*We thank Laura Field, Peter Iliev, Eliezer Fich, Kristine Hansen, William Johnson, Dasol Kim, and seminar participants at Drexel University and Shanghai Advanced Institute of Finance for valuable comments.

Cash Holdings and the Effects of Pre-IPO Financing in Newly Public Firms

Christa H.S. Bouwman Case Western Reserve University

Wharton Financial Institutions Center E-mail: christa.bouwman@case.edu

Phone: (216) 368-3688

Michelle Lowry*

Penn State University E-mail: mlowry@psu.edu

Phone: (814) 865-1483

July 9, 2012

Abstract:

Newly public firms hold far higher levels of cash than mature firms, and these substantially higher cash holdings remain relatively stable for extended periods after the IPO. This is puzzling since many measures of firm growth, which are generally hypothesized to be related to demand for cash, converge to those of mature firms over this period. Interestingly, newly public firms are unique in the extent to which they benefit from these high cash holdings: among newly public firms a high cash portfolio significantly outperforms a lower cash portfolio, but we do not find a similar relation among mature firms. We posit that newly public firms’ cash holdings and the associated benefits are driven by uncertainty regarding the availability of post-IPO financing. Those with pre-IPO syndicated loans (one-third of our sample) likely face the least uncertainty. Consistent with this conjecture, both the high level of cash holdings and the benefits of these cash holdings are restricted to the subsample of newly public firms without syndicated loans prior to the IPO.

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1. Introduction

Cash serves numerous purposes for a firm. It is needed for operating liquidity, and it also

furbishes a firm’s internal capital market for financing investments. However, cash is also a “lazy”

asset, directly reducing the return the firm earns on its assets. In addition, excess cash also exacerbates

the “free cash flow” problem articulated by Jensen (1986). Against this backdrop of both benefits and

costs of cash holdings, the aggregate record high cash holdings of companies in recent years have

received considerable attention. Bates, Kahle and Stulz (2009) document that these record aggregate

cash balances are driven by the particularly high cash holdings of newly public firms. This finding

suggests that the often-posed question ‘are market-wide cash holdings too high’ is perhaps not the

appropriate question. Rather, much can be learned from a more in-depth examination of the cash

holdings of newly public firms.

The cash holdings of newly public firms far exceed those of their more mature and visible

counterparts. The median public firm (excluding firms that have gone public within the past five

years) holds 8% cash/assets. More extreme cases such as Exxon-Mobil and Microsoft, which have

been widely criticized for their excessive cash holdings, held 14% cash/assets in 2008 (and only 3 –

6% of assets in 2010).1 In comparison, the median newly public firm holds 44% cash/assets in the year

of the IPO. Moreover, the high cash holdings of newly public firms persist long after the IPO. Three,

four, and five years after the IPO, the median newly public firm still holds 25% or more of its assets in

cash.

This finding of extreme persistence in newly public firms’ cash positions, for five or more

years after the IPO, represents the first contribution of our paper. The characteristics of IPO firms such

as high growth and high risk suggest that these firms should have high precautionary demands for cash

around the time of the IPO. However, prior literature shows and we confirm that these factors

attenuate in the years following the IPO. For example, Swaminathan and Purnanandam (2004) show

                                                            1 Mature firms of similar size to IPO firms have median cash over assets of 11%, where comparable size is defined as assets between $23 million and $243 million (the 25th and 75th percentile, respectively, of IPO firms’ assets).

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that sales growth decreases substantially over the five years following the IPO, and Brav, Michaely,

Roberts and Zarutskie (2009) suggest that firm risk also decreases after the IPO. While decreases in

growth and risk suggest that cash balances should decline in the years following the IPO, we instead

find that cash holdings are relatively stable for an extended period.

The contrast between the persistence in newly public firms’ cash holdings for years after the

IPO versus the decrease in factors generally perceived as capturing demand for cash is puzzling.

Empirical tests in this paper, however, suggest that newly public firms benefit from these high cash

positions. When we categorize newly public firms into two portfolios based on their cash holdings, we

find that the high cash portfolio significantly outperforms the low cash portfolio, over a period of 36

months. In contrast, we find no evidence of significant performance differences between the high cash

and low cash portfolios among samples of mature firms. Thus, while accumulating cash is frequently

considered a sign of management inefficiency and free-cash-flow problems (e.g., Jensen, 1986), our

results suggest that for newly public firms high cash levels may be beneficial.

In an effort to understand both the persistence in newly public firms’ cash holdings and the

superior performance of IPO firms with high cash positions, we consider in more depth the ways in

which IPO firms differ from their mature counterparts. We focus on the information asymmetry and

general uncertainty surrounding firms, and the ways in which such factors affect firms’ ability to

access external capital markets. The more uncertain information environment typical of newly public

firms suggests that these firms will both find it more difficult to accumulate an optimal level of cash

and face greater uncertainty regarding the availability of future external financing. While in a

Modigliani-Miller type world with perfect information and no transactions costs there is no incentive to

hold high cash, the unique information environment surrounding newly public firms potentially causes

cash to be particularly valuable to these companies.

The premise that market frictions, for example as result from a lack of perfect information,

contribute to young firms’ demands for cash on hand is supported by theoretical literature. Myers and

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Majluf (1984) highlight the ways in which asymmetric information can complicate external capital

raising, and Dittmar and Thakor (2007) show how disagreement between management and investors

regarding firm value can cause firms’ access to liquidity to dry up at a moment’s notice. Boot and

Thakor (2011) show that cash offers benefits of financial flexibility to firms, and theoretical models of

Acharya, Almeida, and Campello (2007) and Gamba and Triantis (2008) suggest that these benefits

would be particularly important for IPO firms. The model of Acharya et al. implies that firms with

investment opportunities arriving in low cash flow states and firms with external financing constraints

should retain more cash. Notably, compared to mature firms, IPO firms are characterized by both low

cash flows and uncertainty regarding future financing. Gamba and Triantis’ model indicates that such

firms will command a relatively large premium for liquidity provided by cash holdings. Indeed,

Faulkender and Wang (2006) find that the marginal value of liquidity is higher for firms with lower

liquidity, with greater investment opportunities, and with higher external financing constraints.

To explore the extent to which market frictions contribute to the high cash holdings of IPO

firms, we categorize our sample based on a proxy for the difficulty of obtaining post-IPO financing.

Based on a broad literature on relationship banking, we posit that the presence of a pre-IPO syndicated

loan represents such a proxy. The banking literature has emphasized that banks invest in customer-

specific information (e.g., Allen, 1990; Ramakrishnan and Thakor, 1984; Diamond, 1984) and that the

ability to reuse this information intertemporally (e.g., Greenbaum and Thakor, 2007; Freixas and

Rochet, 2008) allows them to create long-term relationships that enhance the likelihood of continued

access to credit for the customer.2 Thus, all else equal, newly public firms that have obtained pre-IPO

syndicated loans will find it easier to obtain further bank financing after the IPO, compared to their

counterparts that have not formed such a relationship. Knowing that they have easier access to future

                                                            2 The empirical literature on the benefits of relationship banking suggests that: the announcement effect of bank loans, in particular renewals, is positive (Lummer and McConnell, 1989); the duration of the bank-borrower relationship positively affects the availability of credit (Petersen and Rajan, 1994; Berger and Udell, 1995); intertemporal smoothing of contract terms increases availability of funds to young firms (Petersen and Rajan, 1994). See Bhattacharya and Thakor (1993) and Boot (2000) for in-depth overviews of the theoretical and empirical relationship lending literature.

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financing suggests that the benefit of holding cash is lower for such firms and thus they will rationally

opt to operate with lower cash balances.

Approximately one-third of firms going public raise money through syndicated loans prior to

the IPO. Consistent with predictions, firms with such loans are substantially more likely to raise post-

IPO financing through similar channels. Specifically, 69% of firms with pre-IPO syndicated loans

raise additional syndicated loans within the first five years following the IPO, compared to only 38% of

firms without pre-IPO syndicated loans.3 Through a series of both hazard models and models that

control for the endogeneity of the source of pre-IPO financing, we confirm that the presence of a pre-

IPO loan increases the probability of obtaining post-IPO financing (in particular post-IPO syndicated

loans). In contrast, backing by a venture capitalist prior to the IPO does not appear to have a similar

effect.

Consistent with inferences from the relationship banking literature, this greater assurance

regarding future financing contributes to lower cash holdings among firms with pre-IPO syndicated

loans. Median cash as a percent of assets among firms with a pre-IPO syndicated loan is 9% in the

year prior to the IPO, and 14% and 15% in years 3 and 5 after the IPO. In comparison, median cash as

a percent of assets is substantially higher among firms without such a loan: 25% in the year prior to the

IPO, and 52% and 49% in years 3 and 5 after the IPO. The previously documented extraordinarily

high cash holdings of newly public firms are concentrated within the subset of IPO firms that faces the

most uncertainty regarding the availability of future financing, i.e., those without pre-IPO syndicated

loans.

To the extent that a pre-IPO syndicated loan reduces uncertainty regarding future financing, we

would expect firms with such loans to have both a higher probability of survival and lower benefits of

excess cash holdings. Our last set of results supports both these conjectures. First, a hazard analysis

indicates that the presence of a pre-IPO syndicated loan significantly decreases the probability of

                                                            3 Firms with pre-IPO syndicated loans are also slightly more likely to have an SEO: 31% of the firms with a pre-IPO loan compared to 27% of the non-loan firms have an SEO within the five years following the IPO.

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delisting for poor performance. Notably, we find no such relation among mature firms, who arguably

face less uncertainty regarding the availability of future financing. Second, long-run returns analyses

support the conclusion that the benefits of cash are lower for firms with pre-IPO syndicated loans, a

conclusion that follows from the previous findings that these firms have both lower levels of cash and

higher rates of survival. Among firms without pre-IPO syndicated loans, firms with high cash holdings

significantly outperform those with low cash holdings. Specifically, a portfolio of high-cash firms

outperforms a portfolio of low cash firms by an average 60 to 90 basis points per month, over a three

year period. In contrast, we find no such difference for firms with pre-IPO syndicated loans, i.e.,

among firms without such uncertainty regarding the availability of future financing.

Our paper contributes to the literatures on both IPO firms and the value of cash. While a

variety of papers suggest that the growth rates and risk levels of IPO firms decrease in the years

following the IPO, we find that cash levels are quite persistent. Moreover, IPO firms appear to benefit

from these high cash holdings: controlling for firm characteristics, firms with higher cash balances

earn higher risk-adjusted returns. While IPO firms have significantly higher cash holdings than mature

firms for at least five years after the IPO, we find no evidence that these high cash holdings are value-

depleting – in fact, our results suggest exactly the opposite.

Our findings also highlight the role of syndicated loans prior to the IPO. There exists a

considerable literature on the role that venture capitalists play and their effects on firms both prior and

subsequent to the IPO. In contrast, we know relatively little about the importance of pre-IPO

syndicated loans, despite the fact that one-third of our sample firms have such a loan prior to going

public. Our results suggest that the presence of such a loan is associated with dramatic decreases in

future financing uncertainty.

