Risk and Corporate Holdings of Highly Liquid Assets
Jess Beltz
and
Murray Frank*
September 1996
* Both authors are affiliated with the Department of Finance, Hong Kong University of Science
and Technology, Clear Water Bay, Kowloon, Hong Kong. The second author is also affiliated with
the University of British Columbia. We would like to thank K. C. Chan, Sudipto Dasgupta,
Vojislav Maksimovic, Anjan Thakor, Sheridan Titman, and the seminar participants at the Hong
Kong University of Science and Technology for helpful comments.
Corresponding author: Murray Frank, Faculty of Commerce, University of British Columbia,
Vancouver, BC, Canada V6T 1Z2. e-mail: frank@nervana.commerce.ubc.ca
Abstract
Corporate holdings of money and short term investments is studied for 1975-1994. The
transactions theory as in Miller and Orr (1966), makes successful predictions about economies of
scale in corporate cash demand, and interest rate effects. Beyond that theory, a range of corporate
risk factors are shown to be important. There is an option effect such that in years with high
volatility of short term interest rates, corporate cash holdings are elevated. The stock market crash
of 1987 induced a doubling of corporate cash holdings, as firms moved out of other short term
investments.
1
Risk and Corporate Holdings of Highly Liquid Assets
The standard theory of corporate cash holding is a model in which an inventory of money is
maintained for transactions purposes. The size of that inventory is limited by the higher rates of
return obtainable on other financial assets. The classic formulations of this idea are Allais (1947),
Baumol (1952), Tobin (1956) and Miller and Orr (1966). If this theory drives corporate behavior,
then transactions measures such as sales ought to help account for the observed levels of corporate
cash. Economies of scale should be observed. The relative holdings of money and other liquid
assets should change in reaction to interest rate changes.1
An alternative idea about corporate liquidity was recently suggested by Chrysler Chairman
Robert Eaton. Chrysler’s main shareholder Kirk Kerkorian, claimed that Chrysler was holding too
much cash. He therefore tried to force management to reduce the cash position by increasing the
payouts to shareholders. However Mr. Eaton argued that next time there would be a downturn in
the auto industry, Chrysler would be much better off due to having built up its cash position. Had
it not done so, it would face the risk of bankruptcy.2 This is the same argument that Smith and
Stulz (1985) made with respect to corporate hedging in general.
In this paper we study holdings of money and other short term investments, by companies
in the United States, for the period 1975-1994. There are two basic points to be made. First, the
transaction theory does a good job of accounting for some important features of corporate
behavior. Economies of scale, and interest rate effects are found in the data. Corporate treasurers
react to changes in the term structure of interest rates, by altering the mix of cash and short term
investments in the company portfolio. Second, beyond that theory major factors determining
corporate demand for highly liquid assets including money, are the risks faced by the firm. In other
1
This is the theory of corporate money demand as taught in the finance textbooks, such as Brealey and
Myers (1991), and Ross, Westerfield and Jordan (1995).
2
This conflict generated wide coverage in the business press, see Rescigno (1995) for example.
2
words, holding money appears to be an important element of how companies manage risk. This is
true for firms of all sizes, but of particular importance for smaller firms.
The fact that the transactions theory makes successful predictions would not be so
surprising were it not for the existing empirical literature on money. In that literature the reverse is
commonly asserted. A good example is Friedman and Kuttner (1992) who reached the sweeping
conclusion that there is no “close or reliable relationship between money and nonfinancial
economic activity”. Despite such claims, the cross sectional estimates of the parameter relating
company sales to company cash holdings are almost the same, year after year, over the twenty year
period for which we have data.
Coping with risk plays a role in much corporate financial activity. We provide evidence
that it plays a central role in corporate cash holding. In order to establish this basic claim, we
examine five types of risk; three company risk factors and two aggregate risk factors. In each case
the company risk factors are associated with higher holdings of money and higher holdings of short
term investments. In each case the aggregate risk factors are associated with higher money holdings
and lower holdings of short term investments. An increase an aggregate risk factor is thus
associated with a “flight to quality.”
Consider the firm specific risk factors. First, research and development expenditures are
clearly risky for the firm. The more a firm spends on research and development, the more money
and short term investments the firm holds. Second, sales and total assets have both been used in
previous studies to capture the scale of company operations. We provide evidence that to a fair
degree these variables are actually serving empirically, as proxies for uncertainty in operating cash
flows. Third, the risk of corporate assets is reflected in the financial markets. We constructed two
different estimates of the βassets for the firms. While both of these are measured with considerable
3
error, in each case it was found that the higher the βassets the more money and short term
investments the firm holds.
Next consider the aggregate risk factors. First, in some years interest rates are more
volatile than in other years. There is an option value associated with money. When interest rate
volatility rises, corporate money holding rises at the expense of holdings of short term investments.
Second, the stock market crash of October 1987 is a useful identifying feature in the data.
It is well known that associated with the crash, there was a perception of increased risk in the
financial markets. In response to the crash, firms switched heavily from short term investments into
cash. However there was little change in the total level of real transactions over this period.
Therefore the transactions theory alone is not adequate to account for this shift in the data.
The previous empirical literature on money demand is vast. Notable surveys and
extensions are provided by Laidler (1985), Lucas (1988), Goldfeld and Sichel (1990), and
Mulligan and Sala-i-Martin (1992). This literature has been predominantly time-series analysis of
aggregate data. Mulligan and Sala-i-Martin (1992) point out some advantages of using crosssection methods.
There have been only a handful of papers that look at cross-section evidence on corporate
money holding. Meltzer (1963) reported that the corporate cash position was linear in logs and unit
elastic with respect to sales. He interpreted this as evidence against the Baumol (1952) and Tobin
(1956) theory. This finding stimulated further work by Maddala and Vogel (1965), Whalen (1965),
and Vogel and Maddala (1967). In particular the last of these provided evidence that the total
assets of the firm may have been an important omitted factor in Meltzer’s study. Including dummy
variables for the asset size of the firms markedly reduced the coefficient on sales to less than 0.5.
