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Corporate Cash Holdings and Monetary Shocks

Haibo Yao1

Abstract

This paper examines the impact of monetary shocks on corporate cash holdings. I find

evidence, consistent with credit channel explanations, that industrial firms hold onto cash

when monetary policy is too tight and large industrial firms are less influenced by

monetary policy tightness or ease. The impact of monetary shocks is asymmetric,

industrial firms react more aggressively to tight shocks than to loose shocks. Further tests

examine whether the long lasting loose monetary policy results in pileup of corporate

cash holdings. The evidence supports the assumption that industrial firms take the

opportunity of “long lasting lower interest rate” environment to hoard cash to buffer the

monetary policy effectiveness.

Key words: cash holdings; monetary shocks; Taylor rule

JEL: G30, G32, E30, E43, E52

1. Introduction

Industrial firms hold cash for many reasons. John Maynard Keynes (1936) posits

three motives: a transaction motive, a precautionary motive, and a speculative motive.

1 Ph.D. candidate in Finance at Mississippi State University and Visiting Instructor at Eastern Kentucky University.

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The transaction motive for money demand results from the need for liquidity for day-to-

day transactions to bridge the gap between payments and receipts. The precautionary

demand for money refers to holding cash to minimize the potential loss arising from a

contingency when access to capital markets is costly. Speculative demand for cash refers

to holding cash to take advantage of investment opportunities that may arise in the future.

Bates et al. (2009) find evidence supporting both the transaction motive and the

precautionary motive from firm specific explanations. They also report consistent

evidence supporting an increase in cash holdings in the 2000s cannot be explained by

changes in firm characteristics.

“One way of understanding why U.S. firms have amassed so much cash is to

recognize that holding cash provides firms with unexercised option value, giving them

financial flexibility in times of heightened uncertainty.”2 Hodrick (2013) cites the

example of Google’s CFO Patrick Pichette’s motivating the company’s holding $48.1

billion of cash at the end of 2012 as giving it “the strategic ability to pounce.”3 Another

example is that “Warren Buffet is noted to think of cash held in his portfolio as a call

option allowing him to obtain cheap assets at fire sale prices (such as his $5 billion

investment in Goldman Sachs in the depths of the financial crisis).”4 Industrial firms

choose their optimal cash holdings in response to market challenges including uncertain

economic, fiscal, and monetary environment such as the sustainability of historically low

interest rates. These uncertainties create corporate cash flow volatility, resulting in the

option value of holding cash.

2 “Are U.S. Firms Really Holding Too Much Cash?” by Laurie Simon Hodrick, Stanford Institute for Economic Policy

Research (SIEPR) policy brief, July, 2013. 3 Morgan Stanley Technology Conference, February 29, 2013.

4 “For Warren Buffett, the cash option is priceless,” The Globe and Mail, September 24, 2012.

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As far as I know, however, none of the prior empirical corporate finance studies

define and use the appropriate monetary policy variable to measure monetary policy

shocks (or tightening). As measures of monetary tightening, nominal rates and changes in

nominal rates could prove misleading and could induce perverse results5 (Fisher, 1930;

Friedman, 1968; Mishkin, 1996). Romer and Romer (2004) document that researchers

need to minimize the federal funds rate endogeneity problem6 to specify a “true causal

link” between monetary policy and other economic variables. Prior studies that examine

the impact of monetary policy on corporate cash holdings use changes of federal funds

rate to measure monetary policy shocks or tightening (Choi and Kim, 2001; Zaman,

2011).

None of the studies examine the possible relation between persistent loose

monetary policy and increasing corporate cash holdings in the 2000s, when monetary

policy is specified as “too low for too long” (Kahn, 2010). Kahn (2010) posits that too

low interest rates for too long may contribute to a buildup of financial imbalances,

resulting in misallocation of resources (which includes cash holdings). Increasing

corporate cash holdings (Bates et al., 2009) possibly means essentially taking money out

of circulation, tamping down economic activity and slowing recovery from crises (e.g.,

Sánchez and Yurdagul, 2013). The impact of persistent “too low” interest rates on

corporate cash holdings could help explain the slow recovery of the economy from the

current financial crisis.

5 For instance, the monetary authorities might think they were providing for a steady cost of credit by holding interest

rates constant, but if the expected rate of inflation rose, they would really be fostering easier money and credit conditions, while changes in nominal interest rates reflect changes in inflationary expectations. 6 “…the funds rate often moved endogenously with changes in economic conditions [such as inflation and output gap].

Such endogenous movements may lead to biased estimates of the effects of monetary policy”.

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Furthermore, prior studies have found mixed evidence on the impact of monetary

policy tightness on corporate cash holding. Using changes of funds rates to proxy for

monetary policy tightness, Choi and Kim (2001) document that when monetary policy is

tightened industrial firms initially increase their cash holdings. Zaman (2011), however,

finds the contrary using the same monetary policy variable. Bates et al. (2009) find a

negative relation between 3-month T-bill yield which is closely linked to the federal

funds rate and the transaction demand for cash, but the relation is not significant. There is

a significant need to conduct empirical monetary policy-related research to examine the

existing theories regarding the relation between corporate cash holdings and monetary

policy shocks.

In the analysis that follows, I augment Bates et al.’s (2009) analysis to include

monetary shocks to examine how monetary policy influences corporate cash holdings. I

first examine how corporate cash holdings are affected by monetary shocks during 1980-

2007. I document that industrial firms increase their cash holdings when facing positive

monetary shocks, which implies that monetary policy becoming too tight. The monetary

impact is asymmetric: industrial firms react more aggressively when monetary policy is

too tight than when it is too loose. Using firm size to proxy for financially constrained, I

find that large firms are less reactive to monetary policy than small ones. My findings are

robust when using different proxies for corporate cash holding, when using different

monetary shock specifications, and when using yearly or quarterly data samples.

While individually these monetary shocks may contain limited information,

collectively they potentially provide insight into whether monetary policy contributed to

a buildup of industrial cash holding. I provide direct evidence on the contributing role of

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sustained monetary shocks to increasing cash holdings in the 2000s documented in Bates

et al. (2009). I find that industrial firms in the U.S. accumulate their cash holdings in

response to sustained negative monetary shocks during this period. The result is robust

when I use different specifications of sustained monetary shocks, for different measures

of cash holdings, for different estimation models and for both yearly and quarterly data

samples.

This essay proceeds as follows. In Section 2, I briefly review three main monetary

channels and their theoretical predictions. I then discuss the main monetary policy

variables I use to measure monetary policy tightness and develop my main hypotheses in

Section 3. In Section 4 I discuss my data set and descriptive statistics and Section 5

reports my empirical results. Section 6 concludes.

2. Previous Research and Theoretical Predictions

There are three main channels in explaining the impact of monetary policy on

cash holdings: interest rate channel, Tobin’s q theory, and credit channel.

2.1 Interest rate channel

The interest rate channel regarding the relation between monetary policy and cash

holdings is based on Keynes’ (1936) three distinct motives of demand for holding cash as

discussed above. The nominal interest rate is the opportunity cost of holding cash.

Keynes (1936) documents an inverse relation between interest rate and transaction and

precautionary demand for cash. Keynes (1936) also documents an inverse relation

between interest rate and speculative demand for cash when interest rates would be

expected to rise (fall) if their current levels are low (high). Overall the interest rate

channel predicts an inverse relation between monetary policy tightness and corporate

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cash holdings. Previous research and evidence supporting the inverse relation between

interest rate and transaction and precautionary demand for cash includes Keynes (1936),

Baumol (1952), Tobin (1956), and Miller and Orr (1966).

There is a similarity between problem of managing a cash balance and that of

managing an inventory of some physical commodity. The Baumol-Tobin model (Baumol,

1952; and Tobin, 1956) predicts that when an individual receives her income periodically

but wishes to make purchases continuously, the optimal strategy of holding cash is

inversely related to the square root of the nominal interest rate under some simplifying

assumptions. Unlike those of individuals, industrial firms’ cash balance fluctuates

irregularly and sometimes unpredictably over time for both operating receipts and

expenditures. Miller and Orr (1966) extend Baumol (1952) to incorporate “this ‘up and

down’ cash balance movement characteristic of business operations” and find that a

firm’s optimal average cash balance is inversely related to the nominal interest rate and

the relation is more sensitive than that of individuals. Keynes (1936) assumes that people

store wealth with either money or bonds. When interest rates are high, future interest

rates would then be expected to fall and bond prices would be expected to rise. So bonds

are more attractive than money when interest rates are high, and vice versa. So

speculative cash demand is also inversely related to the interest rate. Pál and Ferrando

(2010) suggest that internal cash flow is used in a systematic pattern for inter-temporal

allocation of capital, implying that industrial firms may hold more speculative cash for

future depreciation of other assets caused by current expansionary monetary policy.

2.2 Tobin’s q theory

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Tobin’s (Tobin, 1969) q theory provides a mechanism through which monetary

policy affects the economy through its effects on the valuation of equities. Tobin (1969)

defines q as the market value of firms divided by the replacement cost of capital. High q

implies that market firm value is high relative to the replacement cost of capital, and new

plant and equipment capital is cheap relative to the market value of industrial firms.

Firms can then issue equity and get a high price for it relative to the cost of the plant and

equipment they are buying. A fall in interest rates stemming from expansionary monetary

policy increases the present value for the future cash flow, leading to higher Tobin’s q.

Then firms can issue equity to purchase new investment goods. Bates et al. (2009) predict

the relation between capital raising and corporate cash holdings as “capital raising tends

to be lumpy, firms should have more cash immediately after raising capital”.

Combining Tobin’s q theory and Bates et al.’s (2009) capital raising statement, I

predict impact of monetary policy on corporate cash holdings as follows: the fall in

interest rates stemming from expansionary monetary policy increases Tobin’s q, and

firms can issue more equity. Industrial firms should have more cash immediately after

equity issuance. Therefore Tobin’s q theory may exhibit a negative relationship between

monetary policy tightness and corporate cash holdings.

2.3 Credit channels

Credit channels (Bernanke and Gertler, 1995) emphasize asymmetric information

in financial markets associated with costly verification and enforcement of financial

contracts. According to the credit channels theory, firms facing more asymmetric

information problems could have difficulty raising external capital or face a higher cost

of external funds. This would suggest that firms build cash to hedge future funding needs.

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Two basic channels of monetary transmission arise as a result of asymmetric information

problems in credit markets: the narrow credit channel (also known as “bank lending

channel”) and the broad credit channel (also known as “balance-sheet channel”). The

broad credit channel stresses the potential impact of changes in monetary policy on

borrowers’ net worth, cash flow and liquid assets, while the narrow credit channel

focuses more narrowly on the possible effect of monetary policy actions on the supply of

loans by depository institutions. Credit channel literature has examined how monetary

policy affects the demand for cash indirectly through the supply of bank loans (e.g.

Bernanke and Blinder, 1988), the liability of firms (e.g. Christiano et al., 1996), and the

balance sheet of firms (e.g. Gertler and Gilchrist, 1994).

My prediction regarding the impact of monetary policy on corporate cash

holdings through the balance-sheet channel works as follows. Tight monetary policy

directly weakens borrowers’ balance sheets either by reducing net cash flows or by

declining asset prices, leading to lower net worth. The lower the net worth of industrial

firms, the more severe the adverse selection and moral hazard problems are in lending to

these firms, the more difficulty industrial firms could have raising external capital. A

weaker financial position with smaller net worth increases the conflict of interest with the

lender, because the borrower cannot offer enough collateral to guarantee the liabilities

she issues, thus resulting in a higher external finance premium. Similarly, a weaker

financial position with smaller net worth could exacerbate stockholder-bondholder

conflicts. Accordingly, bondholders could choose to protect themselves by requiring

covenants that impose minimum liquidity standards or firms could choose to maintain

excess liquidity to blunt the effects of tight monetary policy on the cost of debt. This

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would suggest that industrial firms build cash to hedge future funding needs in response

to tight monetary policy especially for small (as a proxy for financially constrained)

firms.

The narrow credit or bank lending channel also relies on credit market frictions

while banks play a more central role. Because a significant subset of industrial firms

relies heavily or exclusively on bank financing, a reduction in loan supply will force

those industrial firms to resort to internal financing, like holding more cash for current or

future funding use. Expansionary monetary policy, which increases bank reserves and

bank deposits, increases the availability of credit, suggesting that industrial firms may

reduce their cash holding.

As Bernanke and Gertler (1995) point out, “the effects of the corporate cash

squeeze on economic behavior depend largely on firms’ ability to smooth the drop in

cash flows by borrowing.” Firm size could be used to proxy for the net worth of the firm.

The smaller the industrial firms, the more severe the possible adverse selection and moral

hazard problems are in lending to these firms. Gertler and Gilchrist (1993, 1994) study

the differential impact of a cash squeeze on different types of firms and find striking

differences in behavior between large and small firms. Large firms are at least

temporarily able to maintain their levels of production and employment in the face of

higher interest costs caused by tight monetary policy. Therefore the credit channel

predicts that small firms are more sensitive to monetary policy than large firms.

Overall, credit channel theory predicts that there is a positive relationship between

monetary policy tightness and corporate cash holdings, and large firms are less sensitive

to monetary policy.

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I summarize the above analysis with different monetary policy transmission

channels and the predicted relationship between monetary policy tightness (ease) and

corporate cash holdings in Table 1.

2.4 Previous findings

Former researchers mainly use either interest rates or changes of interest rates to

proxy for monetary policy tightness. Choi and Kim (2001) measure the monetary policy

by the change in the federal funds rate7 and find that upon tighter monetary policy, S&P

500 firms initially increase their cash holdings before reducing them, whereas non-S&P

firms reduce cash holdings more quickly. Choi and Kim (2001) also include the current

value and eight lags of change in federal funds rate to examine the effects of monetary

policy over a longer term. Bates et al. (2009) find a negative relation between 3-month T-

bill yield which is closely linked to the federal funds rate and the transaction demand for

cash, although the relation is not significant. Zaman (2011) uses the change in federal

funds rate as a measure of monetary policy change and finds that when monetary policy

is tight, industrial firms tend to reduce their cash holdings. Stern and Miller (2004) define

“policy mistakes” as current policy deviations from optimal monetary policy8 and argue

that “a material policy mistake… would be to allow a significant rate of inflation or

deflation”, leading to misallocations of resources. Stern and Miller (2004) document that

tighter monetary policy (or “policy mistake” in their paper) result in a significant rate of

deflation, holding money relative to physical assets becomes increasingly attractive, so

corporate cash holdings increase. On the contrary loose monetary policy results in a

7 Choi and Kim (2001) also use the negative value of the mix of nonborrowed reserves and one-period-lagged total

reserves for their robustness check. 8 Stern and Miller (2004) do not provide a specific formula of optimal monetary policies, instead they discuss the

general framework in building such a policy and document three properties of optimal policies.

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significant increase in the rate of inflation, holding money relative to physical assets

becomes increasingly costly, so that industrial firms reduce their cash holdings.

3. Policy Deviation and Hypotheses Development

3.1 Policy Deviation

Following the academic literature such as Taylor (1998) and Kahn (2010), I use

the Taylor rule (Taylor, 1993) to evaluate monetary policy.

The general form of the Taylor Rule may be written as:

(1)

where represents the recommended short-term interest rate, represents the

equilibrium real interest rate, represents the deviation of the inflation rate ( )

from its long-run target ( ),

represents the output gap—the level of real GDP

( ) relative to potential GDP ( ), and the coefficients and represent the policy

maker’s responsiveness to deviations from the targeted output and inflation marks. In

short, the Taylor Rule prescribes a target Federal Funds rate based on the deviation of

inflation and output from long-run means.

Taylor (1998) defines “policy mistakes” as large departures from baseline

monetary policy rules. According to his definition, policy mistakes include excessive

monetary tightness and excessive monetary ease. Policy mistakes can be measured

through use of deviations from the Taylor Rule given in Equation (1). Kahn (2010) uses

the same deviation as an indicator of whether policy is too tight or too easy. Bernanke

(2010) also mentions that whether policy is nevertheless easier than necessary is to

compare Federal Reserve policies to the Taylor rule. Stern and Miller (2004) also use

deviations of current funds rate from a possible optimal monetary policy to define

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“material policy mistakes”. This deviation also could be used as “monetary shocks” in the

spirit of Romer and Romer (2004).

Temporary policy deviations relative to the Taylor Rule’s prescribed rate:

(2)

where is the actual (nominal) target Federal Funds rate at time and is the prescribed

Taylor Rule rate set according to Equation (1).

As Kahn (2010) documents, “such [policy] deviations-especially if they are small

and temporary-may represent an appropriate and desirable response to unusual economic

or financial conditions. Larger and more persistent deviations, however, may contribute

to a buildup of financial imbalances.”

Monetary policy usually takes three to eight quarters to take effect (e.g., Olivei

and Tenreyro, 2007; Labonte, 2013). Sustained deviations, therefore, may be a better

indicator of policy mistakes. “The purpose of this variation is to capture the idea that the

cumulative effect of low interest rates over time drives financial imbalances” (Kahn,

2010). For this paper I use sustained deviations to help examine whether keeping policy-

controlled interest rates too low for too long contributed to the increasing cash holdings

for U.S. industrial firms. Kahn (2010) defines the cumulative policy deviation as the sum

of Taylor rule deviations from the first period up to period t. Different from Kahn (2010),

I calculate the cumulative policy deviation as the following:

(3)

where is the cumulative policy deviation at time t, t-s is the starting time I

calculate the cumulative sum. I calculate the cumulative policy deviation as the sum of

Taylor rule deviations within the recent four periods (s=3), eight periods (s=7), and

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twelve periods (s=11) for robustness checks. My definition of the cumulative policy

deviation has practical sense especially when using quarterly data for monetary policy

takes four, eight or twelve quarters to take effect, as discussed above.

3.2 Hypotheses

3.2.1 Taylor rule deviations and corporate cash holdings

Taylor (1999) notes that when monetary policy was too tight, the recovery from

the 1960-61 recession was weak and the eventual expansion was slow for several years

when monetary policy was too easy in the late 1960s and 1970s, inflation skyrocketed.

Taylor (2007) also points out that “large deviations from business-as-usual policy rules

are difficult for market participants to deal with and can lead to surprising changes in

other responses in the economy.” Stern and Miller (2004) document a material policy

mistake would be to allow a significant rate of inflation or deflation, leading to

misallocations of resources.

As discussed from Table 1, the interest rate channel and Tobin’s q theory predict

a negative relationship between policy deviation and corporate cash holdings, while the

credit channel predicts a positive relationship. Furthermore, the credit channel also

predicts a stronger relationship for large than for small firms.

3.2.2 Cumulative policy deviations and corporate cash holdings

Cumulative policy deviation provides a monetary policy measure to examine the

impact of monetary policy on corporate cash holdings in the long run. This test is

extremely important for the 2000s which is specified by the “long lasting lower interest

rates” (Kahn, 2010). “The most commonly cited evidence that monetary policy was too

easy during the period from 2002 to 2006, as the actual federal funds rate is below the

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values implied by the Taylor rule-by about 200 basis points on average over this five-year

period” (Taylor, 2007; Bernanke, 2010). If the predictions of the interest rate channel and

Tobin’s q theory hold in the long run, industrial firms will take the low interest rates

opportunity to increase their cash holdings gradually, while if the credit channel theory

holds, industrial firms will continuously reduce their cash holdings. Bates et al. (2009)

report evidence that an increase in cash holdings in the 2000s cannot be explained by

changes in firm characteristics9. Therefore I also make the hypothesis that industrial firms

kept increasing their cash holdings when facing persistent loose monetary policy in the

2000s.

I summarize different theories and predictions regarding the relation between

three monetary policy variables and cash holdings in Table 2.

4. Data and Descriptive Statistics

I construct my sample from Compustat and CRSP for the period 1980 to 2007,

extending Bates et al.’s (2009) sample period for an extra year10

. It is also reasonable for

me to use Taylor rule prescriptions to evaluate the appropriateness of monetary policy for

this sample period, when the interest rate policy did not experience significant structural

changes. The Federal Reserve System has focused on achieving its objectives for growth

in the supply of money and credit following the Monetary Control Act11

of 1980. In

practice researchers and economists use 2008 as a cut-off point to analyze the impacts of

monetary policy. For example, Hilsenrath12

(2013) uses the PFC era and AFC era to refer

9 In Bates et al.’ (2009) specification, “the dummy variable for the 1990s is significantly negative but the dummy

variable for the 2000s is significantly positive.” “The intercept for the 2000s is higher than for the 1980s or the 1990s.” 10

My results reported here is consistent with results reported for the sample period 1980 to 2006. 11

The Money Control Act of 1980 required the Fed to price its financial services competitively against private sector providers and to establish reserve requirements for all eligible financial institutions. 12

See “Easy-Money Era a Long Game for Fed” by Jon Hilsenrath, March 18, 2013, on page A2 in the U.S. edition of The Wall Street Journal.

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to the “Pre-Financial Crisis” and “After Financial Crisis”. Hilsenrath (2013) also

documents that in the PFC era “the central bank managed just one short-term interest rate

and expected that to be enough to meet its goals for inflation and unemployment. That

rate is the federal funds rate…”

My macro data set includes real GDP data from Bureau of Economic Analysis,

potential real GDP data from Congressional Budget Office, and CPI data from Bureau of

Labor Statistics13

. Consistent with the previous literature, I exclude financial firms (SIC

codes 6000-6999) because they may carry cash to meet capital requirements rather than

for the economic reasons studied in my analysis. I also exclude utilities (SIC codes 4900-

4999) because their cash holdings can be subject to regulatory supervision. Furthermore,

I restrict my sample to firms that are incorporated in the United States to minimize the

impact of repatriation tax burdens (Foley et al., 2007). Firm-specific accounting variables

are obtained from Compustat, and stock returns are obtained from CRSP. Following

Bates et al. (2009), I eliminate firm-years or firm-quarters for which book value of total

assets is negative or the sales revenue is negative. My final sample contains 118,897

firm-year observations for 13,743 unique firms and 439,659 firm-quarter observations for

13,210 unique firms.

Macro-variables are reported on a calendar year while firm specific variables

from Compustat have both fiscal and calendar basis. Most yearly information from

Compustat is on a fiscal basis while quarterly information from Compustat has both fiscal

and calendar basis. Former researchers find inconsistent results when using fiscal and

calendar accounting information14

. For this analysis, I do my research with the same

13 I follow Kahn (2010). 14

For example, using calendar quarterly Compustat data, Choi and Kim (2005) find that trade credit helps firms absorb

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framework as in the finance literature. Specifically, I calculate the average for those

quarterly macro variables based on the fiscal year definition for each firm and merge the

macro information with firm specific information for the same firm, then combine all

those merged information together to get the whole sample. In Compustat, I have the

fiscal year end variable (FYR) ranging from 1 to 12. For example, for one firm if the

fiscal year ending month is January (FYR=01) 1995, then the calendar dates spanned is

from Feb. 1st, 1995 to Jan. 31

st, 1996. While for another firm if fiscal ending month is

July (FYR=07) 1995, then the calendar dates spanned is from Aug. 1st, 1994 to Jul. 31

st,

1995. Because my macro data are quarterly based, I calculate the average of those

quarterly values for those four quarters in the calendar year of 1995 for the first case. And

I calculate the average of four quarterly values for the third and fourth quarters in the

calendar year of 1994 and the first and second quarters in the calendar year of 1995. For

each of the calculations I ensure that for each fiscal quarter, I have at least 2/3 of the

corresponding calendar months.

Panel A of Table 4 reports descriptive statistics for variables used in my cash

holdings regressions. The variables are defined as follows.

Cash: Following Bates et al. (2009), I measure the corporate cash holdings as

cash and marketable securities (data item #1) divided by total assets (data item #6). I also

measure cash holdings as log net cash ratio, defined as log value of cash and marketable

securities (data item #1) divided by (total assets (data item #6)-cash and marketable

securities (data item #1)), for robustness check.

the effect of credit contraction i.e. when monetary policy is tight, industrial firms will increase their trade credit. While Haan and Sterken (2006) find evidence that when monetary policy is tight, industrial firms will reduce their trade credit based on fiscal annual accounting data. Although those researchers use U.S. firms and Euro and UK firms separately.

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Monetary policy variables: Following Kahn (2010), I take the first two

specifications of the Taylor Rule shown in Table 3 to calculate Taylor rule prescriptions

in Equation (1)15

. Inflation is measured by the four-quarter rate of change in the CPI and

the output gap measured as the log ratio of real GDP to the CBO estimate of potential.

Policy deviations are calculated as the difference between the effective funds rates and

Taylor rule prescriptions based on Equation (2). Cumulative policy deviation is

calculated as the sum of policy deviations from four periods ago based on Equation (3). I

include the squared policy deviation to allow for the possibility of a nonlinear

relationship between Taylor rule deviations and corporate cash holdings, that is, the

possibility that large deviations are much more important than small deviations.

Macro control variables: Following Bates et al. (2009), I use credit spread to

proxy for the general economic environment, which determines the default risk and the

precautionary demand for cash for industrial firms. Credit spread is the difference

between the AAA and BBB yields reported by the Federal Reserve. To control for fiscal

policy, I use is the fiscal deficit. Although I have the annual federal deficit available for

each fiscal year from 1930, I cannot find the corresponding quarterly federal deficit data.

