of financial policy, and firms making significant investments
provide a unique sample to test these theories. Since some epsilon transaction costs are required
to explain why firms do not continuously issue securities in the pecking order theory, there may
be an “S” shaped cost of external financing function, concave at first where transaction costs
dominate, and then convex once informational asymmetries become dominant. The significant
17
investment sample is likely to consist of firms that are at the convex portion of the function, if
the convex portion in fact exists, because these firms are raising a substantial amount of funds.
At the same time, the analysis assumes that the shape of the cost of external financing function is
constant through time.10
Again, the sample of firms identified conditioning on major
investments dominates a sample created by conditioning on large security issues. To directly
select the sample based on large security issues would increase the likelihood of identifying firms
that have experienced a “shock” to their cost function. This procedure would identify firms that
have issued securities because the costs of external financing are temporarily low due to the
convex portion of the cost function temporarily disappearing.
B. What Happens to Cash Holdings?
If the cost of external financing is convex in the amount raised, then firms will minimize
this variable when making significant investments, and thus end the period with low levels of
cash holdings. On the other hand, if the cost of external financing is concave in the amount
raised, then firms may raise more funds than they immediately need, and end the period with
financial slack.11 For example, a simple target-adjustment model of capital structure and cash
holdings, where fixed transaction costs govern adjustment, would have concave costs of external
financing (see Miller and Orr (1966)). The predictions are sharper for firms actually requiring
external financing to complete the significant investment, so special attention is focused on these
firms. Specifically, firms raising external funds are identified as those with debt issues plus
equity issues greater than 5% of beginning of period long-term book assets.12
10
Shocks to the cost function that temporarily reduce costs, but do not change the shape of the cost function should
not be problematic. The decision of whether to invest or not may be affected by the level of the cost function, but
the decision of how much external financing to raise is governed by the marginal cost.
11
Firms facing a concave cost of external financing function do not necessarily raise more external funds than the
amount they invest. The amount that the raise and thus the change in cash holdings is dependent on the level of cash
holdings prior to the investment. For example, firms with significant excess cash before the investment event may
reduce cash holdings even if the marginal cost of external financing is decreasing because the marginal benefits of
additional external financing are relatively low in the presence of high levels of internal funds.
12
The results are similar if firms issuing external financing greater than 25% of the significant investment are
examined.
18
The primary variable of interest is the cash-to-sales ratio.13
This ratio should be
comparable across firms in the same industry, and should allow for meaningful comparisons for
the same firm in event-time. Moreover, examining the cash-to-sales ratio is consistent with the
manner in which valuation analyses are typically executed, where cash and working capital
requirements are generally estimated as a percent of forecasted sales. In addition, scaling by
sales avoids the problems inherent in scaling by assets, which are changing dramatically due to
the significant investment event. One concern with using the cash-to-sales ratio is that the tests
will be leaning heavily on the post-event level of sales being at the new equilibrium level. All of
the following tests are repeated using cash in year t, and sales in year t+1, and the results are
materially unchanged.
A simple way of examining what happens to cash levels when external financing is raised
is to compare event firms’ cash-to-sales ratios to other firms in the industry. Specifically, the
cash-to-sales ratios for event firms are compared to those of other firms in the same industry, and
the percentile that minimizes various transformations of the forecast errors is calculated. For
example, the target adjustment model is estimated assuming that the target is the nth percentile
(n = 1 ... 99) of all cash-to-sales ratios of non-event firms in the same industry in year t. Then
the various statistics (mean, square root of the average squared errors (RMSE), median, and
mean absolute deviation (MAD) of the forecast errors) are calculated and the percentile that
minimizes the statistic is reported. The rankings from this procedure are reported in Table 8.
For the full sample, the mean forecast error of the cash-to-sales ratio is minimized at the
62nd percentile of the industry firms, and the median forecast error is minimized at the 46th
percentile. The changes in the cash-to-sales ratio are also analyzed, with the mean forecast error
of changes in the cash-to-sales ratios being minimized at the 36th percentile, and the median at
the 43rd percentile. Recall, a target-adjustment model predicts that firms raising external funds
13
The results are materially unchanged if the tests use cash scaled by long-term assets.
19
will move towards their targetlikely near the center of the industry distributionwhile the
pecking order predicts that these firms will end up somewhere in the left tail. It appears that the
event firms raising external funds finish the year of the event with fairly high levels of cash
relative to their industry peers, and that changes in their cash holdings are not particularly
extreme.
I also separate firms based on the type of external financing because firms that issue debt
may not be at the convex portion of the cost of external financing function. If firms are able to
issue risk-free debt, the pecking order assumes that the cost of external financing is concave in
this range. However, by the time the firm issues equity the firm is surely at the convex portion of
the cost of external financing function, if the convex portion in fact exists. Interestingly, the
results are virtually identical for the full sample, the debt issuing sub-sample, and the equityissuing sub-sample, with each group ending the period with cash holdings near the center of the
industry distribution, or slightly to the right of the center. This suggests that these firms are not
minimizing the amount of external financing raised.
Additional insights come from grouping the event firms into thirds based on their preevent cash-to-sales ratios. Firms raising external funds, that had low cash-to-sales ratios relative
to their industry in the year prior to making a significant investment, increase their cash-to-sales
ratio considerably more than otherwise similar firms in the industrythe mean forecast error for
changes is minimized at the 67th percentile, and the median at the 62nd percentile. Likewise,
firms that raise external financing and have high levels of cash relative to their industry prior to
the event, reduce their cash holdings dramatically over the event year, with the mean error of
changes in cash-to-sales minimized at the 10th percentile, and the median at the 12th percentile.
Clearly, there is a tendency for firms to move towards the center of the industry distribution, and
it appears that the adjustment is somewhat faster for firms making significant investments.
The empirical evidence is roughly consistent with the predictions of a simple targetadjustment model, and largely at odds with the pecking order model. In particular, the fact that
20
firms with low levels of cash prior to the event increase their cash holdings much more than the
typical firm in the industry, and that the firms in the middle grouping of prior-period cash levels
also increase their cash more than other firms in the industry seems to go against the pecking
order, which predicts the opposite.
C. Regression Analysis on the Determinants of Cash Holdings
To statistically test the marginal impact of the investment event on cash holdings, I use
the regression model of cash balances developed by Opler, Pinkowitz, Stulz, and Williamson
(OPSW) (1998). The natural logarithm of both the cash-to-sales and cash-to-assets ratios are
regressed on the determinants used by OPSW and a dummy variable which equals one if the firm
just completed a significant investment that required external financing, and equals zero
otherwise. For each dependent variable, two specifications are estimated: one pooled across all
Value Line firm-years, and one using the time-series of cross-sections approach developed by
Fama and MacBeth (1973). The results are reported in Table 9.
