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Efficient working capital management, financial constraints and
firm value: A text-based analysis
Sandip Dhole, Sagarika Mishra, Ananda Mohan Pal
PII:
S0927-538X(19)30290-2
DOI:
https://doi.org/10.1016/j.pacfin.2019.101212
Reference:
PACFIN 101212
To appear in:
Pacific-Basin Finance Journal
Received date:
14 May 2019
Revised date:
19 August 2019
Accepted date:
24 September 2019
Please cite this article as: S. Dhole, S. Mishra and A.M. Pal, Efficient working capital
management, financial constraints and firm value: A text-based analysis, Pacific-Basin
Finance Journal(2018), https://doi.org/10.1016/j.pacfin.2019.101212
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Efficient Working Capital Management, Financial Constraints and Firm Value: A Text-Based
Analysis
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Sandip Dhole sandip.dhole@monash.edu, Sagarika Mishra2,∗ mishra@deakin.edu.au, Ananda Mohan Pal3
ampbm@caluniv.ac.in
1
Monash Business School Monash University
2
Department of Finance Deakin University
3
Department of Business Management University of Calcutta
∗
Corresponding author.
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Abstract
In this paper we examine the association between efficient working capital management and
financial constraints for a sample of Australian firms. Using a text-based measure of financial
constraints, we show that efficient working capital management is associated with lower financial
constraints in firms in the next two to three years. Ours is the first study to use a text-based measure
of financial constraints for Australian firms. We also show that the negative association between
financial constraints and future share price is significantly weakened for firms with efficient
working capital management, suggesting that such firms have higher market valuations despite
being financially constrained. Finally, analysts seem take into account working capital
management of firms when setting the one year ahead target price.
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Keywords: Financial Constraints, Working Capital Management, Future Stock Price, Analyst Target
Price
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JEL Classification: G12, G32, M41
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1
Introduction
There is increasing scrutiny of financial performance that’s associated with managing
working capital. And, even though it does not appear on an income statement, working
capital can amount to significant revenue for a company.1
In this paper, we study the importance of working capital management in reducing the
likelihood of future financial constraints and signalling higher firm value. Working capital
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management is important because it enables firms to free up cash and improve liquidity. Deloof
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(2003) shows that efficient management of the cash conversion cycle can improve corporate
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profitability significantly. Baños-Caballero et al. (2012) show that an optimal level of working
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capital is associated with higher profitability of Spanish SMEs. Aktas et al. (2015) also find that
an optimal level of working capital improves operating performance. These studies highlight the
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importance of good working capital management.
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The importance of working capital management is highlighted by the fact that firms often
struggle to manage their working capital effectively, and thereby lose significant opportunities to
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create value. Indeed, a recent survey by PriceWaterhouseCoopers revealed that 203 companies in
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Australia and New Zealand saw deteriorations in their working capital performance by more than 5
percent in 2017.2 The survey further revealed that Australian and New Zealand firms could unlock
$90.60 billion by improving their working capital management practices. Given the importance of
working capital, we ask whether efficient working capital reduces the likelihood of firms being
financially constrained in the future and, whether financially constrained firms with more efficient
working capital management have higher future prices.
1
Cassio Calil, Managing Director of Corporate Client International Banking for J.P. Morgan Commercial Banking, `` Optimizing
Working Capital'' webinar, September 24, 2014; https://www.chase.com/commercial-bank/executive-connect/working-capitalwebinar.
2
See https://www.pwc.com.au/publications/pdf/working-capital-survey-nov18.pdf
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There is anecdotal evidence of the benefits of efficient working management. In 1994, Dell,
Inc. turned to the management of its cash conversion cycle in order to reverse its recent losses.
That strategy contributed to Dell growing its return on invested capital to 167 percent, 10 times the
industry average, in the second quarter of 1997.3
Working capital management involves both choosing the amount to invest and managing the
cash conversion cycle (the time it takes to convert working capital into cash). It is not enough for
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companies to invest in working capital. Deciding the amount to invest in working capital is
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important because over-investment in working capital may increase idle investment and therefore
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be value-reducing. This is consistent with the results of some prior research (Kieschnick et al.,
2013; and de Almeida and Eid Jr, 2014) that incremental investment in working capital are could
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be value reducing. To manage working capital effectively, it is also important for firms to manage
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the cash conversion cycle, because that creates liquidity. Given the importance of both level of
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working capital and the cash conversion cycle, we focus on both these aspects of working capital
management in this study. This is an important feature of our study. Specifically, extant research
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on working capital management usually focuses on only one aspect of working capital management
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(for example, Ding et al., 2013 define working capital management in terms of investment in
working capital, whereas Baños-Caballero et al. (2014) define it in terms of the cash conversion
cycle). By focusing on both the level of investment and cash conversion cycle, we provide a more
comprehensive analysis of the importance of working capital.
We focus on whether efficient working capital management affects the likelihood of future
financial constraints. Campello et al. (2010) and Almeida and Campello (2007) argue that financial
constraints negatively affect future performance. Financially constrained firms often pass up potentially profitable investment opportunities, and the ability of firms to avail external financing.
3
See https://www.strategy-business.com/article/9571?gko=be3fe.
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Given the adverse consequences of financial constraints, prior research has identified factors that
reduce the likelihood of financial constraints. For instance, Erel et al. (2015) show that financially
constrained target firms experience financial relief after being acquired. Ratti et al. (2008) show
that bank concentration could reduce financial constraints, and Love (2003) shows that financial
liberalisation could reduce financial constraints. These studies thus show that some external factors
could reduce financial constraints. Internal capital markets could also reduce the likelihood of
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financial constraints, as Shin and Park (1999) and Desai et al. (2007) argue. Using a sample of
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Australian firms and a text-based measure of financial constraints developed by Bodnaruk et al.
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(2015), we show that efficient working capital management reduces the likelihood of the firm
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facing financial constraints up to three years into the future.
We next examine the valuation implications of working capital, by studying whether the
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negative effect of financial constraints on future share prices is less for firms with more efficient
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working capital management. Our research is based on findings in prior research (Denis and
Sibilkov, 2009) that cash holdings enable financially constrained firms to make (value increasing)
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investments. We find that, while there is a negative association between financial constraints and
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one-year ahead share price, this negative association becomes weaker for firms with more efficient
working capital management. In important additional analysis, we show that analysts seem to
recognise the importance of working capital management for financially constrained firms, as
evidenced by higher target prices for such firms.
We focus on Australian firms for the following reasons. First, Australia has a developed capital
market with strong investor protection laws (Leuz et al., 2003). Since firms operating in capital
markets with strong investor protection laws tend to manage earnings less (Leuz et al., 2003) and
have more informative earnings announcements (DeFond et al., 2007), financial statements of
Australian firms are of high quality and useful for analysis. Second, despite the fact that Australia
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has a developed capital market, it has a significant number of small firms, which are more likely
to be affected by financial constraints (Belghitar and Khan, 2013). Indeed, the mean total assets
for our sample of firms is AUD 45.90 million. This contrasts with USD 7,270 million for US firms
(Glendening et al., 2019). The fact that a significant number of small firms is listed on the
Australian Stock Exchange (ASX) and the fact that Australia has a developed capital market makes
it an interesting institutional setting to study financial constraints.
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Further, (poor) working capital management is an issue of relevance for Australian companies.
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Indeed, as discussed above, the 2018 working capital survey of Pricewaterhouse Coopers finds
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that about 50% of the surveyed Australian and New Zealand companies saw their working capital
performance deteriorate by more than 5 per cent between 2017 and 2018, and that these companies
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could unlock $90.6 billion in value by managing their working capital more effectively. These
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survey results make our research setting of particular relevance.
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Our study relates to Ding et al. (2013) and Baños-Caballero et al. (2014). However, in our
opinion, our study differs significantly from these studies. Ding et al. (2013) is more closely related
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to our study, because they analyse the association between financial constraints and working
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capital investment. They argue that high investment in working capital allows firms to invest more
during periods of financial constraint. Ding et al. (2013) focus only on the investment in working
capital; they do not study the importance of the efficiency of working capital management. In
contrast, we consider both the level of working capital (through measures like cash to asset ratio,
and current ratio) and also the efficiency of working capital management (cash conversion cycle).
Investment in working capital, by itself, does not measure the efficiency of working capital
management. Indeed, high level of working capital could suggest that the firm has idle investment
or poor cash conversion issues, as it is not able to generate enough cash from its working capital.
Second, Ding et al. (2013) study how investment in working capital allows financially constrained
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firms to make investments in the current period. This research question is fundamentally different
from ours, since we study whether current working capital management is associated with future
financial constraints, and whether more efficient working capital management allows financially
constrained firms to enjoy relatively higher valuations. Ding et al. (2013) do not study the valuation
implications of working capital management. Finally, unlike Ding et al. (2013), who measure
financial constraints by the ratio of current cash flow to capital stock, we use a novel text-based
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measure of financial constraints that detects financial constraints more accurately than other
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measures (Bodnaruk et al., 2015).
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Our study also differs from Baños-Caballero et al. (2014). Baños-Caballero et al. (2014)
primarily analyse the association between net trading cycle (working capital efficiency) and firm
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performance. They find a U-shaped relation, which suggests that there is a certain level of working
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capital efficiency that improves firm performance. They also find that financial constraints make
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the inflection point lower, i.e., a shorter trade cycle is associated with superior firm performance.