As a proxy, I resort to use the federal government current receipts and current

expenditures data from U.S. department of Commerce: Bureau of Economic Analysis16

.

Specifically, I calculate the quarterly “Federal Deficit” as the difference between

quarterly federal government current receipts and current expenditures divided by

nominal quarterly GDP then multiply by 100. To make my analysis consistent, for the

annual analysis I calculate the annual “Federal Deficit” as an average of the quarterly

15

The difference between Type 1 and Type 4 Taylor rule prescriptions is a constant, and the difference between Type 2 and Type 3 Taylor rule prescriptions is also a constant. 16

http://research.stlouisfed.org/fred2/source?soid=18

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“Federal Deficit” defined above, not the exact annual federal deficit for each fiscal year17

.

I do not include inflation and output gap into my analysis because the specification of the

theoretical Taylor Rule prescription already includes those two key variables. I include

the average effective federal funds rate to proxy for the opportunity cost of holding cash.

Firm specific control variables: The control variables in the cash holdings

regressions are motivated by the variables used in Bates et al. (2009). Industry sigma is

the average across the two-digit SIC code of the firm cash flow standard deviations for

the previous 10 years, and I require at least three observations for the calculation. Market-

to-book is the ratio of the market value of assets to the book value of assets i.e. book

value of assets (#6) minus the book value of equity (#60) plus the market value of equity

(#199* #25) as the numerator of the ratio and the book value of assets (#6) as the

denominator. Real size is the logarithm of book assets (#6). Cash flow/assets is calculated

as earnings after interest, dividends, and taxes but before depreciation divided by book

assets (((#13–#15–#16–#21)/#6). NWC/assets is net working capital (data item #179)

minus cash and marketable securities (data item #1) divided by book assets. Capex is the

ratio of capital expenditures (data item #128) to the book value of total assets (data item

#6). Leverage is the ratio of total debt to the book value of total assets (data item #6),

where debt includes long-term debt (data item #9) plus debt in current liabilities (data

item #34). R&D/sales is the ratio of research and development expense (data item #46) to

sales (data item #12). Dividend dummy is a dummy variable equal to one if the firm paid

a common dividend and zero otherwise. Acquisition activity is the ratio of expenditures

on acquisitions (data item #129) relative to the book value of total assets (data item #6).

Net debt issuance is calculated as annual total debt issuance (data item #111) minus debt

17

For example, http://www.whitehouse.gov/omb/budget/Historicals

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retirement (data item #114), divided by the book value of total assets (data item #6). Net

equity issuance is calculated as equity sales (data item #108) minus equity purchases

(data item #115), divided by the book value of total assets (data item #6). Loss dummy is

a dummy variable equal to one if net income (data item #172) is less than zero, and zero

otherwise. All variables in dollars are inflation-adjusted to 2007 dollars using the

Consumer Price Index.

Outliers in a firm-year are winsorized as follows: Leverage is winsorized so that it

is between zero and one; R&D/assets, R&D/sales, acquisitions/assets, cash flow

volatility, and capital expenditures/assets are winsorized at the 1% level; the bottom tails

of NWC/assets and cash flow/assets are winsorized at the 1% level; and the top tail of the

market-to-book ratio is winsorized at the 1% level18

. After excluding winsorized and

missing explanatory values, I am left with 77,738 firm-year observations for 12,430

unique firms, and 218,502 firm-quarter observations for 10,636 unique firms.

As reported in Panel A-1 from Table 4, the average annual cash holdings is large

at 13.9% of the total assets. The median cash holding, however, is much smaller at 6.7%

of the total assets. Taylor rule prescriptions in the sample range from 2.9% to 19.3% for

both types of Taylor rule prescriptions. Two types of Taylor rule deviations are trivial

with mean of 0.5% and 0.1%, and median of 0% and 0.2%, implying that the Fed closely

follows the Taylor rule prescriptions (e.g. Bernanke, 2010). I report the descriptive

statistics for the cumulative policy deviation for the most recent four periods. For the

yearly data sample, the cumulative policy deviation is the sum of the current policy

deviation and policy deviations within the last three years. Two types of cumulative

policy deviations tell different stories about the monetary policy. Type I cumulative

18

Detailed definitions of those variables were shown in the appendix.

20

policy deviation has an average of -0.2% and median of -0.6%, suggesting that monetary

policy is too loose in the long run. On the contrary, Type II cumulative policy deviation

has an average of 1.3% and median of 1.3%, suggesting that monetary policy is too tight

in the long run.

For comparison, I also report the descriptive statistics of my quarterly data sample

in Panel A-2 of Table 4. Quarterly medians for both types of policy deviation are zero,

consistent with the annual statistics results that the Fed closely follows the Taylor rule

prescriptions. But different from positive yearly averages, quarterly averages for both

types of policy deviation are negative, suggesting possible conflicts between annual and

quarterly analysis. Furthermore, both types of quarterly cumulative policy deviation

report that monetary policy is relatively loose in the long run, averages and medians are

negative for both.

Panel B of Table 4 reports Pearson correlation coefficients among those monetary

policy variables and macro control variables. As seen in the Panel B-1 of Table 4, cash

holdings are negatively related to all those four monetary policy variables for two types

of Taylor rule specifications: Taylor rule prescriptions, policy deviations, squared policy

deviations and cumulative policy deviations. A number of other noteworthy correlations

are evident in the panel. For example, Taylor rule prescriptions are negatively correlated

to policy deviations (-0.286 for Type I monetary variables and -0.422 for Type II

monetary variables). When the federal funds rate should be set high, the monetary policy

is looser than prescribed possibly reflect the “gradualism” of the Fed. Cumulative policy

deviations are significantly and positively related to policy deviation, the correlation

coefficient is 0.518 for Type I monetary variables and 0.516 for Type II monetary

21

variables. Quarterly correlation results reported from Panel B-2 in Table 4 support the

negative correlation between cash ratio and three monetary variables: Taylor rule

prescriptions, policy deviations cumulative policy deviations. But the correlation

coefficient between cash ratio and squared policy deviations are significantly positive.

Quarterly correlation results also show that cumulative policy deviation is more related to

policy deviation for both types of monetary variables on a quarterly basis than on a yearly

basis.

One must be careful not to draw conclusions from these simple correlations,

because Panel B-1 and Panel B-2 in Table 4 also reveal that cash holdings and monetary

policy variables are strongly correlated with two control variables including fiscal deficit

and credit spread. In Bates et al.’s (2009) analysis, the credit spread variable is positive

and significant at the 10% level in explaining the formation of cash holdings. Panel B-1

in Table 4 reports the correlation coefficient between Type I policy deviation and credit

spread is significantly 0.411 and the correlation coefficient between Type II cumulative

policy deviation and fiscal deficit is significantly -0.371. Quarterly correlation results

from Panel B-2 in Table 4 support the above findings. It is possible that when monetary

policy is tight, the default risk will increase correspondingly, and also the monetary

policy could be set continuously easer to lower the default risk for price stability and

economic growth purposes for the Fed.

I also find significant negative relation between credit spread and the fiscal deficit

defined above, the correlation is -0.413 for my quarterly data sample and -0.398 for my

yearly data sample. When default risk is high reflecting a deteriorating economy, federal

22

government current receipts will decrease relative to its current expenditures. This fact is

important in understanding the following regression results.

Table 5 presents univariate comparisons of key descriptive variables by policy

deviation quartiles for both yearly and quarterly data sample. For this analysis, to show

the impacts of tighter or looser monetary policy, I first divide the whole sample into two

subsamples, one with negative policy deviations and the other with positive policy

deviations. For each subsample I construct four quartiles. I am interested in whether

changes of cash or cash ratios will be different for each policy deviation quartile.

Panel A-1 and A-2 in Table 5 present sorting results specified by Type 1 Taylor

rule deviations. Take yearly sample sorting results from Panel A-1 in Table 5 for example,

I find that for the loose monetary policy regimes specified by negative policy deviation

quartiles, industrial firms increase their holding of cash and cash equivalents when

monetary policy is looser. Industrial firms in the U.S. increase their average cash

holdings by about 21.371 million dollars, and median of about 0.345 million dollars per

year in the first quartile, compared with the fourth quartile in which industrial firms

reduce their average cash holdings by 5.024 million dollars. And because the fourth

quartile is with policy deviations closer to zero, I do not see median changes of cash

holdings. For the other quartiles with positive policy deviations when monetary policy is

tighter, industrial firms reduce their cash holdings by an average of 7.215 million dollars

in the fourth quartile, compared with the reduction of cash averaged around 9.376 million

dollars in the first quartile. The median change of cash is not obvious judged by the fact

that in the extreme positive policy deviation quartile, firms also increase their cash

holdings. Panel A-2 in Table 5 reports quarterly variable changes sorted by quarterly

23

Type I policy deviation. I find that for the loose monetary policy regimes specified by

negative policy deviation quartiles, industrial firms increase their holding of cash and

cash equivalents when monetary policy is looser. Industrial firms in the U.S. increase

their average cash holdings by about 3.255 million dollars, and median of about 0.046

million dollars per quarter in the first quartile, compared with the fourth quartile in which

industrial firms reduce their average cash holdings by 1.865 million dollars and median

of about 0.056 million dollars. For the other quartiles with positive policy deviations

when monetary policy is tighter, industrial firms reduce their cash holdings by an average

of 2.946 million dollars and median of 0.055 million dollars in the fourth quartile,

compared with the reduction of cash averaged around 0.511 million dollars in the first

quartile.

Panel B-1and B-2 present the same results as those from Panel A-1 and A-2 in

Table 5 when I sort my data sample by Type 2 Taylor rule policy deviations. Panel A-1 to

Panel B-2 also report changes of cash ratios sorted by two types of Taylor rule policy

deviations.

Mean and median changes of cash ratios and total amount of cash holdings at two

extreme quartiles seem to support both of my hypotheses, when monetary policy is

extremely tight or ease, industrial firms will increase their cash holdings.

5. Empirical Results

Hu (1999) documents that “since monetary policy is more likely to be responsive

to macro-level variables than to firm-level variables…, the endogeneity problem of

monetary policy should not be a cause for concern.” Following Hu (1999), I use lagged

value of monetary policy variables in the estimation to minimize the endogeneity

24

problem. I use both annual and quarterly data sample estimations and different

specifications of sustained cumulative policy deviations, different lags of monetary

policy variables for robustness checks. Furthermore, I use different Taylor rule

specifications for those monetary policy variables for further robustness check.

Table 6 reports regressions of cash holdings on policy deviations and controls for

my yearly data sample and quarterly data sample analysis. All Ordinary Least Square

(OLS) regressions include industry effects i.e. two-digit SIC code dummies. The t-

statistics based on standard errors robust to clustering by firm and year is reported in

parentheses. Models (1) to (4) report the OLS estimations for cash ratio and the log value

of net cash ratio. Models (5) to (8) report firm fixed effects estimations for cash ratio and

the log value of net cash ratio. Models (1), (2), (5) and (6) report regression results using

cash ratio as dependent variable, and Models (3), (4), (7) and (8) report regression results

using log value of net cash ratio as dependent variable. Models (1), (3), (5) and (7) report

regression results with Type I monetary policy deviation as independent variables, and

Models (2), (4), (6) and (8) report regression results with Type II monetary policy

deviation as independent variables. Consistent with Bates et al. (2009), I find that as a

measure of default risk, credit spread is positively significant supporting the

precautionary demand for cash. Consistent with Bates et al. (2009) in predicting that

“firms and financial intermediaries have become more efficient in handling transactions,

thus reducing transactions-based requirements for cash holdings”, federal funds rate, as a

measure of opportunity cost of holding cash, is not significant and the signs for federal

funds rate are mixed for different models. Consistent with my discussion about the

relation between fiscal deficit and economic condition, I find that fiscal deficit is all

25

significantly negative for all for firm fixed effects models and for both yearly and

quarterly data samples. My other findings about firm specific explanations for corporate

cash holdings are consistent with those from Bates et al. (2009).

Panel A in Table 6 presents two regression model results with two different

measures of cash holdings for two types of Taylor rule policy deviations. I find that the

relation between lagged policy deviation and corporate cash holdings is significantly

positive for all those firm fixed effects models and mostly significant for three OLS

models. The log net cash ratio is more sensitive to the lagged policy deviation than the

cash ratio. My findings provide evidence supporting the credit channel prediction that

industrial firms increase their cash holdings in response to monetary policy tightness,

suggesting that industrial firms resort to their internal capital as a buffer for higher

external funds premium or bondholders’ extra lending requirements caused by positive

monetary shocks. Squared policy deviation is significantly positive when monetary

policy is loose, measured by non-positive policy deviations, suggesting that when

monetary policy is extremely loose industrial firms will increase their cash holdings.

When monetary policy is tight, measured by positive policy deviations, although squared

policy deviation is still significant, the sign of squared policy deviation depends on which

measure of cash holdings I use. Furthermore, the interaction variable “Squared policy

deviationt-1×tight” is significantly negative for those firm fixed effects models. The

absolute value of coefficients for the interaction variable “Squared policy deviationt-

1×tight” is greater than those of the lagged squared policy deviation for cash ratio models,

but smaller for log value of net cash ratio models. Industrial firms will increase their cash

holdings in the form of log value of net cash ratio while reduce their cash ratios when

26

monetary policy is tight. My findings regarding the squared policy deviation are

consistent with my sorting results in Table 5 in finding that industrial firms increase their

cash holdings when monetary policy is extremely loose or extremely tight.

Overall I find evidence from my yearly data sample analysis that when monetary

policy is tightened, industrial firms in the U.S. increase their cash holdings, supporting

the credit channel prediction that industrial firms choose to increase excess liquidity to

blunt the effects of tight monetary policy on the cost of external funds. My findings

support the asymmetric effects of monetary policy, although the degree of asymmetry

depends on the exact measure of cash holdings and Taylor rule specifications.

Former literature such as Gertler and Gilchrist (1994) and Choi and Kim (2005)

already discuss the differences between small and large firms in explaining corporate

behavior. Also the piling up of record amounts of cash for big companies attract the

attention for monetary policy researchers (e.g., Sánchez and Yurdagul, 2013). I control

for unobserved heterogeneity with a dummy variable “Large” in Table 6. Specifically for

each fiscal year, I sort all industrial firms in my sample into four quartiles based on firm

size. I then define a dummy variable “Large” equal to one if one firm is in the largest

firm size quartile, and zero otherwise. I then build interaction variables combining

monetary policy variables and firm size: “Policy deviationt-1×tight×large” and “Squared

policy deviationt-1×tight×large”. For the firm fixed effects model, “Squared policy

deviationt-1×tight×large” is not significant for all those four models, and the signs for this

interaction variable are mixed. I find that “Policy deviationt-1×tight×large” is significantly

negative for OLS models, but this significance is weakened for firm fixed effects

27

models19

. My findings regarding the interaction variable “Policy deviationt-1×tight×large”

support the credit channel prediction that when monetary policy is tightened, small firms

increase their cash holdings more than large firms. Consistent with asymmetric

information problems stated in the credit channel that small firms are more likely to be

credit constrained and are more hurt by tight monetary policy than are large firms. Also

these findings are consistent with basic styled fact that small firms in general have limited

access to the capital market and face more asymmetric and moral hazard problems. Small

firms are more restricted to borrow and may be one of the reasons recovery from

recession has been so tepid (see Sánchez and Yurdagul, 2013).

I find consistent results from Panel B in Table 6 for my quarterly data sample

analysis. Furthermore quarterly sample regression results also present that the interaction

variable “Policy deviationt-1×tight×large” is significantly negative all through different

models, different cash ratio measures and different Taylor rule specifications. Quarterly

regression results support the credit channel in showing that large firms adjust their cash

holdings less aggressively than small firms when facing monetary policy tightness. It is

possible that large industrial firms act more aggressively than small firms when monetary

policy is “extremely” tight, which is not consistently supported by regression results from

Panel B in Table 6.

I also examine the impact of current monetary shocks, or the current policy

deviation, on the corporate cash holdings and do not find consistent evidence supporting

a possible relation for both annual and quarterly regression results (see Table 1 of the

Appendix). To see whether these findings are caused by the possible endogeneity

19

Another explanation about the degree of significance lies in the problem of my quartiles of different firm size, because large or small firms size themselves are ambiguous.

28

problem, I include four recent serial policy deviation variables to explain the corporate

cash holdings in Table 2 of the Appendix. Both panels of Table 2 in the Appendix report

evidence supporting a significantly positive relation between corporate cash holdings and

the lagged policy deviation. Although quarterly regression results provide consistent

evidence supporting a significantly negative relation between corporate cash holdings and

current policy deviation from Panel B of Table 2 of the Appendix, annual regression

results do not provide the same consistent evidence.

To test whether keeping policy-controlled interest rates too low for too long

inadvertently exacerbate financial imbalances through corporate cash holdings to buffer

the monetary policy effectiveness, I examine the relation between corporate cash

holdings and the cumulative policy deviation within the most recent four periods. To

show specifically the general long lasting low interest rate impact in the 2000s, I define

interaction variable “Cumulative policy deviation×2000s dummy” in which 2000s

dummy is a dummy variable equal to one if the firm observation is in the fiscal year after

1999, and zero otherwise. Table 7 reports cash regressions results analogous to those in

Table 6, except that I substitute cumulative policy deviation and its interaction variables

for those policy deviation variables.

Yearly regression results from Panel A in Table 7 report that cumulative policy

deviation is significantly positive for all those different regression models, for different

measures of cash holdings and for different types of Taylor rule specification variables.

These findings support the credit channel in the long run: when industrial firms face tight

monetary policy within the recent four periods, industrial firms increase their cash

holdings. Is it possible that the significance of the cumulative policy deviations is caused

29

by any special previous policy deviations, but not by the production of new information?

Table 2 in the Appendix already answers this question: only the lagged policy deviation

is consistently and significantly positive for all firm fixed effects models for different

types of Taylor rule prescriptions, different cash holdings measures and different

frequency of my data sample.

2000s is special in that 2002 to 2006 saw “the actual federal funds rate is below

the values implied by the Taylor rule-by about 200 basis points on average over this five-

year period” (Taylor, 2007; Bernanke, 2010). As reported from Table 5, industrial firms

also increase their cash holdings when monetary policy is extremely loose. I include the

interaction variable “Cumulative policy deviation×2000s dummy” in all those models in

Table 7 to examine whether “keeping policy-controlled interest rates too low for too long”

(Kahn, 2010) contribute to “an increase in cash holdings in the 2000s that cannot be

explained by changes in firm characteristics” (Bates et al., 2009).

Panel A in Table 7 presents yearly regression evidence that the interaction

variable “Cumulative policy deviation×2000s dummy” is all significantly negative across

both OLS and firm fixed effects models, for both types of Taylor rule measures and both

types of cash holdings measures. Comparing the absolute values of cumulative policy

deviation and the interaction variable “Cumulative policy deviation×2000s dummy”, I

find the evidence that industrial firms do accumulate their cash holdings in response to

the “long lasting lower interests” environment.

I include large firm interaction variables such as “Cumulative policy

deviation×large” and “Cumulative policy deviation×large×2000s dummy” in those

regression models in Panel A of Table 7. “Cumulative policy deviation×large” is not

30

significant with signs mixed for different models and different measures of cash holdings

from yearly regression results in Panel A of Table 7. Neither do I find consistent evidence

that “Cumulative policy deviation×large×2000s dummy” is consistently significant.

Panel B in Table 7 reports quarterly regression results for the same estimation

models as in Panel A of Table 7. I find consistent evidence supporting the credit channel

in the long run and furthermore industrial firms take the opportunity of “long lasting

lower interest rates” to pileup their cash holding in the 2000s. I find from Panel B in

Table 7 that “Cumulative policy deviation×large” is significantly negative. Quarterly

regression results from Panel B in Table 7 also provide evidence supporting the credit

channel that large firms are less sensitive to monetary shocks than small firms.

Overall my findings from Table 7 support the credit channel in the long run:

industrial firms tend to increase their cash holdings when facing persistent tight monetary

shocks in the long run, and large industrial firms react less aggressively than small firms

in response to the persistent monetary shocks. My findings also provide evidence

supporting that industrial firms accumulate cash holdings in response to the “long lasting

lower interests” environment in the 2000s.

I test the robustness of my conclusion regarding the impact of cumulative policy

deviation on corporate cash holdings with two other different measures of cumulative

policy deviation: cumulative policy deviation within the most recent eight periods and

twelve periods. My results are robust for different measures of cumulative policy

deviation, for different frequency of data sample and for different specifications of Taylor

rule prescriptions (see Table 3 and Table 4 in the Appendix).

31

I also examine how industrial firms change their cash holdings in response to the

Taylor rule prescriptions, for Taylor rule is well-known and acknowledged by the Federal

Reserve that the FOMC make monetary policy on this basis although not alone

(Bernanke, 2010). Table 8 reports regressions of cash holdings on two different types of

Taylor rule prescriptions and controls. I do not find consistent evidence supporting a

significant relation between Taylor rule prescriptions and corporate cash holdings for

either yearly data sample analysis or quarterly data sample analysis.

As shown from Panel A in Table 8, firm fixed effects models (5) and (6) with

cash ratio as the dependent variable show that there is a possible negative relation

between Type 1 Taylor rule prescription and corporate cash holdings, but the relation is

neither statistically nor economically significant when using Type 2 Taylor rule

prescription. On the contrary, firm fixed effects models (7) and (8) provide evidence

supporting a statistically significant positive relation between two types of Taylor rule

prescriptions and corporate cash holdings when cash holdings is measured by the log

value of net cash ratio. Although I find that the interaction variable “Taylor

prescription×large” is all negative across all those eight regression models, implying that

large firms are less sensitive to Taylor rule prescriptions than small firms, the evidence is

not all significant. I cannot find consistent evidence supporting a possible relation

between corporate cash holdings and Taylor rule prescriptions either, when using

quarterly data sample from Panel B in Table 8.

These findings regarding Taylor rule prescriptions is consistent with economic

intuition that Taylor rule prescriptions provide essentially information about current

inflation and output gap, but no information about firm income level or changes of short-

32

term real interest rates. Another explanation is that “simple rules necessarily leave out

many factors that may be relevant to the making of effective policy in a given episode”

(Bernanke, 2010) and industrial firms do not include them into their corporate decision

making. Furthermore, there are no specific numerical values for those coefficients in

Equation (1), and Taylor rule prescriptions “may also depend sensitively on how inflation

and the output gap are measured” (Bernanke, 2010).

Could federal funds rates and changes of federal funds rates be better monetary

policy proxies? As discuss from the above, based on all those theories it is the monetary

policy deviation that matters. To convince this point, I also report in the Appendix using

either federal funds rates or changes in federal funds rates alone together with other firm

specific and macro control variables to explain the formation of cash holdings. As

reported from Table 7 of the Appendix, neither federal funds rates nor changes in federal

funds rates are consistently significant in explaining the formation of cash holdings for

different types of Taylor rule specifications, for different models, for different cash ratio

measures and for different frequency of data samples.

Overall I find evidence that corporate cash holdings for U.S. industrial firms are

positively correlated with monetary policy tightness measured by policy deviations, and

large industrial firms are less sensitive to monetary shocks than small firms. These

relations still hold in the long run when I use different specifications of sustained

monetary shocks to measure monetary policy. I also find evidence that the “long lasting

lower interest rates” monetary policy results in the pileup of cash for industrial firms in

the 2000s.

6. Conclusion

33

This research examines how the effect of monetary policy tightness or ease on

corporate cash holdings to better understand how monetary policy targeting at price

stability and unemployment, economic growth influence current-future investment

conflicts. I find a positive relation between policy deviation and cash holdings. The

positive effect of policy deviation is robust when using quarterly data. My evidence

primarily supports the credit channel that firms hold onto cash when monetary policy is

tight. Both annual and quarterly regression results suggest that large industrial firms are

less influenced by tight or loose monetary policy, also consistent with credit channel

predictions.

I find that the relation between policy deviation and corporate cash holdings still

hold in the long run for the pre-2000s, quarterly regression results also provide evidence

that large firms are less sensitive to monetary shocks, supporting credit channel

predictions in the long run. The 2000s is specified by “keeping policy-controlled interest

rates too low for too long” (Kahn, 2010), when “monetary policy was too easy during the

period from 2002 to 2006, as the actual federal funds rate is below the values implied by

the Taylor rule-by about 200 basis points on average over this five-year period” (Taylor,

2007; Bernanke, 2010). My findings provide empirical evidence about a different

relationship between sustained policy deviation and corporate cash holdings in the 2000s.

I show that industrial firms accumulate their cash holdings in response to the long lasting

interest rates policy. Quarterly analysis provides extra findings that large industrial firms

increase cash holdings more than small firms for the same persistent monetary shocks in

the 2000s.

34

The evidence suggests that policymakers should monitor financial conditions for

signs that cash are hoarding for industrial firms. Although policymakers may have many

reasons to deviate from simple rule-like behavior, they should be alert to unintended

consequences from maintaining rates too low for too long. My findings raise serious

concerns about the current practice when “too much cash becomes a really serious

business problem”30

. My study urges more exploration on this topic in the future.

