DNB Working Paper
No. 77/December 2005
Allard Bruinshoofd and Leo de Haan
DNB W O R K I N G P A P E R
Financing the New Economy:
are ict firms really that different?
De Nederlandsche Bank
Financing the New Economy: are ICT firms really that different?
Allard Bruinshoofd and Leo de Haan *
* Views expressed are those of the individual authors and do not necessarily reflect
official positions of De Nederlandsche Ba nk.
Working Paper No. 077/2005
December 2005
De Nederlandsche Bank NV
P.O. Box 98
1000 AB AMSTERDAM
The Netherlands
Financing the New Economy: are ICT firms really that different?
W. Allard Bruinshoofd* and Leo de Haan*
December 2005
Abstract
Did ICT firms behave very differently from non-ICT firms during the global ICT boom-bust
cycle on the stock markets? To answer this question we analyze the financial behavior of a
sample of North-American and Western European firms during 1991-2002. We document that
ICT firms are indeed what they are always said to be: relatively information intensive and
risky firms. We show that they therefore hold more precautionary cash and have lower
leverage targets. Though ICT firms issued more equity and debt during the boom, this was
broadly unrelated to stock market conditions, in contrast to the prediction of the market timing
view. ICT firms did not build up excessive cash reserves that lead to overinvestment. All in
all, the financial management of ICT firms has not been all that different from non-ICT firms.
JEL codes: C33, C43, E41, G3
Keywords: Cash Management, Market Timing, Capital Structure, ICT
_______________________
* De Nederlandsche Bank, Research Division, P.O. Box 98, 1000 AB Amsterdam, The Netherlands.
E-mails: [email protected] and [email protected].
We acknowledge useful comments of Jaap Bos, Peter van Els, Jim Kolari, Clemens Kool, participants of the
2005 International Atlantic Economic Conference (New York) and seminar participants at De Nederlandsche
Bank.
1.
Introduction
The boom of New Economy shares in the late 1990s is an opportunity to investigate what
happens if a particular group of firms suddenly gets access to cheap equity capital. Ofek and
Richardson (2003) show that at the end of 1999, Internet firms traded at extremely high prices
relative to earnings, in the aggregate at roughly 35 times their revenue. If those firms had
achieved industry-average net income margins at the time, they would have had priceearnings (P/E) ratios of 605. The growth rates that would have been required to justify such
high P/E ratios would have been extremely high by historical standards. The New Economy
boom manifested itself primarily in a growing number of Venture Capital backed initial
public offerings (IPOs) by Internet firms (Kaplan, 2003). However, spillover effects also
boosted valuations of already existing and sometimes even long-established firms in the
Information and Communication Technology (ICT) sectors. Such companies saw their stock
prices rise as well (Figure 1). Hence, established ICT firms suddenly had access to relatively
cheap equity capital in comparison to their non-ICT counterparts. How did this affect their
financing behavior?
[Figure 1 about here]
We study this issue using financial statement data of publicly traded non-financial
firms, which we split into two sub-samples of ICT firms and non-ICT firms. Both subsamples cover North-America and Western Europe, and the sample period is 1991-2002. We
compare financing, cash management and investment behavior of ICT firms and non-ICT
firms during the sample period which more or less coincides with the ICT boom-bust cycle. In
particular we address the following questions. Did established ICT firms, in comparison to
non-ICT firms enjoy relatively high stock price increases or P/E ratios during the boom? And
did they ‘time the market’, by issuing equity when stock prices were high? If the answers to
these two questions are affirmative, that would imply that ICT firms exploited the ICT boom
by raising extra external funds. Logical subsequent questions would then be; what did ICT
firms do with these extra funds? Did they hold more cash, did they spend it on new
investment projects, or did they pay off their debt?
These questions are quite topical in view of the ongoing research of the repercussions
of financing constraints on corporate investment and its connections with cash management
and capital structure. One early strand of the financing constraints literature (initiated by
1
Fazzari et al. (1988) focuses on the cash or cash flow sensitivity of investment as a measure of
financial constraints. The cash management literature deals mostly with the optimal level of
corporate cash holdings (e.g. Opler et al., 1999). These streams of literature can be connected
(as proposed by Bruinshoofd, 2005) and in this paper we will make a first step in this
direction. We also involve the capital structure literature, which currently disputes the validity
of the static trade-off theory, with its emphasis on an optimal capital structure (see e.g. Harris
and Raviv, 1991, for a review), versus both the pecking order hypothesis (Myers, 1984;
Shyam-Sunder and Myers, 1999) and the more recent market timing view (Baker and
Wurgler, 2002). Neither of the latter two take capital structure targeting as the point of
departure.
The plan of the paper is as follows. In Section 2 we look at the characteristics of the
firms in our sample, with a focus on the differences between ICT and non-ICT firms. In
Section 3 we consider market timing to see whether ICT and non-ICT firms raise larger
amounts of funding in years of good stock market performance. In Section 4 we turn to cash
management in three parts. First, we apply an event-window analysis to get a better
understanding of what happens with corporate cash holdings around major funding and
spending events. Second we analyze the determinants of corporate cash holdings and explore
the role of market timing therein. Third and last, we look briefly at capital structure
determination of ICT firms. In Section 5 we exploit our cash management analysis to define
excess cash holdings and determine whether the ICT boom has induced ICT firms to
accumulate financial slack. Thereafter, we investigate where excess cash holdings go,
returning first to event-window analysis and subsequently to the estimation of investment
equations with excess cash among the explanatory variables. Section 6 summarizes our main
findings and concludes.
2.
Data and descriptive statistics
Our data derives from the COMPUSTAT Global data files, covering publicly traded firms
worldwide for the period 1991-2002. From these files we select non-financial NorthAmerican and Western European firms.1 Our unbalanced panel dataset contains 47,286
observations and 6,107 firms, split about equally between North-American and Western
1
We include firms from Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy,
Luxemburg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and the United
States.
2
European observations. Throughout the paper we report results for the total sample and only
mention differences between the two zones when this is relevant for the analysis.
Firms are denoted ICT-firms if they belong to one of the ICT industries (Appendix A
gives the four-digit SIC87 industry classification key used). About one out of four firms in our
sample is as such defined to be an ICT firm. Table 1 presents median values of selected
variables for ICT and non-ICT firms and tests of the significance of the differences. We refer
to the explanatory notes in the table for the definitions of the variables. For the remainder of
the paper, when we refer to these definitions we use the variable names in italics.
[Table 1 about here]
A few observations catch the eye. First, ICT firms are smaller than non-ICT firms, but
grow faster. This agrees with the view that ICT-related activities really took off in the
nineties.2 Second, and contrary to the usual assertion that ICT firms initially often have
negative cash flows (e.g. Houben and Kakes, 2002), the ICT firms in our data generate
substantial amounts of cash flows. While median ICT cash flow falls statistically short of
median non-ICT cash flow, the economic difference is almost negligible. Third, with 17.86
percent of non-cash assets ICT firms’ median cash ratio is nearly three times as high as that of
non-ICT firms. While the view of excessive cash hoarding by ICT firms seems easily
supported by this observation, alternative explanations are possible.
One alternative explanation for substantially higher cash holdings in ICT firms is a
stronger precautionary motive to hold cash (e.g. Kim et al, 1998; Opler et al., 1999). In
general, a precautionary motive follows from firms’ intrinsic information intensity, inducing
uncertainty over the access to external funding. The information in Table 1 does indeed
suggest that ICT firms are more information intensive than non-ICT firms. Specifically,
investments in ICT stocks are more risky from the perspective of the portfolio holder on
account of a higher beta and zero median dividend payout. In addition, the substantially
higher market-to-book ratio for ICT firms relative to non-ICT firms reveals that ICT firms’
market values consist for a large part of growth opportunities. Growth opportunities make for
poor collateral especially when applying for long-term loans. In fact, ICT firms rely relatively
2
The structure of our data also indicates that ICT firms are younger than non-ICT firms. Firstly, while the
number of non-ICT firms increases by a factor 2.5 during the sample period, the number of ICT firms increases
by a factor of 5. As a corollary, the median number of years that we observe for non-ICT firms in our sample is
9, whereas for ICT firms it is only 6. Additionally, we observe 43% of the non-ICT firms in our data set for the
full sample period, while the corresponding number is only 26% for the ICT firms in our data.
3
heavily on loans with short maturities (have a higher median short debt), while at the same
time they pay a slightly higher average interest rate on their outstanding debt. Other features
that reduce the collateral value of ICT assets is the fact that ICT firms invest more heavily in
R&D and report larger shares of intangible assets (intangibles) in their balance sheets (cf.
