Financial Flexibility and Short-Term FinancingNeeds: Evidence from Seasonal Firms

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Authors Fairhurst, Douglas J.

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FINANCIAL FLEXIBILITY AND SHORT-TERM FINANCING NEEDS: EVIDENCE FROM

SEASONAL FIRMS

by

Douglas Fairhurst

____________________________

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF MANAGEMENT

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR OF PHILOSOPHY

WITH A MAJOR IN FINANCE

In the Graduate College

THE UNIVERSITY OF ARIZONA

2014

2

THE UNIVERSITY OF ARIZONA

GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation

prepared by Douglas Fairhurst, titled Financial Flexibility and Short-Term Financing Needs:

Evidence from Seasonal Firms and recommend that it be accepted as fulfilling the dissertation

requirement for the Degree of Doctor of Philosophy.

_______________________________________________________________________ Date: April 1, 2014

Sandy Klasa

_______________________________________________________________________ Date: April 1, 2014

Satheesh Aradhyula

_______________________________________________________________________ Date: April 1, 2014

Kathleen Kahle

_______________________________________________________________________ Date: April 1, 2014

Lubomir Litov

_______________________________________________________________________ Date: April 1, 2014

Ryan Williams

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission

of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend

that it be accepted as fulfilling the dissertation requirement.

________________________________________________ Date: April 1, 2014

Dissertation Director: Sandy Klasa

3

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of the requirements for an

advanced degree at the University of Arizona and is deposited in the University Library to be

made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided

that an accurate acknowledgement of the source is made. Requests for permission for extended

quotation from or reproduction of this manuscript in whole or in part may be granted by the head

of the major department or the Dean of the Graduate College when in his or her judgment the

proposed use of the material is in the interests of scholarship. In all other instances, however,

permission must be obtained from the author.

SIGNED: Douglas Fairhurst

4

Acknowledgements

I am very grateful to the members of my dissertation committee, Satheesh Aradhyula, Kathleen

Kahle, Lubomir Litov, and Ryan Williams, and especially to the committee chair Sandy Klasa

for support and helpful discussions. I also thank Laura Cardella, Stephen McKeon, Roberto Mura

(FMA discussant), Boris Nikolov (EFA discussant), Matthew Serfling, Richard Sias, Ph.D.

students at the University of Arizona, and seminar participants at the 2013 European Finance

Association Annual Meeting, the 2013 Financial Management Association Annual Meeting and

Doctoral Consortium, Brigham Young University, Clemson University, Southern Illinois

University, Washington State University, and the Universities of Arizona and Mississippi. I will

always be indebted to my wife Kindra whose patience, love, and support makes my work

possible and to my children who are the motivation behind much of what I do.

5

TABLE OF CONTENTS

LIST OF FIGURES AND TABLES……………..………………………………...……………...6

ABSTRACT……………………………………………………………………………………….7

1. Introduction ........................................................................................................................... 8

2. Related Literature, Hypothesis Development, and Empirical Predictions .................... 16

2.1. Financial flexibility and short-term financing needs ......................................................... 16

2.1.1 The costs and benefits of using cash and debt for short-term financing ...................... 17

2.2. Empirical setting and predictions ...................................................................................... 19

3. Data Sample and Empirical Methodology for Seasonal Classification .......................... 21

3.1. Data sample........................................................................................................................ 21

3.2. Seasonal identification methodology ................................................................................. 21

4. Empirical Results ................................................................................................................. 28

4.1. Seasonal fluctuations in financing needs ........................................................................... 28

4.2. Debt as a source of short-term financing ........................................................................... 29

4.2.1. Fluctuations in debt balances around short-term financing needs ............................. 29

4.2.2. Short-term financing and the type of debt ................................................................... 32

4.3. Cash as a source of short-term financing .......................................................................... 35

4.4. The impact of debt for short-term financing on the capital structure of the firm .............. 37

5. Conclusion ........................................................................................................................... 42

REFERENCES……………………………...……………………………………………………59

6

LIST OF FIGURES AND TABLES

Figure 1. Financing Seasonal Fluctuations………………………………………..………..44

Table 1. Seasonal Firms by Industry……..…………………………………….…….……..45

Table 2. Distribution of Peak Seasons and Sample Firms………………………..………..46

Table 3. Summary Statistics…………………………….………………………….………..47

Table 4. Seasonal Investment Activity………………….………………………….………..49

Table 5. Seasonal Debt Balances…………………….…………………………………..…..50

Table 6. Seasonal Firms and Short-Term Debt……………………………………………..51

Table 7. Trade Credit Use by Seasonal Firms………….…………………………….……..52

Table 8. Seasonal Firms and Cash Holdings………………………………………………..53

Table 9. Seasonal Firms and Total Leverage…………………………………………...…..54

Table 10. Seasonal Firms and Debt Maturity…………………………………………..…..55

Table 11. Seasonal Adjustment to Target Leverage………………………………………..57

7

Abstract

Firms that face seasonal demand account for an important fraction of the U.S. economy.

However, there is surprisingly little evidence on these firms’ financing decisions. Yet, studying

these decisions provides a natural setting to shed light on the types of capital (i.e. cash or debt)

that firms use to manage short-term financing needs. Using seasonal firms as a setting to

examine this issue, I show that seasonal financing needs are met with debt with low exposure to

information asymmetry, such as short-term debt and trade credit. I further show that cash

reserves, which have high carrying costs and can at time lead to agency problems, are not used

for seasonal financing needs. Further, as financial flexibility theory would predict, I document

that seasonal firms maintain more conservative financial policies to increase the ability to use

debt for short-term financing needs. Specifically, seasonal firms are less levered and have long-

term debt with a longer average maturity. Further, seasonal firms adjust toward leverage targets

slower during fiscal quarters when debt is used for short-term financing. Overall, my findings

indicate that firms minimize costs associated with short-term financing needs by using debt with

low issuance costs and the use of this debt impacts the overall capital structure of the firm.

8

1. Introduction

Financial flexibility, or the ability to respond to uncertainty in cash flow needs or investment

opportunities, is a key consideration in financial policy choice. For instance, CFOs commonly

cite financial flexibility as the most important factor in corporate capital structure choice

(Graham and Harvey (2001)). Further, prior empirical work shows that firms maintain flexibility

when setting long-term financial policies such as leverage, the level of cash holdings, and the

composition of payout to shareholders.1 Yet, despite evidence that the need for financial

flexibility impacts long-term financial policies, there is little, if any, evidence on how firms build

financial flexibility into short-term financing needs. Specifically, assuming managers try to

minimize financing costs, which sources of financing (i.e. cash or debt) are used to meet

fluctuations in short term financing needs? Managers may hold excess cash in anticipation of

future short-term financing needs. Cash holdings have low issuance costs, such as exposure to

information asymmetry inherent in external financing (Myers and Majluf (1984)). However, the

costs of holding cash can be high because these assets generate low returns and expose firms to

agency costs. Alternatively, managers may minimize financing costs by relying on debt to meet

short-term financing needs. Debt can be costly due to issuance costs stemming from information

asymmetry between the borrower and lender. But, debt balances avoid the holding costs of large

cash reserves. Further, private credit, such as bank debt, has lower exposure to information

asymmetry costs than other external financing as the lender has access to private information

regarding borrower credit.

1 As examples, firms appear to maintain financial flexibility by reserving borrowing capacity, holding precautionary

cash holdings, and relying on flexible share repurchases as a form of payout as shown in Denis and McKeon (2012),

Opler, Pinkowitz, Stulz, and Williamson (1999), and Jagannathan, Stephens, and Weisbach (2000), respectively. See

Denis (2011) and Almeida, Campello, Cunha, and Weisbach (2013) for surveys of work related to this literature.