30

http://www.forbes.com/sites/robertpicard/2011/08/08/liguidity-is-creating-short-term-investment-challenges-for-many-companies/

35

Table 1 Theories and predictions regarding the relation between monetary policy

variables and cash holdings

Transmission mechanisms of

monetary policy

Theory Predicted relationship

Interest rate channel Expansionary monetary policy

leads to a fall in real interest

rates, which in turn lowers the

opportunity cost of holding

cash.

A negative relationship between

monetary policy tightness and

corporate cash holdings.

Tobin’s q theory Expansionary monetary policy

leads to a rise in stock prices,

industrial firms then take the

opportunity to issue more

equities. Corporate cash

holdings increase following

equity issues.

A negative relationship between

monetary policy tightness and

corporate cash holdings.

Credit channel Expansionary monetary policy

helps reduce the external

finance premium, and increase

significantly the rate of

inflation, resulting in decreasing

cash holding.

A positive relationship between

monetary policy tightness and

corporate cash holdings.

Monetary policy will have a

greater effect on smaller firms

that are more dependent on

bank loans than it will on large

firms that can directly access

the credit markets through other

markets.

36

Table 2 Theories and predicted relationship regarding the relation between monetary

policy variables and cash holdings

Theory Policy deviation Cumulative policy deviation

Interest rate channel (-) (-) No prediction about the monetary policy impact difference on

small and large firms

Tobin’s q theory (-) (-)

No prediction about the monetary policy impact difference on

small and large firms

Credit channel (+) (+) Monetary policy impact more on small firms than on large firms

37

Table 3 Taylor Rule Parameterizations (Kahn, 2010)

Taylor rule prescriptions prescribe the Federal Reserve should follow in setting the

federal funds rate in the general Taylor rule form:

where represents the recommended policy rate as measured by the federal

funds rate, represents the equilibrium real interest rate, represents the

deviation of the inflation rate ( ) from its long-run target ( ),

represents the

output gap—the level of real GDP ( ) relative to potential GDP ( ), and α, are

parameters. This table identifies the four specifications of the Taylor rule to be examined.

All of the rules adhere to the “Taylor principle” that policymakers should adjust the

nominal federal funds rate more than one-for-one with an increase in inflation relative to

target. Rule 1 is the original version of the Taylor rule (Taylor, 1993). Rule 2 places a

higher weight on output than the original Taylor rule (Ball, 1997). Rule 3 and Rule 4

assume higher equilibrium real rates and different weights on inflation and the output

gap. For the calculation, I get real GDP from Bureau of Economic Analysis, potential real

GDP from Congressional Budget Office, and CPI data from Bureau of Labor Statistics.

Inflation is measured by the four-quarter rate of change in the CPI and the output gap

measured as the log ratio of real GDP to the CBO estimate of potential.

Rule 1 2.0 0.5 0.5

Rule 2 2.0 0.5 1.0

Rule 3 2.5 0.5 1.0

Rule 4 2.5 0.5 0.5

38

Table 4 Descriptive statistics and correlations of control variable characteristics and

monetary policy variables

The yearly data sample includes all Compustat firm-year observations from 1980 to 2007

with positive values for the book value of total assets and sales revenue for firms

incorporated in the United States. Financial firms (SIC code 6000-6999) and utilities

(SIC codes 4900-4999) are excluded from the sample, yielding a panel of 118,897 firm-

year observations for 13,743 unique firms. Missing explanatory values reduce the panel

used here to 77,738 firm-year observations for 12,430 unique firms for the OLS

regressions. The quarterly sample yields a panel of 439,659 firm-quarter observations for

13,210 unique firms. Missing explanatory values reduce the panel used here to 218,502

firm-quarter observations for 10,636 unique firms for the OLS regressions. Panel A

reports descriptive statistics, and Panel B reports Pearson correlation coefficients together

with p-values for the significance. Cash/Assets is cash and marketable securities (data

item #1) divided by total assets (data item #6). Taylor prescriptions are calculated based

on two types of Taylor rule specifications from Table 1. Policy deviation is the difference

between the actual annual average federal funds rates and Taylor prescriptions. Squared

policy deviation is the squared value of policy deviation. Cumulative policy deviations

are the sum of Taylor rule deviations from the first period up to the current. I calculate

the fiscal deficit as the difference between annual federal government current receipts and

current expenditures divided by nominal GDP. Credit spread is the difference between

the AAA and BBB yields reported by the Federal Reserve. Industry sigma is the average

across the two-digit SIC code of the firm cash flow standard deviations for the previous

10 years, and I require at least three observations for the calculation. Market-to-book is

the ratio of the market value of assets to the book value of assets i.e. book value of assets

(#6) minus the book value of equity (#60) plus the market value of equity (#199* #25) as

the numerator of the ratio and the book value of assets (#6) as the denominator. Real size

is the logarithm of book assets (#6). Cash flow/assets is calculated as earnings after

interest, dividends, and taxes but before depreciation divided by book assets (((#13–#15–

#16–#21)/#6). NWC/assets is net working capital (data item #179) minus cash and

marketable securities (data item #1) divided by book assets. Capex is the ratio of capital

expenditures (data item #128) to the book value of total assets (data item #6). Leverage is

the ratio of total debt to the book value of total assets (data item #6), where debt includes

long-term debt (data item #9) plus debt in current liabilities (data item #34). R&D/sales is

the ratio of research and development expense (data item #46) to sales (data item #12).

Dividend dummy is a dummy variable equal to one if the firm paid a common dividend

and zero otherwise. Acquisition activity is the ratio of expenditures on acquisitions (data

item #129) relative to the book value of total assets (data item #6). Net debt issuance is

calculated as annual total debt issuance (data item #111) minus debt retirement (data item

#114), divided by the book value of total assets (data item #6). Net equity issuance is

calculated as equity sales (data item #108) minus equity purchases (data item #115),

divided by the book value of total assets (data item #6). Loss dummy is a dummy

variable equal to one if net income (data item #172) is less than zero, and zero otherwise.

All variables in dollars are inflation-adjusted to 2007 dollars using the Consumer Price

Index.

39

Panel A-1: Yearly descriptive statistics

Variable Mean Lower

Quartile Median

Upper Quartile

Std Dev Minimum Maximum N

Cash/Assets 0.139 0.02 0.067 0.189 0.175 -0.013 0.995 77738 Taylor prescription 1 0.061 0.043 0.051 0.068 0.032 0.029 0.193 77738 Policy deviation 1 0.001 -0.013 0 0.013 0.024 -0.064 0.061 77738 Squared policy deviation 1 0.001 0 0 0.001 0.001 0 0.004 77738 Cumulative policy deviation 1 -0.002 -0.035 -0.006 0.031 0.073 -0.204 0.17 77738 Taylor prescription 2 0.058 0.037 0.051 0.071 0.033 -0.003 0.19 77738 Policy deviation 2 0.005 -0.011 0.002 0.017 0.029 -0.059 0.094 77738 Squared policy deviation 2 0.001 0 0 0.001 0.002 0 0.009 77738 Cumulative policy deviation 2 0.013 -0.044 0.013 0.042 0.086 -0.182 0.235 77738 Fiscal deficit -0.024 -0.036 -0.027 -0.017 0.018 -0.052 0.019 77738 Credit spread 1.046 0.74 0.92 1.31 0.404 0.6 2.32 77738 Industry sigma 0.087 0.054 0.08 0.114 0.041 0.019 0.217 77738 Market to book 1.704 1.022 1.321 1.91 1.212 0.123 17.84 77738 Real size 5.209 3.759 5.092 6.536 1.994 -1.81 12.671 77738 Cash flow/assets 0.035 0.016 0.065 0.105 0.148 -1.581 1.634 77738 NWC/assets 0.134 0.001 0.127 0.268 0.197 -1.002 0.923 77738 Capex 0.064 0.024 0.046 0.083 0.06 0 0.439 77738 Leverage 0.238 0.069 0.214 0.36 0.196 0 1 77738 R&D/sales 0.079 0 0 0.037 0.491 0 22.986 77738 Dividend dummy 0.362 0 0 1 0.481 0 1 77738 Acquisition activity 0.017 0 0 0.004 0.047 -0.013 0.425 77738 Net debt issuance 0.005 -0.021 0 0.023 0.109 -4.588 0.94 72914 Net equity issuance 0.019 0 0 0.008 0.113 -1.73 2.047 71907 Loss dummy 0.307 0 0 1 0.461 0 1 77738

Panel A-2: Quarterly descriptive statistics

Variable Mean Lower

Quartile Median

Upper Quartile

Std Dev Minimum Maximum N

Cash/Assets 0.14 0.016 0.056 0.184 0.19 -0.054 0.999 218502 Taylor prescription 1 0.054 0.042 0.049 0.067 0.016 0.024 0.093 218502 Policy deviation 1 -0.002 -0.014 -0.002 0.01 0.017 -0.039 0.07 218502 Squared policy deviation 1 0 0 0 0 0 0 0.005 218502 Cumulative policy deviation 1 -1 -1.043 -0.926 -0.887 0.166 -1.511 -0.836 218502 Taylor prescription 2 0.053 0.035 0.05 0.066 0.021 -0.003 0.099 218502 Policy deviation 2 -0.001 -0.013 0 0.01 0.02 -0.038 0.096 218502 Squared policy deviation 2 0 0 0 0.001 0.001 0 0.009 218502 Cumulative policy deviation 2 -0.489 -0.59 -0.439 -0.372 0.19 -1.218 -0.241 218502 Fiscal deficit -0.022 -0.034 -0.027 -0.014 0.018 -0.055 0.021 218502 Credit spread 0.009 0.007 0.009 0.011 0.003 0.006 0.025 218502 Industry sigma 0.025 0.017 0.024 0.03 0.01 0.006 0.076 218502 Market to book 1.799 1.063 1.378 2.013 1.321 0.105 25.146 218502 Real size 5.185 3.735 5.068 6.539 1.984 -1.804 12.635 218502 Cash flow/assets 0.012 0.004 0.019 0.032 0.045 -0.429 2.052 218502 NWC/assets 0.127 -0.006 0.115 0.257 0.195 -0.97 0.923 218502 Capex 0.041 0.01 0.024 0.052 0.049 0 0.483 218502 Leverage 0.252 0.079 0.232 0.38 0.201 0 1 218502 R&D/sales 0.092 0 0 0.013 0.611 0 28.944 218502 Dividend dummy 0.266 0 0 1 0.442 0 1 218502 Acquisition activity 0.012 0 0 0 0.038 -0.009 0.416 218502 Net debt issuance 0.005 -0.013 0 0.01 0.089 -4.588 1.11 201835 Net equity issuance 0.022 0 0 0.004 0.117 -2.19 7.044 202046 Loss dummy 0.313 0 0 1 0.464 0 1 218502

40

Panel B-1: Yearly correlations between cash, monetary policy variables, and macro control variables

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

1 Cash/Assets 1.000

2 Taylor prescription 1 -0.091 1.000

<.0001

3 Policy deviation 1 -0.081 -0.286 1.000 <.0001 <.0001 4 Squared policy deviation 1 -0.047 0.259 0.277 1.000

<.0001 <.0001 <.0001

5 Cumulative policy deviation 1 -0.046 -0.432 0.518 -0.274 1.000

<.0001 <.0001 <.0001 <.0001

6 Fiscal deficit 0.042 0.155 -0.359 -0.340 -0.198 1.000

<.0001 <.0001 <.0001 <.0001 <.0001

7 Credit spread -0.077 0.440 0.411 0.666 -0.079 -0.398 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001

8 Taylor prescription 2 -0.071 0.953 -0.400 0.060 -0.350 0.378 0.231 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

9 Policy deviation 2 -0.084 -0.225 0.950 0.437 0.345 -0.551 0.548 -0.422 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

10 Squared policy deviation 2 -0.050 0.047 0.559 0.889 -0.122 -0.318 0.672 -0.159 0.682 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

11 Cumulative policy deviation 2 -0.070 -0.376 0.645 -0.113 0.958 -0.371 0.049 -0.357 0.516 0.019 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

41

Panel B-2: Quarterly correlations between cash, monetary policy variables, and macro control variables

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

1 Cash/Assets 1.000

2 Taylor prescription 1 -0.039 1.000

<.0001

3 Policy deviation 1 -0.080 -0.097 1.000

<.0001 <.0001

4 Squared policy deviation 1 0.047 -0.112 0.063 1.000

<.0001 <.0001 <.0001

5 Cumulative policy deviation 1 -0.089 0.063 0.884 0.122 1.000

<.0001 <.0001 <.0001 <.0001

6 Fiscal deficit 0.021 0.182 -0.187 -0.289 -0.201 1.000

<.0001 <.0001 <.0001 <.0001 <.0001

7 Credit spread -0.004 0.098 0.275 0.353 0.392 -0.413 1.000

0.054 <.0001 <.0001 <.0001 <.0001 <.0001

8 Taylor prescription 2 -0.029 0.938 -0.107 -0.154 0.034 0.458 -0.009 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

9 Policy deviation 2 -0.074 -0.242 0.919 0.124 0.801 -0.495 0.333 -0.367 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

10 Squared policy deviation 2 0.020 0.028 0.209 0.864 0.254 -0.074 0.290 0.028 0.178 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

11 Cumulative policy deviation 2 -0.080 -0.089 0.826 0.210 0.930 -0.494 0.403 -0.210 0.879 0.258 1.000

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

42

Table 5 Changes of firm characteristics by policy deviation quartiles

Univariate comparison of means and medians of measures of firm characteristics changes of

U.S.-based publicly traded firms. Median values are bracketed. Cash is cash and marketable

securities (data item #1). Cash/Assets is cash and marketable securities (data item #1) divided by

total assets (data item #6). Taylor prescriptions are calculated based on two types of Taylor rule

specifications from Table 1. Policy deviation is the difference between the actual annual average

federal funds rates and Taylor prescriptions. Squared policy deviation is the squared value of

policy deviation. Cumulative policy deviations are the sum of Taylor rule deviations from the

first period up to the current. I calculate the fiscal deficit as the difference between annual federal

government current receipts and current expenditures divided by nominal GDP. Credit spread is

the difference between the AAA and BBB yields reported by the Federal Reserve. Industry

sigma is the average across the two-digit SIC code of the firm cash flow standard deviations for

the previous 10 years, and I require at least three observations for the calculation. Market-to-

book is the ratio of the market value of assets to the book value of assets i.e. book value of assets

(#6) minus the book value of equity (#60) plus the market value of equity (#199* #25) as the

numerator of the ratio and the book value of assets (#6) as the denominator. Real size is the

logarithm of book assets (#6). Cash flow/assets is calculated as earnings after interest, dividends,

and taxes but before depreciation divided by book assets (((#13–#15–#16–#21)/#6). NWC/assets

is net working capital (data item #179) minus cash and marketable securities (data item #1)

divided by book assets. Capex is the ratio of capital expenditures (data item #128) to the book

value of total assets (data item #6). Leverage is the ratio of total debt to the book value of total

assets (data item #6), where debt includes long-term debt (data item #9) plus debt in current

liabilities (data item #34). R&D/sales is the ratio of research and development expense (data item

#46) to sales (data item #12). Dividend dummy is a dummy variable equal to one if the firm paid

a common dividend and zero otherwise. Acquisition activity is the ratio of expenditures on

acquisitions (data item #129) relative to the book value of total assets (data item #6). Net debt

issuance is calculated as annual total debt issuance (data item #111) minus debt retirement (data

item #114), divided by the book value of total assets (data item #6). Net equity issuance is

calculated as equity sales (data item #108) minus equity purchases (data item #115), divided by

the book value of total assets (data item #6). Loss dummy is a dummy variable equal to one if net

income (data item #172) is less than zero, and zero otherwise. All variables in dollars are

inflation-adjusted to 2007 dollars using the Consumer Price Index. ΔXt is notation for the one-

year change, Xt- Xt-1, where t and (t-1) denote end of fiscal year t and (t-1). When monetary

policy deviation, the actual federal funds rate minus the Type 1 (or 2) Taylor rule prescription, is

positive, the economy is defined as in “tight monetary policy regime”; when monetary policy

deviation is negative, the economy is defined as in “loose monetary policy regime”. In this

analysis, I first divide the whole sample into two subsamples, one subsample with positive policy

deviations implying too tight monetary policy; the other subsample with negative policy

deviations implying too loose monetary policy. Within each subsample I divide them into four

quartiles based on the value of negative or positive policy deviations.

43

Panel A-1: Yearly monetary policy regimes specified by Type 1 policy deviations

Loose monetary policy regime Tight monetary policy regime

First quartile

Second quartile

Third quartile

Fourth quartile

First quartile

Second quartile

Third quartile

Fourth quartile

Policy deviation interval [-0.064, -0.024]

(-0.024, -0.013]

(-0.013, -0.008]

(-0.008, -0.000]

[0.000,0.006]

(0.006,0.013]

(0.013, 0.030]

(0.030, 0.061]

ΔCash/Assets 0.006 0.004 -0.002 -0.004 -0.001 -0.001 -0.0002 0.005 [0.001] [0.000] [-0.000] [-0.001] [-0.001] [-0.001] [-0.001] [0.001] Δcash 21.371 7.858 -0.894 -5.024 -9.376 -13.752 -11.500 -7.215 [0.345] [0.040] [-0.008] [0.000] [0.000] [-0.019] [-0.028] [0.035] Δindustry sigma 0.001 0.003 -0.002 0.000 0.001 0.002 0.003 0.004 [0.000] [0.000] [-0.002] [0.000] [0.000] [0.001] [0.002] [0.003] Δmarket to book 0.139 -0.030 -0.097 -0.009 0.072 0.027 0.012 0.028 [0.063] [0.017] [-0.035] [-0.064] [0.032] [0.001] [0.000] [0.023] Δreal size 0.029 0.008 -0.015 -0.003 -0.026 -0.019 -0.023 -0.090 [0.016] [0.000] [0.003] [0.025] [0.015] [0.026] [0.020] [0.013] Δcash flow/assets -0.001 -0.008 -0.001 0.000 -0.002 0.000 -0.004 -0.003 [-0.001] [-0.001] [0.000] [0.001] [0.001] [0.000] [-0.003] [0.000] ΔNWC/assets 0.000 -0.010 -0.004 -0.001 -0.001 -0.002 -0.006 -0.007 [0.000] [-0.004] [-0.001] [-0.001] [-0.002] [-0.001] [-0.005] [-0.005] Δcapex 0.001 -0.001 -0.002 -0.001 -0.002 0.001 0.000 -0.001 [0.001] [-0.002] [-0.001] [-0.001] [-0.001] [0.000] [0.000] [-0.001] Δleverage -0.008 -0.004 -0.003 0.001 -0.002 0.004 0.011 0.002 [-0.003] [-0.002] [-0.001] [0.000] [-0.001] [0.000] [0.000] [-0.004] ΔR&D/sales 0.018 0.011 0.006 0.001 -0.002 0.000 -0.003 0.001 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δdividend dummy 0.015 -0.012 -0.008 -0.007 -0.008 -0.007 -0.016 -0.034 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δacquisition activity 0.003 -0.002 -0.002 -0.001 0.001 0.000 0.004 0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δnet debt issuance 0.004 -0.005 -0.004 -0.002 -0.004 0.002 0.004 -0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δnet equity issuance 0.003 0.005 -0.003 -0.004 0.002 -0.001 -0.001 -0.001 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δloss dummy -0.022 0.028 0.015 -0.016 0.002 0.006 0.034 0.020 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

44

Panel A-2: Quarterly monetary policy regimes specified by Type 1 policy deviations

Loose monetary policy regime Tight monetary policy regime

Policy deviation interval First quartile

Second quartile

Third quartile

Fourth quartile

First quartile

Second quartile

Third quartile

Fourth quartile

ΔCash/Assets 0.002 0.002 -0.002 -0.002 0.001 -0.003 0.002 0.001

[0.000] [0.000] [0.000] [-0.001] [0.000] [-0.001] [0.000] [-0.001]

Δcash 3.255 4.226 -0.670 -1.865 -0.511 -2.064 0.631 -2.946 [0.046] [0.000] [-0.038] [-0.056] [0.000] [-0.070] [0.000] [-0.055] Δindustry sigma 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δmarket to book 0.031 0.057 -0.069 -0.051 0.027 0.047 -0.026 0.011 [0.016] [0.013] [-0.020] [-0.014] [-0.001] [0.015] [-0.018] [-0.002] Δreal size 0.013 -0.003 -0.002 0.001 -0.002 0.005 0.011 -0.031 [0.004] [-0.004] [-0.001] [0.005] [0.003] [0.006] [0.010] [0.002] Δcash flow/assets 0.000 -0.001 -0.001 0.001 0.000 0.000 -0.001 -0.001 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] ΔNWC/assets 0.000 -0.003 0.000 0.002 -0.002 0.003 -0.001 0.000 [0.001] [-0.001] [0.000] [0.001] [-0.001] [0.002] [-0.001] [0.000] Δcapex -0.001 -0.001 -0.001 -0.001 0.006 -0.006 0.005 0.001 [0.004] [0.006] [0.006] [0.007] [0.008] [0.005] [0.008] [0.009] Δleverage -0.002 -0.001 -0.001 0.001 -0.001 0.003 0.003 0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] ΔR&D/sales 0.005 0.004 -0.002 -0.001 0.001 -0.003 -0.003 -0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δdividend dummy 0.004 -0.001 -0.004 -0.001 0.002 0.001 -0.002 -0.005 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δacquisition activity 0.000 -0.001 0.000 -0.001 0.002 -0.002 0.002 0.000 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δnet debt issuance 0.001 -0.002 -0.001 -0.001 -0.001 0.000 0.001 0.000 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δnet equity issuance 0.000 0.000 0.001 -0.001 0.004 -0.006 0.004 0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δloss dummy -0.005 0.011 0.001 -0.017 0.002 -0.005 0.016 0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

45

Panel B-1: Yearly monetary policy regimes specified by Type 2 policy deviations

Loose monetary policy regime Tight monetary policy regime

First quartile

Second quartile

Third quartile

Fourth quartile

First quartile

Second quartile

Third quartile

Fourth quartile

Policy deviation interval [-0.059, -0.028]

(-0.028, -0.018]

(-0.018, -0.007]

(-0.007, -0.000]

[0.000, 0.004]

(0.004,0.012]

(0.012, 0.028]

(0.028, 0.094]

ΔCash/Assets 0.003 0.005 0.000 -0.001 -0.005 -0.005 0.005 0.005 [0.000] [0.001] [0.000] [-0.001] [-0.001] [-0.001] [0.000] [0.001] Δcash 12.257 24.417 6.664 -12.648 -13.890 -9.212 -11.003 -6.849 [0.048] [0.299] [0.020] [-0.027] [-0.018] [0.004] [-0.007] [0.033] Δindustry sigma 0.002 0.002 -0.002 0.001 0.001 0.000 0.001 0.004 [0.001] [0.000] [-0.002] [0.000] [0.000] [0.001] [0.001] [0.003] Δmarket to book -0.016 0.155 -0.093 -0.012 -0.010 -0.036 0.108 0.027 [0.003] [0.059] [-0.054] [-0.012] [0.009] [-0.035] [0.036] [0.021] Δreal size 0.023 0.027 -0.005 -0.054 -0.003 0.027 -0.043 -0.098 [0.008] [0.016] [0.003] [0.008] [0.024] [0.039] [0.008] [0.011] Δcash flow/assets -0.007 -0.004 -0.002 0.001 -0.001 0.001 -0.003 -0.004 [-0.002] [-0.002] [-0.002] [0.001] [0.002] [0.001] [-0.002] [-0.001] ΔNWC/assets -0.003 -0.009 -0.005 0.000 0.000 -0.003 -0.005 -0.007 [0.000] [-0.004] [-0.003] [-0.001] [0.000] [-0.002] [-0.004] [-0.006] Δcapex 0.002 -0.004 -0.004 0.001 0.001 0.000 -0.002 -0.001 [0.001] [-0.002] [-0.001] [0.000] [0.001] [0.000] [-0.002] [-0.001] Δleverage -0.005 0.001 -0.002 -0.007 0.004 0.008 0.000 0.001 [-0.002] [0.000] [-0.001] [-0.003] [0.000] [0.000] [-0.001] [-0.004] ΔR&D/sales 0.016 0.010 0.009 0.000 0.001 -0.002 -0.002 0.001 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δdividend dummy 0.003 -0.001 -0.004 -0.012 -0.003 -0.007 -0.017 -0.034 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δacquisition activity 0.000 -0.002 -0.003 0.000 0.002 0.003 0.000 0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δnet debt issuance -0.003 0.001 -0.007 -0.001 0.003 0.004 -0.002 -0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δnet equity issuance 0.006 0.004 -0.006 -0.001 -0.005 -0.006 0.008 -0.001 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δloss dummy 0.004 0.006 0.015 -0.005 -0.005 -0.002 0.033 0.020 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

46

Panel B-2: Quarterly monetary policy regimes specified by Type 2 policy deviations

Loose monetary policy regime Tight monetary policy regime

First quartile

Second quartile

Third quartile

Fourth quartile

First quartile

Second quartile

Third quartile

Fourth quartile

Policy deviation interval [-0.038, -0.025]