Himmelberg and Petersen, 1994). Not surprisingly then, median leverage for ICT firms is
substantially below that of non-ICT firms. We will explore the precautionary motive in more
detail in Section 4 where we do a regression analysis on the determinants of cash holdings.
Another possible explanation for their substantially higher cash holdings is that ICT
firms have lower investment rates. Here the data are not easy to interpret. Although ICT
firms’ capital expenditures are comparable in economic terms to those of non-ICT firms, it
follows readily from the table that ICT firms invest considerably more in R&D than do nonICT firms. Granted, ICT firms’ investment opportunities as embodied in the market-to-book
ratio and firm growth rates substantiate higher investment rates. Yet without further analysis
we cannot discard the reading that high cash holdings by ICT firms actually facilitate high
investment expenditure, rather than following from a lack of spending. We return to this issue
more comprehensively in Section 5 where we analyze the impact of (excess) cash on capital
expenditures.
The view that ICT firms hoarded (excessive amounts of) cash – on the back of soaring
stock prices – seems substantiated when we turn to issuance activity. We observe from Table
1 that ICT firms’ net debt issues and net equity issues are higher, while the price-earnings
ratio was higher for ICT firms. Whereas these summary statistics seem to be consistent with
the view that ICT firms have ‘timed the market’, we need to connect issuance activity to stock
market performance explicitly to exclude alternative explanations.3 We turn to this issue next.
3.
Market timing
Figure 1 shows MSCI share price indices for the ICT sector and for Industrials, respectively.
The ICT boom manifested itself around 1999. In 2000 the ICT bubble burst. Figure 2 gives
the median and inter-quartile range for the stock price change for our sample of ICT and nonICT firms and shows a similar picture. The peak in 1999 shows in the ICT stock prices. After
1999 the ICT stock price change is more negative for ICT firms than for non-ICT firms. This
is also the reason why the median stock price change over the whole sample period is lower
3
Such as a stronger demand for external funding by ICT firms in line with their higher growth rates.
4
for ICT firms than for non-ICT firms, although this difference is not statistically significant
(Table 1). Figure 3 shows stock price changes for the ten largest ICT industries in our data.
They show that all ICT sectors share a similar stock market development over the sample
period.
[Figures 2 and 3 about here]
In this section we investigate whether the ICT boom on the stock market has lead to hoarding
of external funds by ICT firms. If firms indeed ‘time the market’ by issuing equity and debt
when valuation is high, ICT firms must have capitalized on the ICT boom by acquiring
external funds even if they did not have an immediate use for it. This hoarding behavior
would be understandable because, under normal circumstances, ICT firms are information
intensive firms and therefore likely to be constrained in their access to external equity. Debt
constraints may also be tight for ICT firms whose assets in place are mostly immaterial or
consist only of growth opportunities and whose risk of failure is high (e.g. Himmelberg and
Petersen, 1994, for US firms). Therefore, the ICT boom may be characterized as ‘pennies
from heaven’ for ICT firms and it is our hypothesis that the ICT boom on the equity market
has induced ICT firms to hoard external equity and – because of rising collateral value – debt.
Hence, we investigate whether the ICT boom on the stock exchange has induced ICT
firms, in particular, to hoard external equity and debt. Therefore, we first sort ICT and nonICT firms by year (to control for macroeconomic issuing trends) and then by sales (to control
for firm size) and, within each consecutive set of ten (similarly sized) firms, allocate them into
ten bins on the basis of their stock return performance (cf. Welch, 2004). This procedure
keeps a roughly equal number of firms in each decile and maximizes the spread in stock
returns across deciles, holding calendar year and firm size constant. The results of this
exercise are in Figure 4. The picture that emerges is that it is not so much equity issues that
rise steeply with stock performance, it is debt issues that do. However, it is difficult to
determine whether ICT or non-ICT firms time the market more aggressively.
[Figure 4 about here]
In order to quantify market timing by firms, we follow Baker and Wurgler (2002) who
define an indicator of market timing that measures whether firms extract external funds when
5
their financial market conditions are most favorable. Using market-to-book (MB) as an
indication of favorable market conditions, we define market timing as:


 e +d
MBs 
s

EFWAMBt = ∑  t −1 s
× T

s =1 
 ∑ ( er + d r ) ∑ MBr T 
r =1
 r =1

t −1
(1)
where e and d denote net equity issues and net debt issues, respectively. For a firm at time t,
EFWAMB reflects the extent to which that firm’s external funding coincided with favorable
market conditions from the first year it was observed in the data up to year t-1. In other words,
it tells us how actively a particular firm has been timing the market.4 If market conditions play
no role in the decision of the firm to raise external funding, its value for EFWAMB will be
unity. If, however, a firm raises external funding specifically in years of above-average
market conditions (i.e. when it faces an above-average market-to-book ratio), its value for
EFWAMB will exceed unity. Non-ICT firms are market timers in a statistical sense, as their
median EFWAMB of 1.018 is statistically different from unity (Table 1). For ICT firms
median EFWAMB is only 1.002 and not statistically different from unity. Hence, in a
statistical sense, non-ICT firms are more aggressive market timers than ICT firms.
4.
Corporate cash management
In Section 2 we observed that ICT firms are more information intensive than non-ICT firms.
This may make them more financially constrained, in the sense that they have more restricted
access to external funding. If so, it may be surprising to learn from the previous section that
the median ICT firm does not actively time debt and equity issues to favorable stock market
conditions. Alternatively, constrained firms may adept their cash management, rather than
their issuance behavior, to their intrinsic information intensity. They may for example hoard
cash for future investment projects to avoid dependence on external funding. We therefore
focus on corporate cash management in this section. We first examine cash dynamics around
sizeable funding and spending events (Section 4.1). Subsequently, we analyze the
4
We deviate from Baker and Wurgler’s (2002) definition in one respect. We normalize a firm’s market-to-book
ratio by the mean over the sample period. In this way EFWAMB only reflects market timing and not also growth
opportunities and is equal to 1 if there is no market timing. We thank Clemens Kool for suggesting this
adjustment.
6
determinants of corporate cash holdings in a regression framework (Section 4.2). We also
briefly look at capital structure management (Section 4.3).
4.1
Cash holdings around sizeable funding and spending events
A quick way to gain insight into how corporate cash is managed is to apply an event window
to cash dynamics around sizeable funding and spending events. With sizeable we signify
funding and spending events that make up at least 5% of total assets. For example, a firm is
considered to issue a sizeable amount of equity (debt) when net equity (debt) issued
constitutes at least 5% of the pre-issue value of total assets (cf. Hovakimian et al., 2001;
Korajczyk and Levy, 2003; Hovakimian, 2005). Table 2, panel A reports mean and median
values of changes in cash holdings in the year of the event (year 0) as well as the cumulative
changes in cash holdings prior to and following the event (years -3 to -1 and years +1 to +3,
respectively). We observe the following.
On the funding side, we observe that cash holdings increase in years with sizeable net
equity issues. This observation applies to ICT as well as non-ICT firms. In the years around
sizeable net equity issues, cash holdings decline, though not significantly so for ICT firms.
One reading of this event-window is that cash is being disbursed on a regular basis and is
occasionally replenished by sizeable net equity issues. In contrast, we observe that cash
holdings by ICT as well non-ICT firms decrease in years of sizeable net debt issues. For nonICT firms declining cash holdings characterize the entire window, without displaying a clear
event-related pattern. For ICT firms, however, nothing much seems to be happening to cash
holdings in the years prior to and following a sizeable debt issue. At least for ICT firms,
therefore, debt issues may be connected to events that include cash disbursements.
Alternatively, firms adjust optimal cash holdings following sizeable changes in leverage (this
will be taken up in Section 4.2).
On the spending side, we observe considerable reductions in cash holdings in years of
sizeable investment or acquisition outlays. This suggests that cash financing plays an
important role in such events. For non-ICT firms we observe reductions in cash holdings in
the years surrounding the spending events as well, although these are mostly much smaller.
For ICT firms we actually observe some hoarding of cash in the years before sizeable
spending events, while cash holdings are replenished in the years thereafter. Considering
mean effects around sizeable acquisitions, for example, we observe that about one third of the
reduction in cash holdings in the year of the acquisition is accumulated in advance, while the
7
remaining two thirds are being replenished in the years afterwards. The observation that some
firms save before large cash expenditures raises the issue of what are the determinants of
planned or precautionary cash holdings of firms. This is the issue that we turn to next.