In other words, financial constraints is a moderating variable in Baños-Caballero et al. (2014). In
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contrast, it is one of the main variables of analysis in our paper – we focus explicitly on the
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association between working capital management and financial constraints. Thus our research
objective is different from Baños-Caballero et al. (2014). Second, while net trade cycle is an
important measure of working capital management, it does not describe the level of investment in
working capital. A more complete description of working capital management considers both the
efficiency and level of working capital. Our paper considers both aspects. Third, unlike BañosCaballero et al. (2014), whose financial constraint measure is based on financial statement
variables, we use a more recent text-based measure, as described above.
We make the following contributions to extant literature. First, we provide a more complete
analysis of the effect of working capital management on future financial constraints. Most extant
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literature (for example, Ding et al., 2013; Baños-Caballero et al., 2014) focus on either working
capital investment or the trade cycle. By studying both the cash ratio and the cash conversion cycle,
we present comprehensive evidence on the importance of working capital management in reducing
the likelihood of financial constraints and improving firm value. This analysis is important as increasing working capital investment (or reducing trade cycle) by itself is not always optimal (as
the results of Kieschnick et al., 2013 show).
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Ours is also the first study to use a text-based measure of financial constraints for Australian
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firms. As Bodnaruk et al. (2015) note, this measure has several advantages over other commonly
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used measures, as it captures financial constraint more accurately by focusing specifically on the
language used by financially constrained firms in their annual reports.
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We also contribute to the literature by demonstrating that financially constrained firms with
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efficient working capital management have higher market valuations than those with less efficient
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working capital management. While prior studies (for ex- ample, Denis and Sibilkov, 2009) allude
to the importance of cash holdings to finance investment, ours is the first study, to our knowledge,
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constrained firms.
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to demonstrate the valuation benefits of efficient working capital management for financially
The paper is organised as follows. We discuss prior literature, build our hypotheses and describe our research methodology in Section 2; we describe the data in Section 3, and present the
results of our empirical estimation in Section 4. We describe our robustness tests in Section 5, and
conclude the paper in Section 6.
2
2.1
Literature, Hypotheses and Research Methodology
Working Capital Management
Working capital management significantly impacts firm performance and valuation. Indeed, net
working capital accounts for a significant proportion of a firm’s capital employed. Firms maintain
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their investment in working capital for many reasons. Holding a certain inventory balance at all
times enables firms to reduce supply costs and protect against price fluctuations (Blinder and
Maccini, 1991). Schiff and Lieber (1974) argue that holding inventory allows firms to service
customers better and avoid high production costs that arise from fluctuations in production.
Similarly, allowing trade credit is an important policy to enhance sales and profits. In fact, Emery
(1984) argues that trade credit is a more profitable short-term investment than marketable securi-
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ties. Investment in working capital can provide firms with liquidity, insuring it against the adverse
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effects of a shortfall of cash (Fazzari and Petersen, 1993). However, too much of investment in
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working capital could also create problems for firms in terms of profitability (Deloof, 2003), as
idle investment reduces return and increases cost of financing. Therefore, an optimal investment
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in working capital is desirable.
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While investment in working capital is an important part of working capital management, it is
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also important for firms to convert working capital accounts to cash. This is because the inability
to convert working capital to cash could create liquidity problems for firms. Therefore, efficient
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working capital management also involves managing the cash conversion cycle. Prior research
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identifies the importance of the management of the cash conversion cycle for small businesses,
which could be cash constrained (Belghitar and Khan, 2013) and firms with significant growth
opportunities (Campello et al., 2011). Using a case study of a listed Brazilian company, Zeidan and
Shapir (2017) show that a shorter cash conversion cycle increases shareholder value. The evidence
described above thus suggests that efficient working capital management can affect shareholder
value.
2.2
Financial Constraints
Financial constraints adversely affect firms’ prospects. In a survey of Chief Financial Officers
(CFOs), Campello et al. (2010) report that financial constraints cause firms to reduce their invest8
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ment in tech spending, capital expenditure and employment. Further, such firms pass up potentially
profitable investment opportunities and draw more heavily upon lines of credit. Musso and Schiavo
(2008) show that the presence of financial constraints significantly increases the likelihood of firms
exiting the market. The evidence that financial constraints affect investment is also backed up by
prior empirical research. For example, Almeida and Campello (2007) show that there is a positive
association between asset tangibility and access to external capital for financially con- strained
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firms, suggesting that when firms face financial constraints, higher (lower) tangibility of assets
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makes it easier (more difficult) to obtain external financing. Fazzari et al. (1988) report that
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investment-cash flow sensitivity is high for financially constrained firms and Fazzari and Petersen
(1993) show that financial constraints adversely affect working capital. Campello and Chen (2010)
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show that the operating earnings and capital expenditures of financially constrained firms are
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significantly affected during periods of negative macro-economic shocks, suggesting that the effect
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of financial constraints is more prominent during bad economic periods. Using a sample of
Japanese firms, Gan (2007) shows that collateral losses restrict the ability of firms to obtain bank
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credit. This suggests that the risk of financial constraint is real. Consistent with this idea, Whited
2.3
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and Wu (2006) show that financial constraint is a priced risk factor.
Hypotheses
In light of the above discussion, it becomes important to ask whether there are factors that
reduce the likelihood (or extent) of financial constraints. Erel et al. (2015) argue that acquisitions
could play a role in alleviating the financial constraints problem. Specifically, they report that
financially constrained target firms experienced significant reductions in the level of cash held, the
sensitivity of cash to cash flow, and the sensitivity of investment to cash flow, subsequent to being
acquired. Ratti et al. (2008) argue that greater market power increases banks’ incentive to produce
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information on potential borrowers. Consistent with this argument, they find that greater bank
concentration reduces the extent of financial constraints. Laeven (2003) and Love (2003) present
evidence that financial liberalisation and financial development reduce financial constraints.
The evidence discussed above shows the role of external factors in reducing financial
constraints. In this paper, we focus on an important firm-specific factor – the role of working capital
management. Much prior research (for example, Almeida et al., 2004; and Faulkender and Wang,
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2006) shows that firms facing financial constraints tend to accumulate cash, suggesting that the
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availability of cash can help firms tide over periods of financial constraints. However, these studies
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do not examine how the management of cash affects the likelihood of the firm facing financial
constraints in future. This is an important question, since it could potentially inform practice on
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how to reduce the likelihood of financial constraints.
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Efficient working capital management is important because it enables companies to remain
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liquid and financially viable over the short and long-term. Indeed, Aktas et al. (2015) and Deloof
(2003) find that efficient working capital management is value enhancing. In a study of Chinese
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firms, Ding et al. (2013) show that good working capital management could alleviate the effect of
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financial constraints. These studies highlight the importance of efficient working capital
management. Based on this research, we could argue that firms with more efficient working capital
management would be less likely to face financial constraints.
Efficient management of working capital minimises the idle investment in working capital and,
consequently, reduces the requirement of funds to finance the working capital. In this way, it
increases the return on working capital investment and enables the firm to bear higher cost of
borrowing. Financial constraints arise when the need for finance is high, and the ability to bear the
cost of finance is low. When the need for finance is less and the ability to bear cost of finance
higher, the likelihood of financial constraints becomes lower.
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Despite the theoretical reasons for the value-enhancing effects of efficient working capital
management, some studies find that investment in working capital does not enhance firm value.
For example, Kieschnick et al. (2013) show that a dollar investment in working capital increases
shareholder value by less than a dollar, and that, an incremental dollar invested in financially
unconstrained firms actually reduces firm value. This is because the valuation of an incremental
investment in working capital is influenced by several factors, such as future sales expectation,
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bankruptcy risk, debt load, financial constraints, etc. Indeed, Fazzari and Petersen (2003) note that
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financial constraints could actually depress working capital investment. Similarly, using a sample
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of Brazilian firms, de Almeida and Eid Jr (2014) find that investment in working capital is actually
value reducing.
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Based on the discussion above, we state our first (refutable) hypothesis in the null form as
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follows:
constraints.
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Hypothesis 1: Efficient working capital management is not associated with future financial
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Prior research finds that financial constraints affect firm value. Lamont et al. (2001) show that
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financially constrained firms have low average stock returns, suggesting that financial constraints
adversely affect share price growth and, hence, firm value. As Campello et al. (2010) note, firms
tend to pass up potentially profitable investment opportunities when they are financially
constrained, thereby adversely affecting their future prospects and valuations. Desai et al. (2007)
show that affiliates of US multinational firms increase their investment, assets and sales, relative
to local firms when the local currency depreciates. One of the reasons for this is the availability of
cash from internal capital markets, which local firms do not have access to. Shin and Park (1999)
examine the benefits of internal capital markets in Korea and find that firms affiliated to Korean
chaebols are less likely to be financially constrained, owing to access to internal capital markets.
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Similarly, focusing on the 2007-2009 Global Financial Crisis, Kuppuswamy and Villalonga (2015)
show that there is a value-increasing effect of corporate diversification, owing to the financing and
investment advantages of diversification. Denis and Sibilkov (2009) show that accumulated cash
holdings allow financially constrained firms to have higher investments.
Building on the above literature, we next examine how efficient working capital management
affects the valuations of financially constrained firms. One of the important objectives of working
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capital management is to improve liquidity and ensure that assets are put to their most productive
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use.4 As the above discussion suggests, firms that are able to management their working capital
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effectively, would be more likely to have higher levels of investment when they are financially
constrained. Denis and Sibilkov (2009) show that there is a positive association between
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investment and value for financially constrained firms that have higher cash holdings. This leads
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to our second hypothesis (in the alternate form).
share prices.
Research Methodology
Measuring Financial Constraints
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2.4.1
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2.4
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Hypothesis 2: Firms with more efficient working capital management have higher future
Most financial constraints studies use accounting variables to measure financial constraint in a firm.