(-0.025, -0.017]

(-0.017, -0.007]

(-0.007, -0.000]

[0.000, 0.004]

(0.004,0.010]

(0.010, 0.022]

(0.022, 0.096]

ΔCash/Assets 0.001 0.001 0.001 -0.001 -0.002 0.000 -0.001 0.002 [0.000] [0.000] [0.000] [-0.001] [-0.001] [0.000] [-0.001] [-0.001] Δcash 2.845 3.455 2.472 -1.950 -1.078 -1.365 -1.026 -2.219 [0.000] [0.013] [-0.005] [-0.035] [-0.038] [-0.018] [-0.020] [-0.036] Δindustry sigma 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δmarket to book 0.053 -0.031 -0.022 -0.015 -0.006 -0.034 0.050 0.017 [0.005] [-0.003] [-0.008] [-0.002] [-0.003] [-0.018] [0.013] [0.000] Δreal size 0.020 0.012 -0.013 -0.008 0.003 -0.002 0.012 -0.027 [0.003] [0.002] [-0.006] [0.002] [0.007] [0.005] [0.009] [0.001] Δcash flow/assets -0.001 0.000 -0.001 0.000 0.000 0.000 -0.001 0.000 [0.000] [0.000] [0.000] [0.000] [0.000] [0.001] [0.000] [0.000] ΔNWC/assets -0.001 -0.002 -0.003 0.002 0.001 0.000 0.001 0.000 [0.001] [0.000] [-0.001] [0.001] [0.000] [0.000] [0.001] [0.000] Δcapex 0.000 0.000 -0.001 0.000 0.002 0.007 -0.009 0.003 [0.005] [0.005] [0.006] [0.006] [0.008] [0.008] [0.004] [0.009] Δleverage -0.001 -0.001 0.000 -0.002 0.002 0.000 0.003 0.001 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] ΔR&D/sales 0.007 0.000 0.003 0.002 -0.001 -0.001 -0.006 -0.002 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δdividend dummy 0.000 0.002 -0.002 0.000 0.001 0.000 -0.001 -0.004 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δacquisition activity -0.001 0.001 -0.001 0.000 0.000 0.003 -0.002 0.000 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δnet debt issuance 0.000 0.001 -0.002 0.000 0.001 0.001 -0.001 0.000 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δnet equity issuance 0.000 0.001 0.001 -0.002 0.000 0.003 -0.004 0.004 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Δloss dummy 0.010 -0.002 0.012 -0.012 -0.003 -0.009 0.003 0.007 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

47

Table 6 Regressions of cash holdings on policy deviation variables and controls

Cash/Assets defined as cash and marketable securities (data item #1) divided by total assets (data

item #6). Log net cash ratio defined as log value of cash and marketable securities (data item #1)

divided by (total assets (data item #6)-cash and marketable securities (data item #1)). The sample

includes all Compustat firm-year observations from 1980 to 2007 with positive values for the

book value of total assets and sales revenue for firms incorporated in the United States. Financial

firms (SIC code 6000-6999) and utilities (SIC codes 4900-4999) are excluded from the sample,

yielding a panel of 118,897 observations for 13,743 unique firms. Missing explanatory values

reduce the panel used here to 67,066 firm-year observations for 12,430 unique firms for the firm

fixed effects regressions. Taylor prescriptions are calculated based on two types of Taylor rule

specifications from Table 1. Policy deviation is the difference between the actual annual average

federal funds rates and Taylor prescriptions. Squared policy deviation is the squared value of

policy deviation. I calculate the fiscal deficit as the difference between annual federal government

current receipts and current expenditures divided by nominal GDP. Credit spread is the difference

between the AAA and BBB yields reported by the Federal Reserve. Industry sigma is the

average across the two-digit SIC code of the firm cash flow standard deviations for the previous

10 years, and I require at least three observations for the calculation. Market-to-book is the ratio

of the market value of assets to the book value of assets i.e. book value of assets (#6) minus the

book value of equity (#60) plus the market value of equity (#199* #25) as the numerator of the

ratio and the book value of assets (#6) as the denominator. Real size is the logarithm of book

assets (#6). Cash flow/assets is calculated as earnings after interest, dividends, and taxes but

before depreciation divided by book assets (((#13–#15–#16–#21)/#6). NWC/assets is net working

capital (data item #179) minus cash and marketable securities (data item #1) divided by book

assets. Capex is the ratio of capital expenditures (data item #128) to the book value of total assets

(data item #6). Leverage is the ratio of total debt to the book value of total assets (data item #6),

where debt includes long-term debt (data item #9) plus debt in current liabilities (data item #34).

R&D/sales is the ratio of research and development expense (data item #46) to sales (data item

#12). Dividend dummy is a dummy variable equal to one if the firm paid a common dividend and

zero otherwise. Acquisition activity is the ratio of expenditures on acquisitions (data item #129)

relative to the book value of total assets (data item #6). Net debt issuance is calculated as annual

total debt issuance (data item #111) minus debt retirement (data item #114), divided by the book

value of total assets (data item #6). Net equity issuance is calculated as equity sales (data item

#108) minus equity purchases (data item #115), divided by the book value of total assets (data

item #6). Loss dummy is a dummy variable equal to one if net income (data item #172) is less

than zero, and zero otherwise. 2000s dummy is a dummy variable equal to one if the firm

observation is in the fiscal year after 1999, and zero otherwise. All variables in dollars are

inflation-adjusted to 2007 dollars using the Consumer Price Index. ΔXt is notation for the one-

year change, Xt- Xt-1, where t and (t-1) denote end of fiscal year t and (t-1). In this analysis, I first

divide the whole sample into four quartiles each fiscal year based on the real size and define firms

in the largest real size quartiles as “large” firms. Large is a dummy variable equal to one if the

firm is in the large real size quartile and zero otherwise.

t-statistics based on standard errors robust to clustering by firm and year are reported in

parentheses. I report adjusted-R2 for OLS estimation models and within R

2 for firm fixed effects

estimation models. ***, **, and * denote significance at the 1%, 5%, and 10% levels,

respectively.

48

Panel A: Yearly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Policy deviationt-1 0.157* 0.0645 2.309** 1.440* 0.294*** 0.192*** 2.275** 1.336*

(2.13) (1.11) (2.87) (2.27) (4.22) (3.34) (2.90) (2.06)

Squared policy deviationt-1 5.448** 2.705 98.67*** 74.85*** 5.921*** 3.144** 87.98*** 60.92***

(3.10) (1.89) (5.12) (4.79) (4.58) (2.99) (5.67) (4.73)

Squared policy

deviationt-1×tight

-4.138 -2.135 -89.08** -74.57*** -6.870** -3.782* -70.64* -55.88**

(-1.38) (-1.08) (-2.72) (-3.46) (-2.84) (-2.39) (-2.48) (-2.97)

Policy deviationt-1

×tight×large

-0.833*** -0.572*** -6.762** -4.990** -0.168 -0.133 -2.491 -2.119

(-4.02) (-3.98) (-2.98) (-3.17) (-1.10) (-1.22) (-1.27) (-1.51)

Squared policy deviationt-

1×tight×large

11.34** 5.055** 107.2* 50.97** -0.329 0.0977 25.90 15.79

(2.80) (2.82) (2.42) (2.60) (-0.13) (0.09) (0.78) (1.07)

Funds rate -0.116*** -0.0854*** -0.347 0.0606 0.0206 0.0462 -0.00651 0.401

(-4.25) (-3.37) (-1.15) (0.22) (0.66) (1.52) (-0.02) (1.12)

Fiscal deficit 0.00624 -0.0126 -2.988*** -3.250*** -0.198*** -0.162*** -3.499*** -3.617***

(0.18) (-0.34) (-7.82) (-7.95) (-4.01) (-3.32) (-6.13) (-6.42)

Credit spread 2.377*** 2.416*** 31.31*** 31.39*** 1.525*** 1.611*** 27.56*** 27.64***

(12.01) (12.11) (14.46) (14.38) (6.84) (7.37) (10.99) (11.21)

Industry sigma 0.303*** 0.300*** 3.254*** 3.251*** 0.0892* 0.0896* 2.004*** 1.991***

(13.78) (13.58) (13.52) (13.45) (2.30) (2.31) (4.70) (4.63)

Market to book 0.0160*** 0.0160*** 0.157*** 0.157*** 0.00646*** 0.00647*** 0.0752*** 0.0755***

(33.84) (33.83) (30.39) (30.42) (7.61) (7.62) (10.43) (10.49)

Real size -0.00529*** -0.00532*** -0.0333*** -0.0329*** -0.000458 -0.000505 -0.0411* -0.0400*

(-15.54) (-15.61) (-8.94) (-8.82) (-0.29) (-0.31) (-2.49) (-2.42)

Cash flow/assets -0.0281*** -0.0282*** -0.130* -0.131* 0.0132 0.0131 0.160* 0.159*

(-6.00) (-6.01) (-2.53) (-2.55) (1.61) (1.60) (2.30) (2.29)

NWC/assets -0.266*** -0.266*** -2.431*** -2.432*** -0.263*** -0.263*** -2.263*** -2.263***

(-82.56) (-82.47) (-68.86) (-68.84) (-34.50) (-34.47) (-30.78) (-30.75)

Capex -0.468*** -0.467*** -3.763*** -3.761*** -0.368*** -0.368*** -2.994*** -2.990***

(-49.01) (-48.90) (-36.00) (-35.95) (-27.19) (-27.15) (-20.93) (-20.89)

Leverage -0.336*** -0.336*** -3.725*** -3.727*** -0.248*** -0.247*** -2.813*** -2.815***

(-117.60) (-117.68) (-118.95) (-119.08) (-33.39) (-33.38) (-36.39) (-36.44)

R&D/sales 0.0705*** 0.0705*** 0.404*** 0.404*** 0.0159*** 0.0158*** 0.0993*** 0.0995***

(50.73) (50.74) (26.65) (26.67) (3.51) (3.51) (3.76) (3.77)

Dividend dummy -0.0209*** -0.0209*** -0.199*** -0.199*** -0.000388 -0.000363 -0.0260 -0.0259

(-16.41) (-16.38) (-14.27) (-14.29) (-0.16) (-0.15) (-0.90) (-0.89)

Acquisition activity -0.347*** -0.346*** -2.712*** -2.700*** -0.286*** -0.286*** -2.091*** -2.077***

(-30.79) (-30.74) (-22.00) (-21.89) (-26.01) (-25.95) (-16.67) (-16.54)

Net debt issuance 0.188*** 0.188*** 1.469*** 1.471*** 0.159*** 0.159*** 1.188*** 1.189***

(37.99) (38.01) (27.15) (27.19) (15.37) (15.37) (12.52) (12.53)

Net equity issuance 0.123*** 0.124*** 0.717*** 0.716*** 0.174*** 0.174*** 1.138*** 1.137***

(25.04) (25.05) (13.30) (13.28) (22.98) (22.99) (18.90) (18.88)

Loss dummy -0.0220*** -0.0220*** -0.194*** -0.194*** -0.0234*** -0.0234*** -0.223*** -0.223***

(-16.25) (-16.23) (-13.09) (-13.10) (-17.61) (-17.63) (-14.69) (-14.72)

Intercept 0.252*** 0.251*** -1.834*** -1.862*** 0.222*** 0.221*** -1.920*** -1.948***

(38.64) (38.46) (-25.68) (-26.13) (21.55) (21.23) (-17.74) (-17.84)

Adj. R2/Within R2 0.445 0.445 0.370 0.370 0.214 0.214 0.162 0.162

49

Panel B: Quarterly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Policy deviationt-1 0.468*** 0.241*** 4.520*** 1.334* 0.323*** 0.181** 4.719*** 1.703**

(8.94) (4.99) (7.81) (2.50) (5.06) (3.25) (6.65) (2.73)

Squared policy deviationt-1 24.81*** 12.89*** 320.4*** 140.5*** 15.44*** 8.059*** 294.3*** 127.1***

(12.26) (6.88) (14.35) (6.79) (6.01) (3.97) (10.16) (5.46)

Squared policy

deviationt-1×tight

-32.06*** -16.13*** -403.0*** -167.7*** -15.66*** -7.783** -336.0*** -133.7***

(-10.30) (-6.27) (-11.73) (-5.90) (-4.48) (-3.00) (-8.52) (-4.54)

Policy deviationt-1

×tight×large

-1.143*** -0.886*** -8.520*** -6.571*** -0.668*** -0.608*** -8.650*** -7.135***

(-8.01) (-8.20) (-5.41) (-5.51) (-4.44) (-4.97) (-4.31) (-4.44)

Squared policy deviationt-

1×tight×large

21.73*** 12.61*** 236.6*** 137.4*** 4.376 3.675* 116.9** 71.78**

(6.13) (6.14) (6.05) (6.07) (1.51) (2.09) (3.10) (3.20)

Funds rate -0.0114 -0.0233 0.167 -0.117 0.160*** 0.167*** 0.617 0.578

(-0.57) (-1.29) (0.76) (-0.58) (4.54) (4.77) (1.51) (1.42)

Fiscal deficit 0.0344 -0.0283 -2.061*** -3.211*** -0.225*** -0.266*** -2.989*** -4.110***

(1.70) (-1.23) (-9.18) (-12.66) (-4.67) (-5.54) (-5.29) (-7.25)

Credit spread 2.676*** 2.785*** 36.88*** 37.93*** 1.935*** 1.992*** 31.00*** 31.17***

(17.99) (18.93) (22.44) (23.34) (7.53) (7.66) (10.58) (10.52)

Industry sigma 0.0150 -0.00948 1.433** 1.064* -0.188 -0.204* -1.581 -1.978

(0.33) (-0.21) (2.82) (2.09) (-1.87) (-2.02) (-1.50) (-1.87)

Market to book 0.0181*** 0.0182*** 0.172*** 0.173*** 0.00733*** 0.00737*** 0.0807*** 0.0816***

(67.26) (67.38) (57.91) (58.12) (9.84) (9.87) (12.98) (13.07)

Real size -0.00413*** -0.00407*** -0.0269*** -0.0257*** 0.000647 0.00105 -0.0360* -0.0279

(-18.80) (-18.54) (-11.09) (-10.60) (0.38) (0.62) (-2.09) (-1.62)

Cash flow/assets -0.228*** -0.228*** -1.333*** -1.340*** -0.0582*** -0.0586*** -0.0883 -0.0956

(-24.64) (-24.69) (-13.09) (-13.15) (-3.37) (-3.40) (-0.62) (-0.67)

NWC/assets -0.274*** -0.274*** -2.510*** -2.516*** -0.242*** -0.242*** -2.098*** -2.104***

(-134.62) (-134.82) (-111.55) (-111.79) (-30.28) (-30.27) (-28.70) (-28.71)

Capex -0.406*** -0.407*** -2.915*** -2.934*** -0.207*** -0.207*** -1.077*** -1.091***

(-57.69) (-57.86) (-37.54) (-37.75) (-20.09) (-20.09) (-9.86) (-9.96)

Leverage -0.362*** -0.363*** -3.903*** -3.913*** -0.245*** -0.246*** -2.781*** -2.798***

(-204.26) (-204.77) (-199.25) (-199.84) (-31.83) (-31.92) (-35.80) (-35.96)

R&D/sales 0.0507*** 0.0508*** 0.276*** 0.277*** 0.00918*** 0.00916*** 0.0581*** 0.0581***

(79.24) (79.34) (39.30) (39.43) (3.85) (3.85) (4.10) (4.12)

Dividend dummy -0.0290*** -0.0290*** -0.271*** -0.272*** -0.00428* -0.00422* -0.0688* -0.0676*

(-33.51) (-33.61) (-28.37) (-28.49) (-2.00) (-1.96) (-2.40) (-2.35)

Acquisition activity -0.350*** -0.347*** -2.527*** -2.483*** -0.238*** -0.237*** -1.360*** -1.335***

(-40.42) (-40.09) (-26.39) (-25.93) (-23.26) (-23.11) (-11.42) (-11.19)

Net debt issuance 0.221*** 0.222*** 1.574*** 1.588*** 0.160*** 0.161*** 1.089*** 1.100***

(57.89) (58.15) (37.31) (37.63) (15.83) (15.86) (12.99) (13.07)

Net equity issuance 0.181*** 0.180*** 1.113*** 1.102*** 0.184*** 0.183*** 1.215*** 1.202***

(62.30) (62.00) (34.77) (34.43) (22.46) (22.41) (20.64) (20.51)

Loss dummy -0.00628*** -0.00630*** -0.0645*** -0.0648*** -0.0108*** -0.0108*** -0.130*** -0.130***

(-7.41) (-7.43) (-6.89) (-6.92) (-11.25) (-11.25) (-12.03) (-12.04)

Intercept 0.251*** 0.251*** -1.914*** -1.916*** 0.198*** 0.196*** -2.058*** -2.097***

(55.32) (55.41) (-38.24) (-38.42) (19.43) (19.17) (-18.96) (-19.31)

Adj. R2/Within R2 0.471 0.470 0.387 0.386 0.184 0.184 0.130 0.129

50

Table 7 Regressions of cash holdings on one year cumulative policy deviation

variables and controls

Cash/Assets defined as cash and marketable securities (data item #1) divided by total assets (data

item #6). Log net cash ratio defined as log value of cash and marketable securities (data item #1)

divided by (total assets (data item #6)-cash and marketable securities (data item #1)). The sample

includes all Compustat firm-year observations from 1980 to 2007 with positive values for the

book value of total assets and sales revenue for firms incorporated in the United States. Financial

firms (SIC code 6000-6999) and utilities (SIC codes 4900-4999) are excluded from the sample,

yielding a panel of 118,897 observations for 13,743 unique firms. Missing explanatory values

reduce the panel used here to 67,574 firm-year observations for 12,430 unique firms for the firm

fixed effects regressions. Taylor prescriptions are calculated based on two types of Taylor rule

specifications from Table 1. Policy deviation is the difference between the actual annual average

federal funds rates and Taylor prescriptions. Squared policy deviation is the squared value of

policy deviation. Cumulative policy deviations are the sum of Taylor rule deviations from the

first period up to the current. I calculate the fiscal deficit as the difference between annual federal

government current receipts and current expenditures divided by nominal GDP. Credit spread is

the difference between the AAA and BBB yields reported by the Federal Reserve. Industry

sigma is the average across the two-digit SIC code of the firm cash flow standard deviations for

the previous 10 years, and I require at least three observations for the calculation. Market-to-book

is the ratio of the market value of assets to the book value of assets i.e. book value of assets (#6)

minus the book value of equity (#60) plus the market value of equity (#199* #25) as the

numerator of the ratio and the book value of assets (#6) as the denominator. Real size is the

logarithm of book assets (#6). Cash flow/assets is calculated as earnings after interest, dividends,

and taxes but before depreciation divided by book assets (((#13–#15–#16–#21)/#6). NWC/assets

is net working capital (data item #179) minus cash and marketable securities (data item #1)

divided by book assets. Capex is the ratio of capital expenditures (data item #128) to the book

value of total assets (data item #6). Leverage is the ratio of total debt to the book value of total

assets (data item #6), where debt includes long-term debt (data item #9) plus debt in current

liabilities (data item #34). R&D/sales is the ratio of research and development expense (data item

#46) to sales (data item #12). Dividend dummy is a dummy variable equal to one if the firm paid

a common dividend and zero otherwise. Acquisition activity is the ratio of expenditures on

acquisitions (data item #129) relative to the book value of total assets (data item #6). Net debt

issuance is calculated as annual total debt issuance (data item #111) minus debt retirement (data

item #114), divided by the book value of total assets (data item #6). Net equity issuance is

calculated as equity sales (data item #108) minus equity purchases (data item #115), divided by

the book value of total assets (data item #6). Loss dummy is a dummy variable equal to one if net

income (data item #172) is less than zero, and zero otherwise. All variables in dollars are

inflation-adjusted to 2007 dollars using the Consumer Price Index. ΔXt is notation for the one-

year change, Xt- Xt-1, where t and (t-1) denote end of fiscal year t and (t-1). 2000s dummy is a

dummy variable equal to one if the firm observation is in the fiscal year after 1999, and zero

otherwise. In this analysis, I first divide the whole sample into four quartiles each fiscal year

based on the real size and define firms in the largest real size quartiles as “large” firms. Large is a

dummy variable equal to one if the firm is in the large real size quartile and zero otherwise.

t-statistics based on standard errors robust to clustering by firm and year are reported in

parentheses. I report adjusted-R2 for OLS estimation models and within R

2 for firm fixed effects

estimation models. ***, **, and * denote significance at the 1%, 5%, and 10% levels,

respectively.

51

Panel A: Yearly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.430*** 0.460*** 0.991* 1.457** 0.0182 0.0183 -2.444*** -2.156***

(9.94) (11.31) (2.10) (3.27) (0.32) (0.33) (-3.64) (-3.32)

Cumulative policy deviation

×large

-0.0112 -0.0512*** 0.304 -0.0686 -0.0134 -0.0209 0.181 0.0537

(-0.65) (-3.39) (1.61) (-0.41) (-0.97) (-1.67) (0.98) (0.33)

Cumulative policy deviation

×2000s dummy

-0.349*** -0.365*** -2.872*** -3.138*** -0.165*** -0.166*** -2.225*** -2.264***

(-12.91) (-13.00) (-9.71) (-10.20) (-3.92) (-4.13) (-5.13) (-5.29)

Cumulative policy deviation 0.0591*** 0.0663*** 0.449*** 0.528*** 0.0797*** 0.0744*** 0.586*** 0.577***

(6.45) (7.91) (4.47) (5.75) (7.75) (8.07) (5.09) (5.54)

Funds rate 0.0583* 0.0943** 1.932*** 2.368*** 0.111*** 0.120*** 1.726*** 2.104***

(2.21) (3.27) (6.70) (7.50) (3.85) (4.34) (5.09) (6.37)

Fiscal deficit 0.00749 -0.0226 -2.917*** -3.291*** -0.159*** -0.148** -3.069*** -3.445***

(0.22) (-0.63) (-7.85) (-8.35) (-3.34) (-3.19) (-5.50) (-6.38)

Credit spread 2.098*** 1.773*** 29.60*** 25.92*** 1.574*** 1.448*** 26.54*** 23.43***

(10.41) (8.26) (13.41) (11.03) (7.33) (7.11) (11.05) (10.25)

Industry sigma 0.261*** 0.260*** 2.770*** 2.760*** 0.0298 0.0377 1.179** 1.218**

(11.81) (11.80) (11.46) (11.43) (0.78) (0.99) (2.77) (2.86)

Market to book 0.0160*** 0.0161*** 0.156*** 0.157*** 0.00656*** 0.00656*** 0.0742*** 0.0749***

(33.84) (34.17) (30.16) (30.41) (7.72) (7.75) (10.29) (10.42)

Real size -0.00522*** -0.00479*** -0.0377*** -0.0358*** -0.00146 -0.00136 -0.0687*** -0.0655***

(-15.87) (-14.05) (-10.48) (-9.60) (-0.87) (-0.82) (-3.97) (-3.82)

Cash flow/assets -0.0269*** -0.0271*** -0.112* -0.113* 0.0150 0.0149 0.190** 0.186**

(-5.75) (-5.79) (-2.19) (-2.20) (1.83) (1.83) (2.73) (2.68)

NWC/assets -0.265*** -0.265*** -2.414*** -2.416*** -0.261*** -0.261*** -2.222*** -2.227***

(-82.28) (-82.36) (-68.39) (-68.43) (-34.18) (-34.26) (-30.11) (-30.22)

Capex -0.467*** -0.468*** -3.721*** -3.735*** -0.364*** -0.365*** -2.889*** -2.903***

(-48.85) (-49.04) (-35.56) (-35.68) (-26.72) (-26.84) (-20.08) (-20.21)

Leverage -0.334*** -0.334*** -3.712*** -3.712*** -0.247*** -0.247*** -2.798*** -2.803***

(-116.75) (-116.80) (-118.43) (-118.49) (-33.20) (-33.27) (-36.02) (-36.16)

R&D/sales 0.0696*** 0.0696*** 0.401*** 0.401*** 0.0158*** 0.0159*** 0.101*** 0.101***

(50.08) (50.09) (26.39) (26.40) (3.48) (3.49) (3.74) (3.77)

Dividend dummy -0.0208*** -0.0207*** -0.195*** -0.195*** -0.00004 0.000004 -0.0194 -0.0178

(-16.33) (-16.26) (-14.02) (-13.99) (-0.02) (0.00) (-0.67) (-0.62)

Acquisition activity -0.354*** -0.353*** -2.786*** -2.765*** -0.289*** -0.288*** -2.118*** -2.097***

(-31.50) (-31.35) (-22.58) (-22.41) (-26.25) (-26.12) (-16.92) (-16.72)

Net debt issuance 0.187*** 0.187*** 1.468*** 1.470*** 0.159*** 0.159*** 1.196*** 1.194***

(37.94) (38.00) (27.13) (27.17) (15.43) (15.40) (12.64) (12.60)

Net equity issuance 0.126*** 0.125*** 0.751*** 0.741*** 0.177*** 0.176*** 1.188*** 1.170***

(25.49) (25.33) (13.91) (13.73) (23.29) (23.20) (19.63) (19.38)

