Journal Pre-proof 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 This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2018 Published by Elsevier. Journal Pre-proof Efficient Working Capital Management, Financial Constraints and Firm Value: A Text-Based Analysis 1 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. re -p ro of 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. lP Keywords: Financial Constraints, Working Capital Management, Future Stock Price, Analyst Target Price Jo ur na JEL Classification: G12, G32, M41 Journal Pre-proof 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 of management is important because it enables firms to free up cash and improve liquidity. Deloof ro (2003) shows that efficient management of the cash conversion cycle can improve corporate -p profitability significantly. Baños-Caballero et al. (2012) show that an optimal level of working re 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 lP importance of good working capital management. na 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 ur create value. Indeed, a recent survey by PriceWaterhouseCoopers revealed that 203 companies in Jo 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 2 Journal Pre-proof 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 of companies to invest in working capital. Deciding the amount to invest in working capital is ro important because over-investment in working capital may increase idle investment and therefore -p 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 re be value reducing. To manage working capital effectively, it is also important for firms to manage lP the cash conversion cycle, because that creates liquidity. Given the importance of both level of na 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 ur on working capital management usually focuses on only one aspect of working capital management Jo (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. 3 Journal Pre-proof 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 of financial constraints, as Shin and Park (1999) and Desai et al. (2007) argue. Using a sample of ro Australian firms and a text-based measure of financial constraints developed by Bodnaruk et al. -p (2015), we show that efficient working capital management reduces the likelihood of the firm re facing financial constraints up to three years into the future. We next examine the valuation implications of working capital, by studying whether the lP negative effect of financial constraints on future share prices is less for firms with more efficient na 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) ur investments. We find that, while there is a negative association between financial constraints and Jo 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 4 Journal Pre-proof 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. of Further, (poor) working capital management is an issue of relevance for Australian companies. ro Indeed, as discussed above, the 2018 working capital survey of Pricewaterhouse Coopers finds -p 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 re could unlock $90.6 billion in value by managing their working capital more effectively. These lP survey results make our research setting of particular relevance. na 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 ur to our study, because they analyse the association between financial constraints and working Jo 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 5 Journal Pre-proof 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 of measure of financial constraints that detects financial constraints more accurately than other ro measures (Bodnaruk et al., 2015). -p 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 re performance. They find a U-shaped relation, which suggests that there is a certain level of working lP capital efficiency that improves firm performance. They also find that financial constraints make na 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 ur contrast, it is one of the main variables of analysis in our paper – we focus explicitly on the Jo 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 6 Journal Pre-proof 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). of Ours is also the first study to use a text-based measure of financial constraints for Australian ro firms. As Bodnaruk et al. (2015) note, this measure has several advantages over other commonly -p used measures, as it captures financial constraint more accurately by focusing specifically on the language used by financially constrained firms in their annual reports. re We also contribute to the literature by demonstrating that financially constrained firms with lP efficient working capital management have higher market valuations than those with less efficient na 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, Jo constrained firms. ur 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 7 Journal Pre-proof 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- of ties. Investment in working capital can provide firms with liquidity, insuring it against the adverse ro effects of a shortfall of cash (Fazzari and Petersen, 1993). However, too much of investment in -p 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 re in working capital is desirable. lP While investment in working capital is an important part of working capital management, it is na 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 ur working capital management also involves managing the cash conversion cycle. Prior research Jo 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 Journal Pre-proof 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 