However, textual analysis could also help identify financial constraints. Kaplan and Zingales (1997)
(hereafter KZ) and Hadlock and Pierce (2010) (hereafter HP) examine 10-K text to identify instances where
managers discuss difficulties in obtaining external financing, liquidity problems, or forced reduction in
investment. KZ and HP classify these firms as financially constrained and use accounting characteristics to
predict whether firms will be classified as financially constrained within their framework. Because of the timeconsuming nature of analysis, they focused on small samples of firms.
4
See https://www.cfainstitute.org/membership/professional-development/refresher-readings/2019/working-capital-management
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Hoberg and Maksimovic (2014) also use textual analysis of 10-Ks focus on the liquidity and capital
utilsation subsection of the Management Discussion and Analysis (MD&A) section of the annual report to
identify financial constraints. Buehlmaier and Whited (2018) use manual searches of news articles that feature
financially constrained firms to construct their text-based measure of financial constraints. Their measures
extend the measures of Hoberg and Maksimovic (2014).
Bodnaruk et al (2015) measure of financial constraint expands on the KZ and HP’s approach of using
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subjective information to measure firms' financial constraint. They measure the level of constraints using the
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frequency of negative words within the entire 10-Ks. This is because the tone of managers' words capture
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subtle signs that the company will face greater future financial challenges. Following the idea we use
Bodnauk et al (2015) measure of financial constraint to do our analysis.
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To generate financial constraints, we start with the annual reports of all the Australian Stock
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Exchange Listed firms for the period 2000-2016, obtained from the Connect4 database. We con-
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verted the annual reports to the text format to facilitate our textual analysis. Specifically, graphics
and images in the pdf or word version of the annual report have little textual content. This approach
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is consistent with Bodnaruk et al. (2015).
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We use the financial constraint dictionary of Bodnaruk et al. (2015) to search the raw text files
and construct our financial constraint variable. This dictionary has a list of 184 words (we provide
this word list in Appendix B) commonly used by firms facing financial constraints. We counted
the frequency of the financial constraint words in the annual reports, and also the total number of
words in the annual reports. Finally, we generated our financial constraint variable – the percentage
of financial constraint in the annual report. For example, if a firm has 5,000 words in its 2016
annual report, of which 500 words are financial constraint words, the financial constraint measure
for 2016 for that firm would be 0.10.
2.4.2
Measuring Working Capital Management
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Since working capital management involves both the amount of working capital, and the conversion of working capital into cash, our measures reflect both these aspects of working capital
management. In this study, we use two main measures of working capital management – one
designed to measure the amount of working capital, and one to measure the conversion of working
capital to cash. Our measures are cash ratio (the ratio of cash to total assets), and the cash
conversion cycle (days receivables plus days inventory minus days payable). Our measures of
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working capital management are based on prior research (for example, Deloof, 2003; Ding et al.,
Model to Test Hypothesis 1
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2.4.3
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2011).
We estimate the following model to test Hypothesis 1:
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𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−𝑛 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑𝑗 𝛾𝑗 𝐼𝑛𝑑𝑗 +
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∑𝑡 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
(1)
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In equation (1) above, FC it is our measure of financial constraints described above. The
variable of interest in equation (1) above is WCMit−n . This variable measures the efficiency of
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working capital. More efficient working capital is captured by higher values of current ratio, quick
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ratio, and cash ratio, and lower values of cash conversion cycle. H1 predicts that the coefficient β1
is negative for cash ratio, and positive for cash conversion cycle. We include size (measured by
the natural logarithm of total assets) as a control variable, following prior research (for example,
Hadlock and Pierce, 2010) that finds that size is associated with financial constraints. We also
control for market-to-book ratio (measured as the ratio of market capitalisation to the book value
of equity), since a high market-to-book ratio represent growth opportunities, and leverage (ratio of
long-term and short-term debt to equity), following Denis and Sibilkov (2009). We also control for
profitability (ROA, defined as the ratio of net income to total assets) and stock return volatility
(StdRet, defined as the 12-month standard deviation of monthly stock returns). Finally, we include
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industry and year fixed effects. We present the list of constraining words in Table 1 and detailed
variable definitions in Table 2.
(Insert Tables 1 and 2 here)
2.4.4
Model to Test Hypothesis 2
As mentioned above, Hypothesis 2, predicts that financially constrained firms with more efficient working capital management have higher future prices than those with less efficient working
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capital management. In order to test this hypothesis, we first sort firms into terciles of working
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capital management (measured as above). Then, for each tercile, we estimate the following model:
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𝑃𝑖𝑡+1 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑𝑗 𝛾𝑗 𝐼𝑛𝑑𝑗 +
(2)
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∑𝑡 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
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In equation (2) above, Pit+1, measures the firm’s share price in year t + 1. The variable of interest
is FC it . This variable (defined above), measures financial constraint in year t. Based on the
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association between financial constraints and profitability and investment described above, we
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expect a negative coefficient β1. However, if efficient working capital management improves firm
value, then the negative association between P and FC would become weaker for firms with more
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efficient working capital management. Consequently, we expect β1 to be more negative for firms
in the lowest (highest) tercile of cash ratio (cash conversion cycle).
3
Description of Data
Our empirical analysis is based on a sample of firms listed on the Australian Stock Exchange
during the period 2000-2016. We obtain the annual reports from Thomson Reuters' Connect4
database, financial statement data and annual stock price data from the Morningstar DatAnalysis
Premium database. We obtain stock returns data from the SIRCA Monthly Prices database.
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Finally, we obtain analyst target price data from IBES.5 Our initial sample consists of 8,010 firmyear observations in the intersection of the DatAnalysis Premium, Connect4 and SIRCA Price
databases. This initial sample has a significant number of missing values of the dependent and
independent variables used in the study. For example, the number of missing values of cash
conversion cycle amounts to more than 40 per cent of the initial sample. We therefore lose 3,448
observations with missing values of the variables of interest. We exclude financial institutions and
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utility firms from our sample. This leads to a further loss of 160 observations. Following
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convention, we winsorise our variables of interest in the 1st and 99th percentiles of their
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distribution. Our sample consists of 4,422 firm-year observations with non-missing values of our
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financial constraint variable. We present our sample selection criteria in Panel A of Table 3.
Table 3, Panel B presents descriptive statistics for some important variables. We see that the
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mean (median) value of FC is 0.427 (0.426). This suggests that 0.43 percent of the words used by
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Australian firms are constraining words, as defined by Bodnaruk et al. (2015). This contrasts with
the results reported by Bodnaruk et al. (2015) for their sample of US firms – Bodnaruk et al. (2015)
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report a mean value of 0.69 for their full sample. Since ours is the first paper to use this measure
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of financial constraints for Australian firms, we do not have a benchmark to evaluate these
numbers. The higher proportion of constraining words for US firms could be on account of the
fact that the US environment is highly litigious. This would naturally cause management to use
more defensive language. However, untabulated results show that the financial constraint measure
is significantly correlated with financial leverage (0.329). This is consistent with the fact that debt
is positively correlated with financial constraint (Buehlmaier and Whitted 2018). We further find
that our measure of financial constraints is significantly negatively correlated with return on assets
5
We use the IBES data only for additional analyses. Our final sample is much smaller if we use the IBES data.
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(-0.061), consistent with financial constraints being negatively associated with profitability
(Campello et al., 2010). This validates our financial constraint measure.
The mean (median) cash conversion cycle is 87.442 (20.186) days. Since days payables had
a large number of extreme values, we winsorised days payable at the 1st and 90th percentiles of
its distribution.6 The mean (median) cash ratio is 0.222 (0.126), suggesting that the average cash
balance of Australian firms is approximately 22 percent. The mean (median) current ratio is 5.482
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(1.820), and the mean (median) quick ratio is 5.128 (1.360). This is consistent with Xu et al.
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(2013).
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The mean total assets of our sample firms is $45.90 million, suggesting that larger firms
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dominate our sample. The mean market-to-book ratio is 3.146, and the mean leverage is 0.777.
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Our descriptive statistics are generally consistent with prior studies that use Australian data – for
in- stance, Fergusson et al. (2019), Xu et al. (2013), Kent et al. (2013).
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(Insert Table 3 here)
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We present the industry distribution of our sample in Panel C of Table 3. The Table shows that
firms in the materials sector account for most observations, followed by firms in the energy,
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industrials, healthcare and consumer discretionary sectors. This is consistent with the general
distribution of firms in the Morningstar DatAnalysis Premium database.
4
4.1
Empirical Tests
Working Capital Management and Financial Constraints
We report the results for Hypothesis 1 (the association between working capital management
and financial constraints) in Table 4. Recall that we estimate equation (1) to test the hypothesis and
6
Winsorising at the conventional 1st and 99th percentiles produces a large negative mean cash conversion cycle, although the
median is still positive. Our results do not change as a result of winsorising days payable at the 90th percentile.
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that we predict a negative (positive) coefficient on WCM for cash ratio (cash conversion cycle) –
Table 4, Panel A (Panel B). Columns 1-3 of Panel A present results for WCMt−1 – WCMt−3
respectively. Consistent with expectations, we find that the coefficient on WCM is negative in all
three columns (coefficient=–0.031, -0.036, and -0.042 respectively; p-value=0.000 in all columns).
This suggests that firms with higher cash ratios are less likely to face financial constraints in the
next three years. Consistent with Bodnaruk et al. (2015), we find that the coefficients on MTB and
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Size are negative (coefficient=-0.001, and -0.008 respectively; p-value=0.001 and 0.000
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respectively).7 Consistent with Korajczyk and Levy (2003), we find that the coefficient on Lev is
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positive (coefficient=0.003; p-value=0.038).