All of these studies used IRS data that was a cross-section of industry aggregate data, not firm
level data.
4
Using firm level data there is an interesting paper by Mulligan (1994). Like the present
study he uses Compustat data. He finds that the elasticity of corporate money demand with respect
to sales is 0.75. Firms that have corporate headquarters in counties that have high wages hold more
money. He interprets this as evidence that the firms are using time and money as substitutes. The
focus of the present study is very different from Mulligan (1994). Most importantly, he does not
consider the various issues associated with corporate risk aversion. We also devote more attention
to interest rate effects. Thus while the current paper has overlapping data with Mulligan (1994),
the issues addressed are quite different.
Some basic features of the data are discussed in section 1. Empirical estimates of cross
sectional cash holding are provided in section 2. Risk effects on corporate cash positions are
quantified in section 3. Other short term investments of the firm are analyzed in section 4. Finally
some conclusions are presented in section 5.
1. The Data
The balance sheet data we use is from Compustat. To account for the effect of inflation all dollar
values are deflated using the consumer price index reported by the US Bureau of the Census
(1995). Firms in banking, finance and insurance were excluded. The data available to us begins in
1975 and ends in 1994. The interest rate data is from the Fama files of the Center for Research in
Security Pricing.
The original data contains a variable labeled “cash”. This is a measure of money, which as
noted by Mulligan (1994), is fairly close to M1. Included is “any immediately negotiable medium
of exchange. It includes money and any instrument normally accepted by banks for deposit and
immediate credit to customer account.” So demand deposits and a variety of short term investments
5
that had an original maturity of less than 90 days are included as cash.3 Some of these very short
term assets are actually interest bearing. We follow Compustat in referring to this as cash.
A broader measure also available in the data is labeled “cash and short term investments”.
In addition to cash, this measure includes a variety of financial assets such as certificates of
deposit, commercial paper, marketable securities, and short-term Treasury bills.4 While such items
are said to be readily transferable to cash, they typically have some risk in terms of their
liquidation value; they also typically offer a higher rate of return than the items listed as cash. We
remove cash from the measure of cash and short term investments. This leaves short term
investments, which is next to cash in terms of liquidity.
Many studies use a single variable as “the interest rate”. Laidler (1985) argues that while
it is clearly possible to include more of the term structure of interest rates, doing so is not a major
issue empirically. Since the various interest rates are highly correlated this view is not hard to
understand. For example in our data, the correlation coefficient between the 3 month interest rate
and the 5 year interest rate is 0.95. However we find it important to allow for more than a single
element from the term structure. The empirical definition of cash includes interest bearing assets
with an original maturity of less than 90 days. Accordingly, holdings of these variables ought to
increase when the short term interest rate rises since they are offering a higher rate of return. On
the other hand holdings of such assets ought to fall when the long term interest rate rises.
3
Included are: bank drafts, bankers acceptances, cash, certificates of deposit included in cash by the
company, checks (cashiers or certified), demand certificates of deposits, demand deposits, letters of credit,
money orders.
4
The list of included assets in addition to cash is: accrued interest combined with short-term investments,
brokerage firms’ good faith and clearing-house deposits, cash in escrow, cash segregated under federal
and other regulations, certificates of deposit included in short-term investments by the company,
certificates of deposit reported as a separate item in current assets, commercial paper, gas transmission
companies’ special deposits, government and other marketable securities (including stocks and bonds
listed as short-term), margin deposits on commodity futures contracts, marketable securities, moneymarket fund, repurchase agreements shown as current asset, real estate investment trust shares of
beneficial interest, restricted cash shown as a current asset, time deposits and time certificates of deposit
(saving accounts shown in current assets), treasury bills listed as short term.
6
Both a short term interest rate of 3 months, and a long term rate of 5 years are used. The
data comes from the Fama Files maintained by the Center for Research in Security Prices. The
short-term rate is the yield on a 6 month Treasury bill with 3 months to maturity. This data comes
from the Fama Treasury Bill files. The long term rate is the yield on a 5 year bond taken from the
Fama-Bliss discount bond file. These are based on non-callable, non-flower notes, and bonds. The
original data in these files is monthly. Since our corporate data is annual, we computed annual
averages of the interest rates. Some basic descriptive statistics and correlations in the data are
collected in the appendix.
There are several data issues to be taken into account when interpreting the results. The
interest rate data is undoubtedly of high quality. The book value of total assets and of sales have
both advantages and disadvantages. A major advantage is that this is data that corporate treasurers
actually have and use. Companies put a lot of time, effort and expense, into the construction of
such data. This data is, of course, subject to the many well-known accounting compromises, see
for example White, Sondhi and Fried (1994). Cash is perhaps slightly better measured than short
term investments (inv), since there is less heterogeneity in the included assets.
Many of the variables that we use cannot be negative by definition. Occasionally negative
values are found in the data. Accordingly we exclude firm-years unless cash, sales, and total assets
are recorded as strictly positive.5 Similarly we excluded firm-years for which cash was recorded as
larger than total assets. When using total assets as an explanatory variable for cash, we subtract
cash from total assets.6 For simplicity we still refer to the remainder as total assets. We follow the
same procedure throughout the paper for all cash and short term investment results using total
assets as an explanatory variable.
5
In some estimations not included in this paper, we experimented with taking such truncation explicitly
into account in the estimation. Since it did not seem to be making much difference, to save space we do
not report the results.
7
Missing values are a problem in the data. Mulligan (1994) has pointed out that in this
data, in some cases larger firms do not bother recording cash holdings separately from cash and
short term investments, since it tends to be a small number relative to the rest of their balance
sheet. Accordingly the economies of scale in cash demand can be expected to be underestimated.
Furthermore in 1988 when more firms started paying attention to their cash positions, more firms
report the value of their cash holdings separately, and so the number of firms in the sample
increases.