4.2
Determinants of corporate cash holdings
As argued before, one explanation for the observation of substantially higher cash holdings by
ICT firms is their stronger precautionary motive to hold cash, following from their higher
intrinsic information intensity.5 Indeed, theoretical reflections and empirical results on cash
management broadly agree on the pivotal role played by information problems that provide a
rationale for precautionary cash holdings.6
In this subsection we therefore look at the determination of corporate cash holdings
using regression analysis.7 We draw upon a considerable body of empirical literature in our
search for the determinants of corporate cash holdings.8 Among the potential determinants of
corporate cash holdings are firm size (picking up scale effects in cash management), cash
substitutes (capturing portfolio effects in cash management), earnings (reflecting the annual
inflow of cash) and an interest rate (to capture the opportunity costs of holding cash). Among
the proposed proxies for information intensity are the market-to-book ratio (reflecting firm
value based on growth opportunities), R&D investment and asset tangibility (capturing
collateral value), leverage, debt maturity structure, and earnings volatility. We add to this list
our market timing measure EFWAMB, introduced in the previous section. Experimenting with
this long-list of potential corporate cash determinants lead us to the cash regressions as
reported in Table 3.9 The reported cash regressions include a common time trend and industry
5
Of course, we have also documented that ICT firms have widely benefited from high stock market valuations.
Therefore, one may argue that we observe them at a time when their access to external funds has possibly been
frictionless, thus reducing their need for efficient cash management.
6
See Kim et al. (1998) for the role of capital market frictions in the determination of precautionary cash holdings
and Opler et al. (1999) for the specific role of informational asymmetries. Pinkowitz and Williamson (2001) take
an international comparative perspective of corporate cash holdings in bank-based and market-based financial
systems, Dittmar et al. (2003) consider the degree of shareholder protection in corporate cash demand, and
Ozkan and Ozkan (2004) analyze the effects of specific corporate governance structures. Bruinshoofd and Kool
(2004) examine the precision of measuring desired corporate cash balances with a link to the macroeconomic
money demand.
7
Here we need to address the fact that the ratio of corporate cash holdings to total assets is censored between 0
and 1. For comparability purposes we follow the literature and define cash as the logarithm of corporate cash
holdings as a fraction of total assets less cash holdings. This stretches the domain of cash to run from negative to
positive infinity.
8
E.g. Opler et al. (1999), Pinkowitz and Williamson (2001), Dittmar et al. (2003), Almeida et al. (2004), Ozkan
and Ozkan (2004), Bruinshoofd and Kool (2004).
9
Note that we have not included earnings volatility in the reported cash regressions. While unreported results
shows that volatile earnings associate with high cash holdings – supporting the precautionary motive – its
8
dummies at the four-digit level in addition to the reported explanatory variables. The time
trend is motivated by the trend decline in corporate cash holdings that we observe in our
sample (Table 1). The industry dummies pick up otherwise unobserved heterogeneity in cash
holdings across economic sectors.
[Table 3 about here]
Table 3 reports the results using pooled as well as between-firm and within-firm
regression analysis. Between-firm regressions relate firm-level average cash holdings to firmlevel averages of the explanatory variables and as such emphasize the cross-sectional
dimension of the relation between the former and the latter. Within-firm regressions relate
cash holdings expressed as deviations from firm-level average cash holdings to the
explanatory variables expressed as deviations from their respective firm-level average values.
Within-firm regressions therefore emphasize the time-series dimension of the relation. The
pooled regression results are a weighted average of the within- and between-firm regressions.
The results support the hypothesis that firms hold precautionary cash for reasons
related to their information intensity. Specifically, firms whose value derives to a larger extent
from growth opportunities (high market-to-book) have higher cash balances. The positive
coefficients for beta furthermore suggest that firms that are more risky from an investor’s
point of view and therefore more likely to face higher costs of external funds, have stronger
precautionary motives to hold cash. Lastly, the use of debt finance affects cash holdings in
terms of total indebtedness as well as in terms of debt composition. While higher indebtedness
(leverage) associates with lower cash holdings – possibly because creditors dislike overly
liquid debtors (cf. Myers and Rajan, 1998) – a relatively heavy reliance on short maturities
tends to push (precautionary) cash holdings up (cf. Holmström and Tirole, 2000). The
negative coefficients for size suggest that large firms are more able to exploit scale effects in
cash management, allowing them to economize on (precautionary) cash holdings. Similarly,
we find that firms with ample near liquidity in their balance sheets tend to conserve cash
holdings.
The estimated coefficients of interest rate and cash flow deserve some special
attention. Theoretically, the interest rate captures the opportunity cost of cash holdings; hence
construction associates with a considerable loss of observations. Specifically, the measurement of a firm’s
earnings volatility as the rolling n-year standard deviation of its earnings results in the loss of the first n years of
observations on each firm.
9
we expect firms facing a high interest rate to economize on cash holdings. This would
translate into an expected negative coefficient on the interest rate in the pooled regression.
The positive coefficient that we obtain implies the reverse. We try to get a better
understanding of this counter-intuitive result by comparing the within-firm and between-firm
regression results. Then we see that the positive interest rate effect in the pooled regression
originates from a strong positive between-firm effect. Hence the positive interest rate effect
that we find in the pooled regression mostly reflects the regularity in the data that firms with
on average a higher value for interest rate over the sample period tend to have a higher
average value for cash. The within-firm effect – though positive – is considerably smaller and
not robust to the inclusion of additional variables. We further note that interest rate
(calculated as the ratio of interest payments to the balance sheet value of outstanding loans) is
not the marginal cost of debt but instead is the average interest burden of the debt that among
other things will also reflect the maturity structure of the pool of debt in each firm. As such it
is not a good measure for the opportunity cost of holding cash.
Regarding cash flow we similarly observe that the negative pooled regression
coefficient follows from a negative between-firm effect that outweighs a positive within-firm
effect. Hence, firms with higher average cash inflows over the sample period are typically the
firms that on average hold less cash over the sample period (between-firm effect). This agrees
with the theoretical conjecture that firms with high inflows of cash consequently need lower
levels of precautionary cash (cf. Kim et al., 1998). At the same time, the within-firm effect is
significant and positive, reflecting that in those years where cash flows exceed a firm’s
average, firms also report cash holdings that are above average. Opler et al. (1999) derive a
cash management model from a narrow view of the financing hierarchy model that results in
such a positive within-firm effect.
Last but not least, we explore the role of market timing in corporate cash management
and include EFWAMB into the cash regression equation. As explained in the previous section,
a firm receives a high value for EFWAMB if, historically, it has issued debt and equity
predominantly in years of favorable stock market conditions. If a firm times the market
without regard to its sources and uses of funds, we expect such a history of market timing to
result in a positive effect of EFWAMB on cash. On the contrary, if a firm manages its cash
holdings carefully by consciously accounting for its sources and uses of funds, we expect no
material impact of market timing on its cash holdings. The results in Table 3 support the latter
view on the role of market timing in cash management: EFWAMB does not really affect cash
holdings. In order to allow for a different role of market timing in the cash management of
10
ICT firms, we have also included an interaction term of EFWAMB with DICT, a dummy that
takes value 1 if the firm is an ICT firm and 0 otherwise. Our regression results show that our
previous conclusion extends to ICT firms, too.
We are naturally concerned whether a single cash equation for all firms at all times is
representative. This concern particularly associates with the ICT boom that may have put ICT
firms in a materially different position from a cash management point of view. To address this
concern, Table 3 also reports the within-firm cash regression results for ICT and non-ICT
firms separately. As the table reveals only relatively minor differences in cash determination
between the two types of firms, we do not seem to lose a lot of precision by applying a single
cash equation to ICT and non-ICT firms. We may nevertheless remain suspicious as the ICT
firms in our sample have not faced constant stock market conditions over the sample period.
Specifically, it follows from the previous section that soaring stock prices for ICT firms are
concentrated in a few years. Put differently, while ICT firms may have managed cash
holdings fundamentally differently from non-ICT firms during boom years, the ICT
regression results in Table 3 may conceal this feature by looking at the average cash
management behaviour of ICT firms over the entire sample period. Unreported results show,
however, that cash management did not change materially over time.10 Hence we may
conclude that in our sample the all-firm pooled regression estimate of the corporate cash
equation generates predicted cash levels that constitute representative cash targets for all firms
and years in our sample.
4.3 Market timing and capital structure
Although we find no evidence that market timing has driven cash management in ICT firms,
market timing may have affected other aspects of financial management. Specifically, Baker
and Wurgler (2002) investigate the connection between market timing and capital structure.
They find that corporate leverage is negatively affected by market timing in their sample of
US firms.