(Insert Table 4 here)
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From Panel B, we note that the coefficient on WCM is positive for all columns 1 and 2
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(coefficient=0.026, and respectively; p-value=0.006, and 0.001 respectively). This suggests that
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high cash conversion cycle increases the extent of financial constraint in the next two years. Stated
differently, our results suggest that low cash conversion cycle reduces the extent of financial
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constraint over the next two years. This result is consistent with Panel A, and further supports
above.
4.2
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Hypothesis 1. The signs and significances on the control variables are consistent with Panel A
Working Capital Management and Future Share Price
We report the results for Hypothesis 2 (examining how efficient working capital management
affects the future price of financially constrained firms) in Table 5. Recall that we divide our sample
into terciles of the cash ratio and cash conversion cycle and estimate equation (2) for each tercile
in order to test Hypothesis 2. Hypothesis 2 predicts that the negative association between financial
constraints and future share price would be less negative for the highest (lowest) tercile of the cash
7
For brevity, we only describe results for the first column.
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ratio (cash conversion cycle). We present results for the cash ratio in Panel A of Table 5. Columns
1-3 present the results of the estimation of equation (2) for tercile 1-3 of the cash ratio respectively.
From Panel A, we note that the coefficient on FC is negative in column 1 (coefficient=-2.961; pvalue=0.023), suggesting that financially constrained firms have lower valuations. However, when
we consider Columns 2 and 3, we see that the coefficient on FC is not significant (p-value=0.406
and 0.613). This suggests that for firms with high cash ratios, financial constraints do not
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negatively affect firm value, consistent with the idea that good working capital management reduces
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the negative impact of financial constraints on firm value. We note that the coefficient on MTB,
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Size, and StdRet are positive (coefficient=0.025, 0.228, and 0.003 respectively; p-value=0.001,
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0.000, and 0.014). These results are consistent with prior research (see for example, Barth, 2017).
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(Insert Table 5 here)
From Panel B, we see that the coefficient on FC is negative for column 2 (coefficient=-5.695; p-
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value=0.003), suggesting that financially constrained firms have lower valuations when their cash
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conversion cycles are moderately high. We note, however, that the coefficient on FC in Column 1
is positive, suggesting that financial constraints do not negatively affect firm value for firms with
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low cash conversion cycles (coefficient=1.127; p-values=0.090). Untabulated results suggest that
the coefficient on column 1 is significantly greater than that in Column 3 (p-value=0.000),
suggesting that the market views efficient working capital management favourably. Seen in
conjunction with the results in Table 4, these results highlight the importance of good working
capital management, and echo the sentiments of practitioners described above.
4.3
Additional Analysis
We now examine whether financial analysts recognise the importance of good working capital
management. Specifically, do analysts consider the efficiency of a firm’s working capital
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management in setting target prices, when the firm is financially constrained? This is an important
analysis because analysts are superior users of financial information and serve an important
intermediary role in capital markets. A large volume of research (see for example, Beyer et al.,
2010) shows that financial analysts shape market expectations by generating quality equity
recommendation and forecasts. To perform this additional test, we replace Pt+1 in equation (2) with
analysts’ one-year ahead target prices (AFPt+1) and then we follow the procedure described above
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for test- ing Hypothesis 2. If analysts do recognise the importance of good working capital
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management, we predict that their target prices would be lower for firms with lower (higher) cash
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ratios (cash conversion cycles). We present these results in Table 6.
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We present results for the three terciles of cash ratio in the three columns of Panel A, Table 6.
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We see from Table 8 that the coefficient on FC is negative for the lowest tercile of cash ratio
(coefficient=- 0.949; p-value=0.036). It is not significant in Columns 2 and 3 (p-value=0.0.422 and
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0.572 respectively). This is consistent with the results reported in Table 5, and suggests that
analysts set lower target prices for financially constrained firms with lower cash ratios, consistent
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with the idea that such firms are more likely to face liquidity issues in the future. In contrast, the
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insignificant coefficient on FC for the high cash ratio firms suggests that analysts believe that
financial constraints are unlikely to have lasting impacts on these firms, since they have higher
liquidity and would likely be able to see through the period of financial constraints. The signs and
significances of the control variables are consistent with those reported in Table 5 above.
(Insert Table 8 here)
We present results for the cash conversion cycle in Panel B, Table 8. As in Panel A above, we
present results for the three terciles of the cash conversion cycle in the three columns of Panel B.
We note from the Table, that the coefficient on FC is negative and significant in column 2
(coefficient=-1.160 respectively; p-value=0.001). It is not significant in Column 1 (p20
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value=0.140). Although the coefficient on FC is not significant in column 3 (p-value=0.843), the
fact that the coefficient is not significant in column 1 suggests that analysts believe that financially
constrained firms with low cash conversion cycles to be less likely to face liquidity issues in the
future. Therefore, they do not lower target prices for such firms. Our results also suggest that
analysts do not seem to consider very high cash conversion cycles to impact value when firms are
financially constrained. By itself, this result is difficult to explain. However, when we consider the
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results for cash ratio in Panel A above, we could argue that if financially constrained firms with
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high cash conversion cycles have large cash balances, it likely alleviates analysts’ concerns about
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liquidity for such firms. Our results in Table 6 generally verify the main results in Table 5 above.
They also contribute to the literature by providing evidence that analysts do appear to factor in
Robustness Tests
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5
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working capital, when issuing target price forecasts.
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We perform important robustness tests in this study. Specifically, we replicate our main results
by using an alternate measure of financial constraints. Next, we use alternate measures of working
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capital management. We also test the robustness of our results by explicitly controlling for the
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period of the Global Financial Crisis. Finally, we acknowledge that financial constraints might
influence working capital management and test whether the direction of the association flows from
financial constraints to working capital, rather than the other way, as presented in our main tests.
5.1
Alternate Measures of Working Capital Management
We now replicate Table 4 with an alternate proxy of financial constraints – the current ratio
(Laurent, 1979). Higher values of current ratio suggests higher liquidity, and thus better working
capital management. We present these results in Tables 7 and 8. Table 7 shows the results of
Hypothesis 1 – the association between working capital management and financial constraints.
Recall that Hypothesis 1 predicts that firms with better working capital management are less likely
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to have financial constraints in the future. Accordingly, we expect a negative association between
current ratio and financial constraints, i.e., we expect the coefficient β1 in equation (1) to be
negative. We see from the Table that the coefficient on WC is negative in columns 1 and 2 (t − 1,
and t − 2), suggesting that firms with higher current ratios are less likely to be financially
constrained over the next two years (coefficient=-0.047, and -0.044 respectively; p-value=0.004,
and 0.033 respectively). This is consistent with Table 4 above.
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(Insert Table 7 here)
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We present the results of Hypothesis 2 in Table 8. Hypothesis 2 predicts that the negative
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association between financial constraints and future share price is lower for firms with more
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efficient working capital management. As discussed above, we estimate equation (2) to test the
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Hypothesis. Following the approach above, we divide our sample into terciles of current ratio, and
estimate equation (2) for each tercile. Hypothesis 2 predicts that the coefficient on FC (β1) is less
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negative for tercile 3 of current ratio, since firms in tercile 3 have the highest levels of current ratio,
and therefore have the most efficient working capital management. We find from Table 8 that the
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esti- mated coefficient 𝛽̂1is negative for terciles 1 and 3 of current ratio (coefficient=-3.605 and -1.931
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respectively; p-value=0.031, and 0.092 respectively). We also find that 𝛽̂1 is significantly more
negative for terciles 1 than 3 (p-value=0.000). This suggests that financial constraints affect firm
value significantly more negatively for firms with poorer working capital management, consistent
with the results reported in Table 5 above.
(Insert Table 8 here)
5.2
The Hadlock-Pierce (2010) SA Index as an Alternate Proxy for Financial Constraints
Bodnaruk et al. (2015) argue that their measure of financial constraints (our main measure) is
superior to other commonly used financial constraint proxies. However, we check the robustness
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of our results using the SA Index of Hadlock and Pierce (2010) as an alternate measure of financial
constraints. Unlike the Bodnaruk et al. (2015) measure, the SA Index is not text-based. Rather, it is
based on the size and age of the firm. We replicate our results in Table 3 using this alternate measure
of financial constraints. We present results in Table 9. Table 9 presents the results for Hypothesis 1,
using the SA Index. It shows that the coefficient on WC is positive for all three columns (t – 1 to t
− 3) when we use the cash ratio as the proxy of working capital management, suggesting that firms
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with higher cash ratios are more likely to be financially constrained over the next three years
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(coefficient=0.312, 0.390, and 0.461 respectively; p-value=0.000 in all three columns). In contrast,
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the coefficient on WC is negative when we use the cash conversion cycle (coefficient=- 0.106, 0.143, and -0.224 respectively; p-value=0.084, 0.067, and 0.013 respectively). This is not
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consistent with Tables 3 and 4. However, this result is not surprising because the SA Index is a
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poor proxy of financial constraint. Indeed, as Bodnaruk et al. (2015) show, the SA Index (and
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similar measures) predict liquidity events very poorly.