As can be seen in Figure 1, for all of the firms in our data set the average level of cash is
about 4% of total assets (TA). However for most of the 1980s it was lower than that, until in 1988
the percentage roughly doubled. Since 1988 the average level of cash as a percentage of total assets
has been fairly stable. Short term investments show an almost mirror image pattern in the data;
falling dramatically between 1987 and 1988. When the data is divided by size into deciles, the
same temporal pattern is found in each decile. The notable difference is that the larger the firms in
a given decile, the lower the ratio of cash to total assets, or short term investments to total assets.
The average constant dollar firm size across time is very stable, although in the top decile it is
perhaps slightly increasing on average. In the top decile the average firm had total assets of
$11,264 million 1982-4 dollars. The bottom decile average total assets was $10.9 million. For the
overall data set that average was $1,835 million.
Figure 2 shows nominal interest rates of 3 month and 5 year duration, along with the
within year standard deviation of each rate. The rates were rather high from 1979-81, and the term
structure was inverted. Over the same period there was a lot of within year volatility. This figure
makes it clear that the term structure of interest rate is very unlikely to help account for the shift in
6
This is a minor point. If one does not subtract out the cash from total assets, the coefficient on total
assets will be biased towards one.
8
corporate cash positions in 1988. Indeed the overall level of interest rates in 1988 seem rather
unremarkable.
2. The Elasticity of Corporate Money Demand
2A. Hypotheses
The standard empirical specification of a money demand equation is
1.
ln M = α0 + α1 ln S + α2 ln R + α3 ln X + ε.
In this equation M is money, S is a scale variable often taken to be sales, R is the interest rate, and
X is a vector of other potential covariates. This equation is commonly estimated using time-series
data, often with a lagged value of M as an element of X. The αi are parameters to be estimated.
Vogel and Maddala (1967) argue for the use of this log specification in order to help control for
heteroskedasticity, which is a potential problem in the levels specification. Mulligan (1994)
provides a natural theoretical structure justifying the use of (1) in the Cobb-Douglas case.7
As discussed by Goldfeld and Sichel (1990) and Laidler (1985), the parameters in times
series estimates of equation (1) on aggregate data were found to be unstable in the late 1970s and
early 1980s. This problem of out-of-sample fit has been labeled the problem of the “missing
money.” Friedman and Kuttner (1992) argue that the behavior of money was highly unstable over
the 1980s. Accordingly one might expect that there would be little explanatory power in estimates
of equation (1). Even more importantly, under this hypothesis, the parameter estimates might be
expected to fluctuate a great deal from year to year. Despite such potential problems, we stick with
9
the traditional empirical model given by equation (1); apart from doing some robustness checks. In
fact this specification turns out rather well empirically.
When estimating (1) with cross-section data, there are many observations of S, but only a
single observation of R. Accordingly in cross-section, interest rate effects cannot be determined.
With panel data the same problem arises in a slightly less extreme form. Again there are a large
number of observations on scale variables, and only a few observations on the interest rate
variables. Accordingly a priori it is expected that it will be harder to pin down the impact of
interest rates in the data.8 The predictions are that an increase in the short term interest rate
increases cash holding, while an increase in the long term interest rate reduces cash holding.
There was a debate between Meltzer (1963) and Maddala and Vogel (1965) over whether
sales or total assets is the more appropriate scale variable in equation (1). Transactions theories
such as Miller and Orr (1966) suggest the use of sales.9 Vogel and Maddala (1967) argue that
wealth based theories such as Friedman (1956), suggests that total assets might be an appropriate
scale variable. In section 3, evidence will be presented suggesting that the identification of total
assets with wealth, in a Friedman (1956) sense, may be misleading.
Money has an option like feature. Since money is so liquid, holding money keeps the
corporate treasurer well positioned to take advantage of any newly arising opportunities. This
option is more valuable in a turbulent environment. To get an empirical proxy for this we
computed the standard deviation of the monthly interest rates. Accordingly if the standard deviation
7
He does not introduce the sorts of risks that we consider, into the analysis. Doing so would involve a fair
bit of algebra in order to keep track of the covariance terms. We leave such theory development for the
future.
8
The concern here is about omitted factors. Suppose that there is an omitted macro factor that is
important for corporate cash holdings, but unrelated to interest rates. By accident they might happen to be
related in a couple of years of our small sample of years. The effect of this omitted factor might then be
attributed empirically to the interest rates. Without access to additional years of data, there is little that
can be done about this danger.
9
It would also suggest the use of measures of corporate expenditures, such as cost of goods sold. However
accrual accounting creates a problem. By the matching principle, these expenditures are allocated to the
10
of interest rates is high in a particular year, the money holdings should also be high. If the standard
deviation of interest rates is low, then the cash position is predicted to be low.
2B. Evidence
The results of annual cross-section regressions of log cash on log total assets are shown in
Figure 3a. The striking result from these regressions is the remarkable stability of the parameter
estimates across time periods. In every year the regression has an adjusted R2 of about 0.5. In
every year the elasticity of money holding with respect to total assets is very close to 0.77. Figure
3b shows that if one replaces total assets by sales in the regression an almost identical result is
obtained. If both sales and total assets are included as in Figure 3c, then the sum of their coefficient
is stable across time at about 0.8. However prior to 1988 sales dominates total assets, while the
reverse is found afterwards.
The sum of cash and short term investments constitute the highly liquid corporate assets.
Figure 4 shows that with respect to sales, very similar results are obtained for the set of liquid
corporate assets. Figure 5 shows that if one removes the cash component, then again similar results
are found for the economies of scale in short term investments.
The standard errors reported in the tables are based on an assumption of independence
across periods. If there is serial correlation at the firm level, then these estimates may be biased. In
the individual cross-section estimates of the specification corresponding to column A of Table 1,
the standard error on the scale variable was usually about 0.015. When both sales and total assets
are included in the same cross section regression the standard errors are about 0.03 on each of
them. With respect to serial correlation problems, these seem like reasonable upper bound
estimates for the true standard errors on the scale variables. Introduction of industry dummies had
revenue that they helped to generate, rather than being recorded when they occurred. Accordingly we stay
away from their use.
11
a negligible impact on the standard errors on the scale variables.10 Over all we do not think that
serial correlation is a big problem for our interpretation of the data.