9

One potential reason for the limited research on firms’ short-term financial policy choices is

the difficulty to empirically identify times when short-term financing needs are high. In this

study, I use the financing decisions of firms facing seasonal demand as a natural setting to

provide evidence on the sources of capital, such as cash holdings or debt that managers use for

short-term financing. Surprisingly, we know little about seasonal firms, who account for a

notable fraction of the U.S. economy. As evidence of this, 47% of firms used the word seasonal

in their 10-K in 2010 with 35% of firms using the word more than one time. Importantly,

studying seasonal fluctuations in short-term financing needs offers an ideal setting to provide

insights into the sources of financing used to meet these needs. Notably, the discussion of

seasonality in 10-Ks often centers on how seasonal fluctuations in operations impact financial

policies. For example, the Chalone Wine Group said the following in their 2003 10-K:

Our business is subject to seasonal as well as quarterly fluctuations in revenues...

Seasonal factors also affect our level of borrowing. For example, our borrowing

levels typically are highest during winter when we have to pay growers for grapes

harvested and make payments related to the harvest.

However, this setting is only beneficial if seasonal fluctuations in short-term financing needs

can be identified. Yet, the current body of empirical research does not provide a way to classify

seasonal firms and identify fluctuations in their financing needs within the year. As such, I

develop a methodology to classify firms that face seasonal demand and identify fluctuations in

liquidity needs for firms classified as seasonal. To do so, I use the Compustat Fundamentals

quarterly filings over the 1984-2010 period and test for significant differences in mean quarterly

revenue/assets across the four fiscal quarters over the time series of observations for each firm. A

firm with significant differences in revenue across its fiscal quarters is classified as seasonal. For

these seasonal firms, the peak quarter is defined as the fiscal quarter with the highest mean

10

revenue, and the other three quarters are defined by their position relative to the peak. Based on

this classification, seasonal firms constitute 22.1% of the firms in my sample.

As this is a new proxy, I run several tests to support its validity. Several findings provide this

support. First, although seasonal firms are present in many industries, the concentration of

seasonal firms is high in industries traditionally thought to be seasonal such as the retail industry

and the apparel industry (61.9% and 60.0%, respectively).2 Second, firms classified as seasonal

use the word ‘seasonal’ more than twice as often in their 10-Ks than the remaining firms. Finally,

firms classified as seasonal are 67% more likely to report that they rely substantially on seasonal

or part-time employees.

I also examine the extent to which financing needs vary seasonally for the firms I categorize

as seasonal. Consistent with fluctuations in financing needs for seasonal firms, I find that capital

expenditures, selling, general, and administrative expenses, and inventory-related expenses

increase in the quarter prior to the peak quarter, remain high through the peak quarter, and then

decline for the next two quarters. I next turn to the financial balances of seasonal firms to

determine which sources of capital are used to finance these fluctuations.

I first consider debt. If the carrying costs of cash are high relative to the issuance costs

inherent in external financing, then managers would minimize financing costs by using debt.

This would lead to the prediction of increasing debt balances when seasonal financing needs are

high. Consistent with this prediction, I show that seasonal firms use debt to meet fluctuations in

financing needs. Specifically, I find that debt balances of seasonal firms increase by 5.2% in

quarter prior to the peak quarter, remain high through the peak quarter, and decline following the

receipt of seasonal revenues. This pattern mirrors seasonal financing needs. Further, the seasonal

2 Though the retail industry consists of the highest proportion of seasonal firms, retail firms do not drive the

empirical results. Retail firms only make up 6.2% (17.4%) of firms in my (seasonal) sample. Further, all results are

qualitatively unaffected by removing retail firms.

11

increases in debt balances are more pronounced for firms that have more severe fluctuations in

seasonal demand. The use of debt by seasonal firms to meet their short-term financing needs is

consistent with this type of financing being used to minimize short-term financing costs.

The ability of firms to meet their short-term financing needs with debt should be greater

when credit conditions are stronger. Thus, if the increases in debt balances when seasonal

financing needs are higher is driven by firms trying to minimize their short-term financing costs,

then this finding should be more pronounced during times when credit conditions are stronger.

Consistent with this prediction, I find that seasonal fluctuations in debt balances are more

prominent in years when credit conditions are strong, providing further evidence that firms rely

on debt for short-term financing needs.

External financing is subject to issuance costs such as those associated with information

asymmetry between the firm and the lender. Private debt minimizes issuance costs for two

reasons. First, an advantage of private debt as a source of financial slack is reduced asymmetric

information costs because the lender receives private signals of the quality of the borrower

(Bernanke (1983), Fama (1985), and James (1987)).3 Second, private debt is typically short-term

debt which reduces the ability for borrowers to shift to riskier assets (Barnea, Haugen, and

Senbet (1980)). As such, the use of debt is less costly for private, short-term debt. Consistent

with this idea, I document that seasonal firms hold 16.1% more debt that is short-term at issuance

as a proportion of total debt than non-seasonal firms. This finding suggests that firms prefer

external financing that minimizes issuance costs.

I also consider trade credit, which can be considered a form of private debt, as a type of

short-term financing. Trade credit shares with private debt many of the same advantages as a

3 In these studies, the authors use the term ‘inside debt’ to refer to external lenders with private signals of borrower

quality. However, to avoid confusion with the use of the term ‘inside debt’ in the recent literature on compensation

with debt-like features (e.g. Sundaram and Yermack (2007)), I use the term ‘private debt’ throughout my paper.

12

type of short-term financing. For example, trade credit does not have the same carrying costs (i.e.

low return on investment and exposure to agency costs) as cash. Further, exposure to information

asymmetry costs is low for trade credit as suppliers receive signals about borrower quality

through their operational relationships with customer firms (Biais and Gollier (1997) and

Petersen and Rajan (1997)). I find that trade credit is also an important source of short-term

financing for seasonal firms. Specifically, trade credit balances increase by 11.9% and 10.1% in

the pre-peak and peak quarters, respectively, over the ‘other’ quarter and decline in the post-peak

quarter. Not surprisingly, these seasonal fluctuations in financing closely track seasonal

inventory balances. Also, seasonal firms finance a higher proportion of their assets with trade

credit than non-seasonal firms. These findings further support the benefits of external financing

in managing short-term financing needs.

However, the use of debt to meet short-term financing needs does not imply that firms do not

also rely substantially on cash balances for short-term financing needs. For example, it could be

the case that managers minimize short-term financing costs by using cash holdings to the extent

possible and the use of debt only to the extent that cash reserves are exhausted. Yet, I find that

seasonal firms do not rely on cash balances for short-term financing needs. For instance, in the

quarter prior to the peak quarter, when seasonal financing needs are high, cash balances do not

decline. Further, if firms hold excess cash in quarters when short-term financing needs are low in

anticipation of times when short-term financing needs are high, they should have higher average

cash balances across all fiscal quarters. However, I find that seasonal firms hold less cash than

non-seasonal firms. In sum, these findings suggest that firms do not rely on cash balances for

short-term financing needs.

13

Taken together, these findings on debt and cash suggest that managers minimize financing

costs by using debt with low issuance costs. However, the use of debt for financing needs is an

important consideration is setting capital structure targets (DeAngelo, DeAngelo, and Whited

(2011)). In the case of seasonal firms, the use of short-term debt for financing requires frequent

interactions with the capital markets. As such, financial flexibility predicts seasonal firms will

maintain more conservative financial policies to ensure their ability to use debt to meet short-

term financing needs. For instance, the leverage of the firm impacts its ability to receive this

financing. Flexibility models predict that seasonal firms should hold less debt to ensure the

ability to get short-term debt for seasonal needs. Consistent with this prediction, I find that

seasonal firms hold 7.4% less debt than non-seasonal firms. These findings suggest that the use

of short-term debt to meet short-term financing needs impacts a firm’s target leverage.

