An Empirical Study of Corporate Liquidity Dynamics,
with Conditioning on Earnings
Andrew Carverhill
September 10, 2012
Abstract
We investigate the dynamics of the firm’s liquidity reserve, as profitability varies.
Growth occurs mostly when profitability is high, and can be financed from current
income. The firm saves from an income shock, but saves more when profitability is
low. The liquidity reserve does not serve to facilitate capital investment, though it
might facilitate financial investment. The depletion of the liquidity reserve can predict
the bankruptcy/liquidation of the firm. We discuss our findings in the light of recent
models of corporate liquidity. We finally conclude that the main motive for holding
the liquidity reserve, is to help the firm to survive financial distress, and not to finance
growth.
Address: School of Economics and Finance, University of Hong Kong, Pokfulam Road,
Hong Kong, China SAR
Email: carverhill@business.hku.hk
Telephone: (852) 2857 8358
1
Abstract
We investigate the dynamics of the firm’s liquidity reserve, as profitability varies.
Growth occurs mostly when profitability is high, and can be financed from current
income. The firm saves from an income shock, but saves more when profitability is
low. The liquidity reserve does not serve to facilitate capital investment, though it
might facilitate financial investment. The depletion of the liquidity reserve can predict
the bankruptcy/liquidation of the firm. We discuss our findings in the light of recent
models of corporate liquidity. We finally conclude that the main motive for holding
the liquidity reserve, is to help the firm to survive financial distress, and not to finance
growth.
Introduction
What are the empirical features and motivating factors of corporate liquidity management?
Do firms maintain liquidity reserves (“save”) to facilitate future optional investment (the
“speculative” motive), or to survive hard times (the “precautionary” motive)? Our aim in
this paper is to investigate these questions using regressions that are conditioned on the
firm’s profitability.
The speculative hypothesis for saving is intuitive, and can be traced back to Keynes
(1936). It is more recently supported in the arguments of Almeida, Campello and Weisbach
(2004) (ACW). These authors argue that financially constrained firms, i.e. firms for whom
it would be expensive to raise capital externally, will save from the speculative motive. They
further argue, and support empirically using regressions, that the proportion saved from the
firm’s operating cash flow (the “cash flow sensitivity of cash” - CFSF) will be positive for
financially constrained firms.
The speculative hypothesis has been challenged more recently, however. Riddick and
Whited (2009) (RW), develop a model, supported by a GMM type test, in which the CFSC
is negative - at times when the firm has a higher operating cash flow, then it is more profitable,
and the cash will more profitably be invested immediately, rather than saved. The model of
Anderson and Carverhill (2012) (AC), at least in its basic form, is closely related to that of
RW, and supports the same conclusions. The AC model emphasises financial distress more
2
than the RW model, and in this model, saving to avoid financial distress (the precautionary
motive) is less urgent when the firm is more profitable.
To address the above questions, in this present paper, we present a series of “conditioned
regressions”, in which lagged profitability is allowed to enter in a nonlinear fashion. We first
present some preliminary results on the profiles of various firm characteristics, in terms of
profitability. We confirm some intuitively reasonable results, such as that firm dividends and
capital investment increase with profitability. On the other hand, more is raised from share
issues when profitability is not high. Perhaps more striking, the firms liquidity holding is
not higher for higher profitability.
Most papers characterise liquidity in terms of the firms reserve of cash, but we will
concentrate more on net working capital. We will see that net working capital usually gives
cleaner empirical results, and we will argue that it better represents the liquidity reserve.
Our first main result is related to the CFSC discussed above, or rather the operating
income sensitivity of liquidity (OISL), which is more our focus. This is positive, in our conditioned regressions. However, it is lower, when the firm is more profitable. We can replicate
this result in an extension of the model of AC, in which operating income is distinguished
from profitability. In our extended model, operating income is made up from a component
directly tied to profitability, together with a white-noise component, which is uncorrelated
in time. A cash flow shock tied to an increase in profitability will lead to a reduction in
saving, as in RW and AC, but a cash inflow from the white noise component might be saved,
contingent on the current liquidity position. These effects combine in the actual operating
income, to produce the effect of the empirical regression.
Our second result is a direct test of whether liquidity serves to facilitate investment, via
some unconditioned (plain vanilla) regressions. Regarding capital investment, we see that,
although this depletes the liquidity reserve, such investment is not higher when the liquid
reserve is higher. Thus, we conclude that the purpose of the reserve is not to facilitate capital
investment. This result is reversed however, if we take a broader definition of investment, to
include financial investment in other firms, as well as capital investment. On the other hand,
financial investment is relatively small on average for our firms, which exclude financial firms
and utilities. We also note that financial investment is presumably more liquid than capital
investment, and so the firm can make such investment and preserve liquidity at the same
3
time.
Our third result is that depletion of the liquidity reserve (represented as cash) can predict
bankruptcy or liquidation up to 3 years ahead, in a Logit regression. This variable is not
subsumed by the presence of profitability in the Logit regression.
These three results all point to our overall conclusion, which is that the purpose of the
liquidity reserve is not to facilitate firm growth, but is rather precautionary - to help the
firm survive financial distress.
Our work is related to a number of recent papers on the firm liquidity reserve, apart
from those mentioned above. Opler, Lee, Pinkowitz and Stulz (1999) agrees with our overall
conclusions, though without our empirical and modeling techniques. This paper documents
high levels of cash reserves, which tend to be persistent, until they are depleted by operating losses. Bates, Kahle and Stulz (2009) show that the cash reserves have increased in
recent years, and attribute this to the changing nature of firms in their data, represented
by increasing R&D expenditure and asset intangibility. We will confirm below that cash
reserves have increased over the last 30 years, but we will see that net working capital has
not increased. We suggest that the need for the liquid reserve has not increased, but with
greater asset intangibility, this reserve must be held more as cash, than as tangible assets
such as inventory.
Other related papers are Dasgupta, Noe and Wang (2008), and McLean (2011). Dasgupta, Noe and Wang ask how a firm reacts to an earnings shock, i.e. an unexpectedly
higher earnings cash flow. The answer is that the firm uses a substantial part of the shock to
reduce dependence on external financing, and saves a substantial part of the rest. Later, at
a lag of 2 or 3 years, the firm will have spent a substantial part of this saving on investment.
These effects are shown to be strongly related to the firm being financially constrained. We
confirm that an income shock leads to higher investment and saving, and our result that a
higher liquidity reserve does not lead to more capital investment is not in conflict with this
paper. This paper is particularly related to the present one, because we adapt the Seemingly
Unrelated Regression technique of this paper.
McLean (2011) shows that firms save a substantial part of the proceeds from share issuance, as well as a substantial part of earnings. He argues the motive for this is precautionary, as described above. The firm wants to have cash on hand for investment later, when
4
share issuance would be more expensive. This is also in tune with the present paper, and it
adds a new dimension to the study of liquidity management.
Finally, Bolton, Chan and Wang (2011) present a model of corporate liquidity, which
addresses the some of the issues of the present paper. In this model, as in the models of
RW and AC, the firm puts its earnings into the liquidity reserve, and it ceases to operate,
if the reserve runs down to zero. As in the model of RW, the investment rate is constrained
by a quadratic cost. In this model, the CFSC will be negative, as in RW, but the liquidity
reserve will be high, when the firm is growing. This is not in tune with our empirical results,
and is a consequence of the fact that there is no profitability state variable, and the only
determinant of the firms behaviour is the liquidity level itself.
The plan of this paper is as follows: In Section 1 we present descriptive profiles of the
firms behaviour as functions of profitability. In Section 2 we present conditioned and ordinary
regressions relating to the firms investment, liquidity management, extent of saving from
current income, etc, and we also put our results into the modeling framework of Anderson and
Carverhill (2011). In Section 3 we present our Logit regressions for firm exit, and in section
4 we summarise our conclusions. We finally present Appendices A, B and C. Appendix A
summarizes our variables and their construction from Compustat data. Appendix B gives
details of our non-parametric regressions, and Appendix C gives some details about our
simulation of the model of Anderson and Carverhill (2011) to fit some of our empirical
results.
1
1.1
Average firm behavior, in terms of profitability
The liquidity profile:
Our basic concern in this paper is with the firm’s liquidity profile, by which we mean its
liquidity as a function of the its profitability. Figure 1 and Figure 2A graph these profiles,
representing liquidity by net working capital (abbreviated to nwc) in Panel A, and cash and
equivalent short term marketable securities (che) in Panel B, and representing profitability
5
by operating income 1 . We can see immediately from these figures some important features
and differences between nwc and che: first, nwc is consistently higher than che; second, and
crucially, both profiles are quite flat, though nwc is lower, but che is higher, in the 4th 4ile
of π than in the 3rd 4ile. Also, from Figure 1, both forms of liquidity are higher and the
profitability has a wider range, for lower long term firm leverage.
We will see below that our results for nwc are mostly stronger than for che, and so nwc
seems to be a better measure of liquidity than che. We note here that nwc seems to come
under great scrutiny and control from managers, and it includes a more complete set of
liquid balance sheet items, such as short term debt and accounts receivable.
In detail, Figure 1 is constructed as follows: First, we take all the firms from 1978
to 2011 in the Compustat North America Annual file, which have a lifetime of at least 5
years. We exclude financial and regulated firms. We allocate our firms to categories of
leverage, based on the average of the long term leverage for each firm, over its lifetime. Long
term leverage for firm i and year t is denoted (lt.debt)i,t , and defined as the face value of
long term (maturity more than 1 year) debt outstanding, DLT Ti,t , at time t, divided by
the firm’s book value ATi,t at2 time t. The categories cover average leverage in intervals
[0.0, 0.1], [0.1, 0.2], [0.2, 0.4], and [0.4, 1.0].
