Abnormal accruals and external financing

Theodore H. Goodman Eller College of Management

University of Arizona McClelland Hall

Tucson, AZ 85721-0108 tgoodman@email.arizona.edu

August 2007

ABSTRACT

In this paper, I analyze the information conveyed by abnormal accruals by examining the conditions where debt and equity investors place a weight on abnormal accrual when deciding to provide a firm with external financing. I find evidence consistent with the debt market containing sophisticated investors that are able to identify when abnormal accruals predict future cash flow. Upon observing abnormal accruals that anticipate future operating cash flow, debt investors withhold capital from firms where abnormal accruals signal that future cash flow is low and extend capital to firms that where abnormal accruals signal that future cash flow is high. Debt investors also place a positive weight on abnormal accruals that reverse, but only when these reversals are due to increases in cash flow realized in the following period. While debt investors appear to rely on abnormal accruals when these accruals are informative, equity investors appear less sophisticated consistent with the literature on earnings management at equity offerings.

This paper is based on my dissertation at the University of Pennsylvania. I would like to thank my committee members: Bob Holthausen, Dave Larcker, Scott Richardson, Cathy Schrand (chair), and Joel Waldfogel. I also thank the following for their helpful comments and suggestions: Dan Bens, Brian Bushee, Mary Ellen Carter, Dan Dhaliwal, Joseph Gerakos, Chris Ittner, Sarah McVay, Jonathan Rogers, Tjomme Rusticus, Bill Schwartz, Mark Trombley, Irem Tuna, Rodrigo Verdi, Sarah Zechman, and workshop participants at the University of Arizona, Baruch College, Michigan State University, Northwestern University, Penn State University, the University of Pennsylvania, Purdue University, and the University of Rochester. I am grateful for financial support from the Deloitte & Touche foundation and the University of Arizona. Any remaining errors are my own.

Abnormal accruals and external financing

ABSTRACT

In this paper, I analyze the information conveyed by abnormal accruals by examining the conditions where debt and equity investors place a weight on abnormal accrual when deciding to provide a firm with external financing. I find evidence consistent with the debt market containing sophisticated investors that are able to identify when abnormal accruals predict future cash flow. Upon observing abnormal accruals that anticipate future operating cash flow, debt investors withhold capital from firms where abnormal accruals signal that future cash flow is low and extend capital to firms that where abnormal accruals signal that future cash flow is high. Debt investors also place a positive weight on abnormal accruals that reverse, but only when these reversals are due to increases in cash flow realized in the following period. While debt investors appear to rely on abnormal accruals when these accruals are informative, equity investors appear less sophisticated consistent with the literature on earnings management at equity offerings.

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1. Introduction

While the use of estimates in accrual accounting affords firms the opportunity to

manipulate the resulting accounting figures, the inclusion of estimates also may convey useful

information about firm performance. A large literature has examined earnings management

initiated with the intent of inflating firm valuations through positive abnormal accruals prior to

security offerings (e.g., Teoh, Welch, and Wong, 1998). However, another literature has

provided evidence that accruals provide an informative signal for predicting future cash flows

(e.g., Dechow, Kothari, and Watts, 1998). In this paper, I examine whether abnormal accruals

provide useful information to investors in the context of external financing decisions. I analyze

the information conveyed by abnormal accruals by examining the conditions where debt and

equity investors place a weight on abnormal accrual when deciding to provide a firm with

external financing. This evidence provides information about both the information that may be

gleaned from abnormal accruals and the sophistication of these different groups of investors with

respect to abnormal accruals.

While research on earnings management suggests that positive abnormal accruals reflect

discretionary and opportunistic accounting choices, there is still debate over whether

discretionary accrual models are well specified (e.g., Guay, Kothari, and Watts, 1996; Ball and

Shivakumar, 2006). Specifically, accruals that are above (below) the expected value from a

model (e.g., the Jones Model) may indicate future cash flow is expected to increase (decrease).

In such a case, abnormal levels of accruals result from accrual accounting conveying useful

information about the firm’s future operations and an association between abnormal accruals and

external financing reflects investors’ reliance on informative accruals. In contrast to work that

examines only the mispricing of abnormal accruals, I provide evidence on mispricing, but also

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cases where investors rely on abnormal accruals and these abnormal accruals correctly indicate

when to provide capital and when to withhold capital.

I find evidence consistent with the debt market containing sophisticated investors that are

able to identify when abnormal accruals predict future cash flow. Upon observing abnormal

accruals that accurately predict whether future operating cash flow will increase or decrease, debt

investors withhold capital from firms where there is greater risk that their principal will be

recovered (i.e., future cash flow is low) and extend capital to firms that will be able to repay

funds in the future (e.g., future cash flow is high). These findings suggest that debt investors rely

on abnormal accruals to infer the probability that a claim will be repaid. Debt investors also

place a positive weight on abnormal accruals that reverse, but only when these reversals are due

to increases in cash flow realized in the following period. These results suggest that debt

investors understand when abnormal accruals reflect information useful for forecasting future

cash flow and when accrual reversals are due to the matching principle (e.g., a decrease in

receivables due to a cash collection). The support for this informative role of abnormal accruals

raises further questions on the extent to which abnormal accruals effectively measure

discretionary accruals.

While debt investors appear to rely on abnormal accruals when these accruals are

informative, equity investors appear less sophisticated consistent with the literature on earnings

management at equity offerings. Equity investors rely on abnormal accruals that appear

opportunistic ex-post (i.e., positive abnormal accruals preceding a decrease in operating cash

flow). In addition, equity investors tend to place a positive weight on abnormal accruals that will

reverse, but not due to an increase in cash flow consistent with an accrual correction of

previously inflated abnormal accruals.

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Analysis of both debt and equity financing also provides insight into the conditions that

make each of these financing alternatives preferable. When positive abnormal accruals anticipate

increases in future cash flow firms appear to prefer issuing debt, despite the fact that equity

investors appear to reward positive abnormal accruals that precede decreases in future cash flow.

In addition, debt investors provide less capital to firms whose negative abnormal accruals

anticipate a decrease in cash flow, but equity investors appear to provide these firms with

external financing. This combination of debt and equity results suggests that information about

the distribution of cash flow from abnormal accruals may influence the choice between external

financing alternatives, where positive abnormal accruals that are informative make debt

preferable and negative abnormal accruals that are informative make equity preferable.

A large body of research has examined the level of abnormal accruals around external

financing events and the reversal of these accruals in future periods (e.g., Rao, Teoh, and Wong,

1998; Teoh, Welch, and Wong, 1998). This paper contributes to this literature by further

examining whether abnormal accruals are related to security issuances because these accruals are

informative or opportunistic. This paper’s motivation is to identify which observations contribute

to the overall association between abnormal accruals and external financing. I examine if

investors’ use of abnormal accruals depends upon whether these abnormal accruals are

informative or opportunistic ex-post. Comparing the behavior of investors across these ex-post

partitions provides insight into the extent to which investors can distinguish between these

groups ex-ante. While I do not expect that investors have perfect foresight to discern which cases

abnormal accruals are accurate, evidence of differences across these partitions indicates that

investors possess the ability to interpret accrual information on-average. The results in this paper

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indicate that the debt investors posses a sophistication that enables these investors to distinguish

between these groups.

The remainder of this paper is organized as follows. Section 2 contains the hypothesis

development, the empirical specification, and the predicted coefficient signs. Section 3 outlines

the variable measurement. Section 4 presents the empirical results and section 5 is the

conclusion.

2.1 Hypothesis development

This paper’s research question asks whether accruals convey useful information to

investors. To address this question I examine the weight investors place on a firm’s abnormal

accruals when deciding to provide a firm with financing. Empirical analysis that examines both

debt and equity investors allows for the possibility that these different groups of investors may

have different levels of sophistication with respect to interpreting abnormal accruals. This

paper’s central hypothesis is that sophisticated investors extract information about future cash

flow from abnormal accruals, while less sophisticated investors rely on accruals that are more

opportunistic. Analysis of this hypothesis provides insight into investor sophistication and

whether abnormal accruals effectively measure opportunistic accounting or whether these

accruals provide useful information about future cash flow in some circumstances.

Adverse selection models predict that if a seller has private information, then a rational

buyer will price protect against the possibility that the good is over-valued (e.g., Akerlof, 1970).

Myers and Majluf (1984) extend these models to the setting of firms issuing securities to fund

investing activities, demonstrating that an information asymmetry between the firm and new

investors can raise the cost of acquiring external capital relative to internally generated cash.

This incremental cost forces a value maximizing firm to not issue securities and under-invest

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when internal resources are low (forgone positive NPV projects). Furthermore, the magnitude of

this incremental cost is smaller for securities where the value of the security is less sensitive to

the firm’s private information, suggesting that to minimize information asymmetry related costs

firms prefer to issue safe securities before risky securities (pecking order theory).1

While rational investors, such as those in the Myers and Majluf (1984) model, would not

place a positive weight on a signal that is expected to be uninformative about the value of the

firm, empirical research on earnings management suggests that the association between

abnormally positive accruals and equity financing indicates that equity investors purchase over-

valued securities (e.g., Rao, Teoh, and Wong, 1998; Teoh, Welch, and Wong, 1998; Rangan,

1998; Cassar, 2005). This empirical evidence is consistent with the hypothesis that equity

investors are unsophisticated with respect to accruals. However, this interpretation relies on the

assumption that abnormal accruals effectively measure “discretionary” or uninformative

accruals.2 In addition, there is evidence that upon learning of an equity offering, equity investors

penalize a firm for recent positive abnormal accruals (Shivakumar, 2000).