As all the firms in our sample seem to have been timing the debt market in particular
(Section 3), the question arises whether the negative connection between market timing and
leverage stands up in our sample, too. Specifically, one may wonder whether the ICT firms in
10
Specifically, we ran annual cross-sectional regressions, cf. Fama and MacBeth (1973). The annual regression
coefficients did not display distinct patterns over time or breaks in specific years, nor did the Fama-MacBeth
regression coefficients give us reason to moderate the conclusions based on the presented regression results.
11
our sample have been accumulating excessive debt burdens on the back of soaring stock
prices, thereby sawing the seeds of the eventual collapse of the New Economy mania. In fact,
Ogawa (2004) connects excessive debt accumulation by Japanese firms during the land price
boom in the 1980s with depressed R&D investment in the 1990s. Along these lines he claims
that excessive debt accumulation in the 1980s has contributed to the fall in productivity
growth in the 1990s. We examine whether excessive debt accumulation has also been a
feature of the ICT boom by examining capital structure management by ICT and non-ICT
firms, focusing specifically on the role of market timing.
[Table 4 about here]
To that end Table 4 presents the results of regressions of leverage on EFWAMB and a
set of control variables. The table is analogous in setup to Table 3 for cash management
discussed earlier. The control variables are well-known in the literature on capital structure
(e.g. Harris and Raviv, 1991; Rajan and Zingales, 1995; Shyam-Sunders and Myers, 1999;
Baker and Wurgler, 2002), as are the signs of the coefficient estimates that we obtain.
Specifically, we find static tradeoff elements in capital structure management in the sense that
large firms tend to have higher leverage ratios, while more risky firms (high interest rate11,
market-to-book, beta, intangibles) tend to be less levered. Additionally, we find pecking order
effects through the negative coefficient on cash flow.
Our focus is on EFWAMB – capturing the effect of market timing on capital structure
– and the interaction of EFWAMB with DICT, which deals with the potentially differential
impact of market timing for ICT firms. Interestingly enough, the estimated coefficient on
EFWAMB is negative in all regressions and statistically significant in most cases. Hence, in
general market timing decreases leverage for the firms in our sample.12 The effect of market
timing on capital structure does not seem to be the same for ICT and non-ICT firms, however,
as the interaction of EFWAMB with DICT is significantly different from zero. The positive
coefficient estimate suggests that ICT firms have indeed accumulated more debt than non-ICT
firms (with similar characteristics). The overall effect of market timing on ICT firms’ capital
structure remains negative in the pooled regression, though. This does not support the
11
Ideally, the negative coefficient estimate on interest rate would reflect a cost-of-debt effect. However, recalling
our earlier discussion concerning the definition and economic meaning of interest rate, it may also reflect firmspecific risk.
12
Unreported results show that the effect of market timing on leverage is larger for North-American firms than it
is for European firms. This is in accordance with De Bie and De Haan (2005), who find no market timing effect
on the capital structure for Dutch firms.
12
excessive debt accumulation reading of the evidence. Hence we can only conclude that market
timing has played a distinctly more limited role in capital structure management in ICT firms.
5.
Excess cash: where does it come from, where does it go?
In this Section we connect changes in excess cash to major funding and spending events and
assess whether excess cash leads to additional investment. But first, we explain how we
measure excess cash.
5.1
Measuring excess cash
We use the estimated pooled regression equation for the whole sample to compile cash targets
for the sub-samples of ICT and non-ICT firms (Table 3, column 1). When comparing the
financial characteristics of these groups of firms we already observed that ICT firms are more
information intensive than non-ICT firms. The regression results confirm that information
intensity leads to higher cash targets. This explains why ICT firms have higher cash targets
than non-ICT firms (Figure 5). ICT firms’ median cash targets exceed those of their non-ICT
counterparts.
[Figures 5 and 6 about here]
Figure 6 presents median deviations (in percentage points) of actual from targeted cash
holdings for ICT and non-ICT firms. We call these deviations ‘excess cash’. Note that excess
cash may refer to a surplus (positive excess cash) as well as a shortage of cash (negative
excess cash). The median deviations from targeted cash are always positive in Figure 6
because the distribution of the cash ratio is skewed. The 25th percentiles are negative though.
Are the cash target deviation-patterns in Figure 6 consistent with the hypothesis that
the ICT boom drove up cash holdings above normal levels, and especially so for ICT firms?
Figure 6 does not reveal a distinct ICT boom related pattern. In 1999 there is not a clear rise
of excess cash, neither for ICT nor for non-ICT firms. In 2000 and 2001 the 75th percentile of
the deviation from target does show an increase. However, these are the years in which the
bubble burst (Figure 1). Hence, the hypothesis that the ICT boom lead to excess cash hoarding
by ICT firms is not confirmed by this analysis.
13
5.2
Excess cash around sizeable spending events
We perform an event window analysis for changes in excess cash ratios around funding and
spending events (Table 2, panel B), analogous to the one presented in Section 4.1 for actual
cash ratios. Changes in excess cash are computed from changes in the residuals from the
pooled regression in Section 4.2. Depending on whether the signs of these residuals are
positive or negative, changes in excess cash can in fact be increases in cash surpluses or
decreases in cash shortages, respectively.
The main observations from Table 2 panel B are, surprisingly, that ICT firms do not
experience significant changes in excess cash in years of equity issuance, while non-ICT firms
do. Earlier, we observed that equity issues increase cash holdings. Hence, for ICT firms the
increase in cash holdings at the time of equity issuance is a desired one from a cash
management point of view, as it does not increase financial slack. The equity issues by nonICT firms increase financial slack.
Debt issues coincide with increases of excess cash (or falls in cash shortages),
especially for non-ICT firms. Earlier we documented that debt issuance does not interfere
with the trend decrease in cash holdings during the sample period. Nevertheless excess cash
goes up (or cash shortages diminish), implying that cash holdings and debt are substitutes.
All spending events (acquisition and investment) affect excess cash negatively. There
is also some evidence that excess cash is being accumulated in the years prior to the spending
event, especially by ICT firms.
The last observation raises the issue of causality: do firms anticipate spending events
by saving excess cash in advance (e.g. De Haan et al., 1994)? Or do firms that experience a
build-up of slack initiate sizeable investment- or acquisition outlays to get rid of it (cf.
Harford, 1999)? For investment events, there is some suggestion of the former, as excess cash
accumulation is resumed after the cash outlay. However, the interpretation is difficult. We
turn to this issue more comprehensively next, when we investigate whether excess cash
affects capital expenditure.
5.3
Does excess cash affect corporate investment expenditure?
We observed that investment expenditures go together with decreases in both actual and
excess cash holdings. In this Section we ask ourselves whether excess cash holdings lead to
additional investment expenditure. We follow Opler et al. (1999) who investigate the
14
relationship between excess cash and investment by adding excess cash to a traditional
investment equation for a sample of US firms. We use the measure of excess cash from the
regressions in the previous Section.13
Opler et al. find that, after controlling for the usual determinants of investment, greater
excess cash prompts firms to invest more, mostly within one year. We confirm this finding for
our sub-sample of non-ICT firms, but not for ICT firms (Table 5). The coefficients of excess
cash are statistically insignificant for ICT firms in most regressions. In contrast, for non-ICT
firms we find many statistically significant coefficients for excess cash. A noteworthy finding
is that most of the effect of excess cash takes place within one year; hence the results yield no
evidence in support of the view that it takes time for excess cash to affect investment. This
finding for non-ICT firms also confirms the findings by Opler et al. for US firms.
[Table 5 about here]
Opler et al. further observe that the impact of excess cash on investment is
significantly smaller for positive excess cash than for negative excess cash. This means that
negative excess cash (i.e. a cash shortage) reduces investment more than positive excess cash
increases investment. Again, we confirm their finding in our sample, but only for non-ICT
firms. Opler et al. view this as evidence for credit constraints. For ICT firms, surprisingly,
none of the coefficients for POSX*(Excess cash/assets) is significant, suggesting that ICT firm
along this line of reasoning have not been credit constrained in our sample. The evidence is
inconclusive, however, as we draw the opposite conclusion when looking at the cash flow
sensitivities of investment. Specifically, we observe that ICT firms’ investment is
considerably more sensitive to cash flow than non-ICT firms’ investment. In the financing
constraints literature, this is considered an indication of financing constraints (e.g. Fazzari et
al., 1988). An in depth analysis of credit constraints – which is beyond the scope of this paper
– would additionally need to take into account potential heterogeneity between ICT firms.