The use of more short-term debt likely also impacts the target maturity of seasonal firms’

long-term debt. Because firms with more short-term debt need to frequently return to the capital

markets, flexibility models predict that these firms should hold long-term debt with a longer

average maturity, which does not require frequent refinancing. Consistent with this prediction, I

document seasonal firms hold 4.3% less long-term debt due in the next three years as a percent

of long-term debt than non-seasonal firms. Further, the increased use of long-term debt with a

longer average maturity appears to offset the greater use of short-term debt. Specifically, I find

that the maturity of a firm’s total debt does not differ between seasonal and non-seasonal firms,

providing additional evidence that the use of short-term debt for financing impacts the target

maturity of seasonal firms’ debt, as predicted by flexibility models.

Another way to test whether seasonal financing needs impact the choice of capital structure

targets is to consider the speed at which firms adjust toward target leverage ratios. Financial

14

flexibility models predict that adjustments toward tradeoff-implied leverage targets will be

slower when financing needs are high as firms use a proportion of debt to meet financing needs

and not to adjust toward a target leverage ratio (DeAngelo, DeAngelo, and Whited (2011)). I test

this prediction by estimating the speed at which firms adjust toward a target leverage ratio

following Flannery and Rangan (2006). I find that the average speed of adjustment varies across

the seasons for seasonal firms. Seasonal firms adjust toward a tradeoff-implied target leverage at

less than half the speed in pre-peak quarters, when short-term financing needs are highest, than

they do in post-peak quarters, when short-term financing needs are lowest. The varying speeds of

adjustment are consistent with flexibility models where target leverage ratios are based on

financing needs in addition to market frictions. Further, the results provide an explanation, other

than adjustment costs, for slow average speeds of adjustment and the tendency of CFOs to report

the use of flexible leverage targets (Graham and Harvey (2001)).

The contribution of my paper is three-fold. First, I provide evidence on how seasonal firms

fund fluctuations in short-term financing needs, and in doing so, provide insights on the

importance of financial flexibility in explaining firms’ financing decisions. Notably, my

evidence suggests that managers minimize short-term financing costs by relying on debt,

especially debt with low issuance costs. This evidence on firm behavior supports arguments in

the banking literature that an important role played by banks is the ability to provide firms with

financial slack due to low informational asymmetry between firms and private lenders (e.g.

James (1987)). My evidence is also consistent with survey evidence that debt is used for

investment in good times whereas cash holdings serve as a precautionary hedge against negative

cash flow shocks (Lins, Servaes, and Tufano (2010)).

15

Second, the results that seasonal firms hold more conservative long-term financial policies in

order to use debt for short-term financing needs imply that maintaining financial flexibility in

short-term financing impacts overall capital structure choice. These results are consistent with

the finding in Denis and McKeon (2012) that future investment needs impact the choice of target

leverage ratios. However, my results suggest that firms also consider short-term financing needs,

which are often treated separately from long-term financial policies, when setting the overall

capital structure policies of the firm.

My final contribution is methodological in nature. Using a simple model with data available

for most Compustat firms, I identify whether firms face seasonal demand. I further identify when

demand for the firm’s products peaks. The model provides an intuitive classification of seasonal

and non-seasonal firms. While this paper uses seasonal activity to shed light on our current

understanding of short-term financial management, the use of this measure might prove valuable

in other areas including but not limited to corporate investment, earnings management, and tax

management. At a minimum, the measure provides an important variable for future work to

control for seasonal fluctuations in financial balances as well as work using quarterly data.

The remainder of the paper proceeds as follows. Section 2 reviews the related literature,

develops the hypotheses, and discusses empirical predictions. Section 3 outlines my sample

selection criteria and describes the empirical methodology that I use to identify firms facing

seasonal demand and peak quarters. Section 4 presents my empirical findings. Section 5

concludes.

16

2. Related Literature, Hypothesis Development, and Empirical Predictions

2.1. Financial flexibility and short-term financing needs

According to Denis (2011), “financial flexibility refers to the ability of a firm to respond in a

timely and value-maximizing manner to unexpected changes in the firm’s cash flows or

investment opportunity set.” A large body of empirical work documents that firms maintain

financial flexibility in setting capital structure policies. For instance, Denis and McKeon (2012)

show that firms maintain lower leverage than a tradeoff model would predict in large part to be

able to meet investment needs when they arise supporting theoretical models of financial

flexibility in capital structure choice (see DeAngelo and DeAngelo (2007) and DeAngelo,

DeAngelo, and Whited (2011)). Also, financial flexibility impacts cash holdings as firms with

costly access to external markets hold higher precautionary cash balances to avoid

underinvestment (Opler et al. (1999) and Bates, Kahle, and Stulz (2009)). Finally, firms with

greater investment opportunities tend to rely more on share repurchases, which are flexible in

nature, than dividends to pay cash to shareholders (Jagannathan, Stephens, and Weisbach

(2000)).

However, in contrast to these studies which show how firms maintain financial flexibility in

selecting long-term financial policies, there is little, if any, evidence on how firms build financial

flexibility into short-term financing needs, an important aspect of liquidity management. This is

despite that fact that CFOs report that three out of the top four finance roles key to value creation

are related to liquidity management (Lins, Servaes, and Tufano (2010)). Specifically, assuming

managers try to minimize financing costs, I ask which sources of financing are least costly to use

to maintain flexibility in order to meet uncertainty in short-term needs. As an example, consider

a firm that has uncertainty in short-term expenditures (i.e. inventory, wages, advertising,

17

improvements to equipment, etc.) that fluctuate with year-over-year changes in demand.

Managers may hold excess cash to be used when these short-term needs are relatively high,

building up reserves in the following periods. Another possibility is that managers reserve access

to debt to finance these fluctuations.4 The costs and benefits of using cash holdings for short-

term financing needs differ from the costs and benefits of using debt for this purpose. I next

discuss the relative costs and benefits of each.

2.1.1 The costs and benefits of using cash and debt for short-term financing

To use cash to manage uncertainty in short-term financing needs requires that excess cash be

held in times when short-term financing needs are low. In periods when financing needs

increase, cash balances decline and are then replenished in order to meet future uncertainty.

Alternatively, managers might rely on debt financing for short-term financing needs. In this case,

managers reserve borrowing capacity to meet fluctuations in short-term financing needs. It is

unclear which approach minimizes financing costs as cash and debt financing have different

costs and benefits.

For instance, relying on cash financing avoids issuance costs inherent in debt financing. One

type of issuance cost stems from information asymmetries between suppliers and demanders of

capital. Managers are expected to profit from the inside knowledge of the quality of future

investment opportunities by issuing capital that is the most over-valued (Myers and Majluf

(1984), Graham and Harvey (2001), Baker and Wurgler (2002)). However, rational investors

anticipate this behavior and place a premium on the issuance cost, with larger premiums on

capital more severely affected by information asymmetries. Cash from operations avoid these

information asymmetry costs. Another issuance cost that the use of cash avoids is the potential

4 Given the short-term nature of these financing needs, it is unlikely that firms would use equity financing due to the

time and cost of accessing the equity markets.

18

that the capital is not available when demanded. Debt financing is subject to the availability of

debt in the capital markets, which is conditional on the state of the economy (Lins, Servaes, and

Tufano (2010)). Alternatively, the firm chooses the level of excess cash that they will retain as a

buffer against fluctuations in short-term financing needs. As such, the access to cash is not

limited in poor economic times in the same way that debt is. In sum, cash holdings avoid

issuance costs stemming from repeated trips to the capital markets to access external financing.

However, while the use of cash avoids these costs, they are not the same for all types of debt

financing. Private debt, in which the lender receives a private signal about the quality of the

borrower, has low informational asymmetry costs due to the private signals (Bernanke (1984)

and Fama (1985)).5 James (1987) argues that private debt is an important source of financial

slack because of these reduced information asymmetry costs. So, while cash has lower issuance

costs than external financing generally, the relative difference in costs is lower between cash and

private debt than between cash and public debt.

Further, using cash for short-term financing fluctuations requires retaining large cash

balances in excess of operational needs which is costly for reasons other than issuance costs.