Prior research has examined whether the relatively more sophisticated parties within the

equity market (e.g., insiders) understand the implications of accruals for future earnings and

returns (Beneish and Vargus, 2002). The debt market also presents a group of investors that may

be able to understand the implications of abnormal accruals. The debt market contains a large

proportion of institutional investors, which are expected to have greater skill in interpreting

1 Consistent with theories in which external capital is more costly than internal funds, extant empirical research documents that investing activities are positively associated with cash flow from operations (e.g., Fazzari, Hubbard, and Petersen, 1988). In addition, firms with volatile cash flow invest less on-average due to cash flow shortfalls (Minton and Schrand, 1999). To mitigate these concerns, firms with volatile cash flow tend to hold larger cash reserves (Opler, Pinkowitz, Stulz, and Williamson, 1999) and constrained firms decrease (increase) reserves when cash flow from operations is low (high) (Almeida, Campello, and Weisbach, 2004). However, the benefits of cash reserves must be weighed against the potential agency costs associated with large levels of slack (Harford, 1999). 2 Several papers have debated whether abnormal accrual models are well-specified and if this mis-specification affects the inferences from earnings management research (e.g., Guay, Kothari, and Watts, 1996; Kothari, Leone, and Wasley, 2005; Ball and Shivakumar, 2006).

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financials. In addition, some parties in the debt market (e.g., banks, credit agencies) have access

to non-public information which should be useful in evaluating the information presented in

accrual. As a result, if debt investors are sophisticated then these investors should respond to

abnormal accruals when these abnormal accruals contain useful information about future cash

flow.

Debt investors also differ from equity investors based on the type of information that is

useful for valuation. If positive abnormal accruals signal that a firm will have high future cash

flow, then this signal reduces debt investor concerns about the firm’s ability to repay debt in the

immediate future. As debt claims have a fixed upside (i.e., the principal and coupon payments), a

signal that a firm will have high cash flow reduces the sensitivity of the debt to the firm’s assets

(or private information about those assets). As an extreme example, Myers and Majluf (1984)

note the case of risk-free debt, where the purchasers of risk-free debt are not concerned about

any information asymmetry regarding the total value of assets in place because there is no risk

that this debt is over-valued. It is important to note that for a firm’s debt to be risk-free does not

require removing all uncertainty related to the firm’s assets, only the uncertainty that it will make

its debt payments. It is more difficult to construct cases where there is no uncertainty regarding

an equity claim as this would require removing all uncertainty about these good outcomes.3 In

addition, the longer horizon of equity (relative to debt) further requires that an abnormal accrual

signal be highly persistent for it to reduce uncertainty about cash flows many years in the future.

Recent work by Cassar (2005) presents evidence that the results suggesting earnings

management around equity financing appear to extend to debt financing: debt financing is

3 For example, if debt investors learn that the value of the firm’s assets will be greater than an amount with certainty, then the firm can issue risk-free debt as long as the face value of the debt is less than that amount. In contrast, if equity investors learn that the value of assets is above some amount with certainty, then there still could be uncertainty and possibly asymmetry about the value of the equity claim as long as there is still variation in the value of the firm’s assets conditional on information that the value is above a bound (e.g., the distribution is continuous).

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positively related to contemporaneous abnormal accruals and debt financing is negatively related

to the association between abnormal accruals and future earnings.4 I examine when abnormal

accruals appear to convey useful information to debt and equity investors. I analyze whether

investors appear to distinguish between different types of abnormal accruals. In addition, instead

of examining if an increase in financing coincides with more persistent accruals, I examine

whether investors appear to obtain information from abnormal accruals that informs their

decision to extend or to withhold financing. Specifically, sophisticated investors are expected to

provide capital when positive abnormal accruals are accurate ex-post, but also withhold capital

when negative abnormal accruals are accurate ex-post.5 In addition, I provide further details on

the interpretation of transitory accruals by distinguishing between different types of reversals

based on whether the reversal coincides with an increase in cash flow.

2.2 Empirical models and predictions

This paper’s investigates whether investors with different degrees of sophistication (debt

vs. equity) place different weights on informative and opportunistic abnormal accruals

(RES_ACCt-1) when determining amount of cash to offer the firm in exchange for securities in

year t (DEPVARt). I begin my analysis with the following model:

DEPVARt = α0 + α1RES_ACCt-1 + CONTROLS + error (1)

4 While Cassar (2005) documents a positive association between contemporaneous abnormal accruals and debt financing, this result is sensitive to matching choices and the use of control variables. When the association is estimated after matching based on total financing the association becomes negative and significant when control variables are excluded and positive and insignificant when control variables are included. In addition, when examining whether debt financing affects the association between abnormal accruals and future earnings, these results are sensitive to whether debt financing is also interacted with other independent variables (cash flow from operations and normal accruals). 5 In such a case, sophisticated investors rely on abnormal accruals that convey useful information both when providing and withholding capital. As a result, if persistence is used as a measure of when abnormal accruals convey useful information, then I would predict greater persistence both when sophisticated investors provide capital and also when sophisticated investors withhold capital.

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Where: DEPVARt =Financing cash flow in year t (TOTFINt, ∆DEBTt, ∆EQUITYt) TOTFINt =∆EQUITYt + ∆DEBTt dataXX Refers to Compustat data item number XX ∆DEBTt =Cash proceeds from the issuance of debt (data 111) plus the change

in notes (data 301) less the payments to reduce long term debt (data 114) ) divided by average assets

∆EQUITYt =Cash proceeds from the sale of common/preferred stock (data108) – cash dividends (data127) - repurchases (data 115) divided by average assets

RES_ACCt-1 =Abnormal accruals in year t-1 CONTROLS =Other determinants of DEPVAR

The coefficient on RES_ACCt-1 (α1) in equation 1 provides evidence of whether a group of

investors rely on abnormal accruals. Equation 1 is similar to the empirical specification used in

prior work to examine the association between abnormal accruals and financing activities. Using

a similar model, Dechow et. al (2006) find that after controlling for other changes in the balance

sheet contemporaneous with the measurement of total accruals, total accruals are negatively

associated with net equity financing activities. In fact, firms with high total accruals tend to

distribute cash to equity investors.6

Equation 1 provides evidence of whether investors rely on abnormal accruals on-average.

However, there may be variation in the extent to which abnormal accruals contain useful

information. In such a case, further analysis is necessary to infer whether a positive estimate of

α1 reflects reliance on informative or opportunistic abnormal accruals. To identify whether an

investor’s use of abnormal accruals reflects a reliance on informative or opportunistic abnormal

accruals, I examine whether α1 is significant in different sub-samples and whether the

coefficients are different across sub-samples.

6 In Table 9, Dechow et. al (2006) report that accruals in period t are positively correlated with disbursement to equity investors in period t+1 and negatively related to disbursements to debt investors in period t+1.

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To distinguish between informative or opportunistic abnormal accruals, I evaluate

whether the firm’s realized future performance corresponds to the abnormal accruals.

Specifically, I examine whether the weight investors place on abnormal accruals varies

depending on whether the sign of these abnormal accruals is consistent with the sign of the ex-

post change in cash flow. I use future cash flow to define positive and negative future outcomes

as the incremental ability to accruals to predict future cash flow is often noted as an advantage of

accrual accounting relative to cash basis accounting (Dechow, Kothari, and Watts, 1998; Barth,

Cram, and Nelson, 2001).7 Partitioning based on ex-post cash flow data identifies cases where

the level of accruals is abnormal relative to a benchmark model (e.g., the Jones model), but this

deviation may have been due to an anticipation of future cash flow performance. If investors are

able to distinguish between these different types of abnormal accruals ex-ante, then their weight

on abnormal accruals should vary across these samples.

By examining partitions based on ex-post future cash flow, I assume that firms have

private information about the change in cash flow from pre-issuance to post-issuance. This

assumption is similar to the Myer and Majluf (1984) assumption that firms have private

information about the value of asset in place: firms know the sum of its discounted cash flows

from theses assets. By assuming that some of an issuing firm’s information advantage is due to

knowledge of short-term cash flows, I examine whether abnormal accruals are able to

communicate information to sophisticated investors and reduce the costs related to this

information asymmetry.

The inclusion of ex-post cash flow data into equation 1 is as follows. First, I replace

RES_ACCt-1 in equation 1 with a piece-wise specification splitting RES_ACCt-1 into a positive

7 Dechow, Kothari, and Watts (1998) find that forecasts of cash flows based on earnings yield lower mean squared errors than forecasts based on historical cash flow. In addition, accruals have an incrementally positive coefficient in models predicting future cash flow (Barth, Cram, and Nelson, 2001).

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(POS_RES_ACCt-1) and negative component (NEG_RES_ACCt-1). Next, I interact

POS_RES_ACCt-1 and NEG_RES_ACCt-1 with indicators for whether future cash flow has

increased (POS_∆CFt+1) or decreased (NEG_∆CFt+1) between year t-1 and year t+1. This yields

the following model:

DEPVARt = β0 + β1NEG_∆CFt+1*POS_RES_ACCt-1 + β2POS_∆CFt+1*POS_RES_ACCt-1 + β3NEG_∆CFt+1*NEG_RES_ACCt-1 + β4POS_∆CFt+1*NEG_RES_ACCt-1 + CONTROLS + error

(1a)

Tests of the coefficients in equation 1a are designed to infer whether positive and negative

accruals are associated with financing activities and whether this association is stronger when

accruals predict future cash flow or do not predict future cash flow.8

If equity investors are misled by abnormal accruals, as suggested by the research on

earnings management, then these investors will place a weight on abnormal accruals when pre-

issuance abnormal accruals are high and realized future cash is low (β1>0). In such a case,

issuing firms appear to benefit from issuing equity when accruals are opportunistic given the

firm’s knowledge of future cash flow: abnormal accruals are positive, but future cash flow

performance will decrease.