(Insert Table 9 here)
Controlling for the effect of the Global Financial Crisis
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5.3
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Our sample period includes the period of the Global Financial Crisis (2008-2009). Although
Australia was not significantly affected by the crisis, it could be argued that Australian firms with
exposure to affected markets would feel the effect of the crisis. Thus, they might use the
constraining words more frequently during this period. If this is so, it could affect our empirical
result. To rule out this effect, we now explicitly control for the crisis period. Specifically, we modify
equation (1) as follows:
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡−𝑛 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + 𝛽7 𝐺𝐹𝐶𝑡 +
∑𝑗 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑𝑡 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
(3)
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In equation (3) above, GFC t is a dummy variable equal to 1 for the years 2008 and 2009; it is
zero otherwise. We present these results in Table 10, Panel A. Column 1 of Table 10 reports results
for the cash ratio and Column 2 for the cash conversion cycle. We note that the coefficient on GFC
is positive (coefficient=0.245 and 0.246 respectively; p-value=0.000 and 0.000 respectively),
suggesting that the period of GFC increased the likelihood of the firm being financially constrained
one year later. The Table also shows that the coefficient on cash ratio is negative (coefficient=-
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0.035; p-value=0.000) and that on the cash conversion cycle is positive (coefficient=0.026; p-
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value=0.006), consistent with Tables 4 and 5 above. This shows that our results are not driven by
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the crisis.
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(Insert Table 10 here)
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We also test for the effect of GFC by re-estimating equation (1) for the non-GFC years only.
We present these results in Panel B of Table 10. We present results for cash ratio (cash conversion
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cycle) in column 1 (column 2). Consistent with Table 4, we find that the coefficient on cash ratio is
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negative (coefficient=-0.037; p-value=0.000) and that on cash conversion cycle is positive
(coefficient=0.028; p-value=0.006).
Testing whether Financial Constraints reduce the Efficiency of Working Capital
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5.4
Management
Hypothesis 1 above tests the notion that firms with more efficient working capital management practices would be less likely to be financially constrained in the future. However, one could
plausibly argue that the existence of financial constraints could have an adverse effect on working capital policy. Indeed, Fazzari and Petersen (1993) show that financial constraints negatively
affect the investment in working capital. Fazzari and Petersen (1993), however, examine whether
financially constrained firms reduce their investment in working capital in the current period. As
discussed above, we study whether current working capital management is associated with the firm
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facing financial constraints in future. Therefore, it is unlikely that our empirical results would be
affected by the contemporaneous effect of financial constraints on working capital investment.
However, to rule out this possibility explicitly, we now examine how financial constraints affect
future working capital investment. Specifically, we estimate the following model:
𝑊𝐶𝑀𝑖𝑡 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡−𝑛 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 +
∑𝑗 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑𝑡 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
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(4)
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In equation (4) above, the dependent variable is working capital management (cash ratio and
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cash conversion cycle) and the independent variable of interest is financial constraints. If the
argument that current financial constraints affects future working capital management adversely is
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true, we would observe a negative (positive) coefficient on financial constraints when the
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dependent variable is cash ratio (cash conversion cycle). We present these results in Table 11. The
dependent variable in Column 1 (2) is cash ratio (cash conversion cycle). The Table shows that the
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coefficient on FC it is not significant (p-value=0.455 and 0.206 respectively). This suggests that
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current financial constraints do not affect future working capital management. This result alleviates
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concerns that there might be reverse causality, i.e., future working capital management might be
affected by current financial constraints.
(Insert Table 11 here)
5.5
Potential Endogeneity in Working Capital Management: Propensity Score Matching
It is possible that firms with high working capital have certain other characteristics that reduce
their likelihood of facing financial constraints in the future. To address this issue we use a
propensity score matching approach to identify a sample of control firms that do not differ on other
observable characteristics. Specifically, we estimate the following logit model:
𝐻𝑖𝑔ℎ𝑖𝑡 = 𝛽0 + 𝛽1 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝐿𝑒𝑣𝑖𝑡 + ∑𝑗 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑𝑡 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
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(5)
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In equation (5) above, Highit is a dummy variable equal to 1 if the firm has a lower cash
conversion cycle than the median of the distribution for each year and industry; 0 otherwise. We
use three explanatory variables in the model – size, market-to-book ratio, and leverage to account
for observable firm characteristics that might affect working capital management. We also include
industry and year fixed effects in the model. We form our matched sample based on the propensity score generated by the first-stage estimate from equation (5). For each high working capital
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management firm, we identify one control firm with the closest propensity score within a caliper
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of 0.001. We use this approach following prior research (Rosenbaum and Rubin, 1983). We then
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estimate equation (1) above on the propensity score matched sample.
We present the results for the propensity score matched sample in Table 12. Panel A of Table
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12 compares the descriptive statistics of the observable firm characteristics for the high and low
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working capital samples. As the Table shows, the two samples are not significantly different in
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terms of size (p-value=0.920), market-to-book ratio (p-value=0.620) and leverage (p-value=0.790).
We present the results of equation (1) in Panel B of Table 12. The first (second) column describes
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the results for the cash ratio (cash conversion cycle). For brevity, we only report results for financial
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constraints one year ahead. As the Table shows, the coefficient on cash ratio (cash conversion
cycle) is negative (positive) – coefficient=-0.036 (0.001); p-value=0.038 (0.024). This is consistent
with our main results above.
(Insert Table 12 here)
5.6
Controlling for the Effect of Potential Unobservable Factors on Financial Constraints
Although we have included many control variables in our regressions, it is possible that there
are some unobservable factors that affect financial constraints. Not controlling for these factors
could potentially cause us to over-estimate the effect of working capital management on future
financial constraints. Since it is difficult to identify the unobservable factors, we include firm
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effects in equation (1) above in place of industry fixed effects and re-estimate the model. We
present the results in Table 13. We show results for cash ratio in column 1 and cash conversion
cycle in column 2. Consistent with Table 4, we find that the coefficient on cash ratio is negative
(coefficient=-0.023; p-value=0.064), and that on cash conversion cycle is positive
(coefficient=0.017; p-value=0.035). These results provide further support for our hypothesis.
(Insert Table 13 here)
Conclusion
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We examine whether efficient working capital management reduces the likelihood of firms fac-
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ing financial constraints in the future. Prior research (for instance, Campello et al., 2010; Almeida
and Campello, 2008) shows that financial constraints negatively affect firms’ investment and that
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they could lead to firms exiting the market (Musso and Schiavo, 2008). Given the serious impact
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of financial constraints on firms’ prospects, it is important to understand whether the likelihood
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of financial constraints could be reduced by suitable corporate strategy. We examine the role of
working capital management in this context.
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Working capital is an important source of liquidity (Fazzari and Petersen, 1993). Therefore,
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managing working capital efficiently helps firms tide over adverse economic periods and increase
shareholder value (Zeidan and Shapir, 2017). Working capital management involves managing
both the amount of working capital and the cash conversion cycle. However, extant literature has
typically examined only one aspect of working capital management. In our study, we focus on both
the level of working capital (we focus primarily on the cash ratio) and the cash conversion cycle
and examine how efficient working capital management affects the likelihood of firms facing
financial constraints up to two years into the future. Using a new text-based measure of financial
constraints based on a recent study by Bodnaruk et al. (2015), and focusing on a sample of firms
listed on the Australian Stock Exchange, we show that a high (low) cash ratio (cash conversion
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cycle) significantly reduces the likelihood of financial constraints up to two years into the future.
This suggests that efficient management of working capital can reduce the likelihood of firms
facing financial constraints in the future.
We examine the benefits of working capital management further by next examining whether
financially constrained firms with more efficient working capital policies enjoy higher valuations.
Our conjecture is based on the notion that more efficient working capital management allows firms
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to absorb the liquidity shocks created by financial constraints. Prior research (Ding et al., 2013)
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shows that financially constrained firms with more investment in working capital can invest to a
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significantly greater degree than those with lower investment in working capital. We find that
financially constrained firms with higher (lower) cash ratios (cash conversion cycles) tend to have
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higher one year ahead prices and higher analyst target prices, suggesting that efficient working
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capital policies can mitigate the adverse effects of financial constraints on firm value.
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Our study is timely given recent reports by PricewaterhouseCoopers (PwC) and EY on the
deteriorating trend of working capital performance by firms in the US, Europe and Australia.8
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Specifically, the 2018/19 Working Capital Report by PricewaterhouseCoopers reveals that firms
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seem to be managing cash flows by cutting back on capital expenditure.9 This has potentially
adverse consequences for long-term growth. The PwC survey suggests that firms could pay for a
55 per- cent increase in capital expenditure by managing working capital efficiently. This
highlights the importance of our study. Our findings will therefore be of interest to practitioners,
as it provides large scale empirical evidence on the benefits of efficient working capital
management.
8
See https://www.ey.com/Publication/vwLUAssets/ey-all-tied-up-working-capital-management-2016/\$File/ey-all-tied-upworking-capital-management-2016.pdf.
9
See https://www.pwc.com/gx/en/services/advisory/deals/business-recovery-restructuring/working-capital-opportunity.html.
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Our study has an important caveat.10 Although our measure of financial constraints is based
on prior research (Bodnaruk et al., 2015), it may not always capture financial constraints. Indeed,
it is possible that some of the words that appear in the list, for example, “obligation” could be used
to convey some other information. For instance, a company outlining that it does not have any
more financial obligation towards a lender does convey a positive message. We recognise this as
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a limitation of our study.
10
We thank the anonymous reviewer for pointing this out.