Loss dummy -0.0221*** -0.0221*** -0.194*** -0.194*** -0.0235*** -0.0235*** -0.223*** -0.224***

(-16.34) (-16.36) (-13.06) (-13.10) (-17.70) (-17.72) (-14.67) (-14.75)

Intercept 0.247*** 0.243*** -1.895*** -1.922*** 0.225*** 0.224*** -1.818*** -1.854***

(38.04) (37.35) (-26.72) (-27.01) (21.43) (21.39) (-16.31) (-16.81)

Adj. R2/Within R2 0.447 0.447 0.370 0.370 0.215 0.215 0.164 0.164

52

Panel B: Quarterly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.389*** 0.348*** 0.927*** 1.319*** 0.0580 0.0671* -0.382 0.389

(16.34) (16.36) (3.53) (5.61) (1.36) (2.08) (-0.70) (0.96)

Cumulative policy deviation

×large

-0.0732*** -0.0725*** 0.270 0.0359 -0.114*** -0.106*** -0.807** -0.853***

(-5.11) (-6.09) (1.71) (0.27) (-4.82) (-5.55) (-2.64) (-3.52)

Cumulative policy deviation

×2000s dummy

-0.233*** -0.143*** -2.315*** -1.284*** -0.107*** -0.0521* -1.858*** -0.848***

(-16.45) (-11.64) (-14.81) (-9.43) (-3.74) (-2.45) (-6.11) (-3.73)

Cumulative policy deviation 0.0626*** 0.0260*** 0.405*** 0.00490 0.0887*** 0.0526*** 0.828*** 0.303*

(6.67) (3.37) (3.89) (0.06) (5.10) (3.83) (4.43) (2.03)

Funds rate 0.0326 0.0173 0.739** 0.364 0.157*** 0.172*** 0.953* 0.881*

(1.56) (0.92) (3.20) (1.74) (4.41) (4.91) (2.31) (2.18)

Fiscal deficit -0.0525** -0.0549* -3.210*** -3.465*** -0.272*** -0.258*** -3.921*** -4.230***

(-2.59) (-2.37) (-14.33) (-13.49) (-6.02) (-5.28) (-7.33) (-7.34)

Credit spread 1.989*** 2.467*** 28.26*** 34.56*** 1.501*** 1.822*** 23.31*** 28.58***

(12.24) (16.19) (15.72) (20.52) (6.18) (7.37) (8.41) (10.13)

Industry sigma 0.0208 -0.0107 1.523** 1.066* -0.188 -0.203* -1.468 -1.970

(0.45) (-0.23) (2.99) (2.10) (-1.88) (-2.02) (-1.40) (-1.87)

Market to book 0.0182*** 0.0183*** 0.172*** 0.173*** 0.00733*** 0.00737*** 0.0806*** 0.0820***

(67.57) (67.80) (57.95) (58.31) (9.85) (9.89) (12.98) (13.16)

Real size -0.00344*** -0.00330*** -0.0271*** -0.0240*** 0.000333 0.000911 -0.0462** -0.0324

(-15.58) (-14.76) (-11.11) (-9.71) (0.19) (0.54) (-2.65) (-1.87)

Cash flow/assets -0.231*** -0.231*** -1.339*** -1.346*** -0.0577*** -0.0586*** -0.0735 -0.0921

(-25.06) (-24.99) (-13.15) (-13.20) (-3.34) (-3.39) (-0.51) (-0.64)

NWC/assets -0.274*** -0.274*** -2.504*** -2.513*** -0.242*** -0.242*** -2.089*** -2.099***

(-134.55) (-134.87) (-111.20) (-111.60) (-30.26) (-30.26) (-28.56) (-28.65)

Capex -0.406*** -0.408*** -2.900*** -2.929*** -0.207*** -0.207*** -1.057*** -1.086***

(-57.72) (-57.98) (-37.33) (-37.70) (-20.14) (-20.19) (-9.67) (-9.92)

Leverage -0.361*** -0.362*** -3.893*** -3.904*** -0.245*** -0.246*** -2.774*** -2.795***

(-203.37) (-204.00) (-198.45) (-199.14) (-31.82) (-31.90) (-35.67) (-35.87)

R&D/sales 0.0502*** 0.0503*** 0.273*** 0.274*** 0.00922*** 0.00919*** 0.0584*** 0.0580***

(78.30) (78.50) (38.83) (38.96) (3.87) (3.87) (4.14) (4.13)

Dividend dummy -0.0291*** -0.0292*** -0.272*** -0.273*** -0.00414 -0.00411 -0.0667* -0.0666*

(-33.79) (-33.84) (-28.49) (-28.67) (-1.93) (-1.91) (-2.34) (-2.32)

Acquisition activity -0.350*** -0.348*** -2.509*** -2.478*** -0.237*** -0.237*** -1.329*** -1.327***

(-40.42) (-40.16) (-26.22) (-25.89) (-23.12) (-23.09) (-11.13) (-11.11)

Net debt issuance 0.221*** 0.222*** 1.575*** 1.585*** 0.160*** 0.160*** 1.093*** 1.099***

(57.76) (58.01) (37.32) (37.56) (15.83) (15.83) (13.03) (13.05)

Net equity issuance 0.181*** 0.181*** 1.113*** 1.106*** 0.183*** 0.183*** 1.210*** 1.203***

(62.37) (62.17) (34.77) (34.55) (22.41) (22.39) (20.55) (20.50)

Loss dummy -0.00646*** -0.00643*** -0.0651*** -0.0660*** -0.0108*** -0.0108*** -0.130*** -0.131***

(-7.63) (-7.58) (-6.95) (-7.04) (-11.25) (-11.26) (-12.02) (-12.11)

Intercept 0.249*** 0.247*** -1.911*** -1.931*** 0.202*** 0.198*** -1.997*** -2.078***

(54.20) (54.24) (-37.77) (-38.49) (19.67) (19.32) (-18.28) (-19.12)

Adj. R2/Within R2 0.471 0.471 0.387 0.386 0.184 0.184 0.130 0.129

53

Table 8 Regressions of cash holdings on Taylor rule prescription variables and

controls

Cash/Assets defined as cash and marketable securities (data item #1) divided by total assets (data

item #6). Log net cash ratio defined as log value of cash and marketable securities (data item #1)

divided by (total assets (data item #6)-cash and marketable securities (data item #1)). The sample

includes all Compustat firm-year observations from 1980 to 2007 with positive values for the

book value of total assets and sales revenue for firms incorporated in the United States. Financial

firms (SIC code 6000-6999) and utilities (SIC codes 4900-4999) are excluded from the sample,

yielding a panel of 118,897 observations for 13,743 unique firms. Missing explanatory values

reduce the panel used here to 67,574 firm-year observations for 12,430 unique firms for the firm

fixed effects regressions. Taylor prescriptions are calculated based on two types of Taylor rule

specifications from Table 1. I calculate the fiscal deficit as the difference between annual federal

government current receipts and current expenditures divided by nominal GDP. Credit spread is

the difference between the AAA and BBB yields reported by the Federal Reserve. Industry

sigma is the average across the two-digit SIC code of the firm cash flow standard deviations for

the previous 10 years, and I require at least three observations for the calculation. Market-to-book

is the ratio of the market value of assets to the book value of assets i.e. book value of assets (#6)

minus the book value of equity (#60) plus the market value of equity (#199* #25) as the

numerator of the ratio and the book value of assets (#6) as the denominator. Real size is the

logarithm of book assets (#6). Cash flow/assets is calculated as earnings after interest, dividends,

and taxes but before depreciation divided by book assets (((#13–#15–#16–#21)/#6). NWC/assets

is net working capital (data item #179) minus cash and marketable securities (data item #1)

divided by book assets. Capex is the ratio of capital expenditures (data item #128) to the book

value of total assets (data item #6). Leverage is the ratio of total debt to the book value of total

assets (data item #6), where debt includes long-term debt (data item #9) plus debt in current

liabilities (data item #34). R&D/sales is the ratio of research and development expense (data item

#46) to sales (data item #12). Dividend dummy is a dummy variable equal to one if the firm paid

a common dividend and zero otherwise. Acquisition activity is the ratio of expenditures on

acquisitions (data item #129) relative to the book value of total assets (data item #6). Net debt

issuance is calculated as annual total debt issuance (data item #111) minus debt retirement (data

item #114), divided by the book value of total assets (data item #6). Net equity issuance is

calculated as equity sales (data item #108) minus equity purchases (data item #115), divided by

the book value of total assets (data item #6). Loss dummy is a dummy variable equal to one if net

income (data item #172) is less than zero, and zero otherwise. All variables in dollars are

inflation-adjusted to 2007 dollars using the Consumer Price Index. ΔXt is notation for the one-

year change, Xt- Xt-1, where t and (t-1) denote end of fiscal year t and (t-1). 2000s dummy is a

dummy variable equal to one if the firm observation is in the fiscal year after 1999, and zero

otherwise. In this analysis, I first divide the whole sample into four quartiles each fiscal year

based on the real size and define firms in the largest real size quartiles as “large” firms. Large is a

dummy variable equal to one if the firm is in the large real size quartile and zero otherwise.

t-statistics based on standard errors robust to clustering by firm and year are reported in

parentheses. I report adjusted-R2 for OLS estimation models and within R

2 for firm fixed effects

estimation models. ***, **, and * denote significance at the 1%, 5%, and 10% levels,

respectively.

54

Panel A: Yearly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Taylor

prescription×large

-0.297*** -0.287*** -2.539*** -2.479*** -0.0475* -0.0313 -1.024** -0.842**

(-13.38) (-12.72) (-10.46) (-10.05) (-2.06) (-1.49) (-3.21) (-2.94)

Taylor prescription 0.0664** 0.105*** 1.432*** 1.805*** -0.0543** -0.00935 0.688** 1.044***

(2.70) (4.53) (5.32) (7.12) (-2.67) (-0.50) (2.80) (4.55)

Funds rate -0.0333 -0.0681* 0.253 -0.0953 0.112** 0.0731* 0.599 0.246

(-1.12) (-2.31) (0.77) (-0.30) (3.18) (2.11) (1.46) (0.60)

Fiscal deficit -0.0546 -0.0798* -3.853*** -4.383*** -0.213*** -0.226*** -4.193*** -4.614***

(-1.59) (-2.16) (-10.23) (-10.82) (-4.48) (-4.58) (-7.59) (-8.05)

Credit spread 2.345*** 2.416*** 32.93*** 34.56*** 1.533*** 1.545*** 28.65*** 29.93***

(12.10) (12.31) (15.51) (16.08) (6.81) (6.69) (11.36) (11.54)

Industry sigma 0.298*** 0.297*** 3.134*** 3.103*** 0.0741 0.0760* 1.720*** 1.698***

(13.65) (13.58) (13.10) (12.96) (1.95) (2.01) (4.09) (4.05)

Market to book 0.0162*** 0.0162*** 0.159*** 0.159*** 0.00656*** 0.00661*** 0.0766*** 0.0774***

(34.20) (34.31) (30.74) (30.86) (7.72) (7.78) (10.63) (10.73)

Real size -0.00289*** -0.00318*** -0.0115** -0.0137*** -0.000565 -0.000708 -0.0322* -0.0338*

(-7.49) (-8.39) (-2.71) (-3.30) (-0.35) (-0.44) (-1.96) (-2.06)

Cash flow/assets -0.0325*** -0.0318*** -0.171*** -0.165** 0.0127 0.0127 0.148* 0.149*

(-6.91) (-6.78) (-3.33) (-3.21) (1.56) (1.55) (2.12) (2.14)

NWC/assets -0.267*** -0.267*** -2.442*** -2.440*** -0.262*** -0.262*** -2.256*** -2.256***

(-83.15) (-83.08) (-69.28) (-69.24) (-34.38) (-34.37) (-30.76) (-30.77)

Capex -0.466*** -0.466*** -3.733*** -3.733*** -0.364*** -0.364*** -2.959*** -2.958***

(-48.88) (-48.88) (-35.77) (-35.78) (-26.99) (-26.96) (-20.68) (-20.68)

Leverage -0.337*** -0.336*** -3.734*** -3.732*** -0.246*** -0.246*** -2.823*** -2.825***

(-118.33) (-118.21) (-119.71) (-119.68) (-33.39) (-33.38) (-36.63) (-36.63)

R&D/sales 0.0699*** 0.0699*** 0.399*** 0.399*** 0.0158*** 0.0157*** 0.0985*** 0.0982***

(50.35) (50.35) (26.34) (26.34) (3.49) (3.47) (3.74) (3.72)

Dividend dummy -0.0205*** -0.0207*** -0.195*** -0.196*** -0.000303 -0.000494 -0.0239 -0.0255

(-16.12) (-16.24) (-14.00) (-14.10) (-0.12) (-0.20) (-0.83) (-0.88)

Acquisition activity -0.349*** -0.348*** -2.719*** -2.708*** -0.285*** -0.283*** -2.079*** -2.071***

(-31.07) (-30.95) (-22.06) (-21.98) (-25.97) (-25.87) (-16.57) (-16.50)

Net debt issuance 0.188*** 0.188*** 1.478*** 1.480*** 0.159*** 0.159*** 1.196*** 1.198***

(38.14) (38.15) (27.33) (27.36) (15.37) (15.37) (12.57) (12.58)

Net equity issuance 0.124*** 0.124*** 0.722*** 0.723*** 0.175*** 0.174*** 1.144*** 1.144***

(25.13) (25.09) (13.41) (13.41) (23.04) (23.02) (19.01) (19.01)

Loss dummy -0.0221*** -0.0221*** -0.195*** -0.195*** -0.0236*** -0.0235*** -0.225*** -0.225***

(-16.31) (-16.32) (-13.13) (-13.14) (-17.73) (-17.69) (-14.84) (-14.80)

Intercept 0.236*** 0.236*** -2.029*** -2.043*** 0.222*** 0.222*** -2.006*** -2.020***

(35.67) (35.58) (-28.06) (-28.17) (21.13) (21.07) (-18.27) (-18.37)

Adj. R2/Within R2 0.446 0.446 0.371 0.371 0.213 0.213 0.161 0.161

55

Panel B: Quarterly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Taylor

prescription×large

-0.389*** -0.353*** -2.440*** -2.304*** -0.0795* -0.0549 -1.976*** -1.717***

(-20.28) (-19.15) (-11.52) (-11.31) (-2.11) (-1.70) (-3.92) (-3.97)

Taylor prescription 0.157*** 0.201*** 1.884*** 2.474*** 0.0180 0.0729* 1.140** 1.844***

(5.96) (8.59) (6.48) (9.56) (0.48) (2.23) (2.65) (4.94)

Funds rate -0.0209 -0.0590** -0.836*** -1.289*** 0.190*** 0.152*** 0.302 -0.219

(-1.06) (-2.97) (-3.84) (-5.88) (4.59) (3.72) (0.65) (-0.48)

Fiscal deficit -0.0481* -0.0946*** -2.929*** -3.716*** -0.276*** -0.304*** -3.964*** -4.556***

(-2.37) (-4.06) (-13.07) (-14.42) (-5.98) (-6.29) (-7.29) (-7.98)

Credit spread 2.664*** 2.657*** 38.02*** 37.72*** 1.954*** 1.964*** 30.33*** 30.41***

(18.83) (18.77) (24.29) (24.11) (7.49) (7.54) (10.23) (10.26)

Industry sigma 0.0125 0.00784 1.117* 1.078* -0.208* -0.209* -1.991 -2.028

(0.27) (0.17) (2.20) (2.12) (-2.05) (-2.06) (-1.88) (-1.92)

Market to book 0.0185*** 0.0185*** 0.175*** 0.175*** 0.00757*** 0.00760*** 0.0842*** 0.0845***

(68.55) (68.53) (58.95) (58.97) (10.15) (10.18) (13.49) (13.54)

Real size -0.00125*** -0.00164*** -0.00640* -0.00818** 0.00149 0.00126 -0.0139 -0.0168

(-4.73) (-6.37) (-2.19) (-2.87) (0.86) (0.74) (-0.80) (-0.97)

Cash flow/assets -0.239*** -0.237*** -1.419*** -1.408*** -0.0592*** -0.0590*** -0.114 -0.111

(-25.87) (-25.67) (-13.90) (-13.80) (-3.43) (-3.42) (-0.80) (-0.77)

NWC/assets -0.276*** -0.275*** -2.527*** -2.526*** -0.242*** -0.242*** -2.112*** -2.111***

(-135.61) (-135.50) (-112.27) (-112.22) (-30.26) (-30.24) (-28.79) (-28.78)

Capex -0.409*** -0.409*** -2.966*** -2.963*** -0.208*** -0.208*** -1.108*** -1.108***

(-58.30) (-58.25) (-38.23) (-38.21) (-20.24) (-20.20) (-10.15) (-10.13)

Leverage -0.363*** -0.363*** -3.918*** -3.918*** -0.247*** -0.247*** -2.808*** -2.810***

(-205.16) (-205.09) (-200.33) (-200.32) (-31.97) (-31.98) (-36.08) (-36.12)

R&D/sales 0.0504*** 0.0504*** 0.275*** 0.275*** 0.00910*** 0.00908*** 0.0575*** 0.0573***

(78.74) (78.77) (39.13) (39.13) (3.83) (3.82) (4.11) (4.09)

Dividend dummy -0.0277*** -0.0280*** -0.263*** -0.265*** -0.00451* -0.00462* -0.0680* -0.0694*

(-32.02) (-32.35) (-27.52) (-27.68) (-2.09) (-2.14) (-2.36) (-2.41)

Acquisition activity -0.351*** -0.349*** -2.509*** -2.503*** -0.238*** -0.237*** -1.352*** -1.346***

(-40.48) (-40.36) (-26.21) (-26.16) (-23.20) (-23.15) (-11.34) (-11.28)

Net debt issuance 0.222*** 0.222*** 1.590*** 1.592*** 0.161*** 0.161*** 1.102*** 1.106***

(58.22) (58.26) (37.68) (37.74) (15.85) (15.87) (13.07) (13.11)

Net equity issuance 0.181*** 0.181*** 1.105*** 1.105*** 0.184*** 0.184*** 1.205*** 1.206***

(62.18) (62.17) (34.53) (34.54) (22.43) (22.43) (20.54) (20.52)

Loss dummy -0.00622*** -0.00624*** -0.0644*** -0.0642*** -0.0108*** -0.0108*** -0.131*** -0.130***

(-7.34) (-7.36) (-6.87) (-6.85) (-11.26) (-11.24) (-12.09) (-12.06)

Intercept 0.235*** 0.235*** -2.019*** -2.033*** 0.193*** 0.192*** -2.166*** -2.177***

(50.78) (51.10) (-39.64) (-40.12) (18.35) (18.42) (-19.56) (-19.77)

Adj. R2/Within R2 0.471 0.471 0.386 0.386 0.183 0.183 0.129 0.129

56

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59

Appendix

Table 1 Regressions of cash holdings on current policy deviation variables and controls

Panel A: Annual regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Policy deviation -0.00651 -0.0439 -0.919*** -1.276*** 0.0649*** 0.0165 -0.460* -0.850***

(-0.27) (-1.94) (-3.47) (-5.14) (3.38) (0.92) (-1.98) (-3.93)

Funds rate -0.0606* -0.0528* 0.884*** 0.934*** 0.0438 0.0550 0.995** 1.054**

(-2.51) (-2.21) (3.35) (3.57) (1.49) (1.87) (2.82) (2.99)

Fiscal deficit -0.0202 -0.0507 -3.560*** -4.133*** -0.210*** -0.224*** -4.130*** -4.580***

(-0.59) (-1.37) (-9.47) (-10.21) (-4.42) (-4.55) (-7.47) (-7.98)

Credit spread 2.516*** 2.576*** 34.39*** 35.93*** 1.544*** 1.552*** 28.89*** 30.12***

(12.99) (13.14) (16.23) (16.74) (6.86) (6.71) (11.43) (11.60)

Industry sigma 0.294*** 0.293*** 3.095*** 3.071*** 0.0731 0.0755* 1.699*** 1.685***

(13.43) (13.40) (12.92) (12.82) (1.92) (1.99) (4.05) (4.02)

Market to book 0.0160*** 0.0160*** 0.157*** 0.158*** 0.00656*** 0.00660*** 0.0767*** 0.0773***

(33.84) (33.88) (30.47) (30.54) (7.73) (7.77) (10.63) (10.70)

Real size -0.00590*** -0.00591*** -0.0373*** -0.0373*** -0.000906 -0.000924 -0.0396* -0.0396*

(-18.84) (-18.87) (-10.87) (-10.88) (-0.57) (-0.58) (-2.42) (-2.42)

Cash flow/assets -0.0271*** -0.0272*** -0.125* -0.124* 0.0131 0.0129 0.157* 0.156*

(-5.78) (-5.80) (-2.44) (-2.43) (1.61) (1.58) (2.25) (2.23)

NWC/assets -0.265*** -0.265*** -2.427*** -2.426*** -0.262*** -0.262*** -2.258*** -2.257***

(-82.55) (-82.53) (-68.85) (-68.84) (-34.40) (-34.38) (-30.78) (-30.78)

Capex -0.468*** -0.467*** -3.750*** -3.747*** -0.365*** -0.364*** -2.971*** -2.967***

(-49.02) (-48.98) (-35.91) (-35.88) (-27.05) (-27.01) (-20.78) (-20.76)

Leverage -0.336*** -0.336*** -3.728*** -3.729*** -0.246*** -0.246*** -2.822*** -2.824***

(-117.95) (-117.96) (-119.44) (-119.50) (-33.37) (-33.37) (-36.59) (-36.61)

R&D/sales 0.0706*** 0.0706*** 0.405*** 0.405*** 0.0158*** 0.0157*** 0.0993*** 0.0988***

(50.82) (50.80) (26.73) (26.71) (3.50) (3.48) (3.77) (3.74)

Dividend dummy -0.0212*** -0.0212*** -0.201*** -0.202*** -0.000349 -0.000514 -0.0248 -0.0261

(-16.65) (-16.69) (-14.43) (-14.47) (-0.14) (-0.21) (-0.86) (-0.90)

Acquisition activity -0.347*** -0.346*** -2.694*** -2.691*** -0.285*** -0.283*** -2.075*** -2.069***

(-30.78) (-30.73) (-21.84) (-21.82) (-25.96) (-25.87) (-16.54) (-16.50)

Net debt issuance 0.188*** 0.188*** 1.477*** 1.478*** 0.159*** 0.159*** 1.195*** 1.197***

(38.05) (38.07) (27.28) (27.31) (15.37) (15.37) (12.56) (12.57)

Net equity issuance 0.124*** 0.124*** 0.722*** 0.724*** 0.175*** 0.174*** 1.145*** 1.145***

(25.10) (25.10) (13.40) (13.44) (23.05) (23.03) (19.02) (19.02)

Loss dummy -0.0221*** -0.0221*** -0.195*** -0.195*** -0.0235*** -0.0235*** -0.225*** -0.224***

(-16.32) (-16.29) (-13.15) (-13.13) (-17.72) (-17.68) (-14.79) (-14.76)

Intercept 0.252*** 0.251*** -1.889*** -1.915*** 0.224*** 0.223*** -1.964*** -1.988***

(38.76) (38.35) (-26.56) (-26.80) (21.51) (21.34) (-18.01) (-18.16)

Adj. R2/Within R2 0.445 0.445 0.370 0.370 0.213 0.213 0.161 0.161

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

60

Panel B: Quarterly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Policy deviation -0.0724** -0.124*** -1.352*** -1.967*** 0.00112 -0.0598 -0.661 -1.431***

(-2.79) (-5.37) (-4.71) (-7.72) (0.03) (-1.93) (-1.65) (-4.10)

Funds rate 0.00475 0.0229 0.222 0.403* 0.184*** 0.209*** 0.836* 1.105**

(0.23) (1.24) (0.98) (1.98) (5.02) (5.90) (1.97) (2.71)

Fiscal deficit -0.00638 -0.0607** -2.667*** -3.494*** -0.271*** -0.302*** -3.846*** -4.479***

(-0.32) (-2.61) (-11.96) (-13.59) (-5.89) (-6.23) (-7.07) (-7.84)

Credit spread 2.771*** 2.748*** 38.69*** 38.32*** 1.964*** 1.970*** 30.56*** 30.59***

(19.57) (19.41) (24.73) (24.50) (7.53) (7.56) (10.28) (10.31)

Industry sigma -0.0156 -0.0166 0.935 0.914 -0.212* -0.211* -2.091* -2.105*

(-0.34) (-0.36) (1.84) (1.80) (-2.09) (-2.09) (-1.97) (-1.99)

Market to book 0.0182*** 0.0182*** 0.174*** 0.174*** 0.00756*** 0.00759*** 0.0840*** 0.0843***

(67.70) (67.73) (58.51) (58.54) (10.14) (10.17) (13.46) (13.50)

Real size -0.00465*** -0.00466*** -0.0277*** -0.0279*** 0.00101 0.000939 -0.0258 -0.0269

(-22.72) (-22.77) (-12.30) (-12.35) (0.60) (0.56) (-1.50) (-1.56)

Cash flow/assets -0.226*** -0.226*** -1.337*** -1.336*** -0.0586*** -0.0586*** -0.0991 -0.0997

(-24.50) (-24.50) (-13.13) (-13.12) (-3.39) (-3.39) (-0.69) (-0.70)

NWC/assets -0.274*** -0.274*** -2.517*** -2.516*** -0.242*** -0.242*** -2.112*** -2.110***

(-134.75) (-134.72) (-111.87) (-111.84) (-30.26) (-30.24) (-28.78) (-28.75)

Capex -0.407*** -0.407*** -2.954*** -2.952*** -0.208*** -0.208*** -1.112*** -1.111***

(-57.97) (-57.95) (-38.07) (-38.05) (-20.25) (-20.20) (-10.17) (-10.14)

Leverage -0.363*** -0.363*** -3.919*** -3.919*** -0.247*** -0.247*** -2.809*** -2.810***

(-205.00) (-205.02) (-200.30) (-200.33) (-31.96) (-31.97) (-36.05) (-36.08)

R&D/sales 0.0508*** 0.0508*** 0.278*** 0.278*** 0.00910*** 0.00908*** 0.0577*** 0.0574***

(79.31) (79.30) (39.48) (39.47) (3.83) (3.82) (4.12) (4.09)

Dividend dummy -0.0296*** -0.0296*** -0.275*** -0.275*** -0.00467* -0.00472* -0.0719* -0.0726*

(-34.36) (-34.38) (-28.90) (-28.92) (-2.17) (-2.19) (-2.49) (-2.52)

Acquisition activity -0.347*** -0.347*** -2.484*** -2.484*** -0.237*** -0.237*** -1.350*** -1.348***

(-39.99) (-39.99) (-25.95) (-25.96) (-23.20) (-23.16) (-11.32) (-11.30)

Net debt issuance 0.222*** 0.223*** 1.590*** 1.592*** 0.161*** 0.161*** 1.101*** 1.105***

(58.16) (58.22) (37.67) (37.73) (15.85) (15.87) (13.05) (13.09)

Net equity issuance 0.180*** 0.180*** 1.103*** 1.104*** 0.184*** 0.184*** 1.206*** 1.207***

(62.01) (62.03) (34.46) (34.48) (22.43) (22.43) (20.54) (20.52)

Loss dummy -0.00638*** -0.00635*** -0.0655*** -0.0650*** -0.0108*** -0.0108*** -0.130*** -0.130***

(-7.53) (-7.49) (-6.99) (-6.93) (-11.24) (-11.22) (-12.04) (-12.00)

Intercept 0.254*** 0.252*** -1.898*** -1.921*** 0.196*** 0.194*** -2.094*** -2.117***

(56.08) (55.82) (-38.07) (-38.65) (19.18) (19.01) (-19.19) (-19.42)

Adj. R2/Within R2 0.470 0.470 0.386 0.386 0.183 0.183 0.128 0.129

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

61

Table 2 Regressions of cash holdings on four serial policy deviation variables and controls

Panel A: Annual regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

current policy

deviation

-0.00169 -0.0562 -1.432*** -1.798*** -0.00847 -0.0468* -1.365*** -1.687***

(-0.04) (-1.63) (-3.34) (-4.78) (-0.38) (-2.19) (-4.83) (-6.39)

lagged policy

deviation

-0.0507 -0.00679 -0.00899 0.351 0.0581* 0.0762*** 0.614* 0.838***

(-1.42) (-0.23) (-0.02) (1.07) (2.52) (3.78) (2.19) (3.44)

lag two policy

deviation

0.180*** 0.135*** 2.207*** 1.461*** 0.142*** 0.104*** 1.755*** 1.204***

(5.26) (4.72) (5.88) (4.66) (6.18) (5.41) (6.31) (5.18)

lag three policy

deviation

-0.113*** -0.0807** -1.461*** -0.908*** -0.0215 -0.000977 -1.035*** -0.613*

(-3.67) (-3.21) (-4.33) (-3.30) (-0.82) (-0.04) (-3.42) (-2.44)

Funds rate -0.0875*** -0.0777** 0.664* 0.710** 0.0652 0.0562 1.038** 1.015**

(-3.40) (-3.12) (2.35) (2.60) (1.92) (1.80) (2.59) (2.72)

Fiscal deficit -0.00132 -0.00587 -3.261*** -3.535*** -0.202*** -0.176*** -3.924*** -4.006***

(-0.04) (-0.15) (-8.60) (-8.48) (-4.15) (-3.47) (-6.95) (-6.84)

Credit spread 2.867*** 2.991*** 39.06*** 41.29*** 1.949*** 2.151*** 33.49*** 36.29***

(13.99) (14.12) (17.41) (17.80) (7.90) (8.39) (12.03) (12.50)

Industry sigma 0.290*** 0.291*** 3.053*** 3.055*** 0.0558 0.0651 1.619*** 1.658***

(13.20) (13.29) (12.71) (12.74) (1.46) (1.71) (3.83) (3.94)

Market to book 0.0161*** 0.0160*** 0.158*** 0.158*** 0.00673*** 0.00668*** 0.0777*** 0.0775***

(33.94) (33.93) (30.61) (30.60) (7.90) (7.85) (10.74) (10.71)

Real size -0.00587*** -0.00586*** -0.0366*** -0.0366*** -0.00001 -0.000107 -0.0346* -0.0353*

(-18.67) (-18.65) (-10.64) (-10.63) (-0.00) (-0.07) (-2.09) (-2.14)

Cash flow/assets -0.0264*** -0.0262*** -0.115* -0.113* 0.0136 0.0138 0.162* 0.164*

(-5.63) (-5.60) (-2.25) (-2.20) (1.66) (1.68) (2.33) (2.36)

NWC/assets -0.266*** -0.266*** -2.431*** -2.432*** -0.264*** -0.264*** -2.268*** -2.270***

(-82.57) (-82.60) (-68.94) (-68.96) (-34.49) (-34.53) (-30.84) (-30.87)

Capex -0.470*** -0.470*** -3.780*** -3.781*** -0.369*** -0.370*** -3.013*** -3.017***

(-49.18) (-49.19) (-36.16) (-36.17) (-27.21) (-27.26) (-21.01) (-21.05)

Leverage -0.336*** -0.337*** -3.734*** -3.735*** -0.249*** -0.249*** -2.839*** -2.839***

(-117.63) (-117.75) (-119.22) (-119.35) (-33.48) (-33.51) (-36.53) (-36.61)

R&D/sales 0.0707*** 0.0707*** 0.406*** 0.406*** 0.0159*** 0.0159*** 0.100*** 0.0997***

(50.83) (50.82) (26.77) (26.76) (3.53) (3.52) (3.79) (3.77)

Dividend dummy -0.0213*** -0.0213*** -0.203*** -0.203*** -0.000384 -0.000482 -0.0259 -0.0265

(-16.71) (-16.76) (-14.54) (-14.55) (-0.16) (-0.20) (-0.89) (-0.92)

Acquisition activity -0.348*** -0.348*** -2.710*** -2.718*** -0.287*** -0.288*** -2.097*** -2.105***

(-30.89) (-30.92) (-21.97) (-22.03) (-26.13) (-26.17) (-16.72) (-16.79)

Net debt issuance 0.188*** 0.188*** 1.480*** 1.480*** 0.159*** 0.159*** 1.198*** 1.197***

(38.09) (38.10) (27.35) (27.34) (15.39) (15.38) (12.58) (12.58)

Net equity issuance 0.124*** 0.125*** 0.731*** 0.734*** 0.175*** 0.175*** 1.148*** 1.150***

(25.23) (25.25) (13.56) (13.61) (23.07) (23.08) (19.05) (19.08)

Loss dummy -0.0222*** -0.0221*** -0.196*** -0.195*** -0.0236*** -0.0235*** -0.225*** -0.224***

(-16.39) (-16.35) (-13.22) (-13.17) (-17.77) (-17.69) (-14.83) (-14.74)

Intercept 0.251*** 0.249*** -1.913*** -1.943*** 0.217*** 0.215*** -2.020*** -2.047***

(38.42) (37.91) (-26.78) (-27.06) (20.64) (20.45) (-18.13) (-18.35)

Adj. R2/Within R2 0.445 0.445 0.370 0.370 0.214 0.214 0.162 0.162

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

62

Panel B: Quarterly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

current policy

deviation

-0.192*** -0.251*** -2.094*** -2.935*** -0.0986** -0.176*** -1.461*** -2.441***

(-4.33) (-6.18) (-4.27) (-6.55) (-2.87) (-5.83) (-3.62) (-6.89)

lagged policy

deviation

0.171*** 0.135** 1.613** 1.203* 0.100*** 0.0979*** 1.442*** 1.203***

(3.48) (2.94) (2.97) (2.38) (3.82) (4.18) (4.47) (4.16)

lag two policy

deviation

0.0203 0.0633 -0.200 0.458 0.00430 0.0488* -0.338 0.326

(0.41) (1.36) (-0.36) (0.89) (0.20) (2.47) (-1.15) (1.21)

lag three policy

deviation

-0.0881* -0.0668 -1.217* -0.811 0.0534 0.0226 -0.652 -0.513

(-2.02) (-1.75) (-2.53) (-1.92) (1.49) (0.76) (-1.55) (-1.47)

Funds rate 0.0141 0.0251 0.494* 0.515* 0.164*** 0.192*** 0.951* 1.146**

(0.66) (1.28) (2.08) (2.38) (4.42) (5.35) (2.20) (2.76)

Fiscal deficit -0.00611 -0.0571* -2.669*** -3.534*** -0.274*** -0.281*** -3.803*** -4.460***

(-0.30) (-2.41) (-11.96) (-13.48) (-5.93) (-5.65) (-6.99) (-7.60)

Credit spread 2.840*** 2.735*** 40.71*** 38.59*** 1.803*** 1.870*** 31.79*** 30.61***

(19.02) (19.03) (24.68) (24.30) (6.60) (7.07) (10.19) (10.15)

Industry sigma -0.0167 -0.0183 0.949 0.894 -0.216* -0.208* -2.127* -2.135*

(-0.36) (-0.40) (1.87) (1.76) (-2.14) (-2.06) (-2.01) (-2.01)

Market to book 0.0182*** 0.0182*** 0.173*** 0.174*** 0.00755*** 0.00755*** 0.0840*** 0.0842***

(67.66) (67.71) (58.45) (58.53) (10.12) (10.12) (13.46) (13.48)

Real size -0.00468*** -0.00466*** -0.0284*** -0.0280*** 0.00125 0.00117 -0.0281 -0.0271

(-22.81) (-22.73) (-12.55) (-12.39) (0.74) (0.70) (-1.62) (-1.57)

Cash flow/assets -0.226*** -0.226*** -1.336*** -1.336*** -0.0588*** -0.0589*** -0.0965 -0.0996

(-24.49) (-24.50) (-13.11) (-13.12) (-3.40) (-3.41) (-0.67) (-0.69)

NWC/assets -0.274*** -0.274*** -2.514*** -2.515*** -0.243*** -0.243*** -2.109*** -2.111***

(-134.57) (-134.67) (-111.60) (-111.74) (-30.29) (-30.30) (-28.72) (-28.75)

Capex -0.407*** -0.407*** -2.941*** -2.950*** -0.209*** -0.209*** -1.107*** -1.113***

(-57.83) (-57.95) (-37.85) (-38.00) (-20.39) (-20.38) (-10.12) (-10.19)

Leverage -0.363*** -0.363*** -3.915*** -3.919*** -0.247*** -0.247*** -2.806*** -2.811***

(-204.71) (-204.97) (-199.91) (-200.24) (-31.99) (-32.00) (-36.03) (-36.09)

R&D/sales 0.0508*** 0.0508*** 0.277*** 0.278*** 0.00914*** 0.00912*** 0.0576*** 0.0575***

(79.26) (79.29) (39.39) (39.44) (3.85) (3.84) (4.12) (4.10)

Dividend dummy -0.0296*** -0.0296*** -0.274*** -0.275*** -0.00470* -0.00478* -0.0715* -0.0726*

(-34.31) (-34.39) (-28.79) (-28.91) (-2.18) (-2.21) (-2.48) (-2.52)

Acquisition activity -0.347*** -0.347*** -2.486*** -2.489*** -0.238*** -0.239*** -1.349*** -1.354***

(-40.04) (-40.07) (-25.97) (-26.00) (-23.26) (-23.28) (-11.31) (-11.34)

Net debt issuance 0.222*** 0.223*** 1.591*** 1.593*** 0.161*** 0.161*** 1.103*** 1.105***

(58.17) (58.21) (37.70) (37.74) (15.83) (15.84) (13.06) (13.08)

Net equity issuance 0.180*** 0.180*** 1.107*** 1.104*** 0.183*** 0.183*** 1.209*** 1.207***

(62.03) (61.98) (34.56) (34.48) (22.41) (22.40) (20.59) (20.54)

Loss dummy -0.00641*** -0.00636*** -0.0659*** -0.0653*** -0.0108*** -0.0107*** -0.131*** -0.130***

(-7.56) (-7.50) (-7.03) (-6.96) (-11.20) (-11.14) (-12.08) (-12.01)

Intercept 0.253*** 0.252*** -1.929*** -1.929*** 0.198*** 0.195*** -2.098*** -2.116***

(55.10) (55.40) (-38.15) (-38.51) (19.36) (19.15) (-19.24) (-19.42)

Adj. R2/Within R2 0.470 0.470 0.386 0.386 0.183 0.183 0.128 0.129

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

63

Table 3 Regressions of cash holdings on two year cumulative policy deviation variables and controls

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

Panel A: Annual regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.00183*** 0.00173*** 0.00526** 0.00723*** 0.000135 0.000141 -0.00822** -0.00464*

(10.52) (10.82) (2.77) (4.14) (0.59) (0.72) (-3.08) (-2.00)

Cumulative policy deviation

×large

-0.000150* -0.000211*** 0.000137 -0.000907 -0.0002*** -0.0001*** -0.000468 -0.000884

(-2.19) (-3.78) (0.18) (-1.49) (-3.34) (-3.74) (-0.72) (-1.63)

Cumulative policy deviation

×2000s dummy

-0.00138*** -0.00100*** -0.0110*** -0.00811*** -0.0006*** -0.0003* -0.008*** -0.00456***

(-12.26) (-9.68) (-8.95) (-7.14) (-3.76) (-2.28) (-4.79) (-3.42)

Cumulative policy deviation 0.0002*** 0.000125*** 0.00120** 0.000566 0.000268*** 0.000178*** 0.00190*** 0.00104**

(5.62) (4.14) (3.05) (1.71) (7.95) (6.48) (4.88) (3.25)

Funds rate 0.0792** 0.0337 2.122*** 1.700*** 0.112*** 0.0807** 1.893*** 1.524***

(2.82) (1.21) (6.91) (5.59) (4.08) (2.95) (5.76) (4.63)

Fiscal deficit -0.0501 -0.0948* -3.612*** -4.252*** -0.182*** -0.183*** -3.697*** -4.305***

(-1.45) (-2.40) (-9.51) (-9.81) (-3.85) (-3.76) (-6.75) (-7.64)

Credit spread 1.694*** 1.882*** 25.73*** 27.37*** 1.282*** 1.432*** 22.81*** 24.30***

(7.95) (8.68) (11.02) (11.53) (6.30) (7.07) (10.03) (10.69)

Industry sigma 0.268*** 0.277*** 2.851*** 2.922*** 0.0520 0.0678 1.358** 1.505***

(12.20) (12.61) (11.83) (12.14) (1.37) (1.79) (3.20) (3.56)

Market to book 0.0160*** 0.0162*** 0.157*** 0.158*** 0.00645*** 0.00651*** 0.0743*** 0.0755***

(33.97) (34.30) (30.30) (30.59) (7.61) (7.69) (10.32) (10.51)

Real size -0.00509*** -0.00479*** -0.0370*** -0.0337*** -0.00175 -0.00114 -0.0651*** -0.0533**

(-15.35) (-14.18) (-10.19) (-9.10) (-1.07) (-0.71) (-3.84) (-3.20)

Cash flow/assets -0.0272*** -0.0273*** -0.118* -0.121* 0.0146 0.0139 0.181** 0.170*

(-5.81) (-5.82) (-2.29) (-2.35) (1.79) (1.69) (2.60) (2.44)

NWC/assets -0.265*** -0.265*** -2.413*** -2.420*** -0.261*** -0.262*** -2.225*** -2.239***

(-82.25) (-82.41) (-68.36) (-68.54) (-34.21) (-34.35) (-30.22) (-30.48)

Capex -0.468*** -0.469*** -3.727*** -3.747*** -0.364*** -0.365*** -2.903*** -2.938***

(-48.96) (-49.14) (-35.62) (-35.81) (-26.84) (-27.04) (-20.24) (-20.54)

Leverage -0.333*** -0.334*** -3.707*** -3.712*** -0.245*** -0.246*** -2.791*** -2.806***

(-116.65) (-116.92) (-118.37) (-118.59) (-33.10) (-33.24) (-36.01) (-36.27)

R&D/sales 0.0696*** 0.0698*** 0.401*** 0.402*** 0.0158*** 0.0158*** 0.101*** 0.101***

(50.08) (50.23) (26.37) (26.46) (3.47) (3.49) (3.74) (3.79)

Dividend dummy -0.0207*** -0.0209*** -0.195*** -0.197*** 0.0000219 -0.0000724 -0.0184 -0.0187

(-16.30) (-16.41) (-14.01) (-14.13) (0.01) (-0.03) (-0.64) (-0.65)

Acquisition activity -0.352*** -0.348*** -2.750*** -2.716*** -0.286*** -0.285*** -2.085*** -2.068***

(-31.28) (-30.98) (-22.30) (-22.02) (-26.02) (-25.88) (-16.63) (-16.46)

Net debt issuance 0.187*** 0.188*** 1.469*** 1.475*** 0.159*** 0.159*** 1.192*** 1.193***

(37.95) (38.07) (27.15) (27.25) (15.40) (15.38) (12.58) (12.56)

Net equity issuance 0.125*** 0.123*** 0.738*** 0.725*** 0.176*** 0.175*** 1.168*** 1.151***

(25.30) (25.07) (13.68) (13.45) (23.17) (23.06) (19.35) (19.11)

Loss dummy -0.0221*** -0.0221*** -0.194*** -0.195*** -0.0235*** -0.0235*** -0.224*** -0.226***

(-16.34) (-16.35) (-13.09) (-13.17) (-17.70) (-17.71) (-14.78) (-14.88)

Intercept 0.246*** 0.244*** -1.898*** -1.918*** 0.227*** 0.224*** -1.838*** -1.903***

(37.93) (37.53) (-26.73) (-26.92) (21.74) (21.55) (-16.70) (-17.49)

Adj. R2/Within R2 0.448 0.446 0.370 0.370 0.214 0.214 0.163 0.162

64

Panel B: Quarterly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.216*** 0.201*** 0.500*** 0.761*** 0.0314 0.0322 -0.304 0.109

(18.11) (18.88) (3.79) (6.46) (1.31) (1.68) (-1.00) (0.45)

Cumulative policy deviation

×large

-0.0388*** -0.0382*** 0.110 -0.0186 -0.0600*** -0.0522*** -0.424** -0.434***

(-5.75) (-7.12) (1.47) (-0.31) (-4.83) (-5.49) (-2.67) (-3.59)

Cumulative policy deviation

×2000s dummy

-0.146*** -0.0971*** -1.522*** -0.936*** -0.0694*** -0.0279* -1.289*** -0.587***

(-19.44) (-14.62) (-18.38) (-12.74) (-4.07) (-2.16) (-7.12) (-4.20)

Cumulative policy deviation 0.0407*** 0.0209*** 0.350*** 0.123** 0.0577*** 0.0326*** 0.588*** 0.260***

(8.49) (5.80) (6.59) (3.09) (6.15) (4.69) (5.89) (3.46)

Funds rate 0.0368 0.0237 0.724** 0.420 0.133*** 0.150*** 0.761 0.742

(1.77) (1.22) (3.15) (1.95) (3.81) (4.36) (1.88) (1.86)

Fiscal deficit -0.0622** -0.0664** -3.293*** -3.551*** -0.274*** -0.242*** -3.853*** -4.116***

(-3.09) (-2.89) (-14.79) (-13.99) (-6.08) (-5.09) (-7.23) (-7.32)

Credit spread 1.585*** 2.144*** 22.62*** 29.97*** 1.153*** 1.703*** 18.04*** 25.60***

(9.07) (13.22) (11.72) (16.72) (4.71) (7.12) (6.45) (9.37)

Industry sigma 0.0404 -0.00171 1.847*** 1.237* -0.182 -0.199* -1.114 -1.808

(0.88) (-0.04) (3.62) (2.43) (-1.82) (-1.98) (-1.06) (-1.72)

Market to book 0.0182*** 0.0183*** 0.171*** 0.173*** 0.00726*** 0.00733*** 0.0797*** 0.0815***

(67.47) (67.90) (57.73) (58.26) (9.77) (9.85) (12.84) (13.10)

Real size -0.00336*** -0.00305*** -0.0286*** -0.0240*** 0.000254 0.00104 -0.0562** -0.0361*

(-15.03) (-13.40) (-11.59) (-9.54) (0.15) (0.60) (-3.19) (-2.07)

Cash flow/assets -0.231*** -0.231*** -1.330*** -1.343*** -0.0572*** -0.0585*** -0.0568 -0.0856

(-25.09) (-25.07) (-13.05) (-13.18) (-3.32) (-3.39) (-0.40) (-0.60)

NWC/assets -0.273*** -0.274*** -2.496*** -2.508*** -0.242*** -0.242*** -2.079*** -2.096***

(-134.24) (-134.68) (-110.76) (-111.32) (-30.23) (-30.29) (-28.40) (-28.61)

Capex -0.405*** -0.408*** -2.888*** -2.924*** -0.207*** -0.209*** -1.051*** -1.088***

(-57.64) (-57.97) (-37.17) (-37.62) (-20.22) (-20.34) (-9.63) (-9.96)

Leverage -0.360*** -0.361*** -3.881*** -3.896*** -0.245*** -0.246*** -2.753*** -2.784***

(-202.57) (-203.32) (-197.50) (-198.40) (-31.69) (-31.86) (-35.38) (-35.71)

R&D/sales 0.0500*** 0.0501*** 0.272*** 0.273*** 0.00927*** 0.00924*** 0.0587*** 0.0583***

(78.00) (78.19) (38.60) (38.75) (3.89) (3.89) (4.14) (4.15)

Dividend dummy -0.0290*** -0.0291*** -0.269*** -0.271*** -0.00412 -0.00404 -0.0666* -0.0650*

(-33.57) (-33.65) (-28.25) (-28.45) (-1.92) (-1.88) (-2.34) (-2.27)

Acquisition activity -0.352*** -0.349*** -2.538*** -2.488*** -0.238*** -0.238*** -1.336*** -1.327***

(-40.66) (-40.27) (-26.53) (-25.99) (-23.18) (-23.11) (-11.19) (-11.09)

Net debt issuance 0.220*** 0.221*** 1.569*** 1.582*** 0.160*** 0.160*** 1.090*** 1.096***

(57.69) (57.98) (37.20) (37.49) (15.81) (15.81) (13.02) (13.03)

Net equity issuance 0.182*** 0.181*** 1.121*** 1.110*** 0.184*** 0.183*** 1.219*** 1.205***

(62.55) (62.30) (35.02) (34.68) (22.40) (22.39) (20.61) (20.51)

Loss dummy -0.00646*** -0.00645*** -0.0647*** -0.0662*** -0.0108*** -0.0108*** -0.129*** -0.131***

(-7.63) (-7.61) (-6.91) (-7.07) (-11.21) (-11.24) (-11.97) (-12.13)

Intercept 0.250*** 0.247*** -1.882*** -1.919*** 0.206*** 0.199*** -1.919*** -2.040***

(54.06) (54.05) (-36.95) (-38.15) (20.02) (19.46) (-17.61) (-18.81)

Adj. R2/Within R2 0.472 0.471 0.387 0.386 0.185 0.184 0.131 0.129

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

65

Table 4 Regressions of cash holdings on three year cumulative policy deviation variables and controls

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

Panel A: Annual regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.00126*** 0.00130*** 0.00295* 0.00451*** 0.0000747 0.0000751 -0.0067*** -0.00506**

(10.37) (11.32) (2.23) (3.60) (0.45) (0.48) (-3.51) (-2.75)

Cumulative policy deviation

×large

-0.0000529 -0.000122** 0.000636 -0.000253 -0.00007* -0.00008** 0.0000793 -0.000275

(-1.11) (-3.10) (1.22) (-0.59) (-2.14) (-2.74) (0.17) (-0.71)

Cumulative policy deviation

×2000s dummy

-0.00104*** -0.000932*** -0.00851*** -0.00762*** -0.0005*** -0.0003** -0.007*** -0.0048***

(-13.34) (-11.65) (-9.98) (-8.70) (-3.91) (-2.69) (-5.30) (-4.08)

Cumulative policy deviation 0.000167*** 0.000134*** 0.00132*** 0.001*** 0.0002*** 0.00016*** 0.00183*** 0.00127***

(6.71) (6.30) (4.83) (4.29) (8.54) (7.82) (6.29) (5.33)

