Chapter IV. Empirical Results
6. Robustness Check
6.2 Time Fixed Effect and Auto-correlated Error Terms
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6.2 Time fixed effect and auto-correlated error terms
In this study, I separately test two distinct sample periods to address the potential contamination effects between these two severe economic downturns and do not control for the yearly fixed effect because it might capture the trends of economic cycles. On the other hand, there might be heteroscedasticity or serial correlation existing in the error terms. I address the issue of auto-correlated error terms and time fixed effects as follows.
First, I add yearly dummy variables for 1995-1999 and 2001-2004 in the regression model of the 2000 dot-com crash and yearly dummy variables for 2003-2007 and 2009-2012 in the regression model of the 2008 financial crisis. In unreported results, I find that most of the findings are robust to the time-fixed effect. Second, I employ the Newey-West adjustment to address the concerns about heteroscedasticity and autocorrelation of error terms. After considering an auto-correlated error terms, I find that FCpoor firms tend to increase payout ratios during the 2000 dot-com crash and are not valued lower during the 2008 financial crisis but are more likely to increase the long-term debt and to default during the 2008 financial crisis.
Overall, FCpoor firms may increase dividends, possibly to cater to investors’ preferences during the severe downturns, but I find stronger evidence that financially constrained firms are more likely to default during the severe downturns if they saved less cash previously.
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Chapter V
How cash holdings evolved following the 2000 dot-com crash
In this section, I address whether firms learned from their experience of a prior severe economic downturn. If so, according to H4, I expect that firms that experienced the 2000 dot-com crash are more likely than other firms to accumulate cash before the 2008 financial crisis.
In addition, among surviving firms, those that suffered from liquidity problems in the 2000 dot-com crash should be more aggressive cash savers.
In Table 8, I first report the average cash ratios for three types of firms according to their survival period: those delisted between 2001 and 2008 (non-surviving firms), firms that stayed listed during both the 2000 dot-com crash and the 2008 financial crisis (surviving firms), and firms that went public after 2000 (new public firms). In general, all firms tended to accumulate cash positions after 2000. Surviving firms increased their cash ratio by 2.6% from 2000 to 2006, whereas non-surviving firms increased their cash ratio only by 0.4%. During 1999–2007, surviving firms increased their cash ratio by 1.7%, whereas non-surviving firms decreased their ratio by 0.8%. Therefore, surviving firms are better prepared than non-surviving firms.
After 2004, the average cash ratio of new public firms was greater than that of other groups of firms. Because new public firms usually make more capital investments than other firms, raw cash ratios may not perfectly measure whether firms are short of cash. Following DeAngelo, DeAngelo, and Stulz (2010), I estimate a firm’s free cash as cash and marketable securities (CHE) minus equity-issuance proceeds (SSTK) and lagged capital expenditure (CAPX).22 If the measure of free cash is negative for a firm, it lacks cash. According to the proportion of firms lacking cash within each group, I observe that new public firms are more likely to be out of cash, although they hold more liquid assets. Moreover, surviving firms are
22 According to DeAngelo et al. (2010), firms are short of cash if their remaining cash is insufficient to support the current capital investments and they do not receive the external proceeds from equity-issuance.
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less likely to be short of cash after 2000 compared with other firms: 17.9% of surviving firms eliminated their liquidity problems between 2000 to 2007. In summary, although surviving firms may not have increased their liquidity positions much more than other firms, they actually were better prepared for the 2008 financial crisis after experiencing the 2000 dot-com crash.
[TABLE 8 ABOUT HERE]
I next use regression Model (4) to examine which type of surviving firm was most likely to accumulate cash following the 2000 dot-com crash.
Panel A of Table 9 reports the results. The results of Model (1) show that surviving firms with a smaller size or higher leverage ratios were more likely to increase their cash balances between 2000 and 2007. After controlling for a firm’s liquidity condition, the results of Models (2)–(4) indicate that the firms with the highest –DD values in 2000 did not significantly change their cash holdings between 2000 and 2007. In contrast, surviving firms that saved the least cash before the 2000 dot-com crash, or lacked cash in 2000, significantly increased their cash holdings after the 2000 dot-com crash. Overall, consistent with H4, surviving firms with a smaller size, higher leverage ratios, or fewer liquid assets in 2000 tended to increase their cash positions following the 2000 dot-com crash.