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Jo
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Tables
Table 1: List of Constraining Words
earmark
irrevocable
prevents
abiding
earmarked
irrevocably
prohibit
bound
earmarking
Limit
prohibited
bounded
earmarks
Limiting
prohibiting
commit
encumber
Limits
prohibition
commitment
encumbered
mandate
of
abide
mandated
commits
encumbers
mandates
prohibitively
committed
encumbrance
mandating
-p
prohibitory
committing
encumbrances mandatory
prohibits
compel
entail
mandatorily
refrain
compelled
entailed
necessitate
refraining
compelling
entailing
necessitated
refrains
entails
necessitates
require
entrench
necessitating
required
compulsion
entrenched
noncancelable
requirement
compulsory
escrow
noncancellable requirements
confine
escrowed
obligate
requires
confined
escrows
obligated
requiring
confinement
forbade
obligates
restrain
confines
forbid
obligating
restrained
confining
forbidden
obligation
restraining
constrain
forbidding
obligations
restrains
constrained
forbids
obligatory
restraint
re
lP
Jo
comply
ur
compels
ro
commitments encumbering
na
prohibitions
34
prohibitive
impair
oblige
restraints
constrains
impaired
obliged
restrict
constraint
impairing
obliges
restricted
constraints
impairment
permissible
restricting
covenant
impairments
permission
restriction
covenanted
impairs
permissions
restrictions
covenanting
impose
permitted
restrictive
covenants
imposed
permitting
restrictively
depend
imposes
pledge
dependence
imposing
pledged
restricts
dependences
imposition
pledges
stipulate
dependant
impositions
pledging
stipulated
dependencies
indebted
preclude
stipulates
dependent
inhibit
precluded
stipulating
depending
inhibited
precludes
stipulation
inhibiting
precluding
stipulations
inhibits
precondition
strict
insist
preconditions
stricter
dictates
insisted
preset
strictest
dictating
insistence
prevent
strictly
directive
insisting
prevented
unavailability
directives
insists
preventing
unavailable
ro
re
lP
na
dictate
ur
depends
Jo
dictated
of
constraining
-p
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restrictiveness
The Table above lists the constraining words identified by Bodnaruk et al. (2015). These are the words
typically used by firms facing financial constraints.
35
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Table 2: Variable Definitions
Variable Name Definition
GFC
of
ro
CashRatio
CR
Age
SA_Index
-p
Size
Lev
ROA
StdRet
CCC
re
MTB
Percentage of financially constraining words in the Australian annual
reports. See Table 1 for the list of constraining words.
The ratio of market capitalisation (closing share price for the year multiplied by common shares outstanding) to the book value of equity
Natural logarithm of total asset
Short term debt plus long term debt scaled by total shareholders’ equity.
Net income (net profit after tax) scaled by total assets
Standard deviation of monthly stock returns
Cash conversion cycle. It is the sum days receivables and days inventory, minus days payables. We scale CCC by 1,000 in our regression
models in order to generate meaningful regression coefficients.
Ratio of cash and short-term investments to total assets
Current ratio – defined as the ratio of current assets to current liabilities
The difference between the IPO date and the current date
Following Hadlock and Pierce (2010), the SA index is defined as [0.737*ln(Total Assets)]+[0.043*ln(Total Assets)2 ]-(0.040*Age).
A dummy variable equal to 1 if the observation is from the period of the
Global Financial Crisis (2008-2009); zero otherwise.
lP
FC
Jo
ur
na
The Table above presents definitions of variables used in the empirical analyses.
36
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Table 3: Description of the Sample
Panel A: Sample Selection
Details
N
Initial Sample in the intersection of the
DatAnalysis Premium, Connect4 and SIRCA
Price databases
8,010
Less:
Missing values of key variables
Financial Institutions and Utilities
of
3,448
160
Total
4,402
re
-p
ro
The Table above presents the sample selection criteria for our study. Our study is based on a sample of
Australian firms for the period 2000- 2016. The sample does not include financial institutions and utility
firms. The sample consists of 4,422 firm-year observations with non-missing values of the financial
constraint variable. We present detailed variable definitions in Table 2.
Mean
Median
P75
SD
0.352
-62.474
0.045
0.800
1.120
16.008
0.862
0.123
0.088
-0.24
0.867
0.100
-1.207
0.427
87.442
0.222
5.128
5.482
17.642
3.146
0.777
0.174
-0.241
6.162
2.645
0.058
0.426
20.186
0.126
1.360
1.820
17.336
1.620
0.348
0.134
0.000
2.175
0.390
-0.317
0.502
74.472
0.312
3.450
3.930
19.128
3.293
0.814
0.208
0.080
5.300
2.105
1.002
0.117
1468.828
0.240
28.993
28.980
2.303
4.865
1.370
0.153
0.828
13.026
7.839
1.790
na
P25
Jo
ur
FC
CCC
CashRatio
QuickRatio
CurrentRatio
Size
MTB
Lev
Std_Ret
ROA
AFP
P
SA_Index
lP
Panel B: Descriptive Statistics
The Table above presents the descriptive statistics for our sample of Australian firms for the period 20002016. The sample does not include financial institutions and utility firms. The sample consists of 4,422
firm-year observations with non-missing values of the financial constraint variable. We present detailed
variable definitions in Table 2.
37
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Panel C: Industry Distribution
Industry
N Mean Median Std Dev
144 0.434
0.432
0.117
Consumer Discretionary
374 0.417
0.418
0.126
Consumer Staples
137 0.429
0.445
0.102
Energy
602 0.433
0.434
0.116
Healthcare
470 0.417
0.416
0.111
Industrials
521 0.443
0.437
0.130
Information Technology
261 0.408
0.416
0.116
1,732 0.434
0.432
0.113
ro
Materials
of
Communications
181 0.366
0.364
0.116
4,422 0.427
0.427
0.117
Real Estate
-p
Total
Jo
ur
na
lP
re
The Table above presents the industry distribution for our sample of Australian firms for the period 20002016. The sample does not include financial institutions and utility firms. The sample consists of 4,422
firm-year observations with non-missing values of the financial constraint variable. We present detailed
variable definitions in Table 2
38
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Table 4: Cash Ratio and Financial Constraints
Panel A: Cash Ratio
CashRatio in year
MTB
Size
ro
Lev
-p
ROA
re
StdRet
lP
Constant
t-3
∗∗∗
-0.042
(0.000)
∗∗
-0.002
(0.002)
∗∗∗
-0.008
(0.000)
∗∗∗
0.006
(0.001)
0.004
(0.431)
0.001
(0.927)
∗∗∗
0.459
(0.000)
3,634
Included
0.191
na
Observations
Industry and Year Effect
Adjusted R2
∗∗∗
t-2
∗∗∗
-0.036
(0.000)
∗∗
-0.002
(0.002)
∗∗∗
-0.008
(0.000)
∗∗∗
0.006
(0.000)
0.000
(0.957)
-0.015
(0.212)
∗∗∗
0.413
(0.000)
4,015
Included
0.261
of
WCM
t-1
∗∗∗
-0.031
(0.000)
∗∗
-0.001
(0.001)
∗∗∗
-0.008
(0.000)
∗∗
0.003
(0.038)
0.001
(0.828)
-0.013
(0.316)
∗∗∗
0.397
(0.000)
4,334
Included
0.299
∗∗
ur
, and represent statistical significance at the 1% and 5% levels of significance respectively. The pvalues (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
Jo
The Table above presents results of the equation below, testing the association between financial constraints
and working capital management. In this Table, we use the cash ratio (CashRatio) to measure working
capital management:
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−𝑛 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗
𝑗
+ ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
𝑡
All variables are defined in Table 2.
39
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Panel B: Cash Conversion Cycle
CCC in Year
MTB
Size
ro
Lev
StdRet
Constant
t-3
0.012
(0.274)
∗∗∗
-0.003
(0.000)
∗∗∗
-0.007
(0.000)
∗∗∗
0.008
(0.000)
0.000
(0.987)
0.006
(0.736)
∗∗∗
0.415
(0.000)
2,539
Included
0.217
na
lP
Observations
Industry and Year Effect
Adjusted R2
re
-p
ROA
∗∗∗
t-2
∗∗∗
0.029
(0.001)
∗∗∗
-0.003
(0.000)
∗∗∗
-0.007
(0.000)
∗∗∗
0.008
(0.000)
-0.005
(0.467)
-0.012
(0.408)
∗∗∗
0.375
(0.000)
2,792
Included
0.305
of
WCM
t-1
∗∗
0.026
(0.006)
∗∗∗
-0.003
(0.000)
∗∗∗
-0.007
(0.000)
∗∗
0.005
(0.000)
-0.005
(0.259)
-0.002
(0.913)
∗∗∗
0.359
(0.000)
2,990
Included
0.350
∗∗
ur
, and represent statistical significance at the 1% and 5% levels of significance respectively. The pvalues (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
Jo
The Table above presents results of the equation below, testing the association between financial constraints
and working capital management. In this Table, we use the cash conversion cycle (CCC) to measure
working capital management:
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−𝑛 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗
𝑗
+ ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
𝑡
All variables are defined in Table 2.