First, excess cash is associated with agency costs due to value-decreasing uses of cash by self-

serving managers (e.g. Jensen (1986), Harford (1999), and Harford, Mansi, and Maxwell

(2008)). Considering information asymmetries only at the time of raising capital ignores the

agency costs associated with large internal balances over time (DeAngelo and DeAngelo

(2007)). Another cost of large excess cash balances is the opportunity cost stemming from the

low return generated on investment of cash relative to other investments (Kim, Mauer, and

Sherman (1998)). So, while the use of cash holdings for short-term financing needs avoids

5 As mentioned in the introduction, these authors use term ‘inside debt’ in place of ‘private debt’. I use ‘private debt’

to avoid confusion with recent research on compensation with debt-like features (e.g. Sundaram and Yermack

(2007)).

19

issuance costs inherent in using debt, these cash holdings also expose firms to costs stemming

from the holding of excess cash. If managers try to minimize financing costs, it is unclear which

source of capital they prefer for short-term financing needs.

2.2. Empirical setting and predictions

Empirically testing the extent to which each source of capital is used for short-term financing

needs is difficult. One potential methodological approach to test this might involve identifying

time periods when short-term financing needs are high by looking at actual expenditures that

require short-term financing. For example, a measure could be created that includes increases in

inventory, advertising expense, wage expense, and others. Then, time periods when this measure

is high would be identified as times when financing needs are high. Different financial balances

could be examined around these time periods to determine how firms finance these short-term

needs. Yet, endogeneity concerns are likely high in this empirical approach as it is unclear if the

increased spending is exogenous to the access to short-term financing.

I use the setting of firms facing seasonal demand as an alternative approach. Seasonal firms

have two features that make them an ideal setting to provide evidence on how firms finance

short-term needs. First, seasonal firms have seasonal fluctuations in short-term financing needs

that, despite uncertainty in the level of needs each year, occur at predictable times within the

year. Anecdotally, seasonal firms see increases in the need for inventory, seasonal workers,

advertising expense, storage and manufacturing capacity, and improvements to plants and

equipment around the time that seasonal revenues increase. As such, to the extent that these

fluctuations in financing needs can be identified, they provide a nice setting to test how they are

financed. Second, the concern that the fluctuations in short-term financing needs are driven by

access to short-term financing is reduced in this setting. By using seasonal revenues to identify

20

fluctuations in financing needs, the fluctuations are likely driven by consumer demand and not

the access to capital.

If the issuance costs of using debt as financing for short-term needs are low relative to the

holding costs of excess cash, the debt hypothesis implies that firms will use more debt for short-

term needs. Empirically, this would lead to the prediction that debt balances increase when short-

term financing needs are high and decline in subsequent periods. In my setting, when seasonal

financing needs are high, we would observe high debt balances. Then, debt balances should

decline following the receipt of seasonal revenues. Further, if managers minimize financing

costs, they should rely on sources of debt with the lowest exposure to issuance costs. Private

debt, which has relatively low information asymmetry costs, is preferred for managing short-

term financing needs. Empirically, the debt hypothesis predicts that seasonal firms will use

private debt for short-term financing needs.

Alternatively, if the holding costs of excess cash from the use of cash for short-term

financing are low relative to the issuance costs of cash, the cash hypothesis implies that firms

will hold excess cash until short-term financing needs are high. In this case, this would lead to

the empirical prediction that cash balances are negatively correlated with seasonal financing

needs. Specifically, when seasonal financing needs are high, cash balances should be low. The

cash balances would then be high following the receipt of seasonal revenues as cash is retained

in anticipation of future seasonal needs. Further, holding excess cash in time periods when

seasonal financing needs are low predicts that seasonal firms will hold more cash on average.

21

3. Data Sample and Empirical Methodology for Seasonal Classification

3.1. Data sample

To test these predictions, I draw my sample from the Compustat Quarterly Fundamentals file.

I include firm observations for U.S. firms in fiscal years including 1984 through 2010 with

strictly positive assets and revenues, as 1984 is the first year that variables such as capital

expenditures (hereafter CapEx) are available at a quarterly frequency. I exclude regulated

industries (SIC codes 4900-4999), financial firms (SIC codes 6000-6999), and quasi-public firms

(SIC codes greater than 9900) due to limitations on financial policy choice. I further exclude

firms without a minimum of twelve quarters of consecutive data to avoid misclassification of

seasonal firms due to limited data.6 Finally, any firm that changes its fiscal year-end during the

sample period is removed from the sample as the revenue figures for the firm are not comparable

across time. These restrictions result in a sample with 432,404 firm-quarter observations from

10,042 unique firms.

3.2. Seasonal classification methodology

To use seasonal firms as a setting, I first develop a methodology to classify seasonal firms

from the Compustat universe and identify when revenues peak for these seasonal firms. At first

consideration, it may be assumed that seasonality is an industry factor and, as such, seasonality

should be classified at the industry level. However, there are at least two reasons that make

industry classifications of seasonality problematic. First, while some industries have a higher

concentration of seasonal firms, there is still variation within industries as to whether a firm is

seasonal. For example, retail firms are often thought to be seasonal. However, some retail firms

6 The requirement of 12 consecutive quarters of data, while subjective, is an attempt to increase the likelihood that

the time series model correctly identifies seasonal firms. All results hold when I change the requirement to 24

quarters of consecutive data.

22

with a diverse product line state in their 10-Ks that they are not seasonal as the seasonality in

some products is offset by seasonality in other products at an aggregate level.

Second, even if all firms in an industry were seasonal, there is variation regarding when peak

seasons occur within an industry. For example, according to 10-Ks of firms in the Beer and Wine

Industry, a significant percentage of firms in this industry are seasonal. However, beer sales tend

to peak in the summer months while wine sales tend to peak in the winter months around holiday

sales. Similar issues arise in retail and manufacturing industries as the peak periods vary

depending on the nature of the product. As such, using an industry measure of seasonality would

require an assignment of peak quarter, which would likely be noisy given the within-industry

variation in when demand peaks.

To avoid these issues, I create firm-specific measure of whether a firm faces seasonal

demand. Further, conditional on being classified as seasonal, I identify the fiscal quarter in which

demand peaks. These measures allow me to analyze seasonal variation in financial policies in the

sub-sample of firms facing seasonal demand as well as cross-sectional differences between

seasonal and non-seasonal firms.

To classify firms as seasonal, I estimate a model using quarterly data from Compustat.

Specifically, I run the following model separately for each firm meeting the sample

requirements, including all observations of the firm:

t1 1 2 2 3 3 4 4 t

t

RevQ Q Q Q

Assets (1)

where the left-hand side variable is revenue in quarter t scaled by the mean of total assets over

the four quarters in the fiscal year. I scale by the mean of assets over the four fiscal quarters to

avoid any influence of seasonality in the scaling variable. Each variable i is an indicator

23

variable that takes the value of one in the firm’s ith fiscal quarter and zero otherwise.7 A firm is

classified as seasonal if the following F-test is rejected at the 95% significance level:

1 2 3 4 . (2)

In words, if the mean scaled revenue of a given quarter is significantly different from any of

the other quarters, the firm is classified as seasonal. For seasonal firms, I identify the peak

quarter as the quarter associated with the largest coefficient estimate. I then define the remaining

quarters by their position in time relative to the peak quarter. Specifically, the quarter just prior

to the peak quarter is defined as the pre-peak quarter, the quarter just following the peak quarter

is defined as the post-peak quarter, and the remaining quarter is referred to as the ‘other’ quarter.

I further create a continuous variable to capture variation in the severity to which firms face

seasonal demand. To be specific, more seasonal firms are defined to be firms which have larger

coefficient of variation in average revenue across the four quarters, measured as follows:

i

i

i

( )CV( )

( ) (3)

where ̂i is the vector of four coefficient estimates from equation (1) for firm i, is the standard

deviation of the four coefficient estimates, and μ is the mean of the four coefficient estimates.