To conclude, according to the estimations on our sample of firms, ICT firms’
investment expenditure does not appear to be affected by excess cash holdings; neither do ICT
firms seem to use excess cash for investment in a way as if they are financially constrained. It
13
We note that our measure of excess cash is different from the one used by Opler et al. The difference concerns
the fact that they estimate cross-sectional cash holdings regressions yearly, while we pool all sample years in one
single regression. By consequence, Opler et al.’s regression model implies that average excess cash across firms
is equal to zero in each given sample year. As we see no reason for this strong assumption, our regression model
allows for the fact that excess cash of each particular firm exhibits mean reversion over time. See also note 10.
15
is the non-ICT firms that do use excess cash for investment and that therefore seem to be
constrained during the sample period.
6.
Conclusion
We have analyzed the financial behavior of a sample of North-American and Western
European ICT firms during a decade that coincided with the global ICT boom-bust cycle on
the stock markets. Our research question is: did ICT firms behave very differently from nonICT firms during this episode? Particularly, did they time the market more aggressively by
raising more external funds when the stock market was high? And if they did, what did they
do with this money? In order to get a complete picture, we consider issuing behavior, cash
management, capital investment and capital structure policy. Our main findings are as
follows.
ICT firms indeed issued relatively more debt and equity than non-ICT firms during the
sample period. However, this behavior was not directly related to their higher stock market
valuation at the time. On the contrary, ICT firms turn out to be less aggressive market timers
than non-ICT firms at the time. In any case, both ICT and non-ICT firms raised much more
debt than equity when stock market performance was booming.
The descriptive characteristics show that ICT firms are indeed what they are always
said to be: relatively information intensive and risky firms. They are small, grow fast, use
more short-term debt, and have a lot of intangibles. Regression analysis reveals that ICT firms
hold relatively more cash than non-ICT firms due to these special characteristics. Market
timing does not appear to have played a role in the development of cash holdings of ICT
firms. Neither have ICT firms built up much larger cash reserves during the sample period.
On the other hand, the regression analysis on capital structure indicates that market timing did
have a (negative) effect on the capital structures of the firms in our sample, though less so for
ICT firms in comparison to non-ICT firms. A further regression analysis of capital investment
suggests that in so far as excess cash reserves were available, these did not induce ICT firms
to initiate more investment projects, whereas it did for non-ICT firms.
All in all, it appears that the financial behavior of ICT firms did not differ completely
from that of non-ICT firms, despite the special circumstances.
16
References
Almeida, H., M. Campello and M.S. Weisbach (2004), “The Cash Flow Sensitivity of Cash,”
Journal of Finance, vol. 59, pp. 1777-1804.
Baker, M., and J. Wurgler (2002), “Market timing and capital structure,” Journal of Finance,
vol. 57, pp. 1-32.
Bie, T.M. de, and L. de Haan (2005), “Market timing and capital structure: Evidence for
Dutch firms,” mimeo.
Bruinshoofd, W.A. (2005), “Corporate investment and financing constraints: connections with
cash management,” Kredit und Kapital, forthcoming.
Bruinshoofd, W.A., and C.J.M. Kool (2004), “Dutch corporate liquidity management: new
evidence on aggregation;” Journal of Applied Economics, vol. 7, pp. 195-230.
Dittmar, A., J. Mahrt-Smith and H. Servaes (2003), “International Corporate Governance and
Corporate Liquidity,” Journal of Financial and Quantitative Analysis, vol. 38, pp.
111-133.
Fama, E.F., and J.D. MacBeth (1973), “Risk, return, and equilibrium: empirical tests,”
Journal of Political Economy, vol. 81, pp. 607-636.
Fazzari, S.M., R. Hubbard, and B.C. Petersen (1988), Financing constraints and corporate
investment, Brookings Papers on Economic Activity, no. 1, pp. 141-195.
Haan, L. de, C.G. Koedijk and J.E.J. de Vrijer (1994), “Buffer stock money and pecking order
financing: results from an interview study among Dutch firms,” De Economist, vol.
142, pp. 287-305.
Harford, J. (1999), “Corporate cash reserves and acquisitions,” Journal of Finance, vol. 54,
pp. 1969-1997.
Harris, M. and A. Raviv (1991), “The theory of capital structure,” Journal of Finance, vol. 46,
pp. 297-355.
Himmelberg, C.P., and B.C. Petersen (1994), “R&D and internal finance: a panel study of
small firms in high-tech industries,” Review of Economics and Statistics, vol. 76, pp.
38-51.
Holmström, B., and J. Tirole (2000), “Liquidity and risk management,” Journal of Money,
Credit, and Banking, vol. 32, pp. 295-319.
Houben, A., and J. Kakes (2002), “ICT innovation and economic performance: the role of
financial intermediation,” Kyklos, vol. 55, pp. 543-562.
17
Hovakimian, A. (2005) “Are observed capital structures determined by equity market
timing?,” Journal of Financial and Quantitative Analysis, forthcoming.
Hovakimian, A., T. Opler and S. Titman (2001), “The debt-equity choice,” Journal of
Financial and Quantitative Analysis, 36, pp. 1-24.
Kaplan, S.N. (2003), “Valuation and New Economy firms,” in: W.C. Hunter, G.G. Kaufman
and M. Pomerleano, Asset Price Bubbles: implications for monetary, regulatory, and
international policies, MIT Press, pp. 391-418.
Kim, C.-S., D.C. Mauer and A.E. Sherman (1998), “The determinants of corporate liquidity:
theory and evidence,” Journal of Financial and Quantitative Analysis, vol. 33, pp.
335-359.
Korajczyk, R., and A. Levy (2003), “Capital structure choice: macroeconomic conditions and
financial constraints,” Journal of Financial Economics, vol. 68, pp. 75-109.
Myers, S.C. (1984), “The capital structure puzzle,” Journal of Finance, vol. 39, pp. 575-592.
Myers, S.C., and R.G. Rajan (1998), “The paradox of liquidity,” Quarterly Journal of
Economics, vol. 63, pp. 733-771.
Ofek, E. and M. Richardson (2003), “DotCom Mania: the rise and fall of Internet stock
prices,” Journal of Finance, vol. 58, 1113-1137.
Ogawa, K. (2004), “Debt, R&D investment and technological progress: a panel study of
Japanese manufacturing firms in the 90s, Osaka University: ISER Discussion paper
no. 607.
Opler, T., L. Pinkowitz, R. Stulz and R. Williamson (1999), “The Determinants and
Implications of Corporate Cash Holdings,” Journal of Financial Economics, vol. 52,
pp. 3-46.
Ozkan, A., and N. Ozkan (2004), “Corporate cash holdings: an empirical investigation of UK
companies,” Journal of Banking and Finance, vol. 28, pp. 2103-2134.
Pinkowitz, L., and R. Williamson (2001), “Bank power and cash holdings: evidence from
Japan,” Review of Financial Studies, vol. 14, pp. 1059-1082.
Rajan, R.G., Zingales, L. (1995), “What do we know about capital structure? Some evidence
from international data,” Journal of Finance, 50, pp. 1421-1460.
Shyam-Sunder, L., Myers, S.C. (1999), “Testing static trade-off against pecking order models
of capital structure,” Journal of Financial Economics, 51, pp. 219 -244.
Welch, I. (2004), “Capital structure and stock returns,” Journal of Political Economy, vol.
112, pp. 106-131.
18
Table 1
Descriptive statistics 1991-2002
Variable
Difference in
ICT
Non-ICT
medians
# Obs.
Median
# Obs.
Median
11.60 **
6.26
36244
17.86
11042
Cash ratio
1.184 **
-2.707
36244
-1.523
11042
Cash
0.010 **
-0.032
31532
-0.022
9356
∆Cash
-1.151 **
5.701
36244
4.550
11042
Real size
0.084 **
0.048
31532
0.132
9356
∆Real Size
-0.138 **
0.580
35062
0.442
10494
Leverage
-0.003 **
0.067
33046
0.064
8529
Cash flow
0.187 **
0.563
35993
0.750
10771
Short debt
0.555 **
1.289
29072
1.844
8113
Market-to-book
-0.026 **
0.396
34965
0.370
10645
Near liquidity
0.498 **
0.636
26334
1.134
7032
Beta
-0.004 **
0.004
35857
0.000
10833
Dividend payout
0.000 **
0.000
24462
0.000
8879
Acquisitions
-0.003 **
0.039
29114
0.036
8790
Capital expenditures
0.080 **
0.015
11342
0.095
6727
R&D
0.012 **
0.030
29750
0.042
8581
Net debt issues
a
0.006 **
0.007
30465
0.013
8708
Net equity issues
0.007 **
0.019
33750
0.026
9667
Intangibles
0.002 **
0.081
32136
0.083
8115
Interest rate
6.38 **
14.79
22020
21.17
5086
Price-earnings ratio
-0.019 **
0.024
26235
0.005
6873
Stock price change
-0.016 **
1.018
11839
1.002
2306
EFWAMB
Notes: Cash ratio is cash and short-term investments as a percentage of total assets less cash and short-term
investments; cash is the logarithm of the ratio of cash and short-term investments to total assets less cash and
short-term investments; ∆ denotes the first-difference operator; real size is the logarithm of total assets in 2000
prices; leverage is total debt as a fraction of total assets; cash flow is earnings after interest, taxes and dividends,
but before depreciation and amortization as a fraction of total assets; short debt is short-term debt as a fraction
of total debt; market-to-book is measured as the market value of the firm relative to the book value of assets;
near liquidity is current assets less cash and short-term investments as a fraction of total assets; beta is the
sensitivity of a company’s stock price to the overall fluctuation in the pre-selected index for that company’s
country (we compiled unlevered betas); dividend payout is dividend payments as a fraction of total assets;
acquisitions acquisition spending relative to total assets; capital expenditures is the net investment in gross
property, plant and equipment relative to total assets; R&D is research and development expense as a fraction of
total assets; net debt issues is the change in total debt as a fraction of total assets; net equity issues is the change
in the book value of stock capital as a fraction of total assets; intangibles is the amount of intangible assets
relative to total assets; interest rate is total interest expense as a fraction of total debt; price-earnings ratio is the
average market value of equity divided by the mean of a minimum of five annual observations of non-negative
cash flows; stock price change is the change in the end-of-year stock price. See the main text for the definition
of EFWAMB. Statistical significance of the difference in medians at the 5 or 1 percent error level is indicated by
*
and **, respectively, using the continuity corrected Pearson χ 2 (1) test.
a) For net equity issues means are reported as medians would be zero. Statistical significance of the difference
in means is calculated using the t-test.
19
Table 2
Changes in cash and excess cash in and around funding and spending
events (in percentage points)
A.
Changes in cash ratio
Year 0
_______________________
Median
Mean
Obs.
______ ______ _____
Years +1 to +3
_______________________
Median
Mean
Obs.
______ ______ _____
Years -3 to -1
_______________________
Median
Mean
Obs.
______ ______ _____
ICT firms
Net equity issues
Net debt issues
Acquisitions
Capital expenditures
0.9**
-0.6**
-2.3**
-1.5**
1.5**
-1.3**
-5.9**
-2.7**
825
3631
883
3062
-0.5**
-0.3**
0.0**
-0.2**
-1.8**
-0.5**
1.8**
0.0**
412
2172
534
1942
0.0**
-0.2**
0.6**
0.1**
-0.4**
0.6**
3.7**
1.4**
495
2473
663
2071
0.5**
-0.3**
-0.7**
-0.7**
2.1**
-0.8**
-3.2**
-1.7**
1312
10588
2228
10916
-2.0**
-0.5**
-0.2**
-0.6**
-4.1**
-1.1**
-0.5**
-1.3**
830
6906
1593
7450
-0.7**
-0.4**
-0.1**
-0.4**
-2.0**
-0.9**
-0.1**
-0.9**
801
7826
1760
7845
Non-ICT firms
Net equity issues
Net debt issues
Acquisitions
Capital expenditures
B.
Changes in excess cash ratio
Year 0
_______________________
Median
Mean
Obs.
______ ______ _____
Year +1 to +3
_______________________
Median
Mean
Obs.
______ ______ _____
Year -3 to -1
_______________________
Median
Mean
Obs.
______ ______ _____
A. ICT firms
Net equity issues
Net debt issues
Acquisitions
Capital expenditures
0.7**
0.3**
-0.7**
-0.7**
0.2**
0.4**
-2.8**
-1.3**
152
1294
287
1021
0.3**
0.5**
0.4**
0.8**
-1.4**
0.8**
0.0**
1.4**
66
825
184
708
-0.5**
0.4**
1.1**
0.5**
0.0**
0.5**
2.4**
0.8**
103
873
196
687
0.6**
0.2**
-0.4**
-0.4**
1.3**
0.4**
-1.7**
-1.0**
455
6295
1326
6328
-0.2**
0.1**
0.1**
0.2**
-1.2**
-0.2**
-0.1**
0.1**
345
4302
930
4571
-0.3**
0.1**
0.1**
0.1**
-1.1**
-0.2**
0.5**
0.0**
314
4557
966
4556
B. Non-ICT firms
Net equity issues
Net debt issues
Acquisitions
Capital expenditures
Excess cash ratio is the antilog x 100% of the residual from the regression to determine cash (Table 3, first column). All other
variables have been defined as before. The minimum amount of a transaction is set to 5% of total assets in order to be counted
as an event. For example, a firm is defined as issuing equity when the book value of stock capital increases by 5% or more of
the pre-issue value of total assets. Changes significantly different from zero at 5% and 1% level are marked * and **
respectively. The reported significance levels for medians are based on the Wilcoxon signed rank test while the levels for means
are based on the t-test. The number of observations in panel B is generally smaller than in panel A, due to missing observations
generated by the regression.
20
Table 3
Corporate cash regressions
Market-to-book
Real size
Near liquidity
Leverage
Beta
Short debt
Interest rate
Cash flow
DICT
EFWAMB
EFWAMB*DICT
All firms
___________________________________________________
Pooled
Pooled Between Between Within
Within
0.149**
0.111**
0.075**
0.135**
0.129** 0.073**
**
**
**
**
**
0.018
-0.032
-0.756**
-0.003
-0.451
0.007
**
**
**
**
**
-2.332
-2.416
-4.418**
-2.483
-3.983
-2.330
**
**
**
**
**
-0.668
-0.639
-0.899**
-0.589
-0.746
-0.591
**
**
**
**
**
0.009
0.016
0.032**
0.006
0.004
0.008
**
**
**
**
**
1.717
1.637
0.256**
1.469
0.355
2.275
**
**
**
**
**
0.820
0.976
0.187**
0.836
0.331
1.099
**
**
**
**
**
-1.762
-1.244
0.474**
-1.153
0.790
-2.399
**
**
**
**
**
-0.924
-**
0.380
**
**
**
**
**
- 0.140
0.066
0.118**
**
**
**
**
**
0.142
0.222**
0.189
-
ICT
________________
Within
Within
0.021**
0.050**
**
-0.650**
-0.463
**
-4.269**
-4.241
**
-0.974**
-0.612
**
0.056**
0.003
**
0.588**
0.371
**
0.125**
0.206
**
0.957**
0.807
**
-**
**
0.160**
**
-**
-
Non-ICT
________________
Within
Within
0.083**
0.081**
**
-0.813**
-0.467
**
-4.088**
-3.843
**
-0.799**
-0.741
**
0.011**
0.001
**
0.144**
0.339
**
0.197**
0.358
**
0.422**
0.905
**
-**
**
0.157**
**
-**
**
**
Intercept
Time trend
Industry dummies
**
YES
YES**
4-digit**
**
YES
YES**
4-digit**
**
YES
YES**
4-digit**
**
YES
YES**
4-digit**
**
YES
YES**
NO**
**
YES
YES**
NO**
**
YES
YES**
NO**
**
YES
YES**
NO**
**
YES
YES**
NO**
YES**
YES**
NO**
9319**
20024**
1479**
3999**
24023** 10798** 24023** 10798** 24023** 10798**
# observations
**
**
**
**
**
**
**
**
**
1358**
3394
244
952
1602
4346
1602
4346
1602
4346
# firms
**
**
**
**
**
**
**
**
**
0.168**
0.135
0.326
0.260
0.186
0.151
0.502
0.379
0.385
0.292
R-squared
ICT
Notes: Dependent variable is cash. D is a dummy that is 1 if the firm is classified as an ICT firm and 0 otherwise. See the main
text for the definition of EFWAMB. All other variables have been defined as before. Statistical significance of coefficient estimates
(using robust standard errors) at the 5 or 1 percent error level is marked by * and **, respectively.
21
Table 4
Corporate debt regressions
All firms
ICT
Cash flow
Real size
Interest rate
Market-to-book
Beta
Intangibles
EFWAMB
DICT
EFWAMB*DICT
_______________________________________________________
Pooled
Pooled
Between
Between
Within
Within
-0.543** -0.460** -0.460**
-0.534**
-0.560** -0.560**
0.035**
0.035**
0.031**
0.031**
0.028**
0.028**
**
**
**
**
**
-0.146**
-0.146
-0.137
-0.146
-0.123
-0.123
**
**
**
**
**
0.002**
0.002
-0.019
-0.019
-0.011
-0.011
**
**
**
**
**
-0.001**
-0.001
-0.028
-0.025
-0.012
-0.012
**
**
**
**
**
-0.000**
0.000
-0.044
-0.047
-0.039
-0.040
**
**
**
**
**
-0.082**
-0.080
-0.117
-0.063
-0.091
-0.077
**
**
**
**
**
-**
-0.433
-0.192
**
**
**
**
**
0.011**
0.194
0.065
-
______
Within
-0.410**
-0.006**
-0.176**
0.003**
0.006**
-0.042**
-0.039**
-**
-**
NonICT
______
Within
-0.484**
0.048**
-0.140**
0.002**
0.000**
0.020**
-0.087**
-**
-**
Intercept
Time trend
Industry dummies
YES**
YES**
4-digit**
YES**
YES**
NO**
YES**
YES**
NO**
YES**
YES**
NO**
YES**
YES**
4-digit**
YES**
YES**
4-digit**
YES**
YES**
4-digit**
YES**
YES**
NO**
# observations
10053** 10053**
10053**
10053** 10053** 10053**
1311**
8742**
**
**
**
**
**
**
**
# firms
1569
1569
1569
1569
1569
1569
232
1337**
**
**
**
**
**
**
**
R-squared
0.436
0.127
0.505
0.512
0.135
0.135
0.089
0.168*v
ICT
Notes: Dependent variable is leverage. D is a dummy that is 1 if the firm is classified as an ICT firm and 0
otherwise. See the main text for the definition of EFWAMB. All other variables have been defined as before.
Statistical significance of coefficient estimates (using robust standard errors) at the 5 or 1 percent error level is
marked by * and **, respectively.
22
Table 5
Capital expenditures and excess cash holdings
Independent variable
Cash flow
Sales growth
Market-to-book
Target cash ratio t-1
Target cash ratio t-2
Target cash ratio t-3
Excess cash ratio t-1
Excess cash ratio t-2
Excess cash ratio t-3
POSX t-1
(POSX*Target cash ratio)t-1
(POSX*Excess cash ratio)t-1
Capital expitures t-1
Sample size
Adjusted R2
Sargan
Fixed firm effects
__________________________
All firms
ICT
Non-ICT
_______
_____
______
0.069**
0.207**
0.003**
0.055**
0.020**
0.080**
-0.002**
-0.004**
0.000**
0.184**
0.121**
0.181**
0.111**
0.127**
0.100**
0.088**
0.101**
0.052**
0.208**
-0.033**
0.261**
0.031**
0.054**
0.021**
0.050**
0.060**
0.040**
0.009**
0.000**
0.011**
-0.113**
0.080**
-0.169**
-0.069**
0.156**
-0.117**
17032**
0.070**
2231**
0.074**
14801**
0.086**
Industry dummies
_________________________
All firms
ICT
Non-ICT
_______
_____
______
0.201**
0.287**
0.159**
0.062**
0.023**
0.090**
0.001**
-0.001**
0.002**
0.039**
0.035**
0.048**
0.017**
0.064**
0.007**
0.002**
0.012**
-0.008**
0.054**
-0.045**
0.081**
-0.017**
0.020**
-0.029**
0.002**
0.027**
-0.006**
0.002**
0.001**
0.003**
-0.023**
0.006**
-0.028**
-0.025**
0.060**
-0.042**
17032**
0.148**
2231**
0.164**
14801**
0.165**
GMM
_________________________
All firms
ICT
Non-ICT
_______
_____
______
-0.022**
0.109**
-0.074**
0.059**
0.029**
0.075**
-0.007**
-0.007**
-0.007**
0.281**
0.128**
0.315**
0.098**
0.046**
0.112**
0.026**
-0.032**
0.029**
0.362**
0.102**
0.421**
0.017**
-0.007**
0.023**
0.042**
0.039**
0.036**
0.015**
-0.005**
0.019**
-0.157**
0.121**
-0.230**
-0.101**
0.154**
-0.154**
0.082**
0.154**
0.062**
13248**
1686**
11562**
68,07**
42,31**
62,18**
Sales growth is the growth rate of total turnover. Excess cash ratio (target cash ratio) is the antilog of the residual (predicted value) from the regression to determine cash
(Table 3, first column). POSX is a dummy variable which has value 1 if there is positive excess cash in the firm year, and zero otherwise. All other variables have been
defined as before. Statistical significance of coefficient estimates (using robust standard errors) at the 5 or 1 percent error level is marked by * and *, respectively. Intercepts
are not reported.
23
Figure 1
Global ICT and industrials share price indices
250
200
150
100
50
MSCI ICT sector
Figure 2
31/08/2004
31/03/2004
31/10/2003
30/05/2003
31/12/2002
31/07/2002
28/02/2002
28/09/2001
30/04/2001
30/11/2000
30/06/2000
31/01/2000
31/08/1999
31/03/1999
30/10/1998
29/05/1998
31/12/1997
31/07/1997
28/02/1997
30/09/1996
30/04/1996
30/11/1995
30/06/1995
31/01/1995
0
MSCI Industrials
Median annual changes in stock prices (%) for ICT and non-ICT firms
120
100
80
60
40
20
0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
-20
-40
-60
non-ICT median (lower and upper quartiles in thin lines)
ICT median (lower and upper quartiles in thin lines)
ICT upper quartile
24
Figure 3
Changes in stock prices (%) in the 10 largest ICT sectors
(A) Median annual stock price changes
120
100
80
60
40
20
0
-20
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
-40
-60
(B) Market value-weighted average annual stock price changes
250
200
150
100
50
0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
-50
-100
7372
7373
3674
4813
3663
3661
7371
3679
3577
4812
Note: Panel (A) reports the median – unweighted – stock price change in each sector. Panel (B) presents the
average annual stock price change in each sector where the observations are weighted according to the market
value of the firm in that sector. The 4-digit SIC 1987 classification codes refer to prepackaged software (7372),
computer integrated systems design (7373), semiconductors and related devices (3674), telephone
communications (4813), radio / television broadcasting / communications equipment (3663), telephone and
telegraph apparatus (3661), computer programming services (7371), electronic components (3679), computer
peripheral equipment (3577), radiotelephone communications (4812). Jointly these 10 ICT sectors contain 63,1%
of all ICT observations in our data set.
25
Figure 4
Net debt and equity issues by deciles of stock price change,
cumulative over five years
ICT firms
30
25
Net debt issues t, t+4
Net equity issues t, t+4
20
15
10
5
0
1
2
3
4
5
6
7
8
9
10
9
10
-5
Non-ICT firms
18
16
Net debt issues t, t+4
Net equity issues t, t+4
14
12
10
8
6
4
2
0
1
2
3
4
5
6
7
8
Deciles for stock return
Explanatory note: Net debt issues and net equity issues have been defined as before, but are given in percentages
instead of fractions here. Firms are sorted first by year (to control for macroeconomic trends), then by sales (to
control for firm size), and then allocated to deciles on the basis of their stock return rank (within each group of
10 consecutive firms). The number of observations per decile for the stock return in the two panels is 344 and
1530, respectively.