[TABLE 9 ABOUT HERE]
I further examine whether surviving firms benefit from their saving behavior in terms of a reduction of their default likelihood. Specifically, by employing regression Model (3), I test whether the surviving firms that saved more cash during 2000 and 2007 were less likely to default during the 2008 financial crisis. Small firms, high-leverage firms, and firms with fewer liquid assets in 2000 were more likely to increase cash ratios after 2000, so I regress the change in the value of –DD between 2008 and 2009 on these firm characteristics to examine the
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benefits of cash savings for surviving firms.23 In Panel B of Table 9, except for FCpoor, firms that tended to save more cash after 2000 significantly reduced their default likelihood during the 2008 financial crisis. In summary, smaller firms, higher leverage firms, and firms with fewer liquid assets were more likely to increase their cash positions after they experienced the 2000 dot-com crash. Consistent with H5, this change in cash positions then helped the surviving firms decrease their default likelihood during the 2008 financial crisis.
23 I do not directly regress the changes in –DD on surviving firms’ cash savings from 2000 to 2006, because the extant literature and my findings in Table 5 already show that cash savings may not decrease a firm’s default risk (Acharya et al., 2012; Arnold, 2014). In contrast, I focus on whether the type of surviving firms that tend to be better prepared after a crisis are less likely to default during the next economic downturn.
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Chapter VI Conclusion
This study relies on the 2000 dot-com crash and the 2008 financial crisis as natural experiments to examine the value of pre-saved cash during severe market downturns. Using the previous cash-saving inclinations of financially constrained firms to investigate the value derived from a precautionary motive of cash holdings, I produce several findings. First, financially constrained firms tend to raise their levels of capital investment during severe economic downturns when they have pre-saved more cash. Second, constrained firms with fewer cash pre-savings receive excess returns that were 37% or 73% lower than other firms during the 2000 dot-com crash and the 2008 financial crisis, respectively. Third, consistent with Acharya et al.’s (2007) argument that constrained firms use different combinations of cash and debt to enhance firm value, I find that constrained firms that pre-saved more cash tended to increase their long-term debts before 2000. Therefore, constrained firms were more likely to default during the 2000 dot-com crash, even if they had pre-saved more cash. Fourth, smaller firms, higher leverage firms, and firms with liquidity problems in 2000 tended to accumulate cash positions following the dot-com crash, and these firms were less likely to default during the 2008 financial crisis.
This article adds to the literature on corporate cash holdings by confirming that pre-saved cash helps financially constrained firms in several ways when external financing is costly (Gamba and Triantis, 2008; Ang and Smedema, 2011; Bolton et al., 2013). My study corroborates the value of cash holdings resulting from a precautionary motive, and it thus supports this precautionary motive for cash savings, as advocated in prior literature (Opler et al., 1999; Almeida et al., 2004; Faulkender and Wang, 2006; Riddick and Whited, 2009; Bates et al., 2009; Denis and Sibilkov, 2010; McLean, 2011).
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There are several intriguing directions for future research. Although I closely examine the financially constrained firms to focus on the possible effect of the precautionary motive, it is difficult to completely exclude the possible effect of the agency motive of cash savings during severe economic downturns. For example, managers may pre-save cash based on a precautionary argument, but they may spend that pre-saved cash in unprofitable projects in a few years. Because my empirical setting captures only the firm performance during severe economic downturns, it would be interesting and informative to test the performance of financially constrained firms’ investments and acquisitions to gain a better understanding of the support (or lack thereof) for the precautionary hypothesis with more empirical tests.
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Table 1 Summary statistics
This table shows summary statistics of the sample from 1995 to 2013. Market-to-book ratios is defined as book assets minus book equity plus market equity all divided by book assets. R&D/Sales is equal to 0 when R&D is missing. Dividends payer is an indicator variable which is equal to 1 in years in which a firm pays a common dividend, and 0 otherwise.∆Cash is the difference between cash at fiscal year t and cash at fiscal year t–1, divided by book assets at fiscal year t–1. Cash flow volatility is measured as the standard deviation of industrial cash flows to assets in the previous 10 years for each firm-year. Issuance-cash savings are the cash saved from the proceeds of share issuance. Proceeds of equity issuance is an item from the statement of cash-flows which includes SEOs, private placements, right offerings, stock sales through direct purchase plans, preferred stock issues, the conversions of debt and preferred stock, exercise of employee options, grants, and benefit plans. Debt-cash savings are the cash saved from debt sales. Cash-flows cash savings are the cash saved from cash that flowed from operations. Other cash savings are the cash saved from the sales of assets and investments. Each source of savings is estimated following the method of McLean (2011). Financial distress is estimated following the method of Charitou et al. (2013). Financially constrained firms (FC) are the firms with size–age (SA) index values in the top tercilewithin the year cohort. SA index is estimated as -0.737 (Size) + 0.043 (Size2) – 0.040 (Age), according to Hadlock and Pierce (2010). FCrich (FCpoor) is a dummy variable defining financially constrained firms that saved the most (least) cash during the past three years. In each year, I separate financially constrained firms into quintiles based on the ∆Casht-3 and define the firms in the top (bottom) quintile as FCrich (FCpoor).