We denote profitability in year t, i.e. from year t − 1 to t, by πt , and we define it as
OIBDPt /ATt−1 (operating income in year t, divided by firm book value in year t − 1). Each
leverage category of Figure 1, and our later conditioned regressions, takes N = 4 knot values
of πt−1 . To obtain these knot values, we pool the firm years in each leverage category, form
2 × N quantiles of {πi,t }i,t , and then take the knots at the dividing points between the 1 st
and 2nd quantile; 3rd and 4th quantile; etc.
Our nwct is calculated as the current assets minus current liabilities, normalised by the
firm’s total assets, i.e. (ACTt − LCTt )/ATt . Also chet is calculated as CHEt /ATt . In each
leverage category, we estimate the profiles of nwc, respectively che, using the conditioned
1
We give details below on the construction of these variables from Compustat data. All dollar based
variables are defined in terms of proportions of the firm’s book assets. We note here that Compustat does
not give cash separate from equivalent securities. Appendix A summarises our variable construction.
2
Mnemonics with capital letters, for example “AT ”, are taken directly from the Compustat data base.
See Appendix A.
6
regression of the Appendix, pooled over all years and without fixed firm effects, and without
any regressors, except that corresponding to a constant. This conditioned regression is
equivalent to a non-parametric curve fitting procedure, as a linear spline function of πt . The
conditioned regression returns an estimate of nwc, resp che, at each knot point, the standard
error for the estimate, and also the standard deviation of the residual.
1.2
Profiles of other firm variables:
Figure 2 presents profiles similar to Figure 1, for a comprehensive set of firm variables. Each
panel in Figure 2 includes firms with average lifetime (lt.debt) ranging from 0.0 to 1.0, i.e.
they are not separated into leverage categories. Table 1 presents the full results of these
regressions, also including the standard deviations of the variables at each π-knot point.
Figure 2, Panel A gives the profiles for nwc and che, i.e. repeating Figure 1, but with
pooled average leverage. Consistent with Figure 1, nwc is significantly higher than che, and
both profiles are quite flat. It is interesting that che is higher in the lowest 4ile of π.
Figure 2, Panel B gives profiles of the firm’s investment, taken separately as capital
expenditure3 capxt := CAP Xt /ATt−1 , and as ivncf := −IV N CFt /ATt−1 . The Compustat
variable IV N CF is a broader category than CAP X, and includes financial investment in
other firms4 . Both forms of investment are higher at high π. The ivncf is only slightly
higher than capx, but it is much more volatile (see Table 1, Panel B). In the lower 4iles of
π, capx is close to the level of 6%, which according to Nadiri and Prucha (1996), will on
average suffice only to maintain the firm’s assets against depreciation.
Figure 2, Panel C graphs the operating income π itself, and the net income ni, in terms
of π. Net income is much less than π, but more volatile, and in the lowest 4ile, it is negative.
Figure 2, Panel D is concerned with cash flows to and from investors. The variable
shisst := SST Ct /ATt−1 is total cash received from issuing common and preferred stock,
normalized by total assets. The variable divt := (DVt + P RST KCt )/ATt−1 includes all
dividends to common and preferred stock, plus cash payments for repurchasing such stock.
3
4
The equality symbol “:=” means that the equation defines the term on its LHS.
Also, consistent with its full name ‘investment, net cash flow”, its sign is negative if the firm is making
an expenditure.
7
We include stock repurchases in dividends, because they represent alternative ways to reward
equity, and stock repurchases have become more common in recent years. This present paper
is not concerned with the question of how the firm will choose between these alternatives.
In Figure 2, Panel D, the variable ∆(lt.debt)t is the change in the long term debt
level over the year, normalized by the assets at the beginning of the year, i.e. (DLT Tt −
DLT Tt−1 )/ATt−1 . We prefer this to long term debt issuance, as a measure of debt financing,
because debt issuance will often be rolling over currently maturing debt.
We see in Figure 2, Panel D, not surprisingly, that div increases strongly with π. Perhaps
more surprisingly, ∆(lt.debt) increases with π, but shiss does not increase very much, and
in fact shiss is higher in the lowest 4ile of π.
These observations represent a challenge to the Pecking Order Hypothesis (POH). The
POH asserts that firms finance investment first from internal funds such as current income
and liquidity reserve, until the reserve is depleted to a prudential limit; then the firm will
issue debt, again up to a prudential limit; and only finally will the firm issue equity. We see
that most investment occurs when the operating income π is high, and on average there is
enough operating income to finance the capx or ivncf investment presented in Panel B. The
fact that shiss is not much higher for high π seems in tune with the POH, but fact that
∆(lt.debt) is higher at higher π is not in tune with the POH. We suggest that this profile
∆(lt.debt) is motivated by the firms goal of maintaining long term leverage, as it grows.
Figure 2, Panel E is concerned with changes to balance sheet items. First, ∆att is the
change in the firm book size, defined as (ATt − ATt−1 )/ATt−1 . This grows strongly with
π, and in fact more strongly than capx or ivncf . Then ∆nwct is defined as (N W Ct −
N W Ct−1 )/ATt−1 , with N W Ct := ACTt − LCTt . This increases in parallel to ∆att - see
Figure 2F.
The variables ∆(lt.liabs) and ∆(lt.assets)t will be needed to complete our analysis of liquidity as nwc. The first is the change in long term liabilities, ((lt.LIABS)t −(lt.LIABS)t−1 )/ATt−1 ,
with (lt.LIABS)t defined as LTt − LCTt (in Compustat, Liabilities Total - Liabilities Current). The second is the change in long term assets, ((lt.ASSET S)t −(lt.ASSET S)t−1 )/ATt−1 ,
with (lt.ASSET S)t defined as ATt − ACTt (in Compustat, Assets Total - Assets Current)).
The variable ∆(lt.liabs) is higher for higher π, just like ∆(lt.debt), but it is higher and more
volatile than ∆(lt.debt). The variable ∆(lt.assets) is close to capx, and lower than ivncf ,
8
expect for low π, but much more volatile.
Finally, Figue 2F gives the changes in nwc and che proportional to the growth in the firm
˜
assets. Specifically, we define these to be ∆nwc
t := (N W Ct /ATt ) − (N W Ct−1 )/ATt−1 ), and
˜
similarly for ∆che
t . These are both close to zero for all π; in fact they are slightly negative,
except for che, when π is low. Thus, on average the liquidity level does not change very
much from year to year. In the next subsection, we compare this with the time series results
of McLean (2011) and Bates, Kahle and Stulz (2009).
These definitions, and the balance sheet identity ATt = LTt + SEQt , lead to the identity
∆nwct = ∆(lt.liabs)t − ∆(lt.assets)t + ∆seqt ,
(1)
in which ∆seqt is the change in the total shareholder’s equity, (SEQt − SEQt−1 )/ATt−1 .
Also
∆seqt = nit + shisst − divt .
(2)
Substituting for ∆seqt in Eqn (1), using Eqn (2) gives the following identity:
∆nwct = ∆(lt.liabs)t − ∆(lt.assets)t + nit + shisst − divt .
(3)
These profiles summarise the average behaviour of the firm as a function of operating
income π. Salient observations are that firm investment, growth, dividends and increase in
long term debt are higher when π is higher. One the other hand, share issuance is not higher,
and in fact is highest for low π. Also, the profiles of nwc and che are quiet flat, in terms of
π, and their changes on average just keep up with the firm’s growth.
1.3
Some observations on the time series evolution of the firms
liquidity and leverage:
Figure 3, Panel B gives the average values of ncwt and chet and Figure 3, panel C gives the
˜
˜
average values of ∆nwc
t and ∆chet defined above, over all firms, and separately for each
year t in our data set. Consistent with Bates, Kahle and Stulz (2009), and McLean (2011),
˜
chet has increased sharply over the last 30 years. But ∆che
t has not, and we conclude that
the increase in chet is not a within-firm effect, but rather it is the result of new firms being
9
initiated, which hold more cash. The results replacing chet by nwct are different, in that nwct
has not increased over time, except for the exceptionally high value in 2011, which is also
present in the chet result. We suggest that in more recent years, firms have tended to replace
inventory by cash as the liquidity buffer represented by nwc. This would be consistent with
the finding of Bates, Kahle and Stulz (2009), who show that more firms derive their value
from intangible sources, such as R&D.
Finally, Figure 3, Panel A gives average firm profitability for each year. This has gradually
declined over the last 30 years, in parallel with the decline in interest rates.
2
Liquidity, financing and investment:
2.1
A simple VAR analysis of the firm dynamics:
Here we do a simple VAR analysis on the firm variables operating income πt , liquidity nwct ,
long term leverage (lt.debt)t , market-to-book ratio qt , and growth ∆att . All these have been
defined above, except qt , and we take this to be ((P RCC F t×CSHOt −CEQt )+ATt )/ATt 5 .
In detail, we regress each element of the vector yt := (πt , nwct , (lt.debt)t , qt , ∆att ) on 2 lags,
i.e. yt−1 , yt−2 . This formulation implicitly assumes that the firms exhibit constant returns
to scale, but we will relax this assumption later.
In Table 2 we present regressions with fixed firm effects, which recognise differences
between firm averages that are not explained by the state variables. From this table we
see that all variables are mean reverting and positively autocorrelated (0 < coefficient < 1),
except ∆att , which is slightly negatively autocorrelated. Also ∆att is positively predicted by
πt−1 , nwct−1 , qt−1 and negatively predicted by (lt.debt)t−1 . Finally, nwc and (lt.debt) seem
to have a long memory; their influence on the table with 2 lags, is equally distributed over
each lag.
The main purpose of Table 2 is to show that these 5 variables are important in analysing
the firm dynamics, and to motivate our later regressions. We will estimate the firms behaviour in terms of these variables.
5
The Compustat variables here are: P RCC F - share price; CSHO - number of shares outstanding;
CEQ - book value of common equity, all at the end of the fiscal year.