If debt investors are able to infer useful information from abnormal accruals then the

weight on abnormal accruals will be positive in cases where the sign of abnormal accruals

correctly predicts the sign of future cash flow changes (β2>0 and β3>0). As a result, debt

investors withhold (provide) financing when they infer from abnormal accruals that future cash

flow will be (high) low. The prediction of a positive coefficient on

8 This regression model is similar to the piece-wise model estimated by Beneish and Vargus (2002) in their analysis of whether positive and negative accruals are more strongly associated with a dependent variable (future earnings and returns) when they coincide with another variable (insider trading indicators).

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NEG_∆CFt+1*NEG_RES_ACCt-1 is distinct from the hypothesis that debt investors prefer firms

with less volatile accruals as in this case where accruals are accurate ex-post (i.e., lower

volatility), but because the sign of the accrual is negative the positive coefficient implies lower

debt financing.

Equation 1a also provides insight into the interaction between debt and equity financing.

Specifically, it provides the opportunity to examine if equity financing becomes more appealing

when negative abnormal accruals make debt financing more costly by signaling. In such a case,

debt financing is less attractive as the probability of default is higher due to lower expected

future cash flow. However, equity investors may not penalize firms for negative abnormal

accruals (DEPVAR=∆EQUITYt, β3<0), if the persistence of this bad news is limited.

Alternatively, a negative weight on these negative abnormal accruals by equity investors may

reflect that in addition to over-weighting positive accruals that are inaccurate ex-post, equity

investors also mistakenly under-weight negative abnormal accruals that are accurate ex-post.

In addition a comparison of the use of abnormal accruals by debt and equity investors

provides insights into the extent to which firms prefer debt over equity. When positive abnormal

accruals anticipate positive future cash flow, debt investors should be willing to provide capital

as these investors are able to interpret the abnormal accrual. In this case, equity investors may be

willing to provide capital as well if these investors myopically place a positive weight on

positive abnormal accruals. If both debt and equity investors are willing to provide the same

amount of capital, then a firm’s choice between these alternatives provide insight into the

relative costs of debt and equity. If firms exhibit a preference for debt over equity (pecking order

theory), then these firms may use abnormal accruals to obtain debt financing

(DEPVAR=∆DEBTt, β2>0), but not issue equity (DEPVAR=∆EQUITYt, β2=0). Interestingly,

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this would result in equity investors primarily providing capital to positive abnormal firms,

where these abnormal accruals are opportunistic.

The following table summarizes the predictions for equation 1a:

DEPVARt = ∆DEBTt

DEPVARt = ∆EQUITYt

Predicted sign for β1 ? +

Predicted sign for β2 + 0

Predicted sign for β3 + -

Predicted sign for β4 ? ?

In addition to testing whether the coefficient on a sub-sample of abnormal accruals is different

from zero, these coefficients can be compared with other sub-samples (e.g., β1 with β2 or β3 with

β4). These comparisons do not require perfect foresight of future cash flow by investors. Instead

these comparison only requires that sophisticated investors are able to glean information about

future cash flow from analyzing abnormal accruals and as a result put a more positive weight on

abnormal accruals that are accurate ex-post (β2>β1 and β3>β4).

Data on future cash flow changes also provides the opportunity to further explore the

interpretation of accrual reversals around financing events. Research in earnings management

often implies that reversals of abnormal accruals following an issuance are evidence of

opportunism. These studies suggest that abnormal accruals decrease because firms must

recognize losses following the issuance to reverse the fictional pre-issuance gains. However, it is

important to emphasize that the losses that drive the reversal are some type of accrual correction,

not a cash flow. For example, if inventory had been opportunistically inflated, then inventory

will decrease through a write-down, but this write-down does not affect cash flow. If this

inventory is sold, then there would be a cash inflow. In such a case, the ex-post cash flow

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obtained from liquidating the inventory provides a signal on the extent to which the inventory

was over-valued ex-ante.

While accrual reversals could be evidence of opportunism, a decrease in abnormal

accruals following an issuance could also reflect the application of the matching principle to

account for a transitory cash flow. For example, if a firm builds up its inventory for a sale that is

non-recurring, then the firm will experience a cash inflow from this sale and a decrease in

inventory. However, once the sale is made the firm will not need to restock its inventory, so

inventory accruals will appear to reverse. In such a case, abnormal accruals correctly anticipate a

cash inflow, albeit a transitory cash flow. These types of abnormal accruals reversals should be

particularly useful for valuing debt, due to its relatively shorter maturity compared to equity.

The inclusion of data on ex-post cash flow and reversals in abnormal accruals into

equation 1 is as follows. First, I replace RES_ACCt-1 in equation 1 with a piece-wise

specification splitting RES_ACCt-1 into a cases where there is a reversal (REV_RES_ACCt-1)

and cases where there is no reversal (NOREV_RES_ACCt-1). Next, I interact REV_RES_ACCt-1

with an indicator for whether future cash flow has increased (POS_∆CFt+1) or decreased

(NEG_∆CFt+1) to explore the different types of reversals. This yields the following model:

DEPVARt = γ0 + γ1NEG_∆CFt+1*REV_RES_ACCt-1 + γ2POS_∆CFt+1*REV_RES_ACCt-1 + γ3NOREV_RES_ACCt-1 + CONTROLS + error

(1b)

Tests of the coefficients in equation 1b are designed to examine whether the weight placed on

abnormal accruals depends on the whether these accruals reverse and if this reversal coincides

with an increase in cash flow.

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If debt investors are sophisticated and accruals that eventually decrease due to the

application of matching (i.e., cash flow increases), then debt investors will place a positive

weight on these abnormal accruals (γ1>0). The prediction that debt investors value information

about transitory short-term cash flow is motivated by the relatively shorter horizon relative of

these securities compared to equity. As a result, I expect that empirical support for the

predictions of equation 1b predicting debt financing would be strongest when the debt is short-

term. To address this concern, I examine changes in notes payable as a robustness test (section

4.5)

If equity investors are unable to anticipate that abnormal accruals will decrease in a

manner consistent with opportunistic account (i.e., cash flow from operations does not increase),

then these investors will place a positive weight on these abnormal accruals (γ2>0). This

prediction is motivated by the claims that accrual reversals following equity issuances signal

opportunism, by further refining the definition to measure cases where the reversal are no due to

normal accrual accounting (e.g., a collection of a receivable).

The following table summarizes the predictions for equation 1b:

DEPVARt = ∆DEBTt

DEPVARt = ∆EQUITYt

Predicted sign for γ1 ? +

Predicted sign for γ2 + ?

As with the comparisons of the coefficients in equation 1a, a comparison of γ1 with γ2 provides

the opportunity to examine whether sophisticated investors are able to distinguish between these

types of reversals.

3. Variable measurement

3.1 Measurement of cash flow and accruals

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As the distinction between cash flow and accruals is important for my research design, I

use cash flow statement data to measure CF and RES_ACC. This measurement of accruals and

cash flow is preferable to changes in balance sheet accounts as it is not confounded by non-cash

transactions (e.g., mergers and acquisitions, foreign currency translations) (Collins and Hribar,

2002).

As in Kothari, Leone, and Wasley (2005), I define RES_ACCt as the residual from the

Jones model after removing the level of abnormal accruals for firms with comparable ROA

(return on assets) performance. I follow their specification where a constant is included when

estimating the cross-sectional Jones Model for each industry-year, where industry is equal to a

firm’s 2 digit SIC code. To control for abnormal accruals related to ROA performance, I match

based on performance by taking the average level of abnormal accruals from a firm’s ROA peer

group within the same industry-year. Peer group are assigned by dividing all industry-year firms

into quintiles based on ROA in year t. The following definitions are used to calculate

RES_ACCt:

ACCt =Net Income (data18) – Cash flow from operations (data308) divided by lagged total assets (data6)

Jones Model ACCt = δ0 + δ1(1/ASSETSt-1) + δ2∆SALESt + δ3PP&Et + ε This model is estimated at the 2 digit SIC code level each year. The Jones model is only estimated using observations where the dependent is less than one in absolute value. The model is only estimated for industry-years with at least 10 observations.

(1/ASSETSt-1) =1 divided by lagged total assets ∆SALESt =Change in sales (data12) in year t divided by lagged total assets PP&Et =Gross PP&E (data8) in year t divided by lagged total assets ROA =Net Income (data18) divided by average assets RES_ACCt = εi for firm i - average εi of peer group

ROA peer groups are obtained by dividing the 2 digit SIC code into quintiles based on ROA. When calculating the average ε for firm i’s peer group firm i is excluded.

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I define CF as the amount of funds produced by operations that can be used in investing

activities. To measure cash flow produced by operations before investing choices I remove R&D

from reported operating cash flow. The definition is listed below:

CF = Cash flow from operations, defined as reported operating cash flow (data308) before R&D expense (data46) divided by average assets. R&D expense is set equal to zero when missing.

While this definition of CF is used in Richardson (2006), it is in contrast to research in finance

that has used earnings before interest, taxes, depreciation and amortization (EBITDA) to measure

cash flow. For example, Bushman, Smith, and Zhang (2005) note that research in finance uses

EBITDA to measure cash flow from operations and that a large portion of the association

between EBITDA and investment is attributable to working capital. Because this paper’s

hypotheses center on predicting cash flow using accrual information, the distinction between

cash flow (which can be used in investing activities) and accruals (which can predict future cash

flow, but not directly fund investment) is important.