For the most part, the pooled regressions using the Value Line sample produce results that
are very similar to those of OPSW. The dummy variable indicating whether the observation is
associated with a significant investment is consistently positive, significantly positive in the
pooled regressions, and insignificantly positive in the time series of cross-sections regressions.
In the pecking order model, observed cash holdings are typically well above the minimum
level of cash holdings required for efficient operations of the firm, as financial slack generally
has value. This should especially be true for the successful Value Line firms used in this study,
which have built up slack through retained earnings after years of strong profits. Thus, finding
that the marginal impact of the significant investment on cash holdings is slightly positive,
provides strong evidence against the pecking order theory’s prediction that these firms are
minimizing the amount of external financing that they raise.
Instead, firms appear to be
increasing their financial slack by replenishing their cash reserves.
21
D. Summary and Implications of the Financing Results
The costs of external finance appear to be concave in the amount raised for the sample
firms, rather than convex as the pecking order theory predicts.
Firms making significant
investments that require external financing finish the investment period with high levels of cash
holdings relative to otherwise similar firms. In addition, there is a tendency for dividend-paying
firms to increase their dividend payouts in the years before and during the event, exerting
additional pressure on an already tight sources and uses constraint. The investing firms do not
appear to be minimizing the amount of external financing that they raise, as the pecking order
predicts, or more generally, as any model with convex costs of external financing predicts.
This does not mean that information asymmetry is irrelevant, or that the manager’s
private information is an unimportant determinant of financial policy, only that at the time of the
investment, the increasing costs associated with asymmetric information appear to be small
relative to fixed transaction costs. For example, asymmetric information may be time varying,
such that firms make significant investments and raise external financing at times when the costs
associated with the manager’s private information are low [see Korajczyk, Lucas, and McDonald
(1991)]. However, for time varying information asymmetries to explain the results completely,
firms must have unusually large propensities to invest following shocks to the cost of external
financing. In other words, it must be the case that when external funds are temporarily low
because of reduced information asymmetries, firms not only raise external funds, but decide to
simultaneously engage in major investments.
The empirical results on the amount of external financing and the post-event cash
holdings implicitly assume that the marginal value of financial slack for firms making major
investments is no greater than that for otherwise similar firms. This seems reasonable since the
sample firms have just completed a major investment and are therefore less likely to make large
investments immediately afterwards. In fact, the event-time analysis shows that investment
activity for the sample firms tends to be significantly lower than that for comparable firms.
22
Finally, transaction costs appear to be an important factor in explaining the financial
policy of the sample firms. At least some transaction costs are necessary to explain the amount
and pattern of external financing. Since there is no evidence that the sample firms face convex
costs of external financing, it may be useful to model the costs associated with asymmetric
information as additional fixed transaction costs, rather than as additional increasing costs. This
suggests that further tests of financial policy theories, especially of the pecking order theory,
should focus on the implications of models that explicitly recognize the existence of transaction
costs, or at least specify a reasonable transaction cost based alternative.
VI. Conclusion
This paper documents how financial policy evolves in event-time for firms making major
investments.
The analysis exploits the fact that unconstrained firms making significant
investments reveal their intended financial policies. The sample is restricted to Value Line firms
to ensure that the firms are unconstrained. Theoretically, this may bias the sample against the
pecking order theory, as information asymmetries, which are at the heart of the pecking order
theory, should be substantially reduced for the Value Line firms. Nonetheless, pecking order
behavior shows up very clearly. Despite an increased reliance on external financing by these
firms, internal funds are still the dominant source of financing, accounting for over 60% of their
investment.
Although equity issues are significantly more likely for firms making major
investments, their relative importance is unchanged. Debt issues cover the vast majority of
internal funding deficits. In addition, it appears that once dividends are paid, firms are extremely
reluctant to reduce payouts, or to slow their growth, even to avoid the costs associated with
issuing securities. In fact, firms making major investments are more likely to substantially
increase their dividends per share in the years before and during the investment event than are
other Value Line firms.
In addition, this paper attempts to distinguish between the observed pecking order
23
behavior and the pecking order theory of Myers and Majluf (1984) and Myers (1984). The
empirical results suggest that (a) transaction costs are an important determinant of financial
policy and (b) pecking order behavior does not necessarily provide strong support for the pecking
order theory. In particular, firms making significant investments that require external financing
raise substantially more funds than appears necessary, using industry peers as a benchmark. This
result is robust to several different methodologies and holds for both firms issuing debt and those
issuing equity. Moreover, the event firms are more likely to significantly increase dividends than
other Value Line firms, exerting additional pressure on an already tight financing requirement.
The event firms do not appear to be minimizing the amount of external financing as the pecking
order theory predicts.
24
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26
Appendix A --- Variable Definitions
Variable
Definition
Investment
Advertising (45) + Research & Development (46) + Capital Expenditures (128)
[All items when available, so long as at least one date item is valid.]
Total Book Assets
Assets (6) + Advertising Capital + Research & Development Capital
[Estimation procedure for capitalized intangible assets is described in the next sub-section.]
Long-Term Assets
Total Book Assets – Cash & Marketable Securities (1) – WC
[All items required]
Total Book Equity
Common Book Equity (60) + Advertising Capital + Research & Development Capital
[Estimation procedure for capitalized intangible assets is described in the next sub-section.]
Operating Cash Flow
EBITDA (13) + Advertising (45) + Research & Development (46) - ∆WC
[Advertising and Research & Development when available.]
Pre-Tax Cash Flow
Income Before Significant Items (18) + Significant Items & Discontinued Operations (48) +
Interest (15) + Taxes (16) + Depreciation (14) + Advertising (45) + Research & Development
(46) + Equity in Net Loss (106) + Funds from Other Operations (217) + Loss on Sale of
Equipment (213) - ∆WC
[Significant Items & Discontinued Operations, Advertising, Research & Development, Equity in
Net Loss, Funds from Other Operations, and Loss on Sale of Equipment all when available.]
After-Tax Cash Flow
Pre-Tax Cash Flow – Taxes (16) + Income Statement Deferred Taxes (50)
Working Capital (WC)
Current Assets (Non-Cash) – Current Liabilities (Non-Debt)
Current Assets
(Non-Cash)
Current Assets (4) – Cash & Marketable Securities (1)
or
Accounts Receivable (2) + Inventories (3) + Other Current Assets (68)
[All items required.]
Current Liabilities
(Non-Debt)
Current Liabilities (5) – Debt in Current Liabilities (34)
or
Accounts Payable (70) + Income Taxes Payable (71) + Other Current Liabilities (72)
[All items required.]
Net Debt Issue
Long-term Debt Issues (86) – Long-term Debt Reductions (92) + Change in Current Debt (301)
[All items required, unless the Change in Current Debt shows a combined data code.]
