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Financial Flexibility and Short-Term FinancingNeeds: Evidence from Seasonal Firms
Item Type text; Electronic Dissertation
Authors Fairhurst, Douglas J.
Publisher The University of Arizona.
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Link to Item http://hdl.handle.net/10150/316777
http://hdl.handle.net/10150/316777
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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
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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
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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
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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.
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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
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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
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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.
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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.
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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
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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.
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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.
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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.
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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
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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)).
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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.
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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,
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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.
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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)).
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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
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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.
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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.
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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
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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.
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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.
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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
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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
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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.
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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.
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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.
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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).
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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
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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
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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.
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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
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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.
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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
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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
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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
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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
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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