An implicit assumption of the methodology used is that the seasonality and the peak season

of a firm are time invariant. As such, I use information in future revenues that is not available to

management at the time financing decisions are made. However, managers likely have other

information regarding the extent to which the firm faces seasonal demand. The time series of

firm revenue is considered a proxy for the information that management has at the time financing

7 I run two alternate specifications for this model. First, I re-classify firms by including a time-trend to capture any

linear growth that may lead to a spurious seasonal classification. Second, I re-classify firms leaving the left-hand

side variable unscaled. In each case, the classification is similar to the one used in the paper and all multivariate

results are robust to each specification.

24

choices are made. However, to ensure robustness to concerns of using future information, I also

classify firms using only past information (i.e. an expanding window) still requiring a minimum

of twelve quarters of scaled revenue data. The correlation between the time invariant measure

and the measure using an expanding window is 0.67. Further, in unreported results, all findings

are similar when using this expanding window for seasonal classification.

Given this is a new measure, I run several tests to provide validity for it. To check the

validity of the measure of seasonal demand, I run a few tests using alternate sources of data.

First, I use a scripting language to count the number of times the word ‘seasonal’ is used in a

firm’s 10-K in 2010. Consistent with an accurate classification of seasonal firms, the mean

(median) number of times the word seasonal is used is 5.7 (4.0) for firms that I classify as

seasonal and 2.2 (1.0) for firms that I classify as non-seasonal.8 As an additional check, I use

data from the Compustat footnote files. These files include a footnote indicating whether firms

rely substantially (greater than 10%) on employees that are either part-time or seasonal in nature.

As further support of an accurate classification of seasonal firms, I find that firms that I classify

as seasonal (non-seasonal) report that they rely substantially on these employees in 33.3%

(19.9%) of firm-years.

The sub-sample of seasonal firms is an important part of all Compustat firms. Seasonal firms

represent 22.1% of all firms meeting sample requirements. From a value-weighted perspective,

seasonal firms hold an average of 26.4% of the total sample assets and constitute 32.5% of the

total sample market capitalization. As shown in Table I, firms classified as seasonal are dispersed

across the Fama-French 49 industries. However, there is significant variation in the concentration

8 It is common for firms to use a phrase similar to ‘Our business is not affected by seasonal demand.’ which would

be counted as a use of the phrase under my methodology. Further, firms occasionally provide a brief discussion of

operating segments facing seasonal demand despite no material seasonality at the aggregate reporting level. I use

textual analysis to check how reasonable my classification is. However, using this as the method of seasonal

classification is much more problematic considering issues with reporting choice, false positives, data matching, etc.

25

of seasonal firms within different industries. For example, consistent with what one might

expect, industries such as retail and apparel contain of a significant percentage of firms that are

classified as seasonal (61.9% and 60.0%, respectively). Table II also provides evidence that the

peak quarters fall when one might expect. December is the most common month in which a peak

quarter ends, followed by September, June, and January. Over all, the methodology appears to be

capturing, on average, whether a firm faces seasonal demand.

Table III provides the univariate statistics for the sample. Panel A presents summary statistics

for seasonal and non-seasonal firms. It is apparent that the two groups are different than each

other. As expected, seasonal firms have more variation in quarterly mean revenue. However,

seasonal firms are also larger, have fewer growth opportunities, and pay more in dividends.

Related to the research question, seasonal firms are less levered than their non-seasonal

counterparts. They also have lower cash balances and utilize more trade credit as a percent of

assets than non-seasonal firms. However, many of the important determinants of these financial

policies also vary between seasonal and non-seasonal firms. These differences are more carefully

examined in multivariate setting in subsequent tables.

Panel B of Table III considers the intra-year dynamics of financial and operational variables.

I present means for seasonal firms by season as determined in the classification model. Further,

the ‘other’ season is used as the base season for comparison purposes. The pre-peak, peak, and

post-peak seasons include a test in the difference in means between that quarter and the ‘other’

quarter. Several patterns emerge. First, revenue slightly increases in the pre-peak quarter and

then significantly increases in the peak quarter, relative to the base quarter.

Second, total assets are higher in the pre-peak and peak quarters than they are in the

remaining quarters. The reasons for the fluctuations in assets are likely driven by items such as

26

inventory, cash holdings, and property, plant, and equipment (PP&E). However, a careful

investigation of these fluctuations is beyond the scope of this paper. Yet, it is important to note

that most variables are scaled by total assets. I use the mean asset balance over the four fiscal

quarters as the denominator. This approach still controls for year-over-year fluctuations in firm

size while avoiding the potential to introduce seasonality through the scale variable.

A third important pattern comes from fluctuations in financing needs. For example, inventory

balances increase by 6.4% (=0.0127/0.197) from the ‘other’ season to the pre-peak season. The

increase disappears by the conclusion of the peak season. However, financing needs are not

limited to working capital. CapEx increases by 4.38% (=0.0007/0.016) and 15.6%

(=0.0025/0.016) in the pre-peak and peak quarters over the ‘other’ quarters, respectively. Selling,

general, and administrative expense (SG&A) increases by 2.8% (=0.0025/0.088) and 14.9%

(=0.0131/0.088) in the pre-peak and peak quarters, respectively.

This increase in financing needs is met with an average increase in debt balances by just over

5.1% (=0.0126/0.248) and 3.9% (=0.0096/0.248) and increases in trade credit of 12.6%

(=0.0137/0.109) and 10.1% (=0.011/0.109) in the pre-peak and peak quarters, respectively.

While it is difficult to determine precisely what each source of funds is used for, these univariate

results provide some evidence of the use of seasonal funds. First, not surprisingly, trade credit

balances closely track inventory balances, suggesting that seasonal firms largely finance seasonal

inventory fluctuations with trade credit. Further, if I assume that debt is used to finance CapEx,

then 33.3% (= (0.0007+0.0025)/0.0096) of the increase in debt balances from the ‘other’ quarter

to the peak quarter is due to seasonal CapEx suggesting that the seasonal fluctuations are not

simply changes in working capital.9

9 Anecdotal evidence from the 10-Ks of firms identified as seasonal support these increases. Firms attribute seasonal

CapEx increases increased improvements to plant and equipment due to greater use during peak seasons as well as

27

Alternatively, cash holdings are insignificantly different at the conclusion of the pre-peak

season and significantly higher at the conclusion of the peak season. This evidence is suggestive

of financial and trade credit but not cash being used to fund intra-year investment needs.

However, as noted previously, these univariate results are potentially driven by intra-year

changes in other determinants of financial policies. I further consider these results using a

multivariate analysis in a subsequent section.

seasonal purchases of both equipment and facilities. Firms attribute seasonal SG&A to an increased advertising

during peak seasons.

28

4. Empirical Results

In Section 2, I outlined predictions generated by the theoretical costs and benefits of cash and

debt for short-term financing needs. In this section, I first identify how seasonal demand causes

short-term financing needs to vary within the year for seasonal firms. This information is later

used to test which sources of financing are used to meet these financing needs.

4.1. Seasonal fluctuations in financing needs

To test how short-term financing needs vary within the year for seasonal firms, I create a

measure of investment activity that relies on short-term financing needs consisting of increases

in inventory, selling, general and administrative expense, research and development expense, and

CapEx. I regress this measure of short-term financing needs on control variables based on those

used in Faulkender and Petersen (2012), including size, growth opportunities, and pre-investment

earnings. I further include indicator variables for three of the four seasons to capture how

financing needs vary. In models (1) and (2) of Table IV, I present the results, with and without

industry fixed effects where the regressions include only seasonal firms.10

The results indicate

significantly different short-term financing needs across the seasons. Specifically, short-term

investment is higher in the pre-peak and peak quarters and lower in the post-peak and ‘other’

quarters. In model (3) and (4), I split my sample based on the coefficient of variation in the four

parameter estimates of my identification model with only seasonal firms having a coefficient of

variation below (above) the cross-sectional median being included in model 3 (4).11

Consistent

with this being seasonal short-term financing needs, the effect is more pronounced for seasonal

10

In this model, and in all other models in which the dependent variable is a quarterly variable, there is a potential

concern that the relation with control variables varies due to seasonal fluctuations in variables on the right-hand side.