Net Equity Issue
Equity Issues (84) – Equity Repurchases (93)
[Both items are required.]
* Compustat data item numbers are in parenthesis.
27
Appendix B --- Examples of Significant Investments
Electronic Associates
Electronic Associates (computer industry) is engaged in the development, manufacture, and marketing of
simulation computer systems and energy measurement systems for utilities. The firm completed a
significant investment over the period 1982-1983, which involved a substantial increase in both capital
expenditures and R&D, relative to the firm’s typical investment patterns. In the early 1980s, analog
computers were losing ground to digital computers. Electronic Associates was losing market share in the
simulation end of the market, which had typically been one of the firm’s core businesses. In 1982 and
1983, the firm invested heavily in the development of the SIMSTAR system, which was announced to the
public in 1983. Development of the SIMSTAR system required increased R&D expenditures of $3.2M
and $4.2M in 1982 and 1983, respectively, up from a five-year average of $1.1M. The SIMSTAR project
also required a near doubling of the firm’s capital expenditures in 1982, from $1.3M to $2.2M. The
increase in CAPX was primarily used to for an addition to the firm’s manufacturing facility, which was
necessary for the SIMSTAR project.
UAL
UAL is the parent company of United Airlines (air transport industry). The company completed one
significant investment, lasting from 1990-1992. In 1990, after fighting off a lengthy takeover battle,
UAL agreed to purchase up to 128 Boeing wide-body jets in a $22B order. This was the largest aircraft
order ever placed by an airline. The deal included firm orders for 34 of the new Boeing 777, with an
option to purchase 34 more, and 30 Boeing 747-400 jumbo jets, with an option for 30 more. In 1992,
UAL decided to switch suppliers from Boeing to Airbus Industrie by agreeing to purchase up to 100
A-320s, in a $3B agreement. Initial deliveries were scheduled for the fall of 1993.
28
Appendix C --- Simulation Evidence on Event-Time Methodology
For each of the selected financial variables, all of the firm-year observations in the Value Line
sample are allocated to one of three groups, based on thirds of the rankings of the variable in year t-1.
14
The intersection of these prior-year groupings and the annual industry classifications form the year t
comparison groups.
15
The mean financial policy variable is calculated each year for each of the
comparison groups to form a set of benchmarks. Once the benchmarks are calculated, a pseudo-sample
16
of 500 firms is randomly selected, with replacement, from the Value Line sample.
For each of the
firms in the pseudo-sample, abnormal financial policy variables are calculated as the difference between
the firm’s financial policy variable and the benchmark variable. The mean of the individual firm
abnormal variables is calculated, and statistical significance is assessed. The statistical significance of
the mean is assessed via the time-series of cross-sections t-statistic and the pooled t-statistic of the mean.
These steps are repeated 1,000 times (benchmarks are only calculated once).
The specification (size) of the test statistics is determined by measuring what fraction of the 1,000
pseudo-samples reject the null hypothesisabnormal measure equals zeroat the 5% significance level.
The simulation results on the specification of the various test statistics are presented in Table C.1. In
particular, the fraction of random samples that reject the null hypothesis are reported for both types of
t-statistics.
The statistical significance of the difference between the fraction of random samples
rejecting the null and the theoretical value of 5% is determined using the normal approximation to the
binomial distribution.
The results suggest that both types of t-statistics are generally well specified. For the time-series of
cross-sections t-statistic, the only exceptions are the abnormal cash-to-sales ratio, the dividend-to-equity
income ratio, and the long-term debt-to-sales ratio, all of which reject too often when negative. For the
pooled t-statistic, the problem variables are the ∆cash-to-sales ratio, the dividends-to-equity income ratio,
and the long-term debt-to-sales ratio, which are rejected too infrequently when positive, and the cash-tosales ratio, which rejects too often when negative.
14
Note that the prior year rankings of the financial policy variables are based on all firm-years. The results are
materially unchanged if these rankings are determined each year.
15
At least five firms are required for the comparison group to be considered valid.
16
Results from simulations based on sample sizes of 50, 100, 250, and 750 firms are generally similar.
29
Table C.1 --- Simulated Specification Levels (Size) of Various Test Statistics for Abnormal Financial Policy Variables
Percent of 1,000 random samples rejecting the null hypothesis: statistic equals zero. Random samples of 500 firms are drawn
from the population of Value Line firms from 1972 to 1996, excluding firms in the financial sector. For each random sample, the
mean of the 500 individual firm abnormal financial policy variables is calculated. The mean and standard error for the timeseries of cross-sections t-statistic are calculated from the time-series of annual average abnormal variables, where individual firm
abnormal measures are first averaged in calendar-time. The pooled t-statistic is calculated from the grand average and standard
deviation of all individual firm abnormal variables. Individual firm abnormal variables are calculated by subtracting the mean
variable of the associated comparison group from each of the randomly selected individual firms. Comparison groups are
rd
rd
th
composed of the firms in the same industry, with similar levels (three groupings: less than 33 percentile; 33 to 67 percentile;
th
and greater than 67 percentile) of the variable in year t-1. Sales are Compustat data item (12). Total book assets are calculated
as assets (6) plus advertising capital (ADC), when available, plus R&D capital (RDC), when available. Cash is Compustat data
item (1). EBITDA is Compustat data item (13). Dividends are measured as cash dividends (127). Equity income is measured as
income before significant items available for common (237). Dividends per share (26) are adjusted for stock splits and stock
dividends. Long-term debt (LTD) is Compustat data item (9). Long-term debt issues are measured as long-term debt issuance
(86) minus long-term debt reductions (92). The difference between the current and previous year is denoted by ∆. The statistical
significance of the difference between the fraction of random samples rejecting the null and the theoretical value of 5% is
determined using the normal approximation to the binomial distribution.