Firm size by itself, whether measured by sales or by total corporate assets, does a
remarkably good job of accounting for corporate cash positions. Larger firms hold a lower
proportion of their assets in the form of cash. There is good evidence of economies of scale in firm
size. This relationship is highly stable across years. As found by Vogel and Maddala (1967),
distinguishing between sales and total assets as measures of firm size is difficult since they seem to
play very much the same role in accounting for corporate cash holdings.
An increase in short term interest rates induces an increase in corporate cash holding. An
increase in long term interest rates induces a reduction in corporate cash holding. These effects are
not nearly as strong as the firm size effects, but they are statistically significant at conventional
significance levels. If only a single interest rate variable is included, then whether it is short term or
long term, the coefficient is negative and significant. Numerically it is between the short and long
rates reported in Table 1.
As hypothesized, the option argument accounts for the effect of the variance of short term
interest rates. The variance of long term interest rates, produced an effect that is statistically
indistinguishable from zero. Given the limited number of years in the panel, the interest rate and
option effects are surprisingly significant, and insensitive to minor variations in specification.
Annual dummy variables were not included in the regressions reported in Table 1. In other
regressions, they were included. The coefficients of interest were essentially unaffected. In no case
was an individual year dummy statistically significant at conventional levels. This does not mean
that there are no temporal effects. In the years 1975 through 1987 the sign on the annual dummy
10
The econometric package would not permit us to use firm specific dummies since there are too many
firms. However we tried using firm dummies on a subsample, and this did not have much impact on the
standard error on sales or total assets. Also, we were not able to isolate much evidence of robust dynamic
12
variable was always negative, while in 1988 through 1994 the sign was always positive. This is
consistent with the basic observation that firms doubled their cash holdings in 1988. In Table 1 it is
apparent that the industry specific dummies did not have much impact. While not reported in the
above table, regressions in levels instead of logs were also run. The level specification led to the
same conclusions, but tended to somewhat favor total assets over sales as an explanatory variable.
3. The Effect of Firm-Specific Risk on Cash Holdings
Perhaps surprisingly, risk aversion has not played much of a role in studies of corporate money
demand; and corporate cash holdings have been largely ignored in studies of corporate risk
management.11 The idea that corporations might behave in a risk averse manner due to agency
concerns has been a major issue in finance at least since Jensen and Meckling (1976).
The agency conflict that seems to be important for our purposes, is that between the
owners and the corporate treasurer.12 Suppose the corporate treasurer knows that the company
portfolio he is managing will be marked-to-market and his performance assessed on a quarterly
basis.13 In that case he will be concerned about the ongoing market values of the assets. He will not
just buy-and-hold, ignoring the market values prior to maturity. If the treasurer is risk averse then
changing risk conditions in the financial markets will cause him to alter the company’s money
holdings. Similarly if there is an increase in uncertainty about the operating cash flows of the firm,
then in order to avoid being caught short, the treasurer will hold more cash in the company
effects in the data. This is not surprising since corporate treasurers are not likely to worry too much about
last year when determining the current portfolio. Furthermore there are only twenty years of data.
11
Stulz (1984) developed a theory of corporate hedging based on managerial risk aversion. For recent
theories of corporate hedging, see Froot, Scharfstein and Stein (1993) who stress the lower shadow price
of internal funds, and DeMarzo and Duffie (1995) who stress the revelation of managerial ability. Nance,
Smith and Smithson (1993) provide evidence that small firms hedge less than do large firms.
12
We think of this as running through the employment contract of the treasurer, rather than through the
corporate capital structure.
13
Institutional literature suggests that currently marking-to-market is commonly done monthly, although
in some firms even more frequent assessment is in use. See for example page 3 of the Corporate Finance
Risk Management & Derivatives Yearbook 1996.
13
portfolio. Such an increase in uncertainty can happen directly due to fluctuations in the goods
market that the firm deals in. Or it could happen if the firm increases its research and development
efforts. Such efforts are inherently rather risky and may make it harder for the corporate treasurer
to predict the firm’s need for liquidity.
There are a variety of differing effects all of which come under the general heading of risk.
Evidence of aggregate risk impacts have already been provided above. The upward shift in
corporate cash holding after the stock market crash of 1987 was illustrated. Similarly the option
value of money in years of high volatility of interest rates has also been documented. Firm specific
risks to be considered are: risk associated with fluctuations in operating cash flows, risk due to
high levels of research and development, and risk of corporate assets as measured in the financial
markets.
Perhaps the single most important type of risk for the corporate treasurer is due to
fluctuations in the operating cash flows. The treasurer needs to ensure that the company has money
available when and where it is needed, and yet needs to ensure that the firm’s financial resources
are not being underutilized. We calculated the standard deviation of annual operating cash flows
for the years 1985 to 1994 using the data extending back to 1975. Relative to the day to day time
frame on which many corporate treasurers need to operate, this annual measure is a fairly crude
device. In each case we required that a firm have at least 5 years of prior data with which to
calculate the standard deviation.
The method of estimating the standard deviation of operating cash flows has implications
for the interpretation of Table 2. By construction there is a correlation in this explanatory variable
from year to year since there is overlapping data in use. This lack of independence may be
unfortunate from a statistical perspective. However it is unavoidable, and it does correspond to the
increasing amount of history that occurs naturally each year. A reader who is concerned about this
14
lack of independence should look only at the results for 1994 in Table 2, since that is the year with
the most prior history. There is little loss in interpretation with such a focus.
In most respects it proved very hard to tell apart the effects of sales and total assets as
scale variables. This is unfortunate since, as argued by Vogel and Maddala (1967), they have
potentially rather different economic interpretations. However in this respect Table 2 and Figure 6
proved to be quite informative. To a considerable extent, the explanatory power of both sales and
total assets appears to have derived from the fact that they are correlated with the standard
deviation of operating cash flows, a major omitted corporate risk factor. This appears to be
stronger in the data after 1987 than before. These results call into question the idea that total assets
were actually serving as a proxy for wealth, as suggested by Vogel and Maddala (1967).
