THE DETERMINANTS AND IMPLICATIONS OF

CORPORATE CASH HOLDINGS

by

Tim Opler, Lee Pinkowitz, René Stulz and Rohan Williamson*

August 1997

* Respectively, Assistant Professor, Ph.D. Student, and Reese Chair in Banking and

Monetary Economics, Fisher College of Business, The Ohio State University, and

Assistant Professor, Georgetown University. Stulz was a Bower Fellow at the Harvard

Business School while part of this research was done and is a Research Associate at the

NBER. Tim Opler also holds an appointment at Deutsche Morgan Grenfell. We thank

seminar participants at NYU and Tuck and Jarrad Harford for useful comments.
ABSTRACT

We examine the determinants and implications of holdings of cash and marketable securities by

publicly traded U.S. firms in the 1971-1994 period. Firms with strong growth opportunities and

riskier cash flows hold relatively high ratios of cash to total assets. Firms that have the greatest

access to the capital markets (e.g. large firms and those with credit ratings) tend to hold lower

ratios of cash to total assets. These results are consistent with the view that firms hold liquid assets

to ensure that they will be able to keep investing when cash flow is too low relative to planned

investment and when outside funds are expensive. The short run impact of excess cash on capital

expenditures, acquisition spending and payouts to shareholders is small. The main reason that

firms experience large changes in excess cash is the occurrence of operating losses. There is no

evidence that risk management and cash holdings are substitutes.
“[T]here is no necessity to hold idle cash to bridge over intervals if it can be obtained

without difficulty at the moment when it is actually required.”

John Maynard Keynes, The General Theory of Employment, Interest and Money, 1934.

1. Introduction

On February 8, 1996, Chrysler Corporation’s Chairman Robert J. Eaton and investor Kirk

Kerkorian agreed to a 5-year standstill agreement in which Kerkorian would cease attempts to take

over Chrysler. An important element of the agreement was a commitment from Chrysler that liquid

assets (defined as cash and marketable securities) in excess of a $7.5 billion target be returned to

shareholders in the form of share repurchases or dividends.

The Chrysler/Kerkorian story raises questions that have gone largely unexamined in the

finance literature. Is there an optimal level of liquid asset holdings on a corporate balance sheet?

And, if so, is the relatively large amount of liquid assets held by firms like Chrysler justified? This

question is particularly relevant today. The S&P 500 corporations reported a total of $716 billion

in cash and marketable securities on their balance sheets as of fiscal year 1994. The largest nonfinancial holders of liquid assets were Ford ($13.8 billion), General Motors ($10.7 billion) and

IBM ($10.5 billion).

A corporation may carry significant liquid assets for a variety of reasons. Perhaps the best

recognized motive is to minimize transactions costs. A well-developed stochastic programming

literature identifies optimal cash balances in a world where selling securities to obtain cash is

costly.1 This literature specifies how much cash a firm will hold on average and its key result is

that there are economies of scale in cash management.

Another motive for holding liquid assets is to enable the firm to keep investing when cash

flow is too low to fund positive net present value projects and when outside funds are expensive.

Outside funds are likely to be expensive for firms with significant information asymmetries and
2

agency costs. For instance, in a downturn investors may have a hard time discerning whether a

firm’s poor performance is due to general economic conditions or to the fact that the firm has

exhausted its growth opportunities. In such a situation, outside funds are expensive and a reserve

of liquid assets is valuable if the firm has good investment opportunities but current cash flow is

low because of the business cycle. If, in contrast, the firm no longer has growth opportunities, then

liquid assets may enable management to postpone necessary changes in the firm’s strategy and

would make shareholders worse off. Therefore, holdings of liquid assets can make shareholders

worse off in some circumstances.

This paper proceeds in three steps. We first examine the basic question of whether firms

act as if they have a target level of cash balances. This will shed light on whether firms behave as if

the amount of cash on the balance sheet is a policy variable that is to be controlled for the reasons

indicated above. We find that cash balances exhibit a high degree of mean-reversion, consistent

with the targeting hypothesis. This raises the natural question of how target levels are set. In our

second step, we investigate the cross-sectional determinants of corporate cash employing

Compustat data for U.S. firms in the 1971-1994 period. Our cross-sectional regressions point to

several influential factors that determine cash balances including corporate growth prospects,

short-term working capital imbalances, leverage, industry volatility, and firm size. We argue that

these results are consistent with a precautionary demand for liquid assets induced by the existence

of information asymmetries and agency costs of debt. Strikingly, use of derivatives does not appear

to decrease cash holdings. At the same time, we find that many firms have liquid asset positions

that far exceed those predicted by our cross-sectional regressions. We define excess cash as the

amount of cash held by a firm in excess of the amount predicted by our model.

 

1

See Baumol (1952), Miller and Orr (1966), Penttinen (1991), Vickson (1985), Chand (1982),

Constantinides (1976) and Elton and Gruber (1974).
3

The existence of firms with large amounts of positive excess cash raises three important

related questions that we address in our third step. First, why is it that firms have excess cash?

Second, does the excess cash lead firms to make expenditure decisions that they would not

otherwise make? Third, what is the main reason that firms experience large changes in excess

cash? Firms could choose to have positive excess cash because management is more risk-averse

than it should be to maximize shareholder wealth. The cost of the positive excess cash resulting

from this excessive risk aversion is the liquidity premium. Firms could also have excess cash

because they do not want to pay out funds to shareholders and lack good projects; in this case,

management would want to find ways to spend the excess cash, so that it would accept projects

that are not in the interest of shareholders. We find evidence that firms with abnormally high cash

invest more, after controlling for investment opportunities. However, at the same time, the

propensity to spend excess cash on investment and acquisitions is sufficiently small that, on

average, little is spent out of excess cash in the short run. In other words, if excess cash burns a

hole through management’s pockets, it does so very slowly. This result is verified when we look at

the persistence of excess cash. Firms that have large amounts of excess cash for more than one

year tend to keep their excess cash for a long period. Finally, we find that firms that experience

large changes in excess cash mainly do so because of operating losses. This result implies that the

lumpiness of capital expenditures and acquisitions is not as important as one might have thought as

a motivation to hold cash.

Our results build on an extensive but generally older literature on corporate liquidity.

Chudson (1945), for example, finds that cash/assets ratios tend to vary systematically by industry

and tend to be higher among profitable companies. Vogel and Maddala (1967) find that cash

balances have been declining over time and that larger firms tend to have lower cash/assets and

cash/sales ratios. This suggests that there are economies of scale in the transaction motive for
4

cash.2 Baskin (1987) argues that firms may use cash holdings for competitive purposes. He

concludes that “[t]he empirical evidence is consistent with an oligopolistic model in which liquid

assets are used both to signal a commitment to retaliation against market encroachment and to

enable firms to preempt new opportunities quickly.” Another paper by John (1993) argues that

firms wish to hold greater amounts of cash when they are subject to higher financial distress costs.

Using a sample of 223 large firms in 1980, John finds that firms with high Tobin’s Q ratios and

low tangible asset ratios tend to hold more cash. This is consistent with the financial distress theory

if one agrees that a low Tobin’s Q is a proxy for financial distress. Finally, in a contemporaneous

and closely related paper, Harford (1997) explores the relation between a firm’s acquisition policy

and its liquid asset holdings. He finds that cash rich firms are more likely to make acquisitions, that

these acquisitions are more likely to be diversifying acquisitions, and that they are more likely to

decrease shareholder wealth. He views his evidence as strongly supportive of free cash flow theory.

The next section of this paper motivates our empirical hypotheses. We present our data in

Section 3. In Section 4, we present time-series results. In Section 5, we report cross-sectional

regressions. In Section 6, we investigate whether the investment and payout policies of firms with

given investment opportunities are related to their liquid asset holdings in the short run. Section 7

examines how likely firms are to keep excess cash over a number of years and examines the

characteristics of firms that experience large changes in excess cash. Section 8 summarizes the

findings and suggests future directions for empirical research in this area.

2. Theory and Empirical Hypotheses

In a world of perfect capital markets, holdings of liquid assets are irrelevant. If cash flow turns out

to be unexpectedly low so that a firm has to raise funds to keep operating and to invest, it can do so

at zero cost. Since there is no liquidity premium in such a world, holdings of liquid assets have no

 

2 A number of early studies considered the question of whether there are economies of scale in

holding cash including Frazer (1964) and Meltzer (1963).
5

opportunity cost. Hence, if a firm borrows money and invests it in liquid assets, shareholder wealth

is unchanged.

If it is costly for the firm to be short of liquid assets, the firm equates the marginal cost of

holding liquid assets to the marginal benefit. Holding an additional dollar of liquid assets reduces

the probability of being short of liquid assets and decreases the cost of being short of cash under

the reasonable assumption that the marginal benefit of liquid assets in a state of the world

decreases as the amount of liquid assets increases. We define a firm to be short of liquid assets if it

has to cut back investment, cut back dividends, or raise funds by selling securities or selling assets.

Specifically, consider investment at date t in state of the world s(t), I(s(t)). In state s(t), the firm has

cash on hand equal to liquid assets at time t-1, L(t-1), plus the return on these liquid assets,

r(s(t))L(t-1), and the cash flow from operations, C(s(t)). Out of the cash on hand, the firm must

pay taxes, T(s(t)), make required payments on existing financial contracts, P(s(t)), and dividend

payments, d(s(t)). Required payments on existing financial contracts include not only contractual

payments on debt contracts and bond issues, but also required payments on derivatives. In this

setting, a firm is short of cash if:

I(s(t)) > L(t-1)(1+r(s(t))) + C(s(t)) -T(s(t)) -P(s(t)) - d(s(t)),

and

(1 r(s(t)))

L(t 1)

I(s(t))

= +

∂ −

∂

.

The first equation indicates that the firm uses up all of the liquid assets available before financing

and the second indicates that if it had more liquid assets available it would invest them. Models of

optimal holdings of liquid assets can differ in terms of the costs of being short of cash in each state

of the world and in terms of the marginal cost of holding cash.
6

The above framework has important implications. The firm can avoid being short of liquid

assets in a particular state of the world by holding more liquid assets or by engaging in financing

activities that reduce P(s(t)) in that state of the world. For instance, the firm could hedge so that in

some states of the world P(s(t)) might be negative. Alternatively, the firm could have more equity

so that it would have to make fewer payments to bondholders, in which case P(s(t)) would be

lower. Therefore, an optimal theory of liquid asset holdings has to address the issue of why it is

more efficient for the firm to hold an additional dollar of liquid assets instead of decreasing

leverage by some amount or by hedging.

In the remainder of the section, we first address the role of transaction costs as a

determinant of cash holdings and then turn to the impact of information asymmetries and agency

costs on cash holdings.