Variable
Time Series of Cross-Sections t-statistic
<-1.96
>1.96
Total
<-1.96
Pooled t-statistic
>1.96
Total
Cash(t) / Sales(t)
log(Cash(t) / Sales(t))
∆Cash(t) / Sales(t)
∆(Cash(t) / Sales(t))
4.7%***
3.0%
3.6%
3.3%
2.1%
2.7%
2.0%
3.0%
6.8%*
5.7%
5.6%
6.3%
4.0%**
2.6%
2.9%
2.9%
2.1%
3.0%
1.1%**
1.7%
6.1%
5.6%
4.0%
4.6%
EBITDA(t) / Sales(t)
∆(EBITDA(t) / Sales(t))
EBITDA(t) / Total Assets(t-1)
∆(EBITDA(t) / Total Assets(t-1))
2.6%
2.1%
3.6%
2.0%
2.1%
2.3%
2.2%
3.0%
4.7%
4.4%
5.8%
5.0%
3.0%
3.1%
2.4%
2.1%
2.7%
2.1%
1.8%
1.9%
5.7%
5.2%
4.2%
4.0%
Dividends(t) / Equity Income(t)
Dividends(t) / Sales(t)
Dividends(t) / Total Assets(t-1)
∆(Dividends(t) / Equity Income(t))
∆(Dividends(t) / Sales(t))
∆(Dividends(t) / Total Assets(t-1))
∆DPS(t) / DPS(t-1)
4.3%***
2.9%
2.6%
3.0%
2.0%
2.0%
2.5%
1.9%
3.4%
1.9%
1.8%
3.2%
3.2%
2.9%
6.2%
6.3%
4.5%
4.8%
5.2%
5.2%
5.4%
3.6%
3.6%
3.6%
2.9%
2.4%
2.6%
2.0%
1.2%*
1.5%
2.3%
1.6%
3.4%
3.0%
3.0%
4.8%
5.1%
5.9%
4.5%
5.8%
5.6%
5.0%
LTD(t) / Total Assets(t)
∆(LTD(t) / Total Assets(t))
LTD(t) / Sales(t)
∆(LTD(t) / Sales(t))
2.4%
3.3%
4.0%**
2.7%
3.2%
2.9%
1.6%
2.6%
5.6%
6.2%
5.6%
5.3%
3.0%
2.3%
3.3%
2.7%
2.3%
2.4%
1.3%*
2.3%
5.3%
4.7%
4.6%
5.0%
LTD Issue(t) / Total Assets(t-1)
LTD Issue(t) / Sales(t)
3.0%
3.6%
2.4%
1.8%
5.4%
5.4%
2.5%
2.7%
2.4%
2.0%
4.9%
4.7%
*, **, *** Denotes significantly different from theoretical value at 10%, 5%, and 1% level, respectively.
30
Table 1.A --- Significant Investments by Year
Significant investment events are defined as the intersection of firms making “major” and “non-routine” investments. Firms with
investment intensities greater than 0.25 are identified as making major investments. Non-routine investments are those with an
annual percentage deviation from the firm’s average investment intensity greater than 50%, where there are at least four annual
observations. In addition, the firm must have been in Value Line for at least two years prior to the event; must not be part of a
consecutive series of events lasting longer than three years; must have at least four years between multiple events; and cannot
complete a merger in the year of the significant investment. Annual investment intensities are calculated as the sum of
advertising (45), capital expenditures (128), and research and development (46), all when available, divided by beginning of the
period long-term book assets. Long-term book assets are calculated as assets (6) plus advertising capital (ADC), when available,
plus R&D capital (RDC), when available, minus cash & marketable securities (1), minus working capital (see Appendix A for
details).
Significant
Investments
Percent of Value
Line Firms
Year
Value Line Firms
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
924
1,142
1,141
1,215
1,204
1,242
1,251
1,264
1,250
1,249
1,211
1,215
1,225
1,200
1,131
1,119
1,109
1,111
1,135
1,167
1,193
1,176
1,159
1,142
1,105
21
43
65
34
26
31
43
64
76
70
28
20
37
29
32
20
17
16
16
13
10
16
21
25
21
2.3%
3.8%
5.7%
2.8%
2.2%
2.5%
3.4%
5.1%
6.1%
5.6%
2.3%
1.6%
3.0%
2.4%
2.8%
1.8%
1.5%
1.4%
1.4%
1.1%
0.8%
1.4%
1.8%
2.2%
1.9%
29,280
794
2.7%
31
Table 1.B --- Significant Investments by Industry
Industry
Advertising, Publishing & Newspaper
Aerospace & Defense
Air Transport
Apparel & Shoe
Auto & Truck
Auto Parts
Beverage
Broadcasting & Cable TV
Building Materials, Cement, Furniture & Homebuilding
Chemical
Coal & Alternate Energy
Computer
Diversified
Drug
Drugstore
Electrical Equipment & Home Appliance
Electronics & Semiconductor
Food Processing
Food Wholesalers & Grocery Stores
Hotel & Gaming
Household Products
Industrial Services (Including Environmental)
Machinery
Manufactured Housing & Recreational Vehicles
Maritime
Medical Services
Medical Supplies
Metal Fabricating
Metals and Mining
Natural Gas
Office Equip. & Supplies
Oilfield Services & Equipment
Packaging & Container
Paper & Forest Products
Petroleum
Precision Instrument
Railroad
Real Estate
Recreation
Restaurant
Retail (Special Lines)
Retail Store
Steel
Telecommunications
Textile
Tire & Rubber
Tobacco
Toiletries & Cosmetics
Toys
Trucking & Transportation Leasing
Utilities
Total
Value Line
Firms
Investment
Events
Percent of
Value Line
Firms
801
662
361
741
164
572
306
213
1,445
1,265
170
897
960
461
244
863
957
1,167
613
293
243
490
1,617
220
131
178
562
405
528
1,136
350
451
452
588
965
593
253
74
386
313
613
681
610
497
356
211
195
261
150
350
2,266
11
21
30
28
4
14
13
6
39
39
7
28
4
7
7
20
45
23
15
17
6
12
39
3
10
5
5
9
27
13
13
17
9
30
57
16
1
0
10
7
18
20
20
2
10
7
9
8
5
13
15
1.4%
3.2%
8.3%
3.8%
2.4%
2.4%
4.2%
2.8%
2.7%
3.1%
4.1%
3.1%
0.4%
1.5%
2.9%
2.3%
4.7%
2.0%
2.4%
5.8%
2.5%
2.4%
2.4%
1.4%
7.6%
2.8%
0.9%
2.2%
5.1%
1.1%
3.7%
3.8%
2.0%
5.1%
5.9%
2.7%
0.4%
0.0%
2.6%
2.2%
2.9%
2.9%
3.3%
0.4%
2.8%
3.3%
4.6%
3.1%
3.3%
3.7%
0.7%
29,280
794
2.7%
32
Table 2 --- Abnormal Event-Time Investment Activity by Firms Making Significant Investments
Average abnormal investment activity as a percent of beginning of period long-term book assets for Value Line firms making
significant investments. All observations are adjusted by subtracting the average value from the associated comparison group.
rd
The comparison group is composed of the firms in the same industry, with similar levels (three groupings: less than 33
rd
th
th
percentile; 33 to 67 percentile; and greater than 67 percentile) of the variable in year t-1. The time-series of annual average
abnormal investment activities is used to calculate the mean and standard error of the mean. Long-term book assets (LTA) are
calculated as book assets (6) plus advertising capital (ADC), when available, plus R&D capital (RDC), when available, minus
cash and marketable securities (1), minus working capital (see Appendix A). Advertising is Compustat data item (45) and is
reported when available. Capital expenditures (CAPX) is Compustat data item (128) and is reported when available. Research
and development (R&D) is Compustat data item (46) and is reported when available. Investment is the sum of advertising (when
available), CAPX (when available), and R&D (when available), but is only calculated if at least one item is available.