40
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Table 5: Cash Ratio, Financial Constraints and Future Share Price
Panel A: Cash Ratio
CashRatio Terciles
MTB
ro
Size
-p
Lev
ROA
re
StdRet
lP
Constant
Adjusted R2
Tercile 3
-0.398
(0.613)
∗∗∗
0.158
(0.000)
∗∗∗
0.854
(0.000)
∗∗∗
-0.244
(0.001)
0.146
(0.259)
0.449
(0.407)
∗∗
-10.590
(0.002)
1,039
Included
0.619
ur
∗
na
Observations
Industry and Year Effect
∗∗∗ ∗∗
Tercile 2
-2.815
(0.406)
∗∗∗
0.948
(0.000)
∗∗∗
2.208
(0.000)
∗∗∗
-1.559
(0.000)
-0.741
(0.103)
0.996
(0.462)
∗∗∗
-42.310
(0.000)
1,062
Included
0.315
of
FC
Tercile 1
∗∗
-2.961
(0.023)
∗∗∗
0.427
(0.000)
∗∗∗
1.180
(0.000)
∗∗∗
-0.421
(0.001)
-0.158
(0.596)
∗∗
-2.949
(0.006)
∗∗∗
-19.270
(0.000)
1,070
Included
0.385
Jo
, , and represent statistical significance at the 1% and 5% levels of significance respectively. The pvalues (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
The Table above presents results of the equation below, testing how the association between financial
constraints and future share price (P) is moderated by working capital management. In this Table, we use
the cash ratio (CashRatio) to measure working capital management:
𝑃𝑖𝑡+1 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡
𝑗
+ 𝜖𝑖𝑡
All variables are defined in Table 2.
41
𝑡
Journal Pre-proof
Panel B: Cash Conversion Cycle
CCC Terciles
Size
Lev
ROA
Constant
∗∗∗ ∗∗
lP
Observations
Industry and Year Effect
Adjusted R2
re
-p
StdRet
∗
Tercile 3
-0.423
(0.897)
∗∗∗
0.603
(0.000)
∗∗∗
1.691
(0.000)
∗∗∗
-0.800
(0.001)
0.555
(0.257)
-1.482
(0.261)
∗∗∗
-31.220
(0.000)
1,092
Included
0.355
of
MTB
Tercile 2
∗∗
-5.695
(0.003)
∗∗∗
0.500
(0.000)
∗∗∗
1.642
(0.000)
∗∗
-0.652
(0.003)
-0.078
(0.860)
∗∗
-2.549
(0.006)
∗∗∗
-28.590
(0.000)
1,086
Included
0.304
ro
FC
Tercile 1
∗
1.127
(0.090)
∗∗∗
0.142
(0.000)
∗∗∗
0.759
(0.000)
∗∗∗
-0.345
(0.000)
0.009
(0.919)
-0.098
(0.854)
∗∗∗
-15.450
(0.000)
993
Included
0.598
na
, , and represent statistical significance at the 1%, 5% and 10% levels of significance respectively. The
p-values (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
Jo
ur
The Table above presents results of the equation below, testing how working capital management moderates
the association between financial constraints and future share price (P). In this Table, we use the cash
conversion cycle (CCC) to measure working capital management:
𝑃𝑖𝑡+1 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡
𝑗
+ 𝜖𝑖𝑡
All variables are defined in Table 2.
42
𝑡
Journal Pre-proof
Table 6: Cash Ratio, Financial Constraints and Analyst Target Price
Panel A: Cash Ratio
CashRatio Terciles
Size
Lev
-p
ROA
re
StdRet
lP
Constant
∗∗∗ ∗∗
na
Observations
Industry and Year Effect
Adjusted R2
∗
Tercile 3
-0.231
(0.422)
∗∗∗
0.080
(0.000)
∗∗∗
0.231
(0.000)
∗∗∗
-0.129
(0.000)
-0.087
(0.622)
-0.266
(0.265)
∗∗∗
-3.993
(0.000)
426
Included
0.497
of
MTB
Tercile 2
-0.343
(0.572)
∗∗∗
0.148
(0.000)
∗∗∗
0.317
(0.000)
∗∗∗
-0.272
(0.000)
0.212
(0.482)
-0.029
(0.934)
∗∗∗
-5.386
(0.000)
426
Included
0.592
ro
FC
Tercile 1
∗∗
-0.949
(0.036)
∗∗∗
0.049
(0.001)
∗∗∗
0.223
(0.000)
∗∗
-0.106
(0.019)
∗∗
0.744
(0.013)
∗
-0.460
(0.051)
∗∗∗
-2.414
(0.000)
472
Included
0.472
Jo
ur
, , and represent statistical significance at the 1%, 5% and 10% levels of significance respectively. The
p-values (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
The Table above presents results of the equation below, testing how the association between financial
constraints and analyst target price (AFP ) is moderated by working capital management. In this Table, we
use the cash ratio (CashRatio) to measure working capital management:
𝐴𝐹𝑃𝑖𝑡+1 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡
𝑗
+ 𝜖𝑖𝑡
All variables are defined in Table 2.
43
𝑡
Journal Pre-proof
Panel B: Cash Conversion Cycle
CCC Terciles
Size
Lev
ROA
Constant
∗∗∗ ∗∗
lP
Observations
Industry and Year Effect
Adjusted R2
re
-p
StdRet
∗
Tercile 3
-0.092
(0.843)
∗∗∗
0.098
(0.000)
∗∗∗
0.274
(0.000)
∗
-0.143
(0.068)
0.395
(0.136)
0.244
(0.430)
∗∗∗
-4.154
(0.000)
472
Included
0.528
of
MTB
Tercile 2
∗∗
-1.160
(0.001)
∗∗∗
0.105
(0.000)
∗∗∗
0.327
(0.000)
∗∗∗
-0.303
(0.000)
0.298
(0.414)
∗∗∗
-0.617
(0.000)
∗∗∗
-5.322
(0.000)
469
Included
0.617
ro
FC
Tercile 1
-0.077
(0.870)
∗∗∗
0.087
(0.000)
∗∗∗
0.201
(0.000)
∗∗∗
-0.131
(0.000)
0.208
(0.232)
0.007
(0.990)
∗∗∗
-2.946
(0.000)
383
Included
0.478
na
, , and represent statistical significance at the 1%, 5% and 10% levels of significance respectively. The
p-values (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
Jo
ur
The Table above presents results of the equation below, testing how the association between financial
constraints and analyst target price (AFP ) is moderated by working capital management. In this Table, we
use the cash conversion cycle (CCC) to measure working capital management:
𝐴𝐹𝑃𝑖𝑡+1 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡
𝑗
+ 𝜖𝑖𝑡
All variables are defined in Table 2.
44
𝑡
Journal Pre-proof
Table 7: Current Ratio and Financial Constraints
CR in year
MTB
Size
Lev
Constant
∗∗∗ ∗∗
lP
Observations
Industry and Year Effect
Adjusted R2
re
StdRet
-p
ro
ROA
t-2
∗∗
-0.044
(0.033)
∗∗∗
-0.002
(0.000)
∗∗∗
-0.006
(0.000)
∗∗∗
0.010
(0.000)
0.004
(0.372)
∗
-0.021
(0.072)
∗∗∗
0.368
(0.000)
4,051
Included
0.243
∗
t-3
-0.007
(0.691)
∗∗∗
-0.002
(0.000)
∗∗∗
-0.005
(0.000)
∗∗∗
0.010
(0.000)
0.007
(0.174)
-0.004
(0.755)
∗∗∗
0.405
(0.000)
3,643
Included
0.171
of
WCM
t-1
∗∗
-0.047
(0.004)
∗∗∗
-0.002
(0.000)
∗∗∗
-0.006
(0.000)
∗∗∗
0.007
(0.000)
0.006
(0.147)
-0.017
(0.169)
∗∗∗
0.354
(0.000)
4,421
Included
0.278
na
, , and represent statistical significance at the 1%, 5% and 10% levels of significance respectively. The
p-values (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
Jo
ur
The Table above presents results of the equation below, testing the association between financial constraints and
working capital management. In this Table, we use the current ratio (CR) to measure working capital management:
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−𝑛 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡
𝑗
+ 𝜖𝑖𝑡
All variables are defined in Table 2.
45
𝑡
Journal Pre-proof
Table 8: Current Ratio, Financial Constraints and Future Share Price
CR Terciles
Size
Lev
ROA
Constant
∗∗∗ ∗∗
lP
Observations
Industry and Year Effect
Adjusted R2
re
-p
StdRet
∗
Tercile 3
∗
-1.931
(0.092)
∗∗∗
0.277
(0.000)
∗∗∗
0.813
(0.000)
∗
-1.488
(0.058)
0.163
(0.335)
-0.143
(0.759)
∗∗∗
-12.940
(0.000)
1,077
Included
0.695
of
MTB
Tercile 2
-0.767
(0.759)
∗∗∗
0.707
(0.000)
∗∗∗
1.732
(0.000)
∗∗
-1.417
(0.011)
0.964
(0.184)
∗∗
-3.276
(0.025)
∗∗∗
-31.440
(0.000)
1,086
Included
0.198
ro
FC
Tercile 1
∗∗
-3.605
(0.031)
∗∗∗
0.369
(0.000)
∗∗∗
1.465
(0.000)
∗∗∗
-0.471
(0.000)
-0.240
(0.203)
-0.913
(0.415)
-25.450
(0.000)
1,008
Included
0.419
na
, , and represent statistical significance at the 1%, 5% and 10% levels of significance respectively. The
p-values (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
Jo
ur
The Table above presents results of the equation below, testing how the association between financial
constraints and future share price (P ) is moderated by working capital management. In this Table, we use
the current ratio (CR) to measure working capital management:
𝑃𝑖𝑡+1 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗 + ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡
𝑗
+ 𝜖𝑖𝑡
All variables are defined in Table 2.