of firms, suggesting that when firms face financial constraints, higher (lower) tangibility of assets ro makes it easier (more difficult) to obtain external financing. Fazzari et al. (1988) report that -p 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) re show that the operating earnings and capital expenditures of financially constrained firms are lP significantly affected during periods of negative macro-economic shocks, suggesting that the effect na 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 ur credit. This suggests that the risk of financial constraint is real. Consistent with this idea, Whited 2.3 Jo 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 9 Journal Pre-proof 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, of 2006) shows that firms facing financial constraints tend to accumulate cash, suggesting that the ro availability of cash can help firms tide over periods of financial constraints. However, these studies -p 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 re how to reduce the likelihood of financial constraints. lP Efficient working capital management is important because it enables companies to remain na 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 ur firms, Ding et al. (2013) show that good working capital management could alleviate the effect of Jo 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. 10 Journal Pre-proof 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, of bankruptcy risk, debt load, financial constraints, etc. Indeed, Fazzari and Petersen (2003) note that ro financial constraints could actually depress working capital investment. Similarly, using a sample -p of Brazilian firms, de Almeida and Eid Jr (2014) find that investment in working capital is actually value reducing. re Based on the discussion above, we state our first (refutable) hypothesis in the null form as lP follows: constraints. na Hypothesis 1: Efficient working capital management is not associated with future financial ur Prior research finds that financial constraints affect firm value. Lamont et al. (2001) show that Jo 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. 11 Journal Pre-proof 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 of capital management is to improve liquidity and ensure that assets are put to their most productive ro use.4 As the above discussion suggests, firms that are able to management their working capital -p 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 re investment and value for financially constrained firms that have higher cash holdings. This leads lP to our second hypothesis (in the alternate form). share prices. Research Methodology Measuring Financial Constraints Jo 2.4.1 ur 2.4 na 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 12 Journal Pre-proof 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 of subjective information to measure firms' financial constraint. They measure the level of constraints using the ro frequency of negative words within the entire 10-Ks. This is because the tone of managers' words capture -p 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. re To generate financial constraints, we start with the annual reports of all the Australian Stock lP Exchange Listed firms for the period 2000-2016, obtained from the Connect4 database. We con- na 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 ur is consistent with Bodnaruk et al. (2015). Jo 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 13 Journal Pre-proof 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 of working capital management are based on prior research (for example, Deloof, 2003; Ding et al., Model to Test Hypothesis 1 -p 2.4.3 ro 2011). We estimate the following model to test Hypothesis 1: re 𝐹𝐶𝑖𝑡 = 𝛽0 + 𝛽1 𝑊𝐶𝑀𝑖𝑡−𝑛 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑𝑗 𝛾𝑗 𝐼𝑛𝑑𝑗 + lP ∑𝑡 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡 (1) na 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 ur working capital. More efficient working capital is captured by higher values of current ratio, quick Jo 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 14 Journal Pre-proof 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 of capital management. In order to test this hypothesis, we first sort firms into terciles of working ro capital management (measured as above). Then, for each tercile, we estimate the following model: -p 𝑃𝑖𝑡+1 = 𝛽0 + 𝛽1 𝐹𝐶𝑖𝑡 + 𝛽2 𝑀𝑇𝐵𝑖𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽4 𝐿𝑒𝑣𝑖𝑡 + 𝛽5 𝑅𝑂𝐴𝑖𝑡 + 𝛽6 𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + ∑𝑗 𝛾𝑗 𝐼𝑛𝑑𝑗 + (2) re ∑𝑡 𝛿𝑡 𝑌𝑒𝑎𝑟𝑡 + 𝜖𝑖𝑡 lP 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 na association between financial constraints and profitability and investment described above, we ur 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 Jo 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. 15 Journal Pre-proof 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 of utility firms from our sample. This leads to a further loss of 160 observations. Following ro convention, we winsorise our variables of interest in the 1st and 99th percentiles of their -p distribution. Our sample consists of 4,422 firm-year observations with non-missing values of our re 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 lP mean (median) value of FC is 0.427 (0.426). This suggests that 0.43 percent of the words used by na 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) ur report a mean value of 0.69 for their full sample. Since ours is the first paper to use this measure Jo 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. 