10
2.2
The firms operating strategy, based on current operating income:
We investigate the firm’s operating strategy at time t, in terms of the following variables,
which have been graphed in Figures 1 and 2, and which we summarise as the vector xt :
x1,t
≡
∆nwct
change in NWC
x2,t
≡
∆(lt.debt)t
increase in LT debt outstanding
x3,t
≡
∆(lt.liabs)t
increase in LT liabilities outstanding
x4,t
≡
shisst
total cash raised from share issue
x5,t
≡
divt
total dividends plus cash paid in share repurchases
x6,t
≡
nit
net income
x7,t
≡
capxt
Capital expenditure
x8,t
≡
ivncft
investment
x9,t
≡
∆(lt.assetst ) increase in LT assets outstanding
x10,t
≡
∆att
growth
The construction of these variables is summarised in Appendix A.
We assume that the firm can choose these variables, subject to the adding-up constraint
of Equation (3), and in the light of the contemporaneous operating income πt . We assume
that πt is exogenously determined, and cannot be affected by the choice of xt (though xt can
affect πt+1 )6 .
The regressions of this section are conditioned on πt−1 in the same way as those of
Section 1, and we also include lagged values nwct−1 , qt−1 , (lt.debt)t−1 and log(ATt−1 ) as controls. Specifically, we estimate the following set of seemingly unrelated regressions, for
6
This assumption is necessary to prevent the regressors in the regressions below from being endogenous.
Note that our regressions are basically of standard shape, agreeing those of the papers we cite, except that
they are conditioned, as described in our Appendix B.
11
i = 1, 2, ..., 10:
Regress( xi,t on
nwct−1 , πt , qt−1 , (lt.debt)t−1 , log(ATt−1 ) :
(4)
fixed firm effects; conditioned on πt−1 , with knot 4 values).
Our approach adapts that of Dasgupta, Noe and Wang (2008)7 . Similarly to Dasgupta et al
(and unlike in Section 1 above), we use firm fixed effects, so that the results are telling us
about the reaction to a “shock” in πt , i.e. a deviation from its expected value, conditioned
on πt−1 and the other controls. The firms behaviour on average can be read from the results
of Section 1. The technical design of these conditional regressions is described in Appendix
B.
Table 3 presents our results. Panel A gives the regression coefficients for the 10 regressees
above, against nwct−1 , and panel B gives the regression coefficients against πt . To save
space, we omit the coefficients for the other 3 regressors. As explained, each regressor has 4
regression coefficients, corresponding to to knot values which are chosen based on 4iles πt−1 .
We first discuss Panel A of Table 3, which concerns the reaction to a deviation in nwct−1
from its average value, conditioned on πt−1 . From Table 1, Panel A, this deviation is on
average about 20% of the firms book value. The first entry -0.536 in the first row says that,
conditioned on πt−1 being at its lowest 4ile value, then ∆nwct is -53.6% of the deviation in
nwct−1 . In other words, nwc is strongly mean reverting. We also see that this reduction
in nwc is associated with reducing (lt.debt)t by about 5.1% and (lt.liabs)t by about 18.2%,
and with increasing (lt.assets)t by about 28.8% of the deviation in nwct−1 . On the other
hand, not much of the reduction in nwc is allocated to capx, ivncf or to div, and not much
is allocated to reducing shiss. These patterns in the allocation of excess nwct−1 from its
average value, are consistent across the 4iles of πt−1 . Finally, a high nwct−1 is associated
with a growth in AT , and this effect is stronger at lower 4iles of πt−1 . Note that this cannot
be the direct result of allocation of nwc to another part of the balance sheet, because nwct−1
is already part of the balance sheet. We suggest that this is because the firm is in a more
healthy state if it has a higher liquidity, particularly if the earnings are currently low.
7
Dasgupta et al’s design differs notably from ours, in that they are working with operating cash flow
rather than operating income, and cash savings rather than nwc. Also, their regressions are not conditioned.
12
Table 3, Panel B concerns the reaction to a deviation in πt from its average value (i.e.
a “shock”), conditioned on πt−1 . From Table 1, Panel C, this deviation is 6%, of the firm’s
book value, and quite stable over the πt−1 4iles. The first entry 0.526 in the first row of
Panel B says that, conditioned on πt−1 being at its lowest 4ile value, then nwct increases on
average by 52.6% of the shock to πt . This proportion falls to 29.8% as we move to the top 4ile
of πt−1 . These coefficients represent “saving from current operating income” (related to the
“cash flow sensitivity of cash”), at different levels of “profitability”, defined as the expected
current operating income, and predicted by πt−1 . We will discuss this further below.
Looking at the other rows corresponding to the πt regressor, we see that with a positive
shock to πt , the firm also issues more shares and increases the (lt.debt)t ; presumably since a
positive shock to πt is good news. The coefficient nit is less than the shock to πt , and this
proportion becomes less in the higher 4iles of πt−1 , presumably, because in these 4iles, the
firm is paying more tax on its earnings. Also, with a positive shock to πt , the dividend is
only increased if πt−1 is relatively high, and by a small amount, at 5.1% of the πt shock.
These results are consistent with results of previous regressions in the literature, though
previous regressions are not conditioned on πt−1 .
Most surprisingly in Panel B, a shock to πt is associated with an INREASE in (lt.liabs)t ,
which is only partly accounted for by an increase in (lt.debt)t . Also, (lt.assets)t increases,
in fact more than the shock to πt itself, and this is only partly being accounted for by capx
or ivncf . Related to these observations for (lt.assets) and (lt.liabs), the total assets of the
firm also increases (∆att > 0) in response to a shock in π, and this increase is actually more
than twice the πt shock itself 8 .
Table 3 Panel C gives the same panel regressions as Panels A and B, but without the NP
conditioning on πt−1 . Instead, we simply include πt−1 as a linear regressor 9 . This regression
8
Note that this is not surprising if we refer to the firm’s market value, instead of the book value. That is,
the market value of the firm can increase by more than the shock to the operating income π. This follows
under the assumption that a currently higher π will be expected to persist into the future, and this will be
incorporated into the current market value. Such persistence is well established - see eg Pastor and Veronesi,
(2003) - and is reflected in our Table 2.
9
Note that the conditioned regression involves products of the regressors with linear splines associated
with πt−1 , and so this linear regression is a very crude substitute.
13
can be denoted by
Regress(xi,t
on
πt , πt−1 , nwct−1 , qt−1 , (lt.debt)t−1 , log(ATt−1 ) :
(5)
fixed firm effects; no conditioning).
The results in Panel C for the regressors πt and nwct−1 , are consistent with those of Panels
A and B. The coefficients on the regressor qt−1 confirm the well established result that this
variable can positively predict firm growth taken either as ∆at, capx, or ivncf . Also, qt−1
positively predicts shiss. This is also well established, and forms the basis of the ‘market
timing’ hypothesis, that high q indicates that the share price s “too high”, and that the firm
issues to take advantage of this market imperfection. The coefficients for log(ATt−1 ) are all
-ve, except for that on divt ; smaller companies are more active in all aspects relating to these
regressees. Coefficients for (lt.debt)t−1 are all -ve expect for shisst , and this coefficient is
very small.
Table 4 is a repeat of Table 3, but with nwc replaced by che. The results here are
broadly consistent with those of table 3, but they are weaker, and in particular, the saving
from current income (line 1 of Panel B) is now not monotone decreasing. It is notable that
in Table 4, Panel C, the R2 of the first regression, with ∆chet as regressee, is much lower, at
11.4%, than the corresponding R2 in Table 3, Panel C, at 23.5%, taking ∆nwct as regresee,
To summarise the main points of this section: If the firm has a positive shock to earnings
πt , then a substantial proportion of the extra income is put into liquidity, represented by
nwc, and relatively little is allocated to a higher div or to more capx. Less is allocated
to liquidity and more to div and capx, when πt−1 is higher. The nwc is managed to be
mean reverting, and an excess is mostly associated with reducing lt.liabs or to increasing
lt.assets. These adjustments are not completely accounted for in investment or reduction
in lt.debt. A positive earnings shock is associated with share issuance, and an increase in
lt.debt, and large increases in lt.assets and lt.liabs. In fact the total assets AT increases
beyond the magnitude of the earnings shock, and this increase cannot be accounted for by
either issuance or the increase in lt.debt.
14
2.3
The “cash flow (operating income) sensitivity of cash (liquidity)”:
As mentioned already, we see in Table 3, Panel B, and in Panel C, that the firm saves from an
income shock, by increasing its net nwc. This corresponds to a positive “cash flow sensitivity
to cash” (CFSC >0), as in Almeida, Campello and Weisbach (ACW) (2004). This agrees
with the regressions of ACW and also of other authors, for example Dasgupta, Noe and
Wang (2008). We also see in our Table 3, Panel B, that the amount of saving from current
operating income is lower, when last year’s profitability was higher.
ACW (2004) also develop a 2-period model, in which firms which are financially constrained, in the sense that raising external finance is expensive, save from operating income,
but unconstrained firms do not. They support their model using regressions of savings on
operating income and some other controls, applied to partitioned sets of firms which are
constrained or unconstrained, according to various established proxies. The CFSC is estimated as the regression coefficient on operating income, and this is found to be +ve for the
constrained firms and essentially zero for the unconstrained firms.
This result CFSC>0 seems to be a puzzle, however, in view of some more recent models.
In the model of Riddick and Whited (2009) (RW), the operating income is a persistent, mean
reverting state variable. When operating income is relatively high, the firm is expected to
continue to be more profitable in the near future, before falling back to the average in
the longer term. The high operating income should therefore be invested immediately for
growth, up to a limit determined by a quadratic growth cost, rather than saved. High
operating income is directly related to Tobin’s q, which can be interpreted in terms of the
relative advantage of holding cash, or investing it. The conclusion is that there will be
less saving, and more growth, when operating income is higher10 , i.e. CFSC <0, at least
when there is a surplus of operating income which might be invested, saved, or disbursed as
dividends.