46
𝑡
Journal Pre-proof
Table 9: Working Capital Management and Financial Constraints: Using the Hadlock and Pierce
(2010) SA Index
CashRatio in Year
Lev
ROA
StdRet
Constant
∗∗∗ ∗∗
lP
Observations
Industry and Year Effect
Adjusted R2
∗
t-1
∗
-0.106
(0.084)
∗∗∗
0.030
(0.000)
∗∗∗
0.813
(0.000)
∗∗∗
-0.053
(0.000)
∗∗∗
-0.142
(0.000)
∗∗∗
0.319
(0.000)
∗∗∗
-14.380
(0.000)
4,687
Included
0.885
of
Size
ro
MTB
t-3
∗∗∗
0.461
(0.000)
∗∗∗
0.030
(0.000)
∗∗∗
0.811
(0.000)
∗∗
-0.041
(0.006)
∗∗
-0.118
(0.002)
∗∗
0.236
(0.028)
∗∗∗
-14.390
(0.000)
3,105
Included
0.832
-p
WCM
t-2
∗∗∗
0.390
(0.000)
∗∗∗
0.030
(0.000)
∗∗∗
0.815
(0.000)
∗∗∗
-0.047
(0.000)
∗∗∗
-0.141
(0.000)
∗∗∗
0.331
(0.001)
∗∗∗
-14.490
(0.000)
3,542
Included
0.858
re
t-1
∗∗∗
0.312
(0.000)
∗∗∗
0.025
(0.000)
∗∗∗
0.819
(0.000)
∗∗∗
-0.040
(0.000)
∗∗∗
-0.135
(0.000)
∗∗∗
0.361
(0.000)
∗∗∗
-14.530
(0.000)
4,017
Included
0.889
CCC in Year
t-2
∗
-0.143
(0.067)
∗∗∗
0.038
(0.000)
∗∗∗
0.803
(0.000)
∗∗∗
-0.062
(0.000)
∗∗∗
-0.118
(0.000)
∗∗
0.262
(0.003)
∗∗∗
-14.230
(0.000)
4,224
Included
0.850
t-3
∗∗
-0.224
(0.013)
∗∗∗
0.037
(0.000)
∗∗∗
0.794
(0.000)
∗∗∗
-0.059
(0.000)
∗∗
-0.086
(0.014)
*
0.186
(0.060)
∗∗∗
-14.050
(0.000)
3,786
Included
0.820
ur
na
, , and represent statistical significance at the 1%, 5%, and 10% levels of significance respectively. The
p-values (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
Jo
The Table above presents results of the equation below, testing the association between financial constraints
and working capital management. In this Table, we use the cash ratio (CashRatio) and cash conversion
cycle (CCC) to measure working capital management:
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−1 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗
𝑗
+ ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
𝑡
All variables are defined in Table 2.
47
Journal Pre-proof
Table 10: Working Capital Management and Financial Constraints: Controlling for the Global
Financial Crisis
Panel A: With Year Fixed Effects
WCM
MTB
ro
Lev
-p
ROA
re
StdRet
Constant
lP
GFC
ur
na
Observations
Industry and Year Effect
Adjusted R2
∗∗∗
CCC
∗∗∗
0.026
(0.006)
∗∗∗
-0.002
(0.000)
∗∗∗
-0.008
(0.000)
∗∗
0.003
(0.046)
-0.005
(0.241)
-0.002
(0.888)
∗∗∗
0.246
(0.000)
∗∗∗
0.393
(0.000)
2,990
Included
0.352
of
Size
CashRatio
∗∗∗
-0.035
(0.000)
∗∗∗
-0.003
(0.000)
∗∗∗
-0.007
(0.000)
∗∗
0.005
(0.005)
-0.005
(0.259)
-0.002
(0.913)
∗∗
0.245
(0.000)
∗∗∗
0.359
(0.000)
2,990
Included
0.350
∗∗
Jo
, and represent statistical significance at the 1% and 5% levels of significance respectively. The pvalues (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
The Table above presents results of the equation below, testing the association between financial constraints
and working capital management. In this Table, we use the cash ratio (CashRatio) and cash conversion
cycle (CCC) to measure working capital management:
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−1 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + 𝛽7 𝐺𝐹𝐶𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗
𝑗
+ ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
𝑡
All variables are defined in Table 2.
48
Journal Pre-proof
Panel B: Without GFC years
CashRatio
∗∗∗
-0.035
(0.000)
WCM
∗∗∗
MTB
-0.007
(0.000)
Size
0.004
(0.027)
-0.002
(0.686)
-0.014
(0.418)
∗∗
ro
ROA
∗∗∗
Constant
lP
re
Observations
Industry and Year Effect
Adjusted R2
-0.007
(0.000)
∗∗∗
0.361
(0.000)
2,570
Included
0.370
-p
StdRet
∗∗∗
∗∗∗
-0.008
(0.000)
0.003
(0.144)
-0.001
(0.690)
-0.014
(0.407)
of
Lev
CCC
∗∗∗
0.026
(0.006)
∗∗
∗∗∗
-0.008
(0.000)
∗∗∗
0.396
(0.000)
2,570
Included
0.368
na
, and represent statistical significance at the 1% and 5% levels of significance respectively. The pvalues (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
Jo
ur
The Table above presents results of the equation below, testing the association between financial constraints
and working capital management. In this Table, we use the cash ratio (CashRatio) and cash conversion
cycle (CCC) to measure working capital management:
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−1 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗
𝑗
+ ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
𝑡
All variables are defined in Table 2.
49
Journal Pre-proof
Table 11: The Effect of Financial Constraints on Future Working Capital Management
CashRatio
-0.050
(0.206)
∗∗∗
0.012
(0.000)
∗∗∗
-0.037
(0.000)
∗∗∗
-0.034
(0.000)
∗∗
-0.028
(0.020)
-0.026
(0.321)
∗∗∗
0.869
(0.000)
3,299
Included
0.324
FC
MTB
Size
Lev
of
ROA
CCC
-0.020
(0.455)
0.000
(0.775)
∗∗∗
-0.003
(0.035)
∗∗∗
-0.005
(0.001)
∗∗
0.019
(0.001)
-0.009
(0.646)
∗∗
0.092
(0.017)
3,299
Included
0.009
ro
StdRet
-p
Constant
lP
re
Observations
Industry and Year Effect
Adjusted R2
, and ∗∗ represent statistical significance at the 1% and 5% levels of significance respectively. The pvalues (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
na
∗∗∗
ur
The Table above presents results of the equation below, testing the association between financial constraints
and working capital management. In this Table, we use the cash ratio (CashRatio) and cash conversion
cycle (CCC) to measure working capital management:
Jo
𝑊𝐶𝑀𝑖𝑡 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡−𝑛 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗
𝑗
+ ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
𝑡
All variables are defined in Table 2.
50
Journal Pre-proof
Table 12: Using Propensity Score Matching to Control for Endogeneity in Working Capital
Management
Panel A: Descriptive Statistics of Observable Characteristics
Variable
Size
MTB
Lev
Mean (High)
17.953
2.540
0.640
Mean (Low)
17.963
2.480
0.630
Median (High)
17.530
1.490
0.340
Median (Low)
17.690
1.720
0.350
p-value
0.920
0.620
0.790
of
Panel B: Working Capital Management and Future Financial Constraints
lP
na
ROA
StdRet
ur
Constant
Observations
Jo
0.352
re
Size
***
0.350
-p
MTB
Lev
CCC
0.025***
(0.002)
-0.003***
(0.000)
-0.008***
(0.000)
0.005**
(0.037)
-0.009
(0.138)
0.001
(0.956)
0.388***
(0.000)
2,186
ro
WCM
CashRatio
-0.036***
(0.001)
-0.004***
(0.000)
-0.007***
(0.000)
0.006***
(0.009)
-0.008
(0.158)
0.001
(0.956)
0.354***
(0.000)
2,186
Adjusted R2
, and ** represent statistical significance at the 1% and 5% levels of significance respectively. The pvalues (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
The Table above presents results of the equation below, testing the association between financial constraints
and working capital management. In this Table, we use the cash ratio (CashRatio) and cash conversion
cycle (CCC) to measure working capital management:
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−1 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑗 𝐼𝑛𝑑𝑗
𝑗
+ ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
𝑡
All variables are defined in Table 2.
51
Journal Pre-proof
Table 13: Working Capital Management and Future Financial Constraints with Firm Fixed Effects
lP
re
-p
ro
of
Working Capital Proxy
CashRatio
CCC
WCM
-0.023*
0.017**
(0.064)
(0.035)
***
-0.002
-0.002***
MTB
(0.000)
(0.000)
-0.004*
-0.005*
Size
(0.100)
(0.056)
*
0.004
0.003
Lev
(0.065)
(0.113)
0.000
0.001
ROA
(0.962)
(0.882)
-0.011
-0.011
StdRet
(0.459)
(0.458)
0.281***
0.298***
Constant
(0.000)
(0.000)
Observations
3,030
3,030
Included
Included
Firm and Year Effect
0.649
0.650
Adjusted R2
***
, and ** represent statistical significance at the 1% and 5% levels of significance respectively. The pvalues (presented in parentheses) are based on heteroscedasticity-adjusted robust standard errors.
na
The Table above presents results of the equation below, testing the association between financial constraints
and working capital management. In this Table, we use the cash ratio (CashRatio) and cash conversion
cycle (CCC) to measure working capital management:
ur
𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−1 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑ 𝛾𝑖 𝐹𝑖𝑟𝑚𝑖
𝑖
Jo
+ ∑ 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡
𝑡
All variables are defined in Table 2.
Highlights
We show that firms with efficient working capital management are less likely to be financially
constrained in the future.
We show that financially constrained firms with efficient working capital management have higher
valuations.
We use a novel text-based measure of financial constraints for a sample of Australian firms.
52
University of Groningen
Identifying inventory project management conflicts
Vries, de, Jan
Published in:
International Journal of Production Economics
DOI:
10.1016/j.ijpe.2020.107620
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