16 Journal Pre-proof (-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 of (1.820), and the mean (median) quick ratio is 5.128 (1.360). This is consistent with Xu et al. ro (2013). -p The mean total assets of our sample firms is $45.90 million, suggesting that larger firms re dominate our sample. The mean market-to-book ratio is 3.146, and the mean leverage is 0.777. lP 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). na (Insert Table 3 here) ur 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, Jo 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. 17 Journal Pre-proof 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 of Size are negative (coefficient=-0.001, and -0.008 respectively; p-value=0.001 and 0.000 ro respectively).7 Consistent with Korajczyk and Levy (2003), we find that the coefficient on Lev is -p positive (coefficient=0.003; p-value=0.038). (Insert Table 4 here) re From Panel B, we note that the coefficient on WCM is positive for all columns 1 and 2 lP (coefficient=0.026, and respectively; p-value=0.006, and 0.001 respectively). This suggests that na 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 ur constraint over the next two years. This result is consistent with Panel A, and further supports above. 4.2 Jo 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. 18 Journal Pre-proof 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 of negatively affect firm value, consistent with the idea that good working capital management reduces ro the negative impact of financial constraints on firm value. We note that the coefficient on MTB, -p Size, and StdRet are positive (coefficient=0.025, 0.228, and 0.003 respectively; p-value=0.001, re 0.000, and 0.014). These results are consistent with prior research (see for example, Barth, 2017). lP (Insert Table 5 here) From Panel B, we see that the coefficient on FC is negative for column 2 (coefficient=-5.695; p- na value=0.003), suggesting that financially constrained firms have lower valuations when their cash ur 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 Jo 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 19 Journal Pre-proof 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 of for test- ing Hypothesis 2. If analysts do recognise the importance of good working capital ro management, we predict that their target prices would be lower for firms with lower (higher) cash -p ratios (cash conversion cycles). We present these results in Table 6. re We present results for the three terciles of cash ratio in the three columns of Panel A, Table 6. lP 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 na 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 ur with the idea that such firms are more likely to face liquidity issues in the future. In contrast, the Jo 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 Journal Pre-proof 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 of results for cash ratio in Panel A above, we could argue that if financially constrained firms with ro high cash conversion cycles have large cash balances, it likely alleviates analysts’ concerns about -p 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 lP 5 re working capital, when issuing target price forecasts. na 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 ur capital management. We also test the robustness of our results by explicitly controlling for the Jo 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 21 Journal Pre-proof 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. of (Insert Table 7 here) ro We present the results of Hypothesis 2 in Table 8. Hypothesis 2 predicts that the negative -p association between financial constraints and future share price is lower for firms with more re efficient working capital management. As discussed above, we estimate equation (2) to test the lP 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 na 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 ur esti- mated coefficient 𝛽̂1is negative for terciles 1 and 3 of current ratio (coefficient=-3.605 and -1.931 Jo 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 22 Journal Pre-proof 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 of with higher cash ratios are more likely to be financially constrained over the next three years ro (coefficient=0.312, 0.390, and 0.461 respectively; p-value=0.000 in all three columns). In contrast, -p 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 re consistent with Tables 3 and 4. However, this result is not surprising because the SA Index is a lP poor proxy of financial constraint. Indeed, as Bodnaruk et al. (2015) show, the SA Index (and na similar measures) predict liquidity events very poorly. (Insert Table 9 here) Controlling for the effect of the Global Financial Crisis ur 5.3 Jo 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) 23 Journal Pre-proof 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=- of 0.035; p-value=0.000) and that on the cash conversion cycle is positive (coefficient=0.026; p- ro value=0.006), consistent with Tables 4 and 5 above. This shows that our results are not driven by -p the crisis. re (Insert Table 10 here) lP 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 na cycle) in column 1 (column 2). Consistent with Table 4, we find that the coefficient on cash ratio is ur 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 Jo 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 24 Journal Pre-proof 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 of (4) ro In equation (4) above, the dependent variable is working capital management (cash ratio and -p 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 re true, we would observe a negative (positive) coefficient on financial constraints