The model of Anderson and Carverhill (2012) (AC) has much in common with the model
10
This conclusion would seem difficult to avoid in a time homogeneous model, holding other state variables,
apart from operating income, constant. This argument shows the limitation of ACW’s 2 period modeling
approach.
15
of RW, as least taking σ = 0 in the AC Model, as we explain below. Simulating this
model with growth opportunities, we conclude that such opportunities are not the major
determinants of liquidity holdings. In this model, the dominant motive for saving is the
precautionary motive11 . Under this motive, and consistent with RW, the desire to save will
be lower, when the profitability is higher, at least when there is a surplus of operating income.
To resolve the puzzle, we will simulate the AC Model in the following extended form:
The profitability ρt obeys the Ito Equation
dρt = κ(ρ̄ − ρt )dt + ηdWtρ ,
(9)
and the (cumulative) operating income St obeys the Ito Equation
dSt = ρt dt + σdWtσ .
The annual operating income πt , as in our Compustat data, is calculated as
(10)
∫t
s=t−1
dSs . The
firm puts its operating income, if positive, into its liquidity reserve, and pays for growth,
dividends and debt interest, from this reserve. Operating losses are paid from the reserve,
and the firm is bankrupt, if the liquidity reserve is run down to zero. See AC, and Appendix
C below, for more details on the model, parameter values, and simulations, etc. In the
model, the state variables are ρt and the level of the liquidity reserve, Ct . The AC Model
was developed mostly assuming that σ = 0. In this case, the continuous profitability variable
ρt is a close substitute for the annual operating income πt , and the model is close to that
of RW. However, for σ > 0, operating income πt includes the influence of profitability ρt ,
together with the white noise type cash flow σdWtσ .
Table 5, Panel A, presents the results of simulating the model of AC, with σ = 0, and
11
The AC Model takes growth opportunities to be exogenously determined, rather than limited by
quadratic growth costs. The profitability of new investment is assumed to be the same as that of the
firm, and so growth will not be pursued, if the firm is not currently sufficiently profitable. The AC Model
applies the observation (see Figure 2) that growth is on average sufficiently small, that it can be financed
from current income. The model does indicate holding liquidity for investment, but this is generally relatively
small, and it is tied to the lumpiness of the investment opportunity.
16
applying the conditioned regression
Regress( ∆nwct on
(11)
πt , nwct−1 , qt−1 :
fixed effects; conditioned on πt−1 with 4 knots).
This is just Regression Equation (4), with i = 1, and without the regressors for long term
debt, and firm size, which are not included in the simulation. We see that the CFSC is
negative, as predicted above, except in the lowest 4ile of profitability. In this 4ile, the firm
is likely to be making an operating loss, since the operating income of 0.059 is close to the
fixed payments to debt long term of 0.04, and this will deplete the liquid reserve. A higher
operating income in this region leads to less depletion, and HIGHER saving, i.e. CFSC >
0. We also see strong mean reversion of the nwc.
Table 5, Panel B, presents 3 ordinary regressions, corresponding to the conditioned Regression Equation (11). Regression 1 omits the regressor nwct−1 . RW define the CFSC as the
coefficient on πt in this regression, and we see that this coefficient is -ve. This is consistent
with the argument above, and with Panel A, but also relies on the effect in the bottom 4ile
in Panel A being dominated by the effects in the other 4iles.
Regression 2 in Panel B omits the qt−1 control, and the CFSC becomes positive. This
also agrees with RW, and they note that including this control, and without an excessive
error, is important in their estimation of the CFSC12 . Regression 3 in Panel B includes the
qt−1 control, and also the nwct−1 control. The CFSC is now negative, and we see that the
R2 becomes much higher.
To replicate the result CFSC>0, we take σ > 0 in the AC Model. The results are given
in Table 5, Panels C and D. Panel C is our conditioned regression, corresponding to Panel
A, and we see, consistent with our Table 3, Panel B, that the CFSC is positive, and lower
in higher 4iles. Panel D is the unconditioned regression as in Panel B, and it also gives
CFSC>0.
The following is an interpretation of our results:
12
Like other authors, RW find CFSC>0, when they apply Regression (11) to empirical data. This disagrees
with their model, and they argue that this is due to errors in the empirical qt−1 , as representing growth
opportunities. They obtain CFSC<0 by an alternative GMM estimation of the model.
17
• An increase in the rate of operating income coming from an increase in ρt should not
be saved, if ρt is sufficiently high, consistent with the arguments sketched above. In
the RW perspective, the value of holding cash is lower, because investment is more
profitable. In the AC perspective, the firm is further from financial distress.
• An increase in the rate of operating income coming from the dWtσ element, on the other
hand, does not signal an improvement in the prospects of the firm, and the argument
above does not apply. This increase will be saved, invested in growth, or disbursed as
a dividend, purely on consideration of the liquidity position. On average, some part
of this shock will be saved as a precaution against distress. We will thus see saving
from current income, if the influence of the dWtσ element is sufficient in the operating
income.
• We also expect that more of the dWtσ element will be saved, when profitability ρt is
lower. The ρt is not observable, but in the conditioned regression, the πt−1 plays the
role of a proxy for ρt . This explains the falling pattern of the saving from current
income, as πt−1 increases.
To summarize the conclusions of the above: The firm saves from current operating income,
but is saves more when the profitability is low. This behaviour is consistent with saving to
avoid financial distress later, rather than to facilitate growth later.
We finally note that there might be other reasons to save from current income, which we
have not modeled. For example, it might put more of an income shock into its cash reserve,
if the firm does not want to change its dividend quickly, or if capx will take some time to
plan and implement.
2.4
Are Liquid reserves maintained for the purpose of investment?:
We now answer this question more directly, using some non-conditioned regressions, presented in Table 6. In the panels of this table, investment is taken separately to be capx and
ivncf , and liquidity is taken separately to be nwc and che. Panel A takes liquidity to be
18
nwc, and investment to be capx. In the panel there are 3 regressions. Regression number 1
predicts investment based on liquidity; number 2 looks at how investment is financed; and
number 3 looks at whether more liquid reserves will be used, if they are higher.
Specifically, Regression 1 is
Regress( capxt on
πt−1 , nwct−1 , qt−1 , log(ATt−1 ), (lt.debt)t−1 :
(12)
fixed firm effects; no conditioning).
From the result we see, as expected, that capxt is positively related to πt−1 and qt−1 , and
negatively related to (lt.debt)t−1 and log(ATt−1 ). More interesting is the coefficient of 0.015,
on the regressor nwct−1 . This says that if nwct−1 is say 1 standard deviation (StDev) higher
than average, then capxt is higher by 1.5% of this amount. From Table 1, we see that the
StDev of nwc is about 20%, and the StDev of capx is about 7%. Thus, capx is only weakly
related to nwct−1 .
Regression 2 is
Regress( capxt on
πt − divt , ∆nwct , shisst , ∆(lt.debt)t , qt−1 , log(ATt−1 ), (lt.debtt−1 ) :
(13)
fixed firm effects; no conditioning).
The first 4 of these regressors represent a reduced set of financing sources; in particular
πt − divt represents retained earnings from current operating income. The last 3 regressors
are the usual controls. The important regressor here is ∆nwct . Minus the coefficient on this
regressor, i.e. 6.6%, gives the reduction in nwc that is used to finance the investment. From
Table 1, the StDev of capx is about 7%, and the StDev of ∆nwc is about 11% Thus, only
about
11%×6.6%
7%
of financing for a shock to capx is coming from the shock to ∆nwc.
Regression 3 is
Regress( capxt on
πt − divt , ∆nwct , shisst , ∆(lt.debt)t ,
(πt − divt ) × nwct−1 , ∆nwct × nwct−1 , shisst × nwct−1 , ∆(lt.debt)t × nwct−1 ,
qt−1 , log(ATt−1 ), (lt.debtt−1 ) :
fixed firm effects; no conditioning).
(14)
19
This is the same Regression 2, but also including cross terms of the 4 financing sources,
with nwct−1 . The important regressor now is ∆nwct × nwct−1 . If the coefficient on this is
negative, then a higher liquidity being available from last year, leads to more liquidity being
used for investment. In the table, this coefficient is very small and +ve.
We see from these regressions in Panel A that capx is not much related to the liquidity
reserve. Regarding the question of this subsection, the liquidity reserve represented as of
nwc is not maintained in order to facilitate capx.
Panel B presents the same regressions as Panel A, but with capx replaced by ivncf .
These results are somewhat different. In Regression 1, the coefficient 6.8% says that liquidity
predicts ivncf somewhat more strongly than it predicts capx. In Regression 2, quite a lot,
at 22.4% of extra liquidity, is used for ivncf . In Regression 3, the coefficient -0.048 indicates
that if liquidity is higher, then more will be used for ivncf . From the results of Panel B,
it is tempting to conclude that liquidity is maintained for the purpose of investment other
than capital investment. However, we hesitate to draw this conclusion, because for most
firms, such investment is not the core of the firms activities. We suggest that the firm is
willing to allocate liquid reserves to non-capital investment on a speculative basis, because
such investments themselves have a high liquidity.
Panels C and D repeat the regressions of Panels A and B respectively, but replacing nwc
by che to represent liquidity. These results are weaker, but broadly consistent with those of
Panels A and B.
3
3.1
Liquidity and firm exit:
Average firm behaviour approaching exit:
Table 7 gives averages of π, etc. for firms that exit the Compustat data set, either through
acquisition, bankruptcy or liquidation. The numbers of firms in each of these categories are
respectively 1720, 80 and 50, out of a total of 11116. The table gives the averages of π,
etc. for these firms, 0, 1, 2, 3, 4 years from exit. Leading up to exit via bankruptcy or
liquidation, π and ni become very poor, and the nwc and che are greatly depleted. This is
not surprising though very different from the aggregated behaviour of all firms described in
20
Figures 1 and 2. Perhaps more surprising, these exiting firms do not strikingly reduce their
book assets, until the year of exit. The firms exiting via merger do not exhibit strikingly
atypical behaviour, in this table.
3.2
Predicting firm exit using a LOGIT regression:
Table 8 is a more formal study of firm exit, in the shape of a multivariate LOGIT regression.
This regression compares firms that exit via acquisition, bankruptcy or liquidation (alternatives 1, 2, 3 respectively), with firms that do not exit in these ways. For each firm exiting
in this way, we take the variable as above, n years before exit. The 5 columns correspond
to n = 0, 1, 2, 3, 4. For firms that do not exit in any of these ways, we take the variable
values at times randomly and uniformly distributed over the firms lifetime. The results are
effectively testing whether firm exit can be predicted, on the basis of the variables included.
Results are presented with regressors ∆at (growth),π (operating income) and che (cash
and equivalents). We see that bankruptcy is indeed associated with a depletion of cash, up
to 3 years ahead. This effect is not subsumed by the effect of the operating income, or of
the growth. The result is also present, though weaker, for liquidation.
If we replace che by nwc, then this variable is never significant. This suggests that
bankrupt firms might have working capital that they cannot liquidate. Also, if we include
q, then the the significance of che disappears and it thrown onto q. This seems to indicate
that the market is aware of the risk entailed in the depletion cash reserve. (These results are
not presented.)
4
Summary and Conclusions:
Using Conditioned Regressions, which give coefficients as nonlinear functions of a conditioning variable, and applying these to Compustat data, we have studied the behaviour of a
firm’s liquid reserve, conditioned on the firm’s profitability. Preliminary results from these
regressions are that firm growth, dividends and increase in long term debt are higher when
the firm is more profitable, but more notably, the liquid reserve and share issuance are not
higher.
21
Our first main results concern the “cash flow sensitivity of cash” (or “saving from current
income”), discussed by Almeida, Campello and Weisbach (2004) and Riddick and Whited
(2009). Simple regressions find this to be +ve, i.e. firms do put some of their current income
into the liquidity reserve. We confirm this with our data, using both ordinary and our
conditioned regressions. Our conditional regressions also reveal that the extent of saving is
decreasing as the profitability, rises.
This result CFSC>0 is problematic, however, in the light of the recent theoretical models
of Riddick and Whited (2009) and Anderson and Carverhill (2011) (AC). These models argue
that CFSC should be negative, at least when the firm is currently profitable. If the liquid
reserve is maintained to facilitate growth, then immediate growth should be preferable to
saving, if the firm is currently more profitable. Also, if the liquid reserve is maintained to
help the firm to survive a period operating losses, then this motive is weaker, if the firm is
currently more profitable. These arguments rely on the profitability being a mean reverting
process, so that one can take advantage of current high profitability only temporarily. They
also rely on the identification of the profitability with the operating income.
We resolve this problem in the context of the AC Model, by introducing an extra, white
noise-type component, into the operating income. This has no ramification for the profitability, and so it should partially be saved, to an extent depending on the firms current liquidity
position. We apply our conditioned regressions to data simulated under this extended AC
Model. We can replicate the result CFSC >0, and also the effect that the saving is decreasing
as the profitability rises. This effect can be explained using the theoretical arguments above.
From these results, and based on the AC Model, we argue that the purpose of the liquid
reserve is not to facilitate growth, but to help the firm survive a period of operating losses.
Our second results come from looking directly at whether the liquid reserve facilitates
investment. We present 3 regressions: [1] to predict investment; [2] to see how investment is
financed, and [3] to see whether more investment will be financed from the liquid reserve, if
this reserve is higher. Taking investment as capital expenditure, our results are very weak.
Some liquid reserve is used for investment, in regression [2] here, but there is a negative
result in regression [3]. We take this as more direct evidence consistent with the above, that
the liquid reserve is not maintained to facilitate growth.
Our results are different if we take a broader class of investment, which includes financial
22
investment, as well as capital investment. The firm will use its liquid reserve for this type
of investment, and will make more investment, if the liquid reserve is higher. One might
therefore conclude that the purpose of the liquid reserve is to facilitate “financial” investment.
However, we note that such investment is relatively small, and is not the main activity, for
the firms in our data set, from which financial firms have been excluded. Also, “financial”
investment itself is more liquid than capital investment, and so the firm might regard such
investment as still maintaining the liquid reserve.
Our final results look at firm exit from the Compustat data, either by acquisition,
bankruptcy, or liquidation. The last 2 types of exit here are associated with a depletion
of the liquid reserve, and this depletion can be used to predict bankruptcy or liquidation up
to 3 years in advance, in a Logit regression. We take this to be further direct evidence, that
the liquid reserve is maintained to help the firm survive a period of operating losses.
23
Appendix A - Glossary of Variable Definitions:
Compustat variables:
ACT
AT
CEQ
CHE
DLC
DLT T
LCT
LT
OIBDP
CAP X
DLT IS
DLT R
DV
P RST KC
SST K
CSHO
P RCC F
current assets, total
assets, total
book value of common equity
cash and equivalent marketable securities
Debt in current liabilities (maturity < 1 year))
Long term debt (maturity > 1 year
Current liabilities, total
Liabilities, total
Operating income before depreciation
Capital Expenditure
Long term debt issue
Long term debt repurchase
Cash dividend
Share repurchase
Share issue
Number of shares outstanding (millions)
Share price
State variables discussed in the text:
πt
nwct
(lt.debt)t
(lt.liabs)t
(lt.assets)t
qt
OIBDPt /ATt−1
(ACTt − LCTt )/ATt
DLT Tt /ATt
(LTt − LCTt )/ATt
(ATt − ACTt )/ATt
((P RCC F t × CSHOt − CEQt ) + ATt )/ATt
Operating income
Net working capital
Long term debt
Long term liabilities
Long term asets
Market over book value
Flow variables discussed in the text:
x1,t
x2,t
x3,t
x4,t
x5,t
x6,t
x7,t
x8,t
x9,t
x10,t
≡
≡
≡
≡
≡
≡
≡
≡
≡
≡
∆nwct
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assets)t
∆att
:=
:=
:=
:=
:=
:=
:=
:=
:=
:=
Compustat data formula
(N W Ct − N W Ct−1 )/ATt−1
(LTt − LTt−1 )/ATt−1
((ATt − LCTt ) − (ATt−1 − LCTt−1 ))/ATt−1
SSTKt /ATt−1
(DVt + P RST KCt )/att−1
N It /ATt−1
CAP Xt /ATt−1
−IV N CFt /ATt−1
(ATt − ACTt ) − (ATt−1 − ACTt−1 )/ATt−1
ATt /ATt−1 − 1
24
Description
(change in NWC)
(increase in LT debt)
(increase in LT liabilities)
(total cash raised from share issue)
(tot. div. plus share repurchase)
(net income)
(Capital expenditure)
(investment)
(increase in LT assets)
(growth)
Appendix B - Our Non-Parametric Regressions:
As already stated, the regressions of Section 1, giving the profiles of average firm variables in terms of
operating income, are equivalent to piecewise linear spline fits. Denoting the knot values by πj , denote the
linear spline functions by ϕj (π), for j = 1, ..., n. These are defined such that ϕj (πk ) is 1 if k = j and 0 if
k ̸= j, and linear in between knots. Also, we take ϕ1 (π) (resp. ϕn (π)) to extrapolate linearly, as π falls
though π1 (resp. rises through πn ).
The profile of the variable yi,t (firm i, time t), as in Section 1, can be obtained as the coefficients
αj , j = 1, ..., n, in the regression
n
∑
yi,t ∼
ϕj (πi,t )αj .
(B1)
j=1
These αj ’s are the values at the πj ’s, of the function that is continuous and linear between the knots
π2 , ..., πn−1 (and linear though π1 , πn ), and which minimises the SSQ error in fitting the data {yi,t }i,t .
The above corresponds to regressing yi,t on a constant. To include other regressors, say xi,t , as in Section
2, augment the Equation (B1) to become
yi,t ∼
n
∑
ϕj (πi,t )(αj + βj xi,t ).
(B2)
j=1
For fixed firm effects, subtract from the data (xi,t , yi,t ), the fitted values from the regression (B1), and apply
regression (B2) to this altered data, with the αj coefficients omitted.
We extend these regressions to give also the standard deviation of the residuals, also as
∑an linear spline
function, with knot values say σ1 , ..., σn . For this, divide each data point (xi,t , yi,t ) by σi,t := j=1 ϕj (πi,t )σj ,
and do the regression on this altered data. Denoting the residuals by {si,t }i,t , and assuming they are normally
distributed, then the (i, t)-th data point contributes − 21 log(2πs2i,t ) − 12 s2i,t to the log-likelihood. (Note that
we have si,t in the likelihood, rather than si,t /σi,t , because we have already divided the data by σi,t .) Finally,
maximise the log-likelihood on σ1 , ..., σn .
Appendix C - Modeling Saving from Current Income:
Here we give details of our implementation and simulation of the AC Model, for Section 2.4. We first mention
that our Equation (9) differs from Equation (2) of AC, in that the noise term now is of Guassian type, rather
√
than CIR type. Equation (2) in AC is dρt = κ(ρ̄ − ρt )dt + η ρt dWtρ . This makes essentially no difference
to the model, except that it allows us to absorb the fixed costs, f in AC, into the parameter ρ̄. In our
simulations, we take the parameters κ = 0.4, ρ̄ = 9%, η = 0.16. All other parameters are as in the AC
benchmark case, except that we take f = 0.0. In AC, the parameters are κ = 0.4, ρ̄ = 1.09, η = 0.16, f = 1.0.
√
Since the average value of ρ in AC is close to 1.0, then keeping the same value for η in the present model
version will cause both versions to be close to each other. The benchmark case includes payment of 0.04
to long term (perpetual) debt. As explained in AC, this gives a reasonable firm leverage and other firm
characteristics. The parameters in AC are not fitted econometrically, but they are set to reasonable values.
AC explores the effects of σ = 0.0, 0.1, 0.2, but mostly focuses on σ = 0.0. In our Table 5, Panels A and
B, we take σ = 0, and in Panels C and D, we take σ = 0.2.
The details of our simulation are as follows: We generate 2000 firm histories, each with a burn-in
period of 20 years, and with a firm lifetime of 30 years, or bankruptcy, if this occurs
before 30 years. We
∫t
simulate yearly data corresponding to (πt , nwct , qt ) as in the text, and with πt = s=t−1 dSs . We then apply
regressions similar to those of Section 2.2.
25
TABLE 1 - continued
TABLE 1
Profiles of firm variables
π knot values
0.05277 0.11079
0.15487
Panel A - liquidity measures
Net working capital - nwc
Value
0.1754
0.1666
0.1815
Sterr
0.0018
0.0020
0.0019
StdDev
0.2187
0.1858
0.1734
Cash and equivalents - che
Value
0.1176
0.0801
0.0816
Sterr
0.0012
0.0012
0.0011
StdDev
0.1376
0.1029
0.1023
Panel B - investment
Capital expenditure - capx
Value
0.0484
0.0449
0.0784
Sterr
0.0005
0.0006
0.0007
StdDev
0.0703
0.0731
0.0627
Investment - ivncf
Value
0.0432
0.0578
0.0932
Sterr
0.0012
0.0014
0.0016
StdDev
0.1456
0.1261
0.1426
Panel C - earnings
Operating income - π
Value
0.0712
0.1150
0.1649
Sterr
0.0005
0.0005
0.0006
StdDev
0.0611
0.0463
0.0505
Net income - ni
Value
-0.0060
0.0236
0.0513
Sterr
0.0012
0.0010
0.0009
StdDev
0.1314
0.0781
0.0758
0.220
0.1790
0.0016
0.1950
0.0981
0.0009
0.1108
0.0981
0.0007
0.0840
0.1160
0.0014
0.1701
0.2311
0.0006
0.0677
0.0909
0.0009
0.0995
π knot values
0.05277 0.11079 0.15487
0.220
Panel D - financing
Share issue - shiss
Value
0.0183
0.0133
0.0152
0.0173
Sterr
0.0006
0.0006
0.0005
0.0005
StdDev
0.0659
0.0487
0.0460
0.0623
Dividend - div
Value
0.0168
0.0175
0.0358
0.0600
Sterr
0.0003
0.0003
0.0007
0.0008
StdDev
0.0396
0.0415
0.0597
0.0863
Change in LT debt - ∆(lt.debt)
Value
0.0082
0.0117
0.0322
0.0368
Sterr
0.0014
0.0017
0.0018
0.0015
StdDev
0.1787
0.1585
0.1677
0.1785
Panel E - Balance sheet changes
Change total assets - ∆at
Value
0.0428
0.0698
0.1154
0.1424
Sterr
0.0026
0.0030
0.0031
0.0027
StdDev
0.3245
0.2790
0.2705
0.3232
Change in NWC - ∆nwc
Value
0.0070
0.0081
0.0146
0.0210
Sterr
0.0014
0.0012
0.0010
0.0009
StdDev
0.1625
0.1120
0.0942
0.1130
Growth in LT liabilities - ∆(lt.liabs)
Value
0.0163
0.0229
0.0454
0.0512
Sterr
0.0018
0.0020
0.0022
0.0019
StdDev
0.2197
0.1845
0.1946
0.2245
Growth in LT assets - ∆(lt.assets)
Value
0.0246
0.0400
0.0762
0.0936
Sterr
0.0019
0.0023
0.0026
0.0023
StdDev
0.2413
0.2254
0.2278
0.2749
Panel F - Prop. changes in liquidity measures
˜
Prop. changes in NWC - ∆nwc
Value
-0.0026
-0.0039
-0.0061 -0.0043
Sterr
0.0012
0.0012
0.0010
0.0008
StdDev
0.1424
0.1066
0.0885
0.0985
˜
Prop. changes in cash - ∆che
Value
0.0021
0.0013
-0.0002
0.0000
Sterr
0.0006
0.0006
0.0006
0.0005
StdDev
0.0746
0.0576
0.0536
0.0587
26
TABLE 2
Regress yt := (πt , nwct , (lt.debt)t , qt , ∆att ) on 2 lags, i.e. yt−1 , yt−2 , with fixed firm effects
πt−1
nwct−1
(lt.debt)t−1
qt−1
∆att−1
πt−2
nwct−2
(lt.debt)t−2
qt−2
Coefficient estimates:
Regressee
πt
nwct
(lt.debt)t
qt
∆att
Regressee
πt
nwct
(lt.debt)t
qt
∆att
Regressee
πt
nwct
(lt.debt)t
qt
∆att
Regressee
πt
nwct
(lt.debt)t
qt
∆att
0.450
0.063
-0.013
0.326
0.440
-0.004
0.471
-0.083
0.018
0.222
0.014
-0.135
0.626
0.043
-0.189
0.018
-0.000
-0.001
0.559
0.104
0.001
-0.006
0.007
-0.054
-0.018
0.014
-0.034
0.031
-0.069
-0.000
-0.007
0.044
-0.007
-0.065
0.129
0.008
0.018
-0.033
0.103
-0.110
-0.013
-0.005
0.004
0.012
-0.025
-0.003
0.458
-0.007
0.092
0.001 -0.024
-0.063
0.338
-0.031
0.515
Standard errors:
0.005
0.009
0.009
0.040
0.028
0.002
0.004
0.004
0.020
0.013
0.002
0.004
0.004
0.018
0.012
0.000
0.001
0.001
0.004
0.003
0.000
0.001
0.001
0.007
0.004
0.006
0.011
0.011
0.049
0.033
0.003
0.005
0.005
0.025
0.017
0.003
0.006
0.006
0.026
0.018
0.000
0.001
0.001
0.005
0.003
0.000
0.001
0.001
0.007
0.005
27
0.006
0.011
0.010
0.048
0.033
∆att−2
-0.001
0.453
-0.081
0.050
0.128
0.009
-0.135
0.646
-0.035
-0.117
0.025
0.001
-0.004
0.554
0.114
0.006
-0.005
0.003
-0.050
-0.025
0.003
0.005
0.005
0.025
0.017
0.003
0.006
0.005
0.025
0.017
0.000
0.001
0.001
0.005
0.003
0.001
0.001
0.001
0.008
0.005
TABLE 3
Panel A - Conditioned regression (4): reaction to higher nwct−1
πt−1 Knot values
Regressee
∆nwct
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assetst
∆att
0.053
0.111
0.154
0.220
-0.536
-0.050
-0.181
-0.015
0.023
0.005
0.021
0.085
0.288
0.193
-0.504
-0.067
-0.160
-0.020
0.027
-0.005
0.021
0.089
0.286
0.191
-0.395
-0.086
-0.132
-0.005
0.031
0.091
0.032
0.002
0.190
0.069
-0.463
-0.080
-0.126
-0.004
0.013
-0.082
0.035
-0.002
0.180
0.061
Comments
NWC mean reverting
Reduce LT debt
Reduce LT liabiliites
Reduce Shiss
Increase dividend
More capx
More investment, at low π
Increase LT assets
Growth stronger for low 4iles
Panel B - Conditioned regression (4): reaction to higher pit
πt−1 Knot values
Regressee
∆nwct
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assetst
∆att
0.053
0.111
0.154
0.220
Comments
0.526
0.519
0.574
0.100
-0.014
0.789
-0.026
0.422
1.108
1.891
0.4919
0.6355
0.7269
0.1396
-0.0161
0.7038
-0.0416
0.5041
1.3074
2.1217
0.3648
0.5395
0.8460
0.1311
0.0342
0.6060
0.1600
0.5494
1.4240
2.1733
0.298
0.449
0.886
0.112
0.050
0.548
0.179
0.540
1.570
2.218
Save - less for higher πt
Increase LT debt
More LT liabilities
More share issue
More dividend at higher πt
More ni - proportion less for higher πt
More capx, at high πt
More investment
Much more LT assets
Much more total assets
Panel C - Regression (5)
Regressor
Regressee
∆nwct
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assetst
∆att
Regressee
∆nwct
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assetst
∆att
πt
πt−1
0.361
0.397
0.640
0.079
0.080
0.642
0.123
0.443
1.202
1.835
-0.132
-0.118
-0.158
-0.073
0.072
-0.026
0.069
-0.008
-0.386
-0.622
0.010
0.015
0.018
0.005
0.005
0.009
0.005
0.012
0.021
0.025
0.012
0.017
0.021
0.006
0.006
0.010
0.006
0.014
0.024
0.029
nwct−1
qt−1
log(ATt−1 )
Regression coefficient
-0.500
0.012
-0.028
-0.043
0.008
-0.017
-0.131
-0.001
-0.021
-0.009
0.011
-0.011
0.043
0.007
0.007
-0.062
0.020
-0.006
0.015
0.011
-0.015
0.069
0.028
-0.000
0.232
0.029
-0.025
0.151
0.046
-0.056
Regression StErr
0.005
0.001
0.007
0.001
0.009
0.002
0.002
0.000
0.002
0.000
0.004
0.001
0.002
0.000
0.006
0.001
0.010
0.002
0.012
0.002
28
0.001
0.001
0.002
0.000
0.000
0.000
0.000
0.001
0.002
0.002
(lt.debt)t−1