Acquisitions are measured as Compustat data item (129). The sale of PP&E is Compustat data item (107). The net purchase of
investments is calculated as the difference between purchase of investments (113) and sale of investments (109). Investment
activity is the sum of investment, acquisitions, sale of PP&E, and net purchase of investments. All coefficients are multiplied by
100, t-statistics are reported in parenthesis, and the number of observations is in square brackets.
Variable
t-3
t-2
t-1
t
t+1
t+2
Advertising(t) / LTA(t-1)
0.15
(0.93)
[552]
0.18
(1.43)
[554]
0.18
(1.74)
[550]
0.29
(1.80)
[707]
-0.32
(-2.64)
[555]
-0.19
(-1.41)
[522]
CAPX(t) / LTA(t-1)
-0.15
(-0.41)
[482]
0.14
(0.34)
[471]
2.44
(6.64)
[497]
12.99
(27.73)
[586]
-0.91
(-3.19)
[455]
-0.75
(-1.90)
[467]
R&D(t) / LTA(t-1)
-0.07
(-0.62)
[553]
0.11
(1.11)
[561]
0.25
(2.31)
[553]
0.47
(5.04)
[722]
-0.05
(-0.63)
[554]
0.08
(0.94)
[525]
Investment(t) / LTA(t-1)
-0.44
(-1.16)
[490]
0.24
(0.46)
[492]
2.43
(5.73)
[492]
14.17
(21.70)
[582]
-2.63
(-6.45)
[421]
-1.63
(-4.34)
[431]
Acquisitions(t) / LTA(t-1)
-0.76
(-2.97)
[380]
-0.77
(-3.99)
[429]
-0.85
(-4.15)
[459]
-1.21
(-9.51)
[581]
-0.43
(-1.49)
[417]
-0.03
(-0.13)
[404]
Sale of PP&E(t) / LTA(t-1)
0.06
(0.40)
[248]
0.12
(0.82)
[291]
0.03
(0.15)
[320]
0.15
(1.39)
[415]
0.11
(0.69)
[283]
0.38
(2.22)
[264]
Net Purchase of Inv.(t) / LTA(t-1)
-0.03
(-0.29)
[246]
-0.25
(-2.93)
[267]
-0.05
(-0.62)
[280]
0.11
(1.45)
[403]
0.08
(0.55)
[297]
-0.01
(-0.12)
[268]
Investment Activity(t) / LTA(t-1)
-1.18
(-2.03)
[463]
-1.26
(-1.96)
[463]
1.93
(3.58)
[470]
14.63
(17.37)
[613]
-2.74
(-5.23)
[421]
-1.52
(-2.47)
[408]
Investment Activity(t) / Sales(t)
-0.91
(-1.98)
[494]
-0.43
(-1.48)
[483]
0.96
(1.82)
[489]
6.55
(13.36)
[611]
-1.17
(-1.84)
[419]
-1.47
(-2.86)
[420]
33
Table 3 --- Adjusted Event-Time Cash Flows of Firms Making Significant Investments
Average adjusted cash flows as a percent of sales for Value Line firms making significant investments. All observations are
adjusted by subtracting the average value from the associated comparison group. The comparison group is composed of the firms
rd
rd
th
th
in the same industry, with similar levels (three groupings: less than 33 percentile; 33 to 67 percentile; and greater than 67
percentile) of the variable in year t-1. The time-series of annual average abnormal investment activities is used to calculate the
mean and standard error of the mean. Sales are Compustat data item (12). Earnings before interest, taxes, depreciation, and
amortization (EBITDA) is Compustat data item (13). Operating cash flows are measured as EBITDA plus advertising (45)
(when available) plus R&D (46) (when available) minus the change in working capital. Working capital is measured as the
difference between non-cash current assets minus non-debt current liabilities. Non-cash current assets are current assets (4)
minus cash and marketable securities (1). If either of these items are missing, the sum of inventories (3), accounts receivable (2),
and other current assets (68) is substituted. Non-debt current liabilities are current liabilities (5) minus debt in current liabilities
(34). If either of these items is missing, the sum of accounts payable (70), income taxes payable (71), and other current liabilities
(72) is substituted. Pre-tax cash flow is income before significant items (18) plus significant items & discontinued operations
(48) (when available) plus interest (15) plus taxes (16) plus depreciation (14) plus advertising (when available) plus R&D (when
available) plus equity in net loss (106) (when available) plus funds from other operations (217) (when available) plus loss on sale
of equipment (213) (when available) minus change in working capital. After-tax cash flow is pre-tax cash flow minus taxes, plus
income statement deferred taxes (50). The symbol ∆ denotes the change from the previous year. All coefficients are multiplied
by 100, t-statistics are reported in parenthesis, and the number of observations is in square brackets.
Variable
t-3
t-2
t-1
t
t+1
t+2
EBITDA(t) / Sales(t)
0.02
(0.06)
[462]
0.55
(2.56)
[478]
0.69
(1.83)
[479]
0.07
(0.21)
[616]
-0.87
(-2.40)
[437]
-0.05
(-0.22)
[405]
Operating Cash Flow(t) / Sales(t)
0.54
(1.34)
[446]
0.65
(1.41)
[463]
0.95
(2.34)
[485]
-0.26
(-0.44)
[599]
0.03
(0.06)
[435]
0.27
(0.48)
[422]
Pre-Tax Cash Flow(t) / Sales(t)
0.19
(0.46)
[438]
0.62
(1.03)
[438]
0.66
(1.24)
[463]
-0.09
(-0.16)
[603]
0.18
(0.25)
[430]
-0.02
(-0.03)
[411]
After-Tax Cash Flow(t) / Sales(t)
0.12
(0.41)
[462]
0.51
(0.95)
[459]
0.81
(1.93)
[467]
0.29
(0.65)
[609]
0.73
(1.21)
[434]
-0.01
(-0.01)
[409]
∆(EBITDA(t) / Sales(t))
-0.19
(-0.86)
[462]
0.44
(2.68)
[478]
0.41
(2.02)
[479]
-0.42
(-2.18)
[616]
-0.62
(-2.78)
[437]
0.53
(2.92)
[405]
∆(Operating Cash Flow(t) / Sales(t))
0.56
(1.25)
[446]
0.91
(2.31)
[463]
0.49
(1.45)
[485]
-0.92
(-1.92)
[599]
-0.18
(-0.54)
[435]
-0.05
(-0.10)
[422]
∆(Pre-Tax Cash Flow(t) / Sales(t))
0.05
(0.09)
[438]
1.06
(1.88)
[438]
0.80
(2.13)
[463]
-0.63
(-1.21)
[603]
0.13
(0.30)
[430]
-0.08
(-0.13)
[411]
∆(After-Tax Cash Flow(t) / Sales(t))
0.35
(1.02)
[462]
0.50
(1.12)
[459]
0.44
(1.32)
[467]
-0.40
(-0.86)
[609]
0.56
(1.33)
[434]
0.19
(0.37)
[409]
34
Table 4 --- Various Dividend Policy Descriptive Statistics for Firms Making Significant Investments (1972-1996)
Dividend policy measures for firms making significant investments from 1972-1996. Dividends are measured as common
dividends (21). Dividend increases and decreases are based on dividend per share growth, adjusted for stock splits and stock
dividends. Dividends per share are Compustat data item (26). Dividend initiations are based on positive common dividends in
year t, and zero dividend payments in year t-1. Dividend omissions are based on zero common dividends in year t, and positive
dividend payments in year t-1. Firms raising external funds are identified as those issuing (net) securities greater than 5% of
beginning of period total book assets in the year of the significant investment. External funds are measured as the sum of debt
issues and equity issues. Debt issues are measured as long-term debt issuance (86) minus long-term debt reductions (92) plus the
change in current debt (301). Equity issues are measured as sale of common and preferred stock (108) minus purchase of
common and preferred stock (115). Total book assets is calculated as assets (6) plus advertising capital (ADC), when available,
plus R&D capital (RDC), when available. The t-statistics testing the significance of the difference between the Value Line
sample and the event firms are reported in parenthesis. N denotes the number of firm-years with valid data.