when the lP 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 na coefficient on FC it is not significant (p-value=0.455 and 0.206 respectively). This suggests that ur current financial constraints do not affect future working capital management. This result alleviates Jo 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 25 (5) Journal Pre-proof 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 of management firm, we identify one control firm with the closest propensity score within a caliper ro of 0.001. We use this approach following prior research (Rosenbaum and Rubin, 1983). We then -p 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 re 12 compares the descriptive statistics of the observable firm characteristics for the high and low lP working capital samples. As the Table shows, the two samples are not significantly different in na 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 ur the results for the cash ratio (cash conversion cycle). For brevity, we only report results for financial Jo 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 26 Journal Pre-proof 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 of 6 ro We examine whether efficient working capital management reduces the likelihood of firms fac- -p 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 re they could lead to firms exiting the market (Musso and Schiavo, 2008). Given the serious impact lP of financial constraints on firms’ prospects, it is important to understand whether the likelihood na of financial constraints could be reduced by suitable corporate strategy. We examine the role of working capital management in this context. ur Working capital is an important source of liquidity (Fazzari and Petersen, 1993). Therefore, Jo 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 27 Journal Pre-proof 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 of to absorb the liquidity shocks created by financial constraints. Prior research (Ding et al., 2013) ro shows that financially constrained firms with more investment in working capital can invest to a -p 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 re higher one year ahead prices and higher analyst target prices, suggesting that efficient working lP capital policies can mitigate the adverse effects of financial constraints on firm value. na 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 ur Specifically, the 2018/19 Working Capital Report by PricewaterhouseCoopers reveals that firms Jo 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. 28 Journal Pre-proof 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 Jo ur na lP re -p ro of a limitation of our study. 10 We thank the anonymous reviewer for pointing this out. 29 Journal Pre-proof References Aktas, N., E. Croci, and D. Petmezas (2015). Is working capital management value-enhancing? Evidence from firm performance and investments. Journal of Corporate Finance 30, 98– 113. Almeida, H. and M. Campello (2007). Financial constraints, asset tangibility, and corporate investment. The Review of Financial Studies 20(5), 1429–1460. Almeida, H., M. Campello, and M. S. Weisbach (2004). The cash flow sensitivity of cash. The of Journal of Finance 59(4), 1777–1804. ro Baños-Caballero, S.and García-Teruel, P. J. and P. Martínez-Solano (2014). Working capital management, corporate performance, and financial constraints. Journal of Business Re- -p search 67(3), 332–338. re Baños-Caballero, S., P. J. García-Teruel, and P. Martínez-Solano (2012). How does working capital management affect the profitability of spanish SMEs? Small Business Economics lP 39(2), 517–529. Barth, M. E., S. F. Cahan, L. Chen, and E. R. Venter (2017). The economic consequences na associated with integrated report quality: capital market and real effects. Accounting, ur Organizations and Society 62, 43–64. Belghitar, Y. and J. Khan (2013, sep). Governance mechanisms, investment opportunity set and Jo SMEs cash holdings. Small Business Economics 40(1), 59–72. Beyer, A., D. Cohen, T. Lys, and B. Walther (2010). The financial reporting environment: Review of the recent literature. Journal of Accounting and Economics 50 (2-3), 296–343. Blinder, A. S. and L. J. Maccini (1991). The resurgence of inventory research: What have we learned? Journal of Economic Surveys 5(4), 291–328. Bodnaruk, A., T. Loughran, and B. McDonald (2015). Using 10-k text to gauge financial constraints. Journal of Financial and Quantitative Analysis 50(4), 623–646. Buehlmaier, M. M. M. and T. M. Whited (2018). Are financial constraints priced? Evidence from textual analysis. The Review of Financial Studies 31(7), 2693-2728. Campello, M. and L. Chen (2010). Are financial constraints priced? evidence from firm fundamentals and stock returns. Journal of Money, Credit and Banking 42(6), 1185–1198. 30 Journal Pre-proof Campello, M., E. Giambona, J. R. Graham, and C. R. Harvey (2011, apr). Liquidity management and corporate investment during a financial crisis. Review of Financial Studies 24(6), 1944–1979. Campello, M., J. R. Graham, and C. R. Harvey (2010). The real effects of financial constraints: Evidence from a financial crisis. Journal of Financial Economics 97(3), 470–487. de Almeida, J. R. and W. Eid Jr (2014). Access to finance, working capital management and company value: Evidences from Brazilian companies listed on BM&FBOVESPA. Journal of of Business Research 67(5), 924–934. DeFond, M., M. Hung, and R. Trezevant (2007, mar). Investor protection and the information ro content of annual earnings announcements: International evidence. Journal of Accounting -p and Economics 43(1), 37–67. Deloof, M. (2003). Does working capital management affect profitability