-0.140
-0.401
-0.341
0.022
-0.065
-0.063
-0.032
-0.097
-0.152
-0.156
0.005
0.007
0.008
0.002
0.002
0.004
0.002
0.006
0.009
0.011
Comments
∂nwct
>0
∂q
t−1
∂shisst
∂qt−1
∂∆att
∂qt−1
R2
0.235
0.122
0.037
0.062
0.230
0.118
0.094
0.220
0.156
0.094
>0
>0
TABLE 4
Panel A - Conditioned regression (4): reaction to higher chet−1
πt−1 Knot values
Regressee
∆chet
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assetst
∆att
0.053
0.111
0.154
0.220
-0.461
-0.060
-0.049
-0.025
0.043
0.077
0.012
0.098
0.422
0.102
-0.411
-0.083
-0.039
-0.003
0.046
0.074
0.007
0.122
0.390
0.146
-0.367
-0.138
-0.126
-0.018
0.037
0.066
0.023
-0.044
0.151
-0.149
-0.400
-0.123
-0.099
-0.008
0.012
0.047
0.036
-0.015
0.191
-0.129
Comments
Che mean reverting
Reduce LT debt
Reduce LT liabiliites
Reduce Shiss
Increase dividend
More net income
More capx
More investment, at low π
Increase LT assets
Grow in low 4iles
Panel B - Conditioned regression (4): reaction to higher pit
πt−1 Knot values
Regressee
∆chet
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assetst
∆att
0.053
0.111
0.154
0.220
Comments
0.192
0.517
0.571
0.098
-0.004
0.786
-0.023
0.425
1.122
1.895
0.183
0.631
0.719
0.138
-0.004
0.699
-0.038
0.509
1.321
2.133
0.230
0.544
0.851
0.131
0.032
0.617
0.162
0.550
1.417
2.175
0.211
0.452
0.888
0.113
0.049
0.545
0.181
0.540
1.565
2.220
Save
Increase LT debt
More LT liabilities
More share issue
More dividend at higher πt
More ni - proportion less for higher πt
More capx, at high πt
More investment
Much more LT assets
Much more total assets
Panel C - Regression (5)
Regressor
Regressee
∆chet
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assetst
∆att
Regressee
∆chet
∆(lt.debt)t
∆(lt.liabs)t
shisst
divt
nit
capxt
ivncft
∆(lt.assetst
∆att
πt
πt−1
0.164
0.396
0.641
0.079
0.082
0.644
0.123
0.443
1.207
1.834
-0.094
-0.131
-0.192
-0.076
0.086
-0.038
0.072
0.011
-0.313
-0.583
0.008
0.015
0.018
0.005
0.005
0.009
0.005
0.012
0.021
0.025
0.009
0.017
0.021
0.006
0.006
0.010
0.006
0.014
0.024
0.028
chet−1
qt−1
log(ATt−1 )
Regression coefficient
-0.410
0.013
-0.012
-0.047
0.009
-0.016
-0.030
-0.000
-0.017
-0.022
0.012
-0.011
0.084
0.005
0.006
0.066
0.019
-0.003
-0.011
0.012
-0.016
0.054
0.027
-0.001
0.336
0.023
-0.030
0.039
0.045
-0.061
Regression StErr
0.006
0.000
0.012
0.001
0.015
0.002
0.004
0.000
0.004
0.000
0.007
0.001
0.004
0.000
0.010
0.001
0.017
0.002
0.020
0.003
0.000
0.001
0.002
0.000
0.000
0.000
0.000
0.001
0.002
0.002
29
(lt.debt)t−1
-0.018
-0.407
-0.347
0.020
-0.057
-0.059
-0.032
-0.090
-0.116
-0.149
0.003
0.007
0.009
0.002
0.002
0.004
0.002
0.006
0.010
0.012
Comments
∂nwct
>0
∂q
t−1
∂shisst
∂qt−1
∂∆att
∂qt−1
R2
0.114
0.121
0.037
0.065
0.228
0.117
0.092
0.217
0.154
0.089
>0
>0
TABLE 5
Regressions on simulated data
Panel A - Conditioned regression (11) with σ = 0
Regressee ∆nwct
πt−1 Knot values
Regressor
πt
nwct−1
qt−1
Regressor
πt
nwct−1
qt−1
0.059
0.181
0.283
0.415
Regression coefficient
0.021 -0.119
-0.082
-0.075
-0.526 -0.657
-0.943
-0.985
0.051
0.011
-0.008
-0.002
Regression StErr
0.001
0.001
0.001
0.001
0.003
0.006
0.021
0.019
0.000
0.001
0.001
0.000
Comments
CFSC<0, except in the bottom 4ile
Mean reverting nwc
Panel B - Unconditioned regression with σ = 0
Regressee ∆nwct
Regression 1
Regressor
Coeff.
(StdErr)
πt
-0.057
(0.001)
nwct−1
qt−1
0.042
(0.001)
R2
9.4%
Comments
CFSC <0
Regression 2
Coeff.
(StdErr)
0.009
(0.001)
0.4%
CFSC >0
Regression 3
Coeff.
-0.079
-0.396
0.049
29.7%
CFSC < 0
(StdErr)
(0.001)
(0.004)
(0.001)
Panel C - Conditioned regression (11) with σ = 0.2
Regressee ∆nwct
πt−1 Knot values
Regressor
πt
nwct−1
qt−1
Regressor
πt
nwct−1
qt−1
-0.064
0.144
0.304
0.517
Regression coefficient
0.193
0.170
0.108
0.089
-0.535 -0.463
-0.712
-0.771
-0.011 -0.045
-0.135
-0.144
Regression StErr
0.001
0.002
0.002
0.001
0.004
0.006
0.009
0.010
0.001
0.002
0.002
0.002
Comments
CFSC>0
Mean reverting nwc
Panel D - Unconditioned regression with σ = 0.2
Regressee ∆nwct
Regression 1
Regressor
Coeff.
(StdErr)
πt
0.153
(0.001)
nwct−1
qt−1
-0.041
(0.001)
R2
22%
Comments
CFSC >0
Regression 2
Coeff.
(StdErr)
0.131
(0.001)
20%
CFSC >0
Regression 3
Coeff.
0.128
-0.427
-0.009
40.3%
CFSC > 0
30
(StdErr)
(0.001)
(0.004)
(0.001)
TABLE 6
Panel A, Regress capx investment in nwc-type liquidity measures
Regression 1 - predicting investment
Regressors
πt−1
Coefficients
0.1340474
StdEr
0.0054153
Regression 2 - financing investment
Regr.
πt − divt
Control regr.
Regr. Coeffs.
0.1312348
Control.regr. Coeffs.
Regr. StErr.
0.0036354
Control regr. StErr.
0.0005480
Regression 3 - financing investment, with cross
Regr.
πt − divt
Cross regr.
(πt − divt ) × nwct−1
Control regr.
Regr. coeffs.
0.1604543
Cross regr. coeffs.
-0.1169479
Control regr. coeffs.
Regr. StErr.
0.0046796
Cross regr. StErr.
0.0134468
Control regr. StErr.
nwct−1
0.0153224
0.0027119
log(ATt−1 )
-0.0189307
0.0005553
qt−1
0.0157873
0.0006065
∆nwct
log(ATt−1 )
-0.0669870
-0.0145652
0.0023978
0.0005726
terms
∆nwct
∆nwct × nwct−1
qt−1
-0.0771195
0.0052849
-0.0150572
0.0025056
0.0028326
0.0005481
shisst
qt−1
0.1488701
0.0144352
0.0052532
0.0025510
∆(lt.debt)t
(lt.debtt−1 )
0.0578470
-0.0332237
0.0018428
shisst
shisst × nwct−1
log(ATt−1 )
0.1620707
-0.0376407
0.0147431
0.0068593
0.0210861
0.0005723
∆(lt.debt)t
∆(lt.debt)t × nwct−1
(lt.debtt−1 )
0.0747975
-0.0902357
-0.0325405
0.0022533
0.0069802
0.0025514
(lt.debt)t−1
-0.0320217
0.0024959
Panel B, Regress ivncf investment in nwc-type liquidity measures
Regression 1 - predicting investment
Regressors
πt−1
Coefficients
0.2251603
StdEr
0.0134261
Regression 2 - financing investment
Regr.
πt − divt
Control regr.
Regr. Coeffs.
0.4725660
Control. Coeffs.
Regr. StErr.
0.0071872
Control StErr.
Regression 3 - financing investment, with cross
Regr.
πt − divt
Cross regr.
(πt − divt ) × nwct−1
Control regr.
Regr. coeffs.
0.4619163
Cross regr. coeffs.
0.1052671
Control regr. coeffs.
Regr. StErr.
0.0092511
Cross regr. StErr.
0.0265833
Control regr. StErr.
nwct−1
0.0678334
0.0067234
log(ATt−1 )
-0.0110238
0.0013767
qt−1
0.0428851
0.0015038
∆nwct
log(ATt−1 )
-0.2235886
0.0160118
0.0047405
0.0010835
terms
∆nwct
∆nwct × nwct−1
qt−1
-0.2444835
-0.0484294
0.0163682
0.0049534
0.0055998
0.0010835
shisst
qt−1
0.6391885
0.0198518
0.0103855
0.0011320
∆(lt.debt)t
(lt.debtt−1 )
0.4707222
0.0142147
0.0036433
0.0050433
shisst
shisst × nwct−1
log(ATt−1 )
0.5673090
0.4349044
0.0188190
0.0135602
0.0416855
0.0011313
∆(lt.debt)t
∆(lt.debt)t × nwct−1
(lt.debtt−1 )
0.5006025
-0.1490148
0.0207651
0.0044546
0.0137993
0.0050439
31
(lt.debt)t−1
-0.0968523
0.0061881
TABLE 6 - continued
Panel C, Regress capx investment in che-type liquidity measures
Regression 1 - predicting investment
Regressors
πt−1
chet−1
Coefficients
0.1422534
-0.0051541
StdEr
0.0053889
0.0043152
Regression 2 - financing investment
Regr.
