Disclosure Timing Determinants of Quarterly Earning Release Dates.japp

7/31/2019 Disclosure Timing Determinants of Quarterly Earning Release Dates.japp

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Disclosure timing: Determinants of

quarterly earnings release dates

Partha Sengupta *

Robert H. Smith School of Business, University of Maryland, College Park,

Van Munching Hall, MD 20742, United States

Abstract

Existing research on discretionary disclosures provides valuable insights on the pot-

entials causes and consequences of alternative forms of disclosure. However, relativelylittle is known about how managers choose to time the release of financial information.

This paper focuses on the quarterly earnings release dates and investigates why some

choose to release earnings information relatively early, compared to others. The results

indicate that the reporting lag (days between fiscal period end and quarterly earnings

release date) is shorter for firms facing greater demand for information from investors

and greater litigation costs. The reporting lag, however, is longer for firms with greater

block ownership and those whose operations are somewhat more complex.

2004 Elsevier Inc. All rights reserved.

JEL classification codes: M41; D82Keywords: Disclosure timing; Earnings announcement date

0278-4254/$ - see front matter 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.jaccpubpol.2004.10.001

* Corresponding author. Tel.: +1 301 405 8928; fax: +1 301 314 9414.

E-mail address: [email protected]

Journal of Accounting and Public Policy 23 (2004) 457482www.elsevier.com/locate/jaccpubpol
mailto:[email protected]:[email protected]


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1. Introduction

A large stream of research has evolved in recent years exploring corporatedisclosure choices. This research, well summarized by Healy and Palepu

(2001), Core (2001) and Verrecchia (2001), examines managements disclosure

though earnings and sales forecasts, financial statements and associated foot-

notes, conference calls, analysts presentations, and websites. The literature

provides valuable insights on the causes and consequences of alternative forms

of corporate disclosure. However, researchers have devoted limited attention to

analyzing how managers choose to time the release of financial information. In

this paper I investigate how company managers decide when to release quar-

terly earnings information.Corporate disclosures take various forms but the quarterly earnings

announcement is probably one of the most highly anticipated events and re-

ceives significant media and investor attention. Research consistently shows

that the market assimilates more and more of the information in earnings as

the announcement date approaches (see Kothari, 2001; for a survey of this re-

search starting with Ball and Brown, 1968). Hence, the extent to which an earn-

ings announcement will provide useful information to market participants

should be a function not only of the nature of information released but also

when it is released. Consequently, the Financial Accounting Standards Board,in defining the primarily qualities that make accounting information useful,

highlighted timeliness to be an important factor defined as:

. . .having information available to decision makers before it loses its

capacity to influence decisions, is an ancillary aspect of relevance. If

information is not available when it is needed or becomes available so

long after the reported events that it has no value for future action, it

lacks relevance and is of little or no use (FASB, Statement of Account-

ing Concept No. 2, p. 5).

Recently, in an effort to provide more timely accounting information to

market participants, the SEC changed the deadlines for filing annual and quar-

terly reports for companies with a public float of at least $75 million. Effective

November 15, 2002, the filing deadline for annual reports are being reduced

gradually from 90days to 60days over 3years, whereas the deadline for filing

quarterly reports are being reduced from 45days to 35days. 1

Disclosure literature generally posits that a firms optimal disclosure strategy

would be determined by the costs and benefits of disclosure. Based on an

1 See SEC Release No. 33-8128: Acceleration of Periodic Report Filing Dates and Disclosure

Concerning Website Access to Reports.

458 P. Sengupta / Journal of Accounting and Public Policy 23 (2004) 457482


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assessment of these costs and benefits firm managers should decide on the

nature and content of information to be disclosed, the timing of disclosures,

medium of disclosures, venue etc. This argument suggests that managersshould set the earnings release date based on their evaluation of potential ben-

efits (and costs) of releasing earnings information relatively quickly. I investi-

gate whether the nature of investor base, litigation costs, accounting

complexity, and proprietary costs, impact DELAY, which is defined as the

number of days after fiscal period end that managers release quarterly earnings

information. Results support most of the hypotheses developed in the paper.

First, I find that DELAY is shorter for firms with greater trading volume

and greater institutional ownership, and longer for firms with greater block

ownership. This is consistent with the argument that firms respond to pressuresfrom investors to release earnings quickly. Firms that have large block owner-

ship, however, are less susceptible to such pressures. 2 Second, I find that DE-

LAY is shorter for technology firms and those having a greater percentage of

outside directors on its board. Both variables could capture the effects of liti-

gation costs. Prior research had identified technology firms to be those poten-

tially having greater litigation risk (for example, Francis et al., 1994 and

Kaznik and Lev, 1995). Board of directors should also be sensitive to litigation

risk since such lawsuits often name the directors as defendants. Lawsuits also

cause investors and regulators to evaluate the effectiveness of the board morecarefully. Shareholder lawsuits typically allege that the firm had private infor-

mation that it failed to release in a timely manner suggesting that firms facing

greater litigation risk would have incentives to release earnings relatively early.

Third, I document that DELAY is longer for firms reporting multiple seg-

ments, undergoing acquisitions, and reporting special items suggesting that

firms with greater accounting complexity generally take longer to release earn-

ings information. Finally, I investigated whether firms facing proprietary costs

have incentives to delay the earnings release. Using alternative measures of

proprietary costs I find somewhat mixed evidence of the potential associationbetween DELAY and such costs. 3

The paper can be viewed as making a contribution to two interrelated

streams of research. Firstly, it extends the discretionary disclosure literature

by focusing on the disclosure timing decision, a dimension of corporate disclo-

sure policy that has been relatively neglected by researchers. Results indicate

that costs and benefits of disclosure not only affect the nature of information

2 Blockholders may have private sources of information and thus may not pressure the company

to release earnings information quickly.3 I also included certain control variables to capture the effects of size, market uncertainty and

the nature of earnings news on earnings release dates.

P. Sengupta / Journal of Accounting and Public Policy 23 (2004) 457482 459


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to be disclosed but also the timing of such disclosure. The findings of this paper

may be helpful in evaluating the consequences of the SECs recently imple-

mented change in the filing deadlines for quarterly and annual reports. Forexample, the fact that the reporting lag is found to be negatively associated

with certain variables such as trading volume, institutional ownership, number

of shareholders and firm size, is consistent with the view of SECs supporters

who perceive the reduction in the reporting deadline to result in more efficient

operations of capital markets. However, the reporting lag is also found to be

positively associated with measures of business complexity suggesting that

some companies facing large information processing costs may produce less

reliable financial information in an effort to meet the shorter deadline.

Secondly, this paper extends a line of research that had focused on compar-ing actual release dates of earnings to expected (or pre-announced) release

dates in order to examine if early announcements conveyed good news and late

announcements bad news. This research includes Givoly and Palmon (1982),

Chambers and Penman (1984), Kross and Schroeder (1984), Begley and Fi-

scher (1998) and Bagnoli et al. (2002) and consistently shows that delayed earn-

ings are associated with bad news. These papers also documented somewhat

weaker evidence to suggest that good news was announced earlier than ex-

pected. These papers can be viewed as addressing the question why a firm

may deviate from a pre-committed disclosure strategy, but does not directly ad-dress the question of how company managers plan on when to release their

earnings information, which is the focus of the current study. I find that ana-

lysts forecast error continues to explain the variation in actual earnings

announcement dates but the other measures included explain a significant por-

tion of the variation in DELAY beyond analysts forecast error (average R2 of

over 20%). 4

The rest of the article is organized as follows. Section 2 develops proxies

to explain the reporting lag (DELAY) based on prior literature on discre-

tionary disclosure, and describes the sample used for the study. Section 3 re-ports the empirical results and the last section provides some concluding

remarks.

4 In this paper I use the number of days between fiscal year end and actual earnings release date

as the dependent variable. Thus my measure of DELAY is a combination of expected or planned

delay which has been less addressed in prior research and unexpected delay which has been thefocus of a number of prior studies. Bagnoli et al. (2002), however, had documented that

approximately 74% of the firms in their sample released earnings exactly on the day they had earlier

committed to and another 13% released earnings one day earlier or later. Thus the primary

difference in the reporting delay across firms in my sample can be attributed to variations in the

expected reporting delay.



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2. Research design

2.1. Determinants of reporting lag

This research explores the potential determinants of reporting lag defined as:

DELAY days between the fiscal year-end and fourth

quarter earnings release date: 5

Firm managers have some discretion in choosing the earnings release date.

As with any discretionary disclosure choice, it is expected that company man-

agement will determine the timing of earnings release based on an evaluation of

the costs and benefits of releasing earnings early versus late. Recent studieshave identified a number of costs and benefits of discretionary disclosure

choices (see Healy and Palepu (2001) and Core (2001) for recent surveys of

the empirical research on disclosures and Verrecchia (2001) for a survey of

the theoretical work). Here, I examine the potential impact of the demand pres-

sure from investors, litigation costs, proprietary costs, and the degree of com-

plexity of financial reporting on the reporting lag. Disclosure literature had

also consistently associated discretionary disclosure measures to firm size, mar-

ket uncertainty and the nature of information released. I incorporate controls

for these factors. The choice of the variables is explained below.

2.1.1. Investor base

Prior research has found evidence to suggest that firms respond to investor

demands for greater discretionary disclosure (Bushee and Noe, 2000; Bushee

et al., 2003). Investors are likely to be concerned about receiving timely infor-

mation from firms they are investing in. Their demand for timely disclosures

should be greater when they are trading more frequently suggesting that DE-

LAY could be negatively associated with trading volume. Consistent with this,

Botosan and Harris (2000) found that firms are more likely to initiate segmentdisclosures when trading volume had declined, and Bushee et al. (2003) docu-

mented that firms that are choosing to make open conference calls have greater

trading volume. Demand for timely disclosures could also be higher for firms

that have greater number of shareholders outstanding. When ownership is

widely dispersed, public disclosures are the most effective method of communi-

cating private information to market participants. I include the following two

measures to capture this:

5 I focus initially on fourth quarter earnings release dates because data on certain variables such

as institutional ownership, block ownership, board of directors and number of segments were

available only on an annual basis. Analyses based on report dates for the first three quarters yielded

similar results and these are discussed in Section 3.4.



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VOL total number of shares traded over the fiscal year

divided by total shares outstanding at fiscal year end:

SHRHOLD log of number of shareholders of the company minus

log of mean number of shareholders of firms in the

same asset decile:

Research also documents a positive association between discretionary dis-

closure and institutional ownership (Healy et al., 1999; Bushee and Noe,

2000; Ajinkya et al., 2004). This research argues that institutions continuously

demand financial information from firms and firms yield to pressure from these

investors by making more frequent earnings forecasts and providing more de-

tailed information in financial reports. In order to capture the role of institu-

tional ownership I include the following variable:

INST percentage of common shares held by financial institutions:

Assuming that institutions would prefer earnings information to be released

as quickly as possible, a negative association between DELAY and INST is

expected.

It has been argued that blockholders monitor a firm s operations reducing

agency costs and increasing firm value (Morck et al. (1988), McConnell and

Servaes (1990), Barclay and Holderness (1991), and Bethel et al. (1998)). Onepossible implication of this is that in the presence of large block ownership

other market participants may feel less of a need to get timely information

from the company. Alternatively, it is possible that blockholders would have

access to private information and so in order to maintain their information

advantage they might discourage timely and detailed public disclosures. 6 Both

alternatives suggest a positive association between block ownership and the

reporting lag. Heflin and Shaw (2000) documented that firms with greater

blockholder ownership have larger quoted spreads and smaller quoted depths

in the market suggesting that blockholder ownership may be associated withgreater information asymmetry. Ajinkya et al. (2004) showed further that con-

centrated institutional ownership is negatively associated with the probability

of issuing a management forecast. Block ownership is defined as:

BLOCK percentage of shares held by blockholdersowners with at

least 5% stake in the company:

6 It is expected that blockholders that are affiliated with the company would be more likely to

have access to private information about the companys performance. However, all blockholders,

by virtue of their large share ownership, could demand and have access to some private

information. Thus Ajinkya et al. (2004) argues that institutions with large equity ownership in a

company act as insiders although they may not be affiliated with the company.



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Based on earlier findings I suggest that the reporting lag (DELAY) is posi-

tively associated with block ownership (BLOCK). 7

2.1.2. Litigation costs

Skinner (1994) had argued that the threat of lawsuits arising from large neg-

ative earnings surprises provide managers with incentives to pre-disclose the

information in order to reduce litigation costs. In support of this hypothesis

Skinner (1994, 1997) documented that firms with bad news are more likely

to pre-disclose compared to those with good news. Timely disclosures are likely

play an important role in reducing litigation costs. In a stock based litigation

the debated issue is often whether the company revealed proper information

without delay so early announcements could help reduce the probability of lit-igation and potential damages to be incurred if a lawsuit does arise. In this pa-

per I suggest the following measure of litigation costs:

TECH 1 if the firm belongs to Drugs COMPUSTAT SIC

codes 28332836; R&D Services 87318734;

Programming 73717379; Computers 35703577;

Electronics 36002674; and 0 otherwise:

Kaznik and Lev (1995) had used this variable to capture litigation risk. Theyfound that TECH firms are more likely to warn investors of an earnings sur-

prise supporting the argument that litigation risk provide incentives for early

disclosures. The variable is also similar to the classification used in Francis

et al. (1994) to identify firms facing high litigation risk. If TECH effectively

captures litigation risk and firms perceive early disclosure to reduce potential

litigation costs, DELAY should be negatively associated with TECH. 8

7 In conducted separate analysis using INSIDER (the percentage of shares of the company held

by insiders) as an alternative to BLOCK. Similar to BLOCK, INSIDER could capture the effects of

agency costs and private information flows. The results based on INSIDER (not reported) were

similar to those obtained for BLOCK.8 Although Skinners work supports the view that managers consider early dissemination of

information to reduce litigation risk, other research had provided some mixed evidence. Thus,

Francis et al. (1994) identified firms in specific industries such as computers, drugs etc. that are

likely to face high litigation risk due to large drop in earnings and compared disclosure behavior of

these firms to a control group. They found no evidence to suggest that the disclosures were driven

by potential litigation costs. A couple of recent studies went further to show that litigation risk may

in fact reduce the probability of discretionary disclosures. Thus Johnson et al. (2001) documented asignificant increase in forward looking forecasts by a group of high-tech firms after the passage of

the Private Securities Litigation Reform Act of 1995, which gave management protection from

lawsuits relating to financial projections. Baginski et al. (2002) showed that Canadian firms are

more likely to issue earnings forecasts compared to US firms, presumably because the Canadian

securities laws make it relatively more difficult for investors to win class action lawsuits.



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Class action lawsuits often name corporate board members as defendants.

Board members may be charged for being passive and not performing their fidu-

ciary duties of protecting investor interests by monitoring firm performance andfacilitating timely disclosures about the firms progress. Unlike company manag-

ers who could generate private benefits from delaying disclosures, board mem-

bers who are not insiders usually have little to gain from selective or delayed

disclosures. On the other hand, they may bear large reputation costs and possibly

monetary costs if litigation arises. This suggests that outside directors would

have incentives to encourage timely release of information in an effort to mini-

mize litigation costs. This line of reasoning is consistent with a broader stream

of research that argues that independent directors bear a reputation cost that

leads them to monitor management actions more carefully and take actions inthe interest of shareholders (for example, Fama and Jensen, 1983; Weisbach,

1988; Borokhovich et al., 1996). Within the accounting literature, Beasley

(1996) provided evidence that outsider-dominated boards reduce the likelihood

of financial statement fraud; Klein (2002) showed that firms with greater percent-

age of outsiders on the board are less likely to manage earnings, and Ajinkya et al.

(2004) documented that firms with greater percentage of outside directors are

more likely to issue earnings forecasts and make more frequent forecasts.

In this study I use the following measure to capture the role of outside

directors:

OUTDIR percentage of the board that are not also officers

of the company:

Based on the arguments above I expect OUTDIR to be negatively associ-

ated with DELAY.

2.1.3. Proprietary costs

A number of researchers have suggested that a firms disclosure decision

could be affected by its concern that market competitors can use the informa-

tion revealed to cut into the profits of the disclosing firm (e.g., Verrecchia,

1983; Wagenhofer, 1990; Feltham and Xie, 1992). Firms facing such propri-

etary costs may not only have incentives to withhold certain types of sensitive

information (Scott (1994) found evidence that the propensity to disclose pen-

sion plan related information was negatively associated with measures captur-

ing labor market power) but may also have incentives to delay the release of

financial information that can be used by competing firms or regulators. Bam-

ber and Cheon (1998) examined the association between proxies for proprie-tary costs (market to book and sales concentration) and forecast specificity

and forecast venue. They found somewhat weak evidence to support the pro-

prietary cost hypothesis. The following two measures of proprietary costs used

in this study are based on Bamber and Cheon (1998):



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MKBK the ratio of market value of equity to book value of

equity at the end of the fourth quarter:

CONS sales of the five largest firms within a two-digit SIC

code divided by total sales of all firms within the

two-digit SIC code:

If companies with high proprietary costs perceive that such costs can be re-

duced through delayed earnings release, we would expect a positive association

between the reporting lag and both MKBK and CONS.

2.1.4. Accounting complexity

The reporting lag could also be affected by the companys financial and

accounting complexity. I use the following three measures to capture the nature

of the firms complexity:

DIVERSE 1; if the company had more than one reportable

segment; 0; otherwise:

NACQUIRE number of acquisitions made by the company during

the fourth quarter:

SPECIAL 1; if the company had reported non-zero specialitems in the fourth quarter; 0; otherwise:

DIVERSE is meant to capture the fact that a multi-segment firm could be

more complex resulting in longer information processing time to create finan-

cial statements. NACQUIRE captures acquisition activities in the last quarter

which could also delay the reporting of earnings as combined and pro-forma

numbers may have to be generated. Lastly, companies reporting special items

may also require longer processing time as the auditor may need additional

time to examine the validity of these charges. SPECIAL is included to capturethis. 9

9 I also examined certain other variables that could capture the firms accounting complexity.

One such variable was operating cycle which is the average time between purchasing or acquiring

inventory and receiving cash from its sales. Managers of firms with shorter operating cycles may be

able to process financial information relatively quickly and may thus release earnings information

quickly. A second variable was operating leverage which was captured by the ratio of fixed assets tototal assets. Companies with greater operating leverage may take longer to allocate fixed overhead

costs and consequently make take longer to report the earnings figures. In the regressions, however,

none of these two variables were statistically significant suggesting that measures already included

are capturing a large part of the cross-sectional differences in the accounting complexity of the firms

in the sample.



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Given the conflicting evidence about the potential link between DELAY

and LOSS I do not predict the sign of the coefficient of LOSS.

Analysis of the various determinants of DELAY is performed using the fol-lowing regression:

DELAY a0 a1VOL a2SHRHOLD a3INST a4BLOCK

a5TECH a6OUTDIR a7MKBK a8CONS

a9DIVERSE a0NACQUIRE a11SPECIAL

a12LSALE a13STDRETN a14BADNEWS

a15LOSS e

The expected signs are: a1 < 0, a2 < 0, a3 < 0, a4 > 0, a5 < 0, a6 < 0, a7 > 0,a8 > 0, a9 > 0, a10 > 0, a11 > 0, a12 < 0, a13 > 0, a14 > 0, and a15 = ?.

2.2. Sample selection

The main sample for the study consists of 11,071 firm-year observations col-

lected as follows (see panel A ofTable 1 for a summary of the sample selection

procedure). First, fourth-quarter earnings report dates were collected from

First Call for the years 19952000, resulting in an initial sample of 19,992

observations.12

The sample was restricted to fourth-quarter earnings primarilybecause some variables such as segment information, listing of board of direc-

tors and officers, institutional ownership, and, block ownership were available

only on an annual basis. Tests were conducted to examine the sensitivity of the

reported results to this restriction and these are discussed in Section 3.4. In the

second stage, 1350 observations were deleted because the report date was either

within 7days of the fiscal year-end or more than 90days after the fiscal year-

end. This screen was set primarily to eliminate potential errors in report dates

and some extreme outliers, yielding 18,642 remaining observations.

In the next stage, financial data to compute the independent variables werecollected from various databases. Financial analysts EPS forecasts were ob-

tained from First Call. 1421 observations were eliminated since some First Call

analyst forecast data were unavailable. Financial data required to compute the

independent variables were primarily collected from CRSP and COMPUSTAT

databases. 3833 observations were deleted due to lack of information in CRSP

and COMPUSTAT to compute the requisite independent variables. Finally,

data on board of directors, officers, institutional ownership and block owner-

ship were obtained from the June edition of Compact Disclosure for each of

12 The results using the First Call report dates were compared to those available from

COMPUSTAT. The results reported in this paper were qualitatively similar to those obtained using

COMPUSTAT report dates.



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the years 19952000. Compact Disclosure collects board of directors and offi-

cer information from the latest available proxy statement and block ownership

and institutional ownership from Spectrum. Compact Disclosure information

was missing for 2317 of the remaining observations, resulting in the final sam-

ple of 11,071 observations.

Panel B of Table 1 reports the year-by-year distribution of reporting lag

(DELAY). The table shows a slight increasing trend in mean and median

reporting lag over 19951998 after which it declines slightly. This is in contrast

to Givoly and Palmon (1982) which documented a reduction in reporting lagover time (their sample period was 19601974) with the median delay going

down from 63 to 37days. During 19951998 the median lag increases from

33days to 41days but in 2000 it declines to 39days. The comparison of report-

ing lag between these two studies suggests that the reduction in reporting lags,

arising probably from improvements in the technology of communications

over time, may have stabilized in recent years.

Table 2 reports the summary statistics for the regression variables. The med-

ian DELAY for the sample period is 38 days. There is also substantial variation

in DELAY across the sample as indicated by the standard deviation, which isover 16, and by the inter-quartile range, which is 22days. Average block own-

ership is about 39%, and the average board consists of about 64% non-officers

(or outsiders). While not reported in the table, average asset size was about

$2.9 billion.

Table 1

Sample selection and description

Number of observationsPanel A: Sample selection criteria

Initial sample of fourth quarter reporting dates for the period

19952000

19,992

Less:

Earnings release date not within 790days after fiscal year-

end

(1350)

First Call data needed to compute analysts forecast error

unavailable

(1421)

Financial data from CRSP and COMPSTAT missing (3833)

Ownership data and officers and directors information

unavailable

(2317)

Final sample 11,071

Panel B: Time series distribution of the reporting lag (DELAY)*

1995 1996 1997 1998 1999 2000

Mean 35.62 37.27 39.06 42.36 41.37 41.53

Median 33 36 37 41 40 39

Panel A of this table reports the sample selection screens and Panel B reports the mean and median

values for DELAY (the number of days between the fiscal year end and fourth quarter earnings



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3. Results

3.1. Reporting lag and analysts disclosure ratings

Before proceeding to the tests of the determinants of DELAY, I examined

whether DELAY is correlated with financial analysts disclosure ratings. If

financial analysts value timely earnings releases then firms with shorter

DELAY should be associated with better disclosure quality ratings developed

Table 2

Summary statistics

Variable Mean Median STD Q1 Q3DELAY 39.34 38.00 16.48 27.00 49.00

VOL 1.50 1.00 1.59 0.55 1.84

SHRHOLD 1.12 0.99 1.67 2.16 0.09INST 41.40 40.22 25.14 20.66 61.10

BLOCK 38.80 35.69 27.60 16.97 57.36

TECH 0.21 0.00 0.41 0.00 0.00

OUTDIR 63.80 71.43 25.46 55.56 81.82

MKBK 4.01 2.08 22.72 1.31 3.56

CONS 0.47 0.47 0.17 0.36 0.58

DIVERSE 0.27 0.00 0.44 0.00 1.00

NACQUIRE 0.66 0.00 1.54 0.00 1.00

SPECIAL 0.29 0.00 0.45 0.00 1.00

LSALE 5.60 5.57 1.81 4.45 6.72

STDRETN 0.04 0.03 0.02 0.02 0.05

BADNEWS 0.36 0.00 0.48 0.00 1.00

LOSS 0.28 0.00 0.45 0.00 1.00

This table provides summary statistics for the key variables based on a sample of 11,071 obser-

vations pooled over the years 19952000. DELAY equals the number of days between the fiscal

year end and fourth quarter earnings release date. VOL is the trading volume for the fiscal year

divided by shares outstanding at the end of the fiscal year. SHRHOLD is the natural log of number

of shareholders of the company minus log of mean number of shareholders of firms in the sameasset decile computed using fiscal year end data. INST is the percentage of the company s common

shares held by institutions. BLOCK is the percentage of common shares of the company held by

blockholders. TECH equals 1 if the firm belongs to Drugs (COMPUSTAT SIC codes 2833

2836), R&D Services (87318734), Programming (73717379), Computers (35703577) and Elec-

tronics (36003674); 0 otherwise. OUTDIR represents the percentage of the board of directors

that are not officers of the company. MKBK is the ratio of market value of equity to book value of

equity at the end of the fiscal quarter. CONS is the ratio of total sales of the top five firms within a

two-digit SIC code to total sales of all firms within the two-digit SIC code. DIVERSE equals 1 if

the company reports more than one primary segment, 0 otherwise. NACQUIRE is the total

number of acquisitions made by the company during the fiscal fourth quarter. SPECIAL equals 1 if

COMPUSTAT reported non-zero special items for the fourth quarter; 0 otherwise. LSALE is thelog of total sales for the fiscal year. STDRETN is the standard deviation of daily stock returns over

the fiscal fourth quarter. BADNEWS equals 1 if the actual reported quarterly EPS is less than the

median analyst forecast before the earnings release, 0 otherwise. LOSS equals 1 if the firm reports a

loss for the fourth quarter, 0 otherwise.



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by these analysts. If the correlation between the two is extremely high, variables

that have been found to be associated with disclosure measures will also likely

to be correlated with DELAY so one may argue that a separate analysis of thedeterminants of DELAY is not informative. In order to examine this issue I use

financial analysts disclosure ratings obtained from the Association for Invest-

ment Management and Research (AIMR)s annual Report of the Financial

Analysts Federation Corporate Information Committee. These disclosure ratings

have been used in prior studies such as Lang and Lundholm (1993) and Sengu-

pta (1998) as aggregate measures of corporate disclosure quality. 13 I used data

for the period 19911995 collected from the AIMRs annual reports.14 The

disclosure ratings were then matched with fourth quarter report dates obtained

from First Call resulting in a sample of 1350 observations for which a total dis-closure quality rating (out of 100 points) were available. 15 For a sub-sample of

1004 observations, the AIMR also provided separate disclosure ratings based

on disclosure in annual reports, quarterly reports and other published informa-

tion, and through communications with financial analysts (investor relations).

For these sub-groups separate correlation coefficients are also provided.

Panel A of Table 3 reports the correlations between DELAY and raw ana-

lysts ratings. The correlation coefficients are all negative as expected. The cor-

relation between the annual reports score and DELAY is not statistically

significant but the correlation between DELAY and the other two sub-cate-gory scores, and the total score are statistically significant at the 0.01 level. Pan-

els B and C report correlations between DELAY and disclosure ratings, subject

to some transformations. Lang and Lundholm (1993) had pointed out that

companies in different industries are rated by different groups of analysts, pos-

sibly using different criteria, suggesting that the scores may not necessarily be

comparable across industries. In an attempt to deal with this problem, two

types of transformations were performed. Panel B reports correlations based

on variables that are computed as deviations from industry means. In Panel

C, correlations are based on standardized variables calculated as the deviationfrom industry mean divided by the standard deviation of the variable within

the industry. The correlations based on these transformations are consistently

negative and all are statistically significant at the 0.05 level or less. Overall, the

results are consistent with the argument that disclosure timing is considered to

be a positive attribute of corporate disclosure quality. However the correla-

tions are in the range of 0.03 to 0.14 suggesting that factors that that have

13 See Lang and Lundholm (1993) for a description of the AIMR disclosure ratings.14 The Association for Investment Management and Research discontinued their evaluation of

firms disclosure practices in 1995 so the correlation analysis could not be performed over the 1995

2000 sample period used for the rest of the analyses of the paper.15 The AIMR published disclosure ratings for only a select group of 300400 firms each year.



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been used to explain disclosure ratings (in prior literature) may not necessarily

explain the timing of earnings release.

3.2. Determinants of cross-sectional variation in reporting lags

The primary results of the determinants of DELAY are reported in Table 4.

Column 3 of the table reports the regression results based on the full sample of

11,071 observations for the period 19952000. Since the data are pooled over6years and some firms may be appearing in multiple years, the results could

be potentially affected as the observations may not all be independent. Thus

separate year-by-year results are provided in columns 49 to check the sensitiv-

ity of the results to pooling of observations over time. The regression

Table 3

Correlations between DELAY and analysts disclosure ratings

Annual Reports Quarterly reports andother published info

Investorrelations

Total score

Panel A: Correlations based on raw data

Coefficient 0.03801 0.09373 0.14352 0.12876p-value 0.2289 0.003


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Table 4

Cross-sectional determinants of DELAY

Independent variables Predicted sign Pooled Year-by-year

19952000 1995 1996 1997 1998

INTERCEPT ? 49.4247 46.3198 38.8364 47.4652 59.0027

(53.322)** (16.779)** (15.643)** (19.363)** (28.253

Investor base

VOL 0.7098 0.4136 0.1474 0.8239 1.338(6.973)** (1.471) (0.542) (3.960)** (6.155

SHRHOLD 0.3991 0.1649 0.0262 0.2981 0.5940(4.668)** (0.601) (0.096) (1.176) (3.013

INST 0.0406 0.0466 0.0855 0.0483 0.0200(6.402)** (2.487)** (5.044)** (3.029)** (1.507

BLOCK + 0.0320 0.0302 0.0515 0.0219 0.0125

(5.819)** (1.865)* (3.577)** (1.550) (1.149)

Litigation costs

TECH 5.5631 4.5472 5.1271 3.9182 6.3636(13.940)** (4.164)** (4.955)** (3.897)** (7.721

OUTDIR 0.0374 0.0659 0.0302 0.0246 0.0768(6.681)** (4.304)** (2.851)** (2.278)* (4.833

Proprietary costs

MKBK + 0.0085 0.0066 0.0139 0.0091 0.0151

(1.187) (1.643) (0.622) (0.924) (1.974)*

CONS + 2.2749 1.7226 9.0445 8.4583 0.5412 (2.569)** (

0.800) (4.075)** (3.596)** (0.297)

Accounting complexity

DIVERSE + 3.9470 5.8883 3.0992 4.2640 2.3327

(12.281)** (5.651)** (3.006)** (4.372)** (3.538)*

NACQUIRE + 0.6694 0.5950 1.0785 0.8549 0.5410

(6.803)** (1.631) (3.848)** (3.845)** (3.524)*


17/26


18/26

coefficients reported in the table are followed by t-statistics which are based on

Whites (1980) heteroscedasticity corrected covariance matrix. For the regres-

sion based on pooled data all variables have their expected signs and coeffi-cients are statistically significant at the 0.01 level, except for MKBK, which

is positive as expected but not statistically significant. The coefficients for LOSS

turn out to be negative and statistically significant at 0.01 level which is consist-

ent with the findings of Choi and Ziebart (2001) and Ajinkya et al. (2004).

The results of the year-by-year regressions are largely consistent with the

findings based on pooled data with one primary exception; the coefficient for

CONS, used as a measure of proprietary costs, is statistically significant in

1996 and 1997 only. For the other years, the coefficient is not statistically

significant.Overall, the results provide strong support of most of the hypotheses pre-

sented earlier. Thus DELAY is found to be negatively associated with meas-

ures of investor base (trading volume, number of shareholders and

institutional ownership) consistent with the argument that companies yield

to pressure from investors to release earnings information quickly. A positive

association between DELAY and BLOCK is found suggesting that as com-

pany ownership gets concentrated management is less susceptible to pressure

from outside to release earnings information quickly. The negative association

between DELAY and TECH would be consistent with the argument that firmsfacing litigation costs are more likely to release earnings numbers quickly.

While TECH has been used in the prior literature as a proxy for litigation costs

it could also capture other factors (for example the high tech firms may have

also invested heavily in information technology resulting in quick processing

of financial information) which could explain the results. The negative associ-

ation between DELAY and OUTDIR provides further support of the litiga-

tion cost argument since outside directors have little to gain from delaying

disclosures but can be named in lawsuits arising from lack of disclosures or de-

layed disclosures. My analysis, however, provides little support of the propri-etary cost hypothesis as the coefficient for MKBK is not statistically significant

in the regressions and CONS (the other proxy for proprietary costs), is statis-

tically significant only in the regression using the pooled data. One possible

explanation for the weaker findings relating to the proprietary cost incentives

is that the measures used may not be effectively capturing proprietary costs.

For example, MKBK would be greater for high growth firms which could be

associated with greater stock price volatility and consequently high litigation

risk (which is expected to be negatively associated with DELAY). Another

potential explanation is that firms may not perceive early release of earningsto generate sufficiently high costs. This could be due to the fact that the earn-

ings information has to be released within a certain deadline anyway and com-

panies are free to choose the nature and content of any additional information

they provide with the earnings release (firms facing high proprietary costs may



19/26

choose to provide minimal supplementary information with earnings but may

not choose to delay the earnings release).

The measures of accounting complexity all turn out to be strongly associ-ated with DELAY. As expected I found that the reporting lag is longer for

firms that are diversified, completed acquisitions, and those that reported spe-

cial items on their income statement. Thus DELAY is longer for companies

that are inherently more complex or those that had to deal with certain com-

plex transactions. The control variables (log of sales, standard deviation of

stock returns, and BADNEWS) all turned out to be as expected indicating that

the reporting lag is longer for smaller firms, those with greater market volatility

and those reporting bad news.

Adjusted R2

s for the regressions ranged from 14% to 26% with the averageR2 being about 21%, suggesting that the variables included, explain a signifi-

cant portion of the cross-sectional variation in the reporting lag. The R2 of

21% for the pooled regression could be compared to that of about 3% that

would be obtained if DELAY was regressed on BADNEWS (analysts forecast

error dummy) only. 16 Thus, the variables identified in this study, significantly

adds to our understanding of the determinants of DELAY, beyond forecast er-

ror identified in prior research. 17

3.3. Further analysis of special items

Results reported in Table 4 indicated that the reporting lag is longer for

firms reporting non-zero special items, after controlling for other potential

determinants of DELAY. Given this finding, one may wonder if DELAY is af-

fected by the size of special items and their sign (i.e., if they are income

increasing or income decreasing). I investigate this by running regression (1)

with the following two alternative measures for SPECIAL:

SPECIAL1 special items=sales:

SPECIAL2 absolute value of special items=sales:

The results of this analysis are reported in Table 5. The table indicates that

neither SPECIAL1 nor SPECIAL2 are statistically significant in the regres-

sions. 18 These findings, in conjunction with those reported in Section 3.2

16

Regressions using alternative measures of forecast error such as (actual EPS mediananalyst forecast)/price generated even lower R2.

17 Of course prior research used analyst forecast errors to explain unexpected changes in the

reporting lag and not the actual reporting lag which is the focus of this study.18 Separate year-by-year regression results (not reported) did not provide any conclusive pattern

either.



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Table 5

Effect of size and nature of special items on DELAY

Independent variables Predicted sign Model 1 Model 2INTERCEPT ? 49.0051 49.0139

(52.824)** (52.835)**

Investor base

VOL 0.7045 0.7058

(6.871)** (6.891)**SHRHOLD 0.3974 0.3972

(4.631)** (4.627)**INST 0.0401 0.0400

(6.312)** (6.296)**

BLOCK + 0.0328 0.0327(5.950)** (5.935)**

Litigation costs

TECH 5.5297 5.5307

(13.831)** (13.836)**OUTDIR 0.0368 0.0368

(6.574)** (6.576)**

Proprietary costs

MKBK + 0.0077 0.0078

(1.078) (1.079)

CONS + 2.1430 2.1438(2.417)** (2.418)**


DIVERSE + 3.9840 3.9843

(12.365)** (12.365)**

NACQUIRE + 0.6989 0.6992

(7.070)** (7.075)**

SPECIAL1 + 0.0820

(0.517)SPECIAL2 + 0.0051

(0.033)

Other variables/controls

LSALE 2.4929 2.4946

(25.174)** (25.186)**STDRETN + 106.7562 106.7999

(11.165)** (11.160)**

BADNEWS + 3.0265 3.0268

(9.511)** (9.512)**

LOSS ? 4.0653 4.0799

(10.640)** (10.707)**

Number of observations 11,071 11,071Adjusted R2 0.21 0.21

This table reports results of the regression



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earlier seem to imply that whereas the presence of special items seem to affect

the reporting lag, the size of these special items and their directional effect on

income, does not seem to affect the reporting lag.

3.4. Analysis with first, second and third quarter earnings release dates

The results reported in earlier sections were based on DELAY measured as

the number of days after fiscal year-end that fourth quarter earnings were re-

leased. This was primarily done since ownership and board information were

available on an annual basis. In this section I re-estimate Eq. (1) using quar-

terly data for the first three quarters to see if the factors that explained part

of the variation in fourth quarter reporting lag could also explain variationin reporting lag for the other three quarters. In order to conduct this analysis,

VOL, MKBK, CONS, LSALE and STDRETN are now measured using data

for the appropriate quarter. The results, reported in Table 6, are very similar to

those reported in Table 4 based on fourth quarter reporting lag. Apart from

Table 5 (continued)


a5TECH a6OUTDIR a7MKBK a8CONS a9DIVERSE a10NACQUIRE a11SPECIAL


a15LOSS e

with alternative measures of SPECIAL. In Model 1, SPECIAL1 is the absolute value of special

items reported for the fourth quarter divided by sales for the fourth quarter. In Model 2, SPE-

CIAL2 is the ratio of special items reported in the fourth quarter divided by sales of the fourth

quarter. Other variables are defined as follows. DELAY equals the number of days between the

fiscal year end and fourth quarter earnings release date. VOL is the trading volume for the fiscal

year divided by shares outstanding at the end of the fiscal year. SHRHOLD is the natural log of

number of shareholders of the company minus log of mean number of shareholders of firms inthe same asset decile computed using fiscal year end data. INST is the percentage of the company s

common shares held by institutions. BLOCK is the percentage of common shares of the company

held by blockholders. TECH equals 1 if the firm belongs to Drugs (COMPUSTAT SIC codes

28332836), R&D Services (87318734), Programming (73717379), Computers (35703577) and

Electronics (36003674); 0 otherwise. OUTDIR represents the percentage of the board of direc-

tors that are not officers of the company. MKBK is the ratio of market value of equity to book

value of equity at the end of the fiscal quarter. CONS is the ratio of total sales of the top five firms

within a two-digit SIC code to total sales of all firms within the two-digit SIC code. DIVERSE

equals 1 if the company reports more than one primary segment, 0 otherwise. NACQUIRE is

the total number of acquisitions made by the company during the fiscal fourth quarter. LSALE

is the log of total sales for the fiscal year. STDRETN is the standard deviation of daily stock re-turns over the fiscal fourth quarter. BADNEWS equals 1 if the actual reported quarterly EPS is

less than the median analyst forecast before the earnings release, 0 otherwise. LOSS equals 1 if

the firm reports a loss for the fourth quarter, 0 otherwise. OLS regression coefficients are followed

by t-values in parentheses based on Whites heteroscedasticity adjusted covariance matrix. **indi-

cates statistical significance at 0.01 level and *indicates statistical significance at 0.05 level (one-

tailed tests, except for INTERCEPT).



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Table 6

Determinants of DELAY using first, second, and third quarter earnings announcement dates

Independent variables Predicted sign Quarter 1 Quarter 2 Quarter 3INTERCEPT ? 26.9074 27.8083 27.4618

(51.617)** (54.396)** (56.913)**

Investor base

VOL 72.1197 99.2120 119.1584

(3.636)** (5.075)** (6.335)**SHRHOLD 0.4390 0.3559 0.4043

(8.258)** (6.989)** (8.155)**INST 0.0302 0.0274 0.0316

(7.660)** (7.278)** (8.481)**

BLOCK + 0.0146 0.0185 0.0170(4.166)** (5.548)** (5.219)**

Litigation costs

TECH 2.9106 2.8051 2.8209

(12.394)** (11.694)** (12.390)**OUTDIR 0.0110 0.0123 0.0118

(3.108)** (3.581)** (3.555)**

Proprietary costs

MKBK + 0.0002 0.0008 0.0056(1.200) (0.245) (1.142)

CONS + 0.0711 1.1317 0.9022(0.123) (2.036) (1.680)


DIVERSE + 1.8756 2.0619 1.7503

(9.368)** (10.568)** (9.313)**

NACQUIRE + 0.3640 0.4303 0.3898

(5.342)** (6.306)** (5.651)**

SPECIAL + 0.5463 0.9978 0.9258

(1.951)* (4.192)** (4.099)**

Other variables/controls

LSALE 0.8614 0.8902 0.9108

(13.585)** (14.067)** (15.048)**STDRETN + 40.3729 35.0604 36.7253

(6.210)** (5.595)** (7.887)**

BADNEWS + 1.7509 1.8536 1.7167

(8.151)** (9.462)** (9.341)**

LOSS ? 2.3667 2.5915 2.7132

(8.979)** (10.309)** (11.034)**

Number of observations 9823 10,482 10,808

Adjusted R2 0.12 0.13 0.15

This table reports results of the regression



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MKBK and CONS all other variables have their expected signs and are statis-

tically significant at conventional levels. The coefficient for MKBK is not sta-

tistically significant in any of the regressions (which is consistent with the

results reported for fourth quarter) whereas the coefficient for CONS turns

out to be negative and statistically significant for quarters 2 and 3 (it is positiveis quarter 1 but not statistically significant). Overall, the results ofTable 6 indi-

cate that factors found to be associated with the reporting lag in fourth quarter

earnings delay are also associated with the reporting lag of the other quarters.

3.5. Sensitivity analysis

I performed a battery of tests to examine whether the results presented

above are sensitive to the choice of variables, sample, and outliers. First, as

a robustness check, I re-estimated regression (1) using an alternative samplewhere only firms with at least three analysts following were kept. Tests run

on this reduced sample (sample size was about 60% of that reported in Table

4) using coefficient of variation of analyst forecasts as the uncertainty measure

(instead of STDRETN) showed every variable to be statistically significant at

Table 6 (continued)


a5TECH a6OUTDIR a7MKBK a8CONS a9DIVERSE a10NACQUIRE a11SPECIAL


a15LOSS e

DELAY equals the number of days between the fiscal quarter end and quarterly earnings release

date (for quarters 1, 2 and 3). VOL is the trading volume for the fiscal quarter divided by shares

outstanding at the end of the fiscal quarter. SHRHOLD is the natural log of number of sharehold-

ers of the company minus log of mean number of shareholders of firms in the same asset decile.

INST is the percentage of the companys common shares held by institutions. BLOCK is the per-

centage of common shares of the company held by blockholders. TECH equals 1 if the firm be-

longs to Drugs (COMPUSTAT SIC codes 28332836), R&D Services (87318734), Programming(73717379), Computers (35703577) and Electronics (36003674); 0 otherwise. OUTDIR repre-

sents the percentage of the board of directors that are not officers of the company. MKBK is the

ratio of market value of equity to book value of equity at the end of the fiscal quarter. CONS is the

ratio of total sales of the top five firms within a two-digit SIC code to total sales of all firms within

the two-digit SIC code. DIVERSE equals 1 if the company reports more than one primary segment,

0 otherwise. NACQUIRE is the total number of acquisitions made by the company during the fis-

cal quarter. SPECIAL equals 1 if COMPUSTAT reported non-zero special items for the quarter; 0

otherwise. LSALE is the log of total sales for the fiscal quarter. STDRETN is the standard devi-

ation of daily stock returns over the fiscal quarter. BADNEWS equals 1 if the actual reported quar-

terly EPS is less than the median analyst forecast before the earnings release, 0 otherwise. LOSS

equals 1 if the firm reports a loss for the current quarter, 0 otherwise. OLS regression coefficientsare followed by t-values in parentheses based on Whites heteroscedasticity adjusted covariance ma-

trix. **indicates statistical significance at 0.01 level and *indicates statistical significance at 0.05 le-

vel (one-tailed tests, except for INTERCEPT).



24/26

the 0.01 level except the analyst dispersion measure and CONS, both of which

came out weakly significant (statistically significant at the 0.1 level). I also ran

the analysis after eliminating extreme values of all variables (top and bottomone percent of all variable were eliminated) and the results turned out to be

qualitatively similar to those reported in Table 4. Finally, tests of multicollin-

earity revealed no significant problems.

4. Conclusions

This paper investigated why certain firms choose to release their quarterly

earnings information relatively quickly compared to others. Results indicated

that the nature of investor base, litigation costs, accounting complexity, and

earnings news, are related to the reporting lag which was defined as the number

of days after fiscal quarter end the company released its earnings. Broadly speak-

ing, the paper makes a contribution by attempting to understand firms disclo-

sure timing choice. This line of enquiry is useful since timing is an important

dimension of disclosure that could affect the usefulness (relevance) of the dis-

closed information. More specifically, this paper extends a line of research that

had consistently documented a negative association between the unexpected

changes in earnings announcement dates and nature of the news (as measured

by analysts forecast errors). By examining a large number of factors beyond

analysts forecast error, this paper attempts to provide a more comprehensive

understanding of how managers plan to time the release the earnings informa-

tion. Subsequent research could explore potential determinants of release dates

of other voluntary disclosures such as managements earnings forecasts.

Acknowledgment

I would like to thank Bipin Ajinkya, Oliver Kim, Taewoo Park, James Pe-ters and Mike Peters for comments and suggestions on an earlier draft. I also

thank Thompson Financial for generously providing the First Call analyst

forecast data through their Academic program.

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