The next approach to quantifying the effect of risk is to look at the riskiness of corporate
assets as reflected in the financial markets. We take the simplest approach we could find to do
this.14 Using stock price data from the Center for Research in Security Pricing, we computed a
βassets for each firm in each year.15 The first approach was to regress the stock price on the value
weighted market portfolio during the year in question. We then used the textbook formula16 to
unlever the βequity. This formula assumes no default risk and so is only a crude approximation. The
second approach to calculating the βequity involved using 5 year rolling windows and monthly
returns data. Again the resulting coefficients were unlevered using the textbook formula. These two
It should be stressed that we are using these βassets measures only as rough proxies for the financial
market’s assessment of the riskiness of the corporate assets. For our purposes such measures seem
sufficient. We are not trying to find a best fit asset pricing model. There is a significant debate over which
approach to asset pricing works best, see Fama and French (1992) and Jagannathan and Wang (1996). We
have nothing to add to that debate.
15
In the textbook interpretation βassets captures all risk that matters to the shareholders. This does not
preclude the possibility that other elements of risk might affect corporate behavior. For example suppose
that a firm finds that the supply of physical inputs becomes more erratic. In reaction to this increased risk,
the firm might well react by holding a larger inventory of inputs. Such behavior would not be a
refutation of the CAPM.
16
Let t be the corporate tax rate, D is riskless debt, E is equity. Then the textbook formula is βequity =
(1+(1-t)D/E) βassets.
14
15
approaches generate rather different firm specific values for the βassets. However when used in
subsequent regressions, each measure generated the same basic patterns of significance in the
regressions. Accordingly we only report results using the first approach, and we did not try to
further refine the estimation procedures.
As can be seen in Table 3 the expected βassets effect on corporate money holding are
found.17 Higher asset risk as measured in the financial market is associated with higher money
demand. This effect does not alter the significance of the other parameters very much, and so it
appears to reflect an independent effect. The one exception to this pattern, as discussed below,
concerns the industry dummy variables. Perhaps half or more of the effect accounted for by the
βassets can also be accounted for through the use of industry dummy variables.
It is a much restricted sample of firms that report expenditures on research and
development. Almost by definition, such expenditures are highly risky. Firms who report positive
R&D are larger than the average firm by about 50%. Despite this, the estimated parameters for the
set of firms who report strictly positive R&D expenditures are generally rather similar to the
broader set of firms. If one adds R&D expenditures as a covariate in its own right; the more R&D
the firm does, the greater its demand for cash. Once again, riskier firms hold more cash.
Nance, Smith and Smithson (1993) find that smaller firms engage in less off balance sheet
hedging behavior. They find that firms that do more research and development do more hedging,
and that firms that hedge hold less liquid assets. The results here show that small firms hold much
more in the way of cash than do large firms. Thus the firm size and firm liquidity results of Nance,
Smith, and Smithson (1993) may have a common source. The research and development findings
are essentially the same as in Nance, Smith, and Smithson (1993).
As a robustness check we also tried using the βequity instead. The effects came through less strongly, but
none the less the same effects were still present, and were still statistically significant.
17
16
To directly test the idea that cash is held as a precaution against future volatility in costs
and revenues, one might simply look at the realized future volatility of sales revenues, and their
impact on current cash holdings.18 Under the joint hypothesis that cash is being held for risk
management reasons and that managers expectations are right on average, we expect higher future
volatility to induce higher current cash holdings. In order to implement such a test we required that
firms have at least five years of data available to calculate the volatility of sales revenue. In this
case it is five years of future data. This implies that the estimated coefficient on sales volatility will
be biased towards zero, since only firms that survive for at least five more years will be included.
The extreme cases in which the firms sales fall to zero are removed from the sample. The
coefficient on total assets may be biased upward. The reason is that firms with high total assets are
more likely to survive, and so will be over sampled. Since larger firms also hold more cash, the
coefficient on total assets will be biased upwards. Despite such potential biases, we did try such
regressions.
As is shown in Table 4, the future volatility effect came through as hypothesized. The bias
was not sufficiently strong to wipe out the effect of the volatility of future sales on current cash
holdings. There is a lack of independence between the individual year results. However there is no
easy way around the problem. Focusing only on the results for 1975 (the year with the most future
data available to us) will not alter any interpretations.
4. Short Term Investments
In addition to cash, corporations also hold a range of short term financial assets (inv). These are
somewhat riskier than cash, but they typically have a higher return. From the Compustat data, this
is the category cash and short term investments, minus the cash component. Since these assets
attract a higher return, corporate treasurers will tend to move resources into these assets when they
18
We thank Anjan Thakor for suggesting we try this.
17
are temporarily unneeded. However they are riskier than cash and so they do not strictly dominate
cash.
One possibility would be to analyze the cash and short term investments together, as a
single variable. The idea is that this would serve as a type of robustness check on any results. As
illustrated in Figure 4, we did this with respect to the firm scale variables. However we do not do
this here. The reason is that according to theory, these assets will behave differently in response to
changes in interest rates. Aggregating them into one variable would make it harder to identify
interest rate effects.
In fact a trap is created by the argument that, as a practical matter, any individual interest
rate will suffice to represent the term structure of interest rates. Consider regressing the log of the
aggregate of cash and short term investments, on a constant, log of sales, and log short term
interest rate. Then serious misinterpretation will follow. A positive but insignificant coefficient is
found on the interest rate. Consider replacing the short term interest rate with a long term interest
rate in that regression. In this case a negative, but insignificant coefficient is found. One would
then be tempted conclude that as an empirical matter, theory is wrong and interest rates do not
matter. This misinterpretation would be due to aggregating effects that are working in different
directions.
Short term investments exhibit economies of scale that are similar in magnitude to cash. In
this case there is some evidence that sales are a better scale variable than is total assets. The
riskiness of company assets as measured by βassets has a much stronger effect in elevating short
term investments than it does on corporate cash holdings. Once again βassets seems to be accounting
for many of the same effects as the industry dummy variables.
The firms who carry out more R&D get a lower loading on the βassets. This is reasonable
since for such firms much of the priced risk is presumably associated with their research efforts.
18
High research and development seems to have a greater impact on holdings of short term
investments than it does on cash holding.
The interest rate effects in Table 5 all have the reverse sign and similar magnitudes to their
effects on cash demand. This suggests a particularly simply interpretation. Corporate treasurers are
able to substitute between money and other short term investments. But they are not permitted to
substitute between such financial assets and the physical assets of the firm.19 This is consistent
with institutional descriptions of corporate decentralization of tasks to different divisions.
Table 6 and the results depicted in Figure 7 are important in interpreting the role of the two
scale variables, sales and total assets. In contrast to many other regressions, in this case the effects
of total assets is now indistinguishable from zero. The holdings of short term investments is
independent of the total assets level of the firm. However it is not independent of the level of sales,
nor of the standard deviation of operating cash flows. Variation in operating cash flows is the most
significant single factor, but the level of sales is also important.
Table 7. carries out the test of the extent to which future volatility of cash flow can
account for current holdings of short term investments. As discussed previously, the coefficient on
the volatility of future sales is biased towards zero due to the requirement that the firm continues to
exist in the data for at least a further five years. As hypothesized, future sales revenue volatility
does induce higher current holdings of short term investments. In this case however the impact of
total assets is not wiped out.
19
To corroborate this hypothesis we regressed the total assets of the firm less all the cash and short term
investments, against both the long and the short term interest rates. Under our hypothesis we predicted
that there should be no correlation. As predicted, despite having 32,133 observations, the adjusted R2 for
the regression is only 0.0003. Of course this is a rather weak test.
19
5. Conclusions
According to Friedman and Kuttner (1992) there is not much of a connection between money and
real economic activity. Goldfeld and Sichel (1990) point to innovations in corporate cash
management practices as important in accounting for the out-of-sample misbehavior of time series
estimates of aggregate money demand during the 1970s and early 1980s. Such studies might lead
one to believe that there would be a great deal of instability in corporate cash holdings.
In fact the parameter estimates are not characterized by such instability. Instead there is a
highly stable relationship between firm size and the firm’s cash position.20 An equation estimated
in 1975 for example, will do an excellent job of fitting the data many years later.21 We were
unable to isolate evidence of significant temporal fluctuations in the relationships, with one
important exception. After the stock market crash of 1987, there was a shift in corporate assets,
from short term investments to cash.
The findings can be summarized quite simply. As suggested by the transactions theory,
there are economies of scale in the holdings of both cash and other short term investments. When
the term structure of interest rates changes, companies move resources back and forth between
cash, and other short term investments. To a considerable extent all such highly liquid assets are
held for risk management reasons. The risks that are being handled can be divided into firm
specific and aggregate risks. The firm specific risks are associated with elevated holdings of both
cash, and other short term investments. Increased aggregate risks induce substitutions away from
short term investments and towards cash holding. A particularly striking illustration of this point,
20
Since we know that there were improvements in cash management technology, the extreme stability of
the parameters over a twenty year period might have been puzzling. Recognition that to a large extent
these resources are being held for risk management purposes, makes the parameter stability much less
surprising.
21
Consistent with Lucas (1988), the findings suggest that the time-series method may not be all that
effective at isolating the correct specification in the data. This may be due to the small number of
observations available in the typical time-series study. The poor performance out of sample emphasized by
Goldfeld and Sichel (1990) and others, may well say more about econometric methods and sample sizes,
than it does about instability in the financial structure of the economy.
20
is the reaction to the stock market crash of 1987 which appears to have induced a structural change
in corporate cash holding.
Corporate cash and short term investment holdings appear to be a significant component of
risk management activity, particularly by smaller firms. Firms that have riskier cash flows hold
more of both cash and short term investments. When the economic environment looks riskier,
companies tend to move resources into cash.
21
Appendix - Some Features of the Data
Table A1.
Descriptive Statistics
mean
minimum
maximum
N
σ
cash and short term investments
120.61
558.7
0.001
16,710
27,143
cash
61.996
367.5
0.001
12,290
27,143
sales
1766.3 5961.2
0.001
125,200
27,143
total assets
1786.3 5522.7
0.0650
95,040
27,143
short rate
0.068
0.027
0.0298
0.1407
27,143
long rate
0.083
0.021
0.0515
0.1349
27,143
0.009
0.006
0.0012
0.0266
27,143
σ of short rate
R&D
74.81
264.05
0.002
4,194
8,912
Dollar values are in millions of 1982-84 dollars. All values of zero or less were deleted. N is the
number of observations.
For the 8,912 firm-years that report R&D > 0, the mean value of cash is 111.2, the mean value of
total assets less cash is 2395.4, and the mean value of sales is 2,720.1, measured in millions of
1982-4 dollars.
Table A2. Correlations
log cash
log cash
1.0
log sales
0.745
log
total
0.750
assets
log short
-0.115
rate
log
long
-0.137
rate
-0.089
log σ of
short rate
log R&D
0.749
Calculations are based on
on 8,912 observations.
log sales
log total
assets
log short
rate
log long
rate
1.0
0.932
1.0
0.008
-0.010
1.0
0.0004
-0.015
0.945
1.0
0.012
-0.004
0.789
0.800
log σ of
short rate
log
R&D
1.0
0.826
0.850
-0.013
-0.016
-0.006
1.0
27,146 observations except for the R&D correlations which are based
22
Table 1.
Dependent variable: log cash
independe
A
B
C
D
E
F
G
H
nt variable
constant
-2.570
-6.163
-6.216
-6.006
-6.034
NA
-6.240
NA
(0.025) (0.123) (0.120) (0.122) (0.122)
(0.120)
log total
0.418
0.772
0.772
0.826
0.419
0.548
assets
(0.011) (0.004) (0.004) (0.005) (0.011) (0.015)
log sales
0.772
0.771
0.380
0.380
0.289
(0.004) (0.004) (0.011)
(0.011) (0.015)
log short
0.404
0.458
0.522
0.471
0.492
0.411
0.455
rate
(0.063) (0.062) (0.063) (0.064) (0.058) (0.063) (0.057)
log long
-1.870
-1.901
-1.956
-2.102
-2.020
-2.034
-1.967
rate
(0.108) (0.105) (0.108) (0.112) (0.101) (0.109) (0.100)
0.097
0.097
0.088
0.089
log
σ
(0.020) (0.018) (0.020) (0.018)
short term
rate
industry
no
no
no
no
no
yes
no
yes
dummies
adj. R2
0.555
0.528
0.596
0.579
0.579
0.661
0.596
0.665
N
27,146 27,146 27,146 27,146 27,146 27,146 27,146 27,146
Coefficients on the constant terms and on the dummy variable terms are not reported when industry
dummies are included. N is the number of observations. Total asset is adjusted by removing cash
from it. All variables are significant at a 99% level of confidence.
Table 2.
Dependent variable: log cash
Year:
Independent variables
constant
1985
1990
1994
-0.959
(0.080)
-0.401
(0.066)
-0.559
(0.067)
1985
1990
1994
-2.782
-1.436
-1.868
(0.171) (0.136) (0.127)
log total assets
0.105
0.130
0.219
(0.068) (0.055) (0.052)
log sales
0.492
0.229
0.244
(0.056) (0.048) (0.044)
0.855
0.939
0.959
0.233
0.570
0.486
log σ operating cash flows
(0.026) (0.020) (0.020) (0.061) (0.049) (0.045)
adj. R2
0.585
0.573
0.558
0.659
0.592
0.590
N
785
1,636
1,835
785
1,636
1,835
N is the number of observations. Total assets is adjusted by subtracting the cash component.
23
Table 3.
Dependent variable: log cash
independe
A
B
nt variable
constant
-5.918
-5.944
(0.131) (0.131)
log total 0.770
0.770
assets
(0.005) (0.005)
log sales
log short
rate
log long
rate
log σ of
short rate
βassets
0.557
(0.067)
-2.067
(0.115)
0.233
(0.009)
0.513
(0.068)
-2.200
(0.119)
0.087
(0.021)
0.232
(0.009)
C
D
E
F
G
H
-6.152
(0.132)
-6.174
(0.132)
-6.178
(0.129)
0.429
(0.012)
0.371
(0.012)
0.487
(0.066)
-1.998
(0.113)
-6.201
(0.129)
0.429
(0.012)
0.371
(0.012)
0.447
(0.067)
-2.120
(0.117)
0.079
(0.021)
0.195
(0.009)
-6.086
(0.020)
0.568
(0.030)
0.126
(0.028)
0.241
(0.102)
-1.924
(0.177)
0.091
(0.032)
0.254
(0.037)
0.190
(0.012)
no
NA
0.542
(0.036)
0.158
(0.033)
0.224
(0.097)
-1.857
(0.169)
0.097
(0.030)
0.052
(0.039)
0.169
(0.017)
yes
1980
1984
0.774
(0.005)
0.421
(0.068)
-1.95
(0.116)
0.162
(0.010)
0.774
(0.005)
0.384
(0.069)
-2.061
(0.120)
0.074
(0.021)
0.162
(0.010)
0.195
(0.009)
log R&D
industry
no
no
no
no
no
no
dummies
adj. R2
0.577
0.577
0.571
0.571
0.593
0.593
0.692
0.729
N
22,377 22,377 22,377 22,377 22,377 22,377
8,912
8,912
N is the number of observations. Total assets is adjusted by subtracting out cash. All are
coefficients are significant at 99%, except in column H in which the coefficient on βassets was not
significant, and the coefficient on the long rate was only significant at a 95% level.
Table 4.
Dependent variable: log cash
Year:
Independent variables
constant
1975
1980
1984
-1.843
(0.107)
-2.017
(0.108)
-1.966
(0.115)
1975
-2.757
-2.872
-3.030
(0.085) (0.099) (0.111)
log total assets
0.629
0.527
0.578
(0.021) (0.025) (0.030)
0.751
0.736
0.739
0.226
0.288
0.234
log σ future sales revenue
(0.021) (0.022) (0.025) (0.023) (0.028) (0.034)
adj. R2
0.539
0.513
0.486
0.743
0.657
0.637
N
1127
1062
896
1127
1062
896
N is the number of observations. Total assets is adjusted by subtracting the cash component.
24
Table 5.
Dependent variable: log short term investments
independe
A
B
C
D
nt variable
constant
-1.679
-2.011
-0.992
-0.628
(0.041) (0.043) (0.162) (0.164)
log total
0.707
assets
(0.007)
log sales
0.733
0.721
0.724
(0.007) (0.006) (0.006)
log short
-0.651
-0.508
rate
(0.108) (0.110)
log long
1.402
1.389
rate
(0.150) (0.152)
-0.134
-0.141
log σ of
(0.034) (0.034)
short rate
0.802
0.805
1.030
βassets
(0.035) (0.035) (0.035)
log R & D
E
F
G
H
-0.979
(0.161)
0.253
(0.018)
0.487
(0.018)
-0.613
(0.107)
1.418
(0.149)
-0.138
(0.034)
0.876
(0.035)
NA
0.187
(0.238)
0.242
(0.043)
0.305
(0.040)
-0.516
(0.152)
1.386
(0.213)
-0.131
(0.047)
0.451
(0.050)
0.274
(0.016)
no
NA
0.003
(0.054)
0.443
(0.049)
-0.429
(0.143)
1.344
(0.202)
-0.143
(0.044)
0.395
(0.052)
0.340
(0.024)
yes
1990
1994
0.428
(0.027)
0.320
(0.027)
-0.465
(0.099)
1.504
(0.138)
-0.169
(0.031)
0.433
(0.039)
industry
no
no
no
no
no
yes
dummies
adj. R2
0.453
0.472
0.475
0.457
0.482
0.562
0.591
0.643
N
15,228 15,228 15,228 15,228 15,228 15,228
6,510
6,510
Total assets in this case is adjusted by subtracting short term investments. N is the number of
observations. All are coefficients are significant at 99%, except in column G the constant is
insignificant. In column H, the coefficient on total assets was not significant.