Value Line
Firm Years
t-3
Firms Making Significant Investments
t-2
t-1
t
t+1
29,740
611
611
612
794
569
534
% of firms not paying dividends
18.4
23.2
(3.08)
22.7
(2.77)
21.6
(2.02)
19.5
(0.81)
18.3
(-0.08)
20.4
(1.20)
% of firms increasing dividends > 25%
11.6
17.0
(3.54)
16.4
(3.18)
21.6
(6.64)
19.1
(5.79)
13.7
(1.36)
9.5
(-1.35)
% of firms decreasing dividends > 25%
4.4
3.4
(-1.04)
3.3
(-1.06)
2.0
(-2.51)
2.1
(-2.70)
5.9
(1.58)
8.8
(4.47)
% of firms initiating dividends
1.4
2.5
(2.14)
2.1
(1.46)
2.0
(1.12)
2.4
(2.29)
1.0
(-0.93)
0.5
(-1.81)
% of firms omitting dividends
1.8
2.1
(0.69)
1.6
(-0.24)
0.8
(-1.78)
0.8
(-2.16)
1.3
(-0.86)
3.0
(2.21)
t+2
All Firms Making Significant Investments
N
Firms Making Significant Investments & Raising External Funds
N
7,059
331
332
332
439
322
303
% of firms not paying dividends
18.0
23.6
(2.63)
22.3
(2.03)
21.1
(1.46)
18.9
(0.49)
19.9
(0.87)
23.1
(2.31)
% of firms increasing dividends > 25%
13.1
14.6
(0.69)
16.5
(1.59)
21.8
(4.12)
18.5
(2.96)
13.4
(0.17)
11.7
(-0.60)
% of firms decreasing dividends > 25%
3.3
4.2
(0.78)
3.3
(0.03)
2.8
(-0.44)
1.8
(-1.57)
7.1
(3.44)
10.0
(5.74)
% of firms initiating dividends
1.1
1.8
(1.30)
2.4
(2.36)
1.5
(0.76)
2.3
(2.44)
1.5
(0.70)
0.9
(-0.25)
% of firms omitting dividends
1.2
1.8
(1.08)
1.2
(0.05)
0.3
(-1.48)
0.7
(-0.96)
2.1
(1.51)
3.7
(4.24)
35
Table 5 --- Debt and Equity Issue Frequency for Firms Making Significant Investments (1972-1996)
Number of debt and equity issues of various sizes for firms making significant investments. Security issues are measured relative to long-term book assets at the beginning of the
year. Long-term book assets are calculated as assets (6) plus advertising capital (ADC), when available, plus R&D capital (RDC), when available, minus cash & marketable
securities (1), minus working capital (see Appendix A for details). Debt issues are measured as long-term debt issuance (86) minus long-term debt reductions (92) plus the change
in current debt (301). Equity issues are measured as sale of common and preferred stock (108) minus purchase of common and preferred stock (115). The number of event firms
issuing securities greater than x% of beginning of period total book assets are reported, as are the number of Value Line firms involved in similarly sized transactions (VL(t)). N is
the total number of firm-years with valid data. The t-statistic testing the significance of the difference between the fraction of event firms issuing securities and the fraction of
Value Line firms issuing securities is calculated using the normal approximation to the binomial distribution.
VL(t)
Number of Firms
t-2
t-1
t
t+1
VL(t)
Percent of Firms
t-2
t-1
t
t+1
t-statistic of Difference from VL(t)
t-2
t-1
t
t+1
External Financing (Debt Issues + Equity Issues) > x%
1%
3%
5%
N
13,448
10,491
8,428
29,575
222
164
130
545
255
200
162
556
532
486
439
717
290
221
182
508
45.5%
35.5%
28.5%
40.7%
30.1%
23.9%
45.9%
36.0%
29.1%
74.2%
67.8%
61.2%
57.1%
43.5%
35.8%
-1.42
-1.44
-1.17
0.13
0.15
0.18
13.31
14.89
15.19
3.97
2.50
2.19
12,882
9,897
7,806
31,092
188
141
115
568
201
164
123
577
536
479
431
757
279
213
173
536
41.4%
31.8%
25.1%
33.1%
24.8%
20.2%
34.8%
28.4%
21.3%
70.8%
63.3%
56.9%
52.1%
39.7%
32.3%
-2.32
-1.79
-1.20
-1.90
-0.94
-0.97
13.81
14.77
15.24
3.60
2.48
2.17
124
65
46
560
144
93
75
579
199
120
87
746
109
63
46
537
21.3%
11.8%
8.0%
22.1%
11.6%
8.2%
24.9%
16.1%
13.0%
26.7%
16.1%
11.7%
20.3%
11.7%
8.6%
0.23
-0.05
0.05
1.05
1.27
1.58
1.86
1.45
1.26
-0.25
-0.02
0.14
53
25
17
508
10.0%
4.9%
2.8%
8.3%
3.7%
2.2%
8.5%
4.9%
3.4%
19.1%
10.3%
7.3%
10.4%
4.9%
3.3%
-0.39
-0.25
-0.12
-0.35
0.00
0.17
3.55
2.17
1.95
0.10
0.01
0.14
Debt Issues > x%
1%
3%
5%
N
Equity Issues > x%
1%
3%
5%
N
6,637
3,682
2,493
31,171
Combination Issue (Debt Issues > x% & Equity Issues > x%)
1%
3%
5%
N
2,958
1,442
825
29,575
45
20
12
545
47
27
19
556
137
74
52
717
36
Table 6 --- Adjusted Event-Time External Financing by Firms Making Significant Investments
Average adjusted external financing as a percent of beginning of period total book assets for Value Line firms making significant
investments. All observations are adjusted by subtracting the average value from the associated comparison group. The
rd
comparison group is composed of the firms in the same industry, with similar levels (three groupings: less than 33 percentile;
rd
th
th
33 to 67 percentile; and greater than 67 percentile) of the variable in year t-1. Long-term book assets (LTA) are calculated
as assets (6) plus advertising capital (ADC), when available, plus R&D capital (RDC), when available, minus cash & marketable
securities, minus working capital (see Appendix A for details). Net debt issues are measured as long-term debt issues (86) minus
long-term debt repurchases (92) plus the change in current debt (301). Net equity issues are the difference between equity issues
(84) and equity repurchases (93). All coefficients are multiplied by 100, t-statistics are reported in parenthesis, and the number
of observations is in square brackets.
