The Pecking Order Theory and Time-Varying Adverse Selection Costs

Don Autore*

Tunde Kovacs

Department of Finance Pamplin College of Business

Virginia Tech Blacksburg, VA 24061

First version: May 2003

This version: November 2004

JEL Classification: G32

Keywords :

capital structure, pecking order theory, time-varying adverse selection costs

Abstract

The extensive use of equity financing in the 1990s is in sharp contrast to the prediction of Myers and Majluf�s (1984) pecking order theory that debt issues strictly dominate equity issues. We provide evidence in favor of a multi-period pecking order [Viswanath (1993)], in which time-varying adverse selection costs can make equity issues optimal (even for firms with sufficient debt capacity). We find that time-varying adverse selection costs are directly related to firms� preference for internal over external funds and for debt over equity financing. We verify our results using a sample of SEOs and public debt offers. Additionally, we find that two increasingly common methods of raising equity � shelf and PIPE offers � are less subject to an adverse selection problem than SEOs. Our results support the underlying prediction of the pecking order that adverse selection costs give rise to a hierarchy of financing choices.

* We are grateful to Greg Kadlec for his guidance and for many helpful discussions. We also thank Senay Agca, Don Chance, Jennifer Conrad, Dave Denis, John Easterwood, Ken French, Jeff Hobbs, Raman Kumar, Abon Mozumdar, Alexei Ovtchinnikov, Barbara Remmers, Dilip Shome, and seminar participants at the 2004 Financial Management Association meeting and Virginia Tech for helpful comments and suggestions. The authors gratefully acknowledge the contribution of Thomson Financial for providing earnings per share forecast data, available through the Institutional Brokers Estimate System. This data has been provided as part of a broad academic program to encourage earnings expectations research.

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The Pecking Order Theory and Time-Varying Adverse Selection Costs

First version: May 2003

This version: November 2004

Abstract

The extensive use of equity financing in the 1990s is in sharp contrast to the prediction of Myers and Majluf�s (1984) pecking order theory that debt issues strictly dominate equity issues. We provide evidence in favor of a multi-period pecking order [Viswanath (1993)], in which time-varying adverse selection costs can make equity issues optimal (even for firms with sufficient debt capacity). We find that time-varying adverse selection costs are directly related to firms� preference for internal over external funds and for debt over equity financing. We verify our results using a sample of SEOs and public debt offers. Additionally, we find that two increasingly common methods of raising equity � shelf and PIPE offers � are less subject to an adverse selection problem than SEOs. Our results support the underlying prediction of the pecking order that adverse selection costs give rise to a hierarchy of financing choices.

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In this paper we study the link between time-variation in adverse selection costs

and the pecking order of financing choices. This link is important. We use it to (i)

reconcile conflicting prior evidence in favor of a multi-period pecking order theory, and

(ii) provide evidence that firms are increasingly raising equity capital via methods that

are less prone to adverse selection costs than seasoned equity offerings (SEOs). Our

results are consistent with the underlying prediction of the pecking order that adverse

selection costs give rise to a hierarchy of financing choices.

According to the pecking order theory, formalized by Myers and Majluf (1984)

and Myers (1984), firms seeking to finance new investments prefer to use funds

according to a hierarchy: first internal funds, then debt issuance, and finally equity

issuance. This �pecking order� arises because managers, not wanting to dilute existing

shareholders� claim, will issue only overvalued securities. Aware of this, market

participants discount firm value to reflect adverse selection costs. Myers and Majluf

(1984) show that because adverse selection costs are always larger for equity issues than

for debt issues, issuing equity is never optimal. Viswanath (1993) extends the single-

period framework of Myers and Majluf to a multi-period setting in which adverse

selection costs vary over time. In this setting, equity issuance can be optimal, even if

cash or debt capacity is available.1

Specifically, Viswanath considers a two-period world, in which a firm has the

option to take on a positive NPV project in each period, but has enough cash available to

finance only one of them. Financing both projects requires an equity issue. In this world,

the manager has to consider the timing of an equity issue, in addition to accepting or

1 Myers and Majluf (1984) allude to this possibility in their conclusion, stating that the �[�] way to build slack is by issuing stock in periods when managers� information advantage is small; firms with insufficient

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rejecting a project [Myers and Majluf (1984)]. Stock is issued when two conditions are

satisfied: 1) the expected level of information asymmetry in the future is high, and 2) the

dilution of the equity issue is small compared to the cost of passing up the projects. In

general, issuing equity when adverse selection costs are low allows the manager to save

available cash to ensure financing for future positive NPV projects. Thus, Viswanath�s

(1993) model predicts temporal variation in the pecking order hierarchy as a result of

time-variation in adverse selection costs.2

Although adverse selection costs distinguish the pecking order theory from other

capital structure theories, empirical studies fail to effectively control for this key feature

of the theory. We argue that this is why the empirical evidence concerning the pecking-

order theory is mixed. Shyam-Sunder and Myers (1999) find support for firms� reliance

on debt financing for a sample of large firms drawn from the 1970s and 1980s. By

contrast, Frank and Goyal (2003) find a significant increase in firms� reliance on equity

financing for a sample of firms drawn from the 1990s. Lemmon and Zender (2003) offer

an explanation for these conflicting findings based on debt capacity. They argue that

firms close to their debt capacity may be forced to issue equity. Consistent with this,

Agca and Mozumdar (2003) estimate a piecewise linear model and find that firms prefer

debt to equity before reaching their debt capacity. While the issue of debt capacity is

important, it cannot explain the findings of Fama and French (2004) that equity issues are

common even for large firms that are not under duress. Thus it seems that the debt

capacity argument is not sufficient to explain all equity issues within the pecking order

theory. However, in the multi-period pecking order, equity issuance can be optimal even

slack to cover possible future investment opportunities would issue in periods when managers have no information advantage.� However, Myers and Majluf do not formalize a multi-period model.

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when firms have internal cash or sufficient debt capacity. We provide evidence that the

conflicting empirical results can be reconciled, in favor of the pecking order theory, when

the empirical specification explicitly allows for time-variation in adverse selection costs.

Several empirical studies provide evidence that adverse selection costs are

important in security issues. First, equity issue announcements are met with a negative

market reaction [Asquith and Mullins (1986), Mikkelson and Partch (1986), and Masulis

and Korwar (1986)]. Second, the market�s reaction to security issue announcements is

more negative for issues of riskier securities [Hadlock and James (2002)]. Third, equity

issues cluster immediately after information release events to minimize adverse selection

costs [Korajczyk, Lucas, and McDonald (1991, 1992) and Dierkens (1991)]. Fourth,

equity issues tend to cluster in certain �windows of opportunity,� which are associated

with low aggregate adverse selection costs [Bayless and Chaplinsky (1996)]. Finally,

Choe, Masulis, and Nanda (1993) argue that adverse selection costs are low in business

expansion periods when equity financing is more common. Overall, these studies suggest

that adverse selection costs vary over time and that firms adjust their issuance policies

accordingly. Therefore, tests of the pecking order theory should consider a multi-period

setting in which time-varying adverse selection costs play a crucial role. To our

knowledge, there is no study that explicitly controls for time-varying adverse selection

costs in tests of the pecking order theory.

We provide statistically significant and economically meaningful evidence in

support of Viswanath�s (1993) multi-period pecking order theory. We consider several

potential proxies for time-series variation in adverse selection costs and show that the

2 Cooney and Kalay (1993) provide a different extension to the Myers and Majluf (1984) model. They allow for negative NPV projects and show that the reaction to stock issues can be positive. However, the Cooney and Kalay model is a single-period model.

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dispersion in analysts� earnings forecasts is the most appropriate proxy to test the multi-

period pecking order theory. Using this proxy, we find that firms rely more on the

external markets for financing needs when adverse selection costs are low. Additionally,

although Viswanath�s (1993) model does not explicitly compare debt and equity issues,

predictions can be formulated by noting that the difference in adverse selection costs

between debt and equity issues is smaller when informational discrepancies are smaller

[Myers and Majluf (1984)]. By issuing equity when information asymmetry is low, a firm

can reduce the incremental adverse selection costs imposed from using equity instead of

debt.3 Given that a firm chooses external financing, we find that the choice of debt

financing is positively related to adverse selection costs. Similarly, firms are more likely

to deviate from the strict pecking order when adverse selection costs are low and growth

options are high. In our tests we emphasize time-series relations (and control for

heterogeneity) by including firm fixed effects.

Furthermore, we conduct tests on additional samples to address the findings of

Fama and French (2004) that firms issue equity in ways that are potentially less prone to

adverse selection costs. We verify our results using a restricted sample that is similar to

using a sample of new debt and equity issues. Additionally, we provide support for the

multi-period pecking order using a sample of SEOs and public debt offers. Specifically,

we illustrate that adverse selection costs significantly decline in the three months prior to

SEO announcements, and significantly increase in the three to six month period after

SEOs. We find a small (insignificant) decrease in adverse selection costs prior to public

debt offers.

3 We assume that debt capacity is limited, so firms need to issue equity at some point.

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Finally, we extend the findings of Fama and French (2004) that firms raise more

capital via methods that potentially suffer less from an adverse selection problem [e.g.

mergers and options] than they raise through SEOs. In particular, we examine shelf-

registered and PIPE offers - methods of raising equity that are most similar to SEOs - and

find that the timing of these offers has little relation to time-varying adverse selection

costs. Moreover, the market imposes a significantly larger discount on the stock of SEO

issuers than on the stock of shelf or PIPE issuers. These findings are consistent with the

relative financing mix of SEOs, shelf offers, and PIPEs during 1990-2003, as issuers are

increasingly avoiding SEOs in favor of shelf offers and PIPEs.

Overall, our evidence supports the premise of the pecking order that adverse

selection costs affect financing choices. Using various samples, we find that firms take

adverse selection costs into account by timing their use of internal versus external and

debt versus equity financing, as well as by moving to equity issues that are less prone to

adverse selection problems. These findings suggest the pecking order as a theory of

financing choices lives.

The remainder of the paper is organized as follows. Section 1 describes our data

and variables. Section 2 provides tests of the multi-period pecking order theory. In

section 3 we provide additional tests using various samples. We conclude in section 4.

1. Data and Variables

Our primary sample consists of firms in the intersection of CRSP, quarterly

COMPUSTAT, and IBES files. CRSP and COMPUSTAT provide security return and

quarterly accounting data, respectively. We exclude all financial firms and utilities, firms

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that are likely to be financially distressed (average assets under $ 50 million, average

sales under $ 5 million, or share price under $ 5), non-U.S. companies, and companies

that are not traded on the NYSE, AMEX, or NASDAQ. We further exclude individual

firm quarters with missing values for total book assets, sales, financing deficit, net change

in debt, net change in equity, long term debt, property, plant, and equipment (PPE),

market-to-book ratio, or profitability. We also exclude firm quarters that are

contaminated by significant (greater than 50% of sales) merger and acquisition activity.

In addition, we exclude a small number of companies with extreme values that are likely

due to data errors.4 We use IBES to obtain analysts� earnings forecast data. To be

included in our sample, firms must have a minimum of four analyst earnings forecasts

available (in at least one month of a given fiscal quarter). We choose this cutoff point to

improve the reliability of the standard deviation of analysts� earnings forecasts. Since the

quarterly IBES coverage starts in 1984, our sample period is 1984-2002. Our sample

consists of 69,944 firm quarters over this 19-year period.

The restriction on the number of analysts following a firm introduces a selection

bias towards large, widely followed firms. Several studies have documented that large

firms tend to use more debt, have smaller growth opportunities, and are more transparent

to investors. Although our sample tends to exclude many small, high growth firms by

construction, it still exhibits substantial variation in size, growth opportunities, adverse

selection costs, and collateral.

4 We exclude the following extreme values (all variables are scaled by total book assets): profitability<-1; net change in debt, net change in equity, or total financing deficit < -0.5; market-to-book>20; dispersion in analysts� earnings forecasts scaled by the stock price>0.1. These exclusions decrease our sample by less than 0.5%. As an alternative to excluding extreme values, we winsorize our variables at the 1% level. The results using this alternative dataset do not differ from those reported in the paper.

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1.1. Measuring adverse selection costs

The pecking order theory is distinguished from other capital structure theories by

its prediction about the relation between adverse selection costs and firms� incremental

financing decisions. Inferences about the relevance of the pecking order theory thus

depend critically on choosing an appropriate proxy variable for adverse selection costs.

Several studies reject the pecking order by interpreting firm characteristics such as

size, growth, or age as proxies for adverse selection costs. For instance, Frank and Goyal

(2003) and Fama and French (2002) argue that the pecking order theory should perform

best among small, high-growth firms because these firms are more likely to have

information asymmetry problems, and therefore incur higher adverse selection costs

when issuing securities. Both studies reject the single-period pecking order theory based

on the finding that small, high-growth companies issue more equity and are less levered

than large and old firms. These variables, however, capture cross-sectional differences

rather than time-series variations. Thus, they are inappropriate to test the multi-period

pecking order theory, which predicts a time-series relation between adverse selection

costs and firms� financing decisions.

Previous corporate finance studies [e.g. Krishnaswami and Subramaniam (1999)]

have identified several proxies to measure the level of adverse selection costs (dispersion

in analysts� earnings forecasts, mean analyst forecast error, residual standard deviation,

and trading volume). These variables capture time-series variation, but they assess

different aspects of asymmetric information. While one of them may be useful in one

context, another may be more suitable in another context.

In the context of the pecking order, it is desirable to use a proxy that reflects

dispersion of opinion among investors, because this is arguably the best approximation of

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the market�s reaction to an equity issue announcement. Since managers issue equity only

when the market overvalues the firm, an issue decision signals bad news about the firm�s

value and truncates from above the distribution of investors� expectations about the future

states of the world. The new consensus (mean) value of the firm, conditional on the

equity issue decision, is thus lower than the previous unconditional price. This discount

to the security price reflects adverse selection costs and depends on the dispersion of

investors� beliefs about the firm�s prospects. This is what we would like to capture. A

more formal justification of this argument is presented in Appendix A.

To approximate the dispersion of investor beliefs, we use dispersion in analysts�

earnings forecasts (DISPERSION). This variable is our proxy for adverse selection costs.

D�Mello and Ferris (2000) identify a negative relation between forecast dispersion and

equity issue announcement reactions, providing empirical justification for the link we

make between DISPERSION and adverse selection costs. In a different context, Diether,

Malloy, and Scherbina (2002) also argue that dispersion in analysts� earnings forecasts

captures dispersion in investor beliefs, and not risk. Other proxies such as analysts�

earnings forecast error5, trading volume6, and volatility7 are noisier estimates of

dispersion of investors� opinion.

5 Analysts� forecast error is the difference between the mean analysts� earnings forecast and the actual realized earnings in a period. Forecast error is not observable to the market until after the earnings release and therefore does not necessarily predict the adverse selection costs the market imposes on the firm at a security issue announcement. An issue announcement does not fully reveal the information the manager has, it only signals the quality of it. The market�s new consensus value of the firm�s price, therefore, is not directly related to the value known by the manager, as shown in the appendix. Thus, forecast error is not a suitable proxy to measure adverse selections costs imposed by the market. 6 Higher trading volume could represent more dispersion in beliefs among investors. However, higher volume can also result from increased liquidity trading, portfolio rebalancing, tax-driven trading [Harris and Raviv (1993)], and the influence of other corporate events, so there is not necessarily a direct connection between trading volume and the dispersion of opinions. 7 Volatility (residual standard deviation) is used in the literature as a proxy for both adverse selection costs and risk. In some situations adverse selection costs and risk have similar effects. However, in tests of debt versus equity financing, theories based on adverse selection costs and theories based on risk yield opposite predictions. On the one hand, higher adverse selection costs lead to more debt financing [Myers and Majluf (1984)]. On the other hand, higher risk is associated with more equity issues [Fluck (1998)]. Volatility is

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We should note that this proxy is likely to understate the level of dispersion in

investor beliefs.8 First, analysts tend to be optimistic and publish positive forecasts. When

analysts have bad news for investors they often choose not to cover the firm at all.

Second, analysts tend to herd and publish similar forecasts. This leads again to a smaller

dispersion.

We compute dispersion of analysts� earnings forecasts in the following way. We

calculate the standard deviation of quarterly forecasts for each month in which at least

four analysts covered the firm. We average these standard deviations quarterly and scale

this number by the end-of-quarter stock price.9 This gives us a measure of DISPERSION

for each quarter for each firm. This variable exhibits a large amount of skewness, which

is an undesirable property in the estimation of normal linear regressions, and more

importantly, in assessing the economic significance of adverse selection costs. Therefore,

we transform this variable by taking its natural logarithm.10 Since DISPERSION is zero

when all analysts agree, we need to introduce a new binary variable (DISP_DUMMY)

that takes the value of one when DISPERSION is zero. Figure B.1 in Appendix B

illustrates the skewness in DISPERSION and Figure B.2 in Appendix B depicts how

taking the natural logarithm eliminates this problem. This has implications for any study

that uses dispersion of analysts� earnings forecasts to make inferences about corporate or

investor behavior.

more likely to be associated with risk since it measures time-series fluctuations in the market�s consensus of the firm�s value, in other words, fluctuations in the mean of the distribution of opinions. Accordingly, we use volatility as a control variable for risk in our tests. 8 Diether, Malloy, and Scherbina (2002) document a data handling procedure at IBES that alters the dispersion of analysts� earnings forecasts. When IBES adjusts for stock splits they round off earnings per share forecasts to the nearest cent. However, this should not create a systematic bias, since some forecasts will be rounded down, others will be rounded up. 9 Alternatively, we scale the standard deviation by the mean analyst forecast. The results (unreported) are consistent with those reported in the paper. 10 Our results (not reported) are qualitatively similar when we use DISPERSION without a log transformation.

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1.2. Control variables

Several factors influence firms� choices between debt and equity financing. Rajan

and Zingales (1995) identify four variables that are closely related to leverage: the

market-to-book ratio, asset tangibility, firm size, and profitability. In our empirical tests

we control for these variables. Asset tangibility is defined as the ratio of fixed assets

(plant, property, and equipment) to total assets (PPE). Tangible assets can serve as

collateral in debt contracts. Firms with more tangible assets can, in general, take on debt

easier. The market-to-book ratio (MTB) is defined as market value of equity plus book

value of total assets minus book value of equity, divided by total book assets. Market-to-

book can proxy for growth opportunities or overvaluation. High growth opportunities

mitigate dilution costs [Myers and Majluf (1984)] and increase debt overhang problems

[Myers (1977)]. Overvaluation induces managers to issue equity [Baker and Wurgler

(2002)]. In each case, high market-to-book values are associated more with equity than

with debt issuance. Similarly, the multi-period pecking order theory implies that firms

with high growth opportunities will issue equity to build financial slack for future

financing, and also that firms will prefer to issue equity when their stock is overvalued to

reduce dilution to existing shareholders. We use the natural logarithm of net sales to

proxy for firm size (SIZE). Larger firms are generally better recognized and thus have

easier access to debt. We define profitability as income before extraordinary items

(Compustat data item number 237) divided by total book assets (PROFITABILITY).

More profitable firms are less reliant on external financing, and upon accessing the

external markets, they are more able to issue debt.

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Our tests also include a young firm dummy variable (YOUNG), which equals one

for firms less than ten years old, measured from the time they first appeared on

Compustat, and zero otherwise. Younger companies are generally not as well known and

have less access to debt markets. Additionally, we include the residual standard deviation

of stock returns (VOLATILITY) to capture idiosyncratic risk and cash flow volatility.

We calculate VOLATILITY as the residual standard deviation from a modified market

model in which we regress daily returns in a given quarter on the lead, contemporaneous,

and lagged daily value-weighted market return. This approach is similar to that of

Dimson (1979) and Scholes and Williams (1977) and helps to minimize the effect of

nonsynchronous trading. Firms with higher volatility are likely to have higher costs of

financial distress, because volatile cash flows make it difficult to support promised

payments to bondholders [Fluck (1998)]. Therefore, higher VOLATILITY should be

associated with relatively less debt and more equity financing. We also include an interest

rate variable (INTRATE), which is the 10-year AAA-rated corporate bond rate. Firms�

choice of debt over equity financing should be inversely related to INTRATE. Finally, in

our tests of internal versus external financing, we include the slope of the yield curve

(YIELDSLOPE), defined as the 20-year Treasury bond rate minus the 3-month Treasury

bill rate, as a control variable for business conditions. Interest rate data is obtained from

the St. Louis Federal Reserve.

[INSERT TABLE 1 ABOUT HERE]

Table 1 presents descriptive statistics and correlation coefficients between our

variables. There is substantial variation in financing source variables (net change in debt

[∆D], net change in equity [∆E], and total financing deficit [FINDEF]) as well as in firm

13

characteristics. All correlation coefficients in Panel B are significant. To address potential

multicollinearity, we provide robustness tests using orthogonalized variables.

2. Empirical Tests of the Multi-Period Pecking Order

To motivate our study of the relation between time-varying adverse selection

costs and firms� financing choices, we first illustrate that adverse selection costs and

other firm characteristics vary considerably over time. Figures 1-8 illustrate trends in

financing patterns and characteristics of our sample firms. Figure 1 shows an increasing

reliance on external financing in the mid- to late-1990s, which contradicts the strict

pecking order. Figure 2 illustrates substantial time variation in the average portion of the

current financing deficit covered by debt, with lower values occurring in 1999-2000.

Figures 3-8 plot cross-sectional averages of dispersion in analysts� earnings forecasts,

market-to-book ratios, asset tangibility, size (natural logarithm of sales), profitability, and

return volatility (residual standard deviation) for each quarter in our sample. The figures

reveal that several factors exhibit time-series variation (decrease in analysts� earnings

forecasts dispersion, increase in market-to-book ratios and return volatility, and decrease

in asset tangibility, size, and profitability) that could have contributed to firms� increased

reliance on external financing and on equity issues in the late 1990s. In the following

sections we provide formal tests of how these variables affect firms� financing decisions.

[INSERT FIGURES 1-8 ABOUT HERE]

2.1. Internal versus external financing

Previous tests of capital structure theory are often organized as a �horse race�

between various theories, such as tradeoff, pecking order, and market timing. It is often

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difficult to devise tests that are capable of distinguishing between the predictions of

competing theories. For example, Baker and Wurgler (2002) interpret their findings as

support for the market timing theory, although they concede that they cannot rule out the

dynamic pecking order.

One of our primary contributions is to identify and test a prediction that is unique

to the pecking order: adverse selection costs give rise to a financing hierarchy. We

establish two key hypotheses to test the multi-period pecking order: (i) a firm�s

preference for internal over external financing is stronger when adverse selection costs

are higher, and (ii) given that a firm accesses the external markets, it will prefer debt to

equity when adverse selection costs are higher.

According to the multi-period pecking order, firms should time the use of external

financing to periods of low adverse selection costs. We test the relation between external

financing and adverse selection costs by estimating the pooled cross-sectional time-series

regression of financing deficit (FINDEF) on dispersion in analysts� forecasts

(DISPERSION) and our control variables:11

FINDEFit = a + b1 DISPERSIONit+ b2 DISP_DUMMY it + b3 MTB it + b4 PPEit + b5 SIZEit + b6 YOUNGit+

+ b7 PROFITABILITYit + b8 VOLATILITYit + b9 INTRATEit + b10 YIELDSLOPEit + eit (1)

Note that by estimating Eq. [1] we do not assume that adverse selection costs are

capable of influencing firms� investment policies, rather, we examine how adverse

selection costs influence whether the firm uses internal or external (FINDEF) financing to

11 Our definition of FINDEF follows Frank and Goyal (2003): FINDEFt = DIVt + It + ∆Wt � Ct = ∆Dt + ∆Et. The financing deficit (FINDEF), defined as the sum of dividends (DIV), investment (I), and the change in working capital (∆W), less cash flow after interest and taxes (C), equals the sum of net equity (∆E) and net long-term debt (∆D) issued in the given quarter (all variables are scaled by total book assets).

15

cover its total financing needs. We hypothesize that DISPERSION is positively related to

the use of internal over external funds to cover total financing needs.

The estimates for Eq. (1) are reported in Table 2. We report estimates for both

pooled and firm-fixed effect regressions. The fixed effect estimates emphasize our main

focus, time-series relations. There is a statistically significant negative relation between

DISPERSION and FINDEF, indicating that firms rely more heavily on external funds for

financing needs when adverse selection costs are low. Similarly, DISP_DUMMY has a

positive and significant coefficient in both estimations, also suggesting that firms rely

more on external financing when adverse selection costs are low. The economic

magnitude of these effects is substantial. The coefficient on DISPERSION is -0.0029,

and from Table 1, the standard deviation of DISPERSION is 2.49. A one standard

deviation increase in adverse selection costs decreases external financing by 0.65% of

total assets. Comparing this figure to the average external financing (0.9% of total assets)

implies that the effect of adverse selection costs is both statistically and economically

significant. These findings are consistent with the prediction that low adverse selection

costs provide incentive for firms to 1) issue risky securities [Viswanath (1993)], and 2)

increase the issue size [Krasker (1986)].

[INSERT TABLE 2 ABOUT HERE]

Higher values of MTB are associated with using more external funds, possibly

because firms attempt to issue overvalued equity, or because high growth firms have

limited internal funds available. As expected, larger, older, and more profitable firms rely

less on external funds for financing. Our interest rate macro variable (INTRATE) is

positively associated with the use of external financing, while the slope of the yield curve

(YIELDSLOPE) is not significant. Accounting for firm fixed effects changes the impact

16

of some variables that capture mainly cross-sectional differences (PPE and

VOLATILITY), but the results pertaining to the role of adverse selection costs get

somewhat stronger. A possible reason for the low R-squared in our Table 2 regressions is

that there is more variation in FINDEF due to variation in investments than due to

variations in the internal versus external financing choice.

2.2. Debt versus equity issues for external financing

To test our second hypothesis, that firms prefer debt to equity when adverse

selection costs are higher, we implement several modifications to the methodology used

in Shyam-Sunder and Myers (1999), Frank and Goyal (2003), and Lemmon and Zender

(2003). These studies use the following regression framework:

∆D it = a + bPO FINDEFit + eit , (2)

where ∆D it is the net change in long-term debt of firm i in quarter t.

We extend Eq. (2) to explicitly control for cross-sectional and time-series

variation in adverse selection costs. Specifically, we add analyst forecast dispersion

(DISPERSION) and our control variables, while conditioning on the amount of financing

deficit.

∆Dit = a + b1 FINDEFit + b2 DISPERSIONit* FINDEFit + b3 DISP_DUMMYit * FINDEFit +

+ b4 MTBit * FINDEFit + b5 PPEit * FINDEFit + b6 YOUNGit * FINDEFit + b7 SIZEit * FINDEFit +

+ b8 PROFITABILITYit * FINDEFit + b9 VOLATILITYit * FINDEFit + b10 INTRATEit * FINDEFit + eit (3)

This conditional approach avoids the critique of Chirinko and Singha (2000) that

tests of Eq. [2] can be misleading since it is difficult to determine how close the

coefficient on financing deficit must be to one to provide support for the pecking order.

Lemmon and Zender (2003) also use a conditional model, but focus on debt capacity

17

rather than time-varying adverse selection costs. They also discuss unreported results that

use analysts� earnings forecast error and residual standard deviation as information

asymmetry proxies, both of which we find improper to test the multi-period pecking

order. See our Section 1.1 and Appendix A for a discussion.

We report results for two samples, our full sample and a subsample that includes

only firms that have a financing deficit, not a surplus. The choice of this subsample is

motivated by the fact that the pecking order theory�s predictions are derived for

investment driven external financing. From our full sample of 69,944 firm quarters, only

38,177 (or about 55%) observations contain firms that have a net financing deficit, i.e.

accessed the external markets on net for financing.12 While our subsample is intended to

ensure that our results are not driven by repurchases, we note that Myers and Majluf�s

model implies that equity repurchases will occur when adverse selection costs are low. In

our regression framework, this corresponds to the same prediction for the coefficient of

adverse selection costs when FINDEF is negative. Shyam-Sunder and Myers (1999) state

that �The Myers-Majluf reasoning works in reverse when the company has a surplus

([FIN]DEF<0) and wants to return cash to investors. [�] Thus the simple pecking

order�s predictions do not depend on the sign of [FIN]DEF.� The intuition is that

repurchase announcements drive the stock price up, more so when adverse selection costs

are higher. This makes repurchases expensive to continuing shareholders. For a more

complete discussion, see Shyam-Sunder and Myers (1999), page 225.

12 The use of an even more restrictive data set, requiring both net change in debt and net change in equity to be positive, could also be justified. When ∆Dt and ∆Et have different signs but FINDEF>0, a firm issues equity (debt) sufficient to pay down debt (repurchase equity) and to finance the firm�s deficit. In this case, there is no relationship between the firm�s net change in debt (equity) and its financial deficit. However, using this more restrictive data set would compromise the size of the sample. Also, we might lose valuable information by disregarding companies that rebalance their portfolios by issuing equity and paying down debt to build debt capacity. The multi-period pecking order predicts that firms will pay down debt when

18

Column 1a and 1b of Table 3 report estimates for the pooled cross-sectional time-

series regression [Eq. (3)] for the full sample and for the financing deficit only sample,

respectively. The coefficient on the interaction term of DISPERSION * FINDEF

(DISP_DUMMY * FINDEF) is positive (negative) and significant in both specifications.

Once a firm has decided to access the external markets, higher adverse selection costs are

associated with more debt issuance and lower adverse selection costs are associated with

more equity issuance. These results are economically significant: a one standard

deviation increase in DISPERSION increases the percentage of FINDEF covered with

debt by 10.83%.

[INSERT TABLE 3 ABOUT HERE]

Both profitability and market-to-book are negatively related to debt issues conditional on

external financing. The negative coefficient on MTB is consistent with the multi-period

pecking order as firms with more growth options are more likely to preserve debt

capacity. The negative effect of PROFITABILITY is somewhat surprising considering

that both the pecking order and the trade-off theory predict a positive sign given that a

firm accesses the external markets (conditional model). The variables PPE, YOUNG,

SIZE, VOLATILITY, and INTRATE all have the expected signs in our regressions.

Firms with more tangible assets, as well as older and larger firms, use more debt

compared to equity, consistent with the empirical findings of Rajan and Zingales (1995).

Riskier firms use relatively more equity then debt. This is consistent with the prediction

of Fluck (1998) that riskier firms have less access to the debt markets, and with the

findings of Jung, Kim, and Stulz (1996) that greater stock return volatility correspond to a

adverse selection costs are low. Thus, for our subsample, we require only that change in debt and change in equity together represent a net financing deficit.

19

greater likelihood of equity issues. Finally, firms� reliance on debt compared to equity

increases when interest rates are low.

Column 2a and 2b of Table 3 report firm fixed effect estimates for each sample.

The coefficients of DISPERSION and DISP_DUMMY remain significant at the 1%

level, indicating a positive (negative) time-series relation between debt (equity) financing

and adverse selection costs.

As a robustness check, we re-estimate our results in Table 2 and Table 3 using

orthogonalized variables to address potential multicollinearity between our variables. We

regress DISPERSION on our set of control variables, and use the orthogonal part of

DISPERSION in our tests. In addition to this, we use a second orthogonalization

technique in which we orthogonalize all of our variables in the following order:

VOLATILITY, PROFITABILITY, SIZE, PPE, MTB, and finally DISPERSION.

Specifically, we remove the effects of YOUNG and DISP_DUMMY from

VOLATILITY, and then we remove the effects of these three variables from

PROFITABILITY. We continue this procedure until the effects of all the variables are

removed from DISPERSION. The results using orthogonalized variables (unreported) are

qualitatively similar to those we report, indicating that multicollinearity does not affect

our inferences.

In Eq. (1) FINDEF is the dependent variable and in Eq. (3) FINDEF is an

independent variable. This motivates us to estimate Eq. (1) and Eq. (3) simultaneously

using a seemingly unrelated regression (SUR) system. The results (untabulated) are

qualitatively similar to those we report.

Frank and Goyal (2003) advocate the use of first differences in tests of

incremental financing choices. Accordingly, we estimate a conditional model similar to

20

Eq. (3), but we replace the levels of independent variables with first differences. Table 4

reports the regression estimates. The importance of adverse selection costs is unchanged

from our earlier results: the effect of ∆DISPERSION is significant at the 1% level.

[INSERT TABLE 4 ABOUT HERE]

Our findings extend the prior literature on the role of time-varying adverse

selection costs. We use a direct and firm-specific proxy (analyst forecast dispersion) for

adverse selection costs, whereas previous studies use aggregate measures such as time

varying �hot� and �cold� markets (Bayless and Chaplinsky (1996)) and the business

cycle (Choe, Masulis, and Nanda (1993)). We are the first to our knowledge to explicitly

control for time-variation in adverse selection costs in tests of the pecking order�s

predictions about internal versus external and debt versus equity financing.

2.3. Predictive logistic regressions

A potential problem with models based on Eq. (2) is that the size of FINDEF and

∆D are not related when ∆D and ∆E have opposite signs. If such capital structure

rebalancing occurs, the magnitude and sign of FINDEF is not directly linked to ∆D. For

example, if a firm issues $100 worth of equity and pays down $80 worth of debt,

FINDEF equals $20 and regressing ∆D on FINDEF can give misleading inferences.

In this section we implement logistic regressions that do not suffer from this

problem. In particular, we test how our explanatory variables affect the probability of

firms deviating from the strict pecking order. We define a �mild deviation� from the

strict (single-period) pecking order as accessing the market on net (FINDEF>0), and a

�strong deviation� as issuing equity on net (∆E>0).

21

Table 5 reports the results of logistic regressions for each definition. Since we are

estimating probabilities, we lag all our explanatory variables to comply with the

predictive nature of the estimation. The specifications in columns 1a and 1b show the

effect of DISPERSION and our set of control variables on the probability of deviation

from the pecking order. In Viswanath�s (1993) model the optimal time to deviate from

the strict pecking order is when growth options (MTB) are high and the dilution to

existing shareholders (DISPERSION) from issuing risky securities is low. The sensitivity

of FINDEF to DISPERSION is thus affected by the value of a firm�s growth options. In

particular, when a firm has high growth options that it does not want to pass up, it will

issue risky securities regardless of DISPERSION. Therefore, we include specifications

with an interaction term of DISPERSION * MTB (columns 2a and 2b).

In Table 5, the coefficient on DISPERSION (DISP_DUMMY) is significantly

negative (positive) and the coefficient on MTB is significantly positive in every

specification, all at the 1% level. Consistent with the multi-period pecking order theory,

firms are more likely to deviate from the strict pecking order when adverse selection

costs are low and / or the firm�s growth options are high. When we interact MTB and

DISPERSION, we find that MTB decreases the effect of DISPERSION on the

probability of deviation. When growth options are higher, adverse selection costs play a

lesser role in the type of financing used. This is consistent with the multi-period pecking

order, since high NPV projects can outweigh the costs of dilution in issuing risky

securities.

[INSERT TABLE 5 ABOUT HERE]

The coefficients on our control variables indicate that larger and more profitable

firms with more collateral are less likely to issue equity, suggesting that these firms have

22

enough financial slack and / or debt capacity and thus are less likely to deviate from the

strict pecking order. Younger firms with greater stock return volatility are more likely to

make financing decisions that deviate from the strict pecking order. We note that the

coefficients on both VOLATILITY and PPE are much more significant in our second

specification (∆E>0), indicating that firms with more volatile returns and less tangible

assets rely more on equity financing.

Overall our logistic regressions indicate that, consistent with predictions in

Viswanath (1993), when firms have low adverse selection costs and high growth options

they are more likely to deviate from the strict financing hierarchy. This suggests that

issuing equity can be a strategic decision to maintain / build financial slack or debt

capacity, to avoid deviations from the strict pecking order in the future, when adverse

selection costs are expected to be higher. Thus, it is not surprising that empirical studies

often find support for single-period pecking order behavior in samples drawn from

periods when aggregate adverse selection costs are high. This finding can in fact be a

result of firms� multi-period financing strategy.

Although we find support for the multi-period pecking order theory, we cannot

rule out other capital structure explanations. Similarly, Fama and French (2002) and

Howakimian, Opler, and Titman (2001) find mixed evidence and argue that competing

theories are complementary rather than mutually exclusive. In our tests, several variables

identified in the trade-off theory contribute significantly to firms� financing decisions.

Additionally, we cannot rule out the market timing argument of Baker and Wurgler

(2002) since we find that high market-to-book corresponds to more external financing,

and in particular, to more equity financing. However, the multi-period pecking order not

only explains the positive relation between market-to-book and equity issues, but also

23

makes additional predictions about the importance of adverse selection costs, which we

also confirm. In our tests adverse selection costs have explanatory power beyond the

effect of market-to-book.

In view of our empirical results, the pecking order theory is not a universal, all-

encompassing capital structure theory. Rather, Myers and Majluf�s (1984) and

Visvanath�s (1993) pecking order theories are descriptions of an important time-series

aspect of firms� financing decisions � with the implicit assumption of �everything else

equal�. This leaves room for other considerations, such as debt tax shields or debt

capacity concerns, to enter firms� financing structure decisions. Even though the pecking

order theory may not fully explain firms� capital structure decisions on its own right, its

underlying assumption, that adverse selection costs matter, is supported by our data.

3. The Method of Raising Equity

In a recent study, Fama and French (2004) point out that firms can raise equity

capital in ways that are potentially less subject to adverse selection costs. This, they

argue, is extremely damaging to the pecking order since equity financing is no longer a

last resort. In this section we provide tests that address the above concerns.

3.1. The Fama-French critique

Our tests to this point have used the Compustat variables change in equity and

change in debt to approximate equity and debt issues. A drawback to using this approach

in tests of the pecking order is that Compustat variables group together methods of

raising equity (debt) that are affected differently by adverse selection costs. Fama and

French (2004) report that option exercises and especially mergers and acquisitions have

24

recently accounted for most of the equity raised by the average firm. These alternative

methods of raising equity presumably have lower adverse selection costs than the less

frequent seasoned equity offerings. Fama and French (2004) interpret the large frequency

and the significant amount of capital raised via these methods as a �deadly blow� to the

pecking order since debt does not necessarily precede equity in the financing hierarchy.

So how do our results stand up in light of the Fama-French critique? In general,

grouping methods of raising equity that are more prone to adverse selection costs [e.g.

SEOs] with methods that are less prone to adverse selection costs [e.g. options and

mergers] should bias against us finding support for the multi-period pecking order. We

note that our Compustat variables are calculated from cash flow statements, and not from

balance sheets, thus our ∆E does not incorporate the effect of stock financed acquisitions.

∆E is affected, though, by option exercises and employee stock purchase plans. It is

possible that option exercises occur during periods of relatively high market valuation,

which may correspond to low adverse selection costs.

To address this concern, we re-estimate our specifications in Tables 2 and 3 using

a restricted sample which includes only firm-quarter observations in which ∆D and ∆E

are greater than 5% of book assets. Hovakimian, Opler, and Titman (2001) find that using

this restricted sample is similar to using a sample of new debt and equity issues from

Securities Data Company (SDC). We report results using the restriction that the absolute

value of ∆D (∆E) is greater than 5%, and the narrower restriction that the value of ∆D

(∆E) is greater than +5%. If ∆D (∆E) is less than the absolute value of 5% or is less than

+5%, we set it equal to zero.

Table 6 reports OLS and firm fixed-effect estimates for Eq. (1) with the above

restrictions. The effect of adverse selection costs (DISPERSION and DISP_DUMMY)

25

remains strong: higher DISPERSION decreases firms� reliance on external funds for

financing needs. The only notable change from Table 2 is that the slope of the yield curve

(YIELDSLOPE) is now positively related to firms� choice of external over internal funds.

This is possibly because expansionary periods are associated with more investment

opportunities, causing firms to save internal cash for future financing. It is also possible

that business cycle expansions coincide with reduced aggregate adverse selection costs

[Choe, Masulis, and Nanda (1993)]. Our firm-specific measure of adverse selection costs

(DISPERSION), however, has additional explanatory power beyond the effect of

YIELDSLOPE. Table 7 reports estimates for Eq. (3) with the 5% restrictions. In each

regression, the explanatory power (R2) is improved compared to the Table 3 estimations.

The results are consistent with our earlier findings that firms use relatively less debt and

more equity to cover their investment needs when adverse selection costs are low.

[INSERT TABLE 6 ABOUT HERE]

[INSERT TABLE 7 ABOUT HERE]

To provide specific evidence that seasoned equity offers (SEOs) are timed to

periods of low adverse selection costs, we examine variations in adverse selection costs

in event time around SEOs. Our SEO sample is drawn from SDC and contains 1428

primary common stock issues of U.S. non-financial and non-utility corporations, during

the period 1990-2003. We exclude unit, rights, and shelf offers. We match this SEO

sample with monthly analysts� forecasts dispersion, and identify the event month as the

month of the SEO filing date. We require that companies have dispersion data available

(according to our previous requirement of a minimum of 4 analysts) in every month

during the interval [-9, 9], which results in a sample of 187 SEOs. We also examine a

sample of 814 public debt offers during 1990-2003 that satisfy our analyst coverage

26

requirements. Shortening the data requirement period increases the sample sizes but does

not change our results.

Figure 9 plots mean values of DISPERSION around SEOs in event time. The

multi-period pecking order predicts that equity issues occur when adverse selection costs

are currently low and are expected to increase in the future. Consistent with this, the level

of adverse selection costs declines sharply prior to SEOs, and increases three to six

months after SEOs. To check for statistical significance, we report tri-monthly changes in

DISPERSION in Panel A of Table 8. There is a significant drop in DISPERSION in the

interval starting three months prior to and ending at the month of the SEO. The largest

decline occurs in the intervals [-3,-2] and [-2,-1]. Additionally, there is a significant

increase in DISPERSION in the three to six month period after SEOs. In contrast, Panel

B of Table 8 reports a small and insignificant decline in DISPERSION prior to public

debt offers and virtually no change after public debt offers. The larger decline (increase)

in DISPERSION prior to (after) seasoned equity offers compared to public debt offers is

consistent with our earlier tests and provides support for the multi-period pecking order.

Note that we compare changes in DISPERSION over time rather than the level of

DISPERSION across securities to focus on the time-series predictions of the pecking

order.

[INSERT FIGURE 9 ABOUT HERE]

[INSERT TABLE 8 ABOUT HERE]

Our finding that SEOs are timed to periods of low adverse selection costs

complements and extends the prior literature on the role of time-varying adverse selection

costs in firm financing. We use a direct and firm-specific proxy (analyst forecast

dispersion) for adverse selection costs, whereas previous studies use aggregate measures

27

such as time varying �hot� and �cold� markets (Bayless and Chaplinsky (1996)) and the

business cycle (Choe, Masulis, and Nanda (1993)). Additionally, we provide evidence

that SEOs are filed after the largest drop in adverse selection costs in a 1.5-year window

around the SEO, whereas Korajczyk, Lucas, and McDonald (1991, 1992) find that equity

issues occur shortly after information releases such as earnings announcements, thereby

reducing adverse selection costs. Earnings announcements are periodic events that

happen multiple times during the period we plot, but we cannot observe a cycle in

adverse selection costs corresponding to these.

Still, we agree with Fama and French (2004) that the pecking order in its most

general form � internal equity, external debt, external equity - breaks down when certain

types of equity issues suffer less (or not at all) from an adverse selection problem. At the

time when Myers and Majluf (1984) and Myers (1984) first described the pecking order

theory, stock financed acquisitions and option exercises were rare, thus the pecking order

was a financing hierarchy that considered public debt issues and SEOs as the full extent

of external financing. Over time, academic researchers (for example, Hadlock and James

(2002) and Hertzel and Smith (1993)) added more layers to this two-tier hierarchy,

including bank loans as the debt with the least adverse selection costs, convertible bonds

as the debt with the most adverse selection costs, and private placements of stock as the

equity with the least adverse selection costs. Similarly, the new low-adverse-selection-

cost ways to issue equity reported by Fama and French (2004) give rise to a more

sophisticated, multi-layered pecking order in which equity issues can have lower and

higher ranks depending on the method of their issuance. After all, the �pecking order� is

an outcome of the Myers and Majluf model in which information asymmetry gives rise to

adverse selection costs. The theory was derived to explain incremental financing choices

28

rather than to explain overall capital structure. Instead of discarding the pecking order as

no longer relevant, we believe future research should focus on updating the pecking order

to include today�s new methods of raising capital. In the next section we provide

preliminary evidence that non-specific methods of raising equity capital � in addition to

the specific purpose equity issues (e.g. mergers and option exercises) mentioned by Fama

and French (2004) � can be less subject to adverse selection costs.

3.2. Methods to raise equity and the pecking order

Motivated by Fama and French (2004), we test the hypothesis that different

methods of raising equity are affected by adverse selection costs to different degrees. We

examine ways to issue equity that are the most similar to SEOs and are used for financing

investments or for general corporate purposes. In particular, we compare our sample of

1428 primary SEOs to a sample of primary shelf-registered equity offerings and Private

Investment in Public Equity (PIPE) offers during 1990-2003. Our sample contains 386

primary shelf offers, identical to the sample studied by Autore, Kumar, and Shome

(2004), and 929 PIPE offers collected from the SDC New Issues Database. Although

PIPEs are privately placed, they are similar to SEOs because the issuer is required to file

a registration statement that covers the resale of the shares, thus eliminating the illiquidity

that is associated with Rule 144 private placements.13

We expect offerings of equity securities that are more prone to adverse selection

costs to occur after significant declines in DISPERSION, and offerings of equity

securities that are less prone to adverse selection costs to have less of a relation to time-

variation in DISPERSION. Panels C-E of Table 8 provide statistics on changes in

29

DISPERSION around the filing and offer dates14 of shelf-registered offerings and around

PIPE offers, respectively. Our restriction that companies have dispersion data available (a

minimum of 4 analysts) in every month during the interval [-9, 9] reduces the sample of

shelf filings to 97, shelf offers to 128, and PIPEs to 47. Statistics in Panels C and D

indicate no significant change in DISPERSION prior to shelf filings or shelf offers. Panel

E reports no significant change in DISPERSION prior to PIPE offers. These findings are

in contrast to the significant decline in DISPERSION prior to SEO announcements.

Furthermore, we expect offerings of equity securities that are more subject to

adverse selection costs to be associated with a more severe market reaction, and offerings

of equity securities that are less subject to adverse selection costs to generate a smaller

market reaction. We calculate the market-adjusted mean and median cumulative

abnormal returns (CARs) for the three-day window centered on the announcement date

for our sample of 1428 SEOs and 386 shelf offerings, and on the offer date for our

sample of 929 PIPE offers. The mean (median) market reaction is -1.68% (-2.07%) at

SEO announcements, -0.67% (-0.82%) at shelf announcements, and 1.34% (-0.16%) at

PIPE offers. Differences between means (t-test) and medians (Wilcoxon sign-rank test)

are all significant at the 1% level, with the exception that the median shelf announcement

reaction is less than the median PIPE offer reaction at the 5% level.

The evidence suggests that shelf equity and PIPEs are less affected by adverse

selection costs than are SEOs. Issuers appear to have little or no concern about time-

varying adverse selection costs in the issuance of shelf-registered equity or PIPEs,

although they conduct SEOs after declines in adverse selection costs. In addition, the

13 Ordinary private placements are structurally different from SEOs, due to the resale restriction and the resulting illiquidity. Accordingly, we do not include them in our equity sample. 14 Shelf offer dates are included because shelf issuers can delay offerings for several months or even years after filing a registration statement.

30

market punishes SEO issuers significantly more than it punishes shelf issuers at the offer

announcement, and it does not penalize PIPE issuers.15

Our findings are consistent with the relative mix of SEOs, shelf offers, and PIPEs

in recent years. Figures 10 and 11 illustrate the relative proportions of each type of equity

offer in terms of the number of offers and total proceeds, respectively. Both figures

illustrate that the relative use of traditional SEOs has declined substantially in the past

decade, while the use of shelf registration and PIPEs has grown. For example, during

2000-2003 SEOs account for only about 15% (258 offers) of the sample equity offers,

while PIPEs account for about two-thirds (627 offers) of our sample equity offers.

Additionally, during 2000-2003 the proceeds from SEOs account for only about 30% of

our total sample equity proceeds, while the proceeds from shelf offers account for more

than half of the sample equity proceeds. These findings are consistent with the results of

Fama and French (2004) that issuers are increasingly favoring ways to issue equity that

have low adverse selection costs. Our evidence on shelf and PIPE issues is important

because these are methods of raising equity similar to an SEO. In contrast, options do not

accommodate investment-driven needs and mergers are specific transactions.

[INSERT FIGURES 10-11 ABOUT HERE]

Considering our evidence, it is unlikely that the pecking order can completely

explain firms� overall capital structure, especially if certain types of equity issues become

intertwined with debt issues in a multi-layered hierarchy. However, the pecking order

remains important as a theory of incremental financing choices. The fact that firms seem

15 We note that, in the cross-section, DISPERSION is substantially higher for PIPE issuers than for SEO or shelf issuers. PIPE issuers are, on average, smaller and less recognized firms and thus are likely to have greater informational discrepancies than SEO or shelf issuers. The fact that PIPE issuers are not penalized by the market implies that PIPEs can resolve the adverse selection problems that equity issuers with high information asymmetry are expected to have. Thus PIPE offers can, in part, explain small firms� reliance on equity even within a single-period adverse selection framework.

31

to avoid SEOs and favor instead issuing equity through channels that bear lower adverse

selection costs, is in fact support for the underlying principle of the pecking order:

adverse selection costs play an important role in firms� financing decisions and, holding

all else constant, create a hierarchy of financing choices.

4. Conclusion

In this paper, we find support for a multi-period pecking order [Viswanath

(1993)] that can accommodate equity issues of firms that are not financially constrained.

The key difference between our study and previous studies is that we explicitly control

for time-series variation in adverse selection costs by using dispersion in analysts�

earnings forecasts as a proxy. We find that firms rely more on external markets for

financing needs when adverse selection costs are low, and given that a firm chooses

external financing, we report that the choice of debt over equity is positively related to

adverse selection costs. Furthermore, we find that firms are more likely to deviate from

the strict pecking order when adverse selection costs are low and growth options are high,

consistent with the predictions of the multi-period pecking order.

We conduct tests on additional samples to address the findings of Fama and

French (2004) that firms issue equity in ways that are less prone to adverse selection

costs. We verify our results using a restricted sample that is similar to using a sample of

new debt and equity issues. Additionally, we provide support for the multi-period

pecking order using a sample of SEOs and public debt offers. Specifically, we illustrate

that adverse selection costs significantly decline in the three months prior to SEO

announcements, and significantly increase in the three to six month period after SEOs.

32

We find a small (insignificant) decrease in adverse selection costs prior to public debt

offers.

We also examine methods of raising equity similar to SEOs that appear to suffer

less from an adverse selection problem. In particular, we find that the timing of shelf-

registered and PIPE offers has little relation to time-varying adverse selection costs.

Moreover, the market places a significantly larger discount on the stock of SEO issuers

than on the stock of shelf or PIPE issuers. The evidence is consistent with the relative

financing mix of SEOs, shelf offers, and PIPEs during 1990-2003, as issuers are

increasingly avoiding SEOs in favor of shelf offers and PIPEs.

In sum, our evidence supports the premise of the pecking order that adverse

selection costs affect financing choices. Using various samples, we find that firms take

adverse selection costs into account by timing their use of internal versus external and

debt versus equity financing, as well as by moving to equity issues that are less prone to

adverse selection problems. These findings suggest the pecking order as a theory of

financing choices lives, although a new multi-layered hierarchy is necessary to reflect

current methods of raising capital. We leave the formal development of a new pecking

order that includes various types of equity securities to future research.

33

Appendix A

Myers and Majluf (1984) and Myers (1984) develop the pecking order theory by

assuming that managers have an information advantage over investors. In their model, the

manager knows both the value of assets in place and the value of a project (growth

option), but investors know only the joint distribution of these two random variables. The

manager, acting in the interest of existing shareholders, issues securities to take on a

positive NPV project if the dilution caused by the issue decision is smaller than the value

gain from the project. The dilution costs arise because the market�s expectation of the

firm value changes conditional on whether or not the manager announces a security issue.

To illustrate how adverse selection and dilution are related, we focus on the

asymmetric information between managers and investors about the value of assets in

place and keep the value of growth options (denoted as b) as given. Similar to Myers and

Majluf (1984), let �a� denote the value of assets in place known only by the manager.

Investors know the distribution of the value of assets in place, the distribution of the

random variable �A�. When the manager announces a security issue, investors will

discount the value of assets in place according to their knowledge of A�s distribution,

conditional on the issue decision, E( A | issue). The manager issues equity if the value of

the existing shareholders� stake is greater with an issue than without it. His decision rule

is thus the following, with the simplification that the firm has no cash, and has to collect I

from the issue proceeds to invest:

aIP

PIba ≥+

++'

')( , (A1)

34

where P�= E( A | issue)+b. Let the value of assets in place, for which the manager

is indifferent between issuing and not issuing, be a*. If the market is rational it can apply

the above decision rule and calculate a level for a*. If a > a* the manager forgoes the

investment, and if a < a* the manager will issue and invest. The existing shareholders�

claim, given the issue decision, is: bdaaafPa

a

+= ∫*

min

)('

Intuitively, the issue decision signals bad news about the value of assets in place

and truncates the distribution of A from above. However, the issue decision does not fully

reveal the manager�s information, thus the discounted share price reflects the market�s

conditional expectation.

We consider an example to illustrate how different potential proxies for adverse

selection are associated with the equity issue decision. Let I=1, b=0.2, and A be

distributed uniformly over [amin, amax]. It is not necessary to assume a uniform

distribution; we use it to simplify the discussion. The value of a* can be calculated using

equation (A1) and the distributional assumption; E( A | issue)=(a*+amin)/2 .

When A~U[1, 3], the value of a* is 2.1. Managers will only issue equity if the

value of assets in place is less than 2.1, thus the market�s expected value of assets in

place conditional on an issue falls to 1.55. If we increase the dispersion of A�s

distribution to U[0, 4], a* becomes 0.6 and the conditional value of assets in place is 0.3.

This illustrates that adverse selection costs of an issue are higher when the dispersion in

the market�s assessment of assets in place is larger, all else constant.

Note, however, that the amount of asymmetric information between managers and

investors is not directly related to adverse selection costs, since the issue announcement

does not fully reveal the realized value known by the manager. When A is uniformly

35

distributed [1, 3], the market�s initial expectation is E(A) equals 2. Conditional on equity

issuance, A is uniformly distributed [1, 2.1]. Asymmetric information is the difference

between the market�s initial expectation and the realization of the value of assets in place

(between 1 and 2.1). Thus the level of asymmetric information between managers and

investors can be anywhere between �0.1 and 1, given an equity issue. This illustrates that

there is no direct relation between the amount of asymmetric information and the adverse

selection costs imposed by the market.

The discussion above assumes that the value of growth options is known with

certainty. This assumption facilitates the focus on the origins of adverse selection costs in

the model. In the more realistic case where the market knows only the joint probability

distribution of the value of assets in place and of the growth option, the issue decision

reveals information about possible pairs of (a, b) in a, b space. However, numerical

simulations in Myers and Majluf (1984) show that when the distribution of assets in place

has a wider dispersion, ceteris paribus, adverse selection costs become greater.

36

Appendix B

Histograms of the dispersion in analysts� earnings forecasts (Figure B.1), and its natural logarithm (Figure B.2) Dispersion is calculated as the standard deviation of analysts� earnings forecasts scaled by the end-of-quarter stock price. For the computation of this variable we require that at least 4 analysts follow the stock.

Figure B.1

Histogram of dispersion

0

5000

10000

15000

20000

25000

30000

0.001 0.011 0.021 0.031 0.041 0.051 0.061 0.071 0.081 0.091

dispersion

Figure B.2

Histogram of the natural logarithm of dispersion

0

500

1000

1500

2000

2500

3000

-10.3 -9.7 -9.0 -8.4 -7.7 -7.0 -6.4 -5.7 -5.1 -4.4 -3.8 -3.1 -2.5logdispersion

37

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41

Table 1 Descriptive statistics and correlations between explanatory variables

Panel A displays the mean, median, and standard deviation of variables computed from the full data sample. Panel B exhibits Pearson correlation coefficients computed from the full sample. All correlation coefficients are significant at the 1% level. ∆D, ∆E, and FINDEF are the net change in debt, net change in equity, and total financing deficit in a quarter. The financing deficit (FINDEF) is defined as the sum of net equity and debt issues for a firm in a given quarter. DISPERSION is the natural logarithm of the average standard deviation of analysts� quarterly earnings forecasts scaled by end of quarter stock price. The dispersion dummy (DISP_DUMMY) takes the value of one when DISPERSION is zero and thus the log operation is invalid. Market-to-book (MTB) is the end of quarter market value of equity plus book value of debt divided by total book assets. YOUNG is a dummy variable equal to one for firms under ten years old and zero otherwise. SIZE is defined as the natural logarithm of sales. Tangibility (PPE) is defined as property, plant and equipment to total book assets. PROFITABILITY is income before extraordinary items scaled by total book assets. Residual standard deviation (VOLATILITY) is calculated from daily returns using a modified market model with the contemporaneous, lead, and lagged market return as explanatory variables in the spirit of Scholes and Williams (1977) and Dimson (1979). The sample period is 1984-2003. There are 69,944 firm-quarter observations in our sample.

Panel A: Descriptive statistics

Variable Mean Median Standard deviation

∆D 0.0046 0 0.0492

∆E 0.0043 0.0002 0.0499

FINDEF 0.0090 0.0005 0.0689

DISPERSION -5.8118 -6.5505 2.4868

DISP_DUMMY 0.1311 0 0.3375

MTB 2.2693 1.6932 1.8025

SIZE 19.2346 19.1427 1.6268

PPE 0.3287 0.2761 0.2265

YOUNG 0.1505 0 0.3576

Profitability 0.0111 0.0146 0.0417

VOLATILITY 0.0274 0.0236 0.0156

Panel B: Correlation coefficients Dispersion MTB SIZE PPE YOUNG Profitability

MTB 0.0749 1.0000

SIZE -0.1377 -0.2029 1.0000

PPE -0.0395 -0.2418 0.1843 1.0000

YOUNG 0.0397 0.1934 -0.2759 -0.1520 1.0000

Profitability -0.0095 0.1675 0.1403 0.0317 -0.1045 1.0000

VOLATILITY 0.0656 0.1868 -0.4613 -0.2796 0.3738 -0.3102

42

Table 2 Determinants of financing deficit

OLS and fixed effects regressions of financing deficit regressed on firm characteristics. Financing deficit (FINDEF) is defined as the sum of net equity and debt issues for a firm in a given quarter. DISPERSION is the natural logarithm of the average standard deviation of analysts� quarterly earnings forecasts scaled by end of quarter stock price. The dispersion dummy (DISP_DUMMY) takes the value of one when DISPERSION is zero and thus the log operation is invalid. Market-to-book (MTB) is the end of quarter market value of equity plus book value of debt divided by total book assets. YOUNG is a dummy variable equal to one for firms under ten years old and zero otherwise. SIZE is defined as the natural logarithm of sales. Tangibility (PPE) is defined as property, plant and equipment to total book assets. PROFITABILITY is income before extraordinary items scaled by total book assets. Residual standard deviation (VOLATILITY) is calculated from daily returns using a modified market model with the contemporaneous, lead, and lagged market return as explanatory variables in the spirit of Scholes and Williams (1977) and Dimson (1979). INTRATE equals the rate on 10-year AAA-rated corporate bonds and YIELDSLOPE is the slope of the yield curve (YIELDSLOPE), defined as the 20-year Treasury bond rate minus the 3-month Treasury bill rate. Interest rate data is obtained from the St. Louis Federal Reserve. Heteroskedasticity adjusted standard errors are in parentheses. The sample period is 1984-2002. There are 69,944 firm-quarter observations in our sample.

OLS Fixed effects Intercept

0.0413*** (0.0058) -

DISPERSION

-0.0028*** (0.0003)

-0.0042*** (0.0004)

DISP_DUMMY

0.0201*** (0.0024)

0.0325*** (0.0032)

MTB

0.0027*** (0.0003)

0.0048*** (0.0004)

PPE

0.0034*** (0.0011)

-0.0827*** (0.0055)

SIZE

-0.0036*** (0.0001)

-0.0102*** (0.0007)

YOUNG (dummy)

0.0093*** (0.0009)

0.0084*** (0.0014)

PROFITABILITY

-0.1046*** (0.0119)

-0.0314*** (0.0116)

VOLATILITY

0.1558*** (0.0321)

0.0047 (0.0405)

INTRATE

0.0010*** (0.0002)

0.0011*** (0.0002)

YIELDSLOPE

0.017 (0.0547)

0.026 (0.0533)

Adjusted R2 0.0322

0.0859

*, **, *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

43

Table 3 Determinants of the source of external financing

Conditional model estimates to examine the source of external financing. The dependent variable is the change in debt (∆D). Financing deficit (FINDEF) is defined as the sum of net equity and debt issues for a firm in a given quarter. DISPERSION is the natural logarithm of the average standard deviation of analysts� quarterly earnings forecasts scaled by end of quarter stock price. The dispersion dummy (DISP_DUMMY) takes the value of one when DISPERSION is zero and thus the log operation is invalid. Market-to-book (MTB) is the end of quarter market value of equity plus book value of debt divided by total book assets. YOUNG is a dummy variable equal to one for firms under ten years old and zero otherwise. SIZE is defined as the natural logarithm of sales. Tangibility (PPE) is defined as property, plant and equipment to total book assets. PROFITABILITY is income before extraordinary items scaled by total book assets. Residual standard deviation (VOLATILITY) is calculated from daily returns using a modified market model with the contemporaneous, lead, and lagged market return as explanatory variables in the spirit of Scholes and Williams (1977) and Dimson (1979). INTRATE equals the rate on 10-year AAA-rated corporate bonds and is obtained from the St. Louis Federal Reserve. Heteroskedasticity adjusted standard errors are in parentheses. The sample period is 1984-2002. There are 69,944 firm-quarter observations in our full sample, and 38,177 firm-quarter observations in our financing deficit only sample. Panel A: Full sample Panel B: Findef>0 subsample OLS Fixed effects OLS Fixed effects 1a 2a 1b 2b Intercept

0.0009*** (0.0001) - -0.0019***

(0.0002) -

FINDEF

0.0209 (0.1761)

0.0153 (0.1629)

-0.6839*** (0.2100)

-0.9373*** (0.2010)

Dispersion*FINDEF

0.0435*** (0.0095)

0.0423*** (0.0088)

0.0332*** (0.0118)

0.034*** (0.0108)

DISP_DUMMY *FINDEF

-0.3724*** (0.0727)

-0.3635*** (0.0681)

-0.3124*** (0.0913)

-0.314*** (0.0847)

MTB*FINDEF

-0.0201*** (0.0035)

-0.0202*** (0.0033)

-0.0162*** (0.0036)

-0.0136*** (0.0034)

PPE*FINDEF

0.2031*** (0.0373)

0.1854*** (0.0364)

0.2782*** (0.0443)

0.2835*** (0.0491)

SIZE*FINDEF

0.0755*** (0.0060)

0.0769*** (0.0056)

0.103*** (0.0069)

0.1156*** (0.0072)

YOUNG *FINDEF

-0.0375* (0.0227)

-0.0394* (0.0221)

-0.0731*** (0.0257)

-0.0857*** (0.0267)

PROFITABILITY *FINDEF

-0.743*** (0.1764)

-0.7941*** (0.1712)

-0.8196*** (0.2458)

-0.9633*** (0.2553)

VOLATILITY *FINDEF

-2.3388*** (0.5787)

-2.2246*** (0.5649)

-2.4413*** (0.6624)

-1.777*** (0.6684)

INTRATE*FINDEF

-0.066*** (0.0073)

-0.0685*** (0.0067)

-0.0442*** (0.0098)

-0.0444*** (0.0093)

Adjusted R2 0.5094

0.5225 0.4951

0.5360

*, **, *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

44

Table 4 Robustness checks: determinants of the source of external financing

This table presents a conditional model, in which the independent variables are first differenced. The dependent variable is change in debt, and the conditioning variable is FINDEF. The financing deficit (FINDEF) is defined as the sum of net equity and debt issues for a firm in a given quarter. DISPERSION is the average standard deviation of analysts� quarterly earnings forecasts scaled by end of quarter stock price. Market-to-book (MTB) is the end of quarter market value of equity plus book value of debt divided by total book assets. YOUNG is a dummy variable equal to one for firms under ten years old and zero otherwise. SIZE is defined as the natural logarithm of sales. Tangibility (PPE) is defined as property, plant and equipment to total book assets. PROFITABILITY is income before extraordinary items scaled by total book assets. Residual standard deviation (VOLATILITY) is calculated from daily returns using a modified market model with the contemporaneous, lead, and lagged market return as explanatory variables in the spirit of Scholes and Williams (1977) and Dimson (1979). INTRATE equals the rate on 10-year AAA-rated corporate bonds and is obtained from the St. Louis Federal Reserve. Heteroskedasticity adjusted standard errors are in parentheses. The sample period is 1984-2002. There are 61,251 firm-quarter observations in our sample.

Conditional model with first differences of independent variables

Dependent variable: change in debt (∆D)

Panel A: Full sample Panel B: Findef >0 subsample

OLS Fixed effects OLS Fixed effects

1a 2a 1b 2b

Intercept

0.001*** (0.0001) - 0.001***

(0.0003) -

FINDEF

0.5465*** (0.0101)

0.5565*** (0.0096)

0.5726*** (0.0156)

0.5721*** (0.0149)

∆DISPERSION*FINDEF

4.1976*** (1.3242)

4.2148*** (1.2364)

5.3811*** (1.8086)

5.2818*** (1.6366)

∆MTB*FINDEF

0.013** (0.0064)

0.013** (0.0060)

0.015** (0.0067)

0.0132** (0.0062)

∆PPE*FINDEF

0.501*** (0.0916)

0.5098*** (0.0866)

0.6965*** (0.1004)

0.6923*** (0.0927)

∆SIZE*FINDEF

0.0556*** (0.0151)

0.0588*** (0.0141)

0.0691*** (0.0159)

0.0672*** (0.0144)

∆PROFITABILITY*FINDEF

-0.7095*** (0.2092)

-0.6763*** (0.2120)

-0.9639*** (0.3037)

-0.8603*** (0.3193)

∆VOLATILITY*FINDEF

-0.5925 (0.7974)

-0.4654 (0.7617)

-0.7042 (0.9063)

-0.2243 (0.8992)

∆INTRATE*FINDEF

-0.0164 (0.0119)

-0.0186* (0.0111)

0.0048 (0.0160)

0.0036 (0.0142)

Adjusted R2 0.4866 0.4900 0.4565

0.5013

*, **, *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

45

Table 5 Determinants of the probability that a firm deviates from the strict pecking order

Logit regressions to assess how our variables affect the probability of a firm deviating from the strict pecking order. In Panel A, deviating from the strict pecking order is defined as FINDEF>0. In Panel B, deviating from the strict pecking order is defined as ∆E>0. All independent variables except YOUNG are lagged to ensure compliance with the predictive nature of the regression. Financing deficit (FINDEF) is defined as the sum of net equity and debt issues for a firm in a given quarter. DISPERSION is the natural logarithm of the average standard deviation of analysts� quarterly earnings forecasts scaled by end of quarter stock price. The dispersion dummy (DISP_DUMMY) takes the value of one when DISPERSION is zero and thus the log operation is invalid. Market-to-book (MTB) is the end of quarter market value of equity plus book value of debt divided by total book assets. YOUNG is a dummy variable equal to one for firms under ten years old and zero otherwise. SIZE is defined as the natural logarithm of sales. Tangibility (PPE) is defined as property, plant and equipment to total book assets. PROFITABILITY is income before extraordinary items scaled by total book assets. Residual standard deviation (VOLATILITY) is calculated from daily returns using a modified market model with the contemporaneous, lead, and lagged market return as explanatory variables in the spirit of Scholes and Williams (1977) and Dimson (1979). INTRATE equals the rate on 10-year AAA-rated corporate bonds and is obtained from the St. Louis Federal Reserve. Standard errors are in parentheses. The sample period is 1984-2002. There are 61,251 firm-quarter observations.

Panel A

Dependent variable:

FINDEF>0

Panel B

Dependent variable:

∆E>0

1a 2a 1b 2b

Intercept

1.147*** (0.1640)

0.9411*** (0.1677)

3.2514*** (0.1733)

3.005*** (0.1772)

Lag(DISPERSION)

-0.0504*** (0.0095)

-0.0814*** (0.0109)

-0.0873*** (0.0100)

-0.1234*** (0.0113)

Lag(DISP_DUMMY)

0.4741*** (0.0723)

0.4752*** (0.0723)

0.9003*** (0.0756)

0.8978*** (0.0757)

Lag(MTB)

0.1017*** (0.0064)

0.1581*** (0.0118)

0.0806*** (0.0068)

0.1495*** (0.0129)

Lag(DISPERSION)* Lag(MTB)

-

0.0101*** (0.0017)

-

0.0122*** (0.0019)

Lag(PPE)

0.1275*** (0.0392)

0.1283*** (0.0392)

-0.3371*** (0.0402)

-0.3357*** (0.0402)

Lag(SIZE)

-0.1187*** (0.0059)

-0.1191*** (0.0059)

-0.2114*** (0.0062)

-0.2115*** (0.0062)

YOUNG

0.1825*** (0.0266)

0.1807*** (0.0266)

0.0343 (0.0282)

0.0327 (0.0282)

Lag(PROFITABILITY)

-1.0046*** (0.2494)

-1.0555*** (0.2498)

-3.2097*** (0.3218)

-3.2818*** (0.3225)

Lag(VOLATILITY)

9.3012*** (0.7488)

9.633*** (0.7513)

20.9684*** (0.8360)

21.3747*** (0.8388)

Lag(INTRATE)

0.0574*** (0.0077)

0.0608*** (0.0077)

-0.0124 (0.0080)

-0.0084 (0.0080)

*, **, *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

46

Table 6 Determinants of financing deficit using restricted samples

OLS and fixed effects regressions of financing deficit regressed on firm characteristics. Financing deficit (FINDEF) is defined as the sum of net equity (∆E) and debt (∆D) issues for a firm in a given quarter. In Panel A, if ∆E (∆D) is less in absolute value than 5% in a given quarter then ∆E (∆D) is set equal to zero. In Panel B, if ∆E (∆D) is less than +5% in a given quarter then ∆E (∆D) is set equal to zero. DISPERSION is the natural logarithm of the average standard deviation of analysts� quarterly earnings forecasts scaled by end of quarter stock price. The dispersion dummy (DISP_DUMMY) takes the value of one when DISPERSION is zero and thus the log operation is invalid. Market-to-book (MTB) is the end of quarter market value of equity plus book value of debt divided by total book assets. YOUNG is a dummy variable equal to one for firms under ten years old and zero otherwise. SIZE is defined as the natural logarithm of sales. Tangibility (PPE) is defined as property, plant and equipment to total book assets. PROFITABILITY is income before extraordinary items scaled by total book assets. Residual standard deviation (VOLATILITY) is calculated from daily returns using a modified market model with the contemporaneous, lead, and lagged market return as explanatory variables in the spirit of Scholes and Williams (1977) and Dimson (1979). INTRATE equals the rate on 10-year AAA-rated corporate bonds and YIELDSLOPE is the slope of the yield curve (YIELDSLOPE), defined as the 20-year Treasury bond rate minus the 3-month Treasury bill rate. Interest rate data is obtained from the St. Louis Federal Reserve. Heteroskedasticity adjusted standard errors are in parentheses. The sample period is 1984-2002. There are 69,944 firm-quarter observations in our sample.

Panel A: abs(∆D)>5% and abs(∆E)>5%

Panel B: ∆D>5% and ∆E>5%

OLS Fixed effects OLS Fixed effects 1a 2a 1b 2b INTERCEPT

0.0387*** (0.0055) - 0.0575***

(0.0054) -

DISPERSION

-0.0027*** (0.0003)

-0.0036*** (0.0004)

-0.0018*** (0.0003)

-0.0028*** (0.0003)

DISP_DUMMY

0.0181*** (0.0023)

0.0264*** (0.0030)

0.0108*** (0.0022)

0.0195*** (0.0028)

MTB

0.0020*** (0.0003)

0.0040*** (0.0004)

0.0023*** (0.0003)

0.0043*** (0.0004)

PPE

0.0018* (0.0010)

-0.0852*** (0.0051)

0.0007 (0.0010)

-0.0823*** (0.0048)

SIZE

-0.0031*** (0.0001)

-0.0092*** (0.0007)

-0.0032*** (0.0001)

-0.0091*** (0.0007)

YOUNG (dummy)

0.0082*** (0.0009)

0.0076*** (0.0013)

0.0087*** (0.0009)

0.0077*** (0.0012)

PROFITABILITY

-0.0967*** (0.0113)

-0.0314*** (0.0110)

-0.0683*** (0.0095)

-0.0188** (0.0095)

VOLATILITY

0.1235*** (0.0313)

0.0402 (0.0396)

0.1324*** (0.0299)

0.0848** (0.0381)

INTRATE

0.0004* (0.0002)

0.0005** (0.0002)

-0.0003* (0.0001)

0.0002 (0.0002)

YIELDSLOPE

0.1198** (0.0512)

0.1299*** (0.0500)

0.1512*** (0.0496)

0.1354*** (0.0478)

Adjusted R2 0.0269

0.0788

0.0311

0.0926

*, **, *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

47

Table 7 Determinants of the source of external financing using restricted samples

Conditional model estimates to examine the source of external financing. The dependent variable is the change in debt (∆D). Financing deficit (FINDEF) is defined as the sum of net equity (∆E) and debt (∆D) issues for a firm in a given quarter. For regressions in Panel A, if ∆E (∆D) is less in absolute value than 5% in a given quarter then ∆E (∆D) is set equal to zero. For regressions in Panel B, if ∆E (∆D) is less than +5% in a given quarter then ∆E (∆D) is set equal to zero. DISPERSION is the natural logarithm of the average standard deviation of analysts� quarterly earnings forecasts scaled by end of quarter stock price. The dispersion dummy (DISP_DUMMY) takes the value of one when DISPERSION is zero and thus the log operation is invalid. Market-to-book (MTB) is the end of quarter market value of equity plus book value of debt divided by total book assets. YOUNG is a dummy variable equal to one for firms under ten years old and zero otherwise. SIZE is defined as the natural logarithm of sales. Tangibility (PPE) is defined as property, plant and equipment to total book assets. PROFITABILITY is income before extraordinary items scaled by total book assets. Residual standard deviation (VOLATILITY) is calculated from daily returns using a modified market model with the contemporaneous, lead, and lagged market return as explanatory variables in the spirit of Scholes and Williams (1977) and Dimson (1979). INTRATE equals the rate on 10-year AAA-rated corporate bonds and is obtained from the St. Louis Federal Reserve. Heteroskedasticity adjusted standard errors are in parentheses. The sample period is 1984-2002. There are 69,944 firm-quarter observations. Panel A:

abs(∆D)>5% and abs(∆E)>5% Panel B:

∆D>5% and ∆E>5% OLS Fixed effects OLS Fixed effects 1a 2a 1b 2b Intercept

0.0001 (0.0000) - 0.0005***

(0.0000) -

FINDEF

-0.7142*** (0.2035)

-0.7336*** (0.1872)

-1.0180*** (0.2145)

-1.1204*** (0.2002)

Dispersion*FINDEF

0.0459*** (0.0106)

0.0449*** (0.0099)

0.0407*** (0.0121)

0.0404*** (0.0113)

DISP_DUMMY *FINDEF

-0.4062*** (0.0823)

-0.3989*** (0.0770)

-0.3761*** (0.0940)

-0.3766*** (0.0889)

MTB*FINDEF

-0.0154*** (0.0035)

-0.0154*** (0.0033)

-0.012*** (0.0035)

-0.0113*** (0.0033)

PPE*FINDEF

0.2787*** (0.0415)

0.2811*** (0.0397)

0.2065*** (0.0448)

0.1992*** (0.0434)

SIZE*FINDEF

0.1040*** (0.0068)

0.1052*** (0.0063)

0.1107*** (0.0070)

0.1152*** (0.0066)

YOUNG *FINDEF

-0.0600*** (0.0242)

-0.0634*** (0.0234)

-0.0697*** (0.0250)

-0.0779*** (0.0243)

PROFITABILITY *FINDEF

-0.6950*** (0.1888)

-0.7278*** (0.1806)

-0.7811*** (0.2461)

-0.8394*** (0.2378)

VOLATILITY *FINDEF

-2.7204*** (0.6144)

-2.5843*** (0.5963)

-2.6881*** (0.6577)

-2.4090*** (0.6392)

INTRATE*FINDEF

-0.02800*** (0.0095)

-0.0295*** (0.0088)

-0.0112 (0.0102)

-0.0099 (0.0095)

Adjusted R2 0.5749 0.5880 0.6080 0.6230 *, **, *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

48

Table 8 Change in dispersion around security offerings

Changes in DISPERSION measured in three-month increments prior to and after SEO filings, public debt filings, shelf equity filings and offers, and private investment in public equity (PIPE) offers conducted during 1990-2003 by U.S. firms. The sample of SEOs and shelf-registered offers contains primary offerings of common stock. The sample of SEOs, public debt offers, shelf offers, and PIPEs are each constrained to those offers for which dispersion data is available in every month during the interval [-9, 9] around the event date. Change in dispersion is calculated for each firm for the given interval, and then it is averaged across firms.

Panel A: Change in dispersion around SEO filings

Interval in eventmonth Mean ∆DISPERSION T-value Number of observations

-9 to -6 0.00020 0.77 187

-6 to -3 0.00012 0.40 187

-3 to 0 -0.00087 -2.71 187

0 to 3 -0.00020 -1.14 187

3 to 6 0.00089 1.94 187

6 to 9 0.00054 0.66 187

Panel B: Change in dispersion around public debt filings

Interval in eventmonth Mean ∆DISPERSION T-value Number of observations

-9 to -6 0.00002 0.12 814 -6 to -3 -0.00022 -1.25 814 -3 to 0 -0.00003 -0.20 814 0 to 3 -0.00002 -0.14 814 3 to 6 0.00003 0.26 814 6 to 9 -0.00003 -0.25 814

Panel C: Change in dispersion around shelf filing dates

Interval in eventmonth Mean ∆DISPERSION T-value Number of observations

-9 to -6 -0.00127 -1.26 97 -6 to -3 0.00006 0.28 97 -3 to 0 0.00009 0.25 97 0 to 3 0.00025 0.60 97 3 to 6 0.00059 0.68 97 6 to 9 -0.00090 -1.39 97

49

Table 8 (cont.)

Change in dispersion around security offerings Changes in DISPERSION measured in three-month increments prior to and after SEO filings, public debt filings, shelf equity filings and offers, and private investment in public equity (PIPE) offers conducted during 1990-2003 by U.S. firms. The sample of SEOs and shelf-registered offers contains primary offerings of common stock. The sample of SEOs, public debt offers, shelf offers, and PIPEs are each constrained to those offers for which dispersion data is available in every month during the interval [-9, 9] around the event date. Change in dispersion is calculated for each firm for the given interval, and then it is averaged across firms.

Panel D: Change in dispersion around shelf offer dates

Interval in eventmonth Mean ∆DISPERSION T-value Number of observations

-9 to -6 0.000032 0.07 128 -6 to -3 0.00047 0.99 128 -3 to 0 -0.00022 -0.41 128 0 to 3 0.00077 0.92 128 3 to 6 -0.00055 -0.75 128 6 to 9 0.00057 0.89 128

Panel E: Change in dispersion around PIPEs

Interval in eventmonth Mean ∆DISPERSION T-value Number of observations

-9 to -6 0.00081 0.39 47 -6 to -3 -0.00174 -0.89 47 -3 to 0 0.00002 0.02 47 0 to 3 -0.00032 -0.25 47 3 to 6 0.00013 0.17 47 6 to 9 -0.00105 -1.37 47

50

Figures 1-8 The time-series of average firm characteristics

Figure 1, and Figures 3-8 plot the sample mean of the firm characteristic labeled for each quarter in the sample. Figure 2 plots the coefficient of financing deficit from the cross-sectional regression of net change in debt on financing deficit. The financing deficit is defined as the sum of net equity and debt issues for a firm in a given quarter. Dispersion is the average standard deviation of analysts� quarterly earnings forecasts scaled by the end of quarter stock price. Market-to-book is the end of quarter market value of equity plus book value of debt divided by total book assets. The natural logarithm of sales variable is our SIZE proxy throughout the paper. Property, plant and equipment is scaled by total book assets. Profitability is income before extraordinary items scaled by total book assets. Return volatility is the residual standard deviation calculated from daily returns using a modified market model with the contemporaneous, lead, and lagged market return as explanatory variables in the spirit of Scholes and Williams (1977) and Dimson (1979). The sample period is 1984-2002. There are 69,944 firm-quarter observations.

51

Figure 1: Average financing deficit

-0.02-0.01

00.010.020.030.04

1984 1987 1990 1993 1996 1999 2002

Figure 3: Average Dispersion of Analysts' Earnings Forecasts

-7.0-6.5-6.0

-5.5-5.0

1984 1987 1990 1993 1996 1999 2002

Figure 4: Average Market-to-Book

1.5

2.0

2.5

3.0

3.5

1984 1987 1990 1993 1996 1999 2002

Figure 5: Average Ratio of Property, Plant, end Equipment

0.200.240.280.320.360.40

1984 1987 1990 1993 1996 1999 2002

Figure 6: Average Logarithm of Sales

19.019.119.219.319.419.519.6

1984 1987 1990 1993 1996 1999 2002

Figure 7: Average Profitability

-0.01

0.00

0.01

0.02

1984 1987 1990 1993 1996 1999 2002

Figure 8: Average Return Volatility

0.010

0.020

0.030

0.040

0.050

1984 1987 1990 1993 1996 1999 2002

Figure 2: Coefficient on Financing Deficit

0.10

0.30

0.50

0.70

0.90

1984 1987 1990 1993 1996 1999 2002

52

Figure 9 Dispersion around SEOs in event time

The figure plots the mean monthly dispersion around SEOs in event time. Our sample of SEOs contains primary SEOs of common stock by non-financial and non-utility U.S. firms during 1990-2003. Unit offerings, right offerings and shelf offers are excluded. The sample is constrained to SEOs for which dispersion data is available in every month [-9, 9] around the SEO. There are 187 sample SEOs that meet this requirement.

Figure 9: Average monthly dispersion around SEOs in event time

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

eventmonth

disp

ersi

on

53

Figures 10-11 Relative mix of SEOs, shelf offers, and PIPEs

Figure 10 illustrates the relative number of equity offers (in terms of annual percentages) during our sample period 1990-2003 that are SEOs (middle portion), shelf offers (bottom portion), and PIPEs (top portion). Figure 11 illustrates the relative proceeds of equity offers (in terms of annual percentages) during our sample period 1990-2003 that are SEOs (middle portion), shelf offers (bottom portion), and PIPEs (top portion).