3.2 Measurement of dependent variables

The dependent variable in equation 1 is equal to the amount of financing received from

different sources (TOTFINt, ∆DEBTt, and ∆EQUITYt) calculated using data from the statement

of cash flows and balance sheet as in Richardson and Sloan (2003). By examining the proceeds

of an issuance, I rely on Myers and Majluf’s (1984) result that when information asymmetry

costs are too high firms decide to not issue new securities. Theory would also support a

prediction related to the incremental cost of external capital. Several papers have examined the

association between the cost of external capital and both cash flow volatility (Minton and

Schrand, 1999) and the characteristics of earnings (Francis, Lafond, Olsson, and Schipper, 2005;

17

Bharath, Sunder, and Sunder, 2006; Jiang, 2006). However, empirical analysis of proceeds

requires controlling for the amount of cash needed to avoid under-investment, while analysis of

the cost of external capital requires controlling for the internal cost of capital.9 In my analysis, I

include control variables to measure growth opportunities and internal resources which should

determine the amount of cash needed from external financing to prevent under-investment.

Using a dependent variable that reflects the magnitude of external financing assumes that

when the proceeds from an issuance are larger, the incremental cost of external capital is smaller.

As a sensitivity analysis, I convert the dependent variables into indicators for whether a firm

obtained external financing of a given type and the main results of this study are qualitatively

similar.

3.3 Measurement of control variables

I include control variables in equation 1 to limit four omitted variable concerns. First, I

include cash flow in year t-1 and year t to ensure that RES_ACCt-1 captures information about

expected future cash flow, rather than information about historical cash flow or cash flow that is

contemporaneous with the financing activity. Second, I include proxies for growth opportunities

to limit concerns that RES_ACCt-1 captures the expected benefits from outside financing. Third,

I include balance sheet information about a firm’s liquidation values to examine the incremental

information conveyed by RES_ACCt-1 which is based on income statement data. Fourth, I

include variables measuring the firm’s leverage and past financing activities which may

influence the costs of additional external financing.

9 Examining the cash obtained from investors and the cash used in investing activities in a given firm-year also alleviates concerns inherent in measuring financing constraints indirectly through cash flow-investment sensitivities. For example, Kaplan and Zingales (1997) note that cash flow-investment sensitivities may not be an appropriate measure of firm financing constraints as theory does not necessarily predict a monotonic relation between these sensitivities and the cost of external capital. However, examining financing cash flows in specific firm-years requires identifying a sample that needs external capital to prevent under-investment.

18

To ensure that the association between RES_ACCt-1 and financing activity reflects the

presence of accounting information that is used to shape investor expectations of future cash

flow and not differences in the pattern of cash flows, I include the level of CF in year t-1 and t as

control variables. Including CFt-1 controls for other information that is available in year t-1.

Including CFt also provides a measure of the need for external capital during year t (Richardson,

2006).

When issuing securities a firm balances the cost of external capital with the benefits from

initiating new projects. To control for variation in these benefits I include proxies for growth

opportunities. Growth opportunities are expected to be negatively related to firm age (AGE) and

the firm’s book to market ratio (BTM) and positively related to investing expenditures made

during year t-1 (ITOTAL). The definitions of the growth opportunity variables are listed below:

AGE =Log(years since company first appears in the CRSP monthly stock file) BTM =BV of assets (data6) divided by MV of assets (MV of equity

(data25*data199) + BV of liabilities (data181)) ITOTAL =[Capital expenditures (data128) + R&D (data46) + Acquisitions

(data129) – Sale of PPE (107)] divided by average assets

To examine the association between RES_ACCt-1 and external financing also requires

controlling for the information contained in the balance sheet. Berger (1999) notes that when

assessing the use of income statement data as an indicator of the probability of default, it is

important to control for balance sheet data that provides information on the liquidation value of a

firm’s assets. As an asset’s location on the balance sheet (e.g., current, fixed, etc.) is associated

with its liquidation value (Berger, Ofek, and Swary, 1996), the composition of a firm’s assets is

relevant when evaluating their liquidation value of the firm. In addition, recent work by Almeida

and Campello (2005) indicates that asset liquidation values are related to cash flow-investment

19

sensitivities; firms with higher liquidation values are less likely to be constrained, but among

constrained firms the affect of cash flow from operations on investing activity increases in

liquidation value for financially constrained firms. The definitions of the asset liquidity variables

are listed below (variables measured at the beginning of year t):

CASH =Cash (data1) divided by total assets AR =Accounts Receivable (data 2) divided by total assets INV =Inventory (data3) divided by total assets

By including the ratio of a firm’s primary current assets to total assets (CASH, AR, INV) as

control variables, the remaining percent of total assets composed of non-current assets (PP&E

and intangibles) is the reference group.

I include variables measuring the firm’s leverage and past financing activity which may

reflect various costs associated with raising new capital. The level of firm leverage may reflect

either debt capacity or the presence of other constraints which affect capital structure (Kaplan

and Zingales, 1997). Prior cash flow from financing (∆DEBTt-1, and ∆EQUITYt-1) are included

to limit concerns that expectations formed in year t-1 are related to financing activity in year t-1,

which prior work suggests will have implications for financing activities in year t-1 (Dechow et.

al, 2006). The definitions of the capital structure variables are listed below (all variables are

measured in the beginning of year t):

LEV =Debt/(Debt + Equity), where debt=(data9 + data34) and equity=data60

4 Empirical Results

4.1 Descriptive statistics

20

I begin my sample selection with the universe of non-financial firm-year observations

that have non-missing data for CF and RES_ACC for a given year (year t) and the two adjacent

years (t-1 and t+1). Next, I require that all sample firms have data for all dependent variables in

year t (∆DEBTt and ∆EQUITYt) and all control variables. I also eliminate extremely small firms

(sales or average assets under $10 million) and firms where the absolute value of CF or

RES_ACC exceeds 1 in year t-1, t, or t+1. Finally, I remove firms listed as ADRs on CRSP.10

There are 45,409 firm-year observations that meet these criteria from 1988 to 2004. In addition,

to limit the influence of extreme observations, I winsorize the top and bottom one percent of all

variables before estimating equation 1.

Panel A of table 1 presents descriptive statistics for both the dependent and independent

variables used in this paper’s analysis. The medians for ∆DEBTt and ∆EQUITYt are both zero,

while the means are both positive. Both the mean and median for RES_ACCt-1 are slightly

negative (Mean=-0.008, Median=-0.007). As this residual was estimated using all firms in

Compustat with available data and there are other sample selection criteria to reach this study’s

final sample, this mean may not equal zero for the sample.

Panel B of table 1 presents descriptive statistics on the univariate correlations between

RES_ACCt-1 and financing activities. Consistent with prior research, there is a positive and

significant univariate correlation between RES_ACCt-1 and all measures of external financing

(∆EQUITYt, ∆DEBTt, and ∆TOTFINt) when calculated using either pearson or spearman

correlations. While this univariate correlation is consistent with investors relying on accruals in

general, it does not distinguish between the hypothesis that this correlation is due to earnings

management from the hypothesis that this correlation is due to informative accruals. In addition,

10 ADRs are identified as all firms where the CRSP share code is greater than 29 and less than 40.

21

the correlation matrix documents a negative and significant correlation between ∆EQUITYt and

∆DEBTt, consistent with prior research on external financing (Richardson and Sloan, 2003).

Table 2 presents OLS estimates of equation 1, which predicts the amount of cash flow

from the different financing activities as a function of abnormal accruals and other control

variables. In column 1, where the model is predicting ∆DEBTt, the coefficient on RES_ACCt-1 is

positive and significant (t=11.407) consistent with the univariate correlations in panel b of table

1. However, in column 2, where the model is predicting ∆EQUITYt, the coefficient on

RES_ACCt-1 is negative and significant (t=-4.178) in contrast to the positive univariate

correlation between RES_ACCt-1 and ∆EQUITYt. While this change in sign contrasts with the

univariate descriptive statisics it is consistent with empirical results presented in Dechow et. al

(2006). Dechow et. al (2006) document a positive association between accruals and future debt

financing and a negative association between accruals and future equity financing.11 However,

this analysis does not yet clarify whether the association between external financing and

abnormal accruals reflects earnings management or informative accruals.

Column 3 of table 2 indicates that there is a positive association between RES_ACCt-1

and TOTFINt. While the results in columns 1 and 2 were consistent with the work in Dechow et.

al (2006) the evidence in column 3 differs with Dechow et al.’s finding that total accruals are

positively related to total distributions (debt plus equity). This difference with respect to total

financing may be due to the measurement of accruals, the presence of additional control

variables or a difference in samples.12 In subsequent tests, I explore the factors that influence the

11 The analysis in Dechow et al. (2006) is based on total accruals (both current and long-term) instead of abnormal accruals. 12 Dechow et. al (2006) only include variables capturing the change in the balance sheet accounts during the prior year as independent variables. The model contains lagged total accruals, the lagged change in cash balance, and lagged financing activity as independent variables. Rather than include the change in cash balance, the specification in this paper includes cash flow from operations (CFt-1), cash flow used in investing activities (ITOTALt-1), and financing activities (∆EQUITYt-1, ∆DEBTt-1) which together approximate the total change in the cash balance.

22

weight on abnormal accruals for debt, equity, and total financing, which also clarifies the

association between total financing and abnormal accruals.