of Belgian firms? re Journal of Business Finance & Accounting 30(3-4), 573–588. lP Denis, D. J. and V. Sibilkov (2009). Financial constraints, investment, and the value of cash holdings. The Review of Financial Studies 23(1), 247–269. na Desai, M. A., C. F. Foley, and K. J. Forbes (2007). Financial constraints and growth: Multinational and local firm responses to currency depreciations. The Review of Financial Studies ur 21(6), 2857–2888. Ding, S., A. Guariglia, and J. Knight (2013). Investment and financing constraints in China: Jo Does working capital management make a difference? Journal of Banking & Finance 37(5), 1490–1507. Emery, G. W. (1984). A pure financial explanation for trade credit. Journal of Financial and Quantitative Analysis 19(3), 271–285. Erel, I., Y. Jang, and M. S. Weisbach (2015). Do acquisitions relieve target firms’ financial constraints? The Journal of Finance 70(1), 289–328. Faulkender, M. and R. Wang (2006). Corporate financial policy and the value of cash. The Journal of Finance 61(4), 1957–1990. Fazzari, S. M., R. G. Hubbard, and B. C. Petersen (1988). Financing constraints and corporate investment. Brookings Papers on Economic Activity 1, 141–195. 31 Journal Pre-proof Fazzari, S. M. and B. C. Petersen (1993). Working capital and fixed investment: New evidence on financing constraints. The RAND Journal of Economics 24(3), 328–342. Fergusson, A., P. Lam, and N. Ma. (2019). Further evidence on mandatory partner rotation andaudit pricing: a supply-side perspective. Accounting and Finance 59, 1055–1100 Gan, J. (2007). Financial constraints and corporate investment: Evidence from an exogenous shock to collateral. Journal of Financial Economics 85(3), 709–734. Glendening, M., E. Mauldin, and K. W. Shaw (2019). Determinants and consequences of of quantitative critical accounting estimate disclosures. The Accounting Review. Hadlock, C. J. and J. R. Pierce (2010). New evidence on measuring financial constraints: ro Moving beyond the KZ index. The Review of Financial Studies 23(5), 1909–1940. -p Hoberg, G. and V. Maksimovic (2014). Redefining financial constraints: A text-based analysis. re The Review of Financial Studies 28(5), 1312-1352. Kaplan, S. N. and L. Zingales (1997). Do investment-cash flow sensitivities provide useful lP measures of financing constraints? The Quarterly Journal of Economics 112(1), 169-215. Kent, P., J. Routledge, and J. Stewart (2010). Innate and discretionary accruals quality and na corporate governance. Accounting & Finance 50(1), 171–195. Kieschnick, R., M. Laplante, and R. Moussawi (2013). Working capital management and share- ur holders’ wealth. Review of Finance 17(5), 1827–1852. Jo Korajczyk, R. A. and A. Levy (2003). Capital structure choice: Macroeconomic conditions and financial constraints. Journal of Financial Economics 68(1), 75–109. Kuppuswamy, V. and B. Villalonga (2015). Does diversification create value in the presence of external financing constraints? Evidence from the 2007–2009 financial crisis. Management Science 62(4), 905–923. Laeven, L. (2003). Does financial liberalization reduce financing constraints? Financial Management 32(1), 5–34. Lamont, O., C. Polk, and J. Saaá-Requejo (2001). Financial constraints and stock returns. The Review of Financial Studies 14(2), 529–554. Laurent, C. R. (1979). Improving the efficiency and effectiveness of financial ratio analysis. Journal of Business Finance & Accounting 6(3), 401–413. 32 Journal Pre-proof Leuz, C., D. Nanda, and P. Wysocki (2003). Earnings management and investor protection: an international comparison. Journal of financial economics 69(3), 505–527. Love, I. (2003). Financial development and financing constraints: International evidence from the structural investment model. The Review of Financial Studies 16(3), 765–791. Musso, P. and S. Schiavo (2008). The impact of financial constraints on firm survival and growth. Journal of Evolutionary Economics 18(2), 135–149. Ratti, R. A., S. Lee, and Y. Seol (2008). Bank concentration and financial constraints on firm- of level investment in Europe. Journal of Banking & Finance 32(12), 2684–2694. Rosenbaum, P. R. and D. B. Rubin (1983). The central role of the propensity score in observa- ro tional studies for causal effects. Biometrika 70(1), 41–55. -p Schiff, M. and Z. Lieber (1974). A model for the integration of credit and inventory manage- re ment. The Journal of Finance 29(1), 133–140. Shin, H.-H. and Y. S. Park (1999). Financing constraints and internal capital markets: Evidence lP from Korean chaebols. Journal of Corporate Finance 5(2), 169–191. 19(2), 531–559. na Whited, T. M. and G. Wu (2006). Financial constraints risk. The Review of Financial Stud- ies Xu, Y., E. Carson, N. Fargher, and L. Jiang (2013). Responses by Australian auditors to the ur global financial crisis. Accounting & Finance 53(1), 301–338. Jo Zeidan, R. and O. M. Shapir (2017). Cash conversion cycle and value-enhancing operations: Theory and evidence for a free lunch. Journal of Corporate Finance 45, 203–219. 33 Journal Pre-proof 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 Journal Pre-proof 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 Journal Pre-proof 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 Journal Pre-proof 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 Journal Pre-proof 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 Journal Pre-proof 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 Journal Pre-proof 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 Journal Pre-proof 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 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) ...
Purchase answer to see full attachment