Mean Median SD Minimum Maximum
Panel A: Firm characteristics
Assets 1137.135 144.087 2965.191 0.194 16973.000
Ln (Market-to-book ratios) 1.754 1.297 1.635 0.532 21.843
Cash flows volatility 0.103 0.093 0.056 0.006 0.573
Cash flows/Assets 0.028 0.068 0.200 -2.283 0.270
Net working capital/Assets 0.128 0.132 0.231 -2.389 0.586
Capital expenditure/Assets 0.063 0.045 0.063 0.000 0.436
Leverage/Assets 0.236 0.208 0.216 0.000 1.820
R&D/Sales 0.084 0.000 0.378 0.000 3.473
Dividends payer 0.430 0.000 0.495 0.000 1.000
Acquisitions/Assets 0.016 0.000 0.047 0.000 0.304
Net equity/Assets 0.018 0.000 0.114 -0.144 1.233
Net debt/Assets 0.010 0.000 0.091 -0.358 0.527
Panel B: Types of Savings
Cash/Assets 0.145 0.074 0.180 0.000 0.879
∆Cash -0.001 0.000 0.089 -0.879 0.879
Issuance-cash savings 0.013 0.000 0.045 -0.020 0.339
Debt-cash savings 0.002 0.000 0.015 -0.053 0.080
Cash-flows cash savings 0.020 0.021 0.056 -0.238 0.178
Other cash savings 0.002 0.000 0.013 -0.045 0.080
Panel C: Financial distress and financially constrained
Financial distress -3.303 -2.686 12.850 -47.257 34.718
Financially constrained 0.149 0.000 0.356 0.000 1.000
FCrich 0.030 0.000 0.170 0.000 1.000
FCpoor 0.030 0.000 0.170 0.000 1.000
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Table 2 Time trends of cash holdings
This table reports the trends of cash holding from 1995 to 2013. Cash is cash and marketable securities divided by book assets. Issuance is the cash saved from the proceeds of share issuance. Proceeds of equity issuance is an item from the statement of cash-flows which includes SEOs, private placements, right offerings, stock sales through direct purchase plans, preferred stock issues, the conversions of debt and preferred stock, exercise of employee options, grants, and benefit plans. Debt is the cash saved from debt sales. Cash flows are the cash saved from cash that flowed from operations. Other is the cash saved from the sales of assets and investments. Each source of cash savings is estimated following the method of McLean (2011).
Cash measures
N Cash Issuance Debt Cash flows Other
1995 5,396 0.161 0.025 0.001 0.021 0.001
1996 5,395 0.175 0.026 0.003 0.016 0.001
1997 5,194 0.178 0.022 0.003 0.012 0.002
1998 5,058 0.178 0.022 0.000 0.015 0.001
1999 4,830 0.185 0.021 0.002 0.014 0.002
2000 4,479 0.178 0.024 0.004 0.012 0.002
2001 4,069 0.182 0.019 0.001 -0.004 0.000
2002 3,780 0.187 0.012 0.002 -0.001 0.001
2003 3,540 0.202 0.025 0.006 0.013 0.001
2004 3,369 0.215 0.028 0.000 0.014 0.000
2005 3,180 0.214 0.021 0.002 0.018 0.003
2006 2,992 0.212 0.020 -0.002 0.014 0.001
2007 2,808 0.208 0.023 0.002 0.015 -0.001
2008 2,628 0.195 0.010 0.003 0.001 -0.001
2009 2,529 0.208 0.013 0.002 0.011 0.001
2010 2,433 0.208 0.017 0.003 0.021 0.002
2011 2,377 0.204 0.018 0.003 0.015 0.000
2012 2,358 0.199 0.016 0.002 0.011 0.001
2013 2,308 0.209 0.019 0.002 0.014 0.001
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Table 3 Use of cash during severe downturns
This table reports the panel regression results regressing the changes in the firm’s different decisions including capital expenditures, dividends, and acquisitions on financially constrained firms with different cash saving inclinations. Dot-com crash is defined as the year 2000; financial crisis is defined as the year 2008 (Bliss et al., 2015). FCrich (FCpoor) is a dummy variable defining financially constrained firms that saved the most (least) cash during the past three years. In each year, I separate financially constrained firms into quintiles according to the ∆Casht-3 and define the firms in the top (bottom) quintile as FCrich
(FCpoor). Financially constrained firms are those with size–age index values (SA) or cash flow volatility (CFV) in the top tercile within the year cohort (Hadlock and Pierce, 2010). In Columns (3) and (6), financially constrained firms are firms with cash flows (CF) in the bottom tercilewithin the year cohort.