4.2 Empirical analysis of Equation 1a

Panel A of table 3 provides evidence on whether positive or negative abnormal accruals

have different effects on external financing. This analysis provides a baseline model examining

whether investors’ weight on abnormal accruals depends on the sign, while the models in panel

B provide evidence on whether investors discern between cases where the sign of abnormal

accruals matches the sign of changes in future operating cash flow (equation 1a). Column 1

indicates that debt investors place a positive weight on both positive and negative abnormal

accruals; the coefficients on both POS_RES_ACCt-1 and NEG_RES_ACCt-1 are positive and

significant (t=5.582 and t=8.926, respectively). While the coefficient on NEG_RES_ACCt-1 is

larger than the coefficient on POS_RES_ACCt-1 the difference between these coefficients is not

significant at the ten percent level (p-value=0.1189, not tabled). Overall, the positive coefficients

on both positive and negative abnormal accruals provides further evidence that on-average debt

investors rely on accounting information for both good and bad news when deciding to provide

the firm with capital.

Column 2 suggests that the weight placed on abnormal accruals by equity investors

depends on the sign of abnormal accrual. Equity investors provide capital to firms with positive

abnormal accruals; the coefficient on POS_RES_ACCt-1 is positive and significant (t=3.841). In

addition, equity investors provide financing to firms with negative abnormal accruals; the

coefficient on NEG_RES_ACCt-1 is negative and significant (t=-8.644). This asymmetry

between positive and negative abnormal accruals is consistent with existing work where positive

23

abnormal accruals play a more prominent role in the accrual anomaly (Beneish and Vargus,

2002; Kothari, Loutskina, and Nikolaev, 2006).

These equity results in column 2, in combination with the debt results in column 1, also

provide evidence of pecking order theory. If firms prefer to issue debt as postulated by pecking

order theory, then firms only choose to issue equity when other costs make debt too costly. If

debt investors place a greater weight on accounting signals of near-term performance relative to

equity investors, then firms with negative abnormal accruals choose obtain less debt financing as

it is now more costly (a positive coefficient on NEG_RES_ACCt-1 in column 1) and instead

choose to issue equity (a negative coefficient on NEG_RES_ACCt-1 in column 2).

Column 3 provides evidence on total financing activities. Firms with positive abnormal

accruals tend to obtain more total external financing, a positive and significant coefficient on

POS_RES_ACCt-1 (t=7.081). However, the coefficient on NEG_RES_ACCt-1 is insignificant.

Given the results in the columns 1 and 2, a negative abnormal accrual may limit debt financing,

but this is offset by more equity financing, resulting in no effect on total financing. Furthermore,

these results suggest that the association between abnormal accruals and total financing

estimated for an entire sample is likely to be influenced by the percent of firms in that sample

with positive abnormal accruals in that sample.

Panel B of table 3 presents of estimates of equation 1a. Column 1 indicates that the

positive coefficients on POS_RES_ACCt-1 and NEG_RES_ACCt-1 documented in column 1 of

panel A are due to cases where the sign of the accruals is consistent with the sign of the change

in cash flows from year t- to t+1. Specifically, the coefficients on both

POS_∆CFt+1*POS_RES_ACCt-1 and NEG_∆CFt+1*NEG_RES_ACCt-1 are positive and

significant (t=9.727 and t=7.995, respectively). These positive coefficients indicate that positive

24

abnormal accruals that precede high future cash flow enable firms to borrow against these future

cash flows, while negative abnormal accruals that precede low future cash flow alert debt

investors to not provide capital to firms that will have difficulty repaying obligations in future

years. While there is evidence consistent with equity investors lacking sophistication when

evaluating opportunistic abnormal accruals in column 1, there is no evidence in consistent that

debt investors are misled in a similar way. The coefficient on NEG_∆CFt+1*POS_RES_ACCt-1 is

negative and insignificant (t=-1.049). Additional tests indicate that conditional on the sign of the

abnormal accrual, debt investors place a more positive weight on abnormal accruals that are

accurate ex-post.13 Overall, the evidence implies that the association between debt financing

activities and accruals appear consistent with debt investors relying on abnormal accruals when

these abnormal accruals are informative about the sign of future cash flows and discerning

between these informative accruals and positive abnormal accruals that precede negative future

cash flow performance.

In contrast to column 1, the model predicting ∆EQUITYt in column 2 presents a

coefficient on NEG_∆CFt+1*POS_RES_ACCt-1 that is positive and significant (t=6.239). This

positive coefficient indicates that equity investors rely on positive abnormal accruals when the

firm will experience a decrease in cash flow following the issuance. In addition, the coefficient

on NEG_∆CFt+1*POS_RES_ACCt-1 is significantly greater than POS_∆CFt+1*POS_RES_ACCt-

1 (t=5.296).

Column 2 does not provide any evidence consistent with equity investors placing a

positive weight on abnormal accruals that predict the sign of future changes in cash flow. In fact,

the coefficient on NEG_∆CFt+1*NEG_RES_ACCt-1 is negative and significant (t=-10.276). This

13 The coefficient on POS_∆CFt+1*POS_RES_ACCt-1 is significantly greater than the coefficient on NEG_∆CFt+1*POS_RES_ACCt-1 (t=5.772) and the coefficient on NEG_∆CFt+1*NEG_RES_ACCt-1 is significantly greater than the coefficient on POS_∆CFt+1*NEG _RES_ACCt-1 (t=2.806).

25

implies that firms with negative abnormal accruals that precede low future cash flows choose to

issue equity. When viewed in conjunction with the positive coefficient in column 1, this suggest

that firms that firms follow a pecking order where negative abnormal accruals made debt

issuances more costly and these firms then choose to issue equity. This further clarifies the

results in panel A of table 3 indicating that while negative abnormal accruals alert debt investors

to not provide capital to a firm before a decrease in cash flow, equity investors are still willing to

offer financing to these firms. The willingness of equity investors to offer capital to these firms

suggests that equity investors believe that low cash flow in year t+1 will not persist or it is

unexpected. The large body of literature on the stock market performance under-performance

following equity offerings suggests that unlike debt investors equity investors may not anticipate

the implications of these abnormal accruals.

In summary, the estimates of equation 1a provide support for the hypothesis that

sophisticated debt investors respond to abnormal accruals when these accruals are informative,

while less sophisticated equity investors appear to rely on abnormal accruals that are

opportunistic. Consistent with the large literature on earnings management at equity offerings,

equity investors rely on positive abnormal accruals that do not signal positive cash flows in the

following year. However, debt investors use the information in abnormal accruals when it is

accurate ex-post. In addition, the presence of negative abnormal accruals appears to alter the

costs of debt financing, leading these firms to issue equity instead.

4.3 Empirical analysis of equation 1b

Panel A of table 4 presents evidence on whether the coefficient on pre-issuance abnormal

accruals in obtaining financing in year t is sensitive to whether these abnormal accruals decrease

between year t-1 and year t+1. Panel B refines this analysis by distinguishing reversals that

26

coincide with an increase in cash flow from reversals that appear more opportunistic (equation

1b). Column 1 of panel A indicates that both REV_RES_ACCt-1 and NOREV_RES_ACCt-1 are

positively associated with ∆DEBTt (t=6.215 and t=7.866, respectively). These positive

coefficients imply that debt investors place a positive weight on abnormal accruals both before

decreases and before increases. Column 2 presents evidence that NOREV_RES_ACCt-1 is

negatively associated with ∆EQUITYt (t=-4.976). This negative coefficient on

NOREV_RES_ACCt-1 appears consistent with the negative coefficient on NEG_RES_ACCt-1

documented in table 3, as firms with negative abnormal accruals appear less likely to experience

a negative change in abnormal accruals due to mean reversion. Finally, column 3 indicates that

both REV_RES_ACCt-1 and NOREV_RES_ACCt-1 are positively associated with TOTFINt

(t=6.105 and t=2.156, respectively), indicating that the combination of the effects in columns 1

and 2 result in a net positive weight on accruals regardless of whether there is a reversal or not.

In the next panel, I explore whether the positive weight on REV_RES_ACCt-1 observed in

column 1 and column 3 results in investors providing financing to firms that will experience a

decrease in cash flow.

Panel B of table 4 presents estimates of equation 1b. Column 1 indicates that when

abnormal accruals will reverse and this reversal is not accompanied by an increase in cash flow

(NEG_∆CFt+1*REV_RES_ACCt-1) the weight used by debt investors is not significantly

different from zero (t=-0.951). However, when the reversal in abnormal accruals coincides with

an increase in cash flow (POS_∆CFt+1*REV_RES_ACCt-1) debt investors place a positive

weight on the abnormal accrual information (t=7.063). These results support the prediction that

debt investors appear to rely on the abnormal accruals that eventually reverse when this reversal

is due to a normal accrual transaction (collection of a receivable or liquidation of an asset), but

27

not when it appears more opportunistic. In addition, the coefficient on

POS_∆CFt+1*REV_RES_ACCt-1 is significantly greater than NEG_∆CFt+1*REV_RES_ACCt-1

(t=4.937).

In contrast to the results in column 1, the results in column 2 support the notion that

equity investors are misled into buying firms where abnormal accruals will decrease and this

decrease is not due to a cash inflow. In the model predicting ∆EQUITYt, the coefficient on

NEG_∆CFt+1*REV_RES_ACCt-1 is positive and significant (t=2.655), while the coefficient on

POS_∆CFt+1*REV_RES_ACCt-1 is negative and insignificant (t=-0.378). Thus, equity investors

appear to rely on accruals that reverse when this reversal is more consistent with earnings

management. The coefficient on NEG_∆CFt+1*REV_RES_ACCt-1 in column 2 is also

significantly greater than POS_∆CFt+1*REV_RES_ACCt-1 (t=2.605).

Column 3 presents evidence on the role of reversals in total financing. Interestingly, the

results relating to total financing more closely resemble the debt financing results (column 1).

The coefficient on abnormal accruals that reverse due to a cash inflow is positive and significant

(t=5.010), while the coefficient on abnormal accruals that reverse and do not produce a cash

inflow is positive, but only marginally significant (t=1.666).

In summary, the estimates of equation 1b indicate the debt investors can infer when

reversals in abnormal accruals are due to increases in cash flow, while equity investors provide

financing to firms whose accrual reversals appear more opportunistic. Consistent with the

literature on earnings management at equity offerings, equity investors place a positive weight on

abnormal accruals that reverse, but do not produce an increase in cash flow. Debt investors also

place a positive weight on abnormal accruals that reverse, however only when the reversal

28

produces cash inflow, consistent with the relative shorter maturity of debt investors valuing these

transitory cash flows.

4.4 Alternative specification - cash flow prediction models

An alternative method for testing whether debt and equity investors are able to identify

when abnormal accruals are informative about future cash flow would be to estimate models

predicting future cash flow and include interactions between external financing proxies and

abnormal accruals. The structure of this analysis closely parallels the models used by Beneish

and Vargus (2002) although the dependent variable is future cash flow (as opposed to earnings or

returns) and the indicators interacted with accruals are based on external financing cash flows

(instead of insider trading indicators).

Table 5 presents estimates of this alternative research design. Column 1 contains a model

predicting CFt+1 as a function of CFt-1, RES_ACCt-1, and expected accruals (E[ACCt-1]). Column

1 indicates that the abnormal accruals contain information correlated with future cash flow, there

is a positive and significant coefficient on abnormal accruals (t=30.613).

Columns 2-4 examine if positive and negative abnormal accruals have a stronger

association with future cash flow based on the sign of external financing. Each column contains a

model where POS_RES_ACCt-1 and NEG_RES_ACCt-1 are interacted with a dummy for

whether financing in year t is abnormally positive or negative. Abnormal financing is defined as

external financing that is not related to control variables and is the residual obtained from

regressing each measure of external financing (∆DEBTt, ∆EQUITYt, or TOTFINt) on the control

variables listed equation 1.

Column 2 indicates that the coefficient on abnormal accruals is most positive when

positive abnormal accruals coincide with an abnormally positive ∆DEBTt or when negative

29

abnormal accruals coincide with an abnormally negative ∆DEBTt. In addition, to being positive

and significant these coefficients are also significantly greater than the other cases when the sign

of abnormal ∆DEBTt is not consistent with the sign of abnormal accruals.14 Thus, when the sign

of a abnormal debt financing appears to validate the abnormal accruals, these accruals have a

more positive association with future cash flows.

Column 3 provides evidence that the consistency between the sign of abnormal equity

financing and abnormal accruals does not appear to yield a better prediction model. In fact, when

the issuance of equity coincides with negative abnormal accruals

(POS_RES_∆EQUITYt*NEG_RES_ACCt-1), these abnormal accruals actually have a positive

association with future cash flow (t=17.038) and this coefficient is significantly greater than

cases where negative abnormal accruals do not coincide with equity issuances

(NEG_RES_∆EQUITYt*NEG_RES_ACCt-1). These results are consistent with the evidence in

table 3 that negative abnormal accruals which are accurate ex-post are more likely to result in

equity issuances as debt is more costly.

Finally, column 4 contains interactions between total financing and abnormal accruals in

a cash flow prediction model. This model does not provide any evidence that total abnormal

financing appears to distinguish between more or less informative abnormal accruals. The

coefficients on abnormal accruals are not significantly different across the different interactions.

This alternative specification yields results consistent with the primary analysis of the

informative accruals and earnings management hypotheses. Specifically, when the cash flows

from debt financing are consistent with the sign of the abnormal accruals, these accruals provide

a better signal of future cash flow. However, when abnormal accruals are negative and firms

14 The coefficient on POS_RES_∆DEBTt*POS_RES_ACCt-1 is significantly greater than the coefficient on NEG_RES_∆DEBTt*POS_RES_ACCt-1 (t=7.266) and the coefficient on NEG_RES_∆DEBTt*NEG_RES_ACCt-1 is significantly greater than the coefficient on POS_RES_∆DEBTt*NEG_RES_ACCt-1 (t=8.220).

30

choose to issue equity these abnormal accruals also predict future cash flow well, presumably

because these firms could not issue debt due to greater risk associated with the negative news in

these accruals.

4.5 Sensitivity analysis

I perform five sets of sensitivity tests to examine the robustness of this paper’s results.

First, as the hypotheses related to debt are motivated in part by the shorter maturity of debt, it is

useful to examine if the paper’s results hold for both short-term debt and long-term debt. Second,

I examine replace the dependent variables in equation 1a and 1b with indicator variables identify

when external financing of a given type is positive. Third, I examine the robustness of using

positive and negative as a split for accrual abnormal accruals and the change in future cash

flows. Rather than splitting based on the sign, these variables can be split based into three

categories: high, medium, and low. Fourth, the analysis in the paper has generally assumed all

firms need external financing in year t and included CFt and CASHt-1 as control variables,

instead I can constrain my sample to firm-years where CFt is relatively low.

To examine if short-term or long-term debt drive the results obtained using the ∆DEBTt

dependent variable, I repeat the analysis of equations 1a and 1b using changes in notes payable

(∆NPt) and changes in long-term debt (∆LTDt) as dependent variables.15 There is support for

both the hypothesis that debt investors act in a sophisticated manner in models where the

dependent variable is defined as either ∆NPt or ∆LTDt (not tabled). However, the evidence

appears strongest in models predicting ∆NPt. Specifically, while the tests of individual

coefficients are supported for both dependent variables the tests of differences between

15 ∆NPt is equal to the change in total current debt (data34) less the change in current long term debt (data44) divided by average assets. The remaining change in debt (∆DEBTt) is assumed to be long-term (∆LTDt=∆DEBTt - ∆NPt). As the ∆NPt is often zero due to cases where the beginning and ending balances are equal to zero, I drop these observations. The results discussed in section 4.4 are based on this definition; however, the results are similar if these observations are not dropped.

31

coefficients in a given model are only significant when the dependent variable is equal to ∆NPt.

The evidence relating to ∆NPt is particularly supportive of an informative role for abnormal

accruals due to the short maturity of this security. Due to is short maturity, investors providing

financing through notes payable provides investors with the opportunity to impose costs on the

firm quickly if it manipulated its accruals (i.e., if sufficient cash flow is not generated the next

year the firm will default). As these settling up costs make earnings management less attractive,

the association between abnormal accruals and ∆NPt provides further support for the notion that

investors rely on abnormal accruals when they are useful.

Repeating the analysis in this paper using a discrete dependent variable compares firms

that issue with those that do not, where it is assumed that the sample of firms that do not issue

have an high incremental cost of external capital. As the dependent variables in equations 1a and

1b are now discrete I estimate these models using logistic regression. The main results of the

paper are robust to this specification (not tabled).

The choice to define abnormal accruals and future changes in cash flow as high or low

based on their respective signs was made for two reasons. First, the independent variables in any

single group may have less variation as the number of partitions becomes finer. Splitting into two

groups (positive and negative) maximizes the sample size in each group, limiting concerns that

power will drop due to limited variation. Second, this definition is consistent with prior work

(e.g., Beneish and Vargus, 2002). However, the paper’s hypotheses regarding high and low

abnormal accruals and high and low cash flows could also be tested using other groupings. To

examine the importance of this choice I re-estimated equation 1a and 1b. For analysis of equation

1a, high, medium and low groups for each variable defined as the top, middle, and bottom third

of the distribution for sample firms. In addition, for analysis of equation 1b, reversals are

32

considered cases where the change in abnormal accruals is in the bottom third of the distribution.

The main results relating from equation 1a and 1b are qualitatively similar in this specification

(not tabled).

Firms unable to fund investment internally may be a particularly powerful sample to

examine external financing choices. I repeat my analysis restricting my sample to firms where

free cash flow (FCF) is negative (i.e., operating cash flow is insufficient to fund the expected

level of investment in year t). Similar to Richardson (2006), I measure FCF using expected

investment to identify cases where under-investment would occur unless cash is obtained from

another source.16 The definition of FCF is listed below:

FCF =CF - E[New Investment] – Maint. Investment Where: New Investment =Industry-year fixed effects + λ1BTM + λ2AGE + error

where New Investment = ITOTAL - Maintenance investment (2)

E[New Investment] =Predicted value from equation 2 Maintenance Investment

=Depreciation and amortization (data 125) divided by average assets

While the sample size drops from 45,409 to 23,004, the primary results of this paper remain

qualitatively the same (not tabled). Overall, the evidence relating supporting informative accruals

for debt financing is qualitatively similar using this smaller sample.

5. Conclusion

In this paper I examine whether abnormal accruals convey information to investors, by

analyzing the association between abnormal accruals and financing. I find evidence that debt

investors appear to obtain information about future cash flow from abnormal accruals, consistent

16

This definition of FCF is distinct from some work in finance because it is based on the level of CF relative to

expected investment, not realized investment. To ensure that firms do not have cash holdings in place before year t to offset the need to obtain external financing in year t, I remove firm-years where the sum of cash holdings at the beginning of year t and FCF in year t is greater than zero.

33

with these investors being sophisticated with respect to earnings information and abnormal

accruals providing a useful signal in some cases. Abnormal accruals help debt investors provide

capital to firms that will be able to repay these funds in the future and withhold capital from

firms that will have low cash flow in the future. In addition, debt investors value abnormal

accruals that decrease in future periods, but only when this decrease appears to be due to a cash

inflow. While debt investors appear to be able to use abnormal accruals when these accruals are

informative, equity financing is associated with abnormal accruals that appear opportunistic ex-

post. Consistent with the large literature on earning management at equity offerings, I find that

cash flow from equity financing is more likely to occur when abnormal accruals are positive, but

these positive abnormal accruals do not signal higher future cash flow. In addition, the reversals

around equity offerings do not appear to reflect cash inflows, but may suggest inflated abnormal

accruals. Finally, this paper’s analysis of abnormal accruals also provides insight into the role of

accounting in a firm’s preferences over debt and equity. Firms appear to prefer to issue debt, but

when debt is more risky due to a negative accurate signal from abnormal accruals these firms

choose to issue equity.

This study is subject to a number of limitations. The largest concern is likely that by

examining the association between abnormal accruals and cash flow from financing activities, I

assume that abnormal accruals are exogenous. This is a strong assumption given the existing

evidence that firms choose the level of voluntary disclosure around financing activities.17

However, because abnormal accruals are calculated based on mandatory accounting disclosures

there is less concern that voluntary disclosures made in anticipation of a security issuance will

introduce bias.

17 For example, there is a growing body of research documenting the different channels that firms use to communicate information and reduce underpricing of a security issue (e.g., Schrand and Verrecchia, 2004).

34

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36

Appendix A Variable definitions

Variable Definition

dataXX Refers to Compustat data item number XX ∆DEBT =Cash proceeds from the issuance of debt (data 111) plus the change in notes (data 301)

less the payments to reduce long term debt (data 114) divided by average assets ∆EQUITY =Cash proceeds from the sale of common/preferred stock (data108) – Cash dividends

(data127) - Repurchases (data 115) divided by average assets TOTFINt =∆EQUITYt + ∆DEBTt CF =Cash flow from operations is defined as reported operating cash flow (data308) before

R&D expense (data46) divided by average assets. R&D expense is set equal to zero when missing.

ACCt =Net Income (data18) – Cash flow from operations (data308) divided by lagged total assets (data6)

Jones Model ACCt =α0 + α1(1/ASSETSt-1) + α2∆SALESt + α3PP&Et + ε This model is estimated at the 2 digit SIC code level each year. The Jones model is only estimated using observations where the dependent is less than one in absolute value. The model is only estimated for industry-years with at least 10 observations.

(1/ASSETSt-1) =1 divided by lagged total assets ∆SALESt =Change in sales (data12) in year t divided by lagged total assets PP&Et =Gross PP&E (data8) in year t divided by lagged total assets ROA =Net Income (data18) divided by average assets RES_ACCt = εi for firm i - average εi of peer group

ROA peer groups are obtained by dividing the 2 digit SIC code into quintiles based on ROA. When calculating the average ε for firm i’s peer group firm i is excluded.

POS_RES_ACCt-1 =RES_ACCt-1 when RES_ACCt-1 is greater than zero and zero otherwise NEG_RES_ACCt-1 =RES_ACCt-1 when RES_ACCt-1 is less than zero and zero otherwise REV_RES_ACCt-1 =RES_ACCt-1 when RES_ACCt+1 is greater than or equal toRES_ACCt-1 and zero

otherwise NOREV_RES_ACCt-1 =RES_ACCt-1 when RES_ACCt+1 is less than RES_ACCt-1 and zero otherwise REVt+1 =1 when RES_ACCt+1 is less than RES_ACCt-1 and zero otherwise POS_∆CFt+1 =1 when CFt+1 is greater than or equal to CFt-1 and zero otherwise NEG_∆CFt+1 =1 when CFt+1 is less than CFt-1 and zero otherwise AGE =Log(years since company first appears in the CRSP monthly stock file) BTM =BV of assets (data6) divided by MV of assets (MV of equity (data25*data199) + BV

of liabilities (data181)) ITOTAL =[Capital expenditures (data128) + R&D (data46) + Acquisitions (data129) – Sale of

PPE (107)] divided by average assets CASH =Cash (data1) divided by total assets AR =Accounts Receivable (data 2) divided by total assets INV =Inventory (data3) divided by total assets LEV =Debt/(Debt + Equity), where debt=(data9 + data34) and equity=data60

37

Table 1 Descriptive statistics

Panel A: Descriptive statistics

Variable

N Mean Standard Deviation

Lower Quartile Median

Upper Quartile

∆DEBTt 45,409 0.007 0.092 -0.021 0.000 0.006 ∆EQUITYt 45,409 0.011 0.099 -0.028 0.000 0.035 TOTFINt 45,409 0.018 0.138 -0.047 -0.004 0.046 RES_ACCt-1 45,409 -0.008 0.094 -0.055 -0.007 0.038 CFt-1 45,409 0.111 0.116 0.046 0.102 0.169 CFt 45,409 0.111 0.114 0.046 0.102 0.169 ITOTALt-1 45,409 0.125 0.115 0.045 0.092 0.169 AGEt-1 45,409 2.207 1.111 1.386 2.303 3.091 BTMt-1 45,409 0.751 0.322 0.515 0.753 0.957 CASHt-1 45,409 0.133 0.167 0.017 0.058 0.186 ARt-1 45,409 0.184 0.128 0.085 0.167 0.255 INVt-1 45,409 0.154 0.149 0.023 0.121 0.237 LEVt-1 45,409 0.347 0.294 0.088 0.320 0.526 ∆DEBTt-1 45,409 0.011 0.103 -0.021 0.000 0.007 ∆EQUITYt-1 45,409 0.015 0.103 -0.026 0.000 0.039

Panel B: Pearson correlations (above diagonal) and Spearman correlations (below diagonal)

Variable RES_ACCt-1 ∆DEBTt ∆EQUITYt TOTFINt RES_ACCt-1 1.000 0.017 0.058 0.053 0.000 <.0001 <.0001 ∆DEBTt 0.024 1.000 -0.065 0.689 <.0001 <.0001 <.0001 ∆EQUITYt 0.069 -0.084 1.000 0.659 <.0001 <.0001 <.0001 TOTFINt 0.070 0.714 0.490 1.000 <.0001 <.0001 <.0001 Table 1 presents descriptive statistics for sample firms. Panel A contains univariate statistics on the distributions of the dependent and independent variables used in this study. Panel B contains pearson and spearman correlations between abnormal accruals (RES_ACCt-1) and the dependent variables used in this study. All variables are defined in Appendix A.

38

Table 2

Determinants of financing activities

(1) (2) (3) ∆DEBTt ∆EQUITYt TOTFINt

RES_ACCt-1 0.067 -0.022 0.044 (11.407) (-4.178) (5.684) CFt-1 0.163 -0.106 0.054 (24.186) (-17.397) (6.136) CFt -0.319 -0.132 -0.470 (-64.126) (-29.258) (-72.045) ITOTALt-1 0.080 0.192 0.288 (14.629) (38.890) (40.371) AGEt-1 -0.002 -0.009 -0.011 (-4.975) (-24.113) (-20.429) BTMt-1 -0.044 -0.031 -0.078 (-28.165) (-21.396) (-37.570) CASHt-1 -0.059 0.032 -0.026 (-16.657) (9.921) (-5.584) ARt-1 -0.010 0.053 0.045 (-2.431) (13.755) (8.026) INVt-1 -0.010 0.024 0.015 (-2.323) (5.874) (2.562) LEVt-1 -0.057 0.025 -0.034 (-32.005) (15.386) (-14.330) ∆DEBTt-1 0.003 0.142 0.156 (0.524) (31.488) (23.951) ∆EQUITYt-1 0.049 -0.071 -0.025 (9.056) (-14.504) (-3.479) Industry effects Yes Yes Yes Year effects Yes Yes Yes Adjusted R-square 0.145 0.201 0.246 N 45,409 45,409 45,409 Table 2 contains the coefficients from models predicting external financing estimated using ordinary least squares, t-statistics are reported below each coefficient in parenthesis. All variables are defined in Appendix A.

39

Table 3

Distinction between positive and negative abnormal accruals

Panel A: Piece-wise coefficients on abnormal accruals

(1) (2) (3) ∆DEBTt ∆EQUITYt TOTFINt

POS_RES_ACCt-1 0.055 0.034 0.091 (5.582) (3.841) (7.081) NEG_RES_ACCt-1 0.077 -0.068 0.006 (8.926) (-8.644) (0.525) CFt-1 0.162 -0.103 0.057 (24.012) (-16.794) (6.452) CFt -0.319 -0.132 -0.470 (-64.130) (-29.267) (-72.055) ITOTALt-1 0.081 0.188 0.285 (14.711) (37.968) (39.747) AGEt-1 -0.002 -0.009 -0.011 (-5.084) (-23.418) (-20.001) BTMt-1 -0.045 -0.030 -0.077 (-28.207) (-20.830) (-37.186) CASHt-1 -0.058 0.029 -0.028 (-16.372) (8.964) (-6.071) ARt-1 -0.010 0.050 0.042 (-2.247) (12.820) (7.481) INVt-1 -0.010 0.020 0.012 (-2.153) (5.044) (2.084) LEVt-1 -0.057 0.025 -0.034 (-31.936) (15.126) (-14.477) ∆DEBTt-1 0.003 0.139 0.154 (0.666) (30.646) (23.432) ∆EQUITYt-1 0.049 -0.071 -0.025 (9.060) (-14.533) (-3.491) Industry effects Yes Yes Yes Year effects Yes Yes Yes Adjusted R-square 0.145 0.202 0.246 N 45,409 45,409 45,409

40

Table 3

Panel B: Estimates of equation 1a

DEPVARt = β0 + β1NEG_∆CFt+1*POS_RES_ACCt-1 + β2POS_∆CFt+1*POS_RES_ACCt-1

+ β3NEG_∆CFt+1*NEG_RES_ACCt-1 + β4POS_∆CFt+1*NEG_RES_ACCt-1 + CONTROLS + error

(1a)

(1) (2) (3) ∆DEBTt ∆EQUITYt TOTFINt

NEG_∆CFt+1*POS_RES_ACCt-1 -0.017 0.093 0.089 (-1.049) (6.239) (4.112) NEG_∆CFt+1*NEG_RES_ACCt-1 0.103 -0.099 -0.003 (9.727) (-10.276) (-0.238) POS_∆CFt+1*POS_RES_ACCt-1 0.093 0.002 0.091 (7.995) (0.187) (6.024) POS_∆CFt+1*NEG_RES_ACCt-1 0.056 -0.015 0.043 (3.989) (-1.203) (2.375) POS_∆CFt+1 -0.011 -0.003 -0.014 (-8.784) (-2.575) (-8.709) CFt-1 0.152 -0.128 0.018 (21.063) (-19.577) (1.928) CFt -0.313 -0.119 -0.448 (-60.707) (-25.424) (-66.455) ITOTALt-1 0.082 0.190 0.288 (14.936) (38.303) (40.200) AGEt-1 -0.002 -0.009 -0.011 (-5.128) (-23.473) (-20.083) BTMt-1 -0.045 -0.031 -0.078 (-28.234) (-21.386) (-37.669) CASHt-1 -0.058 0.028 -0.029 (-16.326) (8.732) (-6.228) ARt-1 -0.010 0.049 0.041 (-2.368) (12.640) (7.250) INVt-1 -0.010 0.020 0.011 (-2.242) (4.951) (1.947) LEVt-1 -0.057 0.024 -0.034 (-31.738) (14.995) (-14.450) ∆DEBTt-1 0.003 0.137 0.151

(0.513) (30.251) (23.012) ∆EQUITYt-1 0.048 -0.072 -0.026 (8.837) (-14.605) (-3.710) Industry effects Yes Yes Yes Year effects Yes Yes Yes Adjusted R-square 0.146 0.204 0.249 N 45,409 45,409 45,409 t-statistic for difference (β3 – β1) 5.772 -5.446 0.097 t-statistic for difference (β4 – β2) -2.806 5.296 2.107 Table 3 contains the coefficients from models predicting external financing estimated using ordinary least squares, t-statistics are reported below each coefficient in parenthesis. Panel A contains models where the coefficient on

41

abnormal accruals (RES_ACCt-1) is allowed to vary based on whether the abnormal accruals are positive or negative. Panel B contains estimates of equation 1a, where the coefficient abnormal accruals (RES_ACCt-1) is allowed to vary based on whether the sign of abnormal accruals is the same as the sign of the change in cash flow from year t-1 to year t+1. All variables are defined in Appendix A.

42

Table 4

Distinction between abnormal accruals based on future reversals

Panel A: Piece-wise coefficient on abnormal accruals

(1) (2) (3) ∆DEBTt ∆EQUITYt TOTFINt

REV_RES_ACCt-1 0.053 0.013 0.069 (6.215) (1.629) (6.105) NOREV_RES_ACCt-1 0.064 -0.037 0.023 (7.866) (-4.976) (2.156) REVt+1 0.003 -0.003 0.000 (2.934) (-3.279) (0.021) CFt-1 0.163 -0.105 0.055 (24.118) (-17.165) (6.269) CFt -0.321 -0.130 -0.469 (-64.201) (-28.878) (-71.818) ITOTALt-1 0.080 0.191 0.287 (14.632) (38.620) (40.150) AGEt-1 -0.002 -0.009 -0.011 (-4.981) (-23.939) (-20.288) BTMt-1 -0.044 -0.030 -0.077 (-28.144) (-21.212) (-37.399) CASHt-1 -0.058 0.030 -0.027 (-16.371) (9.347) (-5.801) ARt-1 -0.010 0.052 0.044 (-2.280) (13.277) (7.773) INVt-1 -0.010 0.022 0.014 (-2.170) (5.447) (2.351) LEVt-1 -0.057 0.025 -0.034 (-31.932) (15.265) (-14.366) ∆DEBTt-1 0.003 0.140 0.155 (0.640) (30.987) (23.645) ∆EQUITYt-1 0.049 -0.072 -0.025 (9.071) (-14.602) (-3.543) Industry effects Yes Yes Yes Year effects Yes Yes Yes Adjusted R-square 0.145 0.202 0.246 N 45,409 45,409 45,409

43

Table 4

Panel B: Estimates of equation 1b

DEPVARt = γ0 + γ1NEG_∆CFt+1*REV_RES_ACCt-1 + γ2POS_∆CFt+1*REV_RES_ACCt-1 + γ3NOREV_RES_ACCt-1 + CONTROLS + error

(1b)

(1) (2) (3) ∆DEBTt ∆EQUITYt TOTFINt

NEG_∆CFt+1*REV_RES_ACCt-1 -0.014 0.036 0.033 (-0.951) (2.655) (1.666) POS_∆CFt+1*REV_RES_ACCt-1 0.068 -0.003 0.063 (7.063) (-0.378) (5.010) NOREV_RES_ACCt-1 0.059 -0.043 0.010 (7.188) (-5.831) (0.936) REVt+1 0.007 0.000 0.008 (6.445) (0.075) (5.367) POS_∆CFt+1 -0.010 -0.007 -0.019 (-9.826) (-7.546) (-13.550) CFt-1 0.143 -0.124 0.014 (19.995) (-19.064) (1.469) CFt -0.311 -0.121 -0.448 (-60.442) (-25.982) (-66.681) ITOTALt-1 0.081 0.192 0.289 (14.887) (38.841) (40.566) AGEt-1 -0.002 -0.009 -0.011 (-4.932) (-23.953) (-20.285) BTMt-1 -0.045 -0.031 -0.078 (-28.277) (-21.511) (-37.787) CASHt-1 -0.058 0.030 -0.026 (-16.316) (9.446) (-5.681) ARt-1 -0.011 0.052 0.043 (-2.489) (13.301) (7.650) INVt-1 -0.010 0.022 0.014 (-2.308) (5.569) (2.357) LEVt-1 -0.057 0.025 -0.033 (-31.744) (15.328) (-14.187) ∆DEBTt-1 0.002 0.139 0.152 (0.403) (30.706) (23.262) ∆EQUITYt-1 0.048 -0.072 -0.027 (8.799) (-14.633) (-3.773) Industry effects Yes Yes Yes Year effects Yes Yes Yes Adjusted R-square 0.147 0.203 0.249 N 45,409 45,409 45,409 t-statistic for difference (γ2 – γ1) 4.937 -2.605 1.399 Table 4 contains the coefficients from models predicting external financing estimated using ordinary least squares, t-statistics are reported below each coefficient in parenthesis. Panel A contains models where the coefficient on abnormal accruals (RES_ACCt-1) is allowed to vary based on whether the abnormal accruals decrease from year t-1 to year t+1. Panel B contains estimates of equation 1b, where the coefficient abnormal accruals (RES_ACCt-1) is

44

allowed to vary based on whether the decrease in abnormal accruals coincides with an positive or negative change in cash flow from year t-1 to year t+1. All variables are defined in Appendix A.

45

Table 5

Cash flow prediction models

(1) (2) (3) (4)

RES_XFINt

based on ∆DEBTt

RES_XFINt

based on ∆EQUITYt

RES_XFINt

based on TOTFINt

CFt-1 0.560 0.560 0.561 0.559 (113.040) (112.993) (112.903) (112.862) E[ACCt-1] 0.080 0.079 0.078 0.078 (11.374) (11.244) (11.101) (11.140) RES_ACCt-1 0.178 (30.613) POS_RES_XFINt -0.005 0.001 -0.007 (-3.622) (0.437) (-5.388) POS_RES_XFINt*POS_RES_ACCt-1 0.242 0.151 0.180 (17.982) (10.080) (13.244) POS_RES_XFINt*NEG_RES_ACCt-1 0.100 0.211 0.172 (7.610) (17.038) (13.782) NEG_RES_XFINt*POS_RES_ACCt-1 0.117 0.187 0.169 (9.467) (16.123) (13.791) NEG_RES_XFINt*NEG_RES_ACCt-1 0.233 0.157 0.188 (21.795) (14.111) (17.014) Industry effects Yes Yes Yes Yes Year effects Yes Yes Yes Yes Adjusted R-square 0.322 0.324 0.323 0.323 N 45,409 45,409 45,409 45,409 t-statistic for difference between coefficients on POS_RES_ACCt-1 based on interaction (POS_RES_XFINt - NEG_RES_XFINt)

7.266 -2.012 0.621 t-statistic for difference between coefficients on NEG_RES_ACCt-1 based on interaction (POS_RES_XFINt - NEG_RES_XFINt)

-8.220 3.380 -1.024 Table 5 contains the coefficients from models predicting cash flow in year t+1 estimated using ordinary least squares, t-statistics are reported below each coefficient in parenthesis. Columns 2, 3, and 4 contain interactions allowing the coefficients on positive and negative abnormal accruals to vary based on whether the sign of abnormal financing is positive or negative. POS_RES_XFINt (NEG_RES_XFINt) is an indicator for cases where abnormal financing is positive (negative). Abnormal financing (RES_XFINt) is defined as the residual from regressing external financing (∆DEBTt, ∆EQUITYt, or TOTFINt) on the control variables listed equation 1. All variables are defined in Appendix A.