Table 6.
Dependent variable: log short term investments
Year:
1985
1990
Independent variables
constant
0.381
-0.347
(0.129) (0.158)
log total assets
1994
-0.267
-0.975
-1.143
(0.278) (0.344) (0.289)
-0.217
-0.175
0.004
(0.109) (0.138) (0.124)
log sales
0.420
0.409
0.287
(0.093) (0.126) (0.109)
0.865
0.851
0.855
0.658
0.605
0.556
log σ operating cash flows
(0.038) (0.044) (0.040) (0.100) (0.127) (0.104)
adj. R2
0.467
0.414
0.427
0.484
0.425
0.438
N
584
520
618
584
520
618
Log inv means the log of the short term investments. N is the number of observations. Total assets
is adjusted by subtracting inv.
25
-0.354
(0.144)
1985
Table 7.
Dependent variable: log short term investments
Year:
1975
1980
Independent variables
constant
-1.438
-1.183
(0.203) (0.192)
log total assets
1984
1980
1984
-2.298
-2.047
-1.804
(0.206) (0.198) (0.176)
0.515
0.510
0.673
(0.049) (0.050) (0.047)
0.767
0.767
0.746
0.342
0.321
0.155
log σ future sales revenue
(0.037) (0.037) (0.036) (0.053) (0.056) (0.051)
adj. R2
0.384
0.382
0.397
0.467
0.463
0.544
N
693
690
648
693
690
648
Log inv means the log of the short term investments. N is the number of observations. Total assets
is adjusted by subtracting inv.
26
-0.455
(0.172)
1975
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28
Figure 1a
Cash and Investments - All Firms
0.25
0.2
0.15
Ratio
0.1
0.05
0
1975
1977
1979
1981
1983
1985
1987
1989
Year
(Cash + Inv.)/TA
Cash/TA
Inv./TA
1991
1993
Figure 1b
Cash and Investments - The Largest Firms Decile
0.25
0.2
0.15
Ratio
0.1
0.05
0
1975
1977
1979
1981
1983
1985
1987
1989
Year
(Cash+Inv.)/TA
Cash/TA
1
Inv./TA
1991
1993
Figure 1c
Cash and Investments - The Smallest Firms Decile
0.25
0.2
0.15
Ratio
0.1
0.05
0
1975
1977
1979
1981
1983
1985
1987
1989
Year
(Cash+Inv.)/TA
Cash/TA
2
Inv./TA
1991
1993
Figure 2
Interest Rates 1975-1994
0.16
0.14
0.12
0.1
Percent/100
0.08
0.06
0.04
0.02
0
1975
1977
1979
1981
1983
1985
1987
1989
1991
Year
Short Rate
Short Rate Std. Dev.
Long Rate
3
Long Rate Std. Dev.
1993
Figure 3a
Annual Results
ln(Cash) = a + b ln(Total Assets - Cash)
1.5
5000
0.5
4500
4000
-0.5
3500
-1.5
3000
Parameter
Values
-2.5
2500
-3.5
2000
-4.5
1500
-5.5
1000
-6.5
500
0
-7.5
1975
1977
1979
1981
1983
1985
1987
1989
1991
Year
observations
a
b
Pooled Regression: ln(Cash) = -2.53 + 0.775 ln(Total Assets - Cash)
4
1993
Figure 3b
Annual Results
ln(Cash) = a + b ln(Sales)
1.5
5000
0.5
4500
4000
-0.5
3500
-1.5
3000
Parameter
Values
-2.5
2500
-3.5
2000
-4.5
1500
-5.5
1000
-6.5
500
0
-7.5
1975
1977
1979
1981
1983
1985
1987
1989
1991
Year
observations
a
b
Pooled Regression: ln(Cash) = -2.57 + 0.772 ln(Sales)
5
1993
Figure 3c
Annual Results
ln(Cash) = a + b1 ln(Sales) + b2 ln(Total Assets - Cash)
5000
0.5
4500
-0.5
Parameter
Values
4000
-1.5
3500
-2.5
3000
2500
-3.5
2000
-4.5
1500
-5.5
1000
-6.5
500
0
-7.5
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
Year
observations
a
b2
b1
Pooled Regression: ln(Cash) = -2.70 + 0.368 ln(Sales) + 0.432 ln (Total Assets - Cash)
6
Figure 4
Annual Results
ln(Cash + Inv.) = a + b ln(Sales)
1.5
5000
0.5
4500
4000
-0.5
3500
-1.5
3000
Parameter
Values
-2.5
2500
-3.5
2000
-4.5
1500
-5.5
1000
-6.5
500
-7.5
0
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
Year
observations
a
b
Pooled Regression: ln(Cash + Inv.) = -1.79 + 0.774 ln(Sales)
7
Figure 5
Annual Results
ln(Inv.) = a + b ln(Sales)
1.5
2000
0.5
1800
1600
-0.5
1400
-1.5
1200
Parameter
Values
-2.5
1000
-3.5
800
-4.5
600
-5.5
400
-6.5
200
0
-7.5
1975
1977
1979
1981
1983
1985
1987
1989
1991
Year
observations
a
b
Pooled Regression: ln(Inv.) = -1.74 + 0.728 ln(Sales)
8
1993
Figure 6
Operating Cash Flow Uncertainty Effects
ln(Cash) = a + b1 ln(Total Assets - Cash) + b2 ln(Sales) + b3 ln(Std. Dev. OCF)
0.6
0.5
0.4
0.3
Parameter
Values
0.2
0.1
0
-0.1
1985
1986
1987
1988
1989
1990
1991
Year
b1
b2
9
b3
1992
1993
1994
Figure 7
Operating Cash Flow Uncertainty Effects
ln(Inv.) = a + b1 ln (Total Assets - Inv.) + b2 ln(Sales) + b3 ln(Std. Dev. OCF)
0.8
0.7
0.6
0.5
0.4
Parameter
values
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
1985
1986
1987
1988
1989
1990
1991
Year
b1
b2
10
b3
1992
1993
1994