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.
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
1
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.
2
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
3
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
4
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 overvalued 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).
5
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).
6
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
4
(1)
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.
7
Where:
DEPVARt
TOTFINt
dataXX
∆DEBTt
∆EQUITYt
RES_ACCt-1
CONTROLS
=Financing cash flow in year t (TOTFINt, ∆DEBTt, ∆EQUITYt)
=∆EQUITYt + ∆DEBTt
Refers to Compustat data item number XX
=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
=Cash proceeds from the sale of common/preferred stock (data108) –
cash dividends (data127) - repurchases (data 115) divided by average
assets
=Abnormal accruals in year t-1
=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.
8
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 expost 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).
9
(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 preissuance 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).
10
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,
11
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:
Predicted sign for β1
Predicted sign for β2
Predicted sign for β3
Predicted sign for β4
DEPVARt =
∆DEBTt
?
+
+
?
DEPVARt =
∆EQUITYt
+
0
?
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
12
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.
13
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 shortterm. 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:
Predicted sign for γ1
Predicted sign for γ2
DEPVARt =
∆DEBTt
?
+
DEPVARt =
∆EQUITYt
+
?
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
14
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
Jones Model
(1/ASSETSt-1)
∆SALESt
PP&Et
ROA
RES_ACCt
=Net Income (data18) – Cash flow from operations (data308) divided by
lagged total assets (data6)
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 industryyears with at least 10 observations.
=1 divided by lagged total assets
=Change in sales (data12) in year t divided by lagged total assets
=Gross PP&E (data8) in year t divided by lagged total assets
=Net Income (data18) divided by average assets
= 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.
15
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;
16
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.
17
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
BTM
ITOTAL
=Log(years since company first appears in the CRSP monthly stock file)
=BV of assets (data6) divided by MV of assets (MV of equity
(data25*data199) + BV of liabilities (data181))
=[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
18
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
AR
INV
=Cash (data1) divided by total assets
=Accounts Receivable (data 2) divided by total assets
=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
19
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.
20
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.
21
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
22
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
23
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_ACCt1
(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).
24
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
25
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
26
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
27
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
28
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).
29
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.
30
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
31
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
E[New Investment]
Maintenance
Investment
=Industry-year fixed effects + λ1BTM + λ2AGE + error
where New Investment = ITOTAL - Maintenance investment
=Predicted value from equation 2
=Depreciation and amortization (data 125) divided by average assets
(2)
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.
32
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 expost. 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).
33
References
Akerlof, G., 1970. The market for ‘lemons’: quality uncertainty and the market mechanism.
Quarterly Journal of Economics 90, 629-650.
Almeida, H., and M. Campello, 2005. Financial constraints, asset tangibility, and corporate
investment. Working paper.
Almeida, H., M. Campello, and M. Weisbach, 2004. The cash flow sensitivity of cash. Journal of
Finance 59, 1777-1804.
Ball, R. and L. Shivakumar, 2006. Earnings quality at initial public offerings. Working paper.
Barth, M., D. Cram and K. Nelson, 2001. Accruals and the prediction of future cash flows.
Accounting Review 76, 27-58.
Beneish, M. and M. Vargus, 2002. Insider trading, earnings quality, and accrual mispricing. The
Accounting Review 77: 755-791.
Berger, P., 1999. Discussion of ‘the effect of limited liability on the informativeness of earnings:
evidence from the stock and bond market’. Contemporary Accounting Research 16, 575581.
Berger, P., E. Ofek, and I. Swary, 1996. Investor valuation of the abandonment option. Journal of
Financial Economics 42, 257-287.
Bharath, S., J. Sunder, and S. Sunder, 2006. Accounting quality and debt contracting. Working
paper.
Bushman, R., A. Smith, and F. Zhang, 2005. Investment-cash flow sensitivities are really capital
investment-working capital investment sensitivities. Working paper.
Cassar, G., 2005. External financing and firm operating performance. Working paper.
Dechow, P., S.P. Kothari and R. Watts, 1998. The relation between earnings and cash flows.
Journal of Accounting and Economics 25, 133-168.
Dechow, P., S. Richardson and R. Sloan, 2006. The persistence and pricing of the cash
component of earnings. Working paper.
Fazzari, S., R. Hubbard, and B. Petersen, 1988. Financing constraints on corporate investment.
Brookings paper on economic activity, 141-195.
Francis, J., R. Lafond, P. Olsson, and K. Schipper, 2005. The market pricing of accruals quality.
Journal of Accounting and Economics 39, 295-327.
34
Guay, M., S.P. Kothari, and R. Watts, 1996. A market-based evaluation of discretionary accrual
models. Journal of Accounting Research 34, 83-105.
Harford, J., 1999. Corporate cash reserves and acquisitions. Journal of Finance 54, 1969-1997.
Jiang, X., 2006.Beating earnings benchmarks and the cost of debt. Working paper.
Kaplan, S. and L. Zingales., 1997. Do Investment-cash flow sensitivities provide useful measures
of financing constraints? Quarterly Journal of Economics 112, 169-215.
Kothari, S.P., E. Loutskina, and V. Nikolaev, 2006. Agency theory of overvalued equity as an
explanation of the accrual anomaly. Working paper.
Kothari, S.P., A. Leone, and C. Wasley, 2005. Performance matched discretionary accrual
measures. Journal of Accounting and Economics 39:163-197.
Minton, B. and C. Schrand, 1999. The impact of cash flow volatility on discretionary investment
and the cost of debt and equity financing. Journal of Financial Economics 54, 423-460.
Myers, S. and N. Majluf, 1984. Corporate financing and investment decisions when firms have
information the investors do not have. Journal of Financial Economics 13, 187-221.
Opler, T., L. Pinkowitz, R. Stulz, and R. Williamson, 1999. The determinants and implications of
corporate cash reserves. Journal of Financial Economics 52, 3-46.
Rao, G., S. Teoh, and T.J. Wong, 1998. Are the accruals during an initial public offering
opportunistic? Review of Accounting Studies 3, 175-208.
Richardson, S., 2006. Overinvestment of free cash flow. Review of Accounting Studies,
forthcoming.
Richardson, S. and R. Sloan, 2003. External financing and future stock returns. Working paper.
Schrand, C. and R. Verrecchia, 2004. Disclosure choice and cost of capital: evidence from
underpricing in initial public offerings. Working paper.
Shivakumar, L., 2000. Do firms mislead investors by overstating earnings before seasoned equity
offering. Journal of Accounting and Economics 29: 339-371.
Teoh, S., I. Welch, and T.J. Wong, 1998. Earnings management and the long-term market
performance of initial public offerings. Journal of Finance 53, 1935-1974.
35
Appendix A
Variable definitions
Variable
Definition
dataXX
∆DEBT
Refers to Compustat data item number XX
=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
=Cash proceeds from the sale of common/preferred stock (data108) – Cash dividends
(data127) - Repurchases (data 115) divided by average assets
=∆EQUITYt + ∆DEBTt
=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.
=Net Income (data18) – Cash flow from operations (data308) divided by lagged total
assets (data6)
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 divided by lagged total assets
=Change in sales (data12) in year t divided by lagged total assets
=Gross PP&E (data8) in year t divided by lagged total assets
=Net Income (data18) divided by average assets
= 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.
=RES_ACCt-1 when RES_ACCt-1 is greater than zero and zero otherwise
=RES_ACCt-1 when RES_ACCt-1 is less than zero and zero otherwise
=RES_ACCt-1 when RES_ACCt+1 is greater than or equal toRES_ACCt-1 and zero
otherwise
=RES_ACCt-1 when RES_ACCt+1 is less than RES_ACCt-1 and zero otherwise
=1 when RES_ACCt+1 is less than RES_ACCt-1 and zero otherwise
=1 when CFt+1 is greater than or equal to CFt-1 and zero otherwise
=1 when CFt+1 is less than CFt-1 and zero otherwise
=Log(years since company first appears in the CRSP monthly stock file)
=BV of assets (data6) divided by MV of assets (MV of equity (data25*data199) + BV
of liabilities (data181))
=[Capital expenditures (data128) + R&D (data46) + Acquisitions (data129) – Sale of
PPE (107)] divided by average assets
=Cash (data1) divided by total assets
=Accounts Receivable (data 2) divided by total assets
=Inventory (data3) divided by total assets
=Debt/(Debt + Equity), where debt=(data9 + data34) and equity=data60
∆EQUITY
TOTFINt
CF
ACCt
Jones Model
(1/ASSETSt-1)
∆SALESt
PP&Et
ROA
RES_ACCt
POS_RES_ACCt-1
NEG_RES_ACCt-1
REV_RES_ACCt-1
NOREV_RES_ACCt-1
REVt+1
POS_∆CFt+1
NEG_∆CFt+1
AGE
BTM
ITOTAL
CASH
AR
INV
LEV
36
Table 1
Descriptive statistics
Panel A: Descriptive statistics
Variable
∆DEBTt
∆EQUITYt
TOTFINt
N
45,409
45,409
45,409
Mean
0.007
0.011
0.018
Standard
Deviation
0.092
0.099
0.138
Lower
Quartile
-0.021
-0.028
-0.047
Median
0.000
0.000
-0.004
Upper
Quartile
0.006
0.035
0.046
RES_ACCt-1
45,409
-0.008
0.094
-0.055
-0.007
0.038
CFt-1
CFt
ITOTALt-1
AGEt-1
BTMt-1
CASHt-1
ARt-1
INVt-1
LEVt-1
∆DEBTt-1
∆EQUITYt-1
45,409
45,409
45,409
45,409
45,409
45,409
45,409
45,409
45,409
45,409
45,409
0.111
0.111
0.125
2.207
0.751
0.133
0.184
0.154
0.347
0.011
0.015
0.116
0.114
0.115
1.111
0.322
0.167
0.128
0.149
0.294
0.103
0.103
0.046
0.046
0.045
1.386
0.515
0.017
0.085
0.023
0.088
-0.021
-0.026
0.102
0.102
0.092
2.303
0.753
0.058
0.167
0.121
0.320
0.000
0.000
0.169
0.169
0.169
3.091
0.957
0.186
0.255
0.237
0.526
0.007
0.039
Panel B: Pearson correlations (above diagonal) and Spearman correlations (below diagonal)
Variable
RES_ACCt-1
RES_ACCt-1
1.000
∆DEBTt
0.017
0.000
∆EQUITYt
0.058
<.0001
TOTFINt
0.053
<.0001
∆DEBTt
0.024
<.0001
1.000
-0.065
<.0001
0.689
<.0001
∆EQUITYt
0.069
<.0001
-0.084
<.0001
1.000
0.659
<.0001
TOTFINt
0.070
<.0001
0.714
<.0001
0.490
<.0001
1.000
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.
37
Table 2
Determinants of financing activities
RES_ACCt-1
CFt-1
CFt
ITOTALt-1
AGEt-1
BTMt-1
CASHt-1
ARt-1
INVt-1
LEVt-1
∆DEBTt-1
∆EQUITYt-1
Industry effects
Year effects
Adjusted R-square
N
(1)
∆DEBTt
0.067
(11.407)
0.163
(24.186)
-0.319
(-64.126)
0.080
(14.629)
-0.002
(-4.975)
-0.044
(-28.165)
-0.059
(-16.657)
-0.010
(-2.431)
-0.010
(-2.323)
-0.057
(-32.005)
0.003
(0.524)
0.049
(9.056)
(2)
∆EQUITYt
-0.022
(-4.178)
-0.106
(-17.397)
-0.132
(-29.258)
0.192
(38.890)
-0.009
(-24.113)
-0.031
(-21.396)
0.032
(9.921)
0.053
(13.755)
0.024
(5.874)
0.025
(15.386)
0.142
(31.488)
-0.071
(-14.504)
(3)
TOTFINt
0.044
(5.684)
0.054
(6.136)
-0.470
(-72.045)
0.288
(40.371)
-0.011
(-20.429)
-0.078
(-37.570)
-0.026
(-5.584)
0.045
(8.026)
0.015
(2.562)
-0.034
(-14.330)
0.156
(23.951)
-0.025
(-3.479)
Yes
Yes
Yes
Yes
Yes
Yes
0.145
45,409
0.201
45,409
0.246
45,409
Table 2 contains the coefficients from models predicting external financing estimated using ordinary least squares, tstatistics are reported below each coefficient in parenthesis. All variables are defined in Appendix A.
38
Table 3
Distinction between positive and negative abnormal accruals
Panel A: Piece-wise coefficients on abnormal accruals
POS_RES_ACCt-1
NEG_RES_ACCt-1
CFt-1
CFt
ITOTALt-1
AGEt-1
BTMt-1
CASHt-1
ARt-1
INVt-1
LEVt-1
∆DEBTt-1
∆EQUITYt-1
Industry effects
Year effects
Adjusted R-square
N
(1)
∆DEBTt
0.055
(5.582)
0.077
(8.926)
0.162
(24.012)
-0.319
(-64.130)
0.081
(14.711)
-0.002
(-5.084)
-0.045
(-28.207)
-0.058
(-16.372)
-0.010
(-2.247)
-0.010
(-2.153)
-0.057
(-31.936)
0.003
(0.666)
0.049
(9.060)
(2)
∆EQUITYt
0.034
(3.841)
-0.068
(-8.644)
-0.103
(-16.794)
-0.132
(-29.267)
0.188
(37.968)
-0.009
(-23.418)
-0.030
(-20.830)
0.029
(8.964)
0.050
(12.820)
0.020
(5.044)
0.025
(15.126)
0.139
(30.646)
-0.071
(-14.533)
(3)
TOTFINt
0.091
(7.081)
0.006
(0.525)
0.057
(6.452)
-0.470
(-72.055)
0.285
(39.747)
-0.011
(-20.001)
-0.077
(-37.186)
-0.028
(-6.071)
0.042
(7.481)
0.012
(2.084)
-0.034
(-14.477)
0.154
(23.432)
-0.025
(-3.491)
Yes
Yes
Yes
Yes
Yes
Yes
0.145
45,409
0.202
45,409
0.246
45,409
39
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
(2)
∆EQUITYt
0.093
(6.239)
-0.099
(-10.276)
0.002
(0.187)
-0.015
(-1.203)
-0.003
(-2.575)
-0.128
(-19.577)
-0.119
(-25.424)
0.190
(38.303)
-0.009
(-23.473)
-0.031
(-21.386)
0.028
(8.732)
0.049
(12.640)
0.020
(4.951)
0.024
(14.995)
0.137
(30.251)
-0.072
(-14.605)
(3)
TOTFINt
0.089
(4.112)
-0.003
(-0.238)
0.091
(6.024)
0.043
(2.375)
-0.014
(-8.709)
0.018
(1.928)
-0.448
(-66.455)
0.288
(40.200)
-0.011
(-20.083)
-0.078
(-37.669)
-0.029
(-6.228)
0.041
(7.250)
0.011
(1.947)
-0.034
(-14.450)
0.151
(23.012)
-0.026
(-3.710)
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R-square
N
0.146
45,409
0.204
45,409
0.249
45,409
t-statistic for difference (β3 – β1)
t-statistic for difference (β4 – β2)
5.772
-2.806
-5.446
5.296
0.097
2.107
NEG_∆CFt+1*POS_RES_ACCt-1
NEG_∆CFt+1*NEG_RES_ACCt-1
POS_∆CFt+1*POS_RES_ACCt-1
POS_∆CFt+1*NEG_RES_ACCt-1
POS_∆CFt+1
CFt-1
CFt
ITOTALt-1
AGEt-1
BTMt-1
CASHt-1
ARt-1
INVt-1
LEVt-1
∆DEBTt-1
∆EQUITYt-1
Industry effects
Year effects
(1)
∆DEBTt
-0.017
(-1.049)
0.103
(9.727)
0.093
(7.995)
0.056
(3.989)
-0.011
(-8.784)
0.152
(21.063)
-0.313
(-60.707)
0.082
(14.936)
-0.002
(-5.128)
-0.045
(-28.234)
-0.058
(-16.326)
-0.010
(-2.368)
-0.010
(-2.242)
-0.057
(-31.738)
0.003
(0.513)
0.048
(8.837)
(1a)
Table 3 contains the coefficients from models predicting external financing estimated using ordinary least squares, tstatistics are reported below each coefficient in parenthesis. Panel A contains models where the coefficient on
40
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.
41
Table 4
Distinction between abnormal accruals based on future reversals
Panel A: Piece-wise coefficient on abnormal accruals
REV_RES_ACCt-1
NOREV_RES_ACCt-1
REVt+1
CFt-1
CFt
ITOTALt-1
AGEt-1
BTMt-1
CASHt-1
ARt-1
INVt-1
LEVt-1
∆DEBTt-1
∆EQUITYt-1
Industry effects
Year effects
Adjusted R-square
N
(1)
∆DEBTt
0.053
(6.215)
0.064
(7.866)
0.003
(2.934)
0.163
(24.118)
-0.321
(-64.201)
0.080
(14.632)
-0.002
(-4.981)
-0.044
(-28.144)
-0.058
(-16.371)
-0.010
(-2.280)
-0.010
(-2.170)
-0.057
(-31.932)
0.003
(0.640)
0.049
(9.071)
(2)
∆EQUITYt
0.013
(1.629)
-0.037
(-4.976)
-0.003
(-3.279)
-0.105
(-17.165)
-0.130
(-28.878)
0.191
(38.620)
-0.009
(-23.939)
-0.030
(-21.212)
0.030
(9.347)
0.052
(13.277)
0.022
(5.447)
0.025
(15.265)
0.140
(30.987)
-0.072
(-14.602)
(3)
TOTFINt
0.069
(6.105)
0.023
(2.156)
0.000
(0.021)
0.055
(6.269)
-0.469
(-71.818)
0.287
(40.150)
-0.011
(-20.288)
-0.077
(-37.399)
-0.027
(-5.801)
0.044
(7.773)
0.014
(2.351)
-0.034
(-14.366)
0.155
(23.645)
-0.025
(-3.543)
Yes
Yes
Yes
Yes
Yes
Yes
0.145
45,409
0.202
45,409
0.246
45,409
42
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
(2)
∆EQUITYt
0.036
(2.655)
-0.003
(-0.378)
-0.043
(-5.831)
0.000
(0.075)
-0.007
(-7.546)
-0.124
(-19.064)
-0.121
(-25.982)
0.192
(38.841)
-0.009
(-23.953)
-0.031
(-21.511)
0.030
(9.446)
0.052
(13.301)
0.022
(5.569)
0.025
(15.328)
0.139
(30.706)
-0.072
(-14.633)
(3)
TOTFINt
0.033
(1.666)
0.063
(5.010)
0.010
(0.936)
0.008
(5.367)
-0.019
(-13.550)
0.014
(1.469)
-0.448
(-66.681)
0.289
(40.566)
-0.011
(-20.285)
-0.078
(-37.787)
-0.026
(-5.681)
0.043
(7.650)
0.014
(2.357)
-0.033
(-14.187)
0.152
(23.262)
-0.027
(-3.773)
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R-square
N
0.147
45,409
0.203
45,409
0.249
45,409
t-statistic for difference (γ2 – γ1)
4.937
-2.605
1.399
NEG_∆CFt+1*REV_RES_ACCt-1
POS_∆CFt+1*REV_RES_ACCt-1
NOREV_RES_ACCt-1
REVt+1
POS_∆CFt+1
CFt-1
CFt
ITOTALt-1
AGEt-1
BTMt-1
CASHt-1
ARt-1
INVt-1
LEVt-1
∆DEBTt-1
∆EQUITYt-1
Industry effects
Year effects
(1)
∆DEBTt
-0.014
(-0.951)
0.068
(7.063)
0.059
(7.188)
0.007
(6.445)
-0.010
(-9.826)
0.143
(19.995)
-0.311
(-60.442)
0.081
(14.887)
-0.002
(-4.932)
-0.045
(-28.277)
-0.058
(-16.316)
-0.011
(-2.489)
-0.010
(-2.308)
-0.057
(-31.744)
0.002
(0.403)
0.048
(8.799)
(1b)
Table 4 contains the coefficients from models predicting external financing estimated using ordinary least squares, tstatistics 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
43
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.
44
Table 5
Cash flow prediction models
(1)
CFt-1
E[ACCt-1]
RES_ACCt-1
(2)
RES_XFINt
based on
∆DEBTt
0.560
(112.993)
0.079
(11.244)
0.560
(113.040)
0.080
(11.374)
0.178
(30.613)
POS_RES_XFINt
-0.005
(-3.622)
0.242
(17.982)
0.100
(7.610)
0.117
(9.467)
0.233
(21.795)
POS_RES_XFINt*POS_RES_ACCt-1
POS_RES_XFINt*NEG_RES_ACCt-1
NEG_RES_XFINt*POS_RES_ACCt-1
NEG_RES_XFINt*NEG_RES_ACCt-1
Industry effects
Year effects
(3)
RES_XFINt
based on
∆EQUITYt
0.561
(112.903)
0.078
(11.101)
0.001
(0.437)
0.151
(10.080)
0.211
(17.038)
0.187
(16.123)
0.157
(14.111)
(4)
RES_XFINt
based on
TOTFINt
0.559
(112.862)
0.078
(11.140)
-0.007
(-5.388)
0.180
(13.244)
0.172
(13.782)
0.169
(13.791)
0.188
(17.014)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.322
45,409
0.324
45,409
0.323
45,409
0.323
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
Adjusted R-square
N
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.
45