Variable
t-3
t-2
t-1
t
t+1
t+2
Net Debt Issues(t) / LTA(t-1)
-1.40
(-1.74)
[267]
-0.53
(-0.60)
[280]
0.06
(0.04)
[295]
5.88
(5.37)
[376]
0.60
(0.67)
[244]
-0.06
(-0.03)
[215]
Net Debt Issues(t) / Sales(t)
-1.04
(-2.66)
[269]
-0.52
(-0.93)
[280]
-0.34
(-0.52)
[304]
3.43
(6.63)
[372]
0.51
(0.55)
[254]
-0.21
(-0.19)
[228]
Debt Issues(t) / LTA(t-1)
-0.50
(-0.57)
[371]
-0.82
(-0.92)
[416]
1.50
(1.35)
[438]
6.87
(7.58)
[587]
-0.98
(-1.05)
[399]
-1.05
(-1.14)
[383]
Debt Reductions(t) / LTA(t-1)
-0.66
(-1.32)
[374]
-0.26
(-0.50)
[418]
0.05
(0.06)
[452]
-0.30
(-0.47)
[602]
-0.44
(-0.72)
[419]
-0.16
(-0.36)
[382]
Net Equity Issues(t) / LTA(t-1)
-0.28
(-0.58)
[351]
-0.21
(-0.48)
[401]
0.99
(1.73)
[422]
0.73
(2.39)
[548]
0.51
(1.67)
[408]
0.08
(0.42)
[355]
Net Equity Issues(t) / Sales (t)
-0.35
(-1.37)
[356]
-0.29
(-1.42)
[409]
0.26
(1.31)
[434]
0.18
(1.31)
[552]
0.30
(2.81)
[407]
-0.10
(-0.84)
[371]
Equity Issues(t) / LTA(t-1)
0.64
(1.00)
[361]
-0.25
(-0.76)
[412]
2.15
(1.40)
[439]
0.75
(2.32)
[582]
-0.09
(-0.34)
[414]
0.18
(0.85)
[370]
Equity Repurchases (t) / LTA(t-1)
0.00
(0.01)
[419]
0.21
(1.20)
[479]
0.09
(0.56)
[498]
0.05
(0.34)
[665]
-0.35
(-3.73)
[487]
-0.06
(-0.49)
[464]
Change in Current Debt(t) / LTA(t-1)
0.06
(0.32)
[399]
-0.11
(-0.69)
[452]
0.01
(0.06)
[484]
0.16
(1.71)
[629]
0.14
(1.21)
[460]
0.21
(1.07)
[432]
External Funds (t) / LTA(t-1)
-2.49
(-2.17)
[253]
0.45
(0.27)
[268]
0.16
(0.14)
[268]
6.25
(4.74)
[340]
1.50
(0.61)
[226]
-0.54
(-0.34)
[204]
External Funds(t) / Sales (t)
-1.47
(-2.29)
[256]
-0.79
(-0.93)
[271]
1.56
(1.07)
[280]
3.60
(3.07)
[347]
3.09
(1.21)
[233]
-0.37
(-0.37)
[210]
37
Table 7 --- Adjusted Event-Time Capital Structures of Firms Making Significant Investments
Average adjusted percent of total book assets accounted for by various types of claims for Value Line firms making significant
investments. All observations are adjusted by subtracting the average value from the associated comparison group. The
rd
comparison group is composed of the firms in the same industry, with similar levels (three groupings: less than 33 percentile;
rd
th
th
33 to 67 percentile; and greater than 67 percentile) of the variable in year t-1. Total book assets (TA) is calculated as assets
(6) plus advertising capital (ADC), when available, plus R&D capital (RDC), when available. The book value of long-term debt
(LTD) is Compustat data item (9). The book value of short-term debt (STD) is measured as debt in current liabilities (34). The
book value of preferred stock (PREF) is measured at par value (130). The book value of common stock (EQTY) is Compustat
data item (60) plus ADC (when available) plus RDC (when available) plus balance sheet deferred taxes and investment tax credit
(35) (when available) plus balance sheet minority interest (38) (when available) plus post-retirement benefit liability (330) (when
available). Total liabilities (TOTLB) balance the identity: TOTLB = TA – EQTY – PREF. The difference between the current
and the previous year is denoted by ∆. All coefficients are multiplied by 100, t-statistics are reported in parenthesis. The number
of observations is in square brackets.