The issue is minor for balance variables, such as size and M/B. However, flow variables, such as pre-investment

earnings, are very likely to fluctuate seasonally. In each quarterly regression in the paper, control variables that are

flow variables are measured as the sum of the previous four quarters to avoid seasonal fluctuations. 11

The split on the severity of is done at the firm level as opposed to the firm-year level as the seasonal classification

is done across the entire time series. As such, the numbers of observations in the two sub-samples are not necessarily

equal.

29

firms with more severe seasonality. With evidence of when short-term financing needs are

highest, I next test for fluctuations in financial balances around these seasonal financing needs.

4.2. Debt as a source of short-term financing

I first consider how debt balances fluctuate around seasonal financing needs. If managers

minimize financing costs by using debt, then debt balances should increase when short-term

financing needs are high, or in the pre-peak and peak quarters. Further, managers should use debt

with the lowest financing costs, such as private debt, to minimize financing costs.

4.2.1. Fluctuations in debt balances around short-term financing needs

I use total debt scaled by book assets to observe how debt balances fluctuate within the year.

Book leverage is used as the primary measure of leverage throughout the paper. The primary

concern with market leverage is that variation in market leverage is largely driven by movements

in equity value, not debt balances (Welch (2004)). Further, book leverage is defined as the sum

of short-term and long-term debt, as opposed to total liabilities, as to clearly demonstrate how

debt balances change across the quarters.12

The denominator for book leverage, in addition to

many other variables, is the mean assets across the four quarters of the fiscal year for the firm.

Table V presents the results of leverage regressions using quarterly data. Only seasonal firms

are included in this table. The regressions are estimated using OLS but are similar when using a

Tobit specification with two-way censoring. Control variables are based on past literature.

Growth opportunities are associated with higher agency costs which should reduce total leverage

(Jensen and Meckling (1976)). I control for both M/B and R&D expense as a proxy for growth

opportunities. The use of fixed assets as collateral reduces information asymmetry costs (Titman

and Wessels (1988)). Thus there should be a positive relation between property, plant and

equipment and total leverage. I control for profitability as a negative relation is well-documented

12

Accounts payable are examined in a subsequent table.

30

(Titman and Wessels (1988), Rajan and Zingales (1995), Fama and French (2002), and Fischer,

Heinkel, and Zechner (1989)). Unique assets have a less liquid secondary market and may not be

used as collateral. I include selling expense as a proxy for unique assets. Bankruptcy costs, as

proportion of firm size, decrease in firm size (Titman and Wessels (1988)). Also, larger firms are

more likely to have a credit rating, which allows them better access to capital (Faulkender and

Petersen (2006)). Thus leverage is positively related to the size of the firm. I include the market-

adjusted return over the previous year to control for the tendency of managers to attempt to time

the market (Baker and Wurgler (2002)). All control variables maintain their expected signs.

To determine how debt fluctuates seasonally, I include dummy variables for three of the

seasons with the ‘other’ season included in the intercept. In Table V, I find leverage significantly

increases by 5.2% (=0.013/0.248) in the pre-peak quarter relative to the level in the ‘other’

quarter and remains at that level through the peak quarter.13

As previously shown, these are the

two seasons when short-term financing needs are highest. Following the peak season, when

short-term financing needs decline and seasonal revenues are received, debt balances decline to

be insignificantly different from the ‘other’ quarter. As further evidence that debt is being used

to finance seasonal needs, the pattern is more pronounced for firms with the severity of seasonal

demand above the median (model (3)) than it is for the firms with less severe seasonal demand

(model (2)).14

These findings provide evidence of firms using debt for short-term financing

needs.

Recent work suggests that a firm’s ability to select its leverage ratio is contingent on the

supply of credit (e.g. Becker (2007) and Leary (2009)). The use of debt to finance fluctuations in

short-term financing needs should be greatest when credit market conditions are relatively

13

By comparison, the mean (median) year-over-year change in debt balances for seasonal firms is 12.9% (0.0%). 14

The severity of seasonal demand is based on the coefficient of variation in mean revenues across the four quarters,

as defined in equation (3).

31

strong. I test whether the supply of credit affects the use of seasonal debt in models (4) and (5) of

Table V. A proxy for the supply of credit used in several studies is the four-quarter moving

average of the spread between the rate on commercial and industrial loans and the federal funds

rate (see Harford (2005), Officer (2007), and Harford, Klasa and Maxwell (2013)). I separate my

sample based on whether this measure is above or below the sample period time series median.

Time periods where this measure is below (above) the median are classified as having relatively

strong (weak) credit market conditions.

The seasonal use of debt for short-term financing needs holds in both strong and weak credit

market conditions. However, the increase in debt during the pre-peak and peak quarters is

significantly more pronounced when credit market conditions are strong. Specifically, seasonal

firms increase their debt balances 0.7 (3.6) times more in the pre-peak (peak) quarter when credit

market conditions are relatively strong than they do when credit market conditions are relatively

weak. In unreported results, a Wald test shows that the differences in coefficients are also

statistically significant. Not surprisingly, there is no difference between the post-peak quarters

based on credit market conditions as seasonal needs are low in this quarter. The increased use of

debt financing in strong credit market environments provides further evidence of debt as a source

of financial flexibility for short-term financing needs.

Finally, in model (6) of Table V, I provide stronger evidence that the seasonal fluctuations in

debt are being used for short-term financing needs. The dependent variable in this model is

change in debt over the quarter net of short-term investment, where investment is defined as the

sum of CapEx, SG&A, R&D expense, and increase in inventory. If the debt proceeds are being

used for investment, then the seasonality in debt and the seasonality in short-term investment

32

should offset each other. Consistent with this conjecture and with the use of debt for short-term

financing needs, the seasonal pattern in debt balances disappears when it is net of investment.

4.2.2. Short-term financing and the type of debt

If managers minimize financing costs by using debt for short-term financing needs, then they

should use debt with the lowest issuance costs. Private debt has the advantage of a low exposure

to information asymmetry costs. If managers are minimizing financing costs, they should use

private debt, which is short-term in nature. I test whether seasonal firms use increased amounts

of short-term debt as a proportion of total debt.

I test this prediction in a multivariate setting where the dependent variable in this model is

debt with a short-term maturity at the time of issuance as a proportion of total debt. I use control

variables used in other debt maturity papers such as Harford, Klasa, and Maxwell (2013).

Specifically, I control for both the demand- and supply-side determinants of debt maturity. I

control for scaled total debt as the amount of total leverage can affect the debt’s maturity. I

control for firm size as there are potential economies of scale in issuing debt. I also use M/B to

control for growth opportunities as increased growth opportunities are associated with higher

underinvestment costs (Myers (1977)). Firms have a tendency to match the maturity of assets to

the maturity of debt. To control for this, I include weighted-average maturity of the assets of the

firm. Theoretical work predicts that seasonal firms will use increased amounts of short-term debt

for reasons other than reduced information asymmetry. Specifically, seasonal firms prefer short-

term debt to avoid paying the term premium on long-term debt during the off-peak seasons

(Aigner and Sprenkle (1973)). I control for the term structure, as measured by the difference in

yield between a ten-year and six-month government bond, to ensure that my findings are not

driven by this alternative explanation. I also control for abnormal earnings as a proxy for firm

33

quality. I control for net debt issuance as issuing (retiring) debt increases (decreases) debt

maturity. I also control for whether the firm went public in the last five years as the introduction

of new firms changes average debt maturity for reasons other than firms actively selecting debt

maturity. Additionally, a time trend is included as the maturity of debt has declined over that last

30 years (Custodio, Ferreira, and Laureano (2013)).