2. A. The transaction costs model.

Consider the effect of transaction costs on the irrelevance result within the framework we just

discussed. We now assume that there are costs to buying and selling financial and real assets. In

particular, let’s assume that there is a cost to raising outside funds that takes the form of a fixed

cost plus a variable cost which is proportional to the amount raised. In this case, a firm short of

liquid assets has to raise funds in the capital markets, liquidate existing assets, reduce dividends,

and/or renegotiate existing financial contracts. Unless the firm has assets that can be liquidated at

low cost, it prefers using the capital markets. However, it is costly to raise funds irrespective of

whether the firm does so by selling assets or using the capital markets. The fixed costs of accessing

outside markets induce the firm to raise funds infrequently and to use cash and liquid asset

holdings as a buffer.

Figure 1 shows the marginal cost curve of being short of liquid assets and the marginal

cost curve of holding cash. The marginal cost curve of being short of liquid assets is downward
7

sloping and the marginal cost curve of holding liquid assets is assumed to be horizontal - if the cost

is a liquidity premium, there is little reason to assume that the liquidity premium changes as the

liquid asset holdings increase. If the firm has a shortage of liquid assets, it can cope with this by

either decreasing investment or dividends or by raising outside funds through security sales or asset

sales. A greater shortage has greater costs since it involves decreasing investment more or raising

more outside funds. For a given amount of liquid assets, an increase in the cost of being short of

liquid assets or an increase in the probability of being short of liquid assets will both shift the

marginal cost curve to the right and increase the firm’s holdings of liquid assets.

With the assumptions that lead to Figure 1, one would expect the marginal cost of being

short of funds and hence holdings of liquid assets to increase with the following variables:

1. Size of transaction costs of raising outside funds. One would expect transaction

costs to be lower for firms that have accessed public markets already. This means that

firms with a debt rating have less liquid assets.

2. Cost of raising funds through asset sales, dividend cuts, and renegotiation. A firm

that has assets on its balance sheet that can be cheaply converted into cash can raise

funds at low cost by selling these assets. Hence, firms with mostly firm-specific assets

have more liquid assets and diversified firms have less liquid assets. Also, a firm that

currently pays dividends can raise funds at low cost by reducing its dividend

payments.

3. Investment opportunities. An increase in the number of profitable investment

opportunities means that the firm has to give up better projects with a cash shortage.

4. Cost of hedging instruments. By hedging with financial instruments, a firm can avoid

situations where it has to seek funds in the capital markets because of random

variation in cash flow. Hence, firms for which hedging is expensive are expected to

hold more liquid assets.
8

5. Length of the cash conversion cycle. Firms in multiple product lines and firms with

low inventory relative to sales are firms where one would expect the cash conversion

cycle to be short, hence they should have less liquid assets.

6. Cash flow uncertainty. Uncertainty leads to situations where the firm has more

outlays than expected at times. Therefore, one would expect firms with greater cash

flow uncertainty to hold more cash.

7. Absence of economies of scale. Simple transaction costs models suggest that there are

economies of scale in cash management.

In a world with significant transaction costs, one would expect assets that can be exchanged for

cash with lower transaction costs to have a lower return to reflect this benefit. 3 This means that

there is now a cost to holding liquid assets. One would expect this cost to be highest for cash and to

decrease for assets that are poor substitutes for cash. Consequently, a firm’s liquid assets have an

opportunity cost. For liquid assets held in the form of demand deposits, the opportunity cost

increases with interest rates. To the extent that cash substitutes are deposited in short-maturity

instruments, holding these cash substitutes become more expensive when the liquidity premium

component of the term structure rises.

In summary, the transaction cost model implies that liquid assets divided by total assets

increase with (1) the volatility of cash flow divided by total assets and (2) the length of the cash

conversion cycle. The model also implies that liquid asset holdings decrease (1) with interest rates

and the slope of the term structure, (2) with the cost of raising debt, (3) with the ease of selling of

assets, (4) the cost of hedging risk and (5) with the size of a firm's dividend.

 

3

 See Amihud and Mendelson (1986) for a paper that provides such a result.
9

2.B. Information asymmetries, agency costs of debt, and liquid asset holdings.

We now extend the analysis to allow for information asymmetries and agency costs of debt. For

now, however, we assume that management maximizes shareholder wealth, so that investors are

not worried that management could use the liquid assets to pursue its own objectives.

First, consider the role of information asymmetries. Information asymmetries make it

harder to raise outside funds. Outsiders want to make sure that the securities they purchase are not

overpriced and consequently discount them appropriately. Since outsiders know less than

management, their discounting may underprice the securities given management’s information. 4 In

fact, outsiders may require a discount that is large enough that management may find it more

profitable not to sell the securities and reduce investment instead. Since information asymmetries

make outside funds more expensive, the model with information asymmetries makes many

predictions that are similar to the model with transaction costs discussed earlier. However, the

model provides an explicit reason for why outside funds would be expensive and predicts that the

cost of raising outside funds increases as these securities are more informationally sensitive and as

information asymmetries are more important. This would suggest that firms with no debt capacity

left would hold more liquid assets since their only alternative is to raise capital through relatively

informationally sensitive securities such as equity. However, if firms with little debt capacity are

firms that have poor investment opportunities, then these firms have little need for liquid assets

except to prevent default.

Let’s now consider the role of agency costs of debt. These agency costs arise when the

interests of the shareholders differ from the interests of the debtholders and, possibly, when

interests differ among various classes of debtholders. Because of these costs, highly levered firms

 

4

 See Myers and Majluf (1984) for this argument.
10

find it difficult and expensive to raise additional funds; they also sometimes find it impossible to

renegotiate existing debt agreements to prevent default and bankruptcy. Such firms have high

incentives to engage in asset substitution as argued by Jensen and Meckling (1976), so that debt

will be expensive both in terms of the required promised yield and in terms of the covenants

attached to the debt. They are also likely to face the underinvestment problem emphasized by

Myers (1977), namely that raising funds to invest may benefit debtholders but not shareholders, so

that shareholders prefer not to invest even though the firm has valuable projects.

Firms want to avoid situations where the agency costs of debt are so high that they cannot

raise funds to invest in valuable projects. Obviously, one way to do so is to choose a low level of

leverage. However, one would expect firms with valuable investment opportunities for which the

cost of raising additional outside funds, in the form of debt, is high to hold more liquid assets since

the cost of being short of funds is higher for such firms. The market-to-book ratio is generally used

as a proxy for investment opportunities. 5 This means that, holding the degree of information

asymmetry between managers and investors constant, one would expect firms with a high marketto-book ratio to hold more cash. The problem is that such firms invest a lot, so that if investment

expenditures occur discretely, they hold more cash, on average, to pay for investment expenditures.

Hence, one would expect liquid assets to increase with the market-to-book ratio controlling for the

level of investment expenditures. 6

In general, one would expect that issuing a large amount of debt would be most costly for

firms with high bankruptcy and distress costs. R&D expenditures are a proxy for distress costs. As

discussed in Titman and Wessels (1988), R&D expenditures can proxy for the degree of product

specialization. We expect customers to be more reluctant to purchase products from a distressed firm

with very specialized products that may require future servicing [Titman (1984) and Maksimovic and

 

5

 See Smith and Watts (1992) and Jung, Kim and Stulz (1996).
11

Titman (1991)]. Opler and Titman (1994) provide evidence that firms with high R&D/sales are more

vulnerable to financial distress. Opler and Titman argue that R&D expenses are correlated with

bankruptcy and distress costs. We would also expect that regulated firms will tend to have lower

costs of raising capital since diverting funds to alternative, risky investments is difficult if not

impossible.

2.C. Agency costs of managerial discretion.

In the presence of agency costs of managerial discretion, management can hold cash to pursue its

own objectives. First, management may hold excess cash simply because it is risk averse. More

entrenched management would therefore be more likely to hold excess cash. Hence, one would

expect firms with anti-takeover amendments to be more likely to hold excess cash. Second,

management may accumulate cash to have more flexibility to pursue its own objectives. Cash is

like free cash flow: It allows management to make investments that the capital markets would not

be willing to finance. By enabling management to avoid the discipline of capital markets, investing

in cash can therefore have an adverse effect on firm value. To put it another way, increasing a

firm’s holdings of liquid assets by one dollar may increase firm value by less than one dollar. The

possibility that management could be using cash for its own objectives raises the costs of outside

funds because outsiders do not know whether management is raising cash to increase firm value or

to pursue its own objectives. Third, management may accumulate cash because it does not want to

make payouts to shareholders and wants to keep funds within the firm. Having the cash, however,

management must find ways to spend it and hence chooses poor projects when good projects are

not available. In general, the agency costs of managerial discretion are less important and may be

 

6

 Antunovitch (1996) further argues that firms with higher information asymmetries will have a

greater dispersion of slack since these firms find it harder to access capital markets.
12

trivial for firms with valuable investment opportunities because the objectives of management and

shareholders are more likely to coincide.

When is it more likely that management will not be disciplined so that it can afford to hold

excess cash to pursue its own objectives? One would expect the firms with excess cash to be low

market-to-book firms where:

1. Outside shareholders are highly dispersed. As argued by Shleifer and Vishny

(1986), the existence of large independent shareholders makes a takeover and/or a

proxy contest easier.

2. The firm is large. Firm size is a takeover deterrent. It requires large resources to be

husbanded by the bidder and makes it easier for the target to use the political arena to

its advantage.

3. The firm has low debt. By having low debt, the firm is less subject to monitoring by

the capital markets.

4. The firm is protected from the market for corporate control through antitakeover charter amendments. These amendments make it less likely that the firm

becomes a target.

For entrenched management, accumulating liquid assets can be a double-edged sword.

Holding excess cash makes it easier for management to remain independent from the capital

markets and to pursue its investment policies. At the same time, it increases the gain to a bidder

from taking over the firm since the bidder gains control of liquid assets that can help it to finance

the acquisition.

To the extent that agency costs of managerial discretion are higher for low mar ket-to-book

firms than for high market-to-book firms, one expects low market-to-book firms with entrenched

management to have excess liquid assets. To the extent that low market-to-book firms have poor

investment opportunities and management holds liquid assets to facilitate an investment program
13

that it would find difficult to finance through the capital markets, one would expect low market-tobook firms with more liquid assets to invest more.

Management holdings of shares help align their interests with those of shareholders. At the

same time, however, they protect management against outside pressures and may make

management more risk-averse. 7 If holding cash is costly and management tends to hold more cash

than is optimal from the perspective of maximizing shareholder wealth, then one would expect cash

holdings to fall with managerial ownership. However, to the extent that managerial ownership

makes management more risk averse, then one would expect cash holdings to increase with

managerial ownership.

2.D. Summary

We summarize the predictions made by the various theories of cash holdings discussed in this

section in Table 1. The table shows the expected signs in regressions predicting corporate liquidity

for a series of variables.

3. Data

We construct a sample of firms for our empirical tests by merging the Compustat annual industrial

and full coverage files with the research industrial file for the 1971-1994 period. These data

include survivors and non-survivors that appeared on Compustat at any time in the sample period.

We exclude financial firms (SIC codes between 6000 and 6999), because they may carry cash to

meet capital requirements rather than for the economic reasons studied here. We also exclude

utilities because their cash holdings can be subject to regulatory supervision in a number of states.

We exclude firms with nonpositive sales for the years in which they have nonpositive sales. Finally,

we exclude ADRs and firms designated as pre-FASB. We present regressions predicting cash and

 

7

 See Stulz (1988).
14

the persistence of cash holdings using the entire dataset. We also present a separate regression

analysis of cash holdings in 1994 for the simple reason that data are available for the governance

structure and risk management activities of firms for that year. Insider share ownership is

measured as the fraction of shares outstanding held by officers and directors (reported in

Disclosure). Firm diversification is measured using the Compustat segment tapes.

3.1 Measure of Liquid Asset Holdings

We measure liquid asset holdings as the ratio of cash and marketable securities (Compustat item

#1) to total assets (Compustat item #6) minus cash and marketable securities. We deflate by the

book value of total assets net of liquid assets (which we call net assets in the following) with the

view that a firm’s ability to generate future profits is a function of its assets in place. While not

reported in this paper, we have also measured liquidity using the cash-to-sales ratio. This does not

affect our main results in a material way.

3.2 Definition of Exogenous Variables

Investment Opportunities. We measure the likelihood that a firm will have positive NPV

projects in the future using the ratio of the market value of a firm’s assets to the book value of its

assets. Since the book value of assets does not include future growth options, we would expect the

ratio of the market value of the firm relative to the book value to be higher when a firm has a high

preponderance of growth options. A variety of past papers have found that the market-to-book

ratio is an important determinant of corporate financing choices thought to depend on a firm’s

portfolio of growth options (e.g. Smith and Watts (1992), Jung, Kim and Stulz (1996) and Barclay

and Smith (1995)).

Regulation. We allow for possible effects of regulation by using a dummy variable for

industries that are or have been subject to entry and price regulation. This variable is identical to
15

that employed by Barclay and Smith (1995). Regulated industries include railroads (SIC code

4011) through 1980, trucking (SIC codes 4210, 4213) through 1980, airlines (SIC code 4512)

through 1978 and telecommunications (SIC codes 4812, 4813) through 1982.

Firm size. We measure firm size as the natural logarithm of the book value of assets in

1994 dollars.

Financial Leverage. We measure leverage using the debt/assets ratio defined as (long-term

debt+short-term debt)/book value of assets.

Dividend Payout Dummy. We define a dummy set equal to one in years where a firm pays

a dividend. Otherwise the dummy equals zero.

Cash flow. We measure cash flow as earnings after interest, dividends and taxes but before

depreciation divided by net assets.

Cash flow riskiness. We measure cash flow riskiness using two measures. First, we use

the standard deviation of industry cash flow computed as follows. For each firm, we compute cash

flow standard deviation for the previous twenty years if available using Compustat since 1950

(industry sigma). We then take the average across the 2-digit SIC code of the firm cash flow

standard deviations. Second, we compute a firm’s cash flow standard deviation for 1994 using the

previous twenty years of data if available.

Financial distress costs. We measure the potential for financial distress costs using the

R&D expense-to-sales ratio. Firms that do not report R&D expenses are considered to be firms with no

R&D expenses.

Management-Shareholder Incentive Alignment. It is difficult to measure the extent of

conflict of interest between managers of a corporation and its shareholders. In theory, the severity

of this conflict is affected by a number of hard-to-measure concepts including the efficiency of the

managerial labor market and the extent of product market discipline (Fama and Jensen (1983)).

Nonetheless, there is a large body of literature that suggests that certain types of firms are more
16

likely to suffer from agency conflicts. For example, firms with inside ownership in excess of 5%

but less than 25-40% are known to trade at somewhat higher market valuations than other firms

(Morck, Shleifer and Vishny (1988), McConnell and Servaes (1990)). We employ a dummy for

whether insider ownership of a firm is in the 5 to 25% range and a dummy for whether insider

ownership is greater than 25%.

Availability of Cash Substitutes. Firms may choose to insure themselves against losses by

holding liquid assets besides cash. For example, it is common for firms to sell off non-core assets

in periods of economic distress. It is also becoming increasingly frequent for firms to liquidate

receivables through factoring or securitization as a means of raising liquidity. We use net working

capital minus cash as a measure of liquid asset substitutes. In addition, we employ a count of the

number of reported business segments to measure whether firms have non-core assets that could be

liquidated in periods of economic distress.

Derivatives usage. To assess a firm’s derivatives usage in 1994, we use the Corporate

Risk Management Handbook from Risk Publications for that year. We collect information on

whether an S&P 500 corporation uses derivatives and on the total of the notional amount of the

derivatives it reports.

3.3 Data Description

Table 2 describes the main variables used in the study. There is wide variation in the ratio of cash

and marketable securities to assets. The median firm has cash equal to approximately 6% of net

assets (i.e. total assets - cash). On a dollar basis, the median firm has cash holdings of $6.28

million, a relatively small amount. This reflects the size distribution of firms on Compustat: The

median firm in the sample has an asset base of $90.1 million.
17

3.4 Variation Across Time

Figure 2 shows the median cash-to-assets ratio in the 1952-1994 period for firms with real assets

in the $90-110 million range and in the $900 million to $1.1 billion range in 1994 dollars

(inflation-adjusted with the CPI). There has been a secular decline in cash holdings since the

1950s, mostly accomplished by the mid-1960s. This decline is consistent with the view that

payment automation reduced the overall need for cash in many companies. It is also likely that cash

balances were abnormally high at the beginning of the 1950s because firms were typically not able

to invest in their primary business areas during World War II.

4 The Determinants of Cash Balances

4.1 Do Firms Have Target Cash Levels?

The hypotheses presented in this paper hold that firms adjust their cash holdings for a variety of

reasons. An alternative hypothesis would state that firms passively allow cash holdings to rise and

fall with cash flow. This argument is not unlike that implied by Myers’ (1984) pecking-order story

that firms allow their debt ratios to float with cash flow.

We test this hypothesis by estimating a first order autoregressive model for each

Compustat firm of the form Δ(Cash/Assets)t = α + β Δ(Cash/Assets)t-1 + ε, where ε is an i.i.d

disturbance with zero mean. Figure 3 shows the distribution of the autoregressive coefficients ( β)

from this regression for all Compustat firms with more than five years of data in the 1950-94

period. The median coefficient is negative, indicating that cash balances are mean reverting. It

appears that there are systematic factors that cause firms not to let cash balances rise too high nor

fall too low.
18

4.2 Univariate Tests

Table 3 presents univariate comparisons of key descriptive variables by cash/assets quartile. We

are interested in whether the characteristics of companies which hold high cash balances (say, in

the fourth quartile) differ from those with low cash balances (first quartile). We test the hypothesis

that the fourth-quartile firms differ significantly from the first-quartile firms using a t-test.

Firms that hold high amounts of cash differ substantially from those with less cash along

several dimensions. For example, the mean market-to-book ratio of fourth quartile cash holders is

substantially higher than that of firms in the first quartile. This is consistent with the view that

firms will hold more cash in order to protect the value of growth options and to avoid the high

transactions costs associated with raising capital in the face of informational asymmetries. This is

also evidenced by the very large difference in the R&D/sales ratio across groups.

4.3 Regression tests on the 1971-1994 Sample

Table 4 presents panel regressions predicting liquidity levels in the 1971-1994 period with the

independent variables described earlier. Firms are allowed to enter and leave the panel. We report

panel regressions that include year dummies and a similar regression where all variables are

industry-adjusted. Because liquidity cannot be negative, we use the logarithm of liquidity as our

dependent variable. In cases where we look at industry adjusted variables, our dependent variable

is the log of the ratio of firm liquidity over the median liquidity in the 2-digit SIC industry. Our

qualitative results are not affected by using the level of liquidity as the dependent variable.

However, when we use the level rather than the logarithm of liquidity as the dependent variable, we

have the rather nonsensical result that some firms are predicted to have a negative amount of cash.

The assumption of independent and identically distributed errors may be violated in this regression

due to the persistence in cash holdings noted earlier and, possibly, due to omitted variable bias.
19

Therefore, we also report fixed effects regression results as well as coefficient estimates for a

regression where we use the sample means of the variables for each firm.

The results are consistent with the univariate findings discussed earl ier. Larger firms hold

less cash. From the first regression, it follows that going from small firms to large firms, say

increasing real assets by a factor of 100, multiplies the liquid assets ratio by about 4/10. These are

also firms that would be least likely to face financing problems due to asymmetric information in

periods of poor economic activity. Large firms are also likely to face economies of scale in cash

holdings. The results indicate that firms with high market-to-book ratios and high R&D

expenditures hold more cash. An increase in the market-to-book ratio from the first quartile to the

third quartile multiplies cash holdings by about 1.3. A similar change for R&D multiplies cash

holdings by about 1.16. This is consistent with the idea that firms with high growth potential hold

cash in order to ensure that they will be able to realize expected future profit even if capital

becomes difficult to obtain. These are firms that would be most likely to face financing problems in

periods of economic distress due to asymmetric information. Since firms with better investment

opportunities invest more, it is not surprising that we also find a positive relation between capital

expenditures and cash.

Riskiness of cash flows has a strong positive effect on cash holdings when that riskiness is

proxied by the industry standard deviation of cash flow to assets. Whereas one would expect

regulated firms to be less risky and less subject to information asymmetries, there is no clear

evidence that these firms hold less cash.

We hypothesized that firms with alternative sources of liquidity should hold less cash.

Firms that pay dividends can generate cash by cutting dividends, so that one would expect them to

hold less cash. There is support for this hypothesis in all but the fixed effects regression. The fixed

effects regression estimates the correlation between cash holdings and a change in dividend policy
20

from no dividends to paying dividends. Such a change in dividend policy might be associated with

increased cash because the firm is doing better than it used to.

One would expect a negative relation between net working capital (more money expected

to come in over the next year than to go out) and cash since net working capital is a substitute for

cash. This hypothesis finds considerable support in our regressions.

Leverage and cash holdings could be positively or negatively related. First, a firm with

high leverage faces greater costs of raising outside funds and hence might want a larger buffer of

cash. Second, firms with greater agency costs of debt will have lower leverage, but the greater

agency costs of debt make cash holdings also more useful. Third, firms with more debt have

greater debt service costs and hence cannot accumulate as much cash out of cash flow as firms

with less debt. It turns out that there is a strong negative relation between leverage and cash

holdings, suggesting that it is more worthwhile for firms to reduce debt than to hold more

precautionary cash balances when leverage is high. This could simply result from the fact that

firms with high leverage are firms that have fewer reasons to hold liquid assets because they do not

have valuable investment opportunities that they would lose in the absence of cash holdings. One

might worry that leverage and cash holdings are determined simultaneously and that, consequently,

we should have chosen a different estimation strategy. When we estimate the regressions in Table 4

without leverage, however, none of our inferences change.

Cash holdings increase in past cash flow. This suggests that firms are reluctant to pay out

past cash flow that they do not immediately invest. Finally, cash holdings are highest when a firm

is in an industry where cash flows are relatively volatile (measured by industry sigma).

4.4. Regression tests on the 1994 sample

For 1994, we also have available data on managerial ownership, derivatives usage, bond ratings,

and anti-takeover charter amendments. We restrict the sample to firms for which the degree of
21

diversification as measured by the number of segments is available. Table 5 estimates crosssectional regressions using the explanatory variables from table 4 and additional explanatory

variables. The first two regressions in Table 5 use the full sample. The other two regressions use

only the S&P 500 firms for which derivatives usage information is available and for which all our

other variables are also available. Looking at the first two columns, we find that the explanatory

variables that are in these regressions as well as in the earlier regressions lead to the same

inferences. In most cases, the coefficient estimates are very similar. For instance, the coefficient on

market-to-book is 0.1445 in Table 5 and 0.1422 in the first column of Table 4.

The additional variables are managerial ownership, firm volatility, debt rating, and number

of segments, which proxies for diversification. Firm volatility has a strong positive effect on cash

holdings even when we control for industry volatility (industry sigma). Management ownership has

a positive effect on cash holdings significant at the 0.10 level for low ownership, but cash holdings

do not increase further as ownership increases past 5%. This result is consistent with managerial

risk aversion insofar as managers may wish to protect their human capital with a cash buffer. Not

surprisingly, firms that have an investment grade bond rating hold less cash. Although the

diversification variable has the predicted sign, it is not significant.

In the last two columns, we present regressions for S&P 500 firms only. The results are

mostly similar to the ones for the full sample. One exception is that diversification has a negative

coefficient significant at the 0.10 level. Now whether a firm pays dividends or not does seem to

matter, which may reflect the fact that most firms in this subsample pay dividends. The coefficients

on inside ownership are not significant and neither is the bond rating dummy. In addition, the

presence of anti-takeover amendments does not have a significant effect on cash holdings. We

would note, however, that the great majority of S&P 500 firms have an anti-takeover amendment.

Cash holdings are unrelated to whether a firm uses derivatives or not but not to the intensity of

derivatives usage. A dummy variable that takes value one if a firm’s has derivatives with a notional
22

amount in excess of 10% of assets has a significant positive coefficient. Consequently the

regressions in Table 5 do not provide support for the view that cash holdings and derivatives are

substitutes, but is not inconsistent with the view that cash holdings and derivatives are

complements.

5.0 Does excess cash affect spending?

We compute a measure of excess cash based on the residual from the first regression in Table 4. A

company with positive excess cash is one that holds more cash than predicted by our model. To get

a sense of the patterns of excess cash holdings, we report cash holdings and excess cash holdings

for firms with total assets in 1994 in excess of $25 billion in Table 6. The table shows that there is

high cross-sectional variability in the cash/assets ratio and in excess cash. Not surprisingly, the

companies with the largest ratios of liquid assets to assets are also the companies with the largest

ratios of excess cash to assets. It is interesting to note that Chrysler held $5.15 billion of cash in

1994, of which over $3 billion was excess cash according to our model.

To more fully understand how firms manage their cash, we show in Table 7 how spending

patterns in year t+1 are related to excess cash in year t. We use the Compustat flow of funds data

to identify spending patterns on an annual as well as on a cross-sectional basis. In this section, we

confine our analysis to the firms with positive excess cash. Firm years are separated into quartiles

on the basis of the market-to-book ratio. If the market-to-book ratio (MB) is a good proxy for the

presence of profitable growth opportunities, then our discussion of the agency costs of managerial

discretion predicts that these agency costs are small in high MB firms. We therefore compare firms

in the highest and lowest quartiles of market-to-book for different quartiles of positive excess cash.

The excess cash quartiles are computed separately across all firms each year, so that the number of

firms in each cell varies but firms in the same excess cash quartile have similar amounts of excess

cash irrespective of their market-to-book ratio.
23

For capital expenditures, we find that they increase monotonically in excess cash for both

high MB and low MB firms. For all quartiles of excess cash, high MB firms invest significantly

more than low MB firms but there is no evidence that capital expenditures increase faster for low

MB firms than for high MB firms as excess cash increases. 8 The increase in capital expenditures

across excess cash quartiles is small compared to the increase in excess cash. Moving from the

first quartile of excess cash to the fourth quartile of excess cash, capital expenditures increase by

about 2% of net assets for high MB firms and 1.4% for low MB firms. However, excess cash

increases dramatically since average excess cash is 1.2% of net assets in the first quartile and

58.05% in the fourth quartile. As a result, investment increases by about 2% of assets when excess

cash increases by more than 20% of net assets. Although we do not reproduce results for firms

with negative excess cash, it is interesting to note that capital expenditures are U-shaped in excess

cash when we look both at positive and negative excess cash. Low MB firms in the lowest quartile

of negative excess cash have capital expenditures that are similar to those of low MB firms in the

highest quartile of positive excess cash (0.0773 versus 0.0762). The same result holds for high MB

firms (0.1157 versus 0.1175). In summary, there is no evidence that the firms where one would

expect the agency costs of managerial discretion to be highest, namely low MB firms, have a

relatively high propensity to spend excess cash on capital expenditures.

For acquisitions, the relation between excess cash and spending on acquisitions is not

monotonic for high MB firms. Further, the difference between spending on acquisitions for the

fourth quartile and first quartile of excess cash is barely significant. For low MB firms, spending

on acquisitions more than doubles when one moves from the lowest quartile of excess cash to the

highest. Hence, for firms with poor investment opportunities, there is evidence consistent with

Harford (1997) that more spending takes place on acquisitions as excess cash increases. When one

 

8

 The ratio of high MB to low MB capital expenditures is 1.57 for the first quartile of positive

excess cash, 1.42 for the second, 1.43 for the third, and 1.54 for the fourth quartile.
24

looks at spending on acquisitions in relation to excess cash for both positive and negative amounts

of excess cash, firms with negative excess cash spend less than half as much on acquisitions as do

firms in the fourth quartile of positive excess cash. Again, however, spending increases much more

slowly across excess cash quartiles than does excess cash.

Payments to shareholders, which are the sum of dividends and stock repurchases, do not

seem to be related to excess cash for high MB firms but are related for low MB firms. Low MB

firms pay out less to shareholders in the first three quartiles of excess cash than do high MB firms,

but pay out more in the fourth quartile.

The last panel of the table provides data on operating cash flow divided by net assets. On

average, high MB firms have higher cash flow as one would expect. There seems to be no relation

between cash flow and excess cash across the first three quartiles of excess cash. However, firms

with the highest quartile of excess cash have surprisingly low operating cash flow relative to the

other firms irrespective of MB. Further, high MB firms do not have higher cash flow than low MB

firms. High excess cash holdings in a given year are not associated with high cash flows the next

year. One would not want to interpret this result as suggesting that high excess cash holdings cause

low performance, however. It could well be that firms that know that they will have lower cash

flow the next year accumulate cash to be able to finance their investment program despite the cash

flow deficiency.

The bottom line from all this is that the spending of low MB firms is more sensitive to

excess cash. Rather surprisingly in light of the predictions of free cash flow theory, the impact of

excess cash on payouts to shareholders is of the same magnitude as the impact of excess cash on

investment and spending on acquisitions. Perhaps not surprisingly, firms with negative excess cash

have lower payouts than other firms.

Firms with more excess cash have higher capital expenditures and spend more on

acquisitions even when they have poor investment opportunities. How do we know, however, that
25

this is a manifestation of the agency costs of excess cash as opposed to the outcome of a plan to

spend more? One way to investigate this issue is to estimate investment equations that control for

investment opportunities and allow excess cash to influence investment. The traditional investment

equation allows us to predict the “normal” amount of investment. Table 8 reports such investment

equations for firms in our sample. We find that after controlling for the determinants of investment,

it is still the case that greater excess cash leads firms to invest more whether they have good

investment opportunities or not. At the same time, however, it appears that the impact of excess

cash on investment is significantly smaller for positive excess cash than negative excess cash. In

other words, negative excess cash reduces investment more than positive excess cash increases

investment. This could be viewed as evidence for credit constraints of the type discussed in

Fazzari, Hubbard and Petersen (1988). Again, however, the results suggest that the propensity to

spend out of positive excess cash is small.

6.0 What happens to excess cash?

In Section 5, we saw that an increase in excess cash leads to a surprisingly small increase in capital

expenditures, acquisitions spending, and payout to shareholders. This suggests that there is

substantial persistence in excess cash levels. Firms appear to sit on cash for extended periods of

time. To better document the extent of this persistence, we divide the sample into excess cash

quartiles. In Table 9, we report a transition matrix for firms in the highest excess cash quartile.

Firms are only included in this table for the first time that they enter this quartile. The quartile is a

transitory state for nearly half of the firms. Roughly 55 percent of firms remain in the top excess

cash quartile by the next year. However, firms that remain in the highest excess cash quartile for

one year tend to remain in that quartile for a substantial amount of time. In all, 39.5 percent of

firms that start in the highest excess cash quartile remain in that quartile five years later. A similar
26

result holds for firms in the lowest quartile. There is clearly strong persistence in excess cash

holdings.

The counterpart of this persistence is spending patterns do not change dramatically after a

firm appears in either the first or fourth excess cash quartile. Table 10 shows spending patterns for

firms in the first and fourth quartiles for five years. Firms in the fourth quartile spend more than

firms in the first quartile. They still spend more on acquisitions and shareholder payouts five years

after having been identified as firms in the fourth quartile of excess cash. However, there is no

dramatic upward jump in spending after a firm has been identified to be in the fourth quartile of

excess cash. In other words, there is no evidence that firms in the fourth quartile of excess cash

make quick spending adjustments to reduce their excess cash

Table 10 also provides results on operating cash flow as a proportion of net assets. The

results on operating cash flow confirm our conjecture in the previous section that firms accumulate

excess cash in anticipation of lower cash flow. Firms that are in the fourth quartile of excess cash

in one year experience a fall in operating cash flow the next year. Operating cash flow then

increases steadily to be higher in year five than in year zero. Similarly, firms that are in the lowest

quartile of excess cash have low operating cash flow in year zero. Their operating cash flow

increases steadily after year zero, so that in year five there is no difference between firms that have

high excess cash in year zero and those that have low excess cash.

Why is it that firms experience large changes in excess cash? We have seen that on

average expenditure patterns of high excess cash firms are not such that they use up their excess

cash quickly. We therefore look at firms that go from the top quartile of excess cash to the bottom

quartile of excess cash in one year. We then look at the expenditure and cash flow patterns for

these firms. The results are reproduced in Table 11. The clear result in that table is that firms that

experience large changes in excess cash, on average, experience large negative operating cash

flows. Note that in the table the end of year 0 is used to assign a firm to an excess cash quartile.
27

We then select the firms that go from quartile 4 to quartile 1. The largest swing in the ratios

reported is the one for operating cash flow. For these firms, the average operating cash flow as a

percentage of net assets falls from –1.11 percent to –9.15 percent. This swing represents a change

on average of eight percent of assets. Neither capital expenditures nor acquisitions increase by as

much as one percent of assets. This further confirms the precautionary motive as a major

determinant of cash holdings, in that managers hold cash for a rainy day. The results in Table 11

also show that lumpiness of capital expenditures and acquisitions is not an important reason for

large changes in excess cash. Firms that experience large increases in excess cash also experience

them because of large swings in operating cash flow. In Panel A, firms that go from the first

quartile of excess cash to the fourth quartile experience an average swing in operating cash flow of

more than 10 percent of total assets! Strikingly, however, this dramatic shift in cash flow has a

small impact on capital expenditures, acquisitions, and payments to shareholders. In other words,

the firms that experience such a large increase in excess cash keep it.

7.0 Conclusion

We examine the determinants of corporate holdings of cash and marketable securities

among publicly traded US firms from 1971-1994. Our results indicate that firms with strong

growth opportunities and firms with riskier activities hold more cash than other firms. Firms that

have the greatest access to the capital market (e.g. large firms and those with credit ratings) tend to

hold less cash. These results are consistent with the view that firms hold liquid assets to ensure that

they will be able to keep investing when cash flow is too low relative to investment and when

outside funds are expensive. Our analysis provides limited support for the view that positive excess

cash leads firms to spend more on investment or acquisitions. Whereas acquisitions increase with

excess cash, payouts to shareholders increase with excess cash also. However, in both cases, the

propensity to use excess cash on investment and acquisitions is quite limited. This suggests that
28

risk aversion is an important driver of cash hoarding. An important issue for further research is

whether, when a firm runs into difficulties, excess cash leads firms to avoid making required

changes. 
29

REFERENCES

Antunovich, Peter, 1996, Optimal slack policy under asymmetric information, Manuscript,

Northwestern University.

Amihud, Yakov and Haim Mendelson, 1986, Liquidity and stock returns, Financial Analyst

Journal 42, 43-48.

Barclay, Michael J., and Clifford W. Smith Jr., 1995, The maturity structure of corporate debt,

Journal of Finance 50, 609-631.

Baskin, Jonathan, 1987, Corporate liquidity in games of monopoly power , Review of Economics

and Statistics 69, 312-319.

Baumol, William J., 1952, The transactions demand for cash: An inventory theoretic approach,

Quarterly Journal of Economics, 545-556.

Browne, Sid, 1995, Optimal investment policies for a firm with a random risk process: Exponential

utility and minimizing the probability of ruin, Mathematics of Operations Research 20, 937-958.

Cantwell, Dennis M., 1996, How public corporations use securitization in meeting financial needs:

The case of Chrysler Financial Corporation, in M. J. Fishman and L. T. Kendall (eds.), A primer

on securitization, MIT Press, Cambridge, MA.

Chand, Sheetal K., 1982, Stocks, flows, and market equilibrium in neo-classical monetary growth

models, Journal of Monetary Economics 8, 117-130.

Chudson, Walter, 1945, The pattern of corporate financial structure, National Bureau of Economic

Research, New York, NY.

Constantinides, George M., 1976, Stochastic cash management with fixed and proportional

transaction costs, Management Science 22, 1320-1331.

Donaldson, Gordon 1961, Corporate debt capacity: A study of corporate debt policy and the

determination of corporate debt capacity, Harvard Business School, Division of Research,

Cambridge, MA.

Elton, Edwin J. and Martin Gruber, 1974, On the cash-balance problem , Operational Research

Quarterly 25, 553-572.

Fama, Eugene F., and Michael C. Jensen, 1983, Agency problems and residual claims, Journal of

Law and Economics 26, 327-350.

Fazzari, Steven M., R. Glenn Hubbard and Bruce petersen, 1988, Financing constraints and

corporate investment, Brookings Papers on Economic Activity 19, 141-195.

Frazer, William J., 1964, Financial structure of manufacturing corporations and the demand for

money: Some empirical findings, Journal of Political Economy, 176-183.

Friedman, Milton, 1959, The demand for money: Some theoretical and empirical results , Journal

of Political Economy, 327-351.

Gertler, Mark and Simon Gilchrist, 1994, Monetary policy, business cycles, and the behavior of

small manufacturing firms, The Quarterly Journal of Economics 109, 309-340.
30

Harford, J., 1997, Corporate cash reserves and acquisitions, Unpublished working paper, William

E. Simon Graduate School of Business Administration, University of Rochester, Rochester, NY

14627.

Huberman, Gur, 1984, External financing and liquidity, Journal of Finance 39, 895-908.

Jensen, Michael, 1986, Agency costs of free cash flow, corporate finance and takeovers, American

Economic Review, 323-329.

Jensen, Michael C., and William H. Meckling, 1976, Theory of the firm: Managerial behavior,

agency costs and ownership structure, 305-360.

John, Teresa A., 1993, Accounting measures of corporate liquidity, leverage, and costs of financial

distress, Financial Management 22, 91-100.

Jung, Kooyul, Yong-Cheol Kim, and René Stulz, 1996, Investment opportunities, managerial

discretion, and the security issue decision, Journal of Financial Economics.

Kashyap, Anil, Owen Lamont and Jeremy Stein, 1994, Credit conditions and the cyclical behavior

of inventories, Quarterly Journal of Economics 109, 565-592.

Keynes, John Maynard, 1934, The general theory of employment, interest and money, Harcourt

Brace, London.

Maksimovic, Vojislav, and Sheridan Titman, 1991, Financial theory and reputation for product

quality, Review of Financial Studies 4, 175-200.

McConnell, John and Henri Servaes, 1990, Additional evidence on equity ownership and firm

value, Journal of Financial Economics 27, 595-612.

Meltzer, Allan H., 1963, The demand for money: A cross-section study of business firms,

Quarterly Journal of Economics, 405-422.

Miller, Merton H. and D. Orr, 1966, A model of the demand for money by firms , Quarterly

Journal of Economics, 413-435.

Milne, Alistair and Donald Robertson, 1996, Firm behavior under the threat of liquidation , Journal

of Economic Dynamics & Control 20, 1427-1449.

Morck, Randall, Andrei Shleifer and Robert W. Vishny, 1988, Management ownership and market

valuation: An empirical analysis, Journal of Financial Economics 20, 293-315.

Myers, Stewart C., 1977, Determinants of corporate borrowing, Journal of Financial Economics

5, 147-175.

Myers, Stewart C., 1984, The capital structure puzzle, Journal of Finance 39, 575-592.

Myers, Stewart C. and Nicholas Majluf, 1984, Corporate financing and investment decisions when

firms have information that investors do not have, Journal of Financial Economics 13, 187-221.

Novaes, Walter and Luigi Zingales, 1993, Financial distress as a collapse of incentive schemes,

manuscript, University of Chicago, Chicago, IL.

Opler, Tim C. and Sheridan Titman, 1994, Financial Distress and Corporate Performance, Journal of

Finance 49, 1015-1040.

Opler, Tim C. and Sheridan Titman, 1996, The Debt-equity choice, Manuscript, The Ohio State

University, Columbus, OH.
31

Penttinen, Markku J., 1991, Myopic and stationary solutions for stochastic cash balance problems ,

European Journal of Operational Research 52, 155-166.

Shleifer, Andrei and Robert Vishny, 1993, Liquidation values and debt capacity: A market equilibrium

approach, Journal of Finance 47, 1343-1366.

Shleifer, Andrei and Robert Vishny, 1986, Large shareholders and corprate control, Journal of Political

Economics 94, 461-488.

Shrieves, Ronald E., 1995, Regulation, recession, and bank lending behavior: The 1990 credit crunch ,

Journal of Financial Services Research 9, 5-30.

Smith, Clifford W. and Rene Stulz, 1985, The determinants of firms’ hedging policies, Journal of

Financial and Quantitative Analysis 20, 391-405.

Smith, Clifford W, Jr. and Ross L. Watts, 1992, The investment opportunity set and corporate

financing, dividend and compensation policies, Journal of Financial Economics 32, 263-292.

Stulz, Ren , 1988, Managerial control of voting rights: Financing policies and the market for

corporate control, Journal of Financial Economics 20, 25-54.

Stulz, Ren , 1990, Managerial discretion and optimal financing policies, Journal of Financial

Economics 26, 3-27.

Titman, Sheridan, 1984, The effect of capital structure on a firm's liquidation decision, Journal of

Financial Economics 13, 137-151.

Titman, Sheridan and Roberto Wessels, 1988, The determinants of capital structure choice,

Journal of Finance 43, 1-20.

Vickson, Robert G., 1985, Simple Optimal Policy for Cash Management: The Average Balance

Requirement Case, Journal of Financial and Quantitative Analysis 20, 353-369.

Vogel, Robert C. and G. S. Maddala, 1967, Cross-section estimates of liquid asset demand by

manufacturing corporations, Journal of Finance 22, 557-575.

Zwiebel, Jeffrey, 1995, Dynamic capital structure under managerial entrenchment, Working paper,

Stanford University.
32

Figure 1

Optimal amount of liquid assets

Marginal

costs

Liquid assets

Marginal cost of

liquid asset shortage

Marginal cost of holding

liquid assets

Optimal holdings

of liquid assets
33

Figure 2. Median Cash/Assets, 1952-1994

Median Cash/Assets Ratio in the 1952-1994 period for Compustat firms with real assets in the

$90-110 million range and in the $900 million to $1.1 billion range in 1994 dollars (inflationadjusted with the CPI). Cash/assets is cash and marketable securities over assets less cash.

$90-110 million in assets

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94

$900 million to 1.1 billion in assets

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94
34

Fraction

pers

-1 -.5 0 .5 1 1.5 2

0

.02

.04

.06

.08

Figure 3. Distribution of Coefficients on Lagged Change in Cash/Assets

Distribution of coefficients on lagged change in cash/assets from the firm-wise regression:

D(Cash/Assets)t = α + β D(Cash/Assets)t-1 + e,

where D is a first difference operator and time steps are annual. Cash/assets is defined as

cash and marketable securities over assets less cash and marketable securities. The chart

includes information on 10,441 U.S. based firms on Compustat with at least five years of

data on cash holdings in the 1950-94 period. The median coefficient value is -0.242.
35

Table 1. Expected Signs on Coefficients in Regressions Predicting Cash Holdings

Cost of Raising Cash When Needed Expected Benefits of Cash Reserve

Expected Costs of

Cash Reserve

Costs Due to

Legal, Time

& Filing

Costs Due to

Information

Asymmetry

Costs Due to

Agency

Problems of

Debt

Availability of

Other Sources

of Liquidity

Completion of

Transactions

Protect Value of

Investment

Opportunities

Protect Assets in

Place / Avoid

Financial

Distress

Cost of

Financing

Cash (Risk

Premium)

Agency

Costs of

Cash

Firm Size - - - +

Bond Rating

Dummy

- - -

Cash Flow / Assets - - / + - / +

Market-to-book

ratio

+ + + -

R&D/sales + + + +

Regulated Industry

Dummy

- -

Standard deviation

of firm level Cash

Flow/Assets

+ + + +

Debt/Assets + + -

Dividend Payer

Dummy

- -

Business Segment

Count

- -

Expense of

Hedging

-

(Current AssetsCurrent Liabilities

- Cash) / Assets

-

Corporate

investments /

Assets

-

Takeover defense

Dummies

-

Fraction of inside

share ownership

-

Slope of the term

structure

-
36

Table 2

Description of Variables for the 1971-1994 Compustat Sample

This table presents descriptive statistics on key variables for the 1971-1994 sample of U.S.-based publicly

traded firms. Variables deflated by assets are deflated by the book value of assets less cash and marketable

securities. Real variables are deflated by the Consumer Price Index to obtain 1994 dollars. Size is defined

as the natural logarithm of assets. Market to book is measured by (book value of assets - book value of

equity + market value of equity)/assets. Cash flow is defined as (earnings before interest and taxes - interest

- taxes - common dividends). Net working capital is calculated without cash. Payout to shareholders is the

sum of cash dividends/assets and stock repurchases/assets. Industry sigma is a measure of the volatility of

an industry’s cash flow for a 20-year period. Industries are defined by 2-digit SIC codes. Total leverage is

total debt/total assets. Sample Size is the number of non-missing observations in the sample.

Variable Mean

First

Quartile Median

Third

Quartile

Sample

Size

Cash /Assets 18.03% 2.16% 6.05% 17.22% 127,284

Real Size 4.1909 2.6769 4.1251 5.5984 126,131

Market-to-Book 1.5845 0.9289 1.1832 1.7386 97,801

R&D / Sales 2.96% 0.00% 0.00% 1.50% 127,462

Cash Flow / Assets 0.74% 0.76% 6.37% 10.91% 125,532

Net Working Capital /

Assets

13.36% -0.95% 15.51% 32.30% 122,511

Capital Expenditures /

Assets

8.87% 2.93% 6.10% 11.49% 127,063

Acquisitions / Assets 0.94% 0.00% 0.00% 0.00% 125,311

Payout to Shareholders 1.54% 0.00% 0.18% 2.11% 123,903

Industry Sigma 12.26% 5.59% 8.80% 16.90% 126,067

Total Leverage 27.96% 10.05% 24.94% 40.49% 127,165
37

Table 3

Firm Characteristics by Excess Cash Levels

Univariate comparison of means of measures of firm characteristics of 87,117 firm years which comprise

the 1971-1994 sample of U.S.-based publicly traded firms for which excess cash could be calculated.

Excess cash is determined by a first pass regression to predict log(cash/(assets-cash)). Variables deflated

by assets are deflated by the book value of assets less cash and marketable securities. Real variables are

deflated by the Consumer Price Index to obtain 1994 dollars. Size is defined as the natural log of assets.

Market to book is measured by (book value of assets - book value of equity + market value of

equity)/assets. Cash flow is defined as (earnings before interest and taxes - interest - taxes - common

dividends). Net working capital is calculated without cash. Payout to shareholders is the sum of cash

dividends/assets and stock repurchases/assets. Industry sigma is a measure of the volatility of an industry’s

cash flow for a 20-year period. Industries are defined by 2-digit SIC codes. Total leverage is total debt/total

assets. The t-statistic is for a difference of means from the first to the fourth quartile. Each quartile

contains approximately 21,780 firm years.

Variable

First

Cash/Assets

Quartile

Second

Cash/Assets

Quartile

Third

Cash/Assets

Quartile

Fourth

Cash/Assets

Quartile

t-statistic

(p-value)

Excess Cash /Assets

Range

-850% to

-2.69%

-2.69% to

0.59%

0.59% to

9.02%

9.02% to

337%

Real Size 4.2694 5.0127 4.9209 4.1423 7.13

(0.0001)

Market-to-Book 1.4706 1.2351 1.3432 1.8151 -5.92

(0.0001)

R&D / Sales 0.0366 0.0119 0.0152 0.0430 -5.44

(0.0001)

Cash Flow / Assets 0.0289 0.0432 0.0483 0.0287 0.09

(0.9295)

Net Working

Capital / Assets

0.1697 0.1961 0.1723 0.1648 2.07

(0.0389)

Capital

Expenditures /

Assets

0.0974 0.0793 0.0855 0.0970 0.45

(0.6509)

Acquisitions /

Assets

0.0101 0.0117 0.0122 0.0106 -1.63

(0.1030)

Payout to

Shareholders

0.0151 0.0151 0.0169 0.0224 -24.40

(0.0001)

Industry Sigma 0.1326 0.1026 0.1052 0.1429 -11.06

(0.0001)
38

Table 4

Regressions Estimating the Determinants of Log (Cash/(Assets-Cash))

The dependent variable in all regressions is the natural log of cash/(assets-cash). In all the independent

variable denominators, except for total debt to assets, assets are really (assets-cash). The year dummy

regressions are run with a dummy variable for each year from 1972-1994. Real size is the natural log of

assets deflated by the CPI to 1994 dollars. Market to book is measured by (book value of assets - book

value of equity + market value of equity)/assets. Cash flow is defined as (earnings before interest and taxes

- interest - taxes - common dividends). NWC is defined as (net working capital - cash). Industry sigma is

the mean of standard deviations of cash flow/assets over 20 years for firms in the same industry as defined

by 2 digit SIC code. Dividend dummy is a variable set to one if the firm paid a dividend in the year and set

to 0 if it did not. Regulation dummy is a variable set to 1 if the firm is in a regulated industry for the year

and set to 0 if it isn’t. Industry adjusted means that all variables are adjusted for industry (including

cash/(assets-cash)) by subtracting the median of the 2-digit SIC code from each firm. All t-statistics are

corrected for heteroskedasticity using White’s correction. The adjusted R 2 of the fixed effect model is

computed without the fixed effects. The fixed effects and cross-sectional regressions exclude firms with

only one observation.

(Continued on next page)
39

Independent

Variable

YearDummies

Year-Dummies

IndustryAdjusted Pooled

Pooled

IndustryAdjustedCross

Sectional

Fixed

Effects

Intercept N.A. N.A. -1.9206

(-82.89)

0.0011

(0.15)

-2.0005

(-34.35)

N.A.

Market/Book 0.1422

(27.60)

0.1343

(25.97)

0.1369

(27.11)

0.1259

(25.10)

0.1105

(9.96)

0.1026

(18.74)

Real Size -0.0402

(-13.37)

-0.0412

(-13.96)

-0.0455

(-15.09)

-0.0434

(-14.64)

-0.0326

(-4.27)

-0.1070

(-13.83)

Cash flow

/Assets

0.1618

(4.44)

0.0838

(2.33)

0.1709

(4.71)

0.0759

(2.13)

-0.1163

(-1.83)

0.0792

(2.09)

NWC/Assets -0.8136

(-31.24)

-0.7807

(-26.36)

-0.7537

(-29.04)

-0.7061

(-24.18)

-0.5589

(-8.72)

-0.5016

(-15.58)

Capital

Expenditures/

Assets

0.4850

(7.38)

0.5465

(8.14)

0.5699

(8.77)

0.6191

(9.35)

0.9581

(5.23)

0.7484

(12.20)

Total debt

/Assets

-3.0234

(-101.61)

-3.0361

(-100.40)

-3.0105

(-101.44)

-2.9889

(-100.01)

-3.2697

(-42.73)

-2.3190

(-66.00)

Industry Sigma 1.1636

(14.92)

0.1060

(1.77)

0.4889

(3.08)

R&D/Sales 1.6606

(19.81)

1.5508

(19.87)

1.7756

(21.02)

1.4909

(19.42)

1.3443

(12.36)

0.7591

(9.13)

Dividend

Dummy

-0.1275

(-11.35)

-0.1172

(-11.14)

-0.0997

(-8.94)

-0.0699

(-6.82)

-0.1463

(-4.09)

0.0482

(3.75)

Regulation

Dummy

-0.0968

(-2.16)

0.1006

(2.83)

-0.0103

(-0.23)

0.1923

(5.41)

0.1721

(1.37)

0.0431

(1.00)

N 87,117 88,755 87,117 88,755 10,869 88,587

Adjusted R221.87% 19.41% 20.99% 18.69% 33.27% 9.85%
40

 Table 5

Derivative Use and Cash/Assets

This table shows the relation between a firm's cash to assets and other variables including the use of

derivatives in 1994. In this table we use the actual value of the derivatives as reported by Risk Publications

deflated by the total assets minus cash. INSIDE0to5 equals inside ownership if inside ownership is less

then 5% and 5% if inside ownership is greater than 5%. INSIDE5to25 = 0 if inside ownership is less than

5%, equals inside ownership minus 5% if inside ownership is greater than 5% but less than 25% and equals

20% if inside ownership is greater than 25%. INSIDEover25 = 0 if board ownership is less than 25% and

equals inside ownership minus 25% if inside ownership is greater than 25%. The bond rating dummy is

equal to 1 if the firm’s debt has an investment grade rating (BBB or higher) and 0 if it is below investment

grade (BBB- or lower) or it has no rating reported on Compustat for 1994. Due to some very significant

outliers in the R&D to sales variable, it has been trimmed at the one-percent level. ***, **, * represent 1%,

5%, and 10% significance levels.

(Continued on next page)
41

Full 1994 Sample S&P 500 That Reported Derivatives

Variable Log(Cash/Assets) Log(Cash/Assets) Log(Cash/Assets) Log(Cash/Assets)

Intercept -2.3514

(-16.055)***

-2.050

(-15.140)***

-3.0222

(-3.435)***

-2.9136

(-3.345)***

Market-to-book 0.1445

(7.348)***

0.1597

(8.166)***

0.23505

(1.875)*

0.2422

(1.981)**

Log of Assets -0.0360

(-1.667)*

-0.0463

(-2.144)**

0.0388

(0.406)

0.0336

(0.353)

Firm Sigma 0.4446

(3.646)***

0.5127

(4.206)***

4.7610

(2.079)**

4.7900

(2.093)**

R & D to Sales 0.9018

(6.516)***

1.0394

(7.608)***

6.5442

(2.452)**

7.6400

(3.221)***

Cash Flow/Assets 0.6320

(3.934)***

0.6676

(4.136)***

-1.9262

(-1.009)

-1.9811

(-1.039)

Net Working

Capital/Assets

-1.2330

(-9.394)***

-1.2333

(-9.345)***

-0.8547

(-1.202)

-0.9188

(-1.299)

Capital

Expenditure/Asset

0.6426

(1.670)*

0.4575

(1.187)

1.6739

(0.970)

1.5133

(0.889)

Total Debt to Assets -3.0598

(-20.505)***

--3.1271

(-20.920)***

-4.0950

(-5.993)***

-4.1210

(-6.040)***

Number of Segments -0.0234

(-0.740)

-0.0201

(-0.633)

-0.1011

(-1.736)*

-0.1001

(-1.719)*

Industry Sigma 1.2546

(5.233)***

0.5488

(0.897)

Dividend Payer

Dummy

-0.1422

(-1.954)**

-0.1701

(-2.331)**

-0.2718

(-0.970)

-0.2548

(-0.910)

INSIDE 0% to 5% 3.8038

(1.853)*

4.1918

(2.032)**

3.5415

(0.657)

3.7571

(0.699)

INSIDE 5% to 25% -0.9004

(-1.471)

-1.0007

(-1.626)*

-0.1298

(-0.048)

-0.1029

(-0.038)

INSIDE over 25% -0.0870

(-0.328)

-0.1090

(-0.401)

0.3899

(0.069)

0.5101

(0.090)

Bond Rating Dummy -0.5211

(-4.505)***

-0.4770

(-4.112)***

-0.1240

(-0.624)

-0.1005

(-0.510)

Anti-takeover Dummy -0.1870

(-1.171)

-0.1841

(-1.154)

Derivative Use

Dummy

0.0319

(0.110)

0.0107

(0.037)

Derivative Use > 10%

of Assets

0.2822

(1.681)*

0.3100

(1.880)*

Adjusted R2

N

28.6%

2400

27.8%

2400

36.4%

216

36.4%

216
42

Table 6

Cash Holdings in 1994 Among Firms with Assets Over $25 Billion

Non-financial firms ranked by their cash and marketable securities to assets ratio using Compustat fiscal

1994 data. Cash/assets is defined as cash and marketable securities over assets less cash. Firms are

required to have assets in excess of $25 billion. The bond rating is the S&P senior bond rating as of 1994.

Industry cash/assets is the median industry cash/assets ratio in 1994 where industry is defined by 2-digit

SIC. Excess cash to assets is defined as excess cash over assets less cash. Excess cash is the residual from

a first pass regression predicting the natural log of cash to assets less cash. An entry of n.a. indicates that

one or more data items required to estimate excess cash was missing from the Compustat tape.

Rank Company

Bond

Rating

Total Cash

($ mil) Cash /

Assets

Industry

Cash/ Assets

Excess

Cash / Assets EBITDA

 ($ mil)

1 IBM A 10554 14.96% 6.71% 10.85% 9202

2 PROCTER & GAMBLE AA 2656 11.61% 9.88% 4.24% 4579

3 CHRYSLER A- 5145 11.59% 4.43% 8.27% 7223

4 FORD MOTOR A+ 13822 6.73% 4.43% 5.46% 25408

5 AMOCO AA 1789 6.50% 5.07% 1.76% 4143

6 GENERAL MOTORS A- 10939 5.83% 4.43% 3.67% 21565

7 DOW CHEMICAL A 1134 4.46% 9.88% -2.03% 3795

8 CHEVRON AA 1306 3.95% 5.07% 0.03% 4925

9 DU PONT AA 1109 3.10% 9.88% -4.04% 6944

10 XEROX A 1058 2.82% 8.11% -1.18% 1961

11 SHELL AAA 617 2.40% 5.07% n.a. 2820

12 VIACOM BB 597.7 2.16% 5.55% -1.04% 824

13 EXXON AAA 1775 2.06% 5.07% -2.73% 11942

14 BELLSOUTH AAA 657.3 1.95% 1.78% -2.43% 7317

15 TEXACO INC A 464 1.85% 5.07% -1.44% 2668

16 SEARS ROEBUCK BBB 1421 1.57% 4.25% -1.96% 1999

17 AT&T CORP AA 1208 1.55% 1.78% -2.15% 13137

18 SBC COMMUNICATIONS A 364.6 1.42% 1.78% -2.97% 4828

19 RJR NABISCO BBB- 423 1.37% 3.32% -1.40% 3698

20 GENERAL ELECTRIC AAA 2591 1.35% 7.18% -0.93% 16194

21 MOBIL CORP AA 531 1.30% 5.07% -3.11% 6611

22 GTE CORP BBB+ 323 0.77% 1.78% -2.90% 8072

23 NYNEX CORP A 137.5 0.46% 1.78% -3.26% 5090

24 PHILIP MORRIS INC A 184 0.35% 5.18% -2.81% 11080

25 BELLSOUTH AAA 94.4 0.35% 1.78% n.a. 6541

26 WAL-MART STORES AA 45 0.14% 4.25% -2.42% 5120
43

Table 7

Spending patterns based on Market to Book and Previous Year’s Excess Cash

The sample includes only firm years in which the firm has positive lagged excess cash. Firm years are ranked into

quartiles by market to book as measured by (book value of assets - book value of equity + market value of

equity)/assets in the current year. High (low) market to book firms are those ranked in the top (bottom) quartile.

The firm years are also independently broken into quartiles based on the previous years holdings of excess cash.

The excess cash holding is the antilog of a residual from a first pass regression to predict the natural log of

cash/(assets-cash). The cash quartiles are generated for every year and firms are regrouped each year. Panel A

shows capital expenditures, Panel B shows expenditures on acquisitions, Panel C shows payments to shareholders

which is defined as stock repurchases plus cash dividends, and Panel D shows the operating cash flow. All

variables are from the flow of funds statement and are deflated by (total assets - cash). Number of firm years of

each quartile are also included in brackets. The t-statistic is generated from a difference of means test between the

first and fourth quartiles of excess cash (column) or the difference between high and low market to book (row).

Previous Year Excess Cash Holdings

Panel A: Quartiles

Capital Expenditures First Second Third Fourth t-statistic

(p-value)

High market to book firm

years

0.0969

[1367]

0.1009

[1920]

0.1080

[2573]

0.1177

[3533]

-7.01

(0.0001)

Low market to book firm

years

0.0621

[3838]

0.0705

[3288]

0.0750

[2627]

0.0790

[1634]

-7.34

(0.0001)

t-statistic

(p-value)

-13.67

(0.0001)

-13.39

(0.0001)

-14.63

(0.0001)

-14.12

(0.0001)

Previous Year Excess Cash Holdings

Panel B: Quartiles

Acquisitions First Second Third Fourth t-statistic

(p-value)

High market to book firm

years

0.0135

[1356]

0.0125

[1918]

0.0131

[2571]

0.0157

[3512]

-1.64

(0.1005)

Low market to book firm

years

0.0049

[3824]

0.0061

[3260]

0.0079

[2612]

0.0110

[1630]

-6.14

(0.0001)

t-statistic

(p-value)

-7.40

(0.0001)

-5.88

(0.0001)

-5.34

(0.0001)

-3.78

(0.0002)

Previous Year Excess Cash Holdings

Panel C: Quartiles

Payments to Shareholders First Second Third Fourth t-statistic

(p-value)

High market to book firm

years

0.0208

[1340]

0.0250

[1892]

0.0280

[2456]

0.0216

[3309]

-0.66

(0.5111)

Low market to book firm

years

0.0130

[3791]

0.0147

[3253]

0.0182

[2592]

0.0241

[1567]

-11.71

(0.0001)

t-statistic

(p-value)

-8.00

(0.0001)

-11.22

(0.0001)

-9.94

(0.0001)

2.18

(0.0289)
44

Table 7, Cont’d

Previous Year Excess Cash Holdings

Panel D: Quartiles

Operating Cash Flow First Second Third Fourth t-statistic

(p-value)

High market to book firm

years

0.0932

[756]

0.1063

[1060]

0.1088

[1444]

0.0399

[1714]

4.99

(0.0001)

Low market to book firm

years

0.0679

[3201]

0.0756

[2644]

0.0788

[1952]

0.0335

[973]

4.14

(0.0001)

t-statistic

(p-value)

-3.33

(0.0001)

-4.80

(0.0001)

-4.57

(0.0001)

-0.57

(0.5678)
45

Table 8

Regression Results for Determinants of Capital Expenditures

Regression results for uses of cash using a sample of firms which excess cash could be calculated. A firm had to be

observed for at least 2 years to be included in the regressions. The dependent variable in all regressions is capital

expenditures divided by assets in year t. Denominator of assets is really (assets - cash) in all variables. All right hand

side numerators come from the flow of funds statements. Cash flow is defined as (earnings before interest and taxes -

interest - taxes - common dividends). Sales Growth is the natural log of sales in year t minus the natural log of sales

in year t-1. (Excess Cash / Assets) t-1 is the antilog of a lagged residual from a first pass regression to determine the

natural log of cash/(assets-cash). (Normal Cash / Assets) t-1 is the antilog of a lagged predicted value from a first pass

regression to determine the natural log of cash/(assets-cash). POSX is a dummy variable which is given a value of 1

if there is positive excess cash in the firm year and zero otherwise. For the industry-adjusted data, all the variables

are adjusted by subtracting the industry median. Industries are defined by 2 digit SIC codes. The t subscripts

indicate time periods. t-statistics are in parentheses and calculated using White’s correction for heteroskedasticity.

The adjusted R2 for fixed effects models are computed without the fixed effects.

(Continued on next page)
46

Raw Industry Adjusted

OLS OLS Fixed

Effects

Fixed

Effects

OLS OLS Fixed

Effects

Fixed

Effects

Intercept 0.0563

(61.36)

0.0559

(60.60)

N.A. N.A. 0.0036

(2.51)

0.0056

(3.45)

N.A. N.A.

(Cash Flow/Assets) t 0.0870

(23.42)

0.0860

(23.17)

0.0240

(7.25)

0.0273

(8.42)

0.0806

(23.17)

0.0796

(22.86)

0.0228

(7.42)

0.0246

(7.98)

Sales Growth 0.0158

(11.41)

0.0159

(11.51)

0.0173

(14.42)

0.0156

(13.20)

0.0164

(11.55)

0.0167

(11.73)

0.0162

(14.13)

0.0149

(13.15)

Market to Book 0.0076

(15.00)

0.0076

(15.17)

0.0098

(18.91)

0.0092

(18.42)

0.0083

(15.83)

0.0083

(15.96)

0.0101

(18.21)

0.0099

(18.02)

(Normal Cash/Assets)t-1 0.0381

(3.04)

0.0923

(4.07)

0.0475

(2.86)

0.0945

(7.01)

0.0035

(1.60)

0.0036

(1.63)

0.0078

(3.25)

0.0079

(3.41)

(Normal Cash/Assets) t-2 0.0535

(4.66)

0.0521

(4.41)

0.0632

(6.33)

0.0608

(7.44)

0.0066

(4.60)

0.0066

(4.57)

0.0087

(6.92)

0.0086

(6.86)

(Normal Cash/Assets) t-3 0.0054

(0.77)

0.0028

(0.41)

0.0191

(3.42)

0.0203

(3.67)

-0.0004

(-0.58)

-0.0005

(-0.69)

0.0008

(1.28)

0.0008

(1.30)

(Excess Cash/Assets) t-1 0.0827

(3.35)

0.0636

(14.59)

0.0038

(2.80)

0.0026

(8.13)

(Excess Cash/Assets) t-2 -0.0019

(-0.63)

0.0072

(2.94)

-0.0001

(-0.83)

0.0001

(1.11)

(Excess Cash/Assets) t-3 -0.0073

(-3.38)

0.0022

(1.16)

-0.0004

(-3.69)

-0.0001

(-0.72)

POSX 0.0000

(0.01)

0.0005

(0.41)

-0.0007

(-2.15)

-0.0018

(-5.53)

0.0003

(0.13)

-0.0010

(-0.43)

0.0012

(3.29)

0.0007

(1.86)

POSX *

(Normal Cash/Assets) t-1

0.0499

(3.38)

0.0015

(0.07)

0.0764

(3.52)

0.0208

(1.12)

0.0017

(0.84)

0.0017

(0.84)

0.0002

(0.07)

0.0006

(0.24)

POSX *

(Excess Cash/Assets) t-1

-0.0757

(-3.07)

-0.0396

(-8.04)

-0.0037

(-2.72)

-0.0020

(-6.18)

Sample size 58,938 58,938 58,938 58,938 57,437 57,437 57,437 57,437

Adjusted R26.56% 6.66% 5.04% 6.41% 7.55% 7.61% 5.88% 6.16%
47

Table 9. Highest Quartile Excess Cash Persistence

This table shows the persistence of levels of excess cash for firms selected based on the first

time they enter the highest quartile of excess cash. The are followed for the next five years to

determine the quartile in which they belong in the subsequent years. Quartile 4 represents the

highest excess cash quartile and Year 0 is the measurement year. The number of firm years in

each quartile is in brackets.

% in Quartile 4 % in Quartile 3 % in Quartile 2 % in Quartile 1

Year 0 100.0

[6281]

Year 1 56.1

[2911]

23.0

[1194]

8.8

[456]

12.1

[627]

Year 2 47.0

[2116]

25.0

[1127]

13.1

[591]

14.9

[672]

Year 3 40.7

[1588]

27.0

[1056]

15.1

[588]

17.2

[672]

Year 4 40.6

[1417]

26.4

[920]

15.2

[529]

17.9

[624]

Year 5 39.5

[1229]

26.4

[821]

16.1

[501]

18.0

[561]
48

Table 10

Future Cash Disposition for Firms’ with High and Low Excess Cash

The sample includes firms only the first year that they were in the first or fourth excess cash quartile (year zero).

The firm years are independently broken into quartiles based on the previous year's holdings of excess cash. The

excess cash holding is the antilog of a residual from a first pass regression to predict the natural log of cash/(assetscash). The cash quartiles are generated for every year and firms are regrouped each year. Panel A shows firms in

the fourth quartile and Panel B shows firms in the first quartile. Panel C shows the t-statistics (in parentheses) and

the p-values for the tests of differences of means between the first and fourth quartile for each variable and each year.

The table shows operating cash flow, capital expenditures, expenditures on acquisitions, and payments to

shareholders which is defined as stock repurchases plus cash dividends. All variables are from the flow of funds

statement and are deflated by (total assets - cash). Number of firm years of each quartile are also included in

brackets. t-values for the difference in means from YEAR 0 are shown in parentheses in Panels A and B.

Panel A. First Quartile Excess Cash Holdings in Year 0

Year 0 Year 1 Year 2 Year 3 Year 4 Year 5

Operating Cash Flow 0.0145

[4112]

0.0559

[4044]

(-9.38)

0.0628

[3495]

(-11.07)

0.0695

[3068]

(-12.57)

0.0733

[2699]

(-13.02)

0.0800

[2359]

(-14.65)

Capital Expenditures 0.1130

[6363]

0.0957

[5663]

(9.23)

0.0903

[5036]

(12.03)

0.0875

[4517]

(13.41)

0.0829

[4064]

(15.81)

0.0820

[3673]

(16.25)

Acquisitions 0.0124

[6212]

0.0103

[5596]

(2.95)

0.0099

[4968]

(3.37)

0.0087

[4450]

(5.08)

0.0086

[3999]

(5.19)

0.0086

[3618]

(5.14)

Payments to Shareholders 0.0123

[6153]

0.0124

[5547]

(-0.17)

0.0129

[4922]

(-1.48)

0.0143

[4422]

(-4.16)

0.0149

[3978]

(-5.30)

0.0158

[3586]

(-6.70)

Panel B. Fourth Quartile Excess Cash Holdings in Year 0

Year 0 Year 1 Year 2 Year 3 Year 4 Year 5

Operating Cash Flow 0.0760

[3754]

0.0331

[3806]

(7.00)

0.0599

[3270]

(2.74)

0.0723

[2868]

(0.63)

0.0869

[2482]

(-1.82)

0.0892

[2032]

(-2.22)

Capital Expenditures 0.1151

[6281]

0.1129

[5597]

(1.10)

0.0972

[4951]

(9.34)

0.0898

[4383]

(13.07)

0.0848

[3936]

(15.74)

0.0846

[3542]

(15.33)

Acquisitions 0.0105

[6162]

0.0151

[5542]

(-5.86)

0.0132

[4906]

(-3.54)

0.0128

[4333]

(-2.84)

0.0102

[3910]

(0.30)

0.0107

[3518]

(-0.31)

Payments to Shareholders 0.0143

[6026]

0.0150

[5493]

(-1.37)

0.0155

[4847]

(-2.22)

0.0166

[4302]

(-4.06)

0.0174

[3866]

(-5.25)

0.0181

[3478]

(-6.10)
49

Panel C. t-statistics for Difference in Means of First and Fourth Quartiles

Year 0 Year 1 Year 2 Year 3 Year 4 Year 5

Operating Cash Flow (-10.88)

0.0001

(4.55)

0.0001

(0.62)

0.5351

(-0.59)

0.5563

(-2.77)

0.0056

(-1.91)

0.0569

Capital Expenditures (-1.05)

0.2958

(-9.17)

0.0001

(-3.80)

0.0001

(-1.27)

0.2054

(-0.99)

0.3200

(-1.41)

0.1598

Acquisitions (2.67)

0.0076

(-6.14)

0.0001

(-4.19)

0.0001

(-4.97)

0.0001

(-2.18)

0.0296

(-2.60)

0.0093

Payments to Shareholders (-4.19)

0.0001

(-5.34)

0.0001

(-4.86)

0.0001

(-4.02)

0.0001

(-4.19)

0.0001

(-3.41)

0.0006
50

Table 11

Future Cash Disposition for Firms’ Going From the Highest to the Lowest Quartile, or Vice Versa,

in One Year

The sample includes firms only the first time that they were in the first (fourth) excess cash quartile (year zero) and

then in the fourth (first) excess cash quartile the next year (year 1). The firm years are independently broken into

quartiles based on the previous year's holdings of excess cash. The excess cash holding is the antilog of a residual

from a first pass regression to predict the natural log of cash/(assets-cash). The cash quartiles are generated for

every year and firms are regrouped each year. Panel A shows firms which moved from the first to the fourth

quartiles and Panel B shows firms which moved from the fourth to the first quartile. Panel C shows the t-statistics (in

parentheses) and the p-values for the tests of differences of means between the two groups of firms for each variable

and each year. The table shows operating cash flow, capital expenditures, expenditures on acquisitions, and

payments to shareholders which is defined as stock repurchases plus cash dividends. All variables are from the flow

of funds statement and are deflated by (total assets - cash). Number of firm years of each quartile are also included

in brackets. t-values for the difference in means from YEAR 0 are shown in parentheses in Panels A and B.

Panel A. First Quartile Excess Cash Holdings in Year 0, Fourth Quartile in Year 1

Year 0 Year 1 Year 2 Year 3 Year 4 Year 5

Operating Cash Flow -0.0512

[257]

0.0594

[266]

(-4.09)

0.0242

[224]

(-2.96)

0.0524

[188]

(-4.21)

0.0338

[157]

(-3.06)

0.0668

[137]

(-4.58)

Capital Expenditures 0.1099

[413]

0.1004

[413]

(1.36)

0.1095

[366]

(0.05)

0.0899

[326]

(2.68)

0.0849

[283]

(3.36)

0.0840

[251]

(3.36)

Acquisitions 0.0172

[401]

0.0146

[406]

(0.77)

0.0179

[359]

(-0.17)

0.0108

[317]

(1.98)

0.0129

[280]

(1.18)

0.0089

[249]

(2.49)

Payments to Shareholders 0.0088

[397]

0.0114

[400]

(-1.45)

0.0112

[354]

(-1.42)

0.0125

[320]

(-2.00)

0.0152

[278]

(-3.11)

0.0149

[243]

(-2.85)

Panel B. Fourth Quartile Excess Cash Holdings in Year 0, First Quartile in Year 1

Year 0 Year 1 Year 2 Year 3 Year 4 Year 5

Operating Cash Flow -0.0111

[341]

-0.0915

[413]

(3.38)

0.0095

[346]

(-0.87)

0.0388

[284]

(-2.08)

0.0519

[236]

(-2.59)

0.0577

[179]

(-2.79)

Capital Expenditures 0.1385

[627]

0.1479

[627]

(-1.30)

0.0989

[544]

(6.10)

0.0926

[458]

(6.73)

0.0885

[406]

(7.45)

0.0920

[358]

(6.54)

Acquisitions 0.0131

[611]

0.0197

[610]

(-2.28)

0.0105

[540]

(1.00)

0.0118

[450]

(0.47)

0.0106

[406]

(0.94)

0.0108

[356]

(0.79)

Payments to Shareholders 0.0123

[598]

0.0106

[609]

(1.03)

0.0101

[538]

(1.33)

0.0114

[449]

(0.54)

0.0129

[402]

(-0.33)

0.0127

[353]

(-0.23)
51

Panel C. t-statistics for Difference in Means of Firm Groups

Year 0 Year 1 Year 2 Year 3 Year 4 Year 5

Operating Cash Flow (-1.47)

0.1424

(6.42)

0.0001

(0.69)

0.4899

(0.65)

0.5183

(-0.73)

0.4673

(0.40)

0.6921

Capital Expenditures (-3.94)

0.0001

(-6.85)

0.0001

(1.58)

0.1145

(-0.38)

0.7022

(-0.52)

0.6025

(-1.04)

0.2976

Acquisitions (1.30)

0.1948

(-1.64)

0.1021

(2.29)

0.0222

(-0.37)

0.7106

(0.72)

0.4749

(-0.064)

0.5265

Payments to Shareholders (-2.09)

0.0367

(0.43)

0.6649

(0.68)

0.4950

(0.62)

0.5374

(1.06)

0.2901

(1.00)

0.3188