The remainder of the paper proceeds as follows. Section 2 describes our data and provides

descriptive statistics. Section 3 examines the evolution of IPO firms’ versus mature firms’ cash

balances and compares these patterns to the evolution of IPO firm characteristics. Section 4

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investigates the effects of cash holdings on firm survival. Section 5 examines the link between IPO

firms’ cash balances and pre-IPO financing, and Section 6 asks whether the market recognizes the

benefits of higher cash holdings. Section 7 concludes.

2. Data

Our sample consists of newly public firms between 1996 and 2009. Because we follow firms

for five years following the IPO, we restrict IPO offer dates to the 1996 to 2004 period. The sample of

IPOs is obtained from Thomson Financial. As is standard in the IPO literature, we exclude financial

firms, utilities, firms with offer prices of less than $5, REITs, and unit offerings. Because several of

our analyses involve a matched sample of mature firms, where firms are matched based on year and

industry, we also exclude IPO firms that fall within the ‘other industry’ group (based on the Fama-

French 12 industry groupings). From Thomson Financial, we collect data on the offer price, offer date,

proceeds raised, and identity of the underwriters. We obtain stock price data from CRSP and

accounting data from Compustat. All accounting ratios are winsorized at the 1% and 99% levels.

Following Field and Karpoff (2002) and Loughran and Ritter (2004), we also obtain firm founding

dates to calculate firm age at the IPO.4

We collect information on IPO firms’ financing from five years before until five years after the

IPO. Information about venture capital (VC) backing is obtained from the ThomsonOne

VentureExpert database. Firms are assumed to be VC-backed if they received financing from one or

more venture capitalists before the IPO. Seasoned equity offering (SEO) data are obtained from the

Thomson Financial New Issues Database. Information about pre- and post-IPO bank financing is

retrieved from Loan Pricing Corporation’s (LPC) Dealscan database.5 Dealscan contains information

                                                            4 We thank Jay Ritter for making these data available on his website: http://bear.warrington.ufl.edu/ritter/FoundingDates.htm. 5 We thank Michael Roberts for sharing identifiers which allow us to match Dealscan loan data with Compustat accounting data. Chava and Roberts (2008) discuss the creation of these identifiers.

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on large loans to both public and private companies.6 While the vast majority of these are syndicated

loans, the data do include some sole-lender loans. For simplicity, we refer throughout the paper to these

loans as syndicated loans.

Many of our analyses employ the sample of IPO firms as well as a sample of matched mature

firms. The mature firm sample is formed as follows: for each IPO firm (call it firm i) we randomly

select one mature firm without replacement (defined as a company that has not gone public within the

past five years; call it firm j) in the same Fama-French industry as firm i, with Compustat data in the

year of firm i’s IPO, and with a CRSP stock price in the month of firm i’s IPO.7

We follow firms for up to five years. For IPO firms, we measure year 1 cash holdings at the

first fiscal year end following the IPO date. For mature firms, we analogously measure year 1 as the

first fiscal year end following the offer date of the matched IPO firm. The literature has used various

measures of cash, including cash/total assets, cash/net assets, log(cash/assets), and cash/sales (e.g.,

Pinkowitz and Williamson (2007); Pinkowitz, Stulz, and Williamson (2006); Dittmar and Mahrt-

Smith (2007); Bates et al (2009)). For IPO firms, cash/net assets is unlikely to be a good measure as it

is highly skewed: IPO firms hold a substantial portion of their assets in cash meaning the denominator

of cash/net assets is rather small (even negative) in many cases. Cash/sales is skewed as well because

some IPO firms have relatively low sales around the time of the IPO. Following Bates et al. (2009),

we therefore measure cash as cash/total assets.

To control for firm characteristics as they relate to demand for cash, many of our tests focus on

excess cash. Following Harford, Mansi and Maxwell (2008) and Opler, Pinkowitz, Stulz, and

Williamson (1999), excess cash is defined as the residual from a regression of cash / assets on various

determinants of cash. The models are based on the premise that firms with higher growth

                                                            6 Dealscan contains 50%-75% of the value of all commercial loans in the U.S. during the early 1990s, and covers an even greater fraction of these loans from 1995 onward (Carey and Hrycray (1999)). 7 We match firms based on Fama-French 12 industries to ensure the availability of sufficient mature firms. Using a 49-industry grouping, the portion of all listed firms in certain industry-years that represent newly public firms is so high that we are unable to obtain sufficient mature firms to conduct a match. In all regressions, we include fixed effects based on Fama-French 49 industries as additional controls. We do not match on additional factors such as firm size, because the identification of mature firms of similar size to IPO firms disproportionately selects distressed firms.

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opportunities, less internal cash flow, higher risk, fewer cash substitutes, and less access to capital

markets have higher demands for cash and thus should operate with more cash. We follow the Harford

et al. specification to calculating excess cash, with minor adjustments to accommodate the more

limited data available for IPO firms (in particular for periods prior to the IPO). To obtain the best

possible estimates of excess cash, regressions are estimated over the entire Compustat universe. We

compute excess cash for each firm in our sample as the residual from this cash regression. Precise

variable descriptions are provided in Appendix I, and additional details regarding the excess cash

specification as well as the tabulated regression are shown in Appendix II.

Table 1 provides descriptive statistics on our sample, where all accounting data represent

median values for the first fiscal year end following the IPO. Our sample consists of 2,089 IPO firms.

Looking first at column one in Panel A, the median IPO firm has 46% of its assets in cash in the year

following the IPO. Not surprisingly, this percentage is a substantially lower 35% (not shown for

brevity) in year 2, after the firm has had time to invest proceeds raised.

Prior literature has demonstrated that cash holdings are related to a variety of firm

characteristics. Table 1 provides descriptive evidence on these characteristics (defined in more detail

in Appendix I), and they are used as controls in subsequent regressions. Consistent with their high

growth rates, IPO firms tend to have low leverage (2% book leverage), low profits (1% EBIT/TA), and

high sales growth (40% growth rate in the year prior to the IPO).

In addition, Table 1 also provides IPO-specific descriptive statistics. The median IPO firm is 7

years old. Among IPO firms, the median underwriter rank is 8, where rank is defined following Carter

and Manaster (1990) as updated by Carter, Dark, and Singh (1998) and Loughran and Ritter (2004).

Average initial returns equal 33%, and the median firm raised $46 million in proceeds, which

represents 124% of pre-IPO assets. These statistics are comparable to those shown in other papers for

samples of IPO firms over similar time periods.

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Columns two through five of Panel A show the comparable statistics for subsamples of IPO

firms. Many of our later analyses relate to sources of pre-IPO financing, and as shown here many firm

characteristics vary across pre-IPO financing subsamples. We categorize our IPO sample into four

subgroups: “VC Only” (where VC = pre-IPO VC backing), “Loan Only” (where loan = pre-IPO

syndicated loan), “VC plus Loan”, and “Neither Financing”. Approximately 35% of our sample falls

into the VC-Only subgroup and 32% into the Neither Financing group. The remaining observations

are spread equally between Loan Only and VC plus Loan groups. Consistent with expectations, firms

with pre-IPO syndicated loans tend to be larger, and have higher leverage. Firms with VC backing

tend to be younger, and firms with neither source of pre-IPO financing tend to be smaller.

Finally, column six provides statistics regarding the matched mature firms. Consistent with

common notions regarding mature firms versus IPO firms, the mature firms have substantially lower

cash holdings (9% of assets).

Panel B shows the industry distribution of the IPO sample. The largest concentration of firms

falls within the Business Equipment industry. As noted earlier, finance firms, utilities, and firms in the

other category are excluded from the sample.

3. Determinants of IPO firms’ cash holdings

Table 1 shows that IPO firms have substantially higher cash holdings than matched mature

firms, but that IPO firms also differ on a number of other dimensions that suggest they have higher

demands for cash. Moreover, data shown in Table 1 represent cash holdings at the first fiscal year end

following the IPO, a point when cash holdings may be mechanically high as a result of the IPO

proceeds raised. Figure 1 examines the evolution of cash holdings for IPO firms and mature firms.

Panel A of Figure 1 shows median cash/TA for IPO firms and for matched mature firms, from

the year before the IPO until 5 years after the IPO. The year 1 numbers were shown in Table 1, and

not surprisingly IPO firms’ cash balances are highest in this year, immediately following the IPO.

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Cash decreases markedly between year 1 and year 2 as proceeds are invested and used to cover

expenses. A striking observation from Figure 1 is the persistence in cash holdings after year 2. Cash

decreases slightly between years 2 and 3, and then stays virtually constant. Moreover, cash remains

significantly higher for IPO firms than for mature firms. Five years after the IPO, the median IPO

firm’s cash is nearly double that of mature firms in the same industry groupings (27% vs 11%).

Panel B shows that this persistence is robust to limiting the sample to firms that survive

throughout the entire five-year period. Moreover, Panel C indicates that this persistence is robust to

excluding the IPO Bubble period, which we define following Lowry, Officer, and Schwert (2010) as

IPOs going public between September, 1998 and August, 2000. As discussed by Lowry et al,

Loughran and Ritter (2004), and others, this period was a very unique time in the IPO markets along a

variety of dimensions, and we want to ensure that our results are not driven by these offerings.

Figure 2 provides initial evidence regarding the role of firm characteristics in explaining IPO

firms’ high cash balances. Prior literature shows that firms with higher growth opportunities and

greater risk levels have higher demands for cash. Around the time of the IPO, IPO firms rank highly

on both of these dimensions, making it not surprising that they hold substantially more cash than their

more mature counterparts. However, the persistence in cash holdings documented in Figure 1 raises

the question of whether these higher growth opportunities and risk levels similarly persist far beyond

the IPO. Figure 2 examines this issue by showing a variety of growth and risk metrics, for both IPO

firms and matched mature firms for years 1 to 5.

Panels A to E examine five common metrics of growth opportunities: sales growth, capital

expenditures/assets, acquisitions/assets, market-to-book, and R&D/sales.8 The first four of these five

measures suggest that the growth opportunities of IPO firms have converged to those of the matched

mature firms within three to five years after the IPO. Specifically, IPO firms’ sales growth and

acquisition expenditures/total assets are virtually indistinguishable from those of the matched mature

                                                            8 We show medians for all these metrics except for acquisitions/assets: mean acquisitions/assets are shown because medians equal 0 for both sets of firms.

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firms by year 5, and capital expenditures/total assets and market-to-book of the two groups of firms are

virtually equal by year 3. These patterns differ dramatically from the persistent difference in cash

holdings between these two sets of firms. In contrast, Panel E provides some evidence that the growth

options of IPO firms continue to be substantially higher than those of the matched mature firms:

R&D/sales is substantially higher for the IPO firms even in year 5.

As shown in Panel F, IPO firms appear to have higher risk than mature firms throughout the

entire five year period following the IPO, as evidenced by their higher volatility of stock returns.

While the difference is not large, it is extremely persistent.

One possible factor causing firms to hold high levels of cash, even in the absence of

abnormally high growth opportunities, would be low cash flows. Firms without the assurance of a

regular stream of cash flows will have greater demands for cash, to protect themselves from

uncertainty related to raising external financing. Panel G of Figure 2 provides some support for this

conjecture. By year 5 after the IPO, there has been some convergence between the EBIT/TA of IPO

firms and mature firms, but that of mature firms remains considerably higher.

Table 2 presents regressions that test the statistical significance of the relations shown in

Figures 1 and 2. The sample consists of both IPO firms and matched mature firms, and the table shows

OLS regressions of cash regressed on various firm-specific characteristics to capture firm growth and

firm risk, plus a dummy variable to indicate whether the firm is an IPO firm. In columns 1 and 3 the

dependent variable is cash/assets, with column 1 measuring both the dependent variable and all control

variables in year 3, i.e., the third fiscal year end after the IPO, and column 3 focusing on year 5.

Industry fixed effects based on the Fama-French 49 industries as well as calendar year fixed effects are

included, and robust standard errors are reported in parentheses.

Consistent with inferences from Figure 1, results in columns 1 and 3 provide strong evidence

that IPO firms hold significantly higher cash balances than mature firms. Three years after the IPO,

IPO firms have on average 10.3% greater cash/assets than matched mature firms, even after controlling

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for a battery of firm-specific characteristics. Moreover, five years after the IPO this gap has closed

little, with IPO firms still holding 8.4% more in cash / assets.

As an alternate way to capture differences in firms’ demands for cash, we compare firms’

excess cash holdings, the calculation of which is detailed in Appendix II. We use these excess cash

estimates as dependent variables in two alternative specifications in Table 2. Specifically, in columns

2 and 4 of Table 2 the excess cash estimates of our IPO firms and matched mature firms are regressed

on the IPO dummy and control variables. Notably, inferences based on excess cash are qualitatively

similar to those discussed earlier where cash/assets was the dependent variable. IPO firms have

significantly higher excess cash than mature firms, both three and five years after the IPO.

4. Do newly public firm benefit from their cash holdings?

4.1 Relation between excess cash and firm performance

While Table 2 shows that IPO firms hold significantly more cash than mature firms in the same

industry and year, it says nothing about whether these high cash holdings are good or bad. If all firms

hold the optimal amount of cash, then we would expect to find no relation between excess cash and

firm performance. However, it is possible that newly public firms are more limited than mature firms

in their ability to accumulate an optimal amount of cash, for example because they have a relatively

short track record which makes it difficult for these high information asymmetry firms to obtain

external financing. There are substantial costs associated with insufficient cash balances, including the

inability to undertake positive NPV projects. To the extent that the market cannot / does not

completely price in such dynamics, we will observe a positive relation between excess cash holdings

and subsequent performance.

Panel A of Table 3 examines this conjecture. In the top portion of the panel, we consider all

newly-listed firms at the end of their first fiscal year following the IPO. We divide the firms into two

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categories, those with above-median excess cash at this point and those with below-median excess

cash, and we follow them for 36 months. Thus, each month those newly public firms whose first fiscal

year end just passed get added to one of the portfolios, and those whose first fiscal year end was more

than 36 months ago exit the portfolios.9 Using a calendar-portfolio approach, each month we calculate

the returns (net of the risk free rate) on each portfolio, and we regress these returns on the three Fama-

French (1993) factors plus the Carhart (1997) momentum factor. The Fama and French factors include

the market return minus the risk-free rate (Rm–Rf), returns on a portfolio of small firms minus returns

on a portfolio of big firms (SMB), and returns on a high-book-to-market portfolio minus returns on a

low-book-to-market portfolio (HML). The momentum factor is defined as returns on a high-

momentum portfolio minus returns on a low-momentum portfolio (UMD). The intercept from such a

regression, which is commonly referred to as the alpha, can be interpreted as a measure of abnormal

performance.

The bottom portion of Panel A shows results of a similar analysis, with the exception that firms

are placed into either the high excess cash or low excess cash portfolio based on their cash balances at

the end of their second (instead of first) fiscal year end. The first fiscal year end may not be the best

time to measure firms’ cash holdings: firms whose first fiscal year ends one or two months after the

IPO likely hold much of the recently obtained proceeds in cash, whereas firms whose first fiscal year

ends ten or eleven months after the IPO would have had more time to invest the proceeds. For these

reasons, it is potentially better to focus on cash holdings at the end of the second fiscal year.

Focusing on the t=2 analysis, which is likely to be less noisy, results provide strong support for

the conjecture that firms with higher excess cash holdings outperform those with lower excess cash

holdings. Specifically, the high excess cash portfolio exhibits abnormal performance of 70 basis points

per month, relative to the low excess cash portfolio. The finding is a strong one, given that portfolios

                                                            9 The excess cash measure is not defined prior to the firm going public, thus preventing us from forming portfolios at the time of the IPO. Moreover, partitioning firms at the time of the IPO, i.e., based on pre-IPO cash levels, is similarly noisy: firms are likely less focused on their cash levels at this point as they expect to receive large infusions of cash in the near future.

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are formed based on excess cash holdings, meaning they abstract from the effects of firm

characteristics. Moreover, by definition these abnormal returns tests are forward looking: evidently

the market does not fully appreciate the effects of market frictions and the inability of firms with

insufficient cash to realize their potential.

Panel B replicates the same analysis, but on the sample of matched mature firms. Notably, we

find no evidence of higher excess cash firms having an advantage within this sample. This contrast is

consistent with our conjecture that IPO firms face more uncertainty regarding the availability of

external financing compared to mature firms. As a result, IPO firms with insufficient cash are forced

to bypass positive NPV projects and will exhibit poorer performance than other firms with higher cash

balances. Mature firms are less sensitive to such uncertainties, and therefore do not benefit from

excessive cash holdings.

4.2 The importance of pre-IPO financing

We posit that both the higher cash balances of IPO firms and the greater benefits of cash to IPO

firms are driven by uncertainty regarding the availability of future financing. To investigate this

conjecture more thoroughly, we consider the types of IPO firms that will be most versus least sensitive

to such uncertainty. We turn to the relationship banking literature for guidance. As highlighted by

Allen (1990), Ramakrishnan and Thakor (1984) and Diamond (1984), banks develop customer-specific

information that increases their ability to offer continued access to credit over time.10 In addition,

Panel A in Table 1 indicates that these firms tend to have higher profits, providing further evidence that

uncertainty regarding access to external financing is less of a concern. Based on this literature, we

posit that firms with pre-IPO syndicated loans face considerably less uncertainty regarding the

availability of subsequent financing, and thus will tend to hold less cash.

Figure 3 presents preliminary evidence on this issue. As a starting point, panel A categorizes

all IPO firms into quartiles based on the level of cash/assets prior to the IPO. We then follow these

                                                            10 See Greenbaum and Thakor (2007) and Freixas and Rochet (2008) for in-depth discussions.

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firms for five years. Panel A plots median cash/assets for each quartile, in years 1 to 5. The first thing

to notice is that the stability we observed in the overall sample of IPO firms holds for each of these

quartiles. The high-cash firms’ cash holdings are just as persistent as those of the low-cash firms.

Second, median cash / assets among the highest quartile firms is over 55%, five years after the IPO.

In unreported results, we examine the median sales growth and R&D/Sales for these same four

quartiles (i.e., quartiles are still defined based on cash/TA in the year before the IPO). We find that

sales growth rates slow down markedly, with the four quartiles converging by year 3. That is, by year

3, the high cash portfolio firms have growth rates equal to those of low cash firms, even though their

cash balances are 14 times as large (56% vs 4%). R&D/Sales similarly decreases for the high cash

firms, although it remains substantially higher than for firms in the low cash portfolios (which have

median R&D/Sales of 0).

In Panel B, we similarly group the IPO firms into four portfolios, but here the grouping is

based on the source of pre-IPO financing. Consistent with descriptive statistics presented in Table 1,

we divide firms into four groups: Loan Only, VC Only, VC plus Loan, and Neither Financing. Panel

B shows that the VC Only firms have the highest levels of cash holdings. Three to five years after the

IPO, these firms continue to hold nearly 50% of their assets in cash. It is startling that it can be optimal

for these firms to have nearly half of their assets in nonproductive uses. In contrast, the Loan Only

firms have 5% cash/TA, a percentage that is small and extremely stable over time.

Looking across panels A and B of Figure 3, the similarity in the graphs suggests that the firms

in the highest cash/TA quartile are those firms that are venture backed. In contrast, firms in the lowest

cash/TA quartile are the Loan Only firms. Both other groups (VC plus Loan, and Neither Financing)

fall between these two extremes. In sum, Figure 3 highlights the extent to which both pre-IPO and

post-IPO cash balances are related to the sources of pre-IPO financing.

16

Tables 4 and 5 examine these relations between source of pre-IPO financing and cash balances

in a multivariate framework. The format of Table 4 is similar to that of Table 2, with the exception

that we attempt to differentiate not only between the cash holdings of IPO firms versus mature firms,

but also between the cash holdings of different groups of IPO firms, where IPO firms are grouped

according to their source of pre-IPO financing. The sample consists of IPO firms and matched mature

firms, with columns 1 and 2 focusing on cash holdings three years after the IPO and columns 3 and 4

five years after the IPO. For each time period, we estimate regressions using both cash/assets and

excess cash/assets as the dependent variable. Independent variables of interest include the IPO dummy

interacted with variables to indicate whether or not a firm has a given form of financing. Thus, we

have IPO dummy*VC dummy, IPO dummy*Prior Loan dummy, and IPO dummy*Neither Financing

dummy. By construction, every IPO firm falls into at least one of these categories, where firms having

both VC backing and a pre-IPO syndicated loan will have both the VC dummy and the Prior Loan

dummy equal to 1. Finally, we also include a Loan Prior to Year 0, which equals one for any IPO and

mature firm that has a syndicated loan within the five years prior to the IPO offer date (or offer date of

the matched IPO firm for mature firms). Additional control variables include the same ones used in

Table 2.

Results in Table 4 are consistent with inferences from Table 3. Looking first at column 1,

where the dependent variable is cash/assets three years after the IPO, we see that all three IPO

interactions are significantly positive, indicating that IPO firms have significantly more cash than

matched mature firms. However, looking at the coefficient on IPO * VC dummy compared to that on

IPO * Loan Prior to Year 0 Dummy, we see that the difference in cash holdings is far smaller for firms

that have a prior loan. This is consistent with their greater assurance regarding the availability of

future financing. Findings using excess cash/assets as the dependent variable and findings five years

after the IPO are similar.

17

Somewhat puzzlingly, Table 4 shows that firms with neither form of pre-IPO financing, i.e.,

neither VC backing nor a loan, also hold only moderately higher cash balances than their mature

counterparts. These firms should face just as much uncertainty regarding future financing as their

venture-backed counterparts, suggesting that they should hold much higher cash balances. It is

possible that these firms have been less successful in accumulating substantial cash balances, for

example because they lack the assistance of a venture capitalist, even though they would benefit from

such positions. The next section of the paper discusses this issue further.

One concern with the Table 4 analysis is endogeneity. If VC backed firms are higher quality,

then they might have been better able to raise more cash prior to the IPO, in the IPO, and/or after the

IPO, all of which potentially contribute to higher cash positions three and five years following the IPO.

We note that this concern is at least somewhat mitigated by the fact that VC-backed firms and pre-IPO

loan firms both received certification from financiers and should therefore be just as likely to be high

quality – however, we find no evidence that pre-IPO loan firms hold higher cash positions.

Nevertheless, Table 5 re-examines the relation between pre-IPO financing and cash positions,

controlling for endogeneity.

Columns 1 – 3 of Table 5 focus on the effects of a pre-IPO loan on post-IPO cash levels, and

columns 4 – 6 focus on the effects of VC backing. We follow Wooldridge (2002) and Angrist and

Pischke (2009) in our estimation. Looking first at columns 1 – 3, our first step is to estimate a probit

regression of Pre-IPO Loan on firm age at the time of the IPO, which should be related to whether a

firm has such a loan but not to cash holdings three and five years later, plus various controls. We

obtain the fitted variable from this regression and use that as an instrument in the regression of year

three excess cash holdings (reported in column 2) and year five cash holdings (reported in column 3).11

Our use of firm age at the IPO to identify pre-IPO Loan is based on the reasonable assumption that

                                                            11 Results are qualitatively similar if we run a standard IV regression in which we use firm age at the IPO (instead of the fitted values from the probit) as the instrument in the second stage regression.

18

firms that are older at the time of the IPO are more likely to have a syndicated loan. In contrast, after

three to five years as a public firm, the number of years the firm operated under a private status is

unlikely to be related to post-IPO cash holdings. In column 4, following the findings of Chen,

Gompers, Kovner and Lerner (2010) we use VC Location to identify VC backing, where VC Location

equals one if the firm is located in the New York City, San Francisco, or Boston CMSA, zero

otherwise.

Both the log of firm age at the IPO and the VC Location dummy are highly significant in their

respective regressions, and results from Angrist-Pischke weak instrument tests suggest that our

instruments are not weak. After controlling for endogeneity, we continue to find that IPO firms with

pre-IPO syndicated loans operate with substantially lower cash levels than other IPO firms, while IPO

firms with VC backing operate with substantially higher cash.

4.3 Does Pre-IPO Financing reduce uncertainty regarding Post-IPO financing?

Evidence presented in Tables 4 and 5 demonstrates that newly public firms with pre-IPO

syndicated loans tend to hold lower cash balances than otherwise similar firms without such loans. We

have conjectured that these lower cash balances stem from the lower uncertainty these firms face

regarding the availability of future financing. Tables 6 through 8 examine this conjecture more

directly, with Table 6 presenting descriptive statistics and Tables 7 and 8 showing regression analyses

that examine the extent to which pre-IPO financing affects post-IPO financing. For example, do

relationships formed prior to the IPO with loan syndicates contribute to a higher probability of

obtaining additional syndicate loan financing in later years?

Table 6 categorizes all IPO firms into the same four groups analyzed earlier: VC Only (735

firms), Loan Only (341 firms), VC plus Loan (340 firms), and Neither Financing (673 firms). The first

row presents evidence on proceeds raised in the IPO as a fraction of pre-IPO assets, across the four

19

groups. The VC Only firms raise the highest proceeds, a notable finding given that this group also has

the highest cash/assets prior to the IPO.

The next set of rows provides evidence on the frequency of post-IPO financing. Without

question, the ‘Loan Only’ firms have the highest incidence of future financing. Eighty-four percent of

these firms either issue equity through an SEO or raise money through a syndicated loan within the

first five years following the IPO. Firms with both pre-IPO loans and venture backing are slightly less

likely to raise future financing, with 70% of these firms having either an SEO or syndicated loan within

five years of the IPO. In comparison, among firms in the VC Only group and the Neither Financing

groups the comparable percentage ranges from 50 to 56%. Total dollars raised through these forms of

financing yield similar conclusions, with firms having pre-IPO loans raising substantially higher

amounts of money in the subsequent five years after the IPO. For example, among firms in the Loan

Only and VC and Loan subgroups, the median total dollars raised through syndicated loans and SEOs

in the five years following the IPO equals 84% and 49% of year 1 assets. In comparison, firms in the

VC Only and Neither Financing groups, the analogous percentage is less than 20%. Mean dollars

raised as a percent of year 1 assets (not tabulated) also yield similar conclusions: firms with pre-IPO

loans have an average 131%, compared to 97% for firms without such loans.

As we noted earlier, it is puzzling that firms with Neither Financing hold relatively modest

levels of cash. Absent the certification effects of either a venture capitalist or a banker, we would

expect these firms to be characterized by particularly high information asymmetries and find it difficult

to access external capital. The finding, as reported in Table 6, that only about half of these firms raise

financing subsequent to the IPO supports this intuition. One possible explanation is that these firms

have been unable to access sufficient funding in the past; in fact, the absence of either VC backing or

syndicated loans prior to the IPO is a strong indication that these firms may be especially constrained.

In addition, their cash flows both prior to and subsequent to the IPO are relatively low: 4% cash

flow/assets in year -1 and 0.9% in year 1 (not shown for brevity). We note that the average delisting

20

rates for this group of firms provides strong support for the conjecture that this group of firms is

operating at below-optimal cash levels. As shown in Table 6, among the four groups of firms, the

Neither Financing group has by far the highest levels of delisting for poor performance. While

approximately 8% of firms in each of the other groups delist for poor performance within the first three

years after the IPO, 15% of the Neither Financing group delist. Similarly, 13-18% of firms in each of

the other groups delist within the first five years, compared to 26% of the Neither Financing firms.

The VC Only firms provide an informative contrast to the Neither Financing group. Both

groups exhibit similarly low rates of post-IPO financings, but the VC only firms are no more likely to

delist for poor performance than firms with pre-IPO loans, despite the fact that firms with pre-IPO

loans are much more likely to obtain post-IPO financing. This is potentially because of VC-backed

firms’ higher cash balances prior to the IPO, combined with the greater proceeds raised in the IPO. We

posit that these higher cash balances earlier on are able to sustain them through periods when access to

external capital is limited.

While Table 6 suggests certain relations between pre-IPO financing, post-IPO financing, cash

balances, and delisting rates, the relations shown are all univariate. Tables 7 and 8 provide more robust

evidence on the extent to which pre-IPO financing, in particular syndicated loans, are associated with

higher rates of post-IPO financing. Table 7 examines this issue in a multivariate framework using a

hazard model approach, and Table 8 addresses potential endogeneity concerns. Further examination of

the interplay between pre-IPO financing, delisting rates, and cash balances are left to the next section

of the paper.

Looking first at Table 7, Column 1 investigates whether the presence of VC backing is

associated with a higher likelihood of an SEO in the five years subsequent to the IPO. Venture

capitalists continue to serve on the board for some period after the IPO, and they may assist the firms

in ways that would help it obtain further financing, for example through its connections with

investment bankers and investors. The hazard approach models the probability that the firm will have

21

an SEO. A firm that survives for five years without having an SEO or a firm that leaves the sample for

any other reason, for example a merger or other delisting event, is considered truncated. Column 2 is

similar, with the exception that it models the likelihood of a post-IPO syndicated loan.

Results indicate that, after controlling for other firm characteristics, the presence of a venture

capitalist has no effect on the probability that a firm conducts an SEO. In contrast, the presence of a

pre-IPO syndicated loan dramatically increases the probability that a firm obtains a post-IPO

syndicated loan. This result is striking in the sense that it highlights the extent to which syndicated

loans arranged prior to the IPO continue to benefit the firm for years after their introduction to public

markets. While there exists a considerable literature on the many benefits of venture capitalists, the

benefits of pre-IPO loans have been largely unexplored. Gonzalez and James (2007) find that firms

with pre-IPO bank loans exhibit, on average, superior post-IPO operating performance, and they

attribute this superior performance to banks’ abilities to screen firms ex ante.12 Our results suggest

that, in addition to any screening effects, bank loans also benefit firms by increasing access to post-IPO

financing. In contrast, Sorenson (2007) shows that venture capitalists also actively and successfully

screen firms ex ante, but there is no evidence that firms with VC backing are more likely to obtain

post-IPO financing.

While hazard models have the advantage of accounting for truncated data, it is not

straightforward to control for endogeneity under this approach. Thus, Table 8 shows a two-stage

analysis. Similar to Table 5, the log of firm age at the IPO is used in the first-stage regression to

identify the presence of a pre-IPO loan, and a VC location dummy (equal to one if the firm is

headquartered in the New York City, San Francisco, or Boston CMSAs) is used to identify the

presence of VC backing. Inferences from this analysis are similar to those from Table 7. The presence

of VC backing does not appear to have any significant effect on the likelihood that a firm will raise

subsequent financing through an SEO. However, firms with pre-IPO syndicated loans are significantly

                                                            12 Gonzalez and James focus on all bank loans, while we restrict our analysis to syndicated loans.

22

more likely to conduct a post-IPO syndicated loan. In economic terms, the impact is substantial:

results from the hazard analysis suggest that firms with pre-IPO loans are 57% more likely to obtain

post-IPO loans, and results from the two-stage analysis estimate the impact at 60%.

Finally, we also examine whether a pre-IPO loan affects the likelihood of a post-IPO SEO, or

whether pre-IPO venture funding affects the likelihood of a post-IPO loan. We find no support for

either relation (results not tabulated).

4.4 Pre-IPO Financing and Post-IPO Survival

Results to this point demonstrate that syndicated loans provide certain advantages to young

firms. Specifically, these firms are able to operate with lower cash balances, in part because they have

greater assurance regarding the availability of future financing. Once a firm has formed a relationship

with a bank, it has a higher probability of benefitting from that relationship in the form of future loans.

For firms at the growth stage of their lifecycle, obtaining additional financing in the years subsequent

to the IPO is likely to be critical. If the presence of a pre-IPO syndicated loan increases the probability

of obtaining such financing, it likely also increases the very probability of survival for these firms:

extreme distress is less likely if the availability of funding is greater.

Table 9 employs both hazard analyses and two-stage regressions to examine this prediction that

the presence of a pre-IPO syndicated loan decreases the probability of delisting for poor performance.

The first two columns of Table 9 show hazard models on our sample of IPO firms. This approach

enables us to model whether a firm delists for poor performance in a given year. In column 1, IPO

firms are followed for up to 5 years following the IPO. Firms that survive up to 5 years without

delisting for poor performance or that are acquired prior to year 5 are considered to be truncated

observations in the hazard model. Explanatory variables include those used in earlier regressions. In

addition, we also control for the log of market value of equity, which Shumway (2001) shows to be an

23

important determinant of bankruptcy. While our dependent variable is delisting for poor performance

rather than bankruptcy, the two are likely related.13

Column 1 of Table 9 provides strong evidence in support of the prediction that newly public

firms with pre-IPO loans are less likely to delist for poor performance. Interestingly, in column 2 we

see that the presence of VC backing has no analogous effect. This is consistent with evidence in prior

tables, that a syndicated loan prior to the IPO substantially affects the likelihood of obtaining post-IPO

financing, whereas the presence of a venture capitalist does not have a similar effect. While a wide

body of literature has highlighted the many positive effects of venture capitalists, these effects appear

to be somewhat focused on the pre-IPO period. After the firm has gone public, their influence wanes.

We note that this waning influence is consistent with the fact that many VCs leave the board within the

first year or two after the IPO.

Column 3 shows that inferences regarding the effects of syndicated loans prior to the IPO on

post-IPO delisting are robust to controlling for endogeneity. Similar to earlier tables, in the first stage

regression we instrument pre-IPO syndicated loan with firm age at the time of the IPO (first stage not

shown in Table 9). In the second stage regression reported in the third column of Table 9, we see that

the Pre-IPO Loan variable continues to be significantly negative.

Finally, column 4 examines whether syndicated loans have similar effects for mature firms.

Using our matched sample of mature firms from earlier analyses, we examine whether mature firms

that had a syndicated loan within the five years prior to the offer date of the matched IPO firm are less

likely to delist for poor performance. To the extent that mature firms face fewer financing

uncertainties, we would expect the presence of such loans to be less important among this sample.

Results confirm this conjecture. This contrast provides additional assurance that endogeneity issues

are not driving the differences we observe among the sample of IPO firms.

                                                            13 Shumway also shows that the Z-score, the distance to default, predicts bankruptcy. Because we are focusing on delisting for poor performance rather than bankruptcy, and because Z score measures were developed on samples of mature firms that differ substantially from those in our sample, we do not use the Z score itself, but note that our control variables include variables similar to the ones that constitute the Z-score.

24

In sum, the survival analyses provide additional evidence on the value of syndicated loans for

IPO firms. The fact that this group of IPO firms both operates with significantly lower cash balances

and has a lower rate of delisting for poor performance is marked.

5. Are the net benefits of excess cash restricted to firms with high future financing uncertainty?

Our results suggest that uncertainty regarding the availability of external financing is extremely

important for newly public firms, that this uncertainty persists for many years after the IPO, and that

the relationship dynamics behind syndicated loans substantially mitigate this uncertainty. In light of

this marked influence of syndicated loans, as evidenced in Tables 4 – 9, we revisit our earlier result

regarding the net benefits of excess cash for newly public firms. Table 3 showed that newly public

firms with higher levels of excess cash outperform those with lower levels of excess cash, but the same

effect did not hold for portfolios of matched mature firms. We posited that uncertainty regarding the

availability of external financing explained this contrast. Putting all these results together, we present a

final test of our hypothesis that newly public firms’ high cash holdings stem from external financing

uncertainty. Specifically, we posit that the positive relation between cash holdings and firm

performance should be stronger among the newly public firms without pre-IPO syndicated loans, as

these are the firms that suffer the most uncertainty regarding the availability of financing.

Table 10 shows alphas from regressions similar to those employed in Table 3. In the top

(bottom) panel of Table 10, we divide IPO firms into portfolios based on their level of excess cash at

the end of the first (second) fiscal year after the IPO. Because we want to separately examine the

importance of cash for different subgroups of newly public firms, we form portfolios within these

subgroups. For example, in the first column, we consider only firms that had a syndicated loan prior to

the IPO, and we divide these firms based on their level of excess cash. Firms with above median

(below median) excess cash holdings are placed into the high excess cash (low excess cash) portfolio.

Using a calendar time portfolio approach similar to that employed in Table 3, firms are maintained in

25

the portfolio for 36 months. We compute excess returns on the portfolio of firms each month and

regress these returns on the four factors: Rm-Rf, SMB, HML, and UMD. For conciseness, we only

report alphas from these regressions. Thus, looking at the first column in the top panel of Table 10,

among newly public firms with a pre-IPO syndicated loan, those with above-median excess cash

holdings at the end of the first fiscal year following the IPO did not exhibit any abnormal performance

(alpha insignificantly different form zero).

Regardless of whether we partition firms based on excess cash holdings at the first or second

fiscal year ends after the IPO, results show a dramatic difference between firms with versus without

pre-IPO syndicated loans. Among firms with a pre-IPO syndicated loan, there is no evidence that

those with higher excess cash outperformed those with lower excess cash, i.e., the alpha on the high

minus low portfolio is insignificantly different from zero. In contrast, among firms without such a

loan, the high excess cash portfolio significantly outperforms the low excess cash portfolio, by an

average 60 to 90 basis points per month. These contrasting results are consistent with cash being

extremely valuable only to those firms that face the most uncertainty regarding the availability of

future financing, i.e., firms without a syndicated loan.

For completeness, we conduct a similar analysis on firms with and without VC backing. In

light of our prior findings that venture capitalist backing has no effect on future financing, we do not

expect to find a difference between the value of excess cash in VC-backed companies compared to that

in non VC-backed companies. Overall, results are broadly consistent with this conclusion. The alphas

on the high minus low portfolio for VC firms and non-VC firms are virtually identical when firms are

partitioned based on end of fiscal year 2 excess cash holdings. While alphas are slightly different

based on the fiscal year 1 portfolio formation, the difference is small and, as noted above, the focus on

excess cash holdings at the first fiscal year end after the IPO is likely to be noisier.

While we have employed a variety of techniques in earlier analyses to address endogeneity

concerns, this calendar time portfolio approach to examining post-IPO returns has the advantage of not

26

being susceptible to endogeneity. Because we examine forward-looking risk-adjusted returns, the

endogeneity driven by firm quality that potentially affects earlier results should not be an issue here.

In sum, IPO firms with pre-IPO loans have a high level of assurance that they will be able to

raise post-IPO financing: approximately 84% of these firms obtain either post-IPO syndicated loans or

conduct an SEO within the first three years following the IPO (Table 6). In addition, the relationship-

banking literature suggests that even firms with a pre-IPO bank relationship that did not raise any

financing after the IPO are more likely to have the ability to do so when needed. Thus, these firms can

afford to maintain relatively low cash balances following the IPO (Figure 3, Tables 4 and 5). The

market neither punishes nor rewards this group of firms based on their cash holdings

In contrast, firms without pre-IPO loans are significantly less likely to raise post-IPO financing:

approximately 53% of these firms raise money through either a post-IPO syndicated loan or an SEO.

The greater uncertainty regarding the availability of future financing means that cash balances are

substantially more important. Consistent with this conjecture, within this sample of IPO firms, i.e.,

those without pre-IPO syndicated loans, the firms with higher excess cash holdings earn higher

abnormal stock returns.

These abnormal return regressions split by pre-IPO funding source are interesting in and of

their own right, but also help address a potential concern that our aggregation of firms by cash holdings

is actually picking up another known determinant of post-IPO performance. Specifically, Brav and

Gompers (1997) and Gonzalez and James (2007) find that IPO firms backed by venture capitalists and

by bankers tend to perform better following the IPO. Moreover, we find that cash holdings are related

to these sources of pre-IPO financing. In the full IPO sample regressions, it is possible that our high-

cash portfolio differs systematically from the low-cash portfolio along these dimensions. This concern

is mitigated by two factors. First, firms with pre-IPO loans have among the lowest cash balances and

these are exactly the firms that Gonzalez and James find to perform better, whereas our full-sample

low-cash portfolio underperforms. Second, none of these issues are relevant in the subgroupings

27

shown in Table 10, where we define cash holdings within the samples of pre-IPO loan firms, non-pre-

IPO loan firms, etc, and we continue to find significant differences between the performance of high

and low cash firms.

7. Conclusion

Bates et al. show that the record-high cash holdings of the overall market in recent years are

driven by newly public firms. We take a new angle towards examining the reasons that recent IPO

firms hold such a high portion of their assets in cash, by investigating more directly the benefits of

these cash holdings. Our results suggest that IPO firms’ cash holdings are extremely persistent up to

five years after the IPO.

We show that these high cash holdings are actually restricted to a subset of newly public firms,

most notably those without pre-IPO syndicated loans. The extraordinarily high cash holdings of this

subset of firms, compared to the substantially lower cash holdings of other firms that similarly just

went public leads naturally to questions regarding the source and the value of these cash balances.

Our results regarding the sources of cash balances as well as their value can be broadly

generalized as follows. IPO firms with pre-IPO syndicated loans have a high degree of certainty

regarding future financing. As a result, they do not need to maintain high cash balances. The market

indeed places no premium on those that do hold more of their assets in cash. In contrast, IPO firms

without pre-IPO syndicated loans face substantial uncertainty regarding the availability of future

financing. These firms protect their operational flexibility by maintaining relatively high cash

balances. Consistent with the value of cash among these firms, those with a higher portion of their

assets in cash earn higher abnormal stock returns.

28

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1

Appendix 1

This Appendix defines variables used as controls in the cash regressions, as well as the variables used to estimate excess cash.

(1) Assets: Due to economies to scale in cash holdings, larger firms may require a lower

portion of their assets to be held in cash. Larger firms may also benefit from easier access

to external financing, also causing them to hold less cash.

(2) Leverage: The book value of interest-bearing debt divided by total assets. Firms with

higher leverage may have lower cash if tend to have lower growth opportunities.

(3) EBIT/TA: Firms with higher EBIT have a lower demand for high cash balances.

However, firms generating higher EBIT also can more easily build up their cash balances.

(4) Return Volatility: For each firm fiscal year, we calculate the standard deviation of daily

returns in each of the first three months prior to the fiscal year end. The mean of these

three monthly standard deviations represents the measure of return volatility for the firm.14

Firms with higher return volatility are likely riskier, meaning they face higher costs of

external finance.

(5) Sales Growth: Sales growth equals the percentage change in sales over the prior fiscal

year. Firms with higher sales growth are more likely to be high growth firms, meaning

they are expected to have higher cash holdings.

(6) CapEx/Assets: To the extent that firms with higher capital expenditures are high growth

firms, we would expect them to hold more cash. Alternatively, if capital expenditures are

lumpy, a large expenditure during year t will cause the firm to have less cash at year end.

(7) R&D/Sales: Firms with higher R&D expenditures are likely to hold higher cash balances.

Such firms tend to face greater information asymmetries, making it more difficult for them

to access external capital markets. Also, the high R&D expenditures indicate that these

                                                            14 Return volatility is measured over a three month period (instead of a longer twelve month period) to enable us to calculate this variable relative to IPO firms’ first fiscal year end. For those IPOs whose first fiscal year ends less than three months after the IPO, return volatility (in year 1) is calculated over this shorter period of time. 

2

firms are growth firms. As noted for industry MB below, a shortfall of cash would be

costly because such firms would have to give up valuable growth opportunities.

(8) Acquisitions/Assets: This is another proxy for growth opportunities, suggesting it would

similarly be positively related to cash holdings. However, because acquisitions/assets is

measured in the same year as the cash holdings, a large expenditure on a cash acquisition

during the year may cause cash holdings to be mechanically lower at year end.

(9) NWC (excl. Cash)/Assets: This is defined as working capital minus cash and cash

equivalents, all divided by assets. Inventories and receivables, two of the primary

components of working capital, can be converted relatively easily into cash. Thus, firms

with higher net working capital balances (as a fraction of assets) are expected to hold less

cash.

(10) Industry MB: Median market-to-book ratio of firms in the same Fama-French 49 industry,

in the same calendar year. Market-to-book is defined as the market value of equity plus the

book value of total assets minus the book value of equity, all divided by the book value of

total assets. Firms in industries with higher market-to-book ratios are more likely to have

higher growth options, and thus higher demands for cash: a shortfall in cash would cause a

high growth firm to give up valuable growth opportunities.

(11) Std Dev of Industry CFs: For each firm in the Compustat universe, we calculate the

standard deviation of cash flows over the prior four years. We then calculate the median of

these standard deviations across all firms in the same Fama-French 49 industry in the same

calendar year. Firms in industries with a higher standard deviation of cash flows are likely

to face greater uncertainty and thus have higher demands for cash.

 

3

Appendix 2 

To estimate excess cash, we estimate an OLS regression of cash/assets of all firms with Compustat and CRSP data on explanatory variables related to demand for cash. This set of explanatory variables follows that used in prior literature, and most of the variables are defined in Appendix 1. Additional variables include industry MB and industry standard deviation of cash flows. Industry MB is defined as the median market-to-book ratio across all firms in the same Fama-French 49 industry grouping. In the spirit of Harford et al. (2008) and Opler et al. (1999), the industry standard deviation of cash flows is calculated as follows. First we compute firm cash flows as (operating income before depreciation – interest expense – taxes) / (assets-cash), second we obtain the standard deviation of each firm’s cash flows over the prior four years, and third we take the median standard deviation across all firms in the same Fama-French 49 industry grouping. Finally, we also include Fama-French 12 industry dummies.15 Robust standard errors are clustered by firm and are shown in parentheses.

Dep’t Variable = Cash / Assets (%)

   

Log(Assets) -1.42*** (0.10)

Leverage -32.32*** (1.00)

Industry MB 4.63*** (0.38)

EBITDA / Assets 1.81 (1.11)

NWC (excl. Cash) / Assets -7.63** (3.41)

Std Dev of Industry CFs 28.96*** (2.99)

R&D / Sales 4.55*** (0.15)

CapEx / Assets -35.07*** (2.30)

Acquisitions / Assets -32.26*** (1.09)

Return Volatility -3.59 (4.97)

Sales Growth 0.04 (0.04)

Number Observations 73,295

                                                            15 We do not define industry dummies at the 49 industry level because a large portion of firms in some of these more narrowly-defined industries are IPO firms, causing the regression to downwardly bias the excess cash estimates of the mature firms in these industries (because the regression forces the average residual, i.e., excess cash, in each industry to be zero).

4

Table 1: Summary statistics

Panel A shows summary statistics on variables used in regressions, and Panel B gives the industry composition (based on twelve Fama-French industry groupings) of different samples. The IPO firm subsample consists of firms that went public between 1996 and 2004, and the mature firm (i.e., firms that have been public for at least five years) subsample consists of one matching firm for each IPO firm, where firms are matched on year and Fama-French 12 industry grouping. Firms in the utility (industry 8), finance (industry 11), and other (industry 12) industry groupings are excluded. IPO firms are divided into four groups based on their sources of pre-IPO financing: those with only VC backing, those with only a (syndicated bank) loan, those with both, and those with neither.

Cash/Assets equals cash and cash equivalents divided by total assets. Assets is in millions. Book Leverage equals the book value of interest-bearing debt divided by total assets. EBIT/Assets is earnings before interest and taxes divided by total assets. Return Volatility is the standard deviation of monthly firm returns, averaged across each of the three months prior to the fiscal year end. Sales Growth is measured as percentage growth over the past fiscal year. CapEx/Assets is capital expenditures divided by total assets. R&D/Sales is research and development expenditures divided by sales. Age equals the number of years since the firm was founded, measured at the time of the IPO. Underwriter rank is a measure of the quality of the underwriter, with the minimum being zero and the maximum being nine. % Technology firms is based on the SDC technology dummy, which equals one if the firm is in the biotech, computer equipment, electronics, communications, or general technology industries. Proceeds is dollars raised in the IPO (in millions). Unless noted otherwise, all accounting variables are measured at the first fiscal year end after the IPO for IPO firms. For mature firms they are measured at the first fiscal year end after the matched IPO firm’s offer date. Panel A: Summary statistics: medians

IPO firms

Mature firms

All VC

Only Loan Only

VC and Loan

Neither Financing

All

Cash / Assets 0.46 0.70 0.10 0.39 0.35 0.09

Assets 79.35 77.97 134.06 101.05 56.18 133.66

Leverage 0.02 0.01 0.14 0.04 0.03 0.18

EBIT / Assets 0.01 -0.14 0.09 0.03 0.04 0.06

Return Volatility 0.05 0.06 0.04 0.05 0.04 0.04

Sales Growth 0.40 0.53 0.28 0.41 0.33 0.09

CapEx / Assets 0.04 0.04 0.05 0.05 0.04 0.04

R&D / Sales 0.04 0.24 0.00 0.05 0.00 0.00

Age 7.0 5.0 11.0 7.0 7.0 .

Underwriter Rank 8.0 8.0 8.0 9.0 8.0 .

Initial Return

Proceeds 45.50 46.20 56.00 56.00 35.00 .

Proceeds / Assets 1.24 1.64 0.68 0.96 1.26 .

Obs 2,089 735 341 340 673 2,089

% of IPO sample 100% 35% 16% 16% 32%

5

Panel B: Industry composition based on 12 Fama-French industry groupings

IPO firms

Mature firms

All VC

Only Loan Only

VC and Loan

Neither Financing

All

1: Consumer nondurables 3.3% 1.1% 6.7% 3.5% 3.9% 3.3%

2: Consumer durables 1.2% 0.5% 2.6% 0.9% 1.3% 1.2%

3: Manufacturing 5.2% 1.6% 11.4% 2.9% 7.1% 5.2%

4: Energy 2.1% 0.3% 4.4% 2.9% 2.4% 2.1%

5: Chemicals 0.6% 0.4% 1.5% 0.0% 0.7% 0.6%

6: Business equipment 37.2% 46.5% 19.6% 43.8% 32.7% 37.2%

7: Telecom 4.7% 4.1% 6.5% 7.1% 3.4% 4.7%

9: Shops 12.2% 8.2% 17.0% 11.5% 14.4% 12.2%

10: Healthcare 12.8% 21.0% 5.0% 10.9% 8.8% 12.8%

Obs 2,089 735 341 340 673 2,089

   

6

Table 2: Do IPO firms hold more cash than mature firms?

This table shows OLS regressions in which the cash balances of the sample of IPO and matched mature firms are regressed on an IPO dummy, which equals one if the firm is an IPO firm, plus control variables. In columns 1 and 2, the sample consists of IPO firms at the third fiscal year end after the IPO combined with the matched mature firms from the same event year (i.e., the third fiscal year end after the offer date of the matched IPO firm). Columns 3 and 4 show the same regressions for year 5. The dependent variable in columns 1 and 3 is cash as a percent of total assets, and in column 2 and 4 is excess cash as a percent of total assets, where excess cash/assets equals the residual from the regression shown in Table 3. The burn rate is defined as the negative value of operating cash flow minus investment cash flow, all divided by lagged cash. All firms with a negative burn rate are placed into quintile 1. All other firms are classified equally within each event year (i.e., year relative to the IPO) into quintiles 2 - 5, with the highest (lowest) quintile including those firms with the highest (lowest) burn rates. All other control variables are defined in Table 1. Unless noted otherwise, accounting variables are measured at the same fiscal year end as the dependent variable. All regressions include year and industry fixed effects (based on 49 Fama-French industry groupings). Robust standard errors are shown in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%, respectively.

Sample = IPO + Matched Mature firms 3 years after IPO

Sample = IPO + Matched Mature firms 5 years after IPO VARIABLES

Cash/Assets Excess Cash/Assets Cash/Assets Excess Cash/Assets

IPO Dummy 10.34*** 10.35*** 8.43*** 8.13*** (0.74) (0.74) (0.82) (0.81)

Burn Rate Quintile -1.63*** -1.58*** -0.81*** -0.69**

(0.25) (0.25) (0.29) (0.29)

Log(Assets) -0.69*** 0.58*** -0.80*** 0.51**

(0.21) (0.21) (0.23) (0.23)

Leverage -30.41*** 0.16 -30.41*** -0.28 (1.71) (1.78) (2.06) (2.19)

EBIT/Assets -9.24*** -9.05*** -4.76** -3.97*

(1.62) (1.63) (2.11) (2.12)

Sales Growth -0.10 -0.17 0.79*** 0.78***

(0.16) (0.19) (0.22) (0.21)

Return Volatility -37.01*** -39.03*** -24.80 -27.23 (12.86) (13.24) (18.71) (18.38)

CapEx / Assets -38.95*** -10.10* -52.97*** -24.46***

(5.49) (5.73) (7.66) (7.76)

R&D / Sales 3.58*** -0.99*** 5.27*** 0.74

(0.36) (0.36) (0.47) (0.47)

Acquisitions / Assets -40.51*** -8.64* -41.29*** -9.69* (4.45) (4.49) (5.73) (5.78)

Constant 22.10*** -7.44 19.10*** 1.56

(6.00) (5.90) (4.48) (10.97)

Observations 3,136 3,057 2,408 2,349

R-squared 0.51 0.13 0.50 0.10

7

Table 3: Does the market recognize the benefits of higher cash for IPO firms? Returns on high- vs low-cash portfolios This table examines whether a portfolio of high-cash IPO firms outperforms a portfolio of low-cash IPO firms. We form portfolios of IPO firms based on firms’ excess cash holdings at the first fiscal year end following the IPO (t=1), and the second fiscal year end following the IPO (t=2). Firms with above median excess cash holdings at time t are placed into the high (low) excess cash portfolio, and firms remain in the portfolio for 36 months following time t. Portfolio returns are calculated each month, and returns net of the risk free rate are regressed on the three Fama-French factors (RM-Rf, SMB, and HML) and the momentum factor (UMD). Panel A shows the results for the entire IPO sample, and Panel B shows the results for the matched mature firm sample. Standard errors are shown in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%, respectively. Panel A: 3-year alphas for the IPO firm sample Alpha RM-Rf SMB HML UMD

Excess Cash holdings of all IPO firms at t=1

High Excess Cash 0.011*** (0.004)

1.247*** (0.099)

1.229*** (0.098)

-0.899*** (0.126)

-0.514*** (0.067)

Low Excess Cash 0.007** (0.003)

1.121*** (0.078)

0.988*** (0.077)

-0.291*** (0.100)

-0.547*** (0.053)

High – Low 0.004 (0.003)

0.126* (0.071)

0.241*** (0.070)

-0.608*** (0.090)

0.033 (0.048)

Excess Cash holdings of all IPO firms at t=2

High Excess Cash 0.013*** (0.003)

1.218*** (0.082)

1.115*** (0.086)

-0.7*** (0.106)

-0.43*** (0.058)

Low Excess Cash 0.005* (0.003)

1.155*** (0.073)

1.063*** (0.076)

-0.296*** (0.094)

-0.477*** (0.051)

High – Low 0.007*** (0.002)

0.063 (0.056)

0.051 (0.058)

-0.405*** (0.072)

0.047 (0.039)

8

Panel B: 3-year alphas for the matched mature firm sample Alpha RM-Rf SMB HML UMD

Excess Cash holdings of all Mature firms at t=1

High Excess Cash 0.009*** (0.002)

1.033*** (0.052)

0.833*** (0.052)

-0.027 (0.066)

-0.337*** (0.035)

Low Excess Cash 0.012*** (0.003)

0.843*** (0.07)

0.959*** (0.069)

-0.041 (0.089)

-0.370*** (0.047)

High – Low -0.003 (0.002)

0.190*** (0.05)

-0.126** (0.05)

0.014 (0.064)

0.033 (0.034)

Excess Cash holdings of all Mature firms at t=2

High Excess Cash 0.010*** (0.002)

1.038*** (0.054)

0.865*** (0.056)

0.004 (0.069)

-0.259*** (0.038)

Low Excess Cash 0.012*** (0.003)

0.824*** (0.068)

0.895*** (0.07)

0.000 (0.087)

-0.385*** (0.048)

High – Low -0.001 (0.003)

0.214*** (0.062)

-0.030 (0.064)

0.003 (0.079)

0.126*** (0.043)

9

Table 4: What types of firms hold more cash?

This table shows OLS regressions of Cash/Assets (columns 1 and 3) and Excess Cash/Assets (Columns 2 and 4), where both cash measures are in percentage terms, on IPO dummies interacted with pre-IPO financing sources, plus control variables. IPO is a dummy that equals one if the firm is an IPO firm. The IPO dummy is interacted with: VC, which equals one for firms that were backed by a venture capitalist prior to the IPO; Loan Prior to Yr 0, which equals one for any firm (IPO or mature) that had a syndicated loan within the five years prior to the IPO offer date (or to the matched IPO firm’s offer date for mature firms) (this is labeled Pre-IPO Loan dummy in tables that only include IPO firms); Neither Financing, which equals one for firms that had neither VC backing nor a pre-IPO syndicated loan. We also include Loan Prior to Yr 0. All other control variables are defined in Tables 1 and 2. Unless noted otherwise, accounting variables are measured at the same fiscal year end as the dependent variable. All regressions include year and industry fixed effects (based on 49 Fama-French industry groupings). Robust standard errors are shown in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%, respectively.

Year 3 Year 5

Cash/Assets Excess Cash/Assets Cash/Assets Excess Cash/Assets

IPO*VC Dummy 12.69*** 12.88*** 11.01*** 10.80*** (1.01) (1.01) (1.14) (1.13)

IPO*Loan Prior to Yr 0 Dummy 2.89*** 2.59*** 2.39** 2.41** (0.97) (0.97) (1.07) (1.07)

IPO*Neither Financing 3.08*** 3.17*** 1.00 0.63 (1.17) (1.16) (1.36) (1.34)

Loan Prior to Yr 0 Dummy -6.98*** -6.55*** -7.45*** -7.28*** (0.90) (0.90) (1.00) (1.00)

Burn Rate Quintile -1.54*** -1.48*** -0.90*** -0.78*** (0.25) (0.25) (0.28) (0.28)

Log(Assets) -0.31 0.93*** -0.34 0.96*** (0.21) (0.21) (0.23) (0.23)

Leverage -27.90*** 2.55 -27.97*** 2.19 (1.66) (1.72) (2.01) (2.12)

EBIT/Assets -7.67*** -7.37*** -4.70** -3.96** (1.61) (1.61) (2.01) (2.02)

Sales Growth -0.12 -0.22 0.73*** 0.73*** (0.18) (0.21) (0.22) (0.22)

Return Volatility -29.56** -31.08** -22.18 -24.76 (12.20) (12.52) (17.49) (17.08)

CapEx / Assets -37.58*** -8.82 -52.48*** -24.29*** (5.31) (5.51) (7.33) (7.38)

R&D / Sales 3.32*** -1.25*** 4.81*** 0.28 (0.35) (0.34) (0.45) (0.45)

Acquisitions / Assets -37.25*** -5.28 -38.39*** -7.07 (4.33) (4.37) (5.46) (5.51)

Constant 25.17*** -4.40 23.18*** 1.26 (5.69) (5.50) (5.29) (14.51)

Observations 3,136 3,057 2,408 2,349 R-squared 0.53 0.17 0.53 0.15

1

Table 5: Cash holdings across samples of IPO firms, correcting for endogeneity

This table examines which firms hold more cash, correcting for endogeneity of the pre-IPO funding source. Columns 1 and 4 show first-stage probit regressions of Pre-IPO loans and VC backing, respectively. The instrument for pre-IPO loan equals firms age, and the instrument for VC backing equals VC Location dummy (equal to one if a firm is headquartered in the New York City, San Francisco, or Boston CMSA, zero otherwise). Fitted values from the column 1 probit (column 4 probit) are used as the Pre-IPO loan instrument (VC instrument) in columns 2 and 3 (columns 5 and 6) in 2SLS regressions. The dependent variables equal percent Excess Cash/Assets, expressed as a percentage, in years 3 and 5 after the IPO. Control variables are defined in Tables 1 and 2. All regressions include year and industry fixed effects (based on 49 Fama-French industry groupings). We also include results from Angrist-Pischke tests which test the null hypothesis of weak instruments and the Hausman endogeneity test which tests the null hypothesis that the potentially endogenous variables are exogenous. Robust standard errors are shown in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%, respectively.

Pre-IPO Loan

1st stage probit

Dept = Excess Cash/Assets VC

1st stage probit

Dept = Excess Cash/Assets

3 yrs post-IPO

5 yrs post-IPO

3 yrs post-IPO

5 yrs post-IPO

Log(Firm Age at IPO)

0.17*** (0.04)

Pre-IPO Loan Instrument

-48.96*** -57.38*** (12.31) (17.57)

VC Location dummy

0.38*** (0.08)

VC Instrument 52.83*** 64.85*** (9.31) (14.43)

Burn Rate Quintile 0.03 -1.72*** -0.93 -0.03 -1.54*** -1.27 (0.03) (0.60) (0.76) (0.03) (0.58) (0.77)

Log(Assets) 0.11*** 2.11** 1.68* 0.09*** -1.78** -0.74 (0.03) (0.88) (1.00) (0.03) (0.71) (0.78)

Leverage 0.50*** 9.03** 10.68* -0.43** 8.32** 8.56 (0.18) (4.51) (5.43) (0.17) (3.97) (5.26)

EBIT/Assets 0.46*** -0.70 2.30 -0.41*** 1.75 -1.49 (0.15) (3.19) (4.38) (0.14) (3.08) (3.83)

Sales Growth -0.02 -0.14 0.74*** 0.02 -0.24 0.21 (0.02) (0.34) (0.22) (0.02) (0.43) (0.35)

Return Volatility 2.37* -16.96 -38.50 -0.13 -50.59** -53.20* (1.22) (24.65) (28.74) (1.12) (22.36) (23.02)

CapEx/Assets 1.71*** 16.67 -17.58 0.99* -26.47** -48.76** (0.58) (14.06) (18.96) (0.57) (11.89) (20.91)

R&D/Sales -0.04 -1.66*** 0.16 0.09*** -2.51*** -2.47** (0.03) (0.54) (0.71) (0.03) (0.50) (0.89)

Acquisitions/Assets -0.08 -24.23** -15.18 -1.12** 3.73 -1.76 (0.55) (11.00) (18.43) (0.55) (11.09) (19.94)

Constant -1.09* 12.92 30.89*** -0.90* -11.20 -16.17 (0.57) (9.43) (11.55) (0.52) (8.78) (9.72) Observations 1,479 1,455 1,042 1,533 1,507 1,064 Pseudo R-Squared 0.12 0.17 F-Statistic 2.33*** 1.37** 3.10*** 1.92***

AP Weak Inst. Test 23.49*** 12.85*** 42.77*** 23.56***

Hausman endog. test 28.56*** 26.31*** 45.21*** 33.83***

Table 6: Pre-IPO and post-IPO financing

This table provides descriptive statistics on the sample of IPO firms, where all firms are categorized based on source(s) of Pre-IPO financing: those with only VC backing, those with only a (syndicated bank) loan, those with both, and those with neither. For each group of firms, the table shows proceeds raised as a percent of pre-IPO assets; the percentage of firms that raised financing through a loan, SEO, or either funding source within five years of the IPO; the total dollars raised through loans (defined as the total amount available to draw down) or SEOs, as a fraction of assets at the end of year 1; the percentage of firms that delisted for poor performance within three and five years of the IPO, Cash / Assets at times -1 through +3 relative to the IPO; Excess Cash / Assets at times + 1 through + 3 relative to the IPO; EBIT / Assets at times -1 through +3 relative to the IPO; and Assets (in millions) at times -1 through + 3 relative to the IPO. All financials and financial ratios represent medians.

VC Only (n=735)

Loan Only (n=341)

VC and Loan (n=340)

Neither Financing (n=673)

Proceeds/Assets t= -1 163.9% 67.6% 95.6% 126.2%

Loan w/i 5 yrs 31.3% 77.8% 58.8% 45.3%

SEO w/i 5 yrs 30.2% 30.0% 32.9% 24.5%

Loan or SEO 49.9% 83.7% 69.7% 55.6%

(Total $ raised in Loan or SEO) / TA1

0.0% 83.6% 48.7% 17.5%

Delist for poor perf. w/i 3 yrs 8.3% 8.2% 7.6% 14.9%

Delist for poor perf. w/i 5 yrs 15.7% 17.8% 13.2% 26.2%

Cash /Assets t= -1 42.1% 3.7% 14.2% 7.6%

Cash / Assets t=1 69.6 9.8 38.5 34.5

Cash / Assets t=2 54.1 6.0 28.3 20.2

Cash / Assets t=3 51.5 6.0 23.0 16.0

Excess Cash / Assets t=1 28.4% 0.9% 11.1% 10.3%

Excess Cash / Assets t=2 17.1 -1.6 4.9 2.0

Excess Cash / Assets t=3 13.1 -3.4 1.8 -1.4

EBIT /Assets t= -1 -38.7% 9.7% -0.1% 5.2%

EBIT /Assets t=1 -13.7 9.2 2.9 4.3

EBIT /Assets t=2 -19.1 8.0 3.2 2.1

EBIT /Assets t=3 -20.9 6.4 0.8 0.7

Assets t= -1 (millions) 17.9 67.7 32.8 16.1

Assets t=1 78.0 136.1 101.1 56.2

Assets t=2 85.6 200.6 131.1 72.7

Assets t=3 89.9 242.3 147.5 89.8

Table 7: Hazard model of post-IPO financings (SEO and Post-IPO Loan)

This table shows Hazard models of the time to SEO (column 1) and time to post-IPO loan (column 2). Observations are considered truncated if firms either survive up to 5 years without raising the specified form of post-IPO financing or if they delist prior to 5 years and without raising the specified form of financing. Explanatory variables include dummy variables to designate whether a firm received venture backing and/or pre-IPO syndicated loans (VC Dummy and Pre-IPO Loan Dummy), Excess Cash/Assets (defined as the residual from the regression shown in Appendix 2), plus other control variables defined in Tables 1 and 2. All regressions include year and industry fixed effects (based on 49 Fama-French industry groupings). Standard errors are clustered by Fama-French 49 industry, and are shown in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%, respectively.

Hazard for

SEO Hazard for

Post-IPO Loan VARIABLES

VC Dummy 0.21

(0.13)

Pre-IPO Loan Dummy 0.57*** (0.11)

Excess Cash / Assets -0.40 -2.10*** (0.31) (0.27)

Underwriter Rank -0.02 0.05 (0.04) (0.04)

Burn Rate Quintile 0.14*** 0.02

(0.05) (0.05)

Log(Assets) 0.23*** 0.10 (0.06) (0.06)

Leverage -0.02 0.00 (0.30) (0.28)

EBIT / Assets 0.88*** 0.89*** (0.34) (0.29)

Sales growth 0.13 -0.06 (0.12) (0.05)

Return Volatility -16.71*** -2.19 (3.51) (2.04)

CapEx / Assets 0.14 1.12** (0.89) (0.54)

R&D / Sales 0.07** -0.09 (0.03) (0.06)

Acquisitions / Assets 0.30 1.92** (0.74) (0.91)

Observations 5,047 3,409

Table 8: Analysis of post-IPO financings (SEO and Post-IPO Loan), controlling for endogeneity

Columns 1 and 3 show first-stage probit regressions of VC backing and pre-IPO loans, respectively. Instruments in the first stage regressions include a VC Location Dummy (equal to one if a firm is headquartered in the New York City, San Francisco, or Boston CMSA, zero otherwise) and the log of firm age at the IPO. Fitted values from the column 1 probit (column 3 probit) are used as the VC Instrument (Pre-IPO Loan Instrument) in column 2 (column 4) in 2SLS regressions. The dependent variable in column 2 (column 4) equals one if the firm did an SEO (obtained a post-IPO loan). Control variables, defined in Table 2, are measured at the end of the previous fiscal year. All regressions include year and industry fixed effects (based on 49 Fama-French industry groupings). We also include results from Angrist-Pischke tests which test the null hypothesis of weak instruments and the Hausman endogeneity test which tests the null hypothesis that the potentially endogenous variables are exogenous. Robust standard errors are shown in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%, respectively.

VC: 1st stage

Dep’t = SEO

LOAN: 1st stage

Dep’t = Post-IPO Loan

VC Location Dummy 0.28*** (0.04)

Log(Firm Age at IPO) 0.11*** (0.02)

VC Instrument -0.01 (0.07)

Pre-IPO Loan Instrument 0.69*** (0.19)

Excess Cash / Assets 1.12*** -0.03 -0.86*** -0.11** (0.09) (0.03) (0.09) (0.05)

Underwriter Rank 0.21*** -0.00 0.07*** -0.01* (0.01) (0.01) (0.01) (0.01)

Log(Assets) -0.06*** 0.02*** 0.05** 0.01 (0.02) (0.00) (0.02) (0.01)

Leverage -0.27*** 0.00 0.51*** -0.06 (0.09) (0.02) (0.09) (0.05)

EBIT/Assets -0.29*** 0.01 0.51*** -0.00 (0.07) (0.01) (0.08) (0.03)

Sales Growth 0.04*** 0.00 -0.00 -0.00 (0.02) (0.00) (0.02) (0.01)

Return Volatility 0.13 -0.49*** 1.34* -0.59** (0.69) (0.13) (0.72) (0.28)

CapEx / Assets 0.49* 0.02 1.75*** -0.11 (0.28) (0.05) (0.27) (0.13)

R&D / Sales 0.10*** 0.00 -0.03* -0.00 (0.01) (0.00) (0.01) (0.00)

Acquisitions / Assets -0.18 0.10* 0.57** 0.08 (0.25) (0.05) (0.24) (0.13)

Constant -1.60*** -0.12 -1.00** 0.02 (0.47) (0.10) (0.48) (0.20)

Observations 6,454 4,975 6,277 3,276 Pseudo R-Squared 0.038 -0.436 F-statistic 3.52*** 4.79***

AP Weak Instrument Test 73.31*** 24.41*** Hausman endogeneity test 0.08 16.47***

Table 9: Effects of pre-IPO financing sources on firm survival

This table examines the time to delisting for poor performance. Columns 1 and 2 contain hazard models in which observations are considered truncated if firms either survive up to 5 years without delisting for poor performance or if they delist for a reason other than poor performance (e.g., a merger) prior to year 5. Column 3 deals with funding source endogeneity – it shows the 2nd-stage of instrumental variable regressions. Columns 1-3 include our sample of IPO firms, and column 4 includes a sample of matched mature firms. Explanatory variables include a dummy indicating whether the firm obtained a syndicated loan within the five years prior to the IPO (or the offer date of the matched IPO for mature firms) and a dummy equal to 1 if the IPO firm was backed by a venture capitalist prior to the IPO. Market value of equity is measured at the end of the fiscal year, i.e., at the same time as the accounting variables. All regressions also include controls used throughout other tables. All regressions include year and industry fixed effects (based on 49 Fama-French industry groupings). Column 3 also includes results from an Angrist-Pischke test which test the null hypothesis of weak instruments and a Hausman endogeneity test which tests the null hypothesis that the potentially endogenous variables are exogenous. Standard errors are shown in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%, respectively.

IPO Firms

Hazard IPO Firms

Hazard IPO firms

2nd stage 2SLS Mature Firms

Hazard

Pre-IPO Loan Dummy -0.43*** -0.43*** -0.30*** -0.04 (0.14) (0.14) (0.070) (0.15)

VC Backing Dummy -0.03

(0.14)

Underwriter Rank -0.09*** -0.09*** -0.00

(0.03) (0.03) (0.002)

Excess Cash / Assets -0.68** -0.67** -0.13*** -0.68 (0.28) (0.29) (0.026) (0.45)

Log(Assets) 0.32*** 0.32*** 0.02*** 0.16* (0.09) (0.09) (0.005) (0.09)

Book Leverage 1.44*** 1.43*** 0.17*** 2.14*** (0.29) (0.29) (0.022) (0.31)

EBIT / Assets -1.43*** -1.43*** -0.06*** -1.84*** (0.18) (0.19) (0.018) (0.23)

Sales Growth 0.01 0.01 0.00 -0.04* (0.03) (0.03) (0.003) (0.02)

Return Volatility 6.20*** 6.20*** 0.91*** 8.56*** (1.27) (1.28) (0.134) (1.73)

CapEx / Assets 0.44 0.43 0.21*** -2.52* (0.81) (0.81) (0.064) (1.30)

R&D / Sales -0.06 -0.06 -0.01*** -0.13** (0.04) (0.05) (0.002) (0.06)

Acquisitions / Assets -0.69 -0.70 -0.05 1.06 (0.86) (0.86) (0.047) (1.18)

Log(Mkt Value Equity) -0.51*** -0.51*** -0.03*** -0.54*** (0.06) (0.06) (0.004) (0.07) Observations 6,363 6,363 6,124 8,241 F-statistic 10.50*** AP Weak Instrument Test 64.14*** Hausman endogeneity test 21.30***

Table 10: Does the benefit of higher cash differ by pre-IPO financing source? Returns on high- vs low-cash portfolios

This table examines whether a portfolio of high-cash IPO firms outperforms a portfolio of low-cash IPO firms, where the sample is split by pre-IPO financing source. The Loan (No Loan) sample represents firms that obtained (did not obtain) a syndicated bank loan prior to the IPO. The VC (No VC) sample represents firms that obtained (did not obtain) VC financing before the IPO. Excess Cash holdings equal Excess Cash/Assets, the residual from the regression shown in Table 3. We form portfolios of IPO firms based on firms’ excess cash holdings at the first fiscal year end following the IPO (t=1), and the second fiscal year end following the IPO (t=2). Firms with above median cash holdings at time t are placed into the high (low) cash portfolio, and firms remain in the portfolio for 36 months following time Portfolio returns are calculated each month, and returns net of the risk free rate are regressed on the three Fama-French factors (RM-Rf, SMB, and HML) and the momentum factor (UMD).

Presence of Pre-IPO Loan? Presence of VC Backing?

Loan No Loan VC No VC

Excess Cash holdings of IPO firms at t=1

High Excess Cash 0.007 (1.47)

0.013*** (3.12)

0.014***

(3.27) 0.008*

(1.75)

Low Excess Cash 0.008*** (3.04)

0.007* (1.71)

0.009**

(2.33) 0.006*

(1.82)

High – Low -0.001 (-0.32)

0.006* (1.96)

0.006*

(1.68) 0.002

(0.70)

Excess Cash holdings of IPO firms at t=2

High Excess Cash 0.008* (1.97)

0.014*** (4.08)

0.016***

(4.09) 0.008**

(2.23)

Low Excess Cash 0.007*** (2.65)

0.005 (1.48)

0.009**

(2.43) 0.003

(1.00)

High – Low 0.001 (0.31)

0.009*** (3.23)

0.006**

(2.11) 0.005**

(1.99)

Figure 1: Evolution of cash holdings

This figure examines cash balances of IPO firms and matched mature firms over time. Mature firms are matched based on industry (Fama-French 12 industry group) and calendar year.

Panel A: Median Cash/Assets of IPO firms vs mature firms

 

Panel B: Median Cash/Assets of IPO firms vs mature firms, conditional on firms surviving through year 5

 

Panel C: Median Cash/Assets of IPO firms vs mature firms, excluding IPO Bubble Period of 9/1998 – 8/2000

 

IPO firms Mature Firms 

 

0

0.1

0.2

0.3

0.4

0.5

0 1 2 3 4 5

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5

0

0.1

0.2

0.3

0.4

0 1 2 3 4 5

Pre-IPO

Pre-IPO

Pre-IPO

Figure 2: Characteristics of IPO vs mature firms

This figure examines various characteristics of IPO firms and matched mature firms over time. Mature firms are matched based on industry (Fama-French 12 industry group) and calendar year.

Panel A: Median Sales Growth Panel B: Median CapEx/TA

Panel C: Mean Acquisitions/TA Panel D: Median Market-to-Book

IPO firms Mature Firms

0

0.1

0.2

0.3

0.4

0.5

0 1 2 3 4 5

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 1 2 3 4 5

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0 1 2 3 4 5

0

0.5

1

1.5

2

2.5

3

0 1 2 3 4 5

Pre-IPOPre-IPO

Pre-IPO Pre-IPO

Panel E: Median R&D/Sales Panel F: Median Return Volatility

Panel G: Median EBIT/TA

IPO firms Mature Firms

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 1 2 3 4 5

0

0.01

0.02

0.03

0.04

0.05

0.06

0 1 2 3 4 5

‐0.04

‐0.02

0

0.02

0.04

0.06

0.08

0 1 2 3 4 5

Pre-IPOPre-IPO

Pre

Figure 3: Differences in cash holdings across IPO firms

This figure examines median Cash/Assets of different groups of IPO firms. Panel A divides all IPO firms into quartiles, based on Cash/Assets in the first year after the IPO (year 1). Panel B divides all IPO firms based on source of pre-IPO financing: pre-IPO syndicated loan only ‘Loan Only’, venture-backed only ‘VC Only’, pre-IPO syndicated loan and VC backing ‘Loan plus VC’, and neither a pre-IPO syndicated loan nor VC backing ‘Neither Financing’.

Panel A: Median Cash/Assets, quartiles of IPO firms based on Cash/Assets

Panel B: Median Cash/Assets, groups of IPO firms based on source of pre-IPO financing

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 1 2 3 4 5

High Cash/Assets

Q3

Q2

Low Cash/Assets

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 1 2 3 4 5

VC Only

Loan Plus VC

Neither Financing

Loan Only

Pre-IPO

Pre-IPO