Variable
t-3
t-2
t-1
t
t+1
t+2
LTD(t) / TA(t)
-1.37
(-3.71)
[507]
-1.46
(-3.53)
[513]
-1.99
(-4.74)
[512]
2.41
(5.62)
[653]
0.22
(0.75)
[468]
-0.60
(-1.69)
[432]
STD(t) / TA(t)
-0.20
(-0.89)
[478]
-0.13
(-0.65)
[506]
-0.22
(-1.49)
[502]
0.07
(0.38)
[645]
-0.01
(-0.04)
[458]
-0.15
(-0.64)
[432]
TOTLB(t) / TA(t)
-1.61
(-3.36)
[486]
-1.74
(-3.27)
[480]
-2.82
(-6.13)
[481]
1.24
(2.41)
[634]
-0.48
(-0.90)
[442]
-2.23
(-3.98)
[402]
PREF(t) / TA(t)
-0.08
(-1.25)
[567]
-0.09
(-1.90)
[562]
-0.08
(-2.16)
[564]
-0.07
(-1.72)
[719]
-0.05
(-0.97)
[521]
-0.01
(-0.15)
[488]
EQTY(t) / TA(t)
1.48
(3.33)
[480]
1.33
(1.95)
[482]
2.46
(5.08)
[493]
-1.19
(-2.91)
[637]
0.67
(1.23)
[446]
2.40
(3.99)
[405]
∆(LTD(t) / TA(t))
-1.18
(-3.69)
[507]
-1.20
(-5.15)
[513]
-1.32
(-4.38)
[512]
3.11
(8.44)
[653]
0.55
(2.63)
[468]
-0.55
(-1.62)
[432]
∆(STD(t) / TA(t))
-0.08
(-0.40)
[478]
-0.12
(-0.75)
[506]
-0.39
(-2.31)
[502]
0.08
(0.38)
[645]
-0.20
(-1.08)
[458]
-0.14
(-0.64)
[432]
∆(TOTLB(t) / TA(t))
-0.69
(-1.63)
[486]
-1.08
(-2.78)
[480]
-1.60
(-5.08)
[481]
2.37
(6.31)
[634]
0.23
(0.54)
[442]
-1.40
(-4.22)
[402]
∆(PREF(t) / TA(t))
-0.06
(-1.86)
[567]
-0.03
(-1.09)
[562]
-0.01
(-0.49)
[564]
0.00
(-0.07)
[719]
0.05
(1.47)
[521]
0.05
(0.73)
[488]
∆(EQTY(t) / TA(t))
0.95
(2.33)
[480]
0.90
(1.90)
[482]
1.43
(5.39)
[493]
-2.50
(-6.81)
[637]
-0.31
(-0.65)
[446]
1.31
(3.45)
[405]
38
Table 8
Post-Event Cash-to-Sales Rankings Relative to Industry
For Firms Making Significant Investments & Raising External Funds
This table displays the percentiles that minimize forecast errors of post-event cash-to-sales ratios for firms making significant
investments and raising external funds greater than 5% of beginning of period long-term book assets. Forecast errors are
th
calculated as the difference between the actual cash-to-sales ratio of event firms and the n percentile of all non-event firms in
the industry during the year with available cash (6) and sales (12) data from Compustat. Firms raising external funds are
identified as those issuing (net) securities greater than 5% of beginning of period long-term book assets in the year of the
significant investment. External funds are measured as the sum of debt issues and equity issues. Debt issues are measured as
long-term debt issuance (86) minus long-term debt reductions (92) plus the change in current debt (301). Equity issues are
measured as sale of common and preferred stock (108) minus purchase of common and preferred stock (115). Long-term book
assets is calculated as assets (6) plus advertising capital (ADC), when available, plus R&D capital (RDC), when available. The
percentile is chosen to minimize the forecast error mean (Mean), square root of the average squared errors (RMSE), median
(Median), and mean absolute deviation (MAD). The number of observations is denoted by N.
Mean
Percentile that minimizes forecast error:
RMSE
Median
MAD
N
Full Sample
Cash(t) / Sales(t)
d(Cash(t) / Sales(t))
62
36
57
41
46
43
43
46
349
347
Debt Issuers
Cash(t) / Sales(t)
d(Cash(t) / Sales(t))
62
35
58
40
46
43
43
46
352
350
Equity Issuers
Cash(t) / Sales(t)
d(Cash(t) / Sales(t))
65
59
61
62
50
57
47
62
73
72
Low Level of Cash/Sales in Year t-1
Cash(t) / Sales(t)
d(Cash(t) / Sales(t))
33
67
27
61
21
62
20
54
97
97
Medium Level of Cash/Sales in Year t-1
Cash(t) / Sales(t)
d(Cash(t) / Sales(t))
51
46
49
47
45
45
43
47
95
95
High Level of Cash/Sales in Year t-1
Cash(t) / Sales(t)
d(Cash(t) / Sales(t))
78
10
77
15
71
12
70
15
108
108
39
Table 9 --- Regression Analysis on the Determinants of Cash Holdings for Value Line Firms (1972-1996)
The dependent variable is either the natural log of cash/sales or the natural log of cash/assets, where assets are long-term assets.
Long-term assets (Assets) are measured as assets (6) plus advertising capital (ADC), when available, plus R&D capital (RDC),
when available, minus cash & marketable securities, minus working capital (see Appendix A for details). Sales are Compustat
data item 12. Market assets are calculated as total book assets (assets plus ADC plus RDC) minus book equity (60) plus market
equity (199*54). Size is measured as the natural log of long-term book assets. Operating cash flows are measured as EBITDA
(13) plus advertising (45) (when available) plus R&D (46) (when available) minus the change in working capital. Capital
expenditures are Compustat data item 128. Debt is the sum of short-term debt (34) and long-term debt (9). Industry cash flow
volatility is the cross-sectional standard deviation of EBITDA/Sales for firms in the same industry during the year. The dividend
dummy equals one if the firm pays a cash dividend (127) during the year, and equals zero otherwise. The significant investment
dummy equals one if the firm completes a significant investment and raises external financing during the year.
Pooled
Time Series of Cross-Sections
æ Cash ö
æ Cash ö
logç
logç
÷
÷
è Sales
è Assets
æ Cash ö
logç
è Sales
æ Cash ö
logç
è Assets
Intercept
-3.19
(-52.69)
-2.95
(-49.65)
-2.86
(-34.22)
-2.60
(-28.73)
Market-to-Book Assets
0.20
(14.04)
0.14
(9.86)
0.24
(11.98)
0.16
(9.25)
Size
-0.10
(-20.45)
-0.18
(-36.17)
-0.10
(-15.97)
-0.17
(-25.31)
Operating Cash Flows / Assets
1.22
(21.97)
1.73
(31.79)
1.19
(10.71)
1.72
(13.88)
Working Capital / Assets
-1.12
(-40.60)
-0.13
(-4.65)
-1.10
(-27.30)
-0.11
(-2.11)
Capital Expenditures / Assets
-1.64
(-17.00)
-1.76
(-18.72)
-1.76
(-13.59)
-1.88
(-17.27)
Advertising Expense / Assets
-1.32
(-7.89)
-0.87
(-5.28)
-1.27
(-6.84)
-0.92
(-4.88)
R&D / Assets
5.24
(26.92)
1.84
(9.63)
4.91
(15.25)
1.65
(5.18)
Debt / Assets
-0.36
(-9.79)
-0.90
(-25.16)
-0.38
(-4.20)
-0.92
(-11.14)
Industry Cash Flow Volatility
1.85
(11.75)
5.90
(37.95)
1.82
(5.35)
5.88
(14.88)
Dividend Dummy
-0.19
(-9.58)
-0.18
(-9.59)
-0.15
(-5.09)
-0.15
(-4.49)
Significant Investment Dummy
0.15
(2.23)
0.11
(1.67)
0.05
(0.73)
0.02
(0.29)
R
0.17
0.31
N
30,042
30,059
Dependent Variable
2
40