πt − divt
∆chet
Control regr.
log(ATt−1 )
Regr. Coeffs.
0.1223706
-0.0725100
Control.regr. Coeffs.
-0.0147229
Regr. StErr.
0.0036149
0.0033277
Control regr. StErr.
0.0005504
Regression 3 - financing investment, with cross terms
Regr.
πt − divt
∆chet
Cross regr.
(πt − divt ) × chet−1
∆chet × chet−1
Control regr.
qt−1
Regr. coeffs.
0.1370752
-0.0730723
Cross regr. coeffs.
-0.1047869
-0.0019417
Control regr. coeffs.
-0.0147061
Regr. StErr.
0.0045581
0.0049173
Cross regr. StErr.
0.0200556
0.0173354
Control regr. StErr.
0.0005501
log(ATt−1 )
-0.0199688
0.0005534
qt−1
0.0162115
0.0006118
shisst
qt−1
0.1471784
0.0144013
0.0053541
0.0005751
∆(lt.debt)t
(lt.debtt−1 )
0.0508807
-0.0241441
0.0018178
0.0025474
shisst
shisst × chet−1
log(ATt−1 )
0.1391226
0.0508081
0.0146210
0.0071044
0.0288807
0.0005774
∆(lt.debt)t
∆(lt.debt)t × chet−1
(lt.debtt−1 )
0.0563094
-0.0550944
-0.0251093
0.0022360
0.0126803
0.0025570
(lt.debt)t−1
-0.0003269
0.0017039
Panel D, Regress ivncf investment in che-type liquidity measures
Regression 1 - predicting investment
Regressors
πt−1
chet−1
Coefficients
0.2517661
0.0589109
StdEr
0.0133588
0.0106970
Regression 2 - financing investment
Regr.
πt − divt
∆chet
Control regr.
log(ATt−1 )
Regr. Coeffs.
0.4249277
-0.1404865
Control. Coeffs.
0.0155603
Regr. StErr.
0.0072920
0.0067126
Control StErr.
0.0011102
Regression 3 - financing investment, with cross terms
Regr.
πt − divt
∆chet
Cross regr.
(πt − divt ) × chet−1
∆chet × chet−1
Control regr.
qt−1
Regr. coeffs.
0.3810065
-0.1459934
Cross regr. coeffs.
0.3246499
0.0123558
Control regr. coeffs.
0.0154263
Regr. StErr.
0.0091816
0.0099051
Cross regr. StErr.
0.0403989
0.0349195
Control regr. StErr.
0.0011081
32
log(ATt−1 )
-0.0125275
0.0013718
qt−1
0.0423628
0.0015165
shisst
qt−1
0.5846790
0.0198604
0.0108003
0.0011600
∆(lt.debt)t
(lt.debtt−1 )
0.4419747
0.0426496
0.0036668
0.0051387
shisst
shisst × chet−1
log(ATt−1 )
0.5441282
0.2257574
0.0186741
0.0143106
0.0581757
0.0011630
∆(lt.debt)t
∆(lt.debt)t × chet−1
(lt.debtt−1 )
0.4252329
0.1864526
0.0467557
0.0045040
0.0255424
0.0051508
(lt.debt)t−1
-0.0445459
0.0042238
TABLE 7
Average firm variable values approaching exit
Variable
∆at
Acquisition (1720 firms)
Years to exit
0
1.05774
1
1.06853
2
1.10941
3
1.10334
4
1.13758
Bankruptcy (80 firms)
Years to exit
0
0.85455
1
1.02253
2
1.07172
3
1.05750
4
1.15168
Liquidation (50 firms)
Years to exit
0
0.84973
1
0.95934
2
1.01317
3
1.13993
4
1.08313
π
nwc
div
che
shiss
∆lt.debt
lt.debt
ni
0.13084
0.13455
0.13698
0.13703
0.14434
0.17713
0.18758
0.19821
0.21014
0.22195
0.02797
0.02955
0.02908
0.02739
0.02621
0.11143
0.11102
0.11387
0.12030
0.12461
0.01636
0.01636
0.02340
0.02911
0.04165
0.00254
0.01584
0.03210
0.03151
0.03453
0.25344
0.26667
0.28245
0.27789
0.28255
0.02116
0.03168
0.01861
0.02253
0.02837
-0.02110
0.02115
0.07262
0.09296
0.12021
-0.03963
0.11418
0.17747
0.20796
0.24006
0.00530
0.00441
0.01106
0.00766
0.01397
0.05399
0.06000
0.07146
0.08234
0.10928
0.00765
0.01240
0.01082
0.00959
0.04982
-0.05005
0.00452
0.00002
0.04983
0.03107
0.24245
0.29342
0.30941
0.32437
0.32042
-0.19895
-0.04082
-0.03175
-0.03909
0.01599
-0.00381
0.03847
0.06393
0.07647
0.09486
0.03376
0.12953
0.15781
0.22599
0.24865
0.00745
0.01108
0.00920
0.01092
0.02172
0.09389
0.09490
0.10261
0.11704
0.13412
0.00209
0.02947
0.00970
0.03630
0.03493
-0.07060
-0.00161
-0.00730
0.05629
0.05872
0.22212
0.28643
0.30063
0.37739
0.34697
-0.13037
-0.07543
-0.01087
-0.04773
-0.00253
TABLE 8
A multivariate Logit regression for firm exit
Default: non-exit. Alternatives:1 - acquisition; 2-bankruptcy; 3-liquidation
Years to exit
Regressor
alt1:const
alt2:const
alt3:const
alt1:∆at
alt2:∆at
alt3:∆at
alt1:π
alt2:π
alt3:π
alt1:che
alt2:che
alt3:che
0 years to
-1.371393
-3.748053
-4.306755
0.735535
0.900909
0.816167
1.097505
-3.513939
-3.169773
-0.677158
-4.209316
-2.195979
exit
***
***
***
***
***
***
***
***
***
***
***
*
1 year to exit
-1.505278 ***
-4.196619 ***
-4.596374 ***
0.959434 ***
1.121260 ***
1.010564 ***
0.702530 *
-2.089969 **
-2.781661 ***
-0.780534 ***
-2.594086 **
-1.302323 .
2 years to
-1.774420
-4.464583
-4.859940
1.295405
1.449597
1.354960
0.813682
-2.099380
-2.828139
-0.862448
-2.504286
-1.493074
33
exit
***
***
***
***
***
***
*
*
**
***
**
*
3 years to
-2.55464
-5.54944
-5.55684
2.17690
2.43373
2.03577
1.34630
-0.69839
-1.84022
-1.17218
-1.99932
-1.14232
exit
***
***
***
***
***
***
***
.
***
**
4 years to
-3.201888
-5.888225
-6.583234
2.990491
3.146848
3.111646
1.830918
-1.798231
-0.024106
-0.932302
-2.348072
-1.470075
exit
***
***
***
***
***
***
***
*
***
**
Figure 1: Liquidity profiles
NWC profiles
CHE profiles
0.5
0.5
Leverage Î
Leverage Î
Leverage Î
Leverage Î
0.4
0.4
0.35
0.35
0.3
0.25
0.2
0.25
0.2
0.15
0.1
0.1
0.05
0.05
0
0.05
0.1
0.15
0.2
operating imcome π
0.25
[0.0,0.1]
[0.1,0.2]
[0.2,0.4]
[0.4,1.0]
0.3
0.15
0
Leverage Î
Leverage Î
Leverage Î
Leverage Î
0.45
Fraction of total assets
Fraction of total assets
0.45
[0.0,0.1]
[0.1,0.2]
[0.2,0.4]
[0.4,1.0]
0
0
0.05
0.1
0.15
0.2
operating imcome π
0.25
Notes: These graphs are the profiles (functions of profitability, represented by operating
income), of liquidity, represented by net working capital in the first panel, and cash in the
second. The 4 graphs correspond to categories of long term leverage, as stated in the legend.
Each graph represents an average over all firms in the leverage category, conditioned on the
profitability. In both panels, the x-axis is the operating income, and the y-axis is the fraction
of the firms book assets.
34
Figure 2: Profiles of other firm variables
A Liquidity profiles
NWC
Cash
0.4
B Investments
CAPX
IVNCF
0.4
C Earnings
0.3
0.3
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0
0
0
0
0.1
0.2
0
D Cash flows to investors
Shiss
Dividend
∆ LT debt
0.4
0.3
0.1
Op. income
net income
0.4
0.2
0
E Balance sheet changes
∆ assets
∆ NWC
∆ LT liabs
∆ LT assets
0.4
0.3
0.2
0.2
0.1
0.1
0
0
0.1
0.2
F Prop. liq. Changes
0.4
prop. ∆ NWC
prop. ∆ Cash
0.3
0.2
0.1
0
0
0.1
0.2
0
0.1
0.2
−0.1
0
0.1
0.2
Notes: These graphs are the profiles (functions of profitability, represented by operating
income), of many firm variables, as described in Section 1. The graphs represent averages
over all firms in the data. In all panels, the x-axis is the operating income, and the y-axis is
the fraction of the firms book assets.
35
Figure 3: Liquidity time series
A Average operating income
0.2
0.15
0.1
1985
1990
1995
2000
2005
2010
2005
2010
B Average liquidity: v is NWC; ^ is Cash
0.4
0.3
0.2
0.1
0
1985
1990
1995
2000
B Average proportional liquidity changes: v for NWC; ^ for Cash)
0.05
0
−0.05
1985
1990
1995
2000
2005
2010
Notes: In these graphs, the variables are averaged in each year of the x-axis. Our data
starts in 1978, but the graphs start in 1983, because we restrict attention to firms which have
been operating for at least 5 years. Panel C, gives the proportional changes in net working
capital, resp. cash, i.e. normalized by the contemporaneous book value of the firm. These
changes are calculated for each firm-year separately, before the firm-averaging is done.
36
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37