CFV is measured as the standard deviation of industrial cash flows to assets in the previous 10 years for each firm-year. In all regressions, I follow Harford et al. (2008) and control for logged assets (Size);
market residual, or the market model residual calculated over the estimation period; sales growth (SG);
working capital which is net of cash (NWC); leverage (LEV); and price-earnings ratios (P/E). SG, NWC, LEV, and P/E are averaged over the prior four years. All regressions control for firm fixed effect, and T-statistics reported in parentheses are based on firm-clustered standard errors (Peterson, 2009). ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively.
Crisis 2000 Dot-com crash 2008 Financial crisis
Sample period 1995-2005 2003-2013
Measure of constraints SA CFV CF SA CFV CF
(1) (2) (3) (4) (5) (6)
Panel A: Using ∆Capital expenditurest as dependent variable
Crisis ∗ FCrich 0.011*
Panel B: Using ∆Dividendst as dependent variable
Crisis ∗ FCrich -0.010
Panel C: Using ∆Acquisitionst as dependent variable
Crisis ∗ FCrich 0.008*
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Table 4 Effect of savings on firm’s value during severe downturns
This table reports the panel regression results regressing the firm’s excess stock return on different decisions. Excess return is the difference between a firm’s annual stock return and its value-weighted benchmark return calculated by 25 Fama and French portfolios based on size and book-to-market. Dot-com crash is defined as the year 2000; financial crisis is defined as the year 2008 (Bliss et al., 2015).
FCrich (FCpoor) is a dummy variable defining financially constrained firms that saved the most (least) cash during the past three years. In each year, I separate financially constrained firms into quintiles based on the ∆Casht-3 and define the firms in the top (bottom) quintile as FCrich (FCpoor). Financially constrained firms are the firms with size–age index values (SA) or cash flow volatility (CFV) in the top tercilewithin the year cohort (Hadlock and Pierce, 2010). In Columns (3) and (6), financially constrained firms are the firms with cash flows (CF) in the bottom tercilewithin the year cohort. CFV is measured as the standard deviation of industrial cash flows to assets in the previous 10 years for each firm-year. In all columns, I further control for changes in cash flows (∆CF), net assets (∆NA), R&D (∆RD), interest expense (∆XINT), and dividends (∆DVC). I also control for leverage (LEV), net financing (NF), and lagged cash in the model (Faulkender and Wang, 2006). All regressions control for firm fixed effect, and T-statistics reported in parentheses are based on firm clustered standard errors (Peterson, 2009). ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively.
FCrich (FCpoor) is a dummy variable defining financially constrained firms that saved the most (least) cash during the past three years. In each year, I separate financially constrained firms into quintiles based on the ∆Casht-3 and define the firms in the top (bottom) quintile as FCrich (FCpoor). Financially constrained firms are the firms with size–age index values (SA) or cash flow volatility (CFV) in the top tercilewithin the year cohort (Hadlock and Pierce, 2010). In Columns (3) and (6), financially constrained firms are the firms with cash flows (CF) in the bottom tercilewithin the year cohort. CFV is measured as the standard deviation of industrial cash flows to assets in the previous 10 years for each firm-year. In all columns, I further control for changes in cash flows (∆CF), net assets (∆NA), R&D (∆RD), interest expense (∆XINT), and dividends (∆DVC). I also control for leverage (LEV), net financing (NF), and lagged cash in the model (Faulkender and Wang, 2006). All regressions control for firm fixed effect, and T-statistics reported in parentheses are based on firm clustered standard errors (Peterson, 2009). ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively.