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Equity issues and aggregate market returns underinformation asymmetryXiaoquan Jiang a & Bong-Soo Lee ba Department of Finance, College of Business Administration , Florida InternationalUniversity , Miami , FL 33199 , USAb Department of Finance, College of Business , Florida State University , Tallahassee , FL32306 , USAPublished online: 11 Oct 2012.

To cite this article: Xiaoquan Jiang & Bong-Soo Lee (2013) Equity issues and aggregate market returns under informationasymmetry, Quantitative Finance, 13:2, 281-300, DOI: 10.1080/14697688.2012.717178

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Quantitative Finance, 2013Vol. 13, No. 2, 281–300, http://dx.doi.org/10.1080/14697688.2012.717178

Equity issues and aggregate market returns under

information asymmetry

XIAOQUAN JIANG*y and BONG-SOO LEEz

yDepartment of Finance, College of Business Administration, Florida International University, Miami, FL33199, USA

zDepartment of Finance, College of Business, Florida State University, Tallahassee, FL 32306, USA

(Received 7 November 2009; revised 12 January 2011; in final form 25 July 2012)

We propose a simple time-series model based on information asymmetry that allows us to testthe predictive power of equity and debt issues with respect to future market returns. Using thismethod, we find that managers’ new equity and debt issue decisions have predictive power forfuture market returns, when we take into account potential feedback from past market returnsand structural breaks. We also take into account a cointegration relation among stock prices,equity issues and debt issues. This finding is robust with respect to various measures of marketreturns and consistent with the managerial timing hypothesis.

Keywords: Equity issues; Market return; Information asymmetry; Predictability

JEL Classification: C5, C53, G1, G3, G14, G32

1. Introduction

Recently, corporate managers’ ability to time the equity

market has been debated in the literature. Baker and

Wurgler (2000) find that the share of equity issues in total

new equity and debt issues is a stable predictor of U.S.

stock market returns between 1928 and 1997. They

further find that firms increase equity issues before

market returns decline. They interpret their finding as

being inconsistent with various efficient market explana-

tions and conclude that managerial timing of an ineffi-

cient equity market is the most credible explanation of

their result.However, Butler et al. (2005) argue that the predictive

power of the equity share in total new issues is driven

primarily by pseudo market timing and not by managers’

genuine timing ability of the equity market. They propose

an efficient market explanation for managers’ seeming

ability to time the aggregate market based on Schultz’s

(2003) pseudo market timing hypothesis. In response to

Butler et al. (2005), Baker et al. (2006) estimate the size of

the aggregate pseudo market timing bias using standard

simulation techniques and find that it is too small to

explain the predictive power of the equity share in new

issues and other managerial decision variables. Therefore,the issue remains as to whether managers’ equity issuedecisions have predictive power for future market returnsand, if so, whether this is necessarily evidence of marketinefficiency. In this paper, we reexamine both issues.

In their paper’s introduction, Baker et al. (2006)characterize market timing as the tendency of firms toissue equity before low equity market returns, and thepseudo market timing of Schultz (2003) as the tendency offirms to issue equity following high returns. In testing thepredictive power of the equity share in new issues, priorstudies use regression of current market return on alagged equity share. Our view is that a sheer univariatepredictive power test of the equity share based on thisregression does not fully distinguish between the twohypotheses. This is because the simple regression does notaddress the issue of whether equity issues are in responseto past market returns, in anticipation of future marketreturns or both, as Baker et al. (2006) point out. Inaddition, the equity share variable may not necessarilyreflect (or reveal) all the information managers use intheir equity and debt issue decisions.

By using a more appropriate empirical model thatcontrols for managers’ equity issue decisions in responseto past market returns, we reexamine the predictive powerof equity decisions and find stable evidence for thispredictability. Specifically, we replicate the findings of*Corresponding author. Email: jiangx@fiu.edu

� 2013 Taylor & Francis

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Butler et al. (2005) that the equity share does not havepredictive power for future market returns for their

modified sample. However, when a cointegration relationamong stock prices, equity issues, and debt issues is taken

into account, we find a stable predictive power of equityissues, for not only in-sample but also out-of-sample. Wefind that new equity issues tend to increase in anticipation

of future declines in market returns and in response topast increases in market returns. Therefore, we find some

support for both the genuine and the pseudo markettiming hypotheses. More importantly, however, the find-

ing that equity issues predict future market declines evenin the presence of past market returns is indeed consistent

with the genuine market timing hypothesis.For the finding of the predictive power of the equity

share, Baker and Wurgler (2000) do not find support forseveral possible efficient market explanations and instead

find managerial timing of an inefficient equity market astheir explanation. In this paper we propose a simple time-

series model based on information asymmetry betweeninside managers and outside investors, which is not

necessarily inconsistent with a (semi-strong form of)market efficiency explanation. The model is very usefulbecause it provides a regression model that tests the

predictive power of equity issues under informationasymmetry, which is extensively used in this paper.

The contribution of this study to the literature can be

summarized as follows. First, in view of the ongoingdebate on the managerial market timing hypothesis of

equity and debt issues, we propose a time-series methodbased on the Granger-causality test as a means of testingthe hypothesis. Second, we provide evidence in favor of

the hypothesis by taking into account a feedback frompast market returns, a cointegration among stock prices,

equity issues, and debt issues, and potential structuralchanges over time. Third, we point out that using the

equity share in new issues may unnecessarily restrict theinformation contained separately in equity issues and

debt issues in predicting future returns.The paper proceeds as follows. In section 2, we briefly

review related literature. In section 3, we propose a time-series model that explains the predictive power of equity

issues on the basis of information asymmetry and showthat a Granger-causality test can be used to test the

predictive power of equity issues. In section 4, we replicatethe results of Butler et al. (2005) based on our empirical

model. In section 5, we present our main empirical results,first based on bivariate models, and then based ontrivariate models. In section 6, we provide further

discussions on using the equity share in prediction. Wealso discuss the predictability for the post-Nasdaq sample,

the out-of-sample predictability, the long-term effect ofequity issues, corporate disclosure and information

asymmetries, and adjustment towards target capitalstructure. In section 7, we explore the robustness of ourresults using both CRSP value-weighted andequal-weighted market returns including out-of-samplepredictability. In section 8, we conclude.

2. Background

The relation between equity issues and future stockreturns has been controversial. Early studies found thatequity issues are negatively associated with future stockreturns. In a seminal paper, Ritter (1991) showed thatinitial public offerings (IPOs) underperform relative tomarket indices and matching stocks three to five yearsafter going public. Loughran and Ritter (1995), Spiessand Affleck-Graves (1995), Lee (1997), and Burch et al.(2004) find similar underperformance following seasonedequity offerings (SEOs).y Baker and Wurgler (2000) findthat firms issue relatively more equity than debt justbefore periods of low market returns, and the equity sharein new equity and debt issues is a stable and significantpredictor of aggregate stock returns, which implies thatcorporate managers can time not only the idiosyncraticcomponent but also the systematic component ofreturns.z

Several recent studies challenge these results. Forexample, Brav et al. (2000), Eckbo et al. (2000),Mitchell and Stafford (2000), Li and Zhao (2003), andButler et al. (2005) find little evidence that IPOs and SEOsunderperform the market on a risk-adjusted basis. Bravet al. (2000) show that post-issue IPO returns are similarto those of firms with similar size and book-to-marketcharacteristics, and SEO returns covary with those ofsimilar non-issuing firms. They find that underperfor-mance is concentrated primarily in small issuing firmswith low book-to-market ratios. Eckbo et al. (2000) alsoquestion the existence of underperformance for equityissues. They find that leverage and its attendant risk aresignificantly reduced following equity offerings, whileliquidity is increased. Because of these changes, they claimthat firms that have recently issued equity are less riskythan benchmark firms.

Butler et al. (2005) argue that the negative associationbetween equity issues and future aggregate stock returnsis spurious. They show that the negative relation isprimarily driven by the strong positive correlationbetween market prices and the equity share surroundingthe two structural breaks in U.S. economic activities: theGreat Depression (1929–1931) and the oil crisis(1973–1974) periods. Their arguments are based on fourobservations. First, when they exclude five years of theirsample—the Great Depression of 1929–1931 and the oil

yBurch et al. (2004) compare the stock performance of two types of equity offerings, rights offers and firm commitment seasonedequity offerings, using a unique data set from the 1930s and 1940s. They find that abnormal returns for firms electing the firmcommitment methods were significantly negative over the year following the offer, while those for firms using rights were not, whichsuggests that firm commitments were timed, while rights offers were not.zThere is extensive accounting literature documenting the relations among financial disclosure, asymmetric information, and capitalmarket. For details, see Healy and Palepu (2001).

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crisis of 1973–1974—because they were unpredictable,

they find the in-sample predictive ability of the equity

share disappears. Second, the equity share has no

predictive power in the post-Nasdaq (i.e. post-1975)

sample period. Third, contrary to the prediction of the

managerial timing hypothesis, there is no substitution of

debt for equity in anticipation of high future returns, and

instead both debt and equity tend to commove and are

positively related to current market conditions. Fourth,

the equity share has no out-of-sample predictability in

that a model using the equity share as a predictor of

future market returns does not outperform a naı̈ve model

that includes only a constant term.In response to the critique of Butler et al. (2005), Baker

et al. (2006) point out that aggregate pseudo market

timing is similar to the small-sample bias studied by

Stambaugh (1986, 1999) and othersy and demonstrate

that pseudo market timing bias is much too small to

account for the observed predictive power of the new

equity issues.Given mixed evidence on the relation between equity

issues and aggregate stock returns, two hypotheses have

been proposed as explanations: the managerial timing and

pseudo market timing hypotheses. Lucas and McDonald

(1990) have developed an asymmetric information model

in which firms postpone their equity issue if they know

they are currently undervalued. Under information asym-

metry, overvalued firms issue equities immediately,

whereas undervalued firms will wait to issue until the

undervaluation is corrected. Therefore, the price will fall

following equity issuance. Their model also explains the

fact that equity issuance tends to follow general increases

in the market. We propose an alternative information

asymmetry model in the following section.Ritter and Welch (2002) suggest that a plausible semi-

rational theory without asymmetric information can also

explain a negative relation in issuing activity: entrepre-

neurs’ sense of enterprise value derives more from their

internal perspective, their day-to-day involvement with

the underlying business fundamentals, and less so from

the public stock market. Sudden changes in the value of

publicly traded firms are not as quickly absorbed into the

private sense of value held by entrepreneurs. Thus,

entrepreneurs adjust their valuation with a lag. As a

result, even if the market price is driven by irrational

public sentiment or the entrepreneur’s price is driven by

irrational private sentiment, entrepreneurs are more

inclined to sell shares after valuations in the public

markets have increased.On the other hand, Schultz (2003) has proposed a

pseudo market timing hypothesis. He argues that since

firms are more likely to issue equity after their stock

prices have appreciated, there is a spurious ex-post

relation between a firm’s equity issues and its equity

price in an efficient market. Therefore, when firms can

receive a higher price for their shares, they are more likely

to issue stocks even if the market is efficient and managers

have no timing ability. The pseudo market timing implies

that the issuing firms do not know prices are at a peak

when they issue stocks. If prices keep rising, even more

offerings would be forthcoming until prices eventually fall

and offerings dry up.

3. Information asymmetry, return process, and equity

issue decisions

In this section, we provide a simple, parsimonious time-

series model in which there is information asymmetry

between managers and outside investors. In such a case,

equity issue decisions may contain (or convey) new

information about future stock returns. In fact, some

equity issue decisions may be information events (i.e.

forward-looking), while others may be non-information

events (i.e. backward-looking) with respect to future stock

returns. The equity issue decision will be related to future

stock returns when it is an informative event under

information asymmetry. The idea is that, although

managers and outside investors observe the same finan-

cial variables such as current and past stock returns and

equity issues, investors may not recover all the informa-

tion which managers use in setting equity issues.z Our

model is very useful because it provides a regression

model that tests the predictive power of equity issues

under information asymmetry.Here we utilize a theorem in time-series econometrics

that states that any time-series process has both invertible

and non-invertible representations (see Fuller (1976,

pp. 64–66, theorem 2.6.4)). Although stock returns may

follow a general ARMA process, for expositional sim-

plicity we assume that outside investors, observing current

and past stock returns, infer a first-order moving average,

MA(1), process of the returns:x

Rt ¼ ð1� �LÞut, j�j5 1:0, ð1Þ

where Rt is the stock return at time t, L is the lag (or

backshift) operator (i.e. LnRt¼Rt�n), and ut is white noise

with var(ut)¼ �2u . The autocovariance functions (ACFs)

for this return process are

var Rtð Þ ¼ 1þ �2� �

�2u , cov Rt,Rt�1ð Þ ¼ ���2u ,

cov Rt,Rt�kð Þ ¼ 0, for k � 2: ð2Þ

yA recent literature discusses alternative econometric methods for correcting the Stambaugh bias and conducting valid inference(Cavangh et al. 1995, Lewellen 2004, Torous et al. 2004, Ang and Bekaert 2007, Campbell and Yogo 2006, Jansson and Moreira2006, Polk et al. 2006).zWe can capture this intuition in a time-series concept of the non-invertibility of the moving average representation (see Box andJenkins (1976, p. 69) and Granger and Newbold (1986, p. 145)).xFor expositional simplicity, we use an MA(1) model of the return process. Any higher-order representation of returns (e.g., Khiland Lee (2002)) yields the same dynamic relations with more complicated computations.

Equity issues and aggregate market returns 283

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On the other hand, suppose that managers, observingthe same current and past stock returns, infer thefollowing MA(1) process of the returns:y

Rt ¼ ð1� ��1LÞvt, ð3Þ

where vt is white noise with var(vt)¼ �2v . The ACFs for

this return process are

var Rtð Þ ¼ 1þ ��2� �

�2v , cov Rt,Rt�1ð Þ ¼ ���1�2v ,

cov Rt,Rt�kð Þ ¼ 0, for k � 2: ð4Þ

Note that if we set �2v ¼ �2�2u , then the ACFs in (2) and

(4) are identical. Since the return process can be identifiedin practice only by the observed ACFs, the identicalACFs imply that stock return processes in (1) and (3)represent the same returns process. That is, for a givenreturn process, investors and managers may infer differ-ent MA(1) processes.z In addition, �2v is smaller than �2ubecause �2v ¼ �

2�2u and |�|51.0. This means that thevariance of the one-step-ahead forecast error of the returnprocess in (3) by managers would be smaller than thecorresponding variance of the return process in (1) byinvestors. However, unlike the ut process, the vt processcannot be recovered by outside investors from theinformation about current and past values of stockreturns.x In sum, although both managers and investorsobserve the same (current and past) returns, underinformation asymmetry managers with a larger informa-tion set ��t ¼ {Rt�j, vt�j, ut�j, for j� 0} can forecast futurereturns better than investors with a smaller informationset �t¼ {Rt�j, ut�j, for j� 0}.

We can obtain an important alternative insight bycomparing the corresponding autoregressive representa-tions (ARR) of the moving average representations(MAR) of stock return processes {Rt} in (1) and (3):

ut ¼ ð1� �LÞ�1Rt ¼

X1j¼0

�jRt�j,

vt ¼ ð1� ��1LÞ�1Rt ¼ �ð�L

�1Þð1� �L�1Þ�1Rt

¼ �X1j¼1

�jRtþj: ð5Þ

Note that the innovations {ut} in the investors’ return

process are backward-looking, whereas the innovations

{vt} in the managers’ return process are forward-looking.{How is this information asymmetry between managers

and outside investors related to the dynamic relation

between new equity issues and stock returns (i.e. the

predictive power of equity issues)? Suppose that managers

have an informational advantage in that they can forecast

the firm’s future prospects better than outside investors

by observing vt. If managers use this information in their

equity issue decisions, new equity issues (DEt) will be a

function of the innovation vt that they observe but

outsiders do not:k

DEt ¼ f ðvtÞ ¼X1i¼0

ð�iLiÞvt

¼X1i¼0

�ivt�i, withX1i¼0

�2i 51: ð6Þ

Then, by using vt in (5), equity issue and stock return

processes will be related as follows:

DEt ¼X1i¼0

ð�iLiÞvt ¼

X1i¼0

ð�iLiÞfð1� ��1LÞ�1Rtg

¼X1i¼0

ð�iLiÞ �

X1j¼1

�jRtþj

!¼ �

X1j¼�1

�jRt�j, ð7Þ

where �j for j¼�1, . . . ,�2,�1, 0, 1, 2, . . . ,1 is a function

of �i and �j. That is, these equity issues will be a linear

combination of future, current, and past returns; thus,

they will be forward-looking. In practice, since managers

do not have perfect foresights, (7) will be

DEt ¼ �X1j¼0

�jRt�j þ Et �X�1j¼�1

�jRt�j

" #: ð8Þ

In contrast, suppose that managers do not have

an informational advantage or they simply do not

make equity issue decisions based on their

yThe � parameter in equation (3) should be identical to that in equation (1) to achieve the identical return process.zThe return process in (1) with the innovation ut is an invertible MAR because the root of the determinant of the MAR of Rt isgreater than 1 (i.e. det[1��z]¼ 0, for z¼ ��1). However, the return process with the innovations vt in (3) is a non-invertible MARbecause the root of the determinant is less than 1 (i.e. det[1���1z]¼ 0, for z¼ �).xThis is because the process is not invertible.{In practice, it would be more practical to posit that vt ¼ Et½�

P1j¼1 �

jRtþj�: The innovations {ut} are represented by a squaresummable linear combination of current and past values of Rt’s (i.e. ut lies in the space spanned by current and lagged Rt’s). However,the innovations {vt} are represented by a square summable linear combination of future values of Rt’s (i.e. vt lies in the space spannedby future Rt’s). This is because if we solve (3) backwards, the right-hand side is not square summablekIn fact, as an empirical measure of new equity issues (DEt), we use the log-difference in equity issues scaled by the CPI inregressions because the level of equity issues Et is non-stationary. The summary statistics for DE and equity for our sample period of1927–2003 are as follows:

Variable Mean Variance Standard error t-Stat (mean 0) Skewness Kurtosis (excess) Jarque–Bera

DE 0.0258 0.5011 0.7079 0.3177 �0.8756 2.9398 37.0800(sign. level) (0.7516) (0.0023) (0.0000) (0.0000)Equity 4.7700 1.9125 1.3829 30.2662 �0.8164 0.5603 9.5612(sign. level) (0.0000) (0.0041) (0.3378) (0.0084)

As shown above, the sample mean of changes in equity (DE) for the sample period of 76 years is 0.026, but it is not significantlydifferent from zero. Further, it is negatively (i.e. left) skewed and shows significant fat tails (i.e. leptokurtic). As a result, it shows asignificant departure from normality.

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informational advantage. Then the equity issues will be

a function of the innovation that outside investors

observe, ut:

DEt ¼ f ðutÞ ¼X1i¼0

ð�iLiÞut

¼X1i¼0

�i ut�1, withX1i¼0

�2i 51: ð9Þ

Then, by using ut in (5), equity issue and stock return

processes will be related as follows:

DEt ¼X1i¼0

ð�iLiÞ ut ¼

X1i¼0

ð�iLiÞð1� �LÞ�1Rt

¼X1i¼0

ð�iLiÞX1j¼0

�jRt�j

( )¼X1k¼0

�kRt�k, ð10Þ

where �k for k¼ 0,1,2, . . . ,1 is a function of �i and �j.

That is, in this case, the equity issues will only reflect the

past and current returns and will not be related to future

returns; thus, they will be backward-looking. To summa-

rize, we have shown that, under information asymmetry,

informative equity issues will be related to not only past

and present returns but also future returns. In contrast, in

the absence of information asymmetry, non-informative

equity issues will not be related to future returns.A practical question is how we distinguish between the

two—informative and non-informative—types of equity

issues. When a firm issues new equity, if it contains new

information about future prospects of the firm (i.e. stock

returns) that is not contained in the current and past

values of returns and equity issues, it is an informative

(i.e. forward-looking) equity issue and is related to future

returns. Otherwise, it is a non-informative (i.e. backward-

looking) equity issue. We can empirically test whether

financing decisions are informative or not by using the

following proposition.The equivalence of the two-sided regression in (8) with

Granger-causality has been established by Sims (1972,

theorem 2), which we restate in our context.

Proposition 1: Consider the following two-sided

regression:

DEt ¼ �þXmj¼�m

�jRt�j þ "t, ð11Þ

where E("t, Rt�j)¼ 0 for all j (¼�m, . . . ,�1, 0, 1, . . . ,m). If

the null hypothesis that all the coefficients of future returns

are zero (i.e. �j¼ 0 for all j50) is rejected, then DEt

Granger-causes Rt. Equivalently, this Granger-causality

can be tested by the null hypothesis that �j¼ 0 for all j40

based on the following regression:

Rt ¼ �þXmj¼1

�jRt�j þXmj¼1

�j DEt�j þ t: ð12Þ

That is, we can use the usual Granger-causality test asa means of testing the predictability of equity issues formarket returns, and the finding of the predictive power ofequity issues can be interpreted based on informationasymmetry. An intuition behind this test is that includinglagged values of market returns helps us to control forpotential feedback in equity issue decisions. This modelcan be used for any other predictability tests of financingvariables. It is also interesting to note that Lucas andMcDonald (1990) developed an asymmetric informationmodel in an attempt to explain the potential predictabilityof equity issues, although their approach is differentfrom ours.

4. Preliminary findings

4.1. Data

For aggregate equity and debt issues, we use the sameannual data as Baker and Wurgler (2000) and Butler et al.(2005). These data are obtained from Jeffrey Wurgler’sWeb page.y The sample period is from 1927 to 2003.Following Baker and Wurgler (2000) and Butler et al.(2005), we construct the equity share in total new issues(i.e. S ratio) as St¼ et/(etþ dt), where et and dt arenominal (dollar) values of aggregate new equity issues andnew debt issues at time t, respectively.

We divide the nominal values of equity issues (et) anddebt issues (dt) by the CPI series to obtain real values, andthen take a log to obtain the log of the real values ofequity issues (Et) and debt issues (Dt). That is,Et¼ log(et/CPIt), and Dt¼ log(dt/CPIt). Therefore, thefirst differences in these variables are growth rates in realvalues of equity issues and debt issues, which we use forDEt and DDt, respectively. As a measure of marketreturns, we use the S&P 500 index returns. The nominalvalues of the S&P 500 index returns are also convertedinto real market returns (Rt) using CPI inflation. We useCRSP value- and equal-weighted market returns asalternative measures of market returns in section 7 as atest of the robustness of our results.

4.2. Replication of Butler et al.’s (2005) findings

In this section, as a preliminary step, we replicate theresults of Butler et al. (2005) to ensure that their resultsare reproduced in our model. Previous studies use theregression of current stock market returns on one-periodlagged equity shares (i.e. S ratio) to investigate thepredictive power of the equity share for stock returns.We believe this regression is not very complete for thepurpose of prediction and distinguishing between the twohypotheses: the managerial timing and pseudo timinghypotheses. A better regression would be to examinewhether the S ratio has any additional predictive power inthe presence of past returns, which leads us to conducta Granger-causality test as discussed in section 3.

yhttp://pages.stern.nyu.edu/�jwurgler/data/equity%20share.xls.

Equity issues and aggregate market returns 285

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Including lagged values of market returns amounts to

controlling for potential feedback (either momentum or a

contrarian strategy) in equity or debt issue decisions.In the first part of panel A of table 1, we test whether

the equity share (S) has additional predictive power for

market returns (R) in the presence of two lagged values of

market returns for the sample period of 1928–2003. We

include two lagged values of S ratios to allow for some

time lags in its predictive power. Considering both the

Akaike and Schwarz information criteria, we choose to

include two lagged values in the regression. We observe

that the null hypothesis that the coefficients of S ratios are

zero as a group (i.e. S ratios do not Granger-cause stock

market returns) is rejected at the conventional significance

level of 10% based on the F-test. In particular, we observe

that the predictive power of the S ratio is not from the

one-period lagged S ratio but from the two-period lagged

S ratio, and its effect on market return is negative, as

expected from previous studies.In the second part of panel A of table 1, we test whether

the S ratio has additional predictive power for market

returns for the modified sample. Following Butler et al.

(2005), we exclude the Great Depression period

(1927–1929) and replace the data during the oil crisis

period of 1973–1974 with their interpolated data, and use

this new sample as the modified sample.y Using the

modified sample, we find that the null hypothesis that the

S ratios do not Granger-cause stock market returns is not

rejected at the conventional significance level. The findingin panel A of table 1 confirms Butler et al.’s (2005) claimagainst the managerial timing hypothesis but in favor ofthe pseudo timing hypothesis.

In panel B, we further examine whether market returns(R) have predictive power for the S ratio for either theoriginal sample or the modified sample. Overall, we findthat market returns do not have predictive power for theS ratio at the conventional significance level of 10% basedon F-tests using either sample. Combining findings inpanels A and B of table 1, the S ratio predicts futuremarket returns but is not predicted by past market returnsfor the original sample, yet there is no predictive relationbetween the two variables for the modified sample.

5. Empirical results

5.1. Bivariate models

One of our premises in this paper is that the equity share innew equity and debt issues alone may not capture all theinformation managers use in their equity (or debt) issuedecisions. To capture all the information conveyed by newequity and debt issues, we use these two variablesseparately in regressions. One potential problem is that ifequity issues (or debt issues) and stock market prices arecointegrated (i.e. commove) over time, regressions usingthe first differences in these variables would be

Table 1. Stock returns and S ratio. This table reports the results of the regressions between the (real) S&P 500 index returns Rt

and the S ratio (¼e/(eþ d)), where e and d are nominal values of aggregate new equity and debt issues, respectively. Panel A is basedon the regression equation

Rt ¼ �þX2i¼1

�iRt�i þX2i¼1

�iSt�i þ et, ð1Þ

and panel B is based on the regression equation

St ¼ �þX2i¼1

�iRt�i þX2i¼1

�iSt�i þ et: ð2Þ

The table reports estimates of coefficients of the lagged returns and S ratios, t-statistics (in parentheses), adjusted R2 values,F-statistics and p-values. In panel A, the F-test is for the null hypothesis that coefficients of lagged S ratios are all zero (i.e. the Sratio does not Granger-cause the stock returns). In panel B, the F-test is for the null hypothesis that coefficients of lagged returns areall zero (i.e. stock returns do not Granger-cause the S ratio). Original sample is from 1928 to 2003 while the modified sample is from

1930 to 2003 with the oil shock period of 1973–1974 interpolated.

Sample Rt Constant Rt�1 Rt�2 St�1 St�2 R2 F(2, 69)

Panel A: Market return equation (1) with dependent variable, Rt

Original sample 0.110�� 0.030 �0.078 0.099 �0.544�� 0.057 2.920�(2.09) (0.25) (�0.65) (0.39) (�2.23) 0.061

Modified sample 0.114� 0.004 �0.069 �0.294 �0.152 �0.021 1.140(1.89) (0.03) (�0.56) (�0.96) (�0.51) 0.326

Panel B: S ratio equation (2) with dependent variable, St

Original sample 0.120��� �0.074 �0.055 0.354��� 0.018 0.150 1.816(5.32) (�1.42) (�1.06) (3.26) (0.17) 0.170

Modified sample 0.080��� �0.061 �0.152 0.581��� 0.026 0.350 2.302(3.49) (�1.30) (�1.72) (5.04) (0.23) 0.108

���, �� and � indicate statistical significance at the 0.001, 0.01 and 0.05 levels, respectively.

yFor example, x(1973)¼ x(1972)þ (1/3) � [x(1975)�x(1972)] and x(1974)¼ x(1972)þ (2/3) � [x(1975)�x(1972)].

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misspecified, and we need to employ an error

correction model by including a one-period lagged coin-

tegration term.Therefore, as a preliminary step, we test for a unit root in

Pt (the logged real S&P index prices), Et (the logged real

value of equity issues), andDt (the logged real value of debt

issues), and their corresponding series in the modified

sample (i.e. mPt, mEt, and mDt). We implement the

Augmented Dickey-Fuller test and the Phillips-Perron

test for the null hypothesis of a unit root in a variable. We

also implement the KPSS (Kwiatkowski et al. 1992) tests

for the null of stationarity. Autocorrelations and the unit

root test results for these variables are presented in panels

A and B of table 2. Their autocorrelations decay very

slowly over time (panel A), and the null hypothesis that

each series has a unit root is not rejected at the conven-

tional significance level of 10% regardless of whether we

employ the Augmented Dickey–Fuller regression test or

the Phillips–Perron test (panel B of table 2). That is, Pt, Et,

and Dt (mPt, mEt, and mDt) series are non-stationary, and

we use their first differences in regressions.Then, by regressing Pt on contemporaneous values of Et

(Dt), we obtain the residual series res1 (res2), which is a

spread between the logged real stock market price and

logged real value of equity (debt) issues. The autocorrela-

tions of these residual series, res1 and res2, decay relatively

fast (panel A), and tests of a unit root in these series show

that they are both stationary, indicating that the Pt and Et

(Dt) series are cointegrated of order one and one (panels B

and C of table 2). This indicates that Pt and Et (Dt) series

tend to comove over time, implying that corporate

managers tend to increase equity and debt issues when

the stock market is strong. This finding is consistent with a

theoretical model by Dittmar and Thakor (2007). In

addition, to take into account the comovement, an error

correction model (ECM) with a lagged residual term (e.g.,

res1t�1 or res2t�1) is desirable in examining dynamic

relations between these variables. We also find that the

equity share (i.e. S ratio) is stationary (panels A and B).

These findings are robust whether we use the original

sample or the modified sample.In panel A of table 3, we regress current real market

returns (Rt) on two lagged values of changes in the logged

real value of new equity issues (i.e. growth rates in the real

value of equity issues,DEt�1 andDEt�2) andone lagged res1

term (res1t�1), as well as two lagged values of market

returns (Rt�1 and Rt�2), using the original and modified

samples, respectively. We observe that the null hypothesisthat new equity issues (including the res1 term) do notGranger-cause market returns is rejected at the conven-tional significance level, not only for the original sample butalso for the modified sample. In particular, it is noted thatthe two-period lagged equity issues (DEt�2) have a signif-icant negative effect on the market return in the modifiedsample, which implies that managers increase equity issuesin anticipation of lower market returns. This is in contrast

to the findings of Butler et al. (2005).yIn panel B of table 3, we observe that new debt issues

(DDt) also Granger-cause market returns (Rt) in both theoriginal and modified samples. In particular, the effect ofnew debt issues on market returns is significantly positivein one period, which implies that managers increase debtissues in anticipation of higher market returns. It is alsonoted that the one-period lagged cointegration term res2(res2t�1) is also significant with a negative coefficient atthe conventional significance level of 10%.z

These findings indicate that the absence of the predic-tive power of the equity share in new equity and debt

issues for market returns documented by Butler et al.

(2005) (or replicated in our table 1) is partly due to the use

of the S ratio rather than equity issues and debt issues

separately, which supports our premise. In particular, the

comparison of R2 values in tables 1 and 3 indicates that

the explanatory power of the S ratio is relatively weak

(e.g., from �0.021 to 0.057) compared with that of either

equity issues DEt (e.g., from 0.091 to 0.152) or debt issues

DDt (e.g., 0.159). This indicates that DEt and DDt

separately span a larger information set than the S ratio

does (see also section 6.1). These findings as a whole

suggest that the managerial timing hypothesis is not

rejected when we use appropriate managers’ decision

variables as regressors (or predictors). We discuss in

section 6.1 that using the S ratio amounts to imposing a

particular cointegration relation on Et and Dt.While our main focus is on the predictive power of new

equity (or debt) issues, we further examine whether new

equity (or debt) issue decisions (DEt (DDt)) are in responseto past market returns, which is already addressed tosome extent by including lagged values of market returnsin the regressions. For this purpose, we regress new equity(debt) issues (DEt (DDt)) on past values of market returns(Rt�j) in the presence of past values of new equity (debt)issues (DEt�j (DDt�j)). In panel C of table 3, we find thatpast market returns are not significant as a group in the

yIn this regression, the timing of the dynamic effects cannot be uniquely identified by autoregressive (AR) coefficients because ARvariables are serially correlated, and residual terms (i.e. the cointegration terms) should also be taken into account. Therefore, wehave to exercise caution in interpreting AR coefficients as representing the timing of the dynamic effects. In panel A of table 3, wefind that the one-period lagged equity issues have a positive effect on market returns in the original sample. This provides evidencethat the original sample may not be appropriate to use for the prediction regression due to structural breaks, as Butler et al. (2005)point out.zSince the res2 term is calculated by the regressionPt¼�2.1657þ 0.4255Dtþ res2t, R

2¼ 0.7268, D.W.¼ 0.4697 (using the original sample),

(�11.32) (14.26)Pt¼�2.1959þ 0.4287Dtþ res2t, R

2¼ 0.7363, D.W.¼ 0.4224 (using the modified sample),

(�11.14) (14.12)the negative coefficient on the res2 term indicates a positive effect of the level of one-period lagged debt issues on market returns,which is consistent with a significant positive coefficient of DDt�1.

Equity issues and aggregate market returns 287

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equity issue regression based on F-tests using either the

original sample or the modified sample. This indicates

that the equity issue decision is not really in response to

past market returns. Combined with the results in panel A

of table 3, these indicate that the managers’ decision to

increase equity issues is in anticipation of future

declines in market returns, but is not in response to past

market returns. This supports the managerial timing

hypothesis for new equity issues.Panel D of table 3 exhibits a somewhat different picture

regarding new debt issues. Past market returns are

significant as a group based on F-tests in the debt issue

Table 2. Time-series properties of data. This table reports the times-series properties of variables used in the paper. S ratio, E, D,and R denote the equity share in new equity and debt issues (¼e/(eþ d), where e and d are nominal values of aggregate new equityand debt issues), logged real values of equity issues and debt issues, and the real S&P 500 returns, respectively. Res1 (res2) denote theresiduals from the cotemporaneous regression of P (¼the logged real S&P 500 prices) on E (D) (¼the logged real values of equity(debt) issues). S ratio (mS ratio), E (mE), D (mD), P (mP), res1 (mres1), and res2 (mres2) are variables based on the original(modified) sample. Original sample is from 1928 to 2003 while the modified sample is from 1930 to 2003 with the oil shock period of1973–1974 interpolated. Panel A reports autocorrelations of variables using the original sample up to 12 lags. Panel B reports the

results of the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests. Panel C reports the results of the KPSS test.

Series Lag¼ 1 2 3 4 5 6 8 10 12

Panel A: AutocorrelationS ratio 0.481 0.200 0.093 0.290 0.032 �0.157 0.036 �0.028 �0.117E 0.869 0.768 0.684 0.671 0.633 0.642 0.639 0.601 0.457D 0.957 0.893 0.860 0.855 0.845 0.813 0.791 0.784 0.679P 0.957 0.906 0.867 0.825 0.794 0.764 0.680 0.572 0.440res1 0.818 0.749 0.642 0.528 0.379 0.249 0.006 �0.209 �0.407res2 0.762 0.608 0.611 0.544 0.413 0.242 0.066 �0.155 �0.366

Augmented Dickey–Fuller tests: ADF (�) Phillips–Perron tests: PP (Z(tb))

Series Lag¼ 1 2 3 6 Lag¼ 1 2 3 6

Panel B: Unit root testsS ratio �4.806��� �5.684��� �3.391�� �2.851� �5.09��� �5.024��� �4.904��� �5.025���mS ratio �3.682��� �3.78��� �3.832��� �3.307�� �4.764��� �4.842��� �4.898��� �5.023���E �1.852 �1.736 �1.079 �0.827 �2.06 �2.039 �1.908 �1.830mE �1.633 �1.547 �1.151 �0.973 �1.993 �1.983 �1.906 �1.885D �1.011 0.042 0.339 0.177 �0.681 �0.529 �0.252 �0.049mD �0.878 0.003 0.07 �0.112 �0.112 �0.444 �0.242 0.037P �0.995 �0.443 �0.658 �0.852 �1.073 �1.037 �1.035 �0.953mP �0.608 �0.57 �1.026 �1.026 �0.791 �0.769 �0.806 �0.787res1 �2.191 �2.188 �2.079 �2.653 �2.714 �2.757 �2.824 �3.045�mres1 �1.429 �1.848 �1.775 �3.234� �2.485 �2.505 �2.598 �2.855res2 �2.722 �1.756 �1.778 �2.967 �3.336� �3.212� �3.279� �3.590��mres2 �2.399 �1.793 �1.841 �2.889 �2.773 �2.614 �2.702 �2.952

Panel C: Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) testsLag res1 res2 mres1 mres2

1 0.509�� 0.250 0.375� 0.2612 0.362� 0.182 0.262 0.1873 0.288 0.144 0.206 0.1874 0.244 0.120 0.172 0.122

Lag Equity DE Debt DD SP DSP

4 1.324��� 0.076 1.496��� 0.197 1.202��� 0.067

Notes: �, ��, and ��� represent 10%, 5% and 1% significant levels, respectively. Res1, res2, mres1, and mres2 are calculated as follows. Using the

original sample:

Pt¼�1.2966þ 0.3768Etþ res1t. R2¼ 0.5915, D.W.¼ 0.3546,

(�7.30) (10.74)

Pt¼�2.1657þ 0.4255Dtþ res2t. R2¼ 0.7268, D.W.¼ 0.4697.

(�11.32) (14.26)

Using the modified sample:

mPt¼�1.3121þ 0.3818mEtþmres1t. R2¼ 0.6161, D.W.¼ 0.3416,

(�7.33) (10.72)

mPt¼�2.1959þ 0.4287mDtþmres2t. R2¼ 0.7363, D.W.¼ 0.4224.

(�11.14) (14.12)

For the S ratio, E, D, and P for both original and modified samples, critical values of t-statistics for both � and Z(tb) with 100 observations are:

10%, �2.58; 5%, �2.89; and 1%, �3.51 (Fuller (1976, tables 8.5.1 and 8.5.2, pp. 371–373)). The details of the adjusted t-statistics Z(tb) can be found

in the work of Phillips and Perron (1988). For the ADF and PP unit root tests of res1 and res2 for both original and modified samples, critical values

with 100 (200) observations are 10%, �3.03(�3.02); 5%, �3.37(�3.37); and 1%, �4.07(�4.00), respectively (see Engle and Yoo (1987, table),

p. 157). For KPSS tests, critical values are 10%, 0.347; 5%, 0.463; and 1%, 0.739, respectively.

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regression, and their effect is positive, which implies that

managers tend to issue debts following market price

increases. Combined with the results in panel B of table 3,

these results indicate that debt issues predict future

market returns and are also affected by past market

returns (i.e. there is a feedback relation between new debt

issues, DDt, and market returns, Rt). However, this is still

consistent with the managerial timing hypothesis for debtissues.

5.2. Trivariate models

5.2.1. Trivariate cointegration. So far we have investi-gated the bivariate relation between market returns and

Table 3. Bivariate model: stock returns, new equity issues and new debt issues. This table reports the results of the regressionsbetween the real S&P 500 returns Rt and lagged changes in logged real value of equity issues DEt�j (debt issues DDt�j). Panels A andB are based on the equation

Rt ¼ �þX2i¼1

�iRt�i þX2i¼1

�iXt�i þ ’ rest�1 þ et, ð1Þ

and panels C and D are based on the equation

Xt ¼ �þX2i¼1

�iRt�i þX2i¼1

�iXt�i þ ’ rest�1 þ et, ð2Þ

.

Rt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3,68)

Panel A: Using equity issues in equation (1): Dependent variable, Rt

Original sample 0.019 �0.029 �0.050 0.099��� �0.044 �0.032 0.152 5.084���(0.90) (�0.20) (�0.41) (2.74) (�1.15) (�0.61) 0.003

Modified sample 0.0267 0.081 0.052 0.040 �0.093�� �0.059 0.091 3.817��(1.31) (0.58) (0.42) (0.95) (�2.40) (�1.17) 0.014

Rt Constant Rt�1 Rt�2 DDt�1 DDt�2 res2t�1 R2 F(3,68)

Panel B: Using debt issues in equation (1): Dependent variable, Rt

Original sample 0.017 0.095 �0.259�� 0.174��� �0.055 �0.111� 0.159 5.308���(0.83) (0.81) (�2.25) (2.96) (�0.97) (�1.72) 0.002

Modified sample 0.015 0.033 �0.127 0.188��� �0.017 �0.098� 0.159 5.829���(0.73) (0.27) (�1.09) (3.31) (�0.32) (�1.68) 0.001

DEt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3,68)

Panel C: Using equity issues in equation (2): Dependent variable, DEt

Original sample 0.031 �0.500 �0.525 �0.024 0.056 0.334 0.000 1.101(0.37) (�0.87) (�1.08) (�0.16) (0.36) (1.55) 0.355

Modified sample 0.120� 0.066 �0.376 �0.111 �0.224� 0.157 0.042 0.609(1.84) (0.15) (�0.95) (�0.83) (�1.83) (0.97) 0.612

DDt Constant Rt�1 Rt�2 DDt�1 DDt�2 res2t�1 R2 F(3,68)

Panel D: Using debt issues in equation (2): Dependent variable, DDt

Original sample 0.046 0.914��� �0.155 0.333��� �0.563��� 0.058 0.414 8.282���(1.27) (4.52) (�0.78) (3.29) (�5.79) (0.51) 0.000

Modified sample 0.074�� 0.729��� �0.060 0.261�� �0.527��� 0.095 0.360 5.216���(2.06) (3.36) (�0.28) (2.59) (�5.43) (0.90) 0.003

where X is DE (DD) in panels A and C (B and D) and res is res1 (res2) in panels A and C (B and D), respectively. The table reports the coefficients of

the lagged R, DE, (DD) and residuals, t-statistics (in parentheses), adjusted R2, F-statistics and p-values. In panel A (B), the F-test is for the null

hypothesis that coefficients of lagged equity (debt) issues and a lagged residual term are all zero (i.e. DE (DD) does not Granger-cause stock market

returns). In panel C (D), the F-test is for the null hypothesis that coefficients of lagged stock returns and a lagged residual term are all zero (i.e. stock

returns do not Granger-cause DE (DD)). The original sample is from 1928 to 2003 while the modified sample is from 1930 to 2003 with the oil shock

period of 1973–1974 interpolated.

Notes: ���, �� and � indicate statistical significance at the 0.001, 0.01 and 0.05 levels, respectively. Res1, res2, mres1, and mres2 are calculated as

follows. Using the original sample:

Pt¼�1.2966þ 0.3768Etþ res1t. R2¼ 0.5915, D.W.¼ 0.3546,

(�7.30) (10.74)

Pt¼�2.1657þ 0.4255Dtþ res2t. R2¼ 0.7268, D.W.¼ 0.4697.

(�11.32) (14.26)

Using the modified sample:

mPt¼�1.3121þ 0.3818mEtþmres1t. R2¼ 0.6161, D.W.¼ 0.3416,

(�7.33) (10.72)

mPt¼�2.1959þ 0.4287mDtþmres2t. R2¼ 0.7363, D.W.¼ 0.4224.

(�11.14) (14.12)

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new equity (debt) issues. Our finding of the bivariatecointegrations between Pt and Et and between Pt and Dt

suggests that there may be a trivariate cointegrationrelation among Pt, Et, and Dt. Therefore, it is worthexamining the dynamic relations among these variables ina unified model. If we find a trivariate cointegration, weshould take the cointegration relation into account inexamining the predictive power of new equity and debtissues to avoid misspecification of the model.

For this purpose, we test for possible cointegrationamong the three variables using either the original sampleor the modified sample by following the procedure ofJohansen (1988, 1991). In panels A and B of table 4, wepresent the three-variable—Pt, Et, and Dt—cointegrationtests based on the maximal eigen-value test and the tracetest of Johansen using the original and modified samples,respectively. The term r denotes the number of linearlyindependent cointegrating vectors. In both panels, thenull of zero cointegration vector (r¼ 0) is rejected,whereas the null of either less than one cointegrationvector (r�1) or less than two cointegration vectors (r�2)is not rejected at the conventional significance level of10%.y To confirm this, we follow Johansen and Juselius(1992) and further examine the determination of thenumber of cointegrating vectors based on a formal test.Using three eigen-values, we compute three possiblecointegration terms: S1 (mS1), S2 (mS2), and S3 (mS3)for the original sample (modified sample).Autocorrelations in panel C of table 4 suggests S1(mS1) appears to be stationary, S2 (mS2) seems to beeither stationary or non-stationary, but S3 (mS3) appearsto be non-stationary. Unit root tests and stationary testsin panels D and E show that S1 (mS1) is stationary, butS2 (mS2) and S3 (mS3) are non-stationary. Overall, thetests indicate that there is one cointegration vector.Further unit root tests for possibly as many as threecointegration terms confirm that there is indeed onecointegration term, which we call S1. This indicates thatPt, Et, and Dt series tend to comove over time, sharing acommon stochastic trend, and that a cointegration termneeds to be taken into account in examining theirdynamic relations.

5.2.2. Trivariate vector error correction model. In panelA.1 of table 5, we report the results of the regression ofcurrent market returns (Rt) on past values of equity issues(DEt�j) and debt issues (DDt�j) as well as the one-periodlagged S1 term (S1t�1) and past values of the returns(Rt�j). Here, to save space, we report the results usingonly the modified sample. The F1 (F2) test is for the nullhypothesis that coefficients of DEt�j (DDt�j) and S1t�1 areall zero. We observe that both lagged equity issues andlagged debt issues are significant as a group in theregression based on F-tests. Furthermore, the sign of the

two-period lagged equity issue term (DEt�2) is signifi-cantly negative, while that of the one-period lagged debtissue term (DDt�1) is significantly positive. This impliesthat new equity issues anticipate a decline in marketreturns, while new debt issues anticipate an increase inmarket returns. This finding is consistent with themanagerial timing hypothesis.

In panel A.2, we report the results of regression of DEt

on Rt�j and DDt�j in the presence of DEt�j. The F1 (F2)test is for the null hypothesis that coefficients of Rt�j

(DDt�j) and S1t�1 are all zero. We find based on F-teststhat both Rt�j and DDt�j are significant as a group in theregression. Furthermore, the sign of Rt�2 is significantlynegative, while that of DDt�1 is significantly positive. Thisimplies that equity issues respond negatively to marketreturn increases with a two-period interval, and positivelyto increases in debt issues. In addition, the cointegrationterm S1 is significant in the regression. Since the S1t�1term is calculated by S1t�1¼ 0.822Pt�1þ 1.904Et�1�

2.087Dt�1, the negative coefficient on the S1t�1 termimplies that equity issues respond negatively to one-period lagged market prices and positively to one-periodlagged debt levels, which is consistent with (and furtherstrengthens) equity issues’ response to past market returnsand past debt issues. The negative response of equityissues to past market returns in the trivariate model is incontrast to the absence of any response of equity issues inthe bivariate model (panel C of table 3).

In panel A.3, we report the results of regressing debtissues (DDt) on past values of market returns (Rt�j) andequity issues (DEt�j) in the presence of past values of debtissues (DDt�j). The F1 (F2) test is for the null hypothesisthat coefficients of Rt�j (DEt�j) and S1t�1 are all zero. Wefind that the lagged market returns are significant as agroup in the regression, but lagged equity issues are not.Furthermore, the sign on the Rt�1 term is significantlypositive, which implies that debt issues respond positivelyto market return increases.z

An additional, important observation we can makefrom the trivariate model analyses compared with thebivariate analyses is that there are feedback relationsbetween equity issues and market returns and betweendebt issues and market returns. Managers increase equityissues in anticipation of lower market returns, whereasthey increase debt issues in anticipation of higher marketreturns, which is consistent with the managerial timinghypothesis. At the same time, in response to marketreturn increases, managers tend to decrease equity issuesbut to increase debt issues. Furthermore, managers’ newequity issue decisions tend to respond to the previousperiod’s equilibrium relation among stock market prices,equity issues, and debt issues (i.e. the S1t�1 term). Thisconfirms the importance of considering the cointegrationrelation among Pt, Et, and Dt. This also indicates that the

yTwo lags are taken in computing the VAR of [Pt, Et, Dt] by considering the Akaike information criterion (1974).zWe further performed some sub-sample analysis based on the trivariate model. Overall results are robust. Since the post-Nasdaq(i.e. post-1975) sample period is presented in table 6 and the result for the period of 1932–2003 are almost the same as those of1930–2003, we do not report results of these two periods in table 5 to save space, and we only report results for the sample period of1932–1972 in panel B of table 5.

290 X. Jiang and B.-S. Lee

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regression model would be misspecified in the absence of

the cointegration term.Butler et al. (2005) point out that managers’ equity

share decisions are primarily in response to the market,

and for the modified sample the equity share does not

have predictive power for future market returns. Here,

once we consider equity issues and debt issues separately,

even for the modified sample, we find that both equity

issues and debt issues have predictive power for future

market returns even in the presence of past values of

market returns. Still we find evidence that new equity

(debt) issue decisions seem to be responding to the

market. However, our finding of the predictability of new

equity (or debt) issues is observed even in the presence of

past values of market returns, which is consistent with the

managerial timing hypothesis.

Table 4. Trivariate cointegration test. The table reports the results of the Johansen cointegration tests for Pt (logged real S&Pindex price), Et (logged real value of equity issues), and Dt (logged real value of debt issues), and their residuals’ autocorrelations,

unit root tests, and KPSS tests.

Eigen-value �-max Trace H0: r p–r �-max90 Trace90

Panel A: Original sample0.270 23.320 30.110 r¼ 0 3 13.390 26.7000.082 6.290 6.790 r� 1 2 10.600 13.3100.007 0.500 0.500 r� 2 1 2.710 2.710

Panel B: Modified sample0.204 16.890 24.260 r¼ 0 3 13.390 26.7000.087 6.770 7.360 r� 1 2 10.600 13.3100.008 0.600 0.600 r� 2 1 2.710 2.710

Series Lag¼ 1 2 3 4 5 6 8 10 12

Panel C: AutocorrelationsS1 0.403 0.189 0.164 0.317 0.005 �0.095 0.071 �0.031 �0.027S2 0.821 0.685 0.611 0.520 0.440 0.307 0.121 �0.022 �0.250S3 0.959 0.921 0.881 0.833 0.791 0.747 0.642 0.530 0.406mS1 0.349 0.293 0.383 0.418 0.057 0.198 0.123 0.310 0.157mS2 0.814 0.696 0.668 0.574 0.472 0.362 0.140 0.010 �0.184mS3 0.968 0.937 0.898 0.863 0.822 0.783 0.706 0.631 0.529

Panel D: Unit root tests

Augmented Dickey–Fuller tests: ADF (�) Phillips–Perron tests: PP (Z(tb))

Series Lag¼ 1 2 3 6 Lag¼ 1 2 3 6

S1 �4.556��� �4.372��� �3.075�� �2.606 �5.558��� �5.533��� �5.509��� �5.715���S2 �2.397 �1.930 �1.940 �2.512 �2.733� �2.669� �2.704� �2.921S3 �0.727 �0.586 �0.658 �1.455 �0.894 �0.901 �0.936 �0.950mS1 �3.430�� �2.684� �2.286 �2.993 �6.464��� �6.48��� �6.527��� �6.792���mS2 �2.600� �2.501 �1.743 �2.458 �2.600� �2.576 �2.615� �2.802mS3 �1.095 �0.578 �0.398 �0.308 �1.095 �0.533 �0.521 �0.523

Pane E: Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) tests

Lag S1 S2 S3 mS1 mS2 mS3

0 0.295 1.854��� 5.085��� 0.815��� 1.376��� 5.37���1 0.211 1.025��� 2.635��� 0.616�� 0.766��� 2.777���2 0.179 0.732�� 1.809��� 0.502�� 0.549�� 1.911���3 0.16 0.579�� 1.396��� 0.419� 0.434� 1.478���4 0.143 0.486�� 1.15��� 0.357� 0.364� 1.219���

Notes: In panel A, r is the number of linearly independent cointegrating vectors. Trace statistic¼�TPn

i¼rþ1 lnð1� �iÞ, �-max

statistic¼�T ln(1��i), where T is the number of observations, n is the dimension of the vector (here n¼ 3), and �i is the ith smallest squared

canonical correlations of Johansen (1988, 1991) or Johansen and Juselius (1990, 1992). Various spreads Si and mSi for i¼ 1, 2, and 3 are calculated

as follows. Using the original sample:

S1t¼ 0.863 �Ptþ 1.774 �Et�2.047 �Dt,S2t¼�2.456 �Ptþ 0.528 �Etþ 0.991 �Dt,

S3t¼�1.619 �Ptþ 0.250 �Et�0.188 �Dt.

Using the modified sample:

mS1t¼ 0.822 �mPtþ 1.904 �mEt�2.087 �mDt,mS2t¼�2.758 �mPtþ 0.308 �mEtþ 1.240 �mDt,

mS3t¼�1.185 �mPtþ 0.238 �mEt�0.402 �mDt.

For the ADF and PP unit root tests of the spreads Si for i¼ 1, 2, and 3 for both original and modified samples, critical values with 100 (200)

observations are 10%, �3.03(�3.02); 5%, �3.37(�3.37); and 1%, �4.07(�4.00), respectively (see Engle and Yoo (1987, table, p. 157). For KPSS

tests, critical values are 10%, 0.347; 5%, 0.463; and 1%, 0.739, respectively.

Equity issues and aggregate market returns 291

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6. Further discussions

6.1. Using equity issues and debt issues separately in thepredictive regression

We confirm that the equity share in new issues (S ratio)

alone does not have predictive power for future returns as

in Butler et al. (2005). However, we also find that when

equity issues and debt issues separately enter the predic-

tive equation, together with an error correction term, they

do have predictive power for future returns. This finding

does not necessarily imply that managers’ decisions to

issue equity are exogenous to the debt issue decision (and

vice versa). This implies that the information set spanned

by both (current and past values of) equity issues and debt

issues is greater than that spanned by (current and past

values of) the equity share in new issues alone in

predicting future returns (see also the comparison of R2

values in tables 1 and 3).Another way to understand this finding is that using

the equity share in new issues may unnecessarily restrict

the information contained separately in equity issues and

debt issues in predicting future returns. The equity share

in new issues (St) restricts the cointegration vectorbetween (log) equity issues and (log) debt issues to[1,�1]. However, as we observe from the trivariatecointegration results in table 4, (log) equity issues and(log) debt issues are cointegrated with (logged) real stockprices with a more general cointegration vector (see alsotable 3).

In addition, we show that using only a one-periodlagged value of the equity share in new issues (St�1) in thepredictive regression does not address either potentialfeedback from past stock returns in equity/debt issuedecisions or information asymmetry. Our new predictiveequation, combined with the separate use of equity issuesand debt issues, contributes to the predictive power ofthese variables.

6.2. On the post-Nasdaq period predictability

An argument proposed by Butler et al. (2005) against themanagerial timing hypothesis is that the equity share doesnot have predictive power for future market returns in thepost-Nasdaq period (i.e. after 1975). In table 6, we

Table 5. Trivariate model: Market returns, new equity issues, and new debt issues. The table reports the dynamic relations amongmarket returns, equity issues and debt issues for the modified sample based on the following trivariate model:

Zt ¼ A0 þ A1Zt�1 þ A2Zt�2 þ ’S1t�1 þ et,

where Z is a 3�1 vector consisting of real S&P index returns R, lagged changes in logged real values of equity issues DE and debtissues DD. The modified sample is from 1930 to 2003 with the oil shock period of 1973–1974 interpolated. mS1 is the cointegrationresidual based the Johansen cointegration test using the logged real S&P index prices, logged real values of equity issues and debtissues. The table reports coefficients of the lagged returns, DE, (DD) and a lagged S1, t-statistics (in parentheses), adjusted R2,F-statistics and p-values. In panel A, the F1 (F2) test is for the null hypothesis that coefficients of lagged equity (debt) issues DEt�j

(DDt�j) and a lagged S1 are all zero. In panel B, the F1 (F2) test is for the null hypothesis that coefficients of lagged returns (debtissues), Rt�j (DDt�j), and a lagged S1 are all zero. In panel C, the F1 (F2) test is for the null hypothesis that coefficients of lagged

returns (equity issues), Rt�j (DEt�j), and a lagged S1 are all zero.

Constant Rt�1 Rt�2 DEt�1 DEt�2 DDt�1 DDt�2 mS1t�1 R2 F1(3,61) F2(3,61)

Panel A: Sample period 1930–2003Panel A1: Market return equation: Dependent variable, Rt

Rt �0.017 0.066 �0.067 0.021 �0.085�� 0.180�� �0.002 �0.009 0.196 2.600� 3.915��(�0.21) (0.52) (�0.57) (0.39) (�2.17) (2.68) (�0.03) (�0.43) 0.060 0.013

Panel A2: Equity issue equation: Dependent variable, DEt

DEt �0.394� 0.378 �0.647�� �0.099 �0.160 0.715��� �0.382�� �0.129�� 0.451 4.305��� 16.810���(1.88) (1.17) (�2.14) �0.470 (�1.59) (4.17) (�2.36) (�2.30) 0.008 0.000

Panel A3: Debt issue equation: Dependent variable, DDt

DDt 0.1752 0.883��� 0.045 �0.047 �0.099 0.280�� �0.509��� 0.028 0.352 5.095��� 0.706(1.17) (3.81) (0.21) (�0.48) (�1.38) (2.28) (�4.40) (0.67) 0.003 0.552

Constant Rt�1 Rt�2 DEt�1 DEt�2 DDt�1 DDt�2 mS1t�1 R2 F1(3,30) F2(3,30)

Panel B: Sample period 1932–1972Panel B1: Market return equation: Dependent variable, Rt

Rt 0.093 �0.075 �0.183 �0.051 �0.093� 0.227�� 0.077 0.017 0.224 1.403 3.111��(0.76) (�0.41) (�1.09) (�0.71) (�1.96) (2.66) (0.95) (0.55) 0.261 0.041

Panel B2: Equity issue equation: Dependent variable, DEt

DEt �0.261 0.374 �0.911�� �0.160 �0.165 0.942��� �0.329 �0.099 0.592 3.465�� 16.540���(�0.82) (0.79) (�2.10) (�0.86) (�1.35) (4.25) (�1.57) (�1.21) 0.028 0.000

Panel B3: Debt issue equation: Dependent variable, DDt

DDt �0.019 1.145��� 0.395 0.192 �0.701��� 0.062 �0.101 �0.022 0.522 3.826�� 0.997(�0.09) (3.35) (1.27) (1.20) (�4.64) (0.46) (�1.14) (�0.37) 0.020 0.408

���, �� and � indicate statistical significance at the 0.001, 0.01 and 0.05 levels, respectively.

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replicate their finding using our regression method, which

includes two lagged values of the S ratios in addition to

two lagged values of market returns. In panel A, the null

hypothesis that the lagged S ratios are not significant as agroup in the regression of current market return (Rt) is

not rejected for the post-Nasdaq period, which confirms

Butler et al.’s finding. In panel A, we also regress the S

ratio on past values of market returns (Rt�j) in the

presence of past values of the S ratios (St�j). We find that

past market returns are also insignificant as a group inthis regression for the post-Nasdaq period.

In panel B, we regress current market returns (Rt) on

past values of equity issues (DEt�j) and the res1 term

(res1t�1), as well as past values of market returns (Rt�j).

Here, we find that DEt�j and res1t�1 are significant as a

group at the conventional significance level. In panel B,

we also regress new equity issues (DEt) on past values of

market returns (Rt�j) and the res1 term (res1t�1), as wellas past values of equity issues (DEt�j). We find that Rt�j

and res1t�1 are insignificant as a group. These indicate

that, for the post-Nasdaq period, new equity issues have

predictive power for future market returns, but do not

respond to past market returns, which is consistent

with the managerial timing hypothesis even for thepost-Nasdaq period.

In panel C, we re-estimate the market return regression

in panel B using the res1 term calculated only for the post-

Nasdaq period. The results remain robust in that DEt�j

Table 6. Post-Nasdaq period (1975–2003) predictability. Panel A reports the coefficients of lagged real returns (R) and S ratios,t-statistics (in parentheses), adjusted R2, F-statistics and p-values. In the first regression of panel A, the F-test is for the nullhypothesis that coefficients of lagged S ratios are all zero (i.e. the S ratio does not Granger-cause stock returns). In the secondregression of panel A, the F-test is for the null hypothesis that coefficients of lagged returns are all zero (i.e. stock returns do notGranger-cause the S ratio). Original sample is from 1928 to 2003 while the modified sample is from 1930 to 2003 with the oil shockperiod of 1973–1974 interpolated. In the first regression of panel B (D), the F-test is for the null hypothesis that coefficients of laggedchanges in logged real value of equity issues DEt�j (debt issues DDt�j) and a lagged residual term are all zero (i.e. DE (DD) does notGranger-cause the stock returns). In the second regression of panel B (D), the F-test is for the null hypothesis that coefficients of laggedstock returns and a lagged residual term are all zero (i.e. stock returns do not Granger-cause DE (DD)). In panel C, residuals arecalculated for the sample period of 1975–2003, whereas in other panels residuals are calculated for the sample period of 1930–2003.

Panel A: Using S ratio: Dependent variables Rt and St, respectivelyRt Constant Rt�1 Rt�2 St�1 St�2 R2 F(2, 24)

0.022 0.149 0.090 0.284 �0.250 �0.122 0.125(0.25) (0.71) (0.40) (0.48) (�0.44) 0.883

St Constant Rt�1 Rt�2 St�1 St�2 R2 F(2, 24)0.040 �0.120 0.029 0.764��� 0.006 0.550 1.351(1.31) (�1.64) (0.36) (3.70) (0.03) 0.278

Panel B: Using equity issues: Dependent variables Rt and DEt, respectivelyRt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3, 23)

0.015 0.282 0.184 0.132 �0.1081 �0.100 0.127 2.717�(0.49) (1.34) (0.93) (1.59) (�1.22) (�1.37) 0.068

DEt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3, 23)0.041 0.092 0.120 �0.205 �0.281 �0.015 �0.102 0.030(0.54) (0.17) (0.24) (�0.97) (�1.24) (�0.08) 0.993

Panel C: Res1 is calculated using only the post-Nasdaq period: Dependent variables, Rt

Rt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3, 23)

0.021 0.300 0.156 0.041 �0.165� �0.205� 0.216 3.891��(0.75) (1.52) (0.88) (0.44) (�1.84) (�2.16) 0.022

Panel D: Using debt issues: Dependent variables Rt and DDt, respectivelyRt Constant Rt�1 Rt�2 DDt�1 DDt�2 res2t�1 R2 F(3, 23)

0.006 0.315 0.042 0.207 �0.192 �0.169 0.152 3.024�(0.19) (1.60) (0.21) (1.63) (�1.52) (�1.66) 0.050

DDt Constant Rt�1 Rt�2 DDt�1 DDt�2 res2t�1 R2 F(3, 23)0.080 0.290 �0.271 0.139 0.017 0.115 �0.117 0.593(1.46) (0.86) (�0.80) (0.63) (0.08) (0.66) 0.626

���, �� and � indicate statistical significance at the 0.001, 0.01 and 0.05 levels, respectively.

Pt¼�1.3121þ 0.3818Etþ res1t. R2¼ 0.6161, D.W.¼ 0.3416,

(�7.33) (10.72)

Pt¼�2.1959þ 0.4287Dtþ res2t. R2¼ 0.7363, D.W.¼ 0.4224.

(�11.14) (14.12)

For the post-Nasdaq sample:

Pt¼�4.1392þ 0.8538Etþ res1t. R2¼ 0.5966, D.W.¼ 0.8562.

(�5.24) (6.51)

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and res1t�1 are significant as a group. In particular, thecoefficient of DEt�2 is significantly negative, indicatingthat managers increase new equity issues in anticipationof a decline in market returns.

To see the potential predictive power of debt issues forfuture market returns, in panel D we regress currentmarket returns (Rt) on past values of debt issues (DDt�j)and the res2 term (res2t�1), as well as past values ofmarket returns (Rt�j). Here, we find that DDt�j andres2t�1 are significant as a group. In panel D, we alsoregress current debt issues (DDt) on past values of marketreturns (Rt�j) and the res2 term (res2t�1), as well as pastvalues of debt issues (DDt�j). We find that Rt�j andres2t�1 are insignificant as a group. Therefore, new debtissues also have predictive power for market returns forthe post-Nasdaq period.

Overall, for the post-Nasdaq period, we find that whilethe equity share (i.e. S ratio) does not have predictivepower, both new equity issues and new debt issues havesome predictive power for future market returns withoutresponding to past market returns, which is consistentwith the managerial timing hypothesis.y

6.3. On the out-of-sample predictability

Another argument raised by Butler et al. (2005) againstthe managerial timing hypothesis is that the S ratio doesnot have predictive power in out-of-sample forecasts. Toreexamine this issue, we compare the predictive power ofthe S ratio and our measure of new equity issues (DEt) inout-of-sample forecasts. We compare the mean absoluteerrors (MAE) and root-mean-squared errors (RMSE)from a series of one-year-ahead out-of-sample forecasts,adding an observation at a time and calculating a series of

one-step-ahead forecasts. We compare the resultsobtained from five models: a naı̈ve model using only aconstant (model 1); a model using a constant and lagged Sratios (model 2); a model using a constant, lagged equityissues, and res1 term (model 3); a model using a constant,lagged equity issues, lagged debt issues, and res1 term(model 4); and a model using a constant, lagged equityand debt issues, and S1 and S2 terms (model 5).

We report the results in table 7. First, we use 1960 as astarting point for recursive out-of-sample forecasts. Andthen we use 1980 as another starting point for arobustness check. In both cases, the model 2 using theconstant and two-period lagged S ratio performs worsethan the naı̈ve model 1, which confirms the finding ofButler et al. (2005). However, models using lagged equityissues (DEt�j), whether the model includes lagged debtissues (DDt�j) or not, perform better than the naı̈ve modelregardless of whether we use 1960 or 1980 as a startingperiod in the out-of-sample recursive forecasts.

This finding of the out-of-sample forecast shows againthat while the use of the S ratio yields no predictivepower, which confirms the finding of Butler et al. (2005),the use of equity issues DEt (or debt issues DDt) yields abetter predictive power, even for out-of-sample forecasts.This confirms that the use of the S ratio may not reveal allthe information managers use for the prediction of futuremarket returns.

6.4. The long-term effect of equity (debt) issues on themarket returns

Given the cointegration relation, we may measure thelong-term effect of equity (debt) issues on the real S&P500 returns Rt by assessing the long-term cumulative

Table 7. Out-of-sample forecast. In this table, we compare the results of the predictive power obtained from five models: a naı̈vemodel using only a constant (model 1), a model using a constant and lagged S ratios (model 2), a model using a constant, laggedequity issues, and res1 term (model 3), a model using a constant, lagged equity issues, lagged debt issues, and res1 term (model 4),and a model using a constant, lagged equity and debt issues, and S1 and S2 terms (model 5). First, we use 1960 as a starting point for

recursive out-of-sample forecasts. And then we use 1980 as a starting point for a robustness check.

Forecasting period Measurement Model 1 Model 2 Model 3 Model 4 Model 5

1960–2003 MAE 0.122 0.125 0.114 0.117 0.115RMSE 0.349 0.353 0.337 0.342 0.339

1980–2003 MAE 0.134 0.149 0.122 0.121 0.113RMSE 0.366 0.386 0.349 0.348 0.336

Notes: Models 1–5 use the following variables as regressors:

model 1: constant,

model 2: constant, S ratio,

model 3: constant, DEt�1, DEt�2, res1t�1,

model 4: constant, DEt�1, DEt�2, DDt�1, DDt�2, S1t�1,

model 5: constant, DEt�1, DEt�2, DDt�1, DDt�2, S1t�1, S2t�1,

where S ratio, E, and D denote the equity share in new equity and debt issues (¼ e/(eþ d)), where e and d are nominal values of aggregate new equity

and debt issues, logged real values of equity issues and debt issues, respectively. Res1 is the residuals from the cotemporaneous regression of the

logged real S&P 500 prices on logged real values of equity issues E, and S1 and S2 are from the Johansen three-variable cointegration regression

residuals.

MAE, mean absolute error; RMSE, root mean squared error.

yIn a trivariate cointegration model for the post-Nasdaq period, we do not find any significant predictive relation among threevariables: stock returns, equity issues, and debt issues. One way to understand this lack of predictability would be the weak power ofthe estimates, given that the number of observations is 29 relative to the number of coefficients of eight.

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effect of equity issues on the stock prices because we haveboth first differences and levels of variables on the right-hand side of equation (1) in table 3. Therefore, we firstestimate the VECM (vector error correction model) andconvert it into a VAR (vector autoregression) in levelsrather than in first differences. Then, we collect terms inlevels and examine the long-term cumulative effect (i.e. bysetting L¼ 1). In this case, we find that the long-termcumulative effect, which is given by the sum of coefficientstaking into account that the dependent variable is also inthe first difference, reduces to the error correction(cointegration) term.y This implies that the cointegrationterm captures the long-term comovement relationbetween equity (or debt) issues and the market prices.In sum, the short-term effect can be measured by thecoefficients of the first-differenced terms in the regressionof the market returns as we discuss in previous sections,while the long-term cumulative effect is measured by thecoefficient of Et (or Dt) in the error correction (coin-tegration) term.

Specifically, we find that the coefficient of Et (or Dt) inthe cointegration term is significant and is given by 0.3768(with t¼ 10.74) for equity issues and by 0.4255 (witht¼ 14.26) for debt issues for the original sample, and by0.3818 (with t¼ 10.72) for equity issues and by 0.4287(with t¼ 14.12) for debt issues for the modified sample(see footnotes of table 3). Therefore, we find that themarket prices and equity issues (and debt issues) arecointegrated, and the long-term effect of equity issues anddebt issues on the market prices remains significantover time.

6.5. Corporate disclosure and information asymmetries

As reviewed by Healy and Palepu (2001), there isextensive literature on the relation between corporatedisclosure and information asymmetry. Research onvoluntary disclosure oftentimes focuses on the informa-tion role of financial reporting for capital markets. Forexample, under information asymmetries between insidemanagers and outside investors regarding the firm’sfuture prospects, making public equity or debt offerswould be costly for existing shareholders (e.g., Myers and

Majluf (1984)). Consequently, managers who anticipate

making capital market transactions have incentives to

provide voluntary disclosure to reduce the information

asymmetry problem, thereby reducing the firm’s cost of

external financing.Our econometric method of testing predictability is

based primarily on the information asymmetry between

inside managers and outside investors. As such, one way

to see whether information asymmetries have been

alleviated over time is to examine whether the predictive

power of equity (or debt) issues has declined over time.By examining the predictive power for the post-Nasdaq

period (i.e. after 1975), we find that while the equity share

(i.e. S ratio) does not have predictive power, both new

equity issues and new debt issues have some predictive

power for future market returns without responding to

past market returns, which is consistent with the

managerial timing hypothesis.In addition, we find from the comparison of F-test

statistics in tables 3 and 6, which measure the significance

of the effect of equity (or debt) issues on the market

returns, that the F-test statistic is less significant in recent

period of the post-Nasdaq period (i.e. post-1975 period).

This implies that the information asymmetry between

inside managers and outside investors that makes the

predictability feasible is weaker in recent years. This

finding is consistent, among other things, with the

argument that the information asymmetry has been

reduced in recent years. This may also be associated

with greater corporate disclosure that alleviates

information asymmetry.

7. Robustness of results

So far we have used the S&P 500 index returns as a

measure of the stock market returns. Now we use CRSP

NYSE/Amex/Nasdaq returns, both value-weighted

returns with dividends (vwretd) and equal-weighted

returns with dividends (ewretd), converted into real

terms Rt using the Consumer Price Index as alternative

measures of market returns, and examine whether our

yThe long-term effect for the following regression equation is calculated as follows:

Xt ¼ aþX2

i¼1biRt�i þ

X2

i¼1ciXt�i þ g rest�1 þ et,

where X is DE (DD) and res is res1 (res2). Ignoring a constant term a, the equation can be rewritten as

DEt ¼ b1Rt�1 þ b2Rt�2 þ c1DEt�1 þ c2DEt�2 þ g Pt�1 � kEt�1ð Þ,

Et � Et�1 ¼ b1 Pt�1 � Pt�2ð Þ þ b2 Pt�2 � Pt�3ð Þ þ c1 Et�1 � Et�2ð Þ þ c2 Et�2 � Et�3ð Þ þ g Pt�1 � kEt�1ð Þ:

By collecting terms and using the lag operator L (i.e. LkXt¼Xt�k), we obtain

Et 1� L� c1Lþ c1L2 � c2L

2 þ c2L3 þ gkL

� �¼ Pt b1L� b1L

2 þ b2L2 � b2L

3 þ gL� �

:

To calculate the long-term (or permanent) effect, we set L¼ 1. Then

Et 1� 1� c1 þ c1 � c2 þ c2 þ gkð Þ ¼ Pt b1 � b1 þ b2 � b2 þ gð Þ:

Et gkð Þ ¼ Pt gð Þ or Pt ¼ kEt:

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findings are robust to different measures of marketreturns.

7.1. Empirical results using CRSP value-weightedreturns

In table 8, we replicate our previous results using theCRSP value-weighted returns with dividends. In panel A,we replicate our table 1 and find that lagged S ratios aresignificant as a group (i.e. S ratios Granger-cause marketreturns) in the regression of the CRSP returns when weuse the original sample, but are not significant when weuse the modified sample.

In panel B, in which we replicate panel A of table 3, wefind that DEt�j and res1t�1 are significant as a group (i.e.equity issues Granger-cause returns) in the regression ofthe CRSP returns, regardless of whether we use theoriginal sample or the modified sample. In particular,the coefficient of DEt�2 is negative and significant in themodified sample, indicating that new equity issuesanticipate declines in the CRSP returns.

In panel C, in which we replicate panel B of table 3, wefind that DDt�j and res2t�1 are not significant as a group(i.e. debt issues do not Granger-cause returns) in theregression of the CRSP returns, regardless of whether weuse the original sample or the modified sample. Therefore,in the case of the CRSP value-weighed returns, new equityissues (DE) do have predictive power for future marketreturns, whereas new debt issues (DD) do not.

In panel D, in which we replicate panel C of table 3, wefind that Rt�j and res1t�1 are significant as a group (i.e.CRSP returns Granger-cause equity issues) in the regres-sion of equity issues (DEt), regardless of whether we usethe original sample or the modified sample. We make asimilar observation for DDt�j in panel E, in which wereplicate panel D of table 3.

In sum, we find that managers’ equity issue decisionsanticipate a decline in the CRSP returns, but they respondto past market returns positively. And debt issues do notappear to have predictive power for the CRSP returns butrespond to market returns positively. Therefore, equityissues are consistent with the managerial timinghypothesis for the CRSP value-weighted returns.

7.2. Empirical results using CRSP equal-weightedreturns

In table 9, we replicate our previous results using theCRSP equal-weighted returns with dividends. In panel A,we find that lagged S ratios are significant as a group (i.e.S ratios Granger-cause returns) in the regression of theCRSP returns (Rt) when we use the original sample, butare not significant as a group (i.e. S ratios do notGranger-cause returns) when we use the modified sample.

In panel B, we find that DEt�j and res1t�1 aresignificant as a group (i.e. equity issues Granger-causereturns) in the regression of the CRSP returns (Rt),

regardless of whether we use the original sample or themodified sample. In panel C, we find that DDt�j andres2t�1 are not significant as a group (i.e. debt issues donot Granger-cause returns) in the regression of Rt,regardless of whether we use the original sample or themodified sample. Therefore, in the case of the CRSPequal-weighed returns, new equity issues do have predic-tive power for future market returns, whereas new debtissues do not, as in the case of the CRSP value-weightedreturns.

In panel D, we find that Rt�j and res1t�1 are significantas a group (i.e. CRSP returns Granger-cause equityissues) in the regression of equity issues (DEt), regardlessof whether we use the original sample or the modifiedsample. We make a similar observation for lagged debtissues (DDt�j) in panel E of table 9.

In sum, similar to the case of using the CRSP value-weighted returns, we find that managers’ equity issuedecisions anticipate declines in the CRSP equal-weightedreturns, but they respond to past market returns posi-tively. And new debt issues do not have predictive powerfor future CRSP equal-weighted returns but respond topast market returns. Therefore, using either value-weighted or equal-weighted CRSP returns, we find thatnew equity issues are consistent with the managerialtiming hypothesis in that equity issues anticipate a declinein future CRSP returns even after we control for pastmarket returns.

7.3. Out-of-sample predictability

In table 10, we report the results of out-of-sampleforecasts using CRSP value- and equal-weighted returnswith dividends. When we use CRSP value-weightedreturns with dividends, we find in panel A that model 2using the S ratio does not perform as well as the naı̈vemodel which uses only a constant term, whereas model 3using equity issues (DEt�j) performs better than the naı̈vemodel. This observation is made regardless of whether weuse 1960 or 1980 as a starting point for the recursive out-of-sample forecasts. Models 4 and 5 do not perform aswell as model 1.

When we use CRSP equal-weighted returns withdividends, we find in panel B that the naı̈ve modelperforms better than any other alternative model when weuse 1960 as a starting point for the recursive out-of-sample forecasts.y When we use 1980 as a starting pointfor the recursive out-of-sample forecasts, however, model2 using the S ratio once again does not perform as well asthe naı̈ve model, whereas models 3, 4, and 5 using equityissues (DEt�j) perform better than the naı̈ve model.

In sum, we find that, except for the case of using theCRSP equal-weighted returns with dividends and 1960 asa starting point for the recursive out-of-sample forecasts,model 3 using equity issues (DEt�j) performs better thanthe naı̈ve model in the out-of-sample forecasts.

yThe poor out-of-sample predictive power when using CRSP equal-weighted returns is consistent with the finding of Korajczyk andLevy (2003) that small-size firms may be more constrained in timing their equity issues with favorable macroeconomic conditions.

296 X. Jiang and B.-S. Lee

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8. Concluding remarks

In examining managers’ timing ability, previous studies

use the regression of current market returns on one-

period lagged equity share (e.g., S ratio). This is partly

because the equity share variable isolates potential timing

motives from the level of the investment itself. However,

the equity share variable may not fully reflect all the

information managers use in their equity and debt issue

decisions, and the regression ignores potential feedback

from past returns in the equity and debt issue decisions. In

particular, when new equity and debt issues are coin-

tegrated with stock market prices, we should use new

equity and debt issues together with a potential coin-

tegration relation to avoid potential misspecification of

the regression.By taking into account potential feedback from past

market returns in the equity issue decisions, we find

evidence that managers’ decisions about equity and

debt issues are affected by past market returns.

Table 8. Using value-weighted CRSP returns: Sample period, 1928–2003. This table replicates tables 1 and 3 using value-weighted CRSP returns with dividends. It contains various regressions between (real) value-weighted CRSP returns R, S ratio,changes in logged real values of equity issues DE and debt issues DD. The table reports coefficients of lagged returns, S ratios, equityissues DE, and debt issues DD, t-statistics (in parentheses), adjusted R2, F-statistics and p-values. In panel A (B or C), the F-test is forthe null hypothesis that coefficients of lagged values of S ratios (DE or DD) and a lagged residual term are all zero (i.e. S ratios(equity issues or debt issues) do not Granger-cause stock returns). In panel D (E), the F-test is for the null hypothesis thatcoefficients of lagged stock returns and a lagged residual term are all zero (i.e. returns do not Granger-cause DE (DD)). Originalsample is from 1928 to 2003 while the modified sample is from 1930 to 2003 with the oil shock period of 1973–1974 interpolated.

Rt Constant Rt�1 Rt�2 St�1 St�2 R2 F(2,69)

Panel A: Using S ratio: Dependent variable, Rt

Original sample 0.213��� 0.028 �0.138 �0.528�� �0.193�� 0.111 4.530��(3.80) (0.24) (�1.19) (�2.12) (�2.78) 0.014

Modified sample 0.097 �0.053 �0.023 �0.505� 0.379 �0.004 1.628(1.57) (�0.42) (�0.19) (�1.71) (1.34) 0.204

Rt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3,68)

Panel B: Using equity issues: Dependent variable, Rt

Original sample 0.060 0.033 �0.041 �0.047 �0.046 0.074�� 0.132 3.986��(2.52) (0.25) (�0.29) (�1.12) (�1.24) (2.27) 0.011

Modified sample 0.074��� �0.016 0.107 �0.099�� �0.072� �0.010 0.036 2.343��(3.15) (�0.12) (0.79) (�2.14) (�1.79) (�0.25) 0.014

Rt Constant Rt�1 Rt�2 DDt�1 DDt�2 res2t�1 R2 F(3,68)

Panel C: Using debt issues: Dependent variable, Rt

Original sample 0.072��� 0.051 �0.223� 0.004 �0.025 0.048� 0.005 0.563(2.89) (0.39) (�1.74) (0.06) (�0.38) (0.93) 0.641

Modified sample 0.081 �0.048 �0.006 0.009 �0.114� �0.017 �0.014 1.207(3.39) (�0.35) (�0.04) (0.14) (�1.84) (�0.34) 0.315

DEt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3,68)

Panel D: Equity issue equation: Dependent variable, DEt

Original sample �0.072 1.170��� �0.115 �0.137 0.102 0.453��� 0.388 16.197���(�0.99) (2.89) (�0.27) (�1.09) (0.91) (4.58) 0.000

Modified sample 0.015 1.148��� 0.155 �0.088 �0.119 0.284�� 0.290 8.160���(0.23) (3.23) (0.42) (�0.70) (�1.09) (2.65) 0.000

DDt Constant Rt�1 Rt�2 DDt�1 DDt�2 res2t�1 R2 F(3,68)

Panel E: Debt issue equation: Dependent variable, DDt

Original sample 0.008 �0.149�� 0.800��� 0.347��� �0.446��� 0.203�� 0.494 13.202���(0.23) (�0.80) (4.39) (3.76) (�4.70) (2.76) 0.000

Modified sample 0.053 �0.332 0.731��� 0.322��� �0.461��� 0.127 0.407 7.312���(1.38) (�1.51) (3.62) (3.10) (�4.67) (1.64) 0.0002

���, �� and � indicate statistical significance at the 0.001, 0.01 and 0.05 levels, respectively. Res1, res2, mres1, and mres2 are calculated as follows.

For the original sample:

Pt¼�4.7493þ 0.9316Etþ res1t. R2¼ 0.6997, D.W.¼ 0.5139,

(�13.71) (13.35)

Pt¼�6.8820þ 1.0495Dtþ res2t. R2¼ 0.8551, D.W.¼ 0.5042.

(�21.69) (21.20)

For the modified sample:

mPt¼�4.6562þ 0.9429mEtþmres1t. R2¼ 0.8224, D.W.¼ 0.7313,

(�17.98) (18.16)

mPt¼�6.5164þ 1.0041mDtþmres2t. R2¼ 0.8815, D.W.¼ 0.5396.

(�23.00) (23.00)

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More importantly, we find that managers’ decisions

about equity and debt issues have some predictive

power for future market returns, even after we take into

account potential feedback from past market returns and

structural breaks. This finding is robust with respect to

various measures of market returns and consistent with

the managerial timing hypothesis.We have proposed a simple time-series model on the

basis of information asymmetry between inside managers

and outside investors. In this model, informative equity

issue decisions have predictive power for future market

returns, which is not necessarily inconsistent with a (semi-

strong form of) market efficiency explanation.

Furthermore, we have shown that we can test these

informative equity issue decisions by a two-sided regres-

sion and equivalently by the usual Granger-causality test.

The causality test takes into full account potential

feedback from past market returns in equity issue

Table 9. Using equal-weighted CRSP returns: Sample period, 1927–2003. This table replicates tables 1 and 3 using (real) equal-weighted CRSP returns with dividends. It contains various regressions between (real) equal-weighted CRSP returns R, S ratio,changes in logged real values of equity issues DE and debt issues DD. The table reports coefficients of lagged returns, S ratios, equityissues DE, and debt issues DD, t-statistics (in parentheses), adjusted R2, F-statistics and p-values. In panel A (B or C), the F-test is forthe null hypothesis that coefficients of lagged values of S ratios (DE or DD) and a lagged residual term are all zero (i.e. S ratios(equity issues or debt issues) do not Granger-cause stock returns). In Panel D (E), the F-test is for the null hypothesis thatcoefficients of lagged stock returns and a lagged residual term are all zero (i.e. returns do not Granger-cause DE (DD)). Originalsample is from 1928 to 2003 while the modified sample is from 1930 to 2003 with the oil shock period of 1973–1974 interpolated.

Rt Constant Rt�1 Rt�2 St�1 St�2 R2 F(2,69)

Panel A: Using S ratio: Dependent variable, Rt

Original sample 0.355��� �0.032 �0.114 �0.873�� �0.351 0.146 6.723���(4.43) (�0.27) (�0.99) (�2.66) (�1.00) 0.002

Modified sample 0.237��� �0.171 �0.028 �0.849�� 0.214 0.032 2.380(2.77) (�1.36) (�0.24) (�2.12) (0.56) 0.101

Rt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3,68)

Panel B: Using equity issues: Dependent variable, Rt

Original sample 0.098��� �0.078 0.039 �0.091� �0.122�� 0.092��� 0.246 8.440���(3.08) (�0.59) (0.30) (�1.72) (�2.62) (2.87) 0.000

Modified sample 0.124��� �0.192 0.0532 �0.068 �0.089 0.063 0.088 3.331��(3.81) (�1.36) (0.40) (�0.99) (�1.58) (1.21) 0.025

Rt Constant Rt�1 Rt�2 DDt�1 DDt�2 res2t�1 R2 F(3,68)

Panel C: Using debt issues: Dependent variable, Rt

Original sample 0.113��� 0.046 �0.176 �0.049 �0.084 0.038� 0.013 1.111(3.22) (0.35) (�1.38) (�0.56) (�0.94) (0.79) 0.351

Modified sample 0.126��� �0.155 �0.017 0.024 �0.179� �0.009 0.025 1.772(3.86) (�1.12) (�0.14) (0.27) (�2.19) (�0.18) 0.162

DEt Constant Rt�1 Rt�2 DEt�1 DEt�2 res1t�1 R2 F(3,68)

Panel D: Equity issue equation: Dependent variable, DEt

Original sample �0.074 0.585�� 0.043 �0.127 0.069 0.374��� 0.388 19.858���(�1.06) (2.02) (0.15) (�1.08) (0.67) (5.25) 0.000

Modified sample �0.003 0.784��� 0.314 �0.074 �0.146 0.289�� 0.377 12.229���(�0.05) (2.93) (1.25) (�0.57) (�1.36) (2.91) 0.000

DDt Constant Rt�1 Rt�2 DDt�1 DDt�2 res2t�1 R2 F(3,68)

Panel E: Debt issue equation: Dependent variable, DDt

Original sample 0.014 �0.202 0.576��� 0.353��� �0.455��� 0.143��� 0.533 16.195���(0.40) (�1.56) (4.59) (4.12) (�5.19) (3.06) 0.000

Modified sample 0.051 �0.260 0.543��� 0.320��� �0.452��� 0.100� 0.447 9.362���(1.37) (�1.65) (4.03) (3.17) (�4.84) (1.82) 0.000

���, �� and � indicate statistical significance at the 0.001, 0.01 and 0.05 levels, respectively. Res1, res2, mres1, and mres2 are calculated as follows.

For the original sample:

Pt¼�5.2184þ 1.2855Etþ res1t. R2¼ 0.6805, D.W.¼ 0.4889,

(�10.44) (12.76)

Pt¼�8.1061þ 1.4395Dtþ res2t. R2¼ 0.8215, D.W.¼ 0.4164.

(�16.46) (18.73)

For the modified sample:

mPt¼�5.0398þ 1.3008mEtþmres1t. R2¼ 0.8657, D.W.¼ 1.0037,

(�16.64) (21.42)

mPt¼�7.3682þ 1.3478mDtþmres2t. R2¼ 0.8776, D.W.¼ 0.5549.

(�19.02) (22.58)

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decisions, which has been extensively utilized in this paper

taking into account potential cointegrations.For aggregate equity and debt issues, we use the same

annual data in this paper as in Baker and Wurgler (2000)and Butler et al. (2005). However, annual data may not befrequent enough for today’s stock markets that are veryefficient. Using more frequent data such as monthly or

even weekly data would be more informative and closer tothe reality. Further, it would be interesting to explore thebull–bear stock market cycles as well as sector rotations,which can be important factors for firms to make their

decisions on equity issues and debt issues. We leave theseissues (e.g., using more frequent data and market cycleswith sector rotations) as a future research agenda.

Acknowledgements

We would like to thank an anonymous referee forconstructive suggestions. We would also like to thank

April Knill, Kevin Krieger, and Nate Porter, and seminarparticipants at FSU, KAEA-KEA InternationalConference, 2006 FMA in Salt Lake City, and 2006FMA European Conference, Stockholm, Sweden for

comments and suggestions.

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Table 10. Out-of-sample forecast using various CRSP returns. In this table, we compare the results of predictive power obtainedfrom five models: a naı̈ve model using only a constant (model 1), a model using a constant and lagged S ratios (model 2), a modelusing a constant, lagged equity issues, and res1 term (model 3), a model using a constant, lagged equity issues, lagged debt issues,and res1 term (model 4), and a model using a constant, lagged equity and debt issues, and S1 and S2 terms (model 5). First, we use1960 as a starting point for recursive out-of-sample forecasts. And then we use 1980 as a starting point for a robustness check.

Forecasting period Measurement Model 1 Model 2 Model 3 Model 4 Model 5

Panel A: VWD (value-weighted returns with dividends)1960–2003 MAE 0.1270 0.1280 0.1259 0.1300 0.1400

RMSE 0.3564 0.3578 0.3548 0.3606 0.37421980–2003 MAE 0.1316 0.1318 0.1301 0.1377 0.1392

RMSE 0.3628 0.3630 0.3607 0.3711 0.3731

Panel B: EWD (equal-weighted returns with dividends)1960–2003 MAE 0.1727 0.1728 0.1793 0.1795 0.1953

RMSE 0.4155 0.4157 0.4234 0.4237 0.44191980–2003 MAE 0.1600 0.1603 0.1540 0.1518 0.1519

RMSE 0.3999 0.4004 0.3924 0.3896 0.3898

Notes: Models 1–5 use the following variables as regressors:

model 1: constant,

model 2: constant, S ratio,

model 3: constant, DEt�1, DEt�2, res1t�1,

model 4: constant, DEt�1, DEt�2, DDt�1, DDt�2, S1t�1,

model 5: constant, DEt�1, DEt�2, DDt�1, DDt�2, S1t�1, S2t�1,

where S ratio, E, and D denote the equity share in new equity and debt issues (¼ e/(eþ d)), where e and d are nominal values of aggregate new equity

and debt issues, logged real values of equity issues and debt issues, respectively. Res1 is the residuals from the cotemporaneous regression of the

logged real CRSP index prices on logged real values of equity issues E, and S1 and S2 are from the Johansen three-variable cointegration regression

residuals.

MAE, mean absolute error; RMSE, root mean squared error.

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