Funds rate 0.0838** 0.0865** 2.188*** 2.213*** 0.115*** 0.0967*** 1.954*** 1.925***

(3.05) (2.89) (7.26) (6.76) (4.11) (3.54) (5.90) (5.90)

Fiscal deficit -0.00484 -0.0731 -3.039*** -3.828*** -0.150** -0.157*** -3.089*** -3.885***

(-0.14) (-1.90) (-8.06) (-9.06) (-3.12) (-3.30) (-5.53) (-7.05)

Credit spread 1.824*** 1.635*** 26.86*** 24.84*** 1.475*** 1.498*** 24.23*** 22.76***

(8.73) (7.18) (11.73) (9.95) (6.97) (7.15) (10.26) (9.67)

Industry sigma 0.264*** 0.268*** 2.796*** 2.847*** 0.0438 0.0595 1.252** 1.397***

(11.98) (12.19) (11.60) (11.81) (1.16) (1.57) (2.95) (3.29)

Market to book 0.0160*** 0.0162*** 0.156*** 0.158*** 0.00650*** 0.00656*** 0.0743*** 0.0758***

(33.91) (34.36) (30.23) (30.59) (7.67) (7.76) (10.33) (10.56)

Real size -0.00519*** -0.00481*** -0.0379*** -0.0351*** -0.00181 -0.00127 -0.0707*** -0.0606***

(-15.73) (-14.20) (-10.49) (-9.47) (-1.09) (-0.77) (-4.10) (-3.57)

Cash flow/assets -0.0266*** -0.0265*** -0.109* -0.111* 0.0152 0.0146 0.193** 0.182**

(-5.67) (-5.67) (-2.13) (-2.17) (1.86) (1.78) (2.76) (2.60)

NWC/assets -0.264*** -0.265*** -2.412*** -2.416*** -0.261*** -0.262*** -2.220*** -2.233***

(-82.18) (-82.26) (-68.31) (-68.40) (-34.17) (-34.31) (-30.09) (-30.32)

Capex -0.467*** -0.469*** -3.727*** -3.748*** -0.364*** -0.366*** -2.898*** -2.932***

(-48.91) (-49.13) (-35.61) (-35.79) (-26.79) (-27.01) (-20.16) (-20.44)

Leverage -0.333*** -0.333*** -3.707*** -3.709*** -0.246*** -0.246*** -2.787*** -2.800***

(-116.53) (-116.58) (-118.28) (-118.34) (-33.04) (-33.16) (-35.88) (-36.09)

R&D/sales 0.0696*** 0.0697*** 0.401*** 0.401*** 0.0158*** 0.0159*** 0.101*** 0.102***

(50.04) (50.12) (26.38) (26.43) (3.48) (3.50) (3.75) (3.81)

Dividend dummy -0.0207*** -0.0207*** -0.194*** -0.195*** 0.0000115 -0.0000134 -0.0176 -0.0163

(-16.25) (-16.29) (-13.95) (-14.00) (0.00) (-0.01) (-0.61) (-0.57)

Acquisition activity -0.354*** -0.351*** -2.782*** -2.742*** -0.287*** -0.286*** -2.110*** -2.080***

(-31.49) (-31.18) (-22.55) (-22.23) (-26.19) (-25.97) (-16.87) (-16.57)

Net debt issuance 0.187*** 0.188*** 1.468*** 1.473*** 0.159*** 0.159*** 1.193*** 1.193***

(37.94) (38.07) (27.13) (27.23) (15.41) (15.39) (12.60) (12.56)

Net equity issuance 0.126*** 0.125*** 0.754*** 0.740*** 0.177*** 0.175*** 1.189*** 1.166***

(25.57) (25.35) (13.96) (13.71) (23.26) (23.14) (19.61) (19.32)

Loss dummy -0.0221*** -0.0221*** -0.194*** -0.196*** -0.0235*** -0.0235*** -0.224*** -0.226***

(-16.34) (-16.38) (-13.09) (-13.19) (-17.70) (-17.74) (-14.77) (-14.93)

Intercept 0.246*** 0.244*** -1.893*** -1.916*** 0.226*** 0.223*** -1.812*** -1.874***

(38.02) (37.46) (-26.69) (-26.93) (21.55) (21.39) (-16.28) (-17.06)

Adj. R2/Within R2 0.447 0.447 0.370 0.370 0.215 0.214 0.164 0.163

66

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

Panel B: Quarterly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.151*** 0.153*** 0.255** 0.487*** 0.0208 0.0179 -0.353 -0.127

(18.49) (20.41) (2.84) (5.90) (1.19) (1.14) (-1.61) (-0.64)

Cumulative policy deviation

×large

-0.0234*** -0.0246*** 0.111* 0.0107 -0.0399*** -0.0335*** -0.254* -0.255**

(-5.39) (-7.33) (2.33) (0.29) (-4.54) (-5.04) (-2.29) (-3.04)

Cumulative policy deviation

×2000s dummy

-0.119*** -0.0916*** -1.298*** -0.945*** -0.0619*** -0.0231 -1.201*** -0.644***

(-22.22) (-17.63) (-21.94) (-16.46) (-4.63) (-1.96) (-8.50) (-5.02)

Cumulative policy deviation 0.0378*** 0.0215*** 0.404*** 0.195*** 0.0522*** 0.0284*** 0.604*** 0.300***

(11.17) (8.78) (10.80) (7.20) (7.74) (5.72) (8.43) (5.59)

Funds rate 0.00864 0.0369 0.215 0.572* 0.0891* 0.117*** 0.150 0.577

(0.41) (1.76) (0.93) (2.47) (2.56) (3.41) (0.37) (1.46)

Fiscal deficit -0.0376 -0.0644** -2.886*** -3.409*** -0.259*** -0.224*** -3.323*** -3.794***

(-1.90) (-2.89) (-13.15) (-13.81) (-5.57) (-4.82) (-6.08) (-6.91)

Credit spread 1.046*** 1.541*** 14.54*** 21.16*** 0.667** 1.507*** 9.544*** 18.60***

(5.63) (8.63) (7.09) (10.71) (2.75) (6.36) (3.46) (6.84)

Industry sigma 0.0536 0.0153 2.069*** 1.529** -0.193 -0.204* -0.902 -1.545

(1.16) (0.33) (4.05) (3.00) (-1.94) (-2.04) (-0.86) (-1.47)

Market to book 0.0181*** 0.0183*** 0.170*** 0.172*** 0.00718*** 0.00727*** 0.0779*** 0.0804***

(67.20) (67.85) (57.34) (58.06) (9.66) (9.78) (12.59) (12.96)

Real size -0.00345*** -0.00293*** -0.0311*** -0.0261*** 0.000238 0.00117 -0.0667*** -0.0448*

(-15.36) (-12.66) (-12.55) (-10.21) (0.13) (0.66) (-3.70) (-2.51)

Cash flow/assets -0.231*** -0.232*** -1.308*** -1.329*** -0.0562** -0.0580*** -0.0301 -0.0665

(-25.01) (-25.10) (-12.84) (-13.04) (-3.26) (-3.36) (-0.21) (-0.46)

NWC/assets -0.273*** -0.274*** -2.492*** -2.501*** -0.242*** -0.243*** -2.073*** -2.092***

(-134.03) (-134.36) (-110.51) (-110.91) (-30.22) (-30.32) (-28.28) (-28.52)

Capex -0.405*** -0.407*** -2.887*** -2.916*** -0.209*** -0.210*** -1.056*** -1.089***

(-57.61) (-57.90) (-37.17) (-37.51) (-20.34) (-20.48) (-9.67) (-9.97)

Leverage -0.360*** -0.360*** -3.872*** -3.884*** -0.245*** -0.246*** -2.733*** -2.767***

(-201.99) (-202.44) (-196.81) (-197.42) (-31.54) (-31.78) (-35.06) (-35.43)

R&D/sales 0.0499*** 0.0499*** 0.271*** 0.272*** 0.00935*** 0.00930*** 0.0597*** 0.0590***

(77.89) (77.91) (38.53) (38.57) (3.91) (3.91) (4.19) (4.19)

Dividend dummy -0.0289*** -0.0289*** -0.268*** -0.269*** -0.00417 -0.00401 -0.0678* -0.0637*

(-33.43) (-33.43) (-28.13) (-28.22) (-1.95) (-1.86) (-2.39) (-2.23)

Acquisition activity -0.355*** -0.350*** -2.584*** -2.511*** -0.239*** -0.238*** -1.365*** -1.329***

(-41.01) (-40.49) (-27.01) (-26.25) (-23.36) (-23.16) (-11.46) (-11.10)

Net debt issuance 0.220*** 0.221*** 1.560*** 1.574*** 0.159*** 0.160*** 1.083*** 1.089***

(57.58) (57.86) (36.99) (37.31) (15.80) (15.79) (12.98) (12.99)

Net equity issuance 0.182*** 0.182*** 1.130*** 1.118*** 0.184*** 0.183*** 1.232*** 1.212***

(62.78) (62.56) (35.31) (34.94) (22.38) (22.37) (20.68) (20.55)

Loss dummy -0.00644*** -0.00647*** -0.0639*** -0.0661*** -0.0107*** -0.0108*** -0.127*** -0.131***

(-7.60) (-7.64) (-6.83) (-7.06) (-11.14) (-11.21) (-11.82) (-12.09)

Intercept 0.255*** 0.248*** -1.789*** -1.874*** 0.213*** 0.202*** -1.778*** -1.955***

(54.74) (54.13) (-34.77) (-37.09) (20.50) (19.55) (-16.25) (-17.90)

Adj. R2/Within R2 0.472 0.472 0.388 0.387 0.185 0.184 0.133 0.130

67

Table 5 Regressions of cash holdings lagged cumulative policy deviation from the very beginning

variables and controls Panel A: Yearly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.00955*** 0.0253*** -0.0324 0.0285 -0.00432 -0.00398 -0.170*** -0.241***

(4.22) (6.80) (-1.31) (0.70) (-1.26) (-0.68) (-4.17) (-3.51)

Cumulative policy deviation

×large

0.0247*** 0.0240*** 0.219*** 0.196*** 0.0107*** 0.00813** 0.151*** 0.0947*

(14.52) (10.48) (11.74) (7.84) (4.26) (2.83) (4.52) (2.31)

Cumulative policy deviation

×2000s dummy

-0.0120*** -0.0260*** -0.0913*** -0.173*** -0.00518* -0.0131** -0.0635* -0.139**

(-6.12) (-7.67) (-4.23) (-4.65) (-2.05) (-2.94) (-2.28) (-2.86)

Cumulative policy deviation 0.00530 0.00585 0.0185 -0.0138 0.0168*** 0.0171*** 0.0424 0.0438

(1.40) (1.64) (0.45) (-0.35) (4.08) (5.03) (0.90) (1.09)

Funds rate 0.106** 0.121*** 2.245*** 2.029*** 0.162*** 0.190*** 2.037*** 2.020***

(3.04) (3.30) (5.88) (5.03) (4.77) (5.36) (5.09) (4.95)

Fiscal deficit -0.175*** -0.151*** -4.677*** -4.327*** -0.310*** -0.291*** -4.925*** -4.635***

(-4.67) (-4.21) (-11.34) (-11.01) (-6.44) (-6.25) (-8.83) (-8.62)

Credit spread 1.964*** 2.233*** 27.31*** 28.02*** 1.788*** 2.136*** 24.39*** 25.97***

(8.76) (9.17) (11.11) (10.50) (8.12) (9.04) (9.95) (9.72)

Industry sigma 0.276*** 0.271*** 2.937*** 2.930*** 0.0470 0.0428 1.442*** 1.413**

(12.44) (12.22) (12.07) (12.03) (1.21) (1.10) (3.32) (3.25)

Market to book 0.0164*** 0.0162*** 0.159*** 0.157*** 0.00675*** 0.00665*** 0.0759*** 0.0752***

(34.67) (34.26) (30.77) (30.44) (7.95) (7.84) (10.55) (10.48)

Real size -0.00120** -0.00297*** -0.00200 -0.0192*** 0.000620 -0.000585 -0.0436* -0.0598***

(-2.84) (-7.72) (-0.43) (-4.55) (0.36) (-0.35) (-2.46) (-3.42)

Cash flow/assets -0.0341*** -0.0315*** -0.180*** -0.155** 0.0118 0.0130 0.154* 0.168*

(-7.27) (-6.70) (-3.49) (-3.01) (1.45) (1.59) (2.21) (2.41)

NWC/assets -0.267*** -0.266*** -2.438*** -2.426*** -0.262*** -0.261*** -2.227*** -2.222***

(-83.02) (-82.61) (-68.97) (-68.63) (-34.24) (-34.17) (-30.21) (-30.10)

Capex -0.466*** -0.465*** -3.718*** -3.709*** -0.363*** -0.362*** -2.887*** -2.879***

(-48.85) (-48.69) (-35.56) (-35.44) (-26.72) (-26.65) (-20.08) (-19.99)

Leverage -0.335*** -0.335*** -3.722*** -3.718*** -0.248*** -0.247*** -2.807*** -2.799***

(-117.00) (-116.87) (-118.55) (-118.38) (-33.20) (-33.16) (-36.01) (-35.97)

R&D/sales 0.0690*** 0.0692*** 0.395*** 0.397*** 0.0158*** 0.0158*** 0.0995*** 0.100***

(49.62) (49.79) (25.99) (26.13) (3.50) (3.50) (3.75) (3.76)

Dividend dummy -0.0200*** -0.0204*** -0.187*** -0.192*** 0.0000505 0.0000649 -0.0151 -0.0172

(-15.71) (-16.06) (-13.43) (-13.77) (0.02) (0.03) (-0.53) (-0.60)

Acquisition activity -0.351*** -0.352*** -2.744*** -2.747*** -0.283*** -0.284*** -2.055*** -2.062***

(-31.26) (-31.32) (-22.29) (-22.30) (-25.67) (-25.80) (-16.31) (-16.39)

Net debt issuance 0.188*** 0.188*** 1.474*** 1.472*** 0.160*** 0.160*** 1.199*** 1.197***

(38.15) (38.12) (27.25) (27.22) (15.39) (15.40) (12.60) (12.61)

Net equity issuance 0.124*** 0.125*** 0.731*** 0.738*** 0.174*** 0.175*** 1.159*** 1.170***

(25.16) (25.34) (13.57) (13.67) (23.02) (23.14) (19.23) (19.36)

Loss dummy -0.0220*** -0.0221*** -0.194*** -0.194*** -0.0236*** -0.0236*** -0.225*** -0.225***

(-16.32) (-16.34) (-13.07) (-13.08) (-17.80) (-17.79) (-14.87) (-14.83)

Intercept 0.231*** 0.232*** -2.051*** -2.003*** 0.225*** 0.217*** -1.923*** -1.894***

(33.71) (34.86) (-27.35) (-27.54) (20.48) (20.53) (-16.84) (-16.92)

Adj. R2/Within R2 0.448 0.447 0.371 0.371 0.214 0.214 0.163 0.162

68

Panel B: Quarterly regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.0107*** 0.0128*** 0.00829 0.0290 -0.0110** -0.0277*** -0.193*** -0.392***

(7.22) (4.38) (0.51) (0.90) (-3.09) (-3.75) (-4.25) (-4.16)

Cumulative policy deviation

×large

0.0215*** 0.0384*** 0.134*** 0.163*** 0.0115*** 0.0283*** 0.183*** 0.308***

(15.85) (13.48) (8.94) (5.20) (3.45) (4.12) (4.21) (3.44)

Cumulative policy deviation

×2000s dummy

-0.00381* -0.00365 -0.0360* -0.00669 0.00668* 0.0261*** 0.0408 0.183**

(-2.49) (-0.92) (-2.13) (-0.15) (2.48) (4.27) (1.40) (2.76)

Cumulative policy deviation -0.0216*** -0.0281*** -0.295*** -0.389*** -0.0135* -0.0379*** -0.337*** -0.516***

(-5.47) (-4.66) (-6.76) (-5.83) (-2.03) (-4.57) (-4.60) (-5.53)

Funds rate 0.0616** 0.0386 0.705** 0.155 0.162*** 0.0950** 0.738 0.0324

(2.89) (1.40) (2.99) (0.51) (4.83) (2.61) (1.86) (0.07)

Fiscal deficit -0.0440 -0.0244 -2.837*** -2.306*** -0.233*** -0.159** -3.171*** -2.491***

(-1.61) (-0.75) (-9.36) (-6.36) (-4.51) (-3.06) (-5.24) (-4.07)

Credit spread 2.323*** 2.007*** 31.63*** 26.43*** 2.073*** 1.753*** 27.81*** 22.24***

(14.69) (12.73) (18.09) (15.16) (8.60) (6.96) (10.16) (7.81)

Industry sigma 0.0632 0.0523 2.052*** 1.961*** -0.180 -0.173 -0.798 -0.871

(1.36) (1.13) (4.00) (3.84) (-1.79) (-1.72) (-0.75) (-0.82)

Market to book 0.0184*** 0.0183*** 0.172*** 0.172*** 0.00749*** 0.00742*** 0.0808*** 0.0804***

(68.11) (67.93) (57.94) (57.78) (10.07) (9.99) (13.05) (12.98)

Real size -0.000822** -0.00145*** -0.0106*** -0.0190*** 0.00168 0.00172 -0.0433* -0.0493**

(-2.95) (-5.39) (-3.45) (-6.38) (0.88) (0.92) (-2.29) (-2.64)

Cash flow/assets -0.242*** -0.240*** -1.405*** -1.373*** -0.0593*** -0.0592*** -0.0762 -0.0660

(-26.13) (-25.92) (-13.76) (-13.45) (-3.43) (-3.42) (-0.53) (-0.46)

NWC/assets -0.275*** -0.274*** -2.503*** -2.497*** -0.242*** -0.242*** -2.072*** -2.069***

(-134.70) (-134.45) (-110.86) (-110.58) (-30.11) (-30.09) (-28.18) (-28.11)

Capex -0.407*** -0.406*** -2.909*** -2.889*** -0.208*** -0.208*** -1.053*** -1.039***

(-57.91) (-57.73) (-37.40) (-37.16) (-20.19) (-20.15) (-9.59) (-9.45)

Leverage -0.360*** -0.360*** -3.880*** -3.880*** -0.246*** -0.246*** -2.747*** -2.750***

(-202.04) (-202.30) (-196.93) (-197.07) (-31.50) (-31.61) (-34.97) (-35.14)

R&D/sales 0.0497*** 0.0497*** 0.270*** 0.270*** 0.00916*** 0.00918*** 0.0579*** 0.0580***

(77.56) (77.64) (38.31) (38.39) (3.86) (3.86) (4.12) (4.12)

Dividend dummy -0.0276*** -0.0278*** -0.257*** -0.261*** -0.00429* -0.00418 -0.0640* -0.0644*

(-31.82) (-32.09) (-26.82) (-27.28) (-1.99) (-1.94) (-2.25) (-2.27)

Acquisition activity -0.354*** -0.353*** -2.561*** -2.546*** -0.238*** -0.238*** -1.340*** -1.332***

(-40.89) (-40.84) (-26.73) (-26.61) (-23.12) (-23.12) (-11.19) (-11.13)

Net debt issuance 0.221*** 0.221*** 1.572*** 1.575*** 0.161*** 0.161*** 1.097*** 1.099***

(57.90) (57.96) (37.28) (37.35) (15.81) (15.82) (13.05) (13.09)

Net equity issuance 0.182*** 0.182*** 1.124*** 1.127*** 0.183*** 0.183*** 1.226*** 1.227***

(62.55) (62.65) (35.11) (35.20) (22.38) (22.40) (20.65) (20.68)

Loss dummy -0.00626*** -0.00634*** -0.0635*** -0.0643*** -0.0107*** -0.0107*** -0.129*** -0.129***

(-7.40) (-7.49) (-6.79) (-6.87) (-11.17) (-11.16) (-11.95) (-11.97)

Intercept 0.216*** 0.232*** -2.261*** -2.027*** 0.183*** 0.189*** -2.306*** -2.072***

(39.61) (50.98) (-37.48) (-40.39) (15.53) (17.41) (-19.00) (-18.42)

Adj. R2/Within R2 0.472 0.472 0.387 0.387 0.183 0.184 0.131 0.131

69

Table 6 Regressions of cash holdings on cumulative policy deviation from the very beginning variables

Panel A: Annual regression results

Model OLS FE

Types of Taylor rule T. 1 T. 2 T. 1 T. 2 T. 1 T. 2 T. 1 T. 2

Dependent variable Cash/Assets Log(Cash/Net Assets) Cash/Assets Log(Cash/Net Assets)

Independent variable (1) (2) (3) (4) (5) (6) (7) (8)

Cumulative policy deviation

×large×2000s dummy

0.0103*** 0.0270*** -0.0273 0.0378 -0.00422 -0.00398 -0.169*** -0.246***

(4.50) (7.15) (-1.10) (0.92) (-1.22) (-0.67) (-4.12) (-3.52)

Cumulative policy deviation

×large

0.0245*** 0.0235*** 0.216*** 0.191*** 0.0108*** 0.00852** 0.150*** 0.0963*

(14.50) (10.50) (11.70) (7.81) (4.33) (2.99) (4.54) (2.37)

Cumulative policy deviation

×2000s dummy

-0.0119*** -0.0271*** -0.0959*** -0.191*** -0.00459 -0.0134** -0.0640* -0.150**

(-6.04) (-7.86) (-4.44) (-5.06) (-1.80) (-2.93) (-2.28) (-3.00)

Cumulative policy deviation 0.00475 0.00696 0.0305 0.00903 0.0159*** 0.0182*** 0.0461 0.0607

(1.23) (1.88) (0.72) (0.22) (3.64) (4.91) (0.93) (1.40)

Funds rate 0.102** 0.127*** 2.294*** 2.163*** 0.159*** 0.198*** 2.050*** 2.124***

(2.90) (3.41) (5.96) (5.28) (4.52) (5.35) (4.97) (5.00)

Fiscal deficit -0.176*** -0.158*** -4.721*** -4.378*** -0.312*** -0.306*** -4.958*** -4.705***

(-4.63) (-4.34) (-11.33) (-11.00) (-6.35) (-6.42) (-8.70) (-8.56)

Credit spread 1.979*** 2.303*** 27.68*** 28.96*** 1.811*** 2.230*** 24.63*** 26.79***

(8.75) (9.18) (11.17) (10.53) (8.10) (9.07) (9.91) (9.62)

Industry sigma 0.277*** 0.271*** 2.931*** 2.912*** 0.0501 0.0420 1.445*** 1.391**

(12.49) (12.19) (12.05) (11.96) (1.29) (1.08) (3.33) (3.20)

Market to book 0.0164*** 0.0162*** 0.159*** 0.157*** 0.00676*** 0.00667*** 0.0760*** 0.0753***

(34.66) (34.27) (30.78) (30.44) (7.95) (7.86) (10.55) (10.49)

Real size -0.00121** -0.00297*** -0.00204 -0.0193*** 0.000689 -0.000484 -0.0431* -0.0594***

(-2.86) (-7.72) (-0.44) (-4.56) (0.40) (-0.29) (-2.43) (-3.40)

Cash flow/assets -0.0342*** -0.0314*** -0.179*** -0.154** 0.0117 0.0129 0.154* 0.168*

(-7.28) (-6.70) (-3.49) (-2.99) (1.43) (1.57) (2.20) (2.41)

NWC/assets -0.267*** -0.266*** -2.438*** -2.426*** -0.262*** -0.261*** -2.227*** -2.222***

(-83.03) (-82.61) (-68.97) (-68.63) (-34.24) (-34.17) (-30.21) (-30.10)

Capex -0.466*** -0.465*** -3.719*** -3.709*** -0.363*** -0.362*** -2.887*** -2.880***

(-48.85) (-48.69) (-35.56) (-35.45) (-26.73) (-26.65) (-20.08) (-19.99)

Leverage -0.335*** -0.335*** -3.723*** -3.719*** -0.248*** -0.248*** -2.808*** -2.801***

(-116.97) (-116.87) (-118.54) (-118.38) (-33.17) (-33.16) (-35.97) (-35.96)

R&D/sales 0.0690*** 0.0692*** 0.395*** 0.397*** 0.0157*** 0.0158*** 0.0995*** 0.100***

(49.62) (49.80) (26.00) (26.15) (3.49) (3.50) (3.75) (3.76)

Dividend dummy -0.0200*** -0.0204*** -0.187*** -0.192*** -0.0000171 0.0000450 -0.0152 -0.0169

(-15.72) (-16.06) (-13.44) (-13.76) (-0.01) (0.02) (-0.53) (-0.59)

Acquisition activity -0.350*** -0.352*** -2.742*** -2.748*** -0.283*** -0.284*** -2.054*** -2.063***

(-31.23) (-31.31) (-22.27) (-22.31) (-25.66) (-25.78) (-16.30) (-16.40)

Net debt issuance 0.188*** 0.188*** 1.474*** 1.473*** 0.160*** 0.160*** 1.199*** 1.198***

(38.15) (38.13) (27.26) (27.23) (15.39) (15.40) (12.60) (12.61)

Net equity issuance 0.124*** 0.125*** 0.731*** 0.740*** 0.174*** 0.175*** 1.159*** 1.171***