In Table VI, model (1) presents the results of the relation between seasonal demand and the

use of short-term debt from OLS estimation. The results show a positive and significant relation

between seasonality and the use of short-term debt. Using the mean value of short-term debt for

non-seasonal firms as a comparison, seasonal firms hold 16.1% (=0.024/0.149) more short-term

debt as a percent of total debt than non-seasonal firms.15

This result is robust to the inclusion of

industry fixed effects as presented in model (2) of Table VI. The increased use of short-term debt

by seasonal firms is consistent managers minimizing costs by using private debt to meet short-

term financing needs.

An econometric concern with this finding is that the dependent variable uses total debt as the

denominator. This may introduce a sample selection bias as the sub-sample of Compustat firms

with zero debt is inadvertently omitted from the sample. Any correlation between seasonal firms

and the tendency to hold zero debt may bias my results. To control for this potential bias, I use a

Heckman selection specification in models (3) and (4) (without and with industry fixed effects).

In the first stage of the Heckman model, I follow Strebulaev and Yang (2013) in modeling the

choice of having zero. Specifically, in the first regression, I model the probability of holding zero

debt as a function of EBIT, PP&E, a dividend indicator, age, R&D expense, and size. Several of

15

A potential concern arises from this test as data on short-term debt is only available annually and firms choose

their year-end. Further, 66.6% of firms have a fiscal year-end that corresponds with either the pre-peak or peak

quarters, when debt balances are highest (as shown in a subsequent table). However, in unreported results, I find that

these results are robust even after excluding those seasonal firms with a fiscal year-end falling in either their pre-

peak or peak quarter.

34

these variables are not control variables in the debt maturity literature, which meets the exclusion

restriction of the Heckman model. The positive and significant relation between the use of short-

term debt and seasonal demand is robust to controlling for this selection bias, and, in fact, the

relation increases in economic importance, providing additional support for the use of private

debt as financial slack for seasonal firms.

Trade credit can also be viewed as private debt. Specifically, trade credit suppliers receive

private signals about borrower quality through their operational relationships (Biais and Gollier

(1997) and Petersen and Rajan (1997)). If managers minimize short-term financing costs by

using private debt, then we would also expect that trade credit is used for short-term financing

needs. As such, I test the extent to which trade credit is used to fund seasonal financing needs. In

Table VII, I provide evidence of the extent to which seasonal firms finance their seasonal

investment needs through accounts payable. The dependent variable is the balance of accounts

payable as a percent of mean total assets. I control for both supply and demand factors affecting

the choice of trade credit usage. The ability to use accounts payable is largely determined by the

willingness of suppliers to extend the credit. Suppliers are likely more willing to lend to high

credit quality firms. To capture this, I include controls for firm size and profit margin. One

advantage of trade credit is smaller loss given default as the supplier is likely better able to get

value out of the goods than a bank (Mian and Smith (1992)). I control for this by including the

days of inventory as measured by the inventory balance divided by 365. Theoretical models

predict that firms with more volatile sales (whether driven by seasonality or some other factor)

will use more trade credit to take advantage of transactions cost savings (Ferris (1981)). To

ensure that my measure of seasonality is capturing seasonality and not simply proxying for year-

over-year sales volatility, I also include a control for sales volatility. Suppliers may prefer to

35

invest in a relationship with firms with high growth to capture future profitable business

opportunities (Petersen and Rajan (1997)). To control for this, I include the firm’s one-year sales

growth rate. Finally, trade credit is traditionally thought of as a more expensive form of

financing than bank or public debt. To control for this, I include the total leverage of the firm.

I find that similar to debt, trade credit balances fluctuate with seasonal financing needs. As

seen in model (1), the balance of accounts payable is 11.9% (=0.013/0.109) and 10.1%

(=0.011/0.109) higher in the pre-peak and peak quarters than in the base quarter, respectively.

The post-peak quarter returns to levels consistent with the ‘other’ season. This effect is more

(less) pronounced for seasonal firms with more (less) severe seasonality as seen in model (2)

((3)). Further, in model (4) of Table VII, I find that seasonal firms finance 7.8% (=0.009/0.115)

more of their assets with trade credit, supporting the idea that trade credit is an important source

of short-term financing needs. This result is robust to the use of annual data (model (5)). The use

of trade credit for short-term financing needs supports the use of private debt as a means of

meeting short-term financing needs.

4.3. Cash as a source of short-term financing

The use of debt for short-term financing needs suggests that this choice minimizes financing

costs by avoiding the costs of cash holdings. However, a potential alternative explanation is that

managers may prefer to use cash holdings to minimize financing costs as cash holdings avoid

issuance costs. In this case, the evidence that debt is used for short-term financing may be

evidence that firms have exhausted the cash they have available for slack. To discriminate

between these two explanations, I next consider how cash balances fluctuate around seasonal

financing needs.

36

Table VIII presents the results of multivariate analyses that examine the use of cash holdings

for seasonal needs. The control variables used are based on the determinants of cash holdings as

documented in the literature. One important variable that is potentially correlated with

seasonality is the industry volatility of cash flows. This measure captures cash flow uncertainty

which leads to the precautionary holding of cash (Opler et al. (1999)). I control for this, as is

typical in the literature, using the industry median of the year-over-year coefficient of variation

in cash flows. This measure is more appropriate than the quarterly variation which would be

more related to seasonal fluctuations. The precautionary motive also suggests greater cash

holding for firms with high growth opportunities, which I proxy for with M/B. I control for size

as transactions costs models imply economies of scale in holding cash (Miller and Orr (1966)).

Liquid assets can be substituted for cash. For example, receivables can be factored to meet

liquidity needs. I include a control for net working capital as a proxy for liquid substitutes for

cash. Information asymmetries increase the cost of financial distress (Opler and Titman (1994)).

Asymmetries are likely higher in firms with high R&D expense, which I include as a control. I

also control for CapEx, leverage, and cash flow (Opler et al. (1999)).

In model (1), I present the results of seasonal fluctuations in cash holdings. I find that cash is

not used for short-term financing needs. For instance, despite high short-term financing needs,

cash holdings are highest at the conclusion of the peak quarter, with the cash balance 13.2%

(=0.015/0.114) higher than the ‘other’ quarter. This is likely from seasonally generated cash

flows. In this particular quarter, it is possible that cash balances decline due to financing needs

only to be masked by cash flows from increased revenue. As such, the cleanest test of the

seasonal use of cash holdings is the balance at the end of the pre-peak quarter when seasonal

financing needs are high and cash flows from seasonal revenue have not peaked. I find that cash

37

holdings are also significantly higher after the pre-peak quarter, even if the economic

significance of the difference is relatively small. Yet, if cash holdings were being used to finance

seasonal financing, then the cash holdings would be significantly lower. The pattern holds

whether the firm is highly seasonal or not, as seen in models (2) and (3). These patterns suggest

cash balances are not used for fluctuations in short-term financing needs.

Another way to test the extent to which cash is used is to compare the levels of cash for

seasonal and non-seasonal firms. If seasonal firms rely on cash for short-term financing needs,

then they should hold excess cash in time periods when short-term financing needs are low. In

this case seasonal firms should hold more cash if we average across all seasons. However,

models (4) and (5) show that seasonal firms hold significantly less cash than non-seasonal firms.

The evidence from both cash fluctuations and cash balances suggests that cash holdings are not

an important source of flexibility for short-term financing needs.

Taken together with the use of private debt, the findings to this point suggest that seasonal

firms rely on debt, especially private debt, and not cash for short-term financing needs. These

findings suggest that the relatively low issuance costs of private debt or lower than the costs of

carrying cash for short-term financing needs.

4.4. The impact of debt for short-term financing on the capital structure of the firm

The use of debt for short-term financing needs is consistent with financial flexibility.

However, according to financial flexibility theory, the use of debt for financing needs impacts

overall capital structure choice. For example, the use of debt as a source of slack requires

frequent interactions with the capital markets (DeAngelo, DeAngelo, and Whited (2011)). As

such, if firms are using debt for fluctuations in short-term financing needs, this requires frequent

interactions with the capital markets. As such, financial flexibility predicts these firms will adopt

38

more conservative long-term financial policies to be able to use debt for short-term financing

needs.

To test this prediction, I first consider the leverage of the firm. Firms can increase their

probability of receiving short-term financing by holding less total debt. In Table IX, I test

whether the use of debt by seasonal firms impacts their total leverage. The model is the same

specification used to analyze leverage in Table V. However, Table IX tests for cross-sectional

differences between seasonal and non-seasonal firms. Model (1) includes all firms and utilizes

annual data. The coefficient on the seasonal variable suggests that seasonal firms hold 7.8%

(=0.021/0.270) less book debt than their non-seasonal counterparts. A potential concern is that

annual data ignores the seasonal fluctuations in debt levels that I have previously shown.

However, as seen in model (2), the result is robust to the use of quarterly data. Further, both of

these specifications are robust to the use of market leverage as the primary measure as reported

in models (3) and (4). These findings are consistent with the reduction in total leverage to secure

short-term financing from the debt markets for finance seasonal investment, suggesting that the

use of debt for short-term financing needs is an important determinant of a firm’s target leverage.

In addition to levels of leverage, firms also have been shown to have target debt maturities

(e.g. Barclay and Smith (1995)). Seasonal firms can offset frequent trips to the capital markets

resulting from short-term debt by using long-term debt with a longer average maturity. To test

these predictions, Table X reports the results of the difference in the maturity of long-term debt

for seasonal and non-seasonal firms. The model uses the same control variables used in the short-

term debt model (Table VI). However, the dependent variable here is long-term debt due in the

next three years as a percent of total long-term debt. Model (1) shows that seasonal firms hold

4.3% (=0.023/0.534) less debt that is due in the following three years as a proportion of total

39

long-term debt than non-seasonal firms, suggesting that seasonal firms do offset the use of

private debt for short-term financing needs by holding long-term debt with a longer average

maturity. The result holds after the inclusion of industry fixed effects as presented in model (2).

In model (3), I control for any sample selection bias due to the existence of zero leverage firms

as was done in Table VI. The finding that seasonal firms have long-term debt with a longer

maturity holds when this selection bias is controlled for. Consistent financial flexibility theory,

this finding suggests that the increased use of debt for short-term financing needs impacts the

maturity of long-term debt in addition to total leverage.

However, if seasonal firms hold long-term debt to counter the use of short-term debt for

seasonal financing, then the effects should offset each other. I test this prediction in model (4) of

Table X. The dependent variable in this model is total debt that matures in the next three years as

a proportion of total debt. Consistent with the use of more short-term debt and long-term debt

with a longer average maturity offsetting one another, I find that there is no significant difference

between seasonal and non-seasonal firms in the proportion of their total debt that matures in the

next three years. Similar to the other debt maturity models, this finding is robust to industry fixed

effects and a Heckman specification controlling for the potential sample selection bias (models

(5) and (6), respectively). This finding is further supportive of debt use for short-term financing

as an important factor in the selection of capital structure targets as predicted by the financial

flexibility hypothesis.

As a final test of the impact of short-term financing needs on capital structure targets, Table

XI presents the results of an estimation of the speed at which firms adjust toward their target

leverage. According to financial flexibility theory, when firms use debt for financing needs they

adjust toward a tradeoff-implied leverage target more slowly. Intuitively, if the debt is being used

40

for financing needs that are not included in the target estimation (as they are not a factor in

tradeoff models), it appears as if firms are not moving toward their target as quickly.

To test this prediction, I follow the methodology of estimating adjustment speeds using

partial adjustment models as in Flannery and Rangan (2006) but adapt the model for quarterly

data. The model estimates the portion of the distance from the tradeoff-implied leverage target

that is closed in a one quarter period. The characteristics of the firm that are determinants of

capital structure choice under tradeoff models are multiplied by the coefficient estimates to

proxy for the leverage target. Specifically, the model is estimated as follows:

i ,t 1 i ,t i ,t i ,t 1Lev X 1 Lev (4)

where evi,t+1 is the leverage of firm i in period t+1 and i,t is a vector of firm characteristics in

period t modeling the costs and benefits of leverage representing a trade-off generated leverage

target. These characteristics are similar to those used in previous leverage regressions.16

The

estimated speed of adjustment is given by ̂ , or one minus the estimated coefficient on the

lagged leverage variable. It is interpreted as the percent of the distance between a firm’s leverage

and their target leverage that is closed in a time period. For example, Flannery and Rangan

estimate that, on average, firms close 34% of the gap between their current leverage and their

leverage target annually.

Model (1) of Table XI estimates the model using book leverage as the measure of leverage

on both sides of the equation. The estimates of the speed of adjustment (one minus the

coefficient estimate) are presented in square brackets. In the average firm-quarter, a firm adjusts

11.6% of the difference between their leverage and their leverage target, which is in line with

16

The market adjusted return is the only variable from previous models that is not included in this model. This

variable is a control for the market timing theory of capital structure and as such, is not used to generate trade-off

based target leverage ratios.

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annual estimates suggesting that firms adjust towards a target leverage. Most estimates of

adjustment speeds are made using market leverage. However, estimates of adjustment speeds are

very comparable with either book or market values of leverage (Faulkender, Flannery, Hankins,

and Smith (2012)). Yet, to show that my findings are robust, I use market-based leverage values

in model (2), and the estimate of the speed of adjustment is similar to using book leverage.

Another issue is that lagged leverage is potentially endogenous when modeling leverage.

Acknowledging this, Flannery and Rangan (2006) model market leverage as the dependent

variable but instrument lagged book leverage for lagged market leverage on the right-hand side. I

follow their methodology in model (3) and show that the estimate of the speed of adjustment is

still similar. To this point, these results suggest that moving from annual to quarterly data does

not impact the estimation of the speed at which firms adjust toward target leverage ratios.

I next test whether the speeds vary across the seasons based on short-term financing needs. In

model (4) of Table XI, I interact the lagged measure of book leverage with an indicator for each

of the four seasons. The coefficient estimates represent the average adjustment speed in that

quarter. The result of the estimation is that the speed of adjustment significantly varies across the

seasons.17

Further, the speed of adjustment is negatively correlated with the level of short-term

financing needs. In the pre-peak quarter when seasonal investment needs are high and seasonal

cash flows are low, firms only adjust 5.6% of the distance from their target leverage. In the post-

peak quarter when investment needs are low, firms adjust 17.5% of the distance from their target

leverage. These patterns are robust to the use of market leverage as the primary measure (model

(5)) and instrumenting lagged book leverage for lagged market leverage (model (6)). These

17

An F-test of the equality of the four seasonal estimates of the speed of adjustment toward a target leverage is

significantly rejected at the 95% level.

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findings support the idea that debt is partially used for short-term financing needs and not

adjusting toward a target leverage ratio.

Collectively, the findings that seasonal firms have lower leverage, use long-term debt with a

longer average maturity and demonstrate variation in the speed at which they adjust toward their

target leverage based on financing needs provide further evidence consistent with financial

flexibility models. Debt, especially private debt, is an important financing source for seasonal

financing needs. However, other financial policies are adjusted to ensure the ability to receive

financing with frequent trips to the capital markets. Collectively, this evidence is consistent with

predictions from the flexibility model that financing of investment is an important consideration

in setting capital structure policies.

5. Conclusion

I identify firms that face seasonal demand among public U.S. firms and provide evidence on

how intra-year fluctuations in investment needs are financed. Seasonal firms, which comprise

over 22% of my sample firms, are an important segment of firms in the U.S. economy. They are

concentrated in industries such as retail, apparel, recreation, and beer and wine. By identifying

these firms and when they face peak demand, I am able to provide evidence on the sources of

financing used