26
Figure 5
Median annual cash targets (%)
16
14
12
10
8
6
4
2
0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2000
2001
non-ICT median (lower and upper quartiles in thin lines)
ICT median (lower and upper quartiles in thin lines)
ICT upper quartile
Figure 6
Median annual deviations from cash targets (%-points)
14
12
10
8
6
4
2
0
1992
1993
1994
1995
1996
1997
1998
1999
-2
-4
non-ICT median (lower and upper quartiles in thin lines)
ICT median (lower and upper quartiles in thin lines)
ICT upper quartile
27
APPENDIX A
Table A1
SIC
code
3571
3572
3575
3576
3577
3613
3629
3651
3652
3661
3663
3669
3671
3672
3674
3675
3676
3677
3678
3679
3695
3699
3812
3823
3824
3825
3826
3827
3829
3861
3873
4812
4813
4822
4832
4833
4841
4899
5045
5063
5064
5065
5072
5731
5734
7371
7372
7373
7374
7375
7376
7377
7378
7379
ICT industries (SIC 1987 codes)
Sector description
Electronic Computers
Computer Storage Devices
Computer Terminals
Computer Communications Equipment
Computer Peripheral Equipment, NEC
Switchgear and Switchboard Apparatus
Electrical Industrial Apparatus, NEC
Household Audio and Video Equipment
Phonograph Records and Prerecorded Audio Tapes and Disks
Telephone and Telegraph Apparatus
Radio and Television Broadcasting and Communications Equipment
Communications Equipment, NEC
Electron Tubes
Printed Circuit Boards
Semiconductors and Related Devices
Electronic Capacitors
Electronic Resistors
Electronic Coils, Transformers, and Other Inductors
Electronic Connectors
Electronic Components, NEC
Magnetic and Optical Recording Media
Electrical Machinery, Equipment, and Supplies, NEC
Search, Detection, Navigation, Guidance, and (Aero)nautical Systems and Instruments
Ind. Instr. for Measurement, Display, and Control of Process Variables; and Related
Totalizing Fluid Meters and Counting Devices
Instruments for Measuring and Testing of Electricity and Electrical Signals
Laboratory Analytical Instruments
Optical Instruments and Lenses
Measuring and Controlling Devices, NEC
Photographic Equipment and Supplies
Watches, Clocks, Clockwork Operated Devices and Parts
Radiotelephone Communications
Telephone Communications, Except Radiotelephone
Telegraph and Other Message Communications
Radio Broadcasting Stations
Television Broadcasting Stations
Cable and Other Pay Television Services
Communications Services, NEC"
Computers and Computer Peripheral Equipment and Software
Electrical Apparatus and Equipment Wiring Supplies, and Construction Materials
Electrical Appliances, Television and Radio Sets
Electronic Parts and Equipment, NEC
Hardware
Radio, Television, and Consumer Electronics Stores
Computer and Computer Software Stores
Computer Programming Services
Prepackaged Software
Computer Integrated Systems Design
Computer Processing and Data Preparation and Processing Services
Information Retrieval Services
Computer Facilities Management Services
Computer Rental and Leasing
Computer Maintenance and Repair
Computer Related Services, NEC
# Obs. in our
sample
141
197
19
231
324
60
0
137
35
475
559
172
0
112
952
0
0
33
54
333
22
0
219
182
11
201
191
87
119
156
21
278
586
21
140
232
210
188
210
98
28
181
25
94
25
469
1863
1126
184
0
0
41
0
0
28
Previous DNB Working Papers in 2005
No. 27
No. 28
No. 29
No. 30
No. 31
No. 32
No. 33
No. 34
No. 35
No. 36
No. 37
No. 38
No. 39
No. 40
No. 41
No. 42
No. 43
No. 44
No. 45
No. 46
No. 47
No. 48
No. 49
No. 50
No. 51
No. 52
No. 53
No. 54
No. 55
No. 56
No. 57
No. 58
No. 59
No. 60
No. 61
No. 62
Jan Marc Berk and Beata K. Bierut, On the optimality of decisions made by hup-and-spokes
monetary policy committees
Ard H.J. den Reijer, Forecasting dutch GDP using large scale factor models
Olivier Pierrard, Capital Market Frictions, Business Cycle and Monetary Transmission
Jan Willem van den End and Mostafa Tabbae, Measuring financial stability; applying the MfRisk
model to the Netherlands
Carin van der Cruijsen and Maria Demertzis, The Impact of Central Bank Transparency on
Inflation Expectations
Allard Bruinshoofd, Bertrand Candelon and Katharina Raabe, Banking Sector Strength and the
Transmission of Currency Crises
David-Jan Jansen and Jakob de Haan, Were verbal efforts to support the euro effective? A highfrequency analysis of ECB statements
Riemer P. Faber and Ad C.J. Stokman, Price convergence in Europe from a macro perspective:
Product categories and reliability
Jan Kakes and Cees Ullersma, Financial acceleration of booms and busts
Wilko Bolt and David B. Humphrey, Public Good Aspects of TARGET: Natural Monopoly, Scale
Economies, and Cost Allocation
Piet van Gennip, Loan extension in China: a rational affair?
Joël van der Weele, Financing development: debt versus equity
Michael Hurd and Susann Rohwedder, Changes in Consumption and Activities at Retirement
Monica Paiella and Andrea Tiseno, Stock market optimism and participation cost: a mean-variance
estimation
Monika Bütler, Olivia Huguenin and Federica Teppa, What Triggers Early Retirement? Results
from Swiss Pension Funds
Günther Fink and Silvia Redaelli, Understanding Bequest Motives – An Empirical Analysis of
Intergenerational Transfers
Charles Grant and Tuomas Peltonen, Housing and Equity Wealth Effects of Italian Households
Philipp Maier, A ‘Global Village’ without borders? International price differentials at eBay
Robert-Paul Berben and Teunis Brosens, The Impact of Government Debt on Private
Consumption in OECD Countries
Daniel Ottens and Edwin Lambregts, Credit Booms in Emerging Market Economies: A Recipe for
Banking Crises?
Jaap Bikker and Michiel van Leuvensteijn, An exploration into competition and efficiency in the
Dutch life insurance industry
Paul Cavelaars, Globalisation and Monetary Policy
Janko Gorter, Subjective Expectations and New Keynesian Phillips Curves in Europe
Ralph de Haas and Ilko Naaborg, Does Foreign Bank Entry Reduce Small Firms’ Access to Credit?
Evidence from European Transition Economies
Ralph de Haas and Ilko Naaborg, Internal Capital Markets in Multinational Banks: Implications for
European Transition Countries
Roel Beetsma, Massimo Guiliodori and Franc Klaassen, Trade Spillovers of Fiscal Policy in the
European Union: A Panel Analysis
Nicole Jonker, Payment Instruments as Perceived by Consumers – A Public Survey
Luis Berggrun, Currency Hedging for a Dutch Investor: The Case of Pension Funds and Insurers
Siem Jan Koopman, André Lucas and Robert J. Daniels, A Non-Gaussian Panel Time Series Model
for Estimating and Decomposing Default Risk
Guay C. Lim and Paul D. McNelis, Real Exchange Rate and Current Account Dynamics with Sticky
Prices and Distortionary Taxes
Roel Beetsma, Alex Cukierman and Massimo Giuliodori, Wars, Redistribution and Civilian Federal
Expenditures in the U.S. over the Twentieth Century
S. Fabiani, M. Druant, I. Hernando, C. Kwapil, B. Landau, C. Loupias, F. Martins, T. Mathä,
R. Sabbatini, H. Stahl and A. Stokman, The Pricing Behaviour of Firms in the Euro Area: New
Survey Evidence
Jan Marc Berk and Beata K. Bierut, Communication in Monetary Policy Committees
Robert-Paul Berben and W. Jos Jansen, Bond Market and Stock Market Integration in Europe
Leo de Haan and Elmer Sterken, Asymmetric Price Adjustment in the Dutch Mortgage Market
L. J. Álvarez , E. Dhyne, M. Hoeberichts, C. Kwapil, H. Le Bihan, P. Lünnemann,
F. Martins, R. Sabbatini, H. Stahl, P. Vermeulen and J. Vilmunen, Sticky prices in the euro area: a
summary of new micro evidence
Previous DNB Working Papers in 2005 (continued)
No. 63
No. 64
No. 65
No. 66
No. 67
No. 68
No. 69
No. 70
No. 71
No. 72
No. 73
No. 74
No. 75
No. 76
Peter Vlaar, Defined benefit pension plans and regulation
Hans Fehr and Christian Habermann, Risk sharing and efficiency implications of progressive
pension arrangements
Axel Börsch-Supan, Alexander Ludwig and Joachim Winter, Aging, Pension Reform, and Capital
Flows: A Multi-Country Simulation Model
Zvi Bodie, Pension Insurance
Frank de Jong, Valuation of pension liabilities in incomplete markets
Vincent Bodart, Olivier Pierrard and Henri R. Sneessens, Calvo Wages in a Search Unemployment
Model
Jacob A. Bikker, Laura Spierdijk and Pieter Jelle van der Sluis, Cheap versus Expensive Trades:
Assessing the Determinants of Market Impact Costs
Wilko Bolt and Alexander F. Tieman, On myopic equilibria in dynamic games with endogenous
discouting
Wilko Bolt, David Humphrey and Roland Uittenbogaard, The Effect of Transaction Pricing on the
Adoption of Electronic Payments: A Cross-Country Comparison
Daniel Dorn, Gur Huberman and Paul Sengmueller, Correlated trading and returns
Marco Hoeberichts and Ad Stokman, Price Setting Behaviour in the Netherlands: Results of a
Survey
Arco van Oord and Howie Lin, Modelling inter- and intraday payment flows
David-Jan Jansen and Jakob de Haan, Is a word to the wise indeed enough? ECB statements and the
predictability of interest rate decisions
Nicolien Schermer, The synchronisation of European labour markets: An analysis using aggregate
Phillips curves