(25.15) (25.36) (13.57) (13.71) (23.01) (23.14) (19.22) (19.38)

Loss dummy -0.0220*** -0.0221*** -0.194*** -0.194*** -0.0236*** -0.0236*** -0.225*** -0.225***

(-16.31) (-16.33) (-13.08) (-13.08) (-17.78) (-17.79) (-14.87) (-14.84)

Intercept 0.230*** 0.231*** -2.046*** -2.009*** 0.224*** 0.215*** -1.925*** -1.901***

(33.69) (34.64) (-27.32) (-27.52) (20.41) (20.38) (-16.91) (-16.94)

Adj. R2/Within R2 0.447 0.447 0.370 0.370 0.215 0.214 0.163 0.163

70

Table 7 Regressions of cash holdings on federal funds rate and changes of federal funds rate and controls

Panel A: Annual OLS regression results

Cash/Assets Log(Cash/Net Assets)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

ΔFunds rate -0.0803** 0.0849** -0.143*** 0.179*** -1.581*** 2.080*** -3.052*** 2.251***

(-2.84) (2.76) (-4.91) (4.92) (-5.10) (6.17) (-9.57) (5.66)

Funds rate 0.157*** -0.134*** 0.136*** -0.0621** 3.678*** -0.245 3.229*** 0.664**

(8.89) (-4.87) (7.93) (-2.65) (18.99) (-0.81) (17.17) (2.59)

Fiscal deficit -0.0729* -0.0351 -0.0174 -3.456*** -3.387*** -3.164***

(-2.28) (-1.07) (-0.53) (-9.87) (-9.40) (-8.83)

Credit spread 2.252*** 3.104*** 2.515*** 40.02*** 41.57*** 34.15***

(15.49) (13.64) (12.99) (25.16) (16.68) (16.12)

Industry sigma 0.184*** 0.319*** 0.264*** 0.292*** 0.253*** 0.294*** 0.412 3.111*** 2.278*** 3.063*** 2.043*** 3.086***

(9.43) (15.04) (12.28) (13.34) (11.83) (13.43) (1.92) (13.42) (9.67) (12.79) (8.72) (12.89)

Market to book 0.0154*** 0.0161*** 0.0158*** 0.0160*** 0.0157*** 0.0160*** 0.143*** 0.157*** 0.153*** 0.157*** 0.152*** 0.157***

(32.67) (34.10) (33.37) (33.86) (33.27) (33.84) (27.73) (30.49) (29.46) (30.42) (29.23) (30.41)

Real size -0.00629*** -0.00573*** -0.00579*** -0.00602*** -0.00590*** -0.00590*** -0.0505*** -0.0379*** -0.0387*** -0.0385*** -0.0411*** -0.0370***

(-20.57) (-18.58) (-18.62) (-19.17) (-19.02) (-18.84) (-15.03) (-11.25) (-11.36) (-11.20) (-12.07) (-10.79)

Cash flow/assets -0.0299*** -0.0269*** -0.0279*** -0.0272*** -0.0281*** -0.0270*** -0.162** -0.122* -0.116* -0.123* -0.121* -0.121*

(-6.38) (-5.75) (-5.95) (-5.80) (-6.00) (-5.77) (-3.15) (-2.38) (-2.25) (-2.39) (-2.35) (-2.36)

NWC/assets -0.264*** -0.266*** -0.266*** -0.265*** -0.266*** -0.265*** -2.373*** -2.425*** -2.437*** -2.422*** -2.431*** -2.429***

(-82.26) (-83.15) (-82.78) (-82.39) (-82.69) (-82.56) (-67.24) (-69.07) (-68.91) (-68.70) (-68.70) (-68.90)

Capex -0.461*** -0.472*** -0.473*** -0.466*** -0.473*** -0.468*** -3.553*** -3.753*** -3.836*** -3.742*** -3.824*** -3.764***

(-48.81) (-49.93) (-49.60) (-48.86) (-49.54) (-49.07) (-34.20) (-36.26) (-36.64) (-35.83) (-36.51) (-36.06)

Leverage -0.335*** -0.337*** -0.337*** -0.335*** -0.337*** -0.336*** -3.687*** -3.722*** -3.742*** -3.720*** -3.731*** -3.729***

(-117.80) (-118.57) (-118.19) (-117.53) (-118.07) (-117.96) (-117.90) (-119.70) (-119.46) (-119.03) (-119.10) (-119.49)

R&D/sales 0.0711*** 0.0706*** 0.0711*** 0.0704*** 0.0710*** 0.0707*** 0.414*** 0.404*** 0.414*** 0.404*** 0.412*** 0.407***

(51.09) (50.81) (51.13) (50.66) (51.04) (50.84) (27.13) (26.65) (27.21) (26.62) (27.04) (26.82)

Dividend dummy -0.0185*** -0.0218*** -0.0205*** -0.0211*** -0.0202*** -0.0212*** -0.133*** -0.200*** -0.180*** -0.198*** -0.172*** -0.200***

(-14.80) (-17.24) (-16.16) (-16.55) (-15.91) (-16.65) (-9.66) (-14.43) (-12.93) (-14.23) (-12.40) (-14.35)

Acquisition activity -0.357*** -0.346*** -0.353*** -0.347*** -0.355*** -0.347*** -2.956*** -2.715*** -2.851*** -2.716*** -2.891*** -2.714***

(-31.79) (-30.77) (-31.37) (-30.85) (-31.55) (-30.83) (-23.90) (-22.04) (-23.08) (-22.05) (-23.41) (-22.02)

Net debt issuance 0.187*** 0.188*** 0.187*** 0.188*** 0.186*** 0.188*** 1.451*** 1.471*** 1.459*** 1.471*** 1.446*** 1.475***

(37.71) (37.98) (37.80) (37.98) (37.68) (38.05) (26.63) (27.18) (26.86) (27.17) (26.61) (27.26)

Net equity issuance 0.124*** 0.123*** 0.123*** 0.124*** 0.123*** 0.124*** 0.751*** 0.728*** 0.734*** 0.729*** 0.740*** 0.725***

(25.10) (25.02) (24.96) (25.17) (25.02) (25.10) (13.87) (13.50) (13.59) (13.52) (13.69) (13.44)

Loss dummy -0.0225*** -0.0217*** -0.0220*** -0.0219*** -0.0218*** -0.0221*** -0.209*** -0.193*** -0.197*** -0.193*** -0.193*** -0.196***

(-16.58) (-16.06) (-16.23) (-16.19) (-16.09) (-16.33) (-13.99) (-13.03) (-13.26) (-13.05) (-12.97) (-13.21)

Intercept 0.288*** 0.247*** 0.268*** 0.251*** 0.271*** 0.252*** -1.061*** -1.888*** -1.530*** -1.880*** -1.463*** -1.866***

(48.79) (38.46) (42.43) (38.77) (43.14) (38.96) (-16.37) (-26.92) (-22.12) (-26.54) (-21.24) (-26.34)

Adj. R2 0.443 0.445 0.443 0.445 0.443 0.445 0.361 0.370 0.365 0.370 0.364 0.369

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

71

Panel B: Annual firm fixed effects regression results

Cash/Assets Log(Cash/Net Assets)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

ΔFunds rate -0.130*** 0.0499* -0.196*** 0.0198 -1.621*** 1.674*** -2.699*** 1.595***

(-6.74) (2.08) (-8.44) (0.61) (-6.99) (5.75) (-9.62) (4.21)

Funds rate 0.151*** 0.0486 0.102*** 0.0581 2.445*** 0.127 1.767*** 0.893*

(6.29) (1.28) (4.80) (1.96) (8.14) (0.29) (6.62) (2.52)

Fiscal deficit -0.214*** -0.237*** -0.237*** -3.903*** -3.964*** -3.940***

(-5.64) (-5.13) (-5.11) (-8.90) (-7.37) (-7.31)

Credit spread 1.924*** 1.657*** 1.581*** 35.45*** 34.75*** 28.62***

(9.41) (5.68) (7.07) (14.43) (10.62) (11.41)

Industry sigma -0.0827* 0.0643 -0.00511 0.0757* -0.0261 0.0756* -1.043** 1.663*** 0.210 1.693*** -0.0773 1.681***

(-2.32) (1.67) (-0.14) (2.00) (-0.72) (1.99) (-2.67) (4.05) (0.52) (4.04) (-0.19) (4.01)

Market to book 0.00595*** 0.00659*** 0.00629*** 0.00663*** 0.00625*** 0.00662*** 0.0648*** 0.0766*** 0.0703*** 0.0767*** 0.0697*** 0.0763***

(7.02) (7.77) (7.41) (7.81) (7.35) (7.81) (9.03) (10.66) (9.73) (10.65) (9.65) (10.58)

Real size -0.00337* -0.00141 -0.00197 -0.000996 -0.00278 -0.000927 -0.0821*** -0.0461** -0.0594*** -0.0450** -0.0706*** -0.0394*

(-2.19) (-0.91) (-1.26) (-0.62) (-1.79) (-0.58) (-5.17) (-2.90) (-3.71) (-2.73) (-4.44) (-2.41)

Cash flow/assets 0.0132 0.0131 0.0136 0.0128 0.0132 0.0128 0.164* 0.160* 0.169* 0.159* 0.164* 0.159*

(1.61) (1.60) (1.65) (1.57) (1.60) (1.57) (2.34) (2.30) (2.42) (2.29) (2.34) (2.28)

NWC/assets -0.257*** -0.261*** -0.261*** -0.262*** -0.261*** -0.262*** -2.181*** -2.249*** -2.247*** -2.251*** -2.235*** -2.258***

(-33.91) (-34.37) (-34.24) (-34.36) (-34.15) (-34.37) (-29.74) (-30.79) (-30.58) (-30.68) (-30.43) (-30.80)

Capex -0.355*** -0.361*** -0.367*** -0.364*** -0.366*** -0.364*** -2.834*** -2.955*** -3.024*** -2.961*** -3.017*** -2.977***

(-26.42) (-26.98) (-27.15) (-26.94) (-27.10) (-26.97) (-19.78) (-20.76) (-21.06) (-20.69) (-20.99) (-20.81)

Leverage -0.245*** -0.246*** -0.248*** -0.246*** -0.247*** -0.246*** -2.799*** -2.808*** -2.850*** -2.810*** -2.835*** -2.822***

(-33.33) (-33.33) (-33.56) (-33.32) (-33.47) (-33.38) (-36.23) (-36.53) (-36.67) (-36.39) (-36.55) (-36.58)

R&D/sales 0.0161*** 0.0157*** 0.0162*** 0.0157*** 0.0160*** 0.0157*** 0.105*** 0.0981*** 0.107*** 0.0982*** 0.106*** 0.0998***

(3.58) (3.46) (3.60) (3.47) (3.56) (3.47) (4.03) (3.72) (4.10) (3.72) (4.01) (3.79)

Dividend dummy 0.00279 -0.000313 0.00113 -0.000577 0.00143 -0.000568 0.0337 -0.0233 0.00696 -0.0240 0.0111 -0.0233

(1.15) (-0.13) (0.46) (-0.24) (0.58) (-0.23) (1.15) (-0.81) (0.24) (-0.83) (0.38) (-0.81)

Acquisition activity -0.290*** -0.283*** -0.289*** -0.283*** -0.290*** -0.283*** -2.203*** -2.078*** -2.196*** -2.079*** -2.214*** -2.085***

(-26.41) (-25.79) (-26.40) (-25.81) (-26.49) (-25.83) (-17.37) (-16.58) (-17.37) (-16.57) (-17.50) (-16.63)

Net debt issuance 0.160*** 0.159*** 0.160*** 0.159*** 0.159*** 0.159*** 1.201*** 1.193*** 1.202*** 1.193*** 1.197*** 1.194***

(15.42) (15.38) (15.42) (15.37) (15.40) (15.38) (12.61) (12.54) (12.63) (12.54) (12.59) (12.55)

Net equity issuance 0.177*** 0.175*** 0.176*** 0.174*** 0.177*** 0.174*** 1.194*** 1.154*** 1.177*** 1.153*** 1.191*** 1.146***

(23.43) (23.11) (23.28) (23.02) (23.40) (23.02) (19.90) (19.20) (19.57) (19.11) (19.78) (19.04)

Loss dummy -0.0243*** -0.0235*** -0.0238*** -0.0235*** -0.0235*** -0.0235*** -0.238*** -0.224*** -0.230*** -0.223*** -0.226*** -0.225***

(-18.22) (-17.73) (-17.86) (-17.66) (-17.66) (-17.67) (-15.60) (-14.73) (-15.06) (-14.71) (-14.81) (-14.83)

Intercept 0.273*** 0.226*** 0.251*** 0.222*** 0.260*** 0.222*** -1.067*** -1.932*** -1.418*** -1.941*** -1.293*** -1.950***

(31.33) (22.68) (26.84) (21.43) (28.37) (21.42) (-12.14) (-19.18) (-14.80) (-17.88) (-13.85) (-17.98)

Within R2 0.208 0.213 0.209 0.213 0.208 0.213 0.150 0.161 0.152 0.161 0.150 0.161

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

72

Panel C: Quarterly OLS regression results

Cash/Assets Log(Cash/Net Assets)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

ΔFunds rate -0.0292 0.212*** -0.0440 0.231*** -0.629 3.372*** -0.814 3.654***

(-0.47) (3.40) (-0.71) (3.68) (-0.92) (4.89) (-1.19) (5.26)

Funds rate 0.0581*** -0.0400** 0.0575*** -0.0334* 0.736*** -0.593*** 0.725*** -0.490**

(4.01) (-2.61) (3.97) (-2.19) (4.59) (-3.51) (4.53) (-2.92)

Fiscal deficit -0.000818 0.00712 0.00582 -2.538*** -2.419*** -2.440***

(-0.04) (0.36) (0.30) (-11.79) (-11.11) (-11.20)

Credit spread 2.742*** 2.866*** 2.765*** 38.33*** 40.17*** 38.58***

(20.14) (19.88) (19.53) (25.48) (25.22) (24.66)

Industry sigma 0.0169 -0.0139 0.00468 -0.00908 0.00653 -0.0176 0.693 0.962 0.541 1.033* 0.575 0.896

(0.37) (-0.30) (0.10) (-0.20) (0.14) (-0.38) (1.37) (1.89) (1.07) (2.03) (1.14) (1.76)

Market to book 0.0178*** 0.0182*** 0.0178*** 0.0182*** 0.0178*** 0.0182*** 0.165*** 0.173*** 0.165*** 0.173*** 0.165*** 0.173***

(66.14) (67.68) (66.26) (67.58) (66.27) (67.66) (55.58) (58.44) (55.73) (58.32) (55.72) (58.43)

Real size -0.00493*** -0.00455*** -0.00477*** -0.00466*** -0.00477*** -0.00464*** -0.0359*** -0.0263*** -0.0339*** -0.0279*** -0.0340*** -0.0276***

(-24.84) (-22.70) (-23.60) (-22.77) (-23.63) (-22.68) (-16.37) (-11.89) (-15.18) (-12.35) (-15.22) (-12.22)

Cash flow/assets -0.232*** -0.226*** -0.231*** -0.226*** -0.231*** -0.226*** -1.383*** -1.326*** -1.364*** -1.335*** -1.365*** -1.333***

(-25.15) (-24.43) (-24.97) (-24.49) (-24.98) (-24.48) (-13.55) (-13.02) (-13.36) (-13.10) (-13.37) (-13.08)

NWC/assets -0.274*** -0.275*** -0.275*** -0.274*** -0.275*** -0.274*** -2.517*** -2.527*** -2.528*** -2.518*** -2.528*** -2.521***

(-135.71) (-135.92) (-135.42) (-134.81) (-135.42) (-134.90) (-112.26) (-113.02) (-112.15) (-111.95) (-112.14) (-112.06)

Capex -0.406*** -0.409*** -0.410*** -0.407*** -0.410*** -0.408*** -2.933*** -2.985*** -2.975*** -2.952*** -2.972*** -2.967***

(-58.17) (-58.66) (-58.24) (-57.94) (-58.25) (-58.10) (-37.94) (-38.73) (-38.22) (-38.04) (-38.20) (-38.25)

Leverage -0.362*** -0.364*** -0.363*** -0.363*** -0.363*** -0.363*** -3.898*** -3.925*** -3.910*** -3.916*** -3.909*** -3.919***

(-205.86) (-206.78) (-204.69) (-204.82) (-204.77) (-205.02) (-200.25) (-202.12) (-199.20) (-200.08) (-199.25) (-200.32)

R&D/sales 0.0505*** 0.0509*** 0.0506*** 0.0508*** 0.0506*** 0.0508*** 0.272*** 0.279*** 0.274*** 0.278*** 0.274*** 0.278***

(78.90) (79.51) (79.00) (79.35) (79.00) (79.37) (38.64) (39.70) (38.80) (39.53) (38.80) (39.56)

Dividend dummy -0.0284*** -0.0298*** -0.0288*** -0.0296*** -0.0288*** -0.0296*** -0.246*** -0.278*** -0.251*** -0.274*** -0.251*** -0.275***

(-33.33) (-34.83) (-33.58) (-34.27) (-33.57) (-34.35) (-26.10) (-29.39) (-26.46) (-28.78) (-26.44) (-28.88)

Acquisition activity -0.355*** -0.347*** -0.355*** -0.348*** -0.355*** -0.347*** -2.689*** -2.496*** -2.682*** -2.500*** -2.684*** -2.495***

(-41.03) (-40.08) (-40.97) (-40.11) (-40.98) (-40.07) (-28.08) (-26.08) (-28.00) (-26.12) (-28.03) (-26.07)

Net debt issuance 0.220*** 0.222*** 0.220*** 0.222*** 0.220*** 0.222*** 1.543*** 1.583*** 1.543*** 1.584*** 1.542*** 1.586***

(57.51) (58.07) (57.51) (58.09) (57.51) (58.12) (36.46) (37.51) (36.46) (37.53) (36.44) (37.57)

Net equity issuance 0.179*** 0.180*** 0.179*** 0.180*** 0.179*** 0.180*** 1.097*** 1.100*** 1.097*** 1.102*** 1.097*** 1.102***

(61.50) (61.96) (61.52) (61.98) (61.51) (61.99) (34.22) (34.38) (34.23) (34.42) (34.22) (34.43)

Loss dummy -0.00634*** -0.00620*** -0.00621*** -0.00629*** -0.00619*** -0.00639*** -0.0675*** -0.0626*** -0.0659*** -0.0639*** -0.0655*** -0.0656***

(-7.47) (-7.31) (-7.32) (-7.41) (-7.30) (-7.54) (-7.19) (-6.68) (-7.01) (-6.82) (-6.97) (-7.00)

Intercept 0.281*** 0.254*** 0.278*** 0.255*** 0.278*** 0.256*** -1.399*** -1.885*** -1.440*** -1.866*** -1.440*** -1.852***

(66.34) (57.53) (64.46) (57.45) (64.46) (57.71) (-29.94) (-38.75) (-30.27) (-38.10) (-30.28) (-37.87)

Adj. R2 0.469 0.470 0.469 0.469 0.469 0.470 0.382 0.386 0.382 0.386 0.382 0.386

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001

73

Panel D: Quarterly firm fixed effects regression results

Cash/Assets Log(Cash/Net Assets)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

ΔFunds rate -0.0365 0.164** -0.0956 0.0782 0.170 3.066*** 0.155 2.879***

(-0.64) (2.91) (-1.63) (1.35) (0.25) (4.64) (0.22) (4.22)

Funds rate 0.149*** 0.181*** 0.146*** 0.184*** 0.0375 0.395 0.0423 0.516

(4.99) (5.16) (4.96) (5.33) (0.11) (0.98) (0.13) (1.31)

Fiscal deficit -0.185*** -0.270*** -0.271*** -3.510*** -3.696*** -3.745***

(-4.86) (-5.94) (-5.98) (-7.84) (-6.89) (-6.99)

Credit spread 2.408*** 2.001*** 1.964*** 32.69*** 31.80*** 30.44***

(9.24) (7.62) (7.55) (11.09) (10.66) (10.28)

Industry sigma -0.271** -0.210* -0.300** -0.208* -0.295** -0.212* -3.248** -1.973 -3.255** -1.968 -3.265** -2.115*

(-2.66) (-2.07) (-2.98) (-2.05) (-2.92) (-2.09) (-3.07) (-1.86) (-3.08) (-1.86) (-3.09) (-2.00)

Market to book 0.00697*** 0.00753*** 0.00700*** 0.00756*** 0.00698*** 0.00756*** 0.0750*** 0.0834*** 0.0750*** 0.0834*** 0.0751*** 0.0837***

(9.35) (10.09) (9.40) (10.14) (9.38) (10.15) (12.04) (13.40) (12.04) (13.40) (12.04) (13.44)

Real size -0.00285 -0.000764 -0.00179 0.000957 -0.00185 0.00101 -0.0658*** -0.0308 -0.0655*** -0.0271 -0.0654*** -0.0252

(-1.76) (-0.47) (-1.11) (0.57) (-1.14) (0.60) (-3.96) (-1.83) (-3.95) (-1.57) (-3.95) (-1.47)

Cash flow/assets -0.0589*** -0.0587*** -0.0576*** -0.0586*** -0.0578*** -0.0586*** -0.0889 -0.0991 -0.0886 -0.0990 -0.0884 -0.0977

(-3.38) (-3.39) (-3.32) (-3.39) (-3.33) (-3.39) (-0.62) (-0.69) (-0.62) (-0.69) (-0.61) (-0.68)

NWC/assets -0.239*** -0.239*** -0.242*** -0.242*** -0.241*** -0.242*** -2.102*** -2.104*** -2.102*** -2.112*** -2.102*** -2.114***

(-29.90) (-30.00) (-30.09) (-30.26) (-30.10) (-30.27) (-28.70) (-28.80) (-28.60) (-28.80) (-28.61) (-28.82)

Capex -0.199*** -0.199*** -0.206*** -0.208*** -0.206*** -0.208*** -1.087*** -1.086*** -1.089*** -1.104*** -1.090*** -1.118***

(-19.39) (-19.49) (-20.00) (-20.15) (-19.98) (-20.20) (-9.80) (-9.86) (-9.85) (-10.05) (-9.88) (-10.19)

Leverage -0.244*** -0.244*** -0.246*** -0.246*** -0.246*** -0.247*** -2.802*** -2.797*** -2.802*** -2.802*** -2.803*** -2.808***

(-31.82) (-31.75) (-31.96) (-31.93) (-31.98) (-31.97) (-35.90) (-35.86) (-35.85) (-35.96) (-35.90) (-36.03)

R&D/sales 0.00867*** 0.00893*** 0.00886*** 0.00910*** 0.00886*** 0.00910*** 0.0542*** 0.0576*** 0.0543*** 0.0580*** 0.0543*** 0.0580***

(3.69) (3.76) (3.78) (3.83) (3.77) (3.83) (4.00) (4.12) (4.00) (4.14) (4.00) (4.15)

Dividend dummy -0.00227 -0.00355 -0.00302 -0.00467* -0.00302 -0.00467* -0.0482 -0.0689* -0.0484 -0.0713* -0.0484 -0.0714*

(-1.07) (-1.67) (-1.41) (-2.17) (-1.41) (-2.17) (-1.65) (-2.39) (-1.66) (-2.47) (-1.66) (-2.47)

Acquisition activity -0.243*** -0.236*** -0.244*** -0.237*** -0.244*** -0.237*** -1.456*** -1.355*** -1.457*** -1.357*** -1.456*** -1.356***

(-23.78) (-23.06) (-23.95) (-23.22) (-23.95) (-23.21) (-12.06) (-11.35) (-12.07) (-11.37) (-12.07) (-11.36)

Net debt issuance 0.162*** 0.162*** 0.161*** 0.161*** 0.161*** 0.161*** 1.097*** 1.099*** 1.096*** 1.097*** 1.097*** 1.098***

(15.95) (15.89) (15.93) (15.85) (15.93) (15.86) (13.11) (13.04) (13.11) (13.01) (13.11) (13.02)

Net equity issuance 0.186*** 0.184*** 0.186*** 0.184*** 0.186*** 0.184*** 1.245*** 1.208*** 1.245*** 1.206*** 1.245*** 1.206***

(22.63) (22.43) (22.67) (22.43) (22.66) (22.43) (20.97) (20.56) (20.97) (20.53) (20.97) (20.54)

Loss dummy -0.0114*** -0.0112*** -0.0111*** -0.0108*** -0.0111*** -0.0108*** -0.133*** -0.130*** -0.133*** -0.129*** -0.133*** -0.130***

(-11.80) (-11.63) (-11.52) (-11.21) (-11.47) (-11.24) (-12.25) (-11.97) (-12.30) (-11.90) (-12.31) (-12.03)

Intercept 0.250*** 0.211*** 0.239*** 0.196*** 0.239*** 0.196*** -1.440*** -2.041*** -1.442*** -2.074*** -1.443*** -2.074***

(27.73) (22.15) (26.07) (19.20) (26.10) (19.20) (-15.85) (-20.49) (-15.51) (-19.17) (-15.54) (-19.16)

Within R2 0.177 0.182 0.178 0.183 0.178 0.183 0.121 0.128 0.121 0.128 0.121 0.128

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001