








































60 
 

 
 

 

Preannouncements: Forecast or Realized Earnings? 
 

Debjeet Pradhan 
Tarleton State University 

 
 The literature has long recognized preannouncements as management forecasts issued after 
the fiscal period ends but before the actual earnings announcements. However, with the advent of 
Enterprise Resource Planning (ERP) systems, companies now can gather information promptly and 
process it efficiently, which allows companies to prepare earnings summaries in real-time, thus making 
it unnecessary to issue forecasts in the days following the quarter-end date. This study argues that any 
earnings announcements issued after the quarter-end date (preannouncements) by companies that 
have implemented ERP systems are based on realized earnings. Such announcements are expected to 
be more accurate than management forecasts, which are based on estimates of future earnings, and 
may be issued before the quarter-end date. This study finds that the accuracy of management voluntary 
disclosures and the timeliness of preannouncements improved in the periods following ERP 
implementation.   
 
Keywords: preannouncements, management forecast, enterprise resource planning 
 

Introduction 
 Innovations in information technology helped companies improve worker productivity and 
lead to the economic boom of the 1990s. It also changed the practice of Accounting in meaningful 
ways and influenced the way managers disclosed information to the market. Many companies 
implemented sophisticated Enterprise Resource Planning (ERP) systems to make the processing and 
storage of accounting data more efficient than the legacy systems that preceded them. ERP systems 
helped improve operational efficiency by automating many accounting and data processing tasks and 
by eliminating information silos. ERP systems gave managers the ability to consolidate and analyze 
accounting information rapidly, often within a matter of days following the close of calendar quarters.   

The mere implementation of a software platform such as ERP, however, cannot improve 
company performance. In the initial days of ERP, implementation companies faced resistance from 
employees who did not want to learn new software. They did not understand how the software worked 
and treated it like a black box. In addition, the early versions of ERP failed to produce reports that 
were visually attractive to management and could not be directly used in presentations. Many users 
simply preferred to extract accounting numbers from ERP only to import them into Microsoft Excel 
to conduct further analysis. Although Excel may have helped them prepare better-looking 
presentations, it deprived them of the real power of ERP systems, which was the ability to consolidate 
and summarize large volumes of data accurately in almost real-time. A significant improvement in firm 
performance, expressed in terms of a ratio of cost of goods sold to revenue, was only found 3 years 

http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH 
2021, Volume 11, pages 60-77 



61 
 

after ERP implementation and not in the first or second years (Poston & Grabski, 2001). It is 
reasonable to expect a variation in the level and speed of adoption of ERP among firms.   

Because ERP systems can consolidate and process data in real-time, it is possible to prepare 
financial statements based on realized financial performance within three working days or less.  
Therefore, once a fiscal quarter closes, there is no reason for management to issue guidance based on 
a forecast. Any guidance issued in the days following a quarter-end date is based on actual financial 
performance (e.g., earnings realization), and it is not a forecast. For simplicity, such guidance is referred 
to as “preannouncement” in this study.   

The literature has long recognized preannouncements as “management forecasts issued after the 
fiscal period end but before the actual earnings announcements.” (e.g., Ajinkya et al., 2005 (footnote 
11)). Miller (2005, p.1), refers to preannouncements as “estimates of earnings disclosed after the 
accounting period-end and before the official earnings announcement.” Soffer et al. (2000, p.1) do 
not specifically refer to the period in which preannouncements are made, preferring instead to define 
preannouncements as a “voluntary disclosure of a tentative earnings amount made shortly before the 
formal earnings announcement.” However, the common assumption that preannouncements are 
forecasts or estimates is mistaken, especially after the adoption of Enterprise Resource Planning (ERP) 
software by operating firms (ERP firms). Preannouncements are based on actual earnings results and, 
therefore, are not forecasts at all. Even so, management forecast errors are not expected to be zero 
for ERP firms because the incentives to meet or beat earnings guidance remain (see Brazel & Dang, 
2008; Soffer et al., 2000).  

In this study, preannouncements are defined as any disclosure of quarterly earnings 
corresponding to the recently completed quarter that is released to the market after the quarter-end 
date but before the actual earnings announcement (EA) date. This definition is more precise than 
Soffer et al. (2000) who define preannouncement as “a voluntary disclosure of tentative earnings 
amount made shortly before the formal earnings announcement”.  The preannouncement (PA) timeline 
is depicted in Figure 1 below.   

 
Figure 1 – Preannouncement Timeline 

 
 

Quarter end date   Earnings announcement (EA) date 

 

 

 

 

 

 In contrast to preannouncements, forecasts are based on management projections because 
earnings realizations cannot be determined until the close of the quarter. For the convenience of 
exposition, we use the term guidance to refer to both preannouncements and forecasts. Before ERP 
implementation, the forecast period may have extended beyond the quarter-end date. After ERP 
implementation it is extremely unlikely that management would issue a forecast after the quarter-end 

Preannouncement (PA) Management forecast 

Guidance 



62 
 

date, knowing fully well that they will have realized earnings information within a few days. Therefore, 
the quarter-end date is a good cut-off date to distinguish between a forecast and a preannouncement.  

Despite all the advances of ERP, there have not been many moves by firms to replace their 
preannouncement with formal earnings announcements, although Brazel & Dang (2008) show that 
good news firms with ERP tend to report their earnings earlier than before. There could be several 
reasons for this behavior. First, management may not believe that there is an obvious advantage to 
announcing early.  Second, firms may lose their ability to manage the expectations of analysts and 
investors during earnings season.  Soffer et al. (2000) show that firms with good news tend to release 
about half of the news during preannouncements while reserving the other half for the formal earnings 
announcement. To the extent that firms prefer to retain this flexibility, they will not advance their 
earnings announcement dates. Third, having a time cushion before the earnings announcement may 
be beneficial should there be any auditing issues or other unforeseen events that crop up late in the 
earnings reporting cycle. Earnings announcement dates are usually announced in advance in keeping 
with tradition, but preannouncements allow full flexibility to firms with regard to their timing. Finally, 
companies may prefer to stick to tradition and announce formal earnings around the same time as 
they have been doing in the past. 

This study examines the relationship between ERP systems and the voluntary disclosure 
behavior of firm managers. Empirical analysis of management disclosure and ERP implementation 
data shows that ERP helps firms reduce guidance errors, and issue preannouncements to the market 
in a timelier manner.   

 
Literature Review 

 The Securities and Exchange Commission (SEC) requires all publicly held US corporations to 
disclose all information that may be relevant for an investor to decide whether they may want to buy, 
sell or hold a company’s security. Some disclosures are mandatory (e.g., 10K, 10Q) while others are 
voluntary. However, voluntary disclosures are not optional – if any information is pertinent to an 
investment decision regarding a firm, it must be released. Of course, managers have broad latitude in 
deciding what, when, and how to release non-public information, subject to the limitations imposed 
by Regulation FD. Such flexibility in voluntary disclosures allows managers to either act in a manner 
that enhances the information available to investors and reduces information asymmetry between 
investors and managers (well-intentioned behavior, consistent with the SEC’s stated purpose for 
financial disclosure), or act opportunistically to benefit the firm, management, or a subset of investors.   

The SEC has passed many regulations over the years to encourage voluntary disclosures and 
to provide some legal protections to managers who make such disclosures. Significant regulatory 
changes include allowing companies to include forward-looking information in regulatory filings 
(enacted in 1973), providing safe-harbor to companies providing forward-looking disclosures to shield 
them from frivolous lawsuits related to disclosures made in good faith (enacted in 1979), and extending 
the safe-harbor rules to protect firms from lawsuits related to forecasts that do not ultimately 
materialize (enacted in 1996). 

Voluntary disclosures usually take the form of either earnings forecasts (management 
guidance) or pro-forma (non-GAAP) earnings measures. Earnings forecasts are estimates of what a 
company believes it will report in the future as a measure of its financial performance. Pro-forma 
earnings are alternative earnings measures that do not conform to Generally Accepted Accounting 
Principles (GAAP). They are prepared and disseminated by managers in the belief that they may be 
useful to investors. 

 



63 
 

Management Guidance 
 Prior literature documents several reasons – both well-intentioned and opportunistic – why 
managers voluntarily provide earnings guidance. One reason is the expectations hypothesis (Ajinkya 
& Gift, 1984 and King et al., 1990), which posits that managers issue guidance to align the market’s 
earnings expectations with management beliefs. Guidance reduces information asymmetry between 
managers and investors, reduces bid-ask spreads for a company’s common stock, decreases the 
dispersion of analyst forecasts, and favorably influences stock prices. Another reason why companies 
disclose earnings guidance is to reduce litigation risk associated with shareholder lawsuits (Skinner, 
1994).  If managers withhold material information, which subsequently leads to shareholder losses, 
they may be subject to shareholder lawsuits that may damage their reputation and impose significant 
costs on the company. Therefore, managers have the incentive to release material information, 
especially unfavorable information, to investors. Finally, managers disclose earnings guidance to 
develop a favorable reputation for transparent and accurate reporting (Graham et al., 2005). 

Prior literature also documents examples of opportunistic disclosure behavior. For example, 
Choi & Ziebart (2004) provide evidence that shows that managers’ long-term forecasts are 
systematically higher than actual earnings realizations. This may simply reflect managers’ natural 
tendency to be optimistic. On the other hand, it may also indicate that managers prefer to be overly 
optimistic to maximize their stock price by increasing the market’s expectation of future earnings.   

Several studies (Hutton, 2005; Ke & Yu, 2006; Richardson et al., 2004) have shown that 
managers’ short-term forecasts tend to be systematically lower than subsequent earnings 
announcements. Managers have been known to engage in “expectations management” to talk down 
analysts’ expectations shortly before earnings announcements in an attempt to meet or beat consensus 
analyst estimates (Burgstahler & Eames, 2006; Matsumoto, 2002). The incentives for doing this are 
strong:  the stock market provides a valuation premium to companies that meet or beat analyst 
consensus estimates (Skinner & Sloan, 2002) but punishes companies who miss analyst consensus 
estimates with a large drop in share price. 

Other evidence of opportunistic disclosure behavior includes managers issuing bad news 
earnings guidance around stock option award periods to temporarily depress stock prices (Aboody & 
Kasznik, 2000), and issuing overly optimistic guidance around secondary equity offerings (SEOs) to 
ensure higher prices for new shares (Rogers & Stocken, 2005). 

   
Voluntary Disclosures of Non-GAAP Earnings Measures 
 During the boom years of the 1990s, many companies released pro-forma earnings along with 
their quarterly earnings announcements. Managers claimed that pro-forma earnings better reflected 
the actual economic performance of their companies. Others claimed that since pro-forma earnings 
did not conform to GAAP, managers could use them to mislead investors.  Academic research on 
Pro-forma disclosures finds evidence that supports both claims. Bhattacharya et al. (2003) show that 
pro-forma earnings are more strongly associated with stock prices than GAAP earnings, suggesting 
that investors find pro-forma earnings more informative than GAAP earnings. Other studies (Lougee 
& Marquardt, 2004) show that the quality of GAAP earnings for companies that report pro-forma 
earnings is of a lower quality than those companies that don’t. This suggests that companies that issue 
pro-forma earnings do so to improve on the deficiencies of their GAAP earnings. 
 
Hypotheses Development  
 US firms started implementing ERP systems in the early-1990s. The transition from legacy 
systems-generated to ERP-generated financial results happened over time (it took multiple years to 
complete implementation at many firms). ERP systems gather information on time, help process 
accounting information efficiently (Davenport 1998; Hitt et al. 2002), and provide a unified enterprise 



64 
 

view of a firm’s financial condition (Dillon 1999).  They also help eliminate information silos and allow 
managers unprecedented access to accounting information (O’Leary 2000). Brazel & Dang (2008) 
show that ERP system implementations shorten reporting lags, between quarter-end and earnings 
release dates, for “good news” firms. Hayes et al. (2001) compare ERP adopters with ERP non-
adopters and illustrate that ERP adopters exhibit improved operational performance.  

The above evidence points to an improved internal information environment for firms with 
ERP systems which likely helped managers provide better guidance to the market. Guidance errors 
(ABSERROR) are expected to have decreased significantly in the years following ERP 
implementation: 

 
ABSERROR = |Realized Earnings - Management Guidance|/ Lagged Assets per Share   (1), 

 
where management guidance corresponds to the latest forecast or preannouncement issued before the 
earnings announcement. 
 
 Hypothesis 1:  Management guidance errors in ERP firms decreased in the years following ERP 
implementation 

 
The motivation for managers to manage earnings expectation upward or downward has been 

extensively studied in the literature (see literature review above). The incentives for managing earnings 
are strong: the stock market provides a valuation premium to companies that meet or beat analyst 
consensus estimates (Skinner & Sloan, 2002) but punishes companies who miss analyst consensus 
estimates with a large drop in share price. Consequently, risk-averse managers will prefer not to issue 
forecasts in the days leading up to the quarter-end date when they will be close to having the actual 
performance data from the ERP system. They will postpone the issuance of management forecasts 
until they have actual earnings data. If they need to issue a forecast, it will happen earlier in the quarter. 

 
 Hypothesis 2: For the ERP firms, the number of days to quarter-end of management forecasts issued in the 
days before the quarter-end, will increase in the years following ERP implementation 
 
 ERP systems can gather, process, and consolidate data in real-time. Towards the end of an 
accounting period, although some manual intervention may be required, in the form of adjusting 
entries or managerial reviews, it is possible to produce financial statements in three working days or 
less. In any case, companies can issue preannouncements much earlier after ERP implementation 
compared to prior periods. 
 
 Hypothesis 3: For the ERP firms, the number of days after quarter-end of preannouncements issued in the 
days following quarter-end, will decrease in the years following ERP implementation 

 
Method 

Sample Selection 
 ERP system implementation data are obtained from a proprietary database supplied by a 
leading international provider of ERP systems. The database contains the names of firms that 
implemented ERP systems and the dates on which the system went live, among other details that are 
not used in this paper. There are 315 unique firms with ticker symbols, CUSIP, and PERMNO in the 
database that implemented ERP systems between 1994 and 1999 (go-live dates).    

Other data, which are described in more detail in Appendix 1, are available from Compustat, 
First Call, Audit Analytics, and IBES. The data from Compustat comprise 331,569 firm-quarter 



65 
 

observations for the period 1991 through 2002. This period was chosen to include at least 12 quarters 
of data before the first ERP implementation date and 12 quarters of data beyond the last ERP 
implementation date. This data was merged with management forecast data from First Call, BIG4 data 
from Audit Analytics, and ERP implementation data. Observations with missing values were dropped. 
That left us with 7009 firm-quarter observations. Finally, analyst forecast data obtained from IBES 
were merged and the observations were limited to plus or minus 3 years from the ERP implementation 
dates. That left 1842 firm-quarter observations to estimate our regressions. The top and bottom 1% 
of each continuous variable was winsorized to mitigate the influence of outliers. 
 
Research Design 
 The literature has employed two different approaches to study the accuracy of management 
guidance. One approach focuses on the incentives facing managers and their firms (e.g., Rogers & 
Stocken, 2005). The other approach focuses on the quality of internal control over financial reporting 
(Feng et al., 2009). The quality of internal control is directly related to the quality of the data in the 
financial system. The quality of data is important because, no matter the incentives, management 
guidance based on inferior data will be less accurate. Because ERP systems are expected to enhance 
data quality, the second approach is chosen to conduct our empirical tests.  

Managers’ disclosures influence the market, but their decision to issue guidance, being 
voluntary, may, in turn, be influenced by the market. To control for this endogeneity in managers’ 
disclosure decisions, the two-stage least squares model of Feng et al. (2009) is modified to test our 
hypotheses. Since material weakness disclosures were not required before the implementation of the 
Sarbanes-Oxley Act, the material weakness variable in their regression is replaced with a corporate 
governance proxy (INSTOWN). An ERP dummy variable is also included in the second stage, which 
takes the value of 1 in the years following ERP implementation and zeroes otherwise, to test the 
impact of ERP implementation on management guidance error. In the first stage, the following probit 
regression of the choice to provide guidance is estimated. GUIDANCE is an indicator variable that is 
equal to 1 if the manager issues guidance in the quarter, and zeroes otherwise. 

 
GUIDANCE  =  b0 + b1ERP + b2LN_TA + b3BIG4 + b4ABSCHGROA + b5STD_AF + 

b6VOLATILITY + b7ORGANIZATIONAL CHANGE + b8COMPLEXITY + 
b9FINANCIAL CHALLENGES + b10INSTOWN + b11LN_ANALYSTS + Σb i 
Industry and Year indicators            (2) 

 
 The inverse Mill’s ratio (IMR) from the first stage model of the choice to issue guidance above 
(equation 2) is used to estimate the second stage ordinary least square model below: 
 
ABSERROR  =  b0 + b1ERP + b2INSTOWN + b3LN_TA + b4BIG4 + b5ABSCHGROA + 

b6DISPFOR + b7VOLATILITY + b8ORGANIZATIONAL CHANGE + 
b9COMPLEXITY + b10FINANCIAL CHALLENGES + b11HORIZON + 
b12ABSREVISION + b13IMR + Σbi Industry and Year Indicators + e  (3) 

 
The independent variables used in equations 2 and 3 are taken from prior research. A 

description of the variables is included in Appendix 1. LN_TA is the natural logarithm of a firm’s total 
assets, used here as a proxy for firm size. Prior research (e.g., Kasznik & Lev, 1995) provides evidence 
of a positive association between firm size and management guidance. BIG4 is an indicator variable 
that is equal to 1 if the auditor is a Big 4 auditor, and zero otherwise. Prior research shows that firms 
using Big 4 auditors tend to have better disclosures (e.g. Ajinkya et al., 2005). ABSCHGROA, the 
absolute value of the change in return on assets, is used as a proxy for shocks to earnings, which makes 



66 
 

it more difficult to predict future earnings (Feng et al., 2009). DISPFOR is the standard deviation of 
analysts’ forecast before the issuance of management guidance. This variable captures the inter-analyst 
uncertainty in the earnings prospects of a firm, indicating the level of difficulty of providing accurate 
earnings guidance (e.g., Ajinkya & Gift, 1984). The variables VOLATILITY, ORGANIZATIONAL 
CHANGE, COMPLEXITY, and FINANCIAL CHALLENGES are factors, derived from a principal 
component analysis of 14 variables that proxy for a firm’s underlying volatility and innate uncertainty. 
More detail on these variables is included in Appendix 2. Two additional variables, HORIZON and 
ABSREVISION are used to control for the difficulty in estimating accurate management guidance.  
HORIZON is defined as the number of days before the fiscal period-end that the management 
guidance is issued, where a larger number indicates more timely guidance. Guidance issued after the 
fiscal period-end is not excluded, and thus HORIZON can be negative. ABSREVISION is the 
magnitude of the revision suggested by the management guidance, defined as the absolute value of 
management guidance less the pre-existing median of consensus analyst forecast deflated by lagged 
assets per share. Both HORIZON and ABSREVISION should be positively associated with guidance 
error (Ajinkya et al., 2005).  

To successfully control endogeneity, at least one independent variable should be correlated 
with the dependent variable in the first stage model but not the dependent variable in the second stage 
model. In equation (2), the logarithm of the number of analysts following a firm (LN_ANALYSTS) 
is correlated with the dependent variable (see Feng et al., 2009; Lang & Lundholm, 1996; Ajinkya et 
al., 2005). Analyst following and management guidance errors are not significantly associated (Ajinkya 
et al., 2005). For H1 to be true, the ERP coefficient, b1 in equation (3) should be negative and 
significant.   

 
Hypothesis 2 
For testing hypothesis 2, the variable HORIZON is split into two variables, FCAST_HORIZON and 
PRE_HORIZON, where FCAST_HORIZON = number of days from management forecast date to 
quarter-end date, and PRE_HORIZON = number of days from quarter-end date to 
preannouncement date. 
 The 2nd stage regression, equation (3) is modified to include FCAST_HORIZON as follows: 
 
FCAST_HORIZON = b0 + b1ERP + b2 INSTOWN + b3LN_TA + b4BIG4 + b5ABSCHGROA 

+ b6DISPFOR + b7VOLATILITY + b8ORGANIZATIONAL CHANGE + 
b9COMPLEXITY + b10FINANCIAL CHALLENGES + b11ABSREVISION + 
b12IMR + Σb i Industry and Year Indicators + e     (4) 

 
The ERP coefficient, b1 in the above regression is expected to be positive and significant. 
 
Hypothesis 3  
For testing hypothesis 3, the following regression is estimated: 
 
PRE_HORIZON = b0 + b1ERP + b2INSTOWN + b3LN_TA + b4BIG4 + b5ABSCHGROA + 

b6DISPFOR + b7VOLATILITY + b8ORGANIZATIONAL CHANGE + 
b9COMPLEXITY + b10FINANCIAL CHALLENGES + b11ABSREVISION + 
b12IMR + Σb i Industry and Year Indicators + e     (5) 

 
A negative and significant coefficient on the ERP dummy variable (b1) will support hypothesis 3. 



67 
 

Results 
 Table 1 shows descriptive statistics for all variables used in the two-stage regressions for the 
periods before and after the completion of ERP implementation. Variable definitions are provided in 
Appendix 1. Guidance error (ABSERROR) has been defined as the absolute value of the difference 
between realized earnings per share and management guidance per share, deflated by lagged total 
assets per share. Univariate comparison of ABSERROR (Table 1 Panel A versus Panel B) 
demonstrates that guidance errors decrease in the period immediately following ERP 
implementations, suggesting that ERP helps managers provide more accurate guidance after ERP 
adoption. However, a univariate analysis does not reliably explain the change in guidance accuracy 
because it may be driven by other variables, creating a correlated, omitted variable problem. Therefore, 
multivariate regressions are needed to study the relationship between our dependent and independent 
variables. 
 
Table 1 – Descriptive Statistics  
 

Panel A: Before ERP Implementation 

Variable  N Mean Std. Dev. Min. Max. 
ABSERROR 902 0.0128 0.0298 0 0.2125 
LN_ANALYSTS 902 1.8259 0.7346 0 3.3673 
STD_AF 902 0.0669 0.1120 0 2.0560 
DISPFOR 902 0.0660 0.1032 0 1.4616 
LN_TA 902 7.3650 1.5799 3.9816 11.3012 
ABSREVISION 902 0.0031 0.0272 -0.0762 0.2133 
INSTOWN 902 0.0845 0.0785 0 0.4026 
ABSCHGROA 902 0.0002 0.0009 0 0.0204 
FINANCIAL CHALLENGES 902 0.6281 0.7701 -3.4364 2.7437 
COMPLEXITY 902 0.5343 1.0106 -1.3618 4.9056 
ORGANIZATIONAL CHANGE 902 0.0332 0.7711 -1.8766 6.3817 
VOLATILITY 902 -0.3189 1.1488 -3.7094 2.8026 
HORIZON 902 16.3968 43.7188 -43 191 
FCAST_HORIZON 734 32.2538 34.3408 1 191 
PRE_HORIZON 168 18.5424 42.0367 1 43 
  

Panel B: After ERP Implementation 

Variable  N Mean Std. Dev. Min. Max. 
ABSERROR 940 0.0051 0.0115 0 0.1437 
LN_ANALYSTS 940 2.0994 0.8338 0 3.8067 
STD_AF 940 0.0827 0.1437 0 3.1847 
DISPFOR 940 0.0804 0.1345 0 3.1847 
LN_TA 940 7.8074 1.5297 3.6431 11.8803 
ABSREVISION 940 -0.0013 0.0139 -0.1214 0.1105 
INSTOWN 940 0.0221 0.0506 0 0.4584 
ABSCHGROA 940 0.0002 0.0006 0 0.0167 



68 
 

FINANCIAL CHALLENGES 940 0.6248 0.7665 -3.5444 3.0007 
COMPLEXITY 940 1.2398 1.4453 -1.6843 8.0460 
ORGANIZATIONAL CHANGE 940 0.0882 0.6908 -1.7346 7.1868 
VOLATILITY 940 0.2350 1.3416 -3.6446 4.3402 
HORIZON 940 41.2783 59.6901 -17 162 
FCAST_HORIZON 765 58.0475 34.9062 7 162 
PRE_HORIZON 175 39.5563 83.6112 1 17 

  Variable definitions are provided in Appendix 1 
 
Hypothesis 1 suggests that management guidance became more accurate following ERP 

implementation.  The results of a two-stage least squares test of H1 are presented in Table 2 (1st stage) 
and Table 3 (2nd stage). The instrument variable for analysts following (LN_ANALYSTS) is 
significant, as expected, confirming that it is correlated with the choice to provide guidance 
(GUIDANCE), the dependent variable. Consistent with the literature, the probit regression shows 
that management guidance is more prevalent in larger firms (e.g., Ajinkya et al., 2005). GUIDANCE 
is also significantly correlated with several other of our independent variables shown in bold in Table 
2. 

 
Table 2 – PROBIT Regression (1st Stage) of the Choice to Issue Guidance 

 

Variable  Coefficient Wald chi-square p-value 
INTERCEPT -2.4828 162.65 <.0001 
ERP 1.0044 446.44 <.0001 
LN_TA 0.1004 15.34 <.0001 
BIG4 0.1507 1.76 0.1849 
ABSCHGROA 46.341 3.08 0.0794 
STD_AF -0.8778 22.64 <.0001 
FINANCIAL CHALLENGES -0.1115 11.7 0.0006 
COMPLEXITY 0.0054 0.08 0.7803 
ORGANIZATIONAL CHANGE -0.0361 1.88 0.17 
VOLATILITY 0.1066 34.57 <.0001 
INSTOWN -1.1286 12.26 0.0005 
LN_ANALYSTS 0.1802 37.94 <.0001 
Industry indicators  included  

Year indicators  included  

No. of observations  1842  

Log-likelihood  -3433.61  

 
The coefficient on the dummy variable ERP in Table 3 is negative and significant at the 10% 

level, suggesting that, on average, ERP implementations help firms improve the accuracy of 
management guidance, which is consistent with Hypothesis 1. The fact that the coefficient on the 
inverse Mill’s ratio (IMR) is significant indicates that self-selection in the choice to issue guidance does 
affect management guidance error. 

 
 



69 
 

Table 3 – OLS Regression (2nd Stage) of Management Forecast Error (abserror)  
 

Variable  Coefficient t-stat p-value 
INTERCEPT -0.0089 -1 0.3166 
ERP -0.0024 -1.74 0.0816 
INSTOWN -0.0145 -1.92 0.0544 
LN_TA 0.0001 0.14 0.8906 
BIG4 0.0023 1.22 0.2217 
ABSCHGROA 9.6957 16.78 <.0001 
DISPFOR 0.0286 8 <.0001 
FINANCIAL CHALLENGES -0.0042 -7.57 <.0001 
COMPLEXITY 0.0016 4.57 <.0001 
ORGANIZATIONAL CHANGE 0.0010 2.2 0.0283 
VOLATILITY -0.0004 -1.32 0.188 
HORIZON 0.00002 4.8 <.0001 
ABSREVISION 0.4038 19.92 <.0001 
IMR 0.0057 3.95 <.0001 
Industry indicators  included  

Year indicators  included  

No. of observations  1842  

Adjusted R2  0.335  

 
Table 4 shows the results of the 2nd stage of the two-stage regression (equation 4) for 

FCAST_HORIZON, the number of days to end of the quarter from the date of the forecast.  
Although univariate results in Table 1 show that the forecast horizon increased from 32.25 days before 
to 58.05 days after ERP implementation, supporting Hypothesis 2, the results of multivariate 
regression of FCAST_HORIZON in Table 4 do not confirm the hypothesis. The ERP coefficient, 
though positive, is not significant. 

 
Table 4 – OLS Regression (2nd Stage) of Management Forecast Horizon (fcast_horizon)  

 
Variable  Coefficient t-stat p-value 
INTERCEPT 77.1915 2.2 0.0277 
ERP 5.5139 0.56 0.5746 
INSTOWN -82.7839 -2.97 0.003 
LN_TA -0.1062 -0.05 0.9586 
BIG4 -5.4671 -0.94 0.3449 
ABSCHGROA 3036.0372 1.03 0.3047 
DISPFOR 2.8656 0.19 0.852 
FINANCIAL CHALLENGES -5.7780 -2.73 0.0064 
COMPLEXITY 1.3250 1.25 0.2108 
ORGANIZATIONAL CHANGE 1.9915 1.34 0.182 
VOLATILITY -1.3895 -0.96 0.3385 



70 
 

ABSREVISION 444.2482 7.07 <.0001 
IMR -16.1390 -1.36 0.1733 
Industry indicators  included  

Year indicators  included  

No. of observations  1499  

Adjusted R2  0.089  

 
 
Table 5 shows the regression results of the 2nd stage of the two-stage regression (equation 5) 

for PRE_HORIZON, the number of days after quarter-end to the date of preannouncement. The 
coefficient on the ERP dummy is negative and significant at the 5% level, which supports Hypothesis 
3. The evidence shows that preannouncements are issued sooner in the period following ERP 
implementation compared to prior periods. 

 
Table 5 – OLS Regression (2nd Stage) of Preannouncement Horizon (pre_horizon) 
 

Variable  Coefficient t-stat p-value 
INTERCEPT 194.1816 1.55 0.1214 
ERP -69.4219 -2.04 0.0426 
INSTOWN -47.1383 -0.62 0.5341 
LN_TA -5.6915 -0.77 0.4432 
BIG4 14.5375 0.43 0.6642 
ABSCHGROA -4353.4860 -1.01 0.3142 
DISPFOR 8.6675 0.27 0.7905 
FINANCIAL CHALLENGES -8.2207 -0.95 0.3427 
COMPLEXITY 4.5111 1.13 0.2602 
ORGANIZATIONAL CHANGE 21.8187 3.73 0.0002 
VOLATILITY 16.7339 3.17 0.0017 
ABSREVISION 111.3202 0.42 0.6734 
IMR -70.6082 -1.72 0.0865 
Industry indicators  included  

Year indicators  included  

No. of observations  343  

Adjusted R2  0.2975  

 
 

Conclusion 
 This study examined the relationship between ERP system implementations and voluntary 
disclosure behavior of managers while recognizing the distinction between management guidance, 
forecasts, and preannouncements. Using a two-stage empirical design to control for endogeneity 
between the choice to issue guidance and management guidance error, the absolute guidance errors 
were found to be lower in the periods following ERP implementations compared to prior periods.  
ERP systems were also found to have enabled managers to make preannouncements promptly 



71 
 

following the close of a fiscal quarter. These results are consistent with the improved speed and 
accuracy that ERP systems brought to the consolidation and preparation of financial statements. 
 

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74 
 

APPENDIX 1 – Variables Definition 
 
 
ERP:   An indicator variable that is equal to one for quarters following ERP 

implementation, and zero otherwise 

GUIDANCE:  An indicator variable that is equal to one if managers issue earnings 
guidance in quarter t, and zero otherwise 

 
LN_TA:  The natural logarithm of total assets.  
 
BIG4:  An indicator variable that is equal to one if the auditor is a Big4 auditor, 

and zero otherwise.  
 
BETA: The slope coefficient from estimating Sharpe’s (1964) market model 

using daily return data from quarter t-1. 
 
LN_AGE: The natural logarithm of the number of years that a firm is covered by 

CRSP.  
 
ABSCHGROA:  The absolute value of the change in ROA (earnings before 

extraordinary items scaled by lagged total assets) from quarter t-1 to 
quarter t.  

 
DISPFOR:  The standard deviation of the individual analyst forecasts for quarter t, 

before the management guidance in quarter t.  
 
STD_AF:  The standard deviation of the individual analyst forecasts at the 

beginning of quarter t.  
 
VOLATILITY: A factor comprised of cash flow volatility, sales volatility, and the 

existence of foreign transactions  
 
ORG.CHANGE: A factor comprised of asset growth, sales growth, leverage, and merger 

and acquisition activity.  
 
COMPLEXITY:  A factor comprised of the number of segments, special items, and the 

existence of a restructuring  
 
FINANCIAL CHALLENGES:  A factor comprised of return on assets, losses, research and 

development, and earnings volatility.  
 
ABSREVISION:   The absolute value of the revision implied by the management 

guidance: [|(management guidance less the pre-existing median 
consensus analyst forecast)|/lagged assets per share].  

 
ABSERROR: Absolute forecast error defined as |Realized Earnings - Management 

Guidance|/ Lagged Assets per Share 



75 
 

INSTOWN: Institutional ownership in quarter t-1.  
LN_ANALYSTS:  The natural logarithm of the number of analysts following the firm at 

the beginning of quarter t. 
 
SIC: One-digit SIC code. 
 
IMR: Inverse Mill’s ratio from the stage 1 regression. 
 
HORIZON:  The number of days before the fiscal quarter-end that the management 

guidance is issued, where a larger number indicates more timely 
guidance. Guidance issued after the fiscal quarter-end is not excluded, 
and thus HORIZON can be negative.  

 
FCAST_HORIZON: The number of days from management forecast date to quarter-end 

date 

PRE_HORIZON: The number of days from quarter-end date to preannouncement date 

 



76 
 

APPENDIX 2 – Factors Formation 
 
Specific firm characteristics, as well as its external environment, may have a systematic impact 

on management forecast errors. These variables are expected to be associated with innate variability 
and uncertainty facing a firm (Feng et al. 2009). To control for these factors, 14 variables that are listed 
below in Table A2 are selected and principal component analysis is applied to aggregate them into 4 
factors as shown below. All factor loadings were greater than 0.7. These factors are used in the 
regressions for testing hypotheses 1, 2, and 3. 

 
Table A2 – Factor Formation 

 
Financial Challenges 
ROA 
Losses 
Earnings volatility 
R&D 
 

Complexity 
Segments 
Restructuring 
Special Items 
 

Organizational Change 
Asset growth 
Sales growth 
Leverage 
M&A 
 

Volatility 
Cash flow volatility 
Sales volatility 
Foreign transactions 

 

Variable Definitions for Factor Formation: 

ROA:     Earnings before extraordinary items/lagged total assets 

Losses: An indicator variable equal to 1 if earnings before extraordinary items in 
quarter t and t-1 sum to less than zero, and zero otherwise 

Earnings volatility: The standard deviation of quarterly ROA over the prior 7 quarters (requiring 
at least 3 non-missing observations) 

R&D: Research and Development expense/lagged total assets 



77 
 

Segments: The natural logarithm of the total number of geographic and operating 
segments 

Restructuring: An indicator variable that is equal to 1 if the firm recognized restructuring 
charges in quarter t, and zero otherwise 

Special Items: Absolute value of special items/ lagged total assets 

Asset growth: Asset growth from quarter t-1 to t (assetst – assetst-1)/assetst-1 

Sales growth: Sales growth from quarter t-1 to t (salest – salest-1)/ salest-1 

Leverage: Total liabilities/ lagged total assets 

M&A: An indicator variable that is equal to 1 if the firm undertook a large merger or 
acquisition in quarter t, and zero otherwise 

Cash flow volatility: The standard deviation of quarterly operating cash flows over the prior 7 
quarters (requiring at least 3 non-missing observations) 

Sales volatility: The standard deviation of quarterly sales over the prior 7 quarters (requiring 
at least 3 non-missing observations) 

Foreign transactions: An indicator variable that is equal to 1 if the firm has foreign transactions in 
quarter t, and zero otherwise. 

 


	Preannouncements: Forecast or Realized Earnings?
	Introduction

	Literature Review
	Management Guidance
	Voluntary Disclosures of Non-GAAP Earnings Measures

	Hypotheses Development
	Method
	Sample Selection

	Research Design
	Hypothesis 2
	Hypothesis 3

	Results
	Conclusion
	References